Close httplib2 connections.
createGuest(body=None, x__xgafv=None)
Create a guest user with access to a [subset of Workspace capabilities](https://support.google.com/a/answer/16558545). This feature is currently in Alpha. Please reach out to support if you are interested in trying this feature.
+Create a guest user with access to a [subset of Workspace capabilities](https://support.google.com/a/answer/16558545). This feature is currently in Open Beta.
delete(userKey, x__xgafv=None)
Deletes a user.
@@ -131,15 +131,15 @@createGuest(body=None, x__xgafv=None)
- Create a guest user with access to a [subset of Workspace capabilities](https://support.google.com/a/answer/16558545). This feature is currently in Alpha. Please reach out to support if you are interested in trying this feature.
+ Create a guest user with access to a [subset of Workspace capabilities](https://support.google.com/a/answer/16558545). This feature is currently in Open Beta.
Args:
body: object, The request body.
The object takes the form of:
{ # Directory users guest creation request message.
- "customer": "A String", # Optional. Immutable ID of the Google Workspace account.
- "primaryGuestEmail": "A String", # Immutable. External email of the guest user being created.
+ "customer": "A String", # Optional. Immutable ID of the Google Workspace account. Only required when request is created by a service account. Defaults to the authenticated user's customer ID otherwise.
+ "primaryGuestEmail": "A String", # Required. External email of the guest user being created.
}
x__xgafv: string, V1 error format.
@@ -156,6 +156,7 @@ Method Details
"aliases": [ # Output only. The list of the user's alias email addresses.
"A String",
],
+ "archivalTime": "A String", # Output only. User's account archival time. (Read-only)
"archived": True or False, # Indicates if user is archived.
"changePasswordAtNextLogin": True or False, # Indicates if the user is forced to change their password at next login. This setting doesn't apply when [the user signs in via a third-party identity provider](https://support.google.com/a/answer/60224).
"creationTime": "A String", # User's G Suite account creation time. (Read-only)
@@ -211,6 +212,7 @@ Method Details
"sshPublicKeys": "", # A list of SSH public keys.
"suspended": True or False, # Indicates if user is suspended.
"suspensionReason": "A String", # Output only. Has the reason a user account is suspended either by the administrator or by Google at the time of suspension. The property is returned only if the `suspended` property is `true`.
+ "suspensionTime": "A String", # Output only. User's account suspension time. (Read-only)
"thumbnailPhotoEtag": "A String", # Output only. ETag of the user's photo (Read-only)
"thumbnailPhotoUrl": "A String", # Output only. The URL of the user's profile photo. The URL might be temporary or private.
"websites": "", # The user's websites. The maximum allowed data size for this field is 2KB.
@@ -260,6 +262,7 @@ Method Details
"aliases": [ # Output only. The list of the user's alias email addresses.
"A String",
],
+ "archivalTime": "A String", # Output only. User's account archival time. (Read-only)
"archived": True or False, # Indicates if user is archived.
"changePasswordAtNextLogin": True or False, # Indicates if the user is forced to change their password at next login. This setting doesn't apply when [the user signs in via a third-party identity provider](https://support.google.com/a/answer/60224).
"creationTime": "A String", # User's G Suite account creation time. (Read-only)
@@ -315,6 +318,7 @@ Method Details
"sshPublicKeys": "", # A list of SSH public keys.
"suspended": True or False, # Indicates if user is suspended.
"suspensionReason": "A String", # Output only. Has the reason a user account is suspended either by the administrator or by Google at the time of suspension. The property is returned only if the `suspended` property is `true`.
+ "suspensionTime": "A String", # Output only. User's account suspension time. (Read-only)
"thumbnailPhotoEtag": "A String", # Output only. ETag of the user's photo (Read-only)
"thumbnailPhotoUrl": "A String", # Output only. The URL of the user's profile photo. The URL might be temporary or private.
"websites": "", # The user's websites. The maximum allowed data size for this field is 2KB.
@@ -335,6 +339,7 @@ Method Details
"aliases": [ # Output only. The list of the user's alias email addresses.
"A String",
],
+ "archivalTime": "A String", # Output only. User's account archival time. (Read-only)
"archived": True or False, # Indicates if user is archived.
"changePasswordAtNextLogin": True or False, # Indicates if the user is forced to change their password at next login. This setting doesn't apply when [the user signs in via a third-party identity provider](https://support.google.com/a/answer/60224).
"creationTime": "A String", # User's G Suite account creation time. (Read-only)
@@ -390,6 +395,7 @@ Method Details
"sshPublicKeys": "", # A list of SSH public keys.
"suspended": True or False, # Indicates if user is suspended.
"suspensionReason": "A String", # Output only. Has the reason a user account is suspended either by the administrator or by Google at the time of suspension. The property is returned only if the `suspended` property is `true`.
+ "suspensionTime": "A String", # Output only. User's account suspension time. (Read-only)
"thumbnailPhotoEtag": "A String", # Output only. ETag of the user's photo (Read-only)
"thumbnailPhotoUrl": "A String", # Output only. The URL of the user's profile photo. The URL might be temporary or private.
"websites": "", # The user's websites. The maximum allowed data size for this field is 2KB.
@@ -410,6 +416,7 @@ Method Details
"aliases": [ # Output only. The list of the user's alias email addresses.
"A String",
],
+ "archivalTime": "A String", # Output only. User's account archival time. (Read-only)
"archived": True or False, # Indicates if user is archived.
"changePasswordAtNextLogin": True or False, # Indicates if the user is forced to change their password at next login. This setting doesn't apply when [the user signs in via a third-party identity provider](https://support.google.com/a/answer/60224).
"creationTime": "A String", # User's G Suite account creation time. (Read-only)
@@ -465,6 +472,7 @@ Method Details
"sshPublicKeys": "", # A list of SSH public keys.
"suspended": True or False, # Indicates if user is suspended.
"suspensionReason": "A String", # Output only. Has the reason a user account is suspended either by the administrator or by Google at the time of suspension. The property is returned only if the `suspended` property is `true`.
+ "suspensionTime": "A String", # Output only. User's account suspension time. (Read-only)
"thumbnailPhotoEtag": "A String", # Output only. ETag of the user's photo (Read-only)
"thumbnailPhotoUrl": "A String", # Output only. The URL of the user's profile photo. The URL might be temporary or private.
"websites": "", # The user's websites. The maximum allowed data size for this field is 2KB.
@@ -528,6 +536,7 @@ Method Details
"aliases": [ # Output only. The list of the user's alias email addresses.
"A String",
],
+ "archivalTime": "A String", # Output only. User's account archival time. (Read-only)
"archived": True or False, # Indicates if user is archived.
"changePasswordAtNextLogin": True or False, # Indicates if the user is forced to change their password at next login. This setting doesn't apply when [the user signs in via a third-party identity provider](https://support.google.com/a/answer/60224).
"creationTime": "A String", # User's G Suite account creation time. (Read-only)
@@ -583,6 +592,7 @@ Method Details
"sshPublicKeys": "", # A list of SSH public keys.
"suspended": True or False, # Indicates if user is suspended.
"suspensionReason": "A String", # Output only. Has the reason a user account is suspended either by the administrator or by Google at the time of suspension. The property is returned only if the `suspended` property is `true`.
+ "suspensionTime": "A String", # Output only. User's account suspension time. (Read-only)
"thumbnailPhotoEtag": "A String", # Output only. ETag of the user's photo (Read-only)
"thumbnailPhotoUrl": "A String", # Output only. The URL of the user's profile photo. The URL might be temporary or private.
"websites": "", # The user's websites. The maximum allowed data size for this field is 2KB.
@@ -640,6 +650,7 @@ Method Details
"aliases": [ # Output only. The list of the user's alias email addresses.
"A String",
],
+ "archivalTime": "A String", # Output only. User's account archival time. (Read-only)
"archived": True or False, # Indicates if user is archived.
"changePasswordAtNextLogin": True or False, # Indicates if the user is forced to change their password at next login. This setting doesn't apply when [the user signs in via a third-party identity provider](https://support.google.com/a/answer/60224).
"creationTime": "A String", # User's G Suite account creation time. (Read-only)
@@ -695,6 +706,7 @@ Method Details
"sshPublicKeys": "", # A list of SSH public keys.
"suspended": True or False, # Indicates if user is suspended.
"suspensionReason": "A String", # Output only. Has the reason a user account is suspended either by the administrator or by Google at the time of suspension. The property is returned only if the `suspended` property is `true`.
+ "suspensionTime": "A String", # Output only. User's account suspension time. (Read-only)
"thumbnailPhotoEtag": "A String", # Output only. ETag of the user's photo (Read-only)
"thumbnailPhotoUrl": "A String", # Output only. The URL of the user's profile photo. The URL might be temporary or private.
"websites": "", # The user's websites. The maximum allowed data size for this field is 2KB.
@@ -714,6 +726,7 @@ Method Details
"aliases": [ # Output only. The list of the user's alias email addresses.
"A String",
],
+ "archivalTime": "A String", # Output only. User's account archival time. (Read-only)
"archived": True or False, # Indicates if user is archived.
"changePasswordAtNextLogin": True or False, # Indicates if the user is forced to change their password at next login. This setting doesn't apply when [the user signs in via a third-party identity provider](https://support.google.com/a/answer/60224).
"creationTime": "A String", # User's G Suite account creation time. (Read-only)
@@ -769,6 +782,7 @@ Method Details
"sshPublicKeys": "", # A list of SSH public keys.
"suspended": True or False, # Indicates if user is suspended.
"suspensionReason": "A String", # Output only. Has the reason a user account is suspended either by the administrator or by Google at the time of suspension. The property is returned only if the `suspended` property is `true`.
+ "suspensionTime": "A String", # Output only. User's account suspension time. (Read-only)
"thumbnailPhotoEtag": "A String", # Output only. ETag of the user's photo (Read-only)
"thumbnailPhotoUrl": "A String", # Output only. The URL of the user's profile photo. The URL might be temporary or private.
"websites": "", # The user's websites. The maximum allowed data size for this field is 2KB.
@@ -823,6 +837,7 @@ Method Details
"aliases": [ # Output only. The list of the user's alias email addresses.
"A String",
],
+ "archivalTime": "A String", # Output only. User's account archival time. (Read-only)
"archived": True or False, # Indicates if user is archived.
"changePasswordAtNextLogin": True or False, # Indicates if the user is forced to change their password at next login. This setting doesn't apply when [the user signs in via a third-party identity provider](https://support.google.com/a/answer/60224).
"creationTime": "A String", # User's G Suite account creation time. (Read-only)
@@ -878,6 +893,7 @@ Method Details
"sshPublicKeys": "", # A list of SSH public keys.
"suspended": True or False, # Indicates if user is suspended.
"suspensionReason": "A String", # Output only. Has the reason a user account is suspended either by the administrator or by Google at the time of suspension. The property is returned only if the `suspended` property is `true`.
+ "suspensionTime": "A String", # Output only. User's account suspension time. (Read-only)
"thumbnailPhotoEtag": "A String", # Output only. ETag of the user's photo (Read-only)
"thumbnailPhotoUrl": "A String", # Output only. The URL of the user's profile photo. The URL might be temporary or private.
"websites": "", # The user's websites. The maximum allowed data size for this field is 2KB.
@@ -897,6 +913,7 @@ Method Details
"aliases": [ # Output only. The list of the user's alias email addresses.
"A String",
],
+ "archivalTime": "A String", # Output only. User's account archival time. (Read-only)
"archived": True or False, # Indicates if user is archived.
"changePasswordAtNextLogin": True or False, # Indicates if the user is forced to change their password at next login. This setting doesn't apply when [the user signs in via a third-party identity provider](https://support.google.com/a/answer/60224).
"creationTime": "A String", # User's G Suite account creation time. (Read-only)
@@ -952,6 +969,7 @@ Method Details
"sshPublicKeys": "", # A list of SSH public keys.
"suspended": True or False, # Indicates if user is suspended.
"suspensionReason": "A String", # Output only. Has the reason a user account is suspended either by the administrator or by Google at the time of suspension. The property is returned only if the `suspended` property is `true`.
+ "suspensionTime": "A String", # Output only. User's account suspension time. (Read-only)
"thumbnailPhotoEtag": "A String", # Output only. ETag of the user's photo (Read-only)
"thumbnailPhotoUrl": "A String", # Output only. The URL of the user's profile photo. The URL might be temporary or private.
"websites": "", # The user's websites. The maximum allowed data size for this field is 2KB.
diff --git a/docs/dyn/aiplatform_v1.batchPredictionJobs.html b/docs/dyn/aiplatform_v1.batchPredictionJobs.html
index 0d415f94bf..509080446a 100644
--- a/docs/dyn/aiplatform_v1.batchPredictionJobs.html
+++ b/docs/dyn/aiplatform_v1.batchPredictionJobs.html
@@ -113,10 +113,10 @@ Method Details
"createTime": "A String", # Output only. Time when the BatchPredictionJob was created.
"dedicatedResources": { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -127,7 +127,7 @@ Method Details
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
@@ -483,10 +483,10 @@ Method Details
"createTime": "A String", # Output only. Time when the BatchPredictionJob was created.
"dedicatedResources": { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -497,7 +497,7 @@ Method Details
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
@@ -859,10 +859,10 @@ Method Details
"createTime": "A String", # Output only. Time when the BatchPredictionJob was created.
"dedicatedResources": { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -873,7 +873,7 @@ Method Details
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
@@ -1241,10 +1241,10 @@ Method Details
"createTime": "A String", # Output only. Time when the BatchPredictionJob was created.
"dedicatedResources": { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1255,7 +1255,7 @@ Method Details
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
diff --git a/docs/dyn/aiplatform_v1.endpoints.html b/docs/dyn/aiplatform_v1.endpoints.html
index fcb1a00741..022cd06c50 100644
--- a/docs/dyn/aiplatform_v1.endpoints.html
+++ b/docs/dyn/aiplatform_v1.endpoints.html
@@ -522,6 +522,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -1107,6 +1113,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -2006,6 +2018,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.batchPredictionJobs.html b/docs/dyn/aiplatform_v1.projects.locations.batchPredictionJobs.html
index d002edfb23..57678daf42 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.batchPredictionJobs.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.batchPredictionJobs.html
@@ -144,10 +144,10 @@ Method Details
"createTime": "A String", # Output only. Time when the BatchPredictionJob was created.
"dedicatedResources": { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -158,7 +158,7 @@ Method Details
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
@@ -513,10 +513,10 @@ Method Details
"createTime": "A String", # Output only. Time when the BatchPredictionJob was created.
"dedicatedResources": { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -527,7 +527,7 @@ Method Details
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
@@ -924,10 +924,10 @@ Method Details
"createTime": "A String", # Output only. Time when the BatchPredictionJob was created.
"dedicatedResources": { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -938,7 +938,7 @@ Method Details
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
@@ -1306,10 +1306,10 @@ Method Details
"createTime": "A String", # Output only. Time when the BatchPredictionJob was created.
"dedicatedResources": { # A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. # The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1320,7 +1320,7 @@ Method Details
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.cachedContents.html b/docs/dyn/aiplatform_v1.projects.locations.cachedContents.html
index c1ea0d93a6..e8f060904b 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.cachedContents.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.cachedContents.html
@@ -301,6 +301,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -712,6 +718,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -1148,6 +1160,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -1570,6 +1588,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -2000,6 +2024,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -2412,6 +2442,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.customJobs.html b/docs/dyn/aiplatform_v1.projects.locations.customJobs.html
index 4b03e934dc..b5a158472f 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.customJobs.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.customJobs.html
@@ -221,10 +221,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -365,10 +365,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -551,10 +551,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -708,10 +708,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.deploymentResourcePools.html b/docs/dyn/aiplatform_v1.projects.locations.deploymentResourcePools.html
index 4995592373..4017f26cb5 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.deploymentResourcePools.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.deploymentResourcePools.html
@@ -132,10 +132,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -145,7 +145,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -250,10 +250,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -263,7 +263,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -307,10 +307,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -320,7 +320,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -372,10 +372,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -385,7 +385,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -456,8 +456,8 @@ Method Details
],
"deployedModels": [ # DEPRECATED Use deployed_model_refs instead.
{ # A deployment of a Model. Endpoints contain one or more DeployedModels.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"checkpointId": "A String", # The checkpoint id of the model.
@@ -470,10 +470,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -483,7 +483,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
diff --git a/docs/dyn/aiplatform_v1.projects.locations.endpoints.html b/docs/dyn/aiplatform_v1.projects.locations.endpoints.html
index 5af24c86cf..126af775d0 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.endpoints.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.endpoints.html
@@ -590,6 +590,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -831,8 +837,8 @@ Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon.
"deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
{ # A deployment of a Model. Endpoints contain one or more DeployedModels.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"checkpointId": "A String", # The checkpoint id of the model.
@@ -845,10 +851,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -858,7 +864,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -1138,8 +1144,8 @@ Method Details
{ # Request message for EndpointService.DeployModel.
"deployedModel": { # A deployment of a Model. Endpoints contain one or more DeployedModels. # Required. The DeployedModel to be created within the Endpoint. Note that Endpoint.traffic_split must be updated for the DeployedModel to start receiving traffic, either as part of this call, or via EndpointService.UpdateEndpoint.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"checkpointId": "A String", # The checkpoint id of the model.
@@ -1152,10 +1158,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1165,7 +1171,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -2071,6 +2077,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -2586,8 +2598,8 @@ Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon.
"deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
{ # A deployment of a Model. Endpoints contain one or more DeployedModels.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"checkpointId": "A String", # The checkpoint id of the model.
@@ -2600,10 +2612,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -2613,7 +2625,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -2849,8 +2861,8 @@ Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon.
"deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
{ # A deployment of a Model. Endpoints contain one or more DeployedModels.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"checkpointId": "A String", # The checkpoint id of the model.
@@ -2863,10 +2875,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -2876,7 +2888,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -3109,8 +3121,8 @@ Method Details
{ # Request message for EndpointService.MutateDeployedModel.
"deployedModel": { # A deployment of a Model. Endpoints contain one or more DeployedModels. # Required. The DeployedModel to be mutated within the Endpoint. Only the following fields can be mutated: * `min_replica_count` in either DedicatedResources or AutomaticResources * `max_replica_count` in either DedicatedResources or AutomaticResources * `required_replica_count` in DedicatedResources * autoscaling_metric_specs * `disable_container_logging` (v1 only) * `enable_container_logging` (v1beta1 only) * `scale_to_zero_spec` in DedicatedResources (v1beta1 only) * `initial_replica_count` in DedicatedResources (v1beta1 only)
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"checkpointId": "A String", # The checkpoint id of the model.
@@ -3123,10 +3135,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -3136,7 +3148,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -3336,8 +3348,8 @@ Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon.
"deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
{ # A deployment of a Model. Endpoints contain one or more DeployedModels.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"checkpointId": "A String", # The checkpoint id of the model.
@@ -3350,10 +3362,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -3363,7 +3375,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -3585,8 +3597,8 @@ Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon.
"deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
{ # A deployment of a Model. Endpoints contain one or more DeployedModels.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"checkpointId": "A String", # The checkpoint id of the model.
@@ -3599,10 +3611,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -3612,7 +3624,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -4452,6 +4464,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -5048,8 +5066,8 @@ Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon.
"deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
{ # A deployment of a Model. Endpoints contain one or more DeployedModels.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"checkpointId": "A String", # The checkpoint id of the model.
@@ -5062,10 +5080,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -5075,7 +5093,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
diff --git a/docs/dyn/aiplatform_v1.projects.locations.featureOnlineStores.featureViews.html b/docs/dyn/aiplatform_v1.projects.locations.featureOnlineStores.featureViews.html
index 6bb7374062..05a75fab11 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.featureOnlineStores.featureViews.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.featureOnlineStores.featureViews.html
@@ -186,8 +186,8 @@ Method Details
},
"name": "A String", # Identifier. Name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}`
"optimizedConfig": { # Configuration for FeatureViews created in Optimized FeatureOnlineStore. # Optional. Configuration for FeatureView created under Optimized FeatureOnlineStore.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
},
@@ -544,8 +544,8 @@ Method Details
},
"name": "A String", # Identifier. Name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}`
"optimizedConfig": { # Configuration for FeatureViews created in Optimized FeatureOnlineStore. # Optional. Configuration for FeatureView created under Optimized FeatureOnlineStore.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
},
@@ -662,8 +662,8 @@ Method Details
},
"name": "A String", # Identifier. Name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}`
"optimizedConfig": { # Configuration for FeatureViews created in Optimized FeatureOnlineStore. # Optional. Configuration for FeatureView created under Optimized FeatureOnlineStore.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
},
@@ -751,8 +751,8 @@ Method Details
},
"name": "A String", # Identifier. Name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}`
"optimizedConfig": { # Configuration for FeatureViews created in Optimized FeatureOnlineStore. # Optional. Configuration for FeatureView created under Optimized FeatureOnlineStore.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
},
diff --git a/docs/dyn/aiplatform_v1.projects.locations.html b/docs/dyn/aiplatform_v1.projects.locations.html
index 1f15326b01..f187b030ba 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.html
@@ -358,6 +358,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -636,6 +642,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -1246,10 +1258,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1259,7 +1271,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -1968,7 +1980,7 @@ Method Details
The object takes the form of:
{ # Request message for EvaluationService.EvaluateInstances.
- "autoraterConfig": { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Autorater config used for evaluation.
+ "autoraterConfig": { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Autorater config used for evaluation. Not applicable for predefined metrics (PredefinedMetricSpec); the server uses its own model configuration for predefined metrics and this field is ignored.
"autoraterModel": "A String", # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
"flipEnabled": True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
"generationConfig": { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
@@ -2260,6 +2272,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -2570,6 +2588,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -2951,6 +2975,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -3174,6 +3204,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -5332,6 +5368,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.hyperparameterTuningJobs.html b/docs/dyn/aiplatform_v1.projects.locations.hyperparameterTuningJobs.html
index 98989672fe..3a6e1a43a8 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.hyperparameterTuningJobs.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.hyperparameterTuningJobs.html
@@ -324,10 +324,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -603,10 +603,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -924,10 +924,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1216,10 +1216,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.indexEndpoints.html b/docs/dyn/aiplatform_v1.projects.locations.indexEndpoints.html
index 70c9682302..e2b908bab3 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.indexEndpoints.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.indexEndpoints.html
@@ -134,8 +134,8 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this IndexEndpoint was created.
"deployedIndexes": [ # Output only. The indexes deployed in this endpoint.
{ # A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"createTime": "A String", # Output only. Timestamp when the DeployedIndex was created.
@@ -147,10 +147,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -160,7 +160,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -320,8 +320,8 @@ Method Details
{ # Request message for IndexEndpointService.DeployIndex.
"deployedIndex": { # A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes. # Required. The DeployedIndex to be created within the IndexEndpoint.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"createTime": "A String", # Output only. Timestamp when the DeployedIndex was created.
@@ -333,10 +333,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -346,7 +346,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -571,8 +571,8 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this IndexEndpoint was created.
"deployedIndexes": [ # Output only. The indexes deployed in this endpoint.
{ # A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"createTime": "A String", # Output only. Timestamp when the DeployedIndex was created.
@@ -584,10 +584,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -597,7 +597,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -707,8 +707,8 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this IndexEndpoint was created.
"deployedIndexes": [ # Output only. The indexes deployed in this endpoint.
{ # A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"createTime": "A String", # Output only. Timestamp when the DeployedIndex was created.
@@ -720,10 +720,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -733,7 +733,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -846,8 +846,8 @@ Method Details
The object takes the form of:
{ # A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"createTime": "A String", # Output only. Timestamp when the DeployedIndex was created.
@@ -859,10 +859,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -872,7 +872,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -963,8 +963,8 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this IndexEndpoint was created.
"deployedIndexes": [ # Output only. The indexes deployed in this endpoint.
{ # A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"createTime": "A String", # Output only. Timestamp when the DeployedIndex was created.
@@ -976,10 +976,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -989,7 +989,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -1087,8 +1087,8 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this IndexEndpoint was created.
"deployedIndexes": [ # Output only. The indexes deployed in this endpoint.
{ # A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"createTime": "A String", # Output only. Timestamp when the DeployedIndex was created.
@@ -1100,10 +1100,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1113,7 +1113,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
diff --git a/docs/dyn/aiplatform_v1.projects.locations.nasJobs.html b/docs/dyn/aiplatform_v1.projects.locations.nasJobs.html
index f2d0904810..789bb575ef 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.nasJobs.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.nasJobs.html
@@ -277,10 +277,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -387,10 +387,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -585,10 +585,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -695,10 +695,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -935,10 +935,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1045,10 +1045,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1256,10 +1256,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1366,10 +1366,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.notebookExecutionJobs.html b/docs/dyn/aiplatform_v1.projects.locations.notebookExecutionJobs.html
index 2a018030ce..3feb5a650f 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.notebookExecutionJobs.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.notebookExecutionJobs.html
@@ -116,10 +116,10 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this NotebookExecutionJob was created.
"customEnvironmentSpec": { # Compute configuration to use for an execution job. # The custom compute configuration for an execution job.
"machineSpec": { # Specification of a single machine. # The specification of a single machine for the execution job.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -134,9 +134,9 @@ Method Details
"network": "A String", # The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and-firewalls#networks)
"subnetwork": "A String", # The name of the subnet that this instance is in. Format: `projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}`
},
- "persistentDiskSpec": { # Represents the spec of persistent disk options. # The specification of a persistent disk to attach for the execution job.
+ "persistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # The specification of a persistent disk to attach for the execution job.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
},
"dataformRepositorySource": { # The Dataform Repository containing the input notebook. # The Dataform Repository pointing to a single file notebook repository.
@@ -268,10 +268,10 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this NotebookExecutionJob was created.
"customEnvironmentSpec": { # Compute configuration to use for an execution job. # The custom compute configuration for an execution job.
"machineSpec": { # Specification of a single machine. # The specification of a single machine for the execution job.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -286,9 +286,9 @@ Method Details
"network": "A String", # The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and-firewalls#networks)
"subnetwork": "A String", # The name of the subnet that this instance is in. Format: `projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}`
},
- "persistentDiskSpec": { # Represents the spec of persistent disk options. # The specification of a persistent disk to attach for the execution job.
+ "persistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # The specification of a persistent disk to attach for the execution job.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
},
"dataformRepositorySource": { # The Dataform Repository containing the input notebook. # The Dataform Repository pointing to a single file notebook repository.
@@ -363,10 +363,10 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this NotebookExecutionJob was created.
"customEnvironmentSpec": { # Compute configuration to use for an execution job. # The custom compute configuration for an execution job.
"machineSpec": { # Specification of a single machine. # The specification of a single machine for the execution job.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -381,9 +381,9 @@ Method Details
"network": "A String", # The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and-firewalls#networks)
"subnetwork": "A String", # The name of the subnet that this instance is in. Format: `projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}`
},
- "persistentDiskSpec": { # Represents the spec of persistent disk options. # The specification of a persistent disk to attach for the execution job.
+ "persistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # The specification of a persistent disk to attach for the execution job.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
},
"dataformRepositorySource": { # The Dataform Repository containing the input notebook. # The Dataform Repository pointing to a single file notebook repository.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimeTemplates.html b/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimeTemplates.html
index 3c75c241ee..d59a88d119 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimeTemplates.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimeTemplates.html
@@ -126,9 +126,9 @@ Method Details
{ # A template that specifies runtime configurations such as machine type, runtime version, network configurations, etc. Multiple runtimes can be created from a runtime template.
"createTime": "A String", # Output only. Timestamp when this NotebookRuntimeTemplate was created.
- "dataPersistentDiskSpec": { # Represents the spec of persistent disk options. # Optional. The specification of persistent disk attached to the runtime as data disk storage.
+ "dataPersistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # Optional. The specification of persistent disk attached to the runtime as data disk storage.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
"description": "A String", # The description of the NotebookRuntimeTemplate.
"displayName": "A String", # Required. The display name of the NotebookRuntimeTemplate. The name can be up to 128 characters long and can consist of any UTF-8 characters.
@@ -149,10 +149,10 @@ Method Details
"a_key": "A String",
},
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine for the template.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -284,9 +284,9 @@ Method Details
{ # A template that specifies runtime configurations such as machine type, runtime version, network configurations, etc. Multiple runtimes can be created from a runtime template.
"createTime": "A String", # Output only. Timestamp when this NotebookRuntimeTemplate was created.
- "dataPersistentDiskSpec": { # Represents the spec of persistent disk options. # Optional. The specification of persistent disk attached to the runtime as data disk storage.
+ "dataPersistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # Optional. The specification of persistent disk attached to the runtime as data disk storage.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
"description": "A String", # The description of the NotebookRuntimeTemplate.
"displayName": "A String", # Required. The display name of the NotebookRuntimeTemplate. The name can be up to 128 characters long and can consist of any UTF-8 characters.
@@ -307,10 +307,10 @@ Method Details
"a_key": "A String",
},
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine for the template.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -421,9 +421,9 @@ Method Details
"notebookRuntimeTemplates": [ # List of NotebookRuntimeTemplates in the requested page.
{ # A template that specifies runtime configurations such as machine type, runtime version, network configurations, etc. Multiple runtimes can be created from a runtime template.
"createTime": "A String", # Output only. Timestamp when this NotebookRuntimeTemplate was created.
- "dataPersistentDiskSpec": { # Represents the spec of persistent disk options. # Optional. The specification of persistent disk attached to the runtime as data disk storage.
+ "dataPersistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # Optional. The specification of persistent disk attached to the runtime as data disk storage.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
"description": "A String", # The description of the NotebookRuntimeTemplate.
"displayName": "A String", # Required. The display name of the NotebookRuntimeTemplate. The name can be up to 128 characters long and can consist of any UTF-8 characters.
@@ -444,10 +444,10 @@ Method Details
"a_key": "A String",
},
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine for the template.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -526,9 +526,9 @@ Method Details
{ # A template that specifies runtime configurations such as machine type, runtime version, network configurations, etc. Multiple runtimes can be created from a runtime template.
"createTime": "A String", # Output only. Timestamp when this NotebookRuntimeTemplate was created.
- "dataPersistentDiskSpec": { # Represents the spec of persistent disk options. # Optional. The specification of persistent disk attached to the runtime as data disk storage.
+ "dataPersistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # Optional. The specification of persistent disk attached to the runtime as data disk storage.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
"description": "A String", # The description of the NotebookRuntimeTemplate.
"displayName": "A String", # Required. The display name of the NotebookRuntimeTemplate. The name can be up to 128 characters long and can consist of any UTF-8 characters.
@@ -549,10 +549,10 @@ Method Details
"a_key": "A String",
},
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine for the template.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -614,9 +614,9 @@ Method Details
{ # A template that specifies runtime configurations such as machine type, runtime version, network configurations, etc. Multiple runtimes can be created from a runtime template.
"createTime": "A String", # Output only. Timestamp when this NotebookRuntimeTemplate was created.
- "dataPersistentDiskSpec": { # Represents the spec of persistent disk options. # Optional. The specification of persistent disk attached to the runtime as data disk storage.
+ "dataPersistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # Optional. The specification of persistent disk attached to the runtime as data disk storage.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
"description": "A String", # The description of the NotebookRuntimeTemplate.
"displayName": "A String", # Required. The display name of the NotebookRuntimeTemplate. The name can be up to 128 characters long and can consist of any UTF-8 characters.
@@ -637,10 +637,10 @@ Method Details
"a_key": "A String",
},
"machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine for the template.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimes.html b/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimes.html
index 5bd64dc5fd..53bf9bdc3d 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimes.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimes.html
@@ -119,9 +119,9 @@ Method Details
{ # Request message for NotebookService.AssignNotebookRuntime.
"notebookRuntime": { # A runtime is a virtual machine allocated to a particular user for a particular Notebook file on temporary basis with lifetime. Default runtimes have a lifetime of 18 hours, while custom runtimes last for 6 months from their creation or last upgrade. # Required. Provide runtime specific information (e.g. runtime owner, notebook id) used for NotebookRuntime assignment.
"createTime": "A String", # Output only. Timestamp when this NotebookRuntime was created.
- "dataPersistentDiskSpec": { # Represents the spec of persistent disk options. # Output only. The specification of persistent disk attached to the notebook runtime as data disk storage.
+ "dataPersistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # Output only. The specification of persistent disk attached to the notebook runtime as data disk storage.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
"description": "A String", # The description of the NotebookRuntime.
"displayName": "A String", # Required. The display name of the NotebookRuntime. The name can be up to 128 characters long and can consist of any UTF-8 characters.
@@ -143,10 +143,10 @@ Method Details
"a_key": "A String",
},
"machineSpec": { # Specification of a single machine. # Output only. The specification of a single machine used by the notebook runtime.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -294,9 +294,9 @@ Method Details
{ # A runtime is a virtual machine allocated to a particular user for a particular Notebook file on temporary basis with lifetime. Default runtimes have a lifetime of 18 hours, while custom runtimes last for 6 months from their creation or last upgrade.
"createTime": "A String", # Output only. Timestamp when this NotebookRuntime was created.
- "dataPersistentDiskSpec": { # Represents the spec of persistent disk options. # Output only. The specification of persistent disk attached to the notebook runtime as data disk storage.
+ "dataPersistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # Output only. The specification of persistent disk attached to the notebook runtime as data disk storage.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
"description": "A String", # The description of the NotebookRuntime.
"displayName": "A String", # Required. The display name of the NotebookRuntime. The name can be up to 128 characters long and can consist of any UTF-8 characters.
@@ -318,10 +318,10 @@ Method Details
"a_key": "A String",
},
"machineSpec": { # Specification of a single machine. # Output only. The specification of a single machine used by the notebook runtime.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -406,9 +406,9 @@ Method Details
"notebookRuntimes": [ # List of NotebookRuntimes in the requested page.
{ # A runtime is a virtual machine allocated to a particular user for a particular Notebook file on temporary basis with lifetime. Default runtimes have a lifetime of 18 hours, while custom runtimes last for 6 months from their creation or last upgrade.
"createTime": "A String", # Output only. Timestamp when this NotebookRuntime was created.
- "dataPersistentDiskSpec": { # Represents the spec of persistent disk options. # Output only. The specification of persistent disk attached to the notebook runtime as data disk storage.
+ "dataPersistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # Output only. The specification of persistent disk attached to the notebook runtime as data disk storage.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
"description": "A String", # The description of the NotebookRuntime.
"displayName": "A String", # Required. The display name of the NotebookRuntime. The name can be up to 128 characters long and can consist of any UTF-8 characters.
@@ -430,10 +430,10 @@ Method Details
"a_key": "A String",
},
"machineSpec": { # Specification of a single machine. # Output only. The specification of a single machine used by the notebook runtime.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.persistentResources.html b/docs/dyn/aiplatform_v1.projects.locations.persistentResources.html
index 4cffce0c67..991866d4f9 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.persistentResources.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.persistentResources.html
@@ -163,10 +163,10 @@ Method Details
},
"id": "A String", # Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -335,10 +335,10 @@ Method Details
},
"id": "A String", # Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -448,10 +448,10 @@ Method Details
},
"id": "A String", # Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -567,10 +567,10 @@ Method Details
},
"id": "A String", # Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically.
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.publishers.models.html b/docs/dyn/aiplatform_v1.projects.locations.publishers.models.html
index b5e97ef962..f4676f6faf 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.publishers.models.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.publishers.models.html
@@ -529,6 +529,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -1263,6 +1269,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -2390,6 +2402,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.memories.html b/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.memories.html
index 940999c606..200d5512e4 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.memories.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.memories.html
@@ -559,7 +559,7 @@ Method Details
"updateTime": "A String", # Output only. Represents the timestamp when this Memory was most recently updated.
}
- updateMask: string, Optional. Mask specifying which fields to update. Supported fields: * `display_name` * `description` * `fact`
+ updateMask: string, Optional. Mask specifying which fields to update. The following fields are immutable: * `scope` * `memory_type`
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
diff --git a/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.sandboxEnvironmentTemplates.html b/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.sandboxEnvironmentTemplates.html
index 29b0d1e5f9..09c27cb5bf 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.sandboxEnvironmentTemplates.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.sandboxEnvironmentTemplates.html
@@ -135,6 +135,14 @@ Method Details
},
"defaultContainerEnvironment": { # The default sandbox runtime environment for default container workloads. # The sandbox environment for default container workloads.
"defaultContainerCategory": "A String", # Required. The category of the default container image.
+ "resources": { # Message to define resource requests and limits (mirroring Kubernetes) for each sandbox instance created from this template. # Optional. Resource requests and limits for the default container.
+ "limits": { # Optional. The maximum amounts of compute resources allowed. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "500m", "1Gi").
+ "a_key": "A String",
+ },
+ "requests": { # Optional. The requested amounts of compute resources. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "250m", "512Mi").
+ "a_key": "A String",
+ },
+ },
},
"displayName": "A String", # Required. The display name of the SandboxEnvironmentTemplate.
"egressControlConfig": { # Configuration for egress control of sandbox instances. # Optional. The configuration for egress control of this template.
@@ -246,6 +254,14 @@ Method Details
},
"defaultContainerEnvironment": { # The default sandbox runtime environment for default container workloads. # The sandbox environment for default container workloads.
"defaultContainerCategory": "A String", # Required. The category of the default container image.
+ "resources": { # Message to define resource requests and limits (mirroring Kubernetes) for each sandbox instance created from this template. # Optional. Resource requests and limits for the default container.
+ "limits": { # Optional. The maximum amounts of compute resources allowed. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "500m", "1Gi").
+ "a_key": "A String",
+ },
+ "requests": { # Optional. The requested amounts of compute resources. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "250m", "512Mi").
+ "a_key": "A String",
+ },
+ },
},
"displayName": "A String", # Required. The display name of the SandboxEnvironmentTemplate.
"egressControlConfig": { # Configuration for egress control of sandbox instances. # Optional. The configuration for egress control of this template.
@@ -300,6 +316,14 @@ Method Details
},
"defaultContainerEnvironment": { # The default sandbox runtime environment for default container workloads. # The sandbox environment for default container workloads.
"defaultContainerCategory": "A String", # Required. The category of the default container image.
+ "resources": { # Message to define resource requests and limits (mirroring Kubernetes) for each sandbox instance created from this template. # Optional. Resource requests and limits for the default container.
+ "limits": { # Optional. The maximum amounts of compute resources allowed. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "500m", "1Gi").
+ "a_key": "A String",
+ },
+ "requests": { # Optional. The requested amounts of compute resources. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "250m", "512Mi").
+ "a_key": "A String",
+ },
+ },
},
"displayName": "A String", # Required. The display name of the SandboxEnvironmentTemplate.
"egressControlConfig": { # Configuration for egress control of sandbox instances. # Optional. The configuration for egress control of this template.
diff --git a/docs/dyn/aiplatform_v1.projects.locations.schedules.html b/docs/dyn/aiplatform_v1.projects.locations.schedules.html
index 1e757c9b50..d3bcc30f61 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.schedules.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.schedules.html
@@ -129,10 +129,10 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this NotebookExecutionJob was created.
"customEnvironmentSpec": { # Compute configuration to use for an execution job. # The custom compute configuration for an execution job.
"machineSpec": { # Specification of a single machine. # The specification of a single machine for the execution job.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -147,9 +147,9 @@ Method Details
"network": "A String", # The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and-firewalls#networks)
"subnetwork": "A String", # The name of the subnet that this instance is in. Format: `projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}`
},
- "persistentDiskSpec": { # Represents the spec of persistent disk options. # The specification of a persistent disk to attach for the execution job.
+ "persistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # The specification of a persistent disk to attach for the execution job.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
},
"dataformRepositorySource": { # The Dataform Repository containing the input notebook. # The Dataform Repository pointing to a single file notebook repository.
@@ -462,10 +462,10 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this NotebookExecutionJob was created.
"customEnvironmentSpec": { # Compute configuration to use for an execution job. # The custom compute configuration for an execution job.
"machineSpec": { # Specification of a single machine. # The specification of a single machine for the execution job.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -480,9 +480,9 @@ Method Details
"network": "A String", # The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and-firewalls#networks)
"subnetwork": "A String", # The name of the subnet that this instance is in. Format: `projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}`
},
- "persistentDiskSpec": { # Represents the spec of persistent disk options. # The specification of a persistent disk to attach for the execution job.
+ "persistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # The specification of a persistent disk to attach for the execution job.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
},
"dataformRepositorySource": { # The Dataform Repository containing the input notebook. # The Dataform Repository pointing to a single file notebook repository.
@@ -837,10 +837,10 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this NotebookExecutionJob was created.
"customEnvironmentSpec": { # Compute configuration to use for an execution job. # The custom compute configuration for an execution job.
"machineSpec": { # Specification of a single machine. # The specification of a single machine for the execution job.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -855,9 +855,9 @@ Method Details
"network": "A String", # The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and-firewalls#networks)
"subnetwork": "A String", # The name of the subnet that this instance is in. Format: `projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}`
},
- "persistentDiskSpec": { # Represents the spec of persistent disk options. # The specification of a persistent disk to attach for the execution job.
+ "persistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # The specification of a persistent disk to attach for the execution job.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
},
"dataformRepositorySource": { # The Dataform Repository containing the input notebook. # The Dataform Repository pointing to a single file notebook repository.
@@ -1184,10 +1184,10 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this NotebookExecutionJob was created.
"customEnvironmentSpec": { # Compute configuration to use for an execution job. # The custom compute configuration for an execution job.
"machineSpec": { # Specification of a single machine. # The specification of a single machine for the execution job.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1202,9 +1202,9 @@ Method Details
"network": "A String", # The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and-firewalls#networks)
"subnetwork": "A String", # The name of the subnet that this instance is in. Format: `projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}`
},
- "persistentDiskSpec": { # Represents the spec of persistent disk options. # The specification of a persistent disk to attach for the execution job.
+ "persistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # The specification of a persistent disk to attach for the execution job.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
},
"dataformRepositorySource": { # The Dataform Repository containing the input notebook. # The Dataform Repository pointing to a single file notebook repository.
@@ -1535,10 +1535,10 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this NotebookExecutionJob was created.
"customEnvironmentSpec": { # Compute configuration to use for an execution job. # The custom compute configuration for an execution job.
"machineSpec": { # Specification of a single machine. # The specification of a single machine for the execution job.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1553,9 +1553,9 @@ Method Details
"network": "A String", # The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and-firewalls#networks)
"subnetwork": "A String", # The name of the subnet that this instance is in. Format: `projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}`
},
- "persistentDiskSpec": { # Represents the spec of persistent disk options. # The specification of a persistent disk to attach for the execution job.
+ "persistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # The specification of a persistent disk to attach for the execution job.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
},
"dataformRepositorySource": { # The Dataform Repository containing the input notebook. # The Dataform Repository pointing to a single file notebook repository.
@@ -1869,10 +1869,10 @@ Method Details
"createTime": "A String", # Output only. Timestamp when this NotebookExecutionJob was created.
"customEnvironmentSpec": { # Compute configuration to use for an execution job. # The custom compute configuration for an execution job.
"machineSpec": { # Specification of a single machine. # The specification of a single machine for the execution job.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1887,9 +1887,9 @@ Method Details
"network": "A String", # The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and-firewalls#networks)
"subnetwork": "A String", # The name of the subnet that this instance is in. Format: `projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}`
},
- "persistentDiskSpec": { # Represents the spec of persistent disk options. # The specification of a persistent disk to attach for the execution job.
+ "persistentDiskSpec": { # Represents the spec of persistent disk and hyperdisk options. # The specification of a persistent disk to attach for the execution job.
"diskSizeGb": "A String", # Size in GB of the disk (default is 100GB).
- "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)
+ "diskType": "A String", # Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) "hyperdisk-balanced" (Hyperdisk Balanced) "hyperdisk-extreme" (Hyperdisk Extreme) "hyperdisk-balanced-high-availability" (Hyperdisk Balanced High Availability) "hyperdisk-ml" (Hyperdisk ML) "hyperdisk-throughput" (Hyperdisk Throughput)
},
},
"dataformRepositorySource": { # The Dataform Repository containing the input notebook. # The Dataform Repository pointing to a single file notebook repository.
diff --git a/docs/dyn/aiplatform_v1.publishers.models.html b/docs/dyn/aiplatform_v1.publishers.models.html
index 9a358c7eb9..567068a491 100644
--- a/docs/dyn/aiplatform_v1.publishers.models.html
+++ b/docs/dyn/aiplatform_v1.publishers.models.html
@@ -515,6 +515,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -1100,6 +1106,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -1647,8 +1659,8 @@ Method Details
},
"deploy": { # Model metadata that is needed for UploadModel or DeployModel/CreateEndpoint requests. # Optional. Deploy the PublisherModel to Vertex Endpoint.
"artifactUri": "A String", # Optional. The path to the directory containing the Model artifact and any of its supporting files.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"containerSpec": { # Specification of a container for serving predictions. Some fields in this message correspond to fields in the [Kubernetes Container v1 core specification](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core). # Optional. The specification of the container that is to be used when deploying this Model in Vertex AI. Not present for Large Models.
@@ -1785,10 +1797,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1798,7 +1810,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -1827,8 +1839,8 @@ Method Details
"multiDeployVertex": [ # Optional. One click deployment configurations.
{ # Model metadata that is needed for UploadModel or DeployModel/CreateEndpoint requests.
"artifactUri": "A String", # Optional. The path to the directory containing the Model artifact and any of its supporting files.
- "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
+ "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.
"minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
},
"containerSpec": { # Specification of a container for serving predictions. Some fields in this message correspond to fields in the [Kubernetes Container v1 core specification](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core). # Optional. The specification of the container that is to be used when deploying this Model in Vertex AI. Not present for Large Models.
@@ -1965,10 +1977,10 @@ Method Details
},
],
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
"key": "A String", # Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value.
"reservationAffinityType": "A String", # Required. Specifies the reservation affinity type.
@@ -1978,7 +1990,7 @@ Method Details
},
"tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
},
- "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
+ "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
"minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
"requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
@@ -2574,6 +2586,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
diff --git a/docs/dyn/aiplatform_v1.reasoningEngines.sandboxEnvironmentTemplates.html b/docs/dyn/aiplatform_v1.reasoningEngines.sandboxEnvironmentTemplates.html
index 645d06e1bc..9d0a06baac 100644
--- a/docs/dyn/aiplatform_v1.reasoningEngines.sandboxEnvironmentTemplates.html
+++ b/docs/dyn/aiplatform_v1.reasoningEngines.sandboxEnvironmentTemplates.html
@@ -135,6 +135,14 @@ Method Details
},
"defaultContainerEnvironment": { # The default sandbox runtime environment for default container workloads. # The sandbox environment for default container workloads.
"defaultContainerCategory": "A String", # Required. The category of the default container image.
+ "resources": { # Message to define resource requests and limits (mirroring Kubernetes) for each sandbox instance created from this template. # Optional. Resource requests and limits for the default container.
+ "limits": { # Optional. The maximum amounts of compute resources allowed. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "500m", "1Gi").
+ "a_key": "A String",
+ },
+ "requests": { # Optional. The requested amounts of compute resources. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "250m", "512Mi").
+ "a_key": "A String",
+ },
+ },
},
"displayName": "A String", # Required. The display name of the SandboxEnvironmentTemplate.
"egressControlConfig": { # Configuration for egress control of sandbox instances. # Optional. The configuration for egress control of this template.
@@ -246,6 +254,14 @@ Method Details
},
"defaultContainerEnvironment": { # The default sandbox runtime environment for default container workloads. # The sandbox environment for default container workloads.
"defaultContainerCategory": "A String", # Required. The category of the default container image.
+ "resources": { # Message to define resource requests and limits (mirroring Kubernetes) for each sandbox instance created from this template. # Optional. Resource requests and limits for the default container.
+ "limits": { # Optional. The maximum amounts of compute resources allowed. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "500m", "1Gi").
+ "a_key": "A String",
+ },
+ "requests": { # Optional. The requested amounts of compute resources. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "250m", "512Mi").
+ "a_key": "A String",
+ },
+ },
},
"displayName": "A String", # Required. The display name of the SandboxEnvironmentTemplate.
"egressControlConfig": { # Configuration for egress control of sandbox instances. # Optional. The configuration for egress control of this template.
@@ -300,6 +316,14 @@ Method Details
},
"defaultContainerEnvironment": { # The default sandbox runtime environment for default container workloads. # The sandbox environment for default container workloads.
"defaultContainerCategory": "A String", # Required. The category of the default container image.
+ "resources": { # Message to define resource requests and limits (mirroring Kubernetes) for each sandbox instance created from this template. # Optional. Resource requests and limits for the default container.
+ "limits": { # Optional. The maximum amounts of compute resources allowed. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "500m", "1Gi").
+ "a_key": "A String",
+ },
+ "requests": { # Optional. The requested amounts of compute resources. Keys are resource names (e.g., "cpu", "memory"). Values are quantities (e.g., "250m", "512Mi").
+ "a_key": "A String",
+ },
+ },
},
"displayName": "A String", # Required. The display name of the SandboxEnvironmentTemplate.
"egressControlConfig": { # Configuration for egress control of sandbox instances. # Optional. The configuration for egress control of this template.
diff --git a/docs/dyn/aiplatform_v1.v1.html b/docs/dyn/aiplatform_v1.v1.html
index c49956c1b0..fc245f4d6a 100644
--- a/docs/dyn/aiplatform_v1.v1.html
+++ b/docs/dyn/aiplatform_v1.v1.html
@@ -597,7 +597,7 @@ Method Details
The object takes the form of:
{ # Request message for EvaluationService.EvaluateInstances.
- "autoraterConfig": { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Autorater config used for evaluation.
+ "autoraterConfig": { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Autorater config used for evaluation. Not applicable for predefined metrics (PredefinedMetricSpec); the server uses its own model configuration for predefined metrics and this field is ignored.
"autoraterModel": "A String", # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
"flipEnabled": True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
"generationConfig": { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
@@ -889,6 +889,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -1199,6 +1205,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -1580,6 +1592,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -1803,6 +1821,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -3960,6 +3984,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
diff --git a/docs/dyn/aiplatform_v1beta1.batchPredictionJobs.html b/docs/dyn/aiplatform_v1beta1.batchPredictionJobs.html
index b3343a149c..93d2e5d6c1 100644
--- a/docs/dyn/aiplatform_v1beta1.batchPredictionJobs.html
+++ b/docs/dyn/aiplatform_v1beta1.batchPredictionJobs.html
@@ -116,10 +116,10 @@ Method Details
"maxRuntimeDuration": "A String", # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days.
},
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure.
"multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
@@ -133,7 +133,7 @@ Method Details
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
@@ -622,10 +622,10 @@ Method Details
"maxRuntimeDuration": "A String", # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days.
},
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure.
"multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
@@ -639,7 +639,7 @@ Method Details
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
@@ -1134,10 +1134,10 @@ Method Details
"maxRuntimeDuration": "A String", # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days.
},
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure.
"multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
@@ -1151,7 +1151,7 @@ Method Details
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
@@ -1652,10 +1652,10 @@ Method Details
"maxRuntimeDuration": "A String", # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days.
},
"machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
- "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
+ "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
"acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
"gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1.
- "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+ "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
"minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure.
"multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
"reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
@@ -1669,7 +1669,7 @@ Method Details
},
"maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
"spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms).
- "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
+ "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
},
"disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
"displayName": "A String", # Required. The user-defined name of this BatchPredictionJob.
diff --git a/docs/dyn/aiplatform_v1beta1.endpoints.html b/docs/dyn/aiplatform_v1beta1.endpoints.html
index 1142b2ef46..47cf2a83fb 100644
--- a/docs/dyn/aiplatform_v1beta1.endpoints.html
+++ b/docs/dyn/aiplatform_v1beta1.endpoints.html
@@ -531,6 +531,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -1130,6 +1136,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
@@ -2050,6 +2062,12 @@ Method Details
"A String",
],
},
+ "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation
+ "apiKey": "A String", # Required. The API key for ExaAiSearch.
+ "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API.
+ "a_key": "", # Properties of the object.
+ },
+ },
"functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
{ # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
"behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method.
diff --git a/docs/dyn/aiplatform_v1beta1.html b/docs/dyn/aiplatform_v1beta1.html
index 8b9d912064..85a75c6fa9 100644
--- a/docs/dyn/aiplatform_v1beta1.html
+++ b/docs/dyn/aiplatform_v1beta1.html
@@ -294,11 +294,6 @@ Instance Methods
Returns the semanticGovernancePolicies Resource.
-
- semanticGovernancePolicyEngine()
-
-Returns the semanticGovernancePolicyEngine Resource.
-
diff --git a/docs/dyn/aiplatform_v1beta1.interactions.html b/docs/dyn/aiplatform_v1beta1.interactions.html
index d35744131e..8e85b5fd25 100644
--- a/docs/dyn/aiplatform_v1beta1.interactions.html
+++ b/docs/dyn/aiplatform_v1beta1.interactions.html
@@ -80,9 +80,6 @@ Instance Methods
Close httplib2 connections.
-
-Deletes an interaction.
getPoll(name, include_input=None, last_event_id=None, stream=None, x__xgafv=None)
Fully typed proto, unary version of GetInteraction that returns Interaction proto.
@@ -109,6 +106,7 @@ Method Details
"agent": "A String", # The name of the `Agent` used for generating the completion.
"deep_research_config": { # Configuration for the Deep Research agent.
"collaborative_planning": True or False, # Enables human-in-the-loop planning for the Deep Research agent. If set to true, the Deep Research agent will provide a research plan in its response. The agent will then proceed only if the user confirms the plan in the next turn.
+ "enable_bigquery_tool": True or False, # Enables bigquery tool for the Deep Research agent.
"thinking_summaries": "A String", # Whether to include thought summaries in the response.
"visualization": "A String", # Whether to include visualizations in the response.
},
@@ -1031,6 +1029,8 @@ Method Details
],
},
"created": "A String", # Required. Output only. The time at which the response was created in ISO 8601 format (YYYY-MM-DDThh:mm:ssZ).
+ "env_id": "A String", # The environment ID for the interaction. Can be 'remote' for default environment.
+ "environment_id": "A String", # Output only. The environment ID for the interaction. Only populated if environment config is set in the request.
"id": "A String", # Required. Output only. A unique identifier for the interaction completion.
"modelInteraction": { # Interaction for generating the completion using models. # Interaction for generating the completion using models.
"generation_config": { # Configuration parameters for model interactions. # Input only. Configuration parameters for the model interaction.
@@ -1515,6 +1515,28 @@ Method Details
},
],
"previous_interaction_id": "A String", # The ID of the previous interaction, if any.
+ "remoteEnvironment": { # Configuration for a custom environment.
+ "networkAllowlist": { # Network egress configuration for the environment. # Allow only specific domains.
+ "allowlist": [ # List of allowed domains and their configurations.
+ { # A network egress rule that controls which external domains the environment is allowed to reach. Each rule identifies a target domain and, optionally, a set of HTTP headers to inject into every matching outbound request.
+ "domain": "A String", # The domain pattern to match for this rule. Use an exact hostname (e.g., `github.com`), a wildcard prefix (e.g., `*.googleapis.com`), or `*` to match all domains.
+ "transform": { # Headers to inject into requests matching this rule. Key: header name (e.g., "Authorization"). Value: header value (e.g., "Bearer your-token").
+ "a_key": "A String",
+ },
+ },
+ ],
+ },
+ "networkMode": "A String", # Network egress mode.
+ "sources": [
+ { # A source to be mounted into the environment.
+ "content": "A String", # The inline content if `type` is `INLINE`.
+ "encoding": "A String", # Optional encoding for inline content (e.g. `base64`).
+ "source": "A String", # The source of the environment. For GCS, this is the GCS path. For GitHub, this is the GitHub path.
+ "target": "A String", # Where the source should appear in the environment.
+ "type": "A String",
+ },
+ ],
+ },
"responseFormatList": {
"response_formats": [
{
@@ -1571,8 +1593,6 @@ Method Details
],
},
},
- "video": { # Configuration for video output format.
- },
},
],
},
@@ -1630,8 +1650,6 @@ Method Details
],
},
},
- "video": { # Configuration for video output format.
- },
},
"response_format": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. # Enforces that the generated response is a JSON object that complies with the JSON schema specified in this field.
"boolValue": True or False, # Represents a boolean value.
@@ -2470,487 +2488,301 @@ Method Details
},
},
"userInput": { # Input provided by the user.
- "content": [
- { # The content of the response.
- "audio": { # An audio content block.
- "channels": 42, # The number of audio channels.
- "data": "A String", # The audio content.
- "mime_type": "A String", # The mime type of the audio.
- "sample_rate": 42, # The sample rate of the audio.
- "uri": "A String", # The URI of the audio.
- },
- "document": { # A document content block.
- "data": "A String", # The document content.
- "mime_type": "A String", # The mime type of the document.
- "uri": "A String", # The URI of the document.
- },
- "image": { # An image content block.
- "data": "A String", # The image content.
- "mime_type": "A String", # The mime type of the image.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the image.
- },
- "text": { # A text content block.
- "annotations": [ # Citation information for model-generated content.
- { # Citation information for model-generated content.
- "end_index": 42, # End of the attributed segment, exclusive.
- "file_citation": { # A file citation annotation. # A file citation annotation.
- "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
+ "contentList": { # A list of Content. # The content of the step. An array of Content objects.
+ "contents": [ # The contents of the list.
+ { # The content of the response.
+ "audio": { # An audio content block.
+ "channels": 42, # The number of audio channels.
+ "data": "A String", # The audio content.
+ "mime_type": "A String", # The mime type of the audio.
+ "sample_rate": 42, # The sample rate of the audio.
+ "uri": "A String", # The URI of the audio.
+ },
+ "document": { # A document content block.
+ "data": "A String", # The document content.
+ "mime_type": "A String", # The mime type of the document.
+ "uri": "A String", # The URI of the document.
+ },
+ "image": { # An image content block.
+ "data": "A String", # The image content.
+ "mime_type": "A String", # The mime type of the image.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the image.
+ },
+ "text": { # A text content block.
+ "annotations": [ # Citation information for model-generated content.
+ { # Citation information for model-generated content.
+ "end_index": 42, # End of the attributed segment, exclusive.
+ "file_citation": { # A file citation annotation. # A file citation annotation.
+ "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
+ ],
+ },
+ "document_uri": "A String", # The URI of the file.
+ "file_name": "A String", # The name of the file.
+ "media_id": "A String", # Media ID in-case of image citations, if applicable.
+ "page_number": 42, # Page number of the cited document, if applicable.
+ "source": "A String", # Source attributed for a portion of the text.
+ },
+ "place_citation": { # A place citation annotation. # A place citation annotation.
+ "name": "A String", # Title of the place.
+ "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
+ "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
+ { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
+ "review_id": "A String", # The ID of the review snippet.
+ "title": "A String", # Title of the review.
+ "url": "A String", # A link that corresponds to the user review on Google Maps.
},
],
+ "url": "A String", # URI reference of the place.
+ },
+ "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
+ "url_citation": { # A URL citation annotation. # A URL citation annotation.
+ "title": "A String", # The title of the URL.
+ "url": "A String", # The URL.
},
- "document_uri": "A String", # The URI of the file.
- "file_name": "A String", # The name of the file.
- "media_id": "A String", # Media ID in-case of image citations, if applicable.
- "page_number": 42, # Page number of the cited document, if applicable.
- "source": "A String", # Source attributed for a portion of the text.
- },
- "place_citation": { # A place citation annotation. # A place citation annotation.
- "name": "A String", # Title of the place.
- "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
- "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
- { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
- "review_id": "A String", # The ID of the review snippet.
- "title": "A String", # Title of the review.
- "url": "A String", # A link that corresponds to the user review on Google Maps.
- },
- ],
- "url": "A String", # URI reference of the place.
- },
- "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
- "url_citation": { # A URL citation annotation. # A URL citation annotation.
- "title": "A String", # The title of the URL.
- "url": "A String", # The URL.
- },
- },
- ],
- "text": "A String", # Required. The text content.
- },
- "thought": { # A thought content block.
- "signature": "A String", # Signature to match the backend source to be part of the generation.
- "summary": [ # A summary of the thought.
- {
- "image": { # An image content block.
- "data": "A String", # The image content.
- "mime_type": "A String", # The mime type of the image.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the image.
},
- "text": { # A text content block.
- "annotations": [ # Citation information for model-generated content.
- { # Citation information for model-generated content.
- "end_index": 42, # End of the attributed segment, exclusive.
- "file_citation": { # A file citation annotation. # A file citation annotation.
- "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
+ ],
+ "text": "A String", # Required. The text content.
+ },
+ "thought": { # A thought content block.
+ "signature": "A String", # Signature to match the backend source to be part of the generation.
+ "summary": [ # A summary of the thought.
+ {
+ "image": { # An image content block.
+ "data": "A String", # The image content.
+ "mime_type": "A String", # The mime type of the image.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the image.
+ },
+ "text": { # A text content block.
+ "annotations": [ # Citation information for model-generated content.
+ { # Citation information for model-generated content.
+ "end_index": 42, # End of the attributed segment, exclusive.
+ "file_citation": { # A file citation annotation. # A file citation annotation.
+ "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
+ ],
+ },
+ "document_uri": "A String", # The URI of the file.
+ "file_name": "A String", # The name of the file.
+ "media_id": "A String", # Media ID in-case of image citations, if applicable.
+ "page_number": 42, # Page number of the cited document, if applicable.
+ "source": "A String", # Source attributed for a portion of the text.
+ },
+ "place_citation": { # A place citation annotation. # A place citation annotation.
+ "name": "A String", # Title of the place.
+ "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
+ "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
+ { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
+ "review_id": "A String", # The ID of the review snippet.
+ "title": "A String", # Title of the review.
+ "url": "A String", # A link that corresponds to the user review on Google Maps.
},
],
+ "url": "A String", # URI reference of the place.
+ },
+ "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
+ "url_citation": { # A URL citation annotation. # A URL citation annotation.
+ "title": "A String", # The title of the URL.
+ "url": "A String", # The URL.
},
- "document_uri": "A String", # The URI of the file.
- "file_name": "A String", # The name of the file.
- "media_id": "A String", # Media ID in-case of image citations, if applicable.
- "page_number": 42, # Page number of the cited document, if applicable.
- "source": "A String", # Source attributed for a portion of the text.
- },
- "place_citation": { # A place citation annotation. # A place citation annotation.
- "name": "A String", # Title of the place.
- "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
- "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
- { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
- "review_id": "A String", # The ID of the review snippet.
- "title": "A String", # Title of the review.
- "url": "A String", # A link that corresponds to the user review on Google Maps.
- },
- ],
- "url": "A String", # URI reference of the place.
- },
- "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
- "url_citation": { # A URL citation annotation. # A URL citation annotation.
- "title": "A String", # The title of the URL.
- "url": "A String", # The URL.
},
- },
- ],
- "text": "A String", # Required. The text content.
+ ],
+ "text": "A String", # Required. The text content.
+ },
+ },
+ ],
+ },
+ "toolCall": { # Tool call content.
+ "codeExecutionCall": { # Code execution content.
+ "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution.
+ "code": "A String", # The code to be executed.
+ "language": "A String", # Programming language of the `code`.
},
},
- ],
- },
- "toolCall": { # Tool call content.
- "codeExecutionCall": { # Code execution content.
- "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution.
- "code": "A String", # The code to be executed.
- "language": "A String", # Programming language of the `code`.
+ "fileSearchCall": { # File Search content.
},
- },
- "fileSearchCall": { # File Search content.
- },
- "functionCall": { # A function tool call content block.
- "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
+ "functionCall": { # A function tool call content block.
+ "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- },
- ],
+ ],
+ },
+ "name": "A String", # Required. The name of the tool to call.
},
- "name": "A String", # Required. The name of the tool to call.
- },
- "googleMapsCall": { # Google Maps content.
- "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool.
- "queries": [ # The queries to be executed.
- "A String",
- ],
+ "googleMapsCall": { # Google Maps content.
+ "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool.
+ "queries": [ # The queries to be executed.
+ "A String",
+ ],
+ },
},
- },
- "googleSearchCall": { # Google Search content.
- "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search.
- "queries": [ # Web search queries for the following-up web search.
- "A String",
- ],
+ "googleSearchCall": { # Google Search content.
+ "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search.
+ "queries": [ # Web search queries for the following-up web search.
+ "A String",
+ ],
+ },
+ "search_type": "A String", # The type of search grounding enabled.
},
- "search_type": "A String", # The type of search grounding enabled.
- },
- "id": "A String", # Required. A unique ID for this specific tool call.
- "mcpServerToolCall": { # MCPServer tool call content.
- "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
+ "id": "A String", # Required. A unique ID for this specific tool call.
+ "mcpServerToolCall": { # MCPServer tool call content.
+ "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- },
- ],
+ ],
+ },
+ "name": "A String", # Required. The name of the tool which was called.
+ "server_name": "A String", # Required. The name of the used MCP server.
+ },
+ "signature": "A String", # A signature hash for backend validation.
+ "urlContextCall": { # URL context content.
+ "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context.
+ "urls": [ # The URLs to fetch.
+ "A String",
+ ],
+ },
},
- "name": "A String", # Required. The name of the tool which was called.
- "server_name": "A String", # Required. The name of the used MCP server.
},
- "signature": "A String", # A signature hash for backend validation.
- "urlContextCall": { # URL context content.
- "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context.
- "urls": [ # The URLs to fetch.
- "A String",
+ "toolResult": { # Tool result content.
+ "call_id": "A String", # Required. ID to match the ID from the function call block.
+ "codeExecutionResult": { # Code execution result content.
+ "is_error": True or False, # Whether the code execution resulted in an error.
+ "result": "A String", # Required. The output of the code execution.
+ },
+ "fileSearchResult": { # File Search result content.
+ "result": [ # Optional. The results of the File Search.
+ { # The result of the File Search.
+ },
],
},
- },
- },
- "toolResult": { # Tool result content.
- "call_id": "A String", # Required. ID to match the ID from the function call block.
- "codeExecutionResult": { # Code execution result content.
- "is_error": True or False, # Whether the code execution resulted in an error.
- "result": "A String", # Required. The output of the code execution.
- },
- "fileSearchResult": { # File Search result content.
- "result": [ # Optional. The results of the File Search.
- { # The result of the File Search.
- },
- ],
- },
- "functionResult": { # A function tool result content block.
- "contentList": {
- "contents": [
- {
- "image": { # An image content block.
- "data": "A String", # The image content.
- "mime_type": "A String", # The mime type of the image.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the image.
- },
- "text": { # A text content block.
- "annotations": [ # Citation information for model-generated content.
- { # Citation information for model-generated content.
- "end_index": 42, # End of the attributed segment, exclusive.
- "file_citation": { # A file citation annotation. # A file citation annotation.
- "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
+ "functionResult": { # A function tool result content block.
+ "contentList": {
+ "contents": [
+ {
+ "image": { # An image content block.
+ "data": "A String", # The image content.
+ "mime_type": "A String", # The mime type of the image.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the image.
+ },
+ "text": { # A text content block.
+ "annotations": [ # Citation information for model-generated content.
+ { # Citation information for model-generated content.
+ "end_index": 42, # End of the attributed segment, exclusive.
+ "file_citation": { # A file citation annotation. # A file citation annotation.
+ "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
+ ],
+ },
+ "document_uri": "A String", # The URI of the file.
+ "file_name": "A String", # The name of the file.
+ "media_id": "A String", # Media ID in-case of image citations, if applicable.
+ "page_number": 42, # Page number of the cited document, if applicable.
+ "source": "A String", # Source attributed for a portion of the text.
+ },
+ "place_citation": { # A place citation annotation. # A place citation annotation.
+ "name": "A String", # Title of the place.
+ "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
+ "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
+ { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
+ "review_id": "A String", # The ID of the review snippet.
+ "title": "A String", # Title of the review.
+ "url": "A String", # A link that corresponds to the user review on Google Maps.
},
],
+ "url": "A String", # URI reference of the place.
+ },
+ "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
+ "url_citation": { # A URL citation annotation. # A URL citation annotation.
+ "title": "A String", # The title of the URL.
+ "url": "A String", # The URL.
},
- "document_uri": "A String", # The URI of the file.
- "file_name": "A String", # The name of the file.
- "media_id": "A String", # Media ID in-case of image citations, if applicable.
- "page_number": 42, # Page number of the cited document, if applicable.
- "source": "A String", # Source attributed for a portion of the text.
- },
- "place_citation": { # A place citation annotation. # A place citation annotation.
- "name": "A String", # Title of the place.
- "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
- "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
- { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
- "review_id": "A String", # The ID of the review snippet.
- "title": "A String", # Title of the review.
- "url": "A String", # A link that corresponds to the user review on Google Maps.
- },
- ],
- "url": "A String", # URI reference of the place.
- },
- "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
- "url_citation": { # A URL citation annotation. # A URL citation annotation.
- "title": "A String", # The title of the URL.
- "url": "A String", # The URL.
},
- },
- ],
- "text": "A String", # Required. The text content.
- },
- },
- ],
- },
- "is_error": True or False, # Whether the tool call resulted in an error.
- "name": "A String", # The name of the tool that was called.
- "stringResult": "A String",
- "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
],
+ "text": "A String", # Required. The text content.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
- },
- },
- ],
- },
- },
- "googleMapsResult": { # Google Maps result content.
- "result": [ # Required. The results of the Google Maps.
- { # The result of the Google Maps.
- "places": [ # The places that were found.
- {
- "name": "A String", # Title of the place.
- "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
- "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
- { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
- "review_id": "A String", # The ID of the review snippet.
- "title": "A String", # Title of the review.
- "url": "A String", # A link that corresponds to the user review on Google Maps.
- },
- ],
- "url": "A String", # URI reference of the place.
},
],
- "widget_context_token": "A String", # Resource name of the Google Maps widget context token.
- },
- ],
- },
- "googleSearchResult": { # Google Search result content.
- "is_error": True or False, # Whether the Google Search resulted in an error.
- "result": [ # Required. The results of the Google Search.
- { # The result of the Google Search.
- "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview.
- },
- ],
- },
- "mcpServerToolResult": { # MCPServer tool result content.
- "contentList": {
- "contents": [
- {
- "image": { # An image content block.
- "data": "A String", # The image content.
- "mime_type": "A String", # The mime type of the image.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the image.
- },
- "text": { # A text content block.
- "annotations": [ # Citation information for model-generated content.
- { # Citation information for model-generated content.
- "end_index": 42, # End of the attributed segment, exclusive.
- "file_citation": { # A file citation annotation. # A file citation annotation.
- "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
- },
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
- },
- },
- ],
- },
- "document_uri": "A String", # The URI of the file.
- "file_name": "A String", # The name of the file.
- "media_id": "A String", # Media ID in-case of image citations, if applicable.
- "page_number": 42, # Page number of the cited document, if applicable.
- "source": "A String", # Source attributed for a portion of the text.
- },
- "place_citation": { # A place citation annotation. # A place citation annotation.
- "name": "A String", # Title of the place.
- "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
- "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
- { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
- "review_id": "A String", # The ID of the review snippet.
- "title": "A String", # Title of the review.
- "url": "A String", # A link that corresponds to the user review on Google Maps.
- },
- ],
- "url": "A String", # URI reference of the place.
- },
- "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
- "url_citation": { # A URL citation annotation. # A URL citation annotation.
- "title": "A String", # The title of the URL.
- "url": "A String", # The URL.
- },
- },
- ],
- "text": "A String", # Required. The text content.
- },
- },
- ],
- },
- "name": "A String", # Name of the tool which is called for this specific tool call.
- "server_name": "A String", # The name of the used MCP server.
- "stringResult": "A String",
- "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
- },
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
- },
- },
- ],
- },
- },
- "signature": "A String", # A signature hash for backend validation.
- "urlContextResult": { # URL context result content.
- "is_error": True or False, # Whether the URL context resulted in an error.
- "result": [ # Required. The results of the URL context.
- { # The result of the URL context.
- "status": "A String", # The status of the URL retrieval.
- "url": "A String", # The URL that was fetched.
},
- ],
- },
- },
- "video": { # A video content block.
- "data": "A String", # The video content.
- "mime_type": "A String", # The mime type of the video.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the video.
- },
- },
- ],
- },
- },
- ],
- },
- "steps": [ # Output only. The steps that make up the interaction.
- { # A step in the interaction.
- "modelOutput": { # Output generated by the model.
- "content": [
- { # The content of the response.
- "audio": { # An audio content block.
- "channels": 42, # The number of audio channels.
- "data": "A String", # The audio content.
- "mime_type": "A String", # The mime type of the audio.
- "sample_rate": 42, # The sample rate of the audio.
- "uri": "A String", # The URI of the audio.
- },
- "document": { # A document content block.
- "data": "A String", # The document content.
- "mime_type": "A String", # The mime type of the document.
- "uri": "A String", # The URI of the document.
- },
- "image": { # An image content block.
- "data": "A String", # The image content.
- "mime_type": "A String", # The mime type of the image.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the image.
- },
- "text": { # A text content block.
- "annotations": [ # Citation information for model-generated content.
- { # Citation information for model-generated content.
- "end_index": 42, # End of the attributed segment, exclusive.
- "file_citation": { # A file citation annotation. # A file citation annotation.
- "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
+ "is_error": True or False, # Whether the tool call resulted in an error.
+ "name": "A String", # The name of the tool that was called.
+ "stringResult": "A String",
+ "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values.
"fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
{ # Represents a single field in a struct.
"name": "A String",
@@ -2969,45 +2801,234 @@ Method Details
},
],
},
- "document_uri": "A String", # The URI of the file.
- "file_name": "A String", # The name of the file.
- "media_id": "A String", # Media ID in-case of image citations, if applicable.
- "page_number": 42, # Page number of the cited document, if applicable.
- "source": "A String", # Source attributed for a portion of the text.
},
- "place_citation": { # A place citation annotation. # A place citation annotation.
- "name": "A String", # Title of the place.
- "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
- "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
- { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
- "review_id": "A String", # The ID of the review snippet.
- "title": "A String", # Title of the review.
- "url": "A String", # A link that corresponds to the user review on Google Maps.
+ "googleMapsResult": { # Google Maps result content.
+ "result": [ # Required. The results of the Google Maps.
+ { # The result of the Google Maps.
+ "places": [ # The places that were found.
+ {
+ "name": "A String", # Title of the place.
+ "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
+ "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
+ { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
+ "review_id": "A String", # The ID of the review snippet.
+ "title": "A String", # Title of the review.
+ "url": "A String", # A link that corresponds to the user review on Google Maps.
+ },
+ ],
+ "url": "A String", # URI reference of the place.
+ },
+ ],
+ "widget_context_token": "A String", # Resource name of the Google Maps widget context token.
},
],
- "url": "A String", # URI reference of the place.
- },
- "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
- "url_citation": { # A URL citation annotation. # A URL citation annotation.
- "title": "A String", # The title of the URL.
- "url": "A String", # The URL.
},
- },
- ],
- "text": "A String", # Required. The text content.
- },
- "thought": { # A thought content block.
- "signature": "A String", # Signature to match the backend source to be part of the generation.
- "summary": [ # A summary of the thought.
- {
- "image": { # An image content block.
- "data": "A String", # The image content.
- "mime_type": "A String", # The mime type of the image.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the image.
+ "googleSearchResult": { # Google Search result content.
+ "is_error": True or False, # Whether the Google Search resulted in an error.
+ "result": [ # Required. The results of the Google Search.
+ { # The result of the Google Search.
+ "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview.
+ },
+ ],
},
- "text": { # A text content block.
- "annotations": [ # Citation information for model-generated content.
+ "mcpServerToolResult": { # MCPServer tool result content.
+ "contentList": {
+ "contents": [
+ {
+ "image": { # An image content block.
+ "data": "A String", # The image content.
+ "mime_type": "A String", # The mime type of the image.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the image.
+ },
+ "text": { # A text content block.
+ "annotations": [ # Citation information for model-generated content.
+ { # Citation information for model-generated content.
+ "end_index": 42, # End of the attributed segment, exclusive.
+ "file_citation": { # A file citation annotation. # A file citation annotation.
+ "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
+ },
+ },
+ ],
+ },
+ "document_uri": "A String", # The URI of the file.
+ "file_name": "A String", # The name of the file.
+ "media_id": "A String", # Media ID in-case of image citations, if applicable.
+ "page_number": 42, # Page number of the cited document, if applicable.
+ "source": "A String", # Source attributed for a portion of the text.
+ },
+ "place_citation": { # A place citation annotation. # A place citation annotation.
+ "name": "A String", # Title of the place.
+ "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
+ "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
+ { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
+ "review_id": "A String", # The ID of the review snippet.
+ "title": "A String", # Title of the review.
+ "url": "A String", # A link that corresponds to the user review on Google Maps.
+ },
+ ],
+ "url": "A String", # URI reference of the place.
+ },
+ "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
+ "url_citation": { # A URL citation annotation. # A URL citation annotation.
+ "title": "A String", # The title of the URL.
+ "url": "A String", # The URL.
+ },
+ },
+ ],
+ "text": "A String", # Required. The text content.
+ },
+ },
+ ],
+ },
+ "name": "A String", # Name of the tool which is called for this specific tool call.
+ "server_name": "A String", # The name of the used MCP server.
+ "stringResult": "A String",
+ "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
+ },
+ },
+ ],
+ },
+ },
+ "signature": "A String", # A signature hash for backend validation.
+ "urlContextResult": { # URL context result content.
+ "is_error": True or False, # Whether the URL context resulted in an error.
+ "result": [ # Required. The results of the URL context.
+ { # The result of the URL context.
+ "status": "A String", # The status of the URL retrieval.
+ "url": "A String", # The URL that was fetched.
+ },
+ ],
+ },
+ },
+ "video": { # A video content block.
+ "data": "A String", # The video content.
+ "mime_type": "A String", # The mime type of the video.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the video.
+ },
+ },
+ ],
+ },
+ "contentString": "A String", # The content of the step. A single string.
+ },
+ },
+ ],
+ },
+ "steps": [ # Required. Output only. The steps that make up the interaction.
+ { # A step in the interaction.
+ "modelOutput": { # Output generated by the model.
+ "content": [
+ { # The content of the response.
+ "audio": { # An audio content block.
+ "channels": 42, # The number of audio channels.
+ "data": "A String", # The audio content.
+ "mime_type": "A String", # The mime type of the audio.
+ "sample_rate": 42, # The sample rate of the audio.
+ "uri": "A String", # The URI of the audio.
+ },
+ "document": { # A document content block.
+ "data": "A String", # The document content.
+ "mime_type": "A String", # The mime type of the document.
+ "uri": "A String", # The URI of the document.
+ },
+ "image": { # An image content block.
+ "data": "A String", # The image content.
+ "mime_type": "A String", # The mime type of the image.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the image.
+ },
+ "text": { # A text content block.
+ "annotations": [ # Citation information for model-generated content.
+ { # Citation information for model-generated content.
+ "end_index": 42, # End of the attributed segment, exclusive.
+ "file_citation": { # A file citation annotation. # A file citation annotation.
+ "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
+ },
+ },
+ ],
+ },
+ "document_uri": "A String", # The URI of the file.
+ "file_name": "A String", # The name of the file.
+ "media_id": "A String", # Media ID in-case of image citations, if applicable.
+ "page_number": 42, # Page number of the cited document, if applicable.
+ "source": "A String", # Source attributed for a portion of the text.
+ },
+ "place_citation": { # A place citation annotation. # A place citation annotation.
+ "name": "A String", # Title of the place.
+ "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
+ "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
+ { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
+ "review_id": "A String", # The ID of the review snippet.
+ "title": "A String", # Title of the review.
+ "url": "A String", # A link that corresponds to the user review on Google Maps.
+ },
+ ],
+ "url": "A String", # URI reference of the place.
+ },
+ "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
+ "url_citation": { # A URL citation annotation. # A URL citation annotation.
+ "title": "A String", # The title of the URL.
+ "url": "A String", # The URL.
+ },
+ },
+ ],
+ "text": "A String", # Required. The text content.
+ },
+ "thought": { # A thought content block.
+ "signature": "A String", # Signature to match the backend source to be part of the generation.
+ "summary": [ # A summary of the thought.
+ {
+ "image": { # An image content block.
+ "data": "A String", # The image content.
+ "mime_type": "A String", # The mime type of the image.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the image.
+ },
+ "text": { # A text content block.
+ "annotations": [ # Citation information for model-generated content.
{ # Citation information for model-generated content.
"end_index": 42, # End of the attributed segment, exclusive.
"file_citation": { # A file citation annotation. # A file citation annotation.
@@ -3740,454 +3761,457 @@ Method Details
},
},
"userInput": { # Input provided by the user.
- "content": [
- { # The content of the response.
- "audio": { # An audio content block.
- "channels": 42, # The number of audio channels.
- "data": "A String", # The audio content.
- "mime_type": "A String", # The mime type of the audio.
- "sample_rate": 42, # The sample rate of the audio.
- "uri": "A String", # The URI of the audio.
- },
- "document": { # A document content block.
- "data": "A String", # The document content.
- "mime_type": "A String", # The mime type of the document.
- "uri": "A String", # The URI of the document.
- },
- "image": { # An image content block.
- "data": "A String", # The image content.
- "mime_type": "A String", # The mime type of the image.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the image.
- },
- "text": { # A text content block.
- "annotations": [ # Citation information for model-generated content.
- { # Citation information for model-generated content.
- "end_index": 42, # End of the attributed segment, exclusive.
- "file_citation": { # A file citation annotation. # A file citation annotation.
- "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
- },
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
- },
- },
- ],
- },
- "document_uri": "A String", # The URI of the file.
- "file_name": "A String", # The name of the file.
- "media_id": "A String", # Media ID in-case of image citations, if applicable.
- "page_number": 42, # Page number of the cited document, if applicable.
- "source": "A String", # Source attributed for a portion of the text.
- },
- "place_citation": { # A place citation annotation. # A place citation annotation.
- "name": "A String", # Title of the place.
- "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
- "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
- { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
- "review_id": "A String", # The ID of the review snippet.
- "title": "A String", # Title of the review.
- "url": "A String", # A link that corresponds to the user review on Google Maps.
- },
- ],
- "url": "A String", # URI reference of the place.
- },
- "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
- "url_citation": { # A URL citation annotation. # A URL citation annotation.
- "title": "A String", # The title of the URL.
- "url": "A String", # The URL.
- },
- },
- ],
- "text": "A String", # Required. The text content.
- },
- "thought": { # A thought content block.
- "signature": "A String", # Signature to match the backend source to be part of the generation.
- "summary": [ # A summary of the thought.
- {
- "image": { # An image content block.
- "data": "A String", # The image content.
- "mime_type": "A String", # The mime type of the image.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the image.
+ "contentList": { # A list of Content. # The content of the step. An array of Content objects.
+ "contents": [ # The contents of the list.
+ { # The content of the response.
+ "audio": { # An audio content block.
+ "channels": 42, # The number of audio channels.
+ "data": "A String", # The audio content.
+ "mime_type": "A String", # The mime type of the audio.
+ "sample_rate": 42, # The sample rate of the audio.
+ "uri": "A String", # The URI of the audio.
+ },
+ "document": { # A document content block.
+ "data": "A String", # The document content.
+ "mime_type": "A String", # The mime type of the document.
+ "uri": "A String", # The URI of the document.
+ },
+ "image": { # An image content block.
+ "data": "A String", # The image content.
+ "mime_type": "A String", # The mime type of the image.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the image.
+ },
+ "text": { # A text content block.
+ "annotations": [ # Citation information for model-generated content.
+ { # Citation information for model-generated content.
+ "end_index": 42, # End of the attributed segment, exclusive.
+ "file_citation": { # A file citation annotation. # A file citation annotation.
+ "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
+ },
+ },
+ ],
+ },
+ "document_uri": "A String", # The URI of the file.
+ "file_name": "A String", # The name of the file.
+ "media_id": "A String", # Media ID in-case of image citations, if applicable.
+ "page_number": 42, # Page number of the cited document, if applicable.
+ "source": "A String", # Source attributed for a portion of the text.
+ },
+ "place_citation": { # A place citation annotation. # A place citation annotation.
+ "name": "A String", # Title of the place.
+ "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
+ "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
+ { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
+ "review_id": "A String", # The ID of the review snippet.
+ "title": "A String", # Title of the review.
+ "url": "A String", # A link that corresponds to the user review on Google Maps.
+ },
+ ],
+ "url": "A String", # URI reference of the place.
+ },
+ "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
+ "url_citation": { # A URL citation annotation. # A URL citation annotation.
+ "title": "A String", # The title of the URL.
+ "url": "A String", # The URL.
+ },
},
- "text": { # A text content block.
- "annotations": [ # Citation information for model-generated content.
- { # Citation information for model-generated content.
- "end_index": 42, # End of the attributed segment, exclusive.
- "file_citation": { # A file citation annotation. # A file citation annotation.
- "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
+ ],
+ "text": "A String", # Required. The text content.
+ },
+ "thought": { # A thought content block.
+ "signature": "A String", # Signature to match the backend source to be part of the generation.
+ "summary": [ # A summary of the thought.
+ {
+ "image": { # An image content block.
+ "data": "A String", # The image content.
+ "mime_type": "A String", # The mime type of the image.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the image.
+ },
+ "text": { # A text content block.
+ "annotations": [ # Citation information for model-generated content.
+ { # Citation information for model-generated content.
+ "end_index": 42, # End of the attributed segment, exclusive.
+ "file_citation": { # A file citation annotation. # A file citation annotation.
+ "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
+ ],
+ },
+ "document_uri": "A String", # The URI of the file.
+ "file_name": "A String", # The name of the file.
+ "media_id": "A String", # Media ID in-case of image citations, if applicable.
+ "page_number": 42, # Page number of the cited document, if applicable.
+ "source": "A String", # Source attributed for a portion of the text.
+ },
+ "place_citation": { # A place citation annotation. # A place citation annotation.
+ "name": "A String", # Title of the place.
+ "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
+ "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
+ { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
+ "review_id": "A String", # The ID of the review snippet.
+ "title": "A String", # Title of the review.
+ "url": "A String", # A link that corresponds to the user review on Google Maps.
},
],
+ "url": "A String", # URI reference of the place.
+ },
+ "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
+ "url_citation": { # A URL citation annotation. # A URL citation annotation.
+ "title": "A String", # The title of the URL.
+ "url": "A String", # The URL.
},
- "document_uri": "A String", # The URI of the file.
- "file_name": "A String", # The name of the file.
- "media_id": "A String", # Media ID in-case of image citations, if applicable.
- "page_number": 42, # Page number of the cited document, if applicable.
- "source": "A String", # Source attributed for a portion of the text.
- },
- "place_citation": { # A place citation annotation. # A place citation annotation.
- "name": "A String", # Title of the place.
- "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
- "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
- { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
- "review_id": "A String", # The ID of the review snippet.
- "title": "A String", # Title of the review.
- "url": "A String", # A link that corresponds to the user review on Google Maps.
- },
- ],
- "url": "A String", # URI reference of the place.
- },
- "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
- "url_citation": { # A URL citation annotation. # A URL citation annotation.
- "title": "A String", # The title of the URL.
- "url": "A String", # The URL.
},
- },
- ],
- "text": "A String", # Required. The text content.
+ ],
+ "text": "A String", # Required. The text content.
+ },
+ },
+ ],
+ },
+ "toolCall": { # Tool call content.
+ "codeExecutionCall": { # Code execution content.
+ "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution.
+ "code": "A String", # The code to be executed.
+ "language": "A String", # Programming language of the `code`.
},
},
- ],
- },
- "toolCall": { # Tool call content.
- "codeExecutionCall": { # Code execution content.
- "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution.
- "code": "A String", # The code to be executed.
- "language": "A String", # Programming language of the `code`.
+ "fileSearchCall": { # File Search content.
},
- },
- "fileSearchCall": { # File Search content.
- },
- "functionCall": { # A function tool call content block.
- "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
+ "functionCall": { # A function tool call content block.
+ "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- },
- ],
- },
- "name": "A String", # Required. The name of the tool to call.
- },
- "googleMapsCall": { # Google Maps content.
- "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool.
- "queries": [ # The queries to be executed.
- "A String",
- ],
+ ],
+ },
+ "name": "A String", # Required. The name of the tool to call.
},
- },
- "googleSearchCall": { # Google Search content.
- "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search.
- "queries": [ # Web search queries for the following-up web search.
- "A String",
- ],
+ "googleMapsCall": { # Google Maps content.
+ "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool.
+ "queries": [ # The queries to be executed.
+ "A String",
+ ],
+ },
},
- "search_type": "A String", # The type of search grounding enabled.
- },
- "id": "A String", # Required. A unique ID for this specific tool call.
- "mcpServerToolCall": { # MCPServer tool call content.
- "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
+ "googleSearchCall": { # Google Search content.
+ "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search.
+ "queries": [ # Web search queries for the following-up web search.
+ "A String",
+ ],
+ },
+ "search_type": "A String", # The type of search grounding enabled.
+ },
+ "id": "A String", # Required. A unique ID for this specific tool call.
+ "mcpServerToolCall": { # MCPServer tool call content.
+ "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- },
- ],
+ ],
+ },
+ "name": "A String", # Required. The name of the tool which was called.
+ "server_name": "A String", # Required. The name of the used MCP server.
+ },
+ "signature": "A String", # A signature hash for backend validation.
+ "urlContextCall": { # URL context content.
+ "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context.
+ "urls": [ # The URLs to fetch.
+ "A String",
+ ],
+ },
},
- "name": "A String", # Required. The name of the tool which was called.
- "server_name": "A String", # Required. The name of the used MCP server.
},
- "signature": "A String", # A signature hash for backend validation.
- "urlContextCall": { # URL context content.
- "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context.
- "urls": [ # The URLs to fetch.
- "A String",
+ "toolResult": { # Tool result content.
+ "call_id": "A String", # Required. ID to match the ID from the function call block.
+ "codeExecutionResult": { # Code execution result content.
+ "is_error": True or False, # Whether the code execution resulted in an error.
+ "result": "A String", # Required. The output of the code execution.
+ },
+ "fileSearchResult": { # File Search result content.
+ "result": [ # Optional. The results of the File Search.
+ { # The result of the File Search.
+ },
],
},
- },
- },
- "toolResult": { # Tool result content.
- "call_id": "A String", # Required. ID to match the ID from the function call block.
- "codeExecutionResult": { # Code execution result content.
- "is_error": True or False, # Whether the code execution resulted in an error.
- "result": "A String", # Required. The output of the code execution.
- },
- "fileSearchResult": { # File Search result content.
- "result": [ # Optional. The results of the File Search.
- { # The result of the File Search.
- },
- ],
- },
- "functionResult": { # A function tool result content block.
- "contentList": {
- "contents": [
- {
- "image": { # An image content block.
- "data": "A String", # The image content.
- "mime_type": "A String", # The mime type of the image.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the image.
- },
- "text": { # A text content block.
- "annotations": [ # Citation information for model-generated content.
- { # Citation information for model-generated content.
- "end_index": 42, # End of the attributed segment, exclusive.
- "file_citation": { # A file citation annotation. # A file citation annotation.
- "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
+ "functionResult": { # A function tool result content block.
+ "contentList": {
+ "contents": [
+ {
+ "image": { # An image content block.
+ "data": "A String", # The image content.
+ "mime_type": "A String", # The mime type of the image.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the image.
+ },
+ "text": { # A text content block.
+ "annotations": [ # Citation information for model-generated content.
+ { # Citation information for model-generated content.
+ "end_index": 42, # End of the attributed segment, exclusive.
+ "file_citation": { # A file citation annotation. # A file citation annotation.
+ "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
+ ],
+ },
+ "document_uri": "A String", # The URI of the file.
+ "file_name": "A String", # The name of the file.
+ "media_id": "A String", # Media ID in-case of image citations, if applicable.
+ "page_number": 42, # Page number of the cited document, if applicable.
+ "source": "A String", # Source attributed for a portion of the text.
+ },
+ "place_citation": { # A place citation annotation. # A place citation annotation.
+ "name": "A String", # Title of the place.
+ "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
+ "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
+ { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
+ "review_id": "A String", # The ID of the review snippet.
+ "title": "A String", # Title of the review.
+ "url": "A String", # A link that corresponds to the user review on Google Maps.
},
],
+ "url": "A String", # URI reference of the place.
+ },
+ "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
+ "url_citation": { # A URL citation annotation. # A URL citation annotation.
+ "title": "A String", # The title of the URL.
+ "url": "A String", # The URL.
},
- "document_uri": "A String", # The URI of the file.
- "file_name": "A String", # The name of the file.
- "media_id": "A String", # Media ID in-case of image citations, if applicable.
- "page_number": 42, # Page number of the cited document, if applicable.
- "source": "A String", # Source attributed for a portion of the text.
- },
- "place_citation": { # A place citation annotation. # A place citation annotation.
- "name": "A String", # Title of the place.
- "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
- "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
- { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
- "review_id": "A String", # The ID of the review snippet.
- "title": "A String", # Title of the review.
- "url": "A String", # A link that corresponds to the user review on Google Maps.
- },
- ],
- "url": "A String", # URI reference of the place.
- },
- "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
- "url_citation": { # A URL citation annotation. # A URL citation annotation.
- "title": "A String", # The title of the URL.
- "url": "A String", # The URL.
},
- },
- ],
- "text": "A String", # Required. The text content.
- },
- },
- ],
- },
- "is_error": True or False, # Whether the tool call resulted in an error.
- "name": "A String", # The name of the tool that was called.
- "stringResult": "A String",
- "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
],
+ "text": "A String", # Required. The text content.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
- },
- },
- ],
- },
- },
- "googleMapsResult": { # Google Maps result content.
- "result": [ # Required. The results of the Google Maps.
- { # The result of the Google Maps.
- "places": [ # The places that were found.
- {
- "name": "A String", # Title of the place.
- "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
- "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
- { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
- "review_id": "A String", # The ID of the review snippet.
- "title": "A String", # Title of the review.
- "url": "A String", # A link that corresponds to the user review on Google Maps.
- },
- ],
- "url": "A String", # URI reference of the place.
},
],
- "widget_context_token": "A String", # Resource name of the Google Maps widget context token.
- },
- ],
- },
- "googleSearchResult": { # Google Search result content.
- "is_error": True or False, # Whether the Google Search resulted in an error.
- "result": [ # Required. The results of the Google Search.
- { # The result of the Google Search.
- "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview.
},
- ],
- },
- "mcpServerToolResult": { # MCPServer tool result content.
- "contentList": {
- "contents": [
- {
- "image": { # An image content block.
- "data": "A String", # The image content.
- "mime_type": "A String", # The mime type of the image.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the image.
+ "is_error": True or False, # Whether the tool call resulted in an error.
+ "name": "A String", # The name of the tool that was called.
+ "stringResult": "A String",
+ "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
+ },
},
- "text": { # A text content block.
- "annotations": [ # Citation information for model-generated content.
- { # Citation information for model-generated content.
- "end_index": 42, # End of the attributed segment, exclusive.
- "file_citation": { # A file citation annotation. # A file citation annotation.
- "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
- ],
+ ],
+ },
+ },
+ "googleMapsResult": { # Google Maps result content.
+ "result": [ # Required. The results of the Google Maps.
+ { # The result of the Google Maps.
+ "places": [ # The places that were found.
+ {
+ "name": "A String", # Title of the place.
+ "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
+ "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
+ { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
+ "review_id": "A String", # The ID of the review snippet.
+ "title": "A String", # Title of the review.
+ "url": "A String", # A link that corresponds to the user review on Google Maps.
+ },
+ ],
+ "url": "A String", # URI reference of the place.
+ },
+ ],
+ "widget_context_token": "A String", # Resource name of the Google Maps widget context token.
+ },
+ ],
+ },
+ "googleSearchResult": { # Google Search result content.
+ "is_error": True or False, # Whether the Google Search resulted in an error.
+ "result": [ # Required. The results of the Google Search.
+ { # The result of the Google Search.
+ "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview.
+ },
+ ],
+ },
+ "mcpServerToolResult": { # MCPServer tool result content.
+ "contentList": {
+ "contents": [
+ {
+ "image": { # An image content block.
+ "data": "A String", # The image content.
+ "mime_type": "A String", # The mime type of the image.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the image.
+ },
+ "text": { # A text content block.
+ "annotations": [ # Citation information for model-generated content.
+ { # Citation information for model-generated content.
+ "end_index": 42, # End of the attributed segment, exclusive.
+ "file_citation": { # A file citation annotation. # A file citation annotation.
+ "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
+ ],
+ },
+ "document_uri": "A String", # The URI of the file.
+ "file_name": "A String", # The name of the file.
+ "media_id": "A String", # Media ID in-case of image citations, if applicable.
+ "page_number": 42, # Page number of the cited document, if applicable.
+ "source": "A String", # Source attributed for a portion of the text.
+ },
+ "place_citation": { # A place citation annotation. # A place citation annotation.
+ "name": "A String", # Title of the place.
+ "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
+ "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
+ { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
+ "review_id": "A String", # The ID of the review snippet.
+ "title": "A String", # Title of the review.
+ "url": "A String", # A link that corresponds to the user review on Google Maps.
},
],
+ "url": "A String", # URI reference of the place.
+ },
+ "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
+ "url_citation": { # A URL citation annotation. # A URL citation annotation.
+ "title": "A String", # The title of the URL.
+ "url": "A String", # The URL.
},
- "document_uri": "A String", # The URI of the file.
- "file_name": "A String", # The name of the file.
- "media_id": "A String", # Media ID in-case of image citations, if applicable.
- "page_number": 42, # Page number of the cited document, if applicable.
- "source": "A String", # Source attributed for a portion of the text.
- },
- "place_citation": { # A place citation annotation. # A place citation annotation.
- "name": "A String", # Title of the place.
- "place_id": "A String", # The ID of the place, in `places/{place_id}` format.
- "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps.
- { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps.
- "review_id": "A String", # The ID of the review snippet.
- "title": "A String", # Title of the review.
- "url": "A String", # A link that corresponds to the user review on Google Maps.
- },
- ],
- "url": "A String", # URI reference of the place.
- },
- "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes.
- "url_citation": { # A URL citation annotation. # A URL citation annotation.
- "title": "A String", # The title of the URL.
- "url": "A String", # The URL.
},
- },
- ],
- "text": "A String", # Required. The text content.
- },
- },
- ],
- },
- "name": "A String", # Name of the tool which is called for this specific tool call.
- "server_name": "A String", # The name of the used MCP server.
- "stringResult": "A String",
- "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values.
- "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
- { # Represents a single field in a struct.
- "name": "A String",
- "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
- "boolValue": True or False, # Represents a boolean value.
- "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
- "values": [ # Repeated field of dynamically typed values.
- # Object with schema name: GenaiValue
],
+ "text": "A String", # Required. The text content.
+ },
+ },
+ ],
+ },
+ "name": "A String", # Name of the tool which is called for this specific tool call.
+ "server_name": "A String", # The name of the used MCP server.
+ "stringResult": "A String",
+ "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values.
+ "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control.
+ { # Represents a single field in a struct.
+ "name": "A String",
+ "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error.
+ "boolValue": True or False, # Represents a boolean value.
+ "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`.
+ "values": [ # Repeated field of dynamically typed values.
+ # Object with schema name: GenaiValue
+ ],
+ },
+ "nullValue": "A String", # Represents a null value.
+ "numberValue": 3.14, # Represents a double value.
+ "stringValue": "A String", # Represents a string value.
+ "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
- "nullValue": "A String", # Represents a null value.
- "numberValue": 3.14, # Represents a double value.
- "stringValue": "A String", # Represents a string value.
- "structValue": # Object with schema name: GenaiStruct # Represents a structured value.
},
+ ],
+ },
+ },
+ "signature": "A String", # A signature hash for backend validation.
+ "urlContextResult": { # URL context result content.
+ "is_error": True or False, # Whether the URL context resulted in an error.
+ "result": [ # Required. The results of the URL context.
+ { # The result of the URL context.
+ "status": "A String", # The status of the URL retrieval.
+ "url": "A String", # The URL that was fetched.
},
],
},
},
- "signature": "A String", # A signature hash for backend validation.
- "urlContextResult": { # URL context result content.
- "is_error": True or False, # Whether the URL context resulted in an error.
- "result": [ # Required. The results of the URL context.
- { # The result of the URL context.
- "status": "A String", # The status of the URL retrieval.
- "url": "A String", # The URL that was fetched.
- },
- ],
+ "video": { # A video content block.
+ "data": "A String", # The video content.
+ "mime_type": "A String", # The mime type of the video.
+ "resolution": "A String", # The resolution of the media.
+ "uri": "A String", # The URI of the video.
},
},
- "video": { # A video content block.
- "data": "A String", # The video content.
- "mime_type": "A String", # The mime type of the video.
- "resolution": "A String", # The resolution of the media.
- "uri": "A String", # The URI of the video.
- },
- },
- ],
+ ],
+ },
+ "contentString": "A String", # The content of the step. A single string.
},
},
],
@@ -4771,31 +4795,6 @@ Method Details
Close httplib2 connections.
delete(name, x__xgafv=None)
- Deletes an interaction.
-
-Args:
- name: string, Required. The name of the interaction to delete. Format: interactions/{interaction} (required)
- x__xgafv: string, V1 error format.
- Allowed values
- 1 - v1 error format
- 2 - v2 error format
-
-Returns:
- An object of the form:
-
- { # Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged.
- "contentType": "A String", # The HTTP Content-Type header value specifying the content type of the body.
- "data": "A String", # The HTTP request/response body as raw binary.
- "extensions": [ # Application specific response metadata. Must be set in the first response for streaming APIs.
- {
- "a_key": "", # Properties of the object. Contains field @type with type URL.
- },
- ],
-}
-getPoll(name, include_input=None, last_event_id=None, stream=None, x__xgafv=None)
Fully typed proto, unary version of GetInteraction that returns Interaction proto. @@ -4818,6 +4817,7 @@Method Details
"agent": "A String", # The name of the `Agent` used for generating the completion. "deep_research_config": { # Configuration for the Deep Research agent. "collaborative_planning": True or False, # Enables human-in-the-loop planning for the Deep Research agent. If set to true, the Deep Research agent will provide a research plan in its response. The agent will then proceed only if the user confirms the plan in the next turn. + "enable_bigquery_tool": True or False, # Enables bigquery tool for the Deep Research agent. "thinking_summaries": "A String", # Whether to include thought summaries in the response. "visualization": "A String", # Whether to include visualizations in the response. }, @@ -5740,6 +5740,8 @@Method Details
], }, "created": "A String", # Required. Output only. The time at which the response was created in ISO 8601 format (YYYY-MM-DDThh:mm:ssZ). + "env_id": "A String", # The environment ID for the interaction. Can be 'remote' for default environment. + "environment_id": "A String", # Output only. The environment ID for the interaction. Only populated if environment config is set in the request. "id": "A String", # Required. Output only. A unique identifier for the interaction completion. "modelInteraction": { # Interaction for generating the completion using models. # Interaction for generating the completion using models. "generation_config": { # Configuration parameters for model interactions. # Input only. Configuration parameters for the model interaction. @@ -6224,6 +6226,28 @@Method Details
}, ], "previous_interaction_id": "A String", # The ID of the previous interaction, if any. + "remoteEnvironment": { # Configuration for a custom environment. + "networkAllowlist": { # Network egress configuration for the environment. # Allow only specific domains. + "allowlist": [ # List of allowed domains and their configurations. + { # A network egress rule that controls which external domains the environment is allowed to reach. Each rule identifies a target domain and, optionally, a set of HTTP headers to inject into every matching outbound request. + "domain": "A String", # The domain pattern to match for this rule. Use an exact hostname (e.g., `github.com`), a wildcard prefix (e.g., `*.googleapis.com`), or `*` to match all domains. + "transform": { # Headers to inject into requests matching this rule. Key: header name (e.g., "Authorization"). Value: header value (e.g., "Bearer your-token"). + "a_key": "A String", + }, + }, + ], + }, + "networkMode": "A String", # Network egress mode. + "sources": [ + { # A source to be mounted into the environment. + "content": "A String", # The inline content if `type` is `INLINE`. + "encoding": "A String", # Optional encoding for inline content (e.g. `base64`). + "source": "A String", # The source of the environment. For GCS, this is the GCS path. For GitHub, this is the GitHub path. + "target": "A String", # Where the source should appear in the environment. + "type": "A String", + }, + ], + }, "responseFormatList": { "response_formats": [ { @@ -6280,8 +6304,6 @@Method Details
], }, }, - "video": { # Configuration for video output format. - }, }, ], }, @@ -6339,8 +6361,6 @@Method Details
], }, }, - "video": { # Configuration for video output format. - }, }, "response_format": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. # Enforces that the generated response is a JSON object that complies with the JSON schema specified in this field. "boolValue": True or False, # Represents a boolean value. @@ -7179,487 +7199,301 @@Method Details
}, }, "userInput": { # Input provided by the user. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "contentList": { # A list of Content. # The content of the step. An array of Content objects. + "contents": [ # The contents of the list. + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "toolCall": { # Tool call content. + "codeExecutionCall": { # Code execution content. + "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. }, }, - ], - }, - "toolCall": { # Tool call content. - "codeExecutionCall": { # Code execution content. - "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. - "code": "A String", # The code to be executed. - "language": "A String", # Programming language of the `code`. + "fileSearchCall": { # File Search content. }, - }, - "fileSearchCall": { # File Search content. - }, - "functionCall": { # A function tool call content block. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "functionCall": { # A function tool call content block. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], + ], + }, + "name": "A String", # Required. The name of the tool to call. }, - "name": "A String", # Required. The name of the tool to call. - }, - "googleMapsCall": { # Google Maps content. - "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. - "queries": [ # The queries to be executed. - "A String", - ], + "googleMapsCall": { # Google Maps content. + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], + }, }, - }, - "googleSearchCall": { # Google Search content. - "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. - "queries": [ # Web search queries for the following-up web search. - "A String", - ], + "googleSearchCall": { # Google Search content. + "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], + }, + "search_type": "A String", # The type of search grounding enabled. }, - "search_type": "A String", # The type of search grounding enabled. - }, - "id": "A String", # Required. A unique ID for this specific tool call. - "mcpServerToolCall": { # MCPServer tool call content. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "id": "A String", # Required. A unique ID for this specific tool call. + "mcpServerToolCall": { # MCPServer tool call content. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], + ], + }, + "name": "A String", # Required. The name of the tool which was called. + "server_name": "A String", # Required. The name of the used MCP server. + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { # URL context content. + "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, }, - "name": "A String", # Required. The name of the tool which was called. - "server_name": "A String", # Required. The name of the used MCP server. }, - "signature": "A String", # A signature hash for backend validation. - "urlContextCall": { # URL context content. - "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. - "urls": [ # The URLs to fetch. - "A String", + "toolResult": { # Tool result content. + "call_id": "A String", # Required. ID to match the ID from the function call block. + "codeExecutionResult": { # Code execution result content. + "is_error": True or False, # Whether the code execution resulted in an error. + "result": "A String", # Required. The output of the code execution. + }, + "fileSearchResult": { # File Search result content. + "result": [ # Optional. The results of the File Search. + { # The result of the File Search. + }, ], }, - }, - }, - "toolResult": { # Tool result content. - "call_id": "A String", # Required. ID to match the ID from the function call block. - "codeExecutionResult": { # Code execution result content. - "is_error": True or False, # Whether the code execution resulted in an error. - "result": "A String", # Required. The output of the code execution. - }, - "fileSearchResult": { # File Search result content. - "result": [ # Optional. The results of the File Search. - { # The result of the File Search. - }, - ], - }, - "functionResult": { # A function tool result content block. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "functionResult": { # A function tool result content block. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "is_error": True or False, # Whether the tool call resulted in an error. - "name": "A String", # The name of the tool that was called. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - }, - "googleMapsResult": { # Google Maps result content. - "result": [ # Required. The results of the Google Maps. - { # The result of the Google Maps. - "places": [ # The places that were found. - { - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. + ], + "text": "A String", # Required. The text content. + }, }, ], - "widget_context_token": "A String", # Resource name of the Google Maps widget context token. - }, - ], - }, - "googleSearchResult": { # Google Search result content. - "is_error": True or False, # Whether the Google Search resulted in an error. - "result": [ # Required. The results of the Google Search. - { # The result of the Google Search. - "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. - }, - ], - }, - "mcpServerToolResult": { # MCPServer tool result content. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. }, - ], - }, - }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. - }, - }, - ], - }, - }, - ], - }, - "steps": [ # Output only. The steps that make up the interaction. - { # A step in the interaction. - "modelOutput": { # Output generated by the model. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "is_error": True or False, # Whether the tool call resulted in an error. + "name": "A String", # The name of the tool that was called. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. { # Represents a single field in a struct. "name": "A String", @@ -7678,50 +7512,239 @@Method Details
}, ], }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. + "googleMapsResult": { # Google Maps result content. + "result": [ # Required. The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. }, ], - "url": "A String", # URI reference of the place. }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. + "googleSearchResult": { # Google Search result content. + "is_error": True or False, # Whether the Google Search resulted in an error. + "result": [ # Required. The results of the Google Search. + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, + ], }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + "mcpServerToolResult": { # MCPServer tool result content. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. + }, + ], + }, + }, + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. + }, + }, + ], + }, + "contentString": "A String", # The content of the step. A single string. + }, + }, + ], + }, + "steps": [ # Required. Output only. The steps that make up the interaction. + { # A step in the interaction. + "modelOutput": { # Output generated by the model. + "content": [ + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. { # Represents a single field in a struct. "name": "A String", "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. @@ -8449,454 +8472,457 @@Method Details
}, }, "userInput": { # Input provided by the user. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. + "contentList": { # A list of Content. # The content of the step. An array of Content objects. + "contents": [ # The contents of the list. + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "toolCall": { # Tool call content. + "codeExecutionCall": { # Code execution content. + "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. }, }, - ], - }, - "toolCall": { # Tool call content. - "codeExecutionCall": { # Code execution content. - "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. - "code": "A String", # The code to be executed. - "language": "A String", # Programming language of the `code`. + "fileSearchCall": { # File Search content. }, - }, - "fileSearchCall": { # File Search content. - }, - "functionCall": { # A function tool call content block. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "functionCall": { # A function tool call content block. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], - }, - "name": "A String", # Required. The name of the tool to call. - }, - "googleMapsCall": { # Google Maps content. - "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. - "queries": [ # The queries to be executed. - "A String", - ], + ], + }, + "name": "A String", # Required. The name of the tool to call. }, - }, - "googleSearchCall": { # Google Search content. - "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. - "queries": [ # Web search queries for the following-up web search. - "A String", - ], + "googleMapsCall": { # Google Maps content. + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], + }, }, - "search_type": "A String", # The type of search grounding enabled. - }, - "id": "A String", # Required. A unique ID for this specific tool call. - "mcpServerToolCall": { # MCPServer tool call content. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "googleSearchCall": { # Google Search content. + "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], + }, + "search_type": "A String", # The type of search grounding enabled. + }, + "id": "A String", # Required. A unique ID for this specific tool call. + "mcpServerToolCall": { # MCPServer tool call content. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], + ], + }, + "name": "A String", # Required. The name of the tool which was called. + "server_name": "A String", # Required. The name of the used MCP server. + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { # URL context content. + "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, }, - "name": "A String", # Required. The name of the tool which was called. - "server_name": "A String", # Required. The name of the used MCP server. }, - "signature": "A String", # A signature hash for backend validation. - "urlContextCall": { # URL context content. - "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. - "urls": [ # The URLs to fetch. - "A String", + "toolResult": { # Tool result content. + "call_id": "A String", # Required. ID to match the ID from the function call block. + "codeExecutionResult": { # Code execution result content. + "is_error": True or False, # Whether the code execution resulted in an error. + "result": "A String", # Required. The output of the code execution. + }, + "fileSearchResult": { # File Search result content. + "result": [ # Optional. The results of the File Search. + { # The result of the File Search. + }, ], }, - }, - }, - "toolResult": { # Tool result content. - "call_id": "A String", # Required. ID to match the ID from the function call block. - "codeExecutionResult": { # Code execution result content. - "is_error": True or False, # Whether the code execution resulted in an error. - "result": "A String", # Required. The output of the code execution. - }, - "fileSearchResult": { # File Search result content. - "result": [ # Optional. The results of the File Search. - { # The result of the File Search. - }, - ], - }, - "functionResult": { # A function tool result content block. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "functionResult": { # A function tool result content block. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "is_error": True or False, # Whether the tool call resulted in an error. - "name": "A String", # The name of the tool that was called. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue ], + "text": "A String", # Required. The text content. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - }, - "googleMapsResult": { # Google Maps result content. - "result": [ # Required. The results of the Google Maps. - { # The result of the Google Maps. - "places": [ # The places that were found. - { - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. }, ], - "widget_context_token": "A String", # Resource name of the Google Maps widget context token. - }, - ], - }, - "googleSearchResult": { # Google Search result content. - "is_error": True or False, # Whether the Google Search resulted in an error. - "result": [ # Required. The results of the Google Search. - { # The result of the Google Search. - "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. }, - ], - }, - "mcpServerToolResult": { # MCPServer tool result content. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. + "is_error": True or False, # Whether the tool call resulted in an error. + "name": "A String", # The name of the tool that was called. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + ], + }, + }, + "googleMapsResult": { # Google Maps result content. + "result": [ # Required. The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. + }, + ], + }, + "googleSearchResult": { # Google Search result content. + "is_error": True or False, # Whether the Google Search resulted in an error. + "result": [ # Required. The results of the Google Search. + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, + ], + }, + "mcpServerToolResult": { # MCPServer tool result content. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. }, ], }, }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. }, }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. - }, - }, - ], + ], + }, + "contentString": "A String", # The content of the step. A single string. }, }, ], @@ -10409,6 +10435,7 @@Method Details
"agent": "A String", # The name of the `Agent` used for generating the completion. "deep_research_config": { # Configuration for the Deep Research agent. "collaborative_planning": True or False, # Enables human-in-the-loop planning for the Deep Research agent. If set to true, the Deep Research agent will provide a research plan in its response. The agent will then proceed only if the user confirms the plan in the next turn. + "enable_bigquery_tool": True or False, # Enables bigquery tool for the Deep Research agent. "thinking_summaries": "A String", # Whether to include thought summaries in the response. "visualization": "A String", # Whether to include visualizations in the response. }, @@ -11331,6 +11358,8 @@Method Details
], }, "created": "A String", # Required. Output only. The time at which the response was created in ISO 8601 format (YYYY-MM-DDThh:mm:ssZ). + "env_id": "A String", # The environment ID for the interaction. Can be 'remote' for default environment. + "environment_id": "A String", # Output only. The environment ID for the interaction. Only populated if environment config is set in the request. "id": "A String", # Required. Output only. A unique identifier for the interaction completion. "modelInteraction": { # Interaction for generating the completion using models. # Interaction for generating the completion using models. "generation_config": { # Configuration parameters for model interactions. # Input only. Configuration parameters for the model interaction. @@ -11815,6 +11844,28 @@Method Details
}, ], "previous_interaction_id": "A String", # The ID of the previous interaction, if any. + "remoteEnvironment": { # Configuration for a custom environment. + "networkAllowlist": { # Network egress configuration for the environment. # Allow only specific domains. + "allowlist": [ # List of allowed domains and their configurations. + { # A network egress rule that controls which external domains the environment is allowed to reach. Each rule identifies a target domain and, optionally, a set of HTTP headers to inject into every matching outbound request. + "domain": "A String", # The domain pattern to match for this rule. Use an exact hostname (e.g., `github.com`), a wildcard prefix (e.g., `*.googleapis.com`), or `*` to match all domains. + "transform": { # Headers to inject into requests matching this rule. Key: header name (e.g., "Authorization"). Value: header value (e.g., "Bearer your-token"). + "a_key": "A String", + }, + }, + ], + }, + "networkMode": "A String", # Network egress mode. + "sources": [ + { # A source to be mounted into the environment. + "content": "A String", # The inline content if `type` is `INLINE`. + "encoding": "A String", # Optional encoding for inline content (e.g. `base64`). + "source": "A String", # The source of the environment. For GCS, this is the GCS path. For GitHub, this is the GitHub path. + "target": "A String", # Where the source should appear in the environment. + "type": "A String", + }, + ], + }, "responseFormatList": { "response_formats": [ { @@ -11871,8 +11922,6 @@Method Details
], }, }, - "video": { # Configuration for video output format. - }, }, ], }, @@ -11930,8 +11979,6 @@Method Details
], }, }, - "video": { # Configuration for video output format. - }, }, "response_format": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. # Enforces that the generated response is a JSON object that complies with the JSON schema specified in this field. "boolValue": True or False, # Represents a boolean value. @@ -12770,459 +12817,462 @@Method Details
}, }, "userInput": { # Input provided by the user. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue + "contentList": { # A list of Content. # The content of the step. An array of Content objects. + "contents": [ # The contents of the list. + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, ], }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, }, ], + "text": "A String", # Required. The text content. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. + ], + }, + "toolCall": { # Tool call content. + "codeExecutionCall": { # Code execution content. + "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. + }, + }, + "fileSearchCall": { # File Search content. + }, + "functionCall": { # A function tool call content block. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, ], - "url": "A String", # URI reference of the place. }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + "name": "A String", # Required. The name of the tool to call. + }, + "googleMapsCall": { # Google Maps content. + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], }, }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. + "googleSearchCall": { # Google Search content. + "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, + "search_type": "A String", # The type of search grounding enabled. + }, + "id": "A String", # Required. A unique ID for this specific tool call. + "mcpServerToolCall": { # MCPServer tool call content. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue ], }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, }, ], - "text": "A String", # Required. The text content. }, + "name": "A String", # Required. The name of the tool which was called. + "server_name": "A String", # Required. The name of the used MCP server. }, - ], - }, - "toolCall": { # Tool call content. - "codeExecutionCall": { # Code execution content. - "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. - "code": "A String", # The code to be executed. - "language": "A String", # Programming language of the `code`. + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { # URL context content. + "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, }, }, - "fileSearchCall": { # File Search content. - }, - "functionCall": { # A function tool call content block. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "name": "A String", # Required. The name of the tool to call. - }, - "googleMapsCall": { # Google Maps content. - "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. - "queries": [ # The queries to be executed. - "A String", - ], + "toolResult": { # Tool result content. + "call_id": "A String", # Required. ID to match the ID from the function call block. + "codeExecutionResult": { # Code execution result content. + "is_error": True or False, # Whether the code execution resulted in an error. + "result": "A String", # Required. The output of the code execution. }, - }, - "googleSearchCall": { # Google Search content. - "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. - "queries": [ # Web search queries for the following-up web search. - "A String", + "fileSearchResult": { # File Search result content. + "result": [ # Optional. The results of the File Search. + { # The result of the File Search. + }, ], }, - "search_type": "A String", # The type of search grounding enabled. - }, - "id": "A String", # Required. A unique ID for this specific tool call. - "mcpServerToolCall": { # MCPServer tool call content. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue + "functionResult": { # A function tool result content block. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "is_error": True or False, # Whether the tool call resulted in an error. + "name": "A String", # The name of the tool that was called. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + }, + "googleMapsResult": { # Google Maps result content. + "result": [ # Required. The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. }, ], }, - "name": "A String", # Required. The name of the tool which was called. - "server_name": "A String", # Required. The name of the used MCP server. - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextCall": { # URL context content. - "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. - "urls": [ # The URLs to fetch. - "A String", + "googleSearchResult": { # Google Search result content. + "is_error": True or False, # Whether the Google Search resulted in an error. + "result": [ # Required. The results of the Google Search. + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, ], }, - }, - }, - "toolResult": { # Tool result content. - "call_id": "A String", # Required. ID to match the ID from the function call block. - "codeExecutionResult": { # Code execution result content. - "is_error": True or False, # Whether the code execution resulted in an error. - "result": "A String", # Required. The output of the code execution. - }, - "fileSearchResult": { # File Search result content. - "result": [ # Optional. The results of the File Search. - { # The result of the File Search. - }, - ], - }, - "functionResult": { # A function tool result content block. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "mcpServerToolResult": { # MCPServer tool result content. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "is_error": True or False, # Whether the tool call resulted in an error. - "name": "A String", # The name of the tool that was called. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue ], + "text": "A String", # Required. The text content. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], - }, - }, - "googleMapsResult": { # Google Maps result content. - "result": [ # Required. The results of the Google Maps. - { # The result of the Google Maps. - "places": [ # The places that were found. - { - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. + ], + }, + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "url": "A String", # URI reference of the place. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, ], - "widget_context_token": "A String", # Resource name of the Google Maps widget context token. }, - ], + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. + }, + ], + }, }, - "googleSearchResult": { # Google Search result content. - "is_error": True or False, # Whether the Google Search resulted in an error. - "result": [ # Required. The results of the Google Search. - { # The result of the Google Search. - "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. - }, - ], + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. }, - "mcpServerToolResult": { # MCPServer tool result content. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], - }, - }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. }, - }, - ], + ], + }, + "contentString": "A String", # The content of the step. A single string. }, }, ], }, - "steps": [ # Output only. The steps that make up the interaction. + "steps": [ # Required. Output only. The steps that make up the interaction. { # A step in the interaction. "modelOutput": { # Output generated by the model. "content": [ @@ -14040,454 +14090,457 @@Method Details
}, }, "userInput": { # Input provided by the user. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "contentList": { # A list of Content. # The content of the step. An array of Content objects. + "contents": [ # The contents of the list. + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], }, - ], - "url": "A String", # URI reference of the place. + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "toolCall": { # Tool call content. + "codeExecutionCall": { # Code execution content. + "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. + }, + }, + "fileSearchCall": { # File Search content. + }, + "functionCall": { # A function tool call content block. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, }, ], - "text": "A String", # Required. The text content. }, + "name": "A String", # Required. The name of the tool to call. }, - ], - }, - "toolCall": { # Tool call content. - "codeExecutionCall": { # Code execution content. - "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. - "code": "A String", # The code to be executed. - "language": "A String", # Programming language of the `code`. + "googleMapsCall": { # Google Maps content. + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], + }, }, - }, - "fileSearchCall": { # File Search content. - }, - "functionCall": { # A function tool call content block. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "googleSearchCall": { # Google Search content. + "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], + }, + "search_type": "A String", # The type of search grounding enabled. + }, + "id": "A String", # Required. A unique ID for this specific tool call. + "mcpServerToolCall": { # MCPServer tool call content. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], + ], + }, + "name": "A String", # Required. The name of the tool which was called. + "server_name": "A String", # Required. The name of the used MCP server. }, - "name": "A String", # Required. The name of the tool to call. - }, - "googleMapsCall": { # Google Maps content. - "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. - "queries": [ # The queries to be executed. - "A String", - ], + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { # URL context content. + "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, }, }, - "googleSearchCall": { # Google Search content. - "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. - "queries": [ # Web search queries for the following-up web search. - "A String", - ], + "toolResult": { # Tool result content. + "call_id": "A String", # Required. ID to match the ID from the function call block. + "codeExecutionResult": { # Code execution result content. + "is_error": True or False, # Whether the code execution resulted in an error. + "result": "A String", # Required. The output of the code execution. }, - "search_type": "A String", # The type of search grounding enabled. - }, - "id": "A String", # Required. A unique ID for this specific tool call. - "mcpServerToolCall": { # MCPServer tool call content. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, + "fileSearchResult": { # File Search result content. + "result": [ # Optional. The results of the File Search. + { # The result of the File Search. }, ], }, - "name": "A String", # Required. The name of the tool which was called. - "server_name": "A String", # Required. The name of the used MCP server. - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextCall": { # URL context content. - "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. - "urls": [ # The URLs to fetch. - "A String", - ], - }, - }, - }, - "toolResult": { # Tool result content. - "call_id": "A String", # Required. ID to match the ID from the function call block. - "codeExecutionResult": { # Code execution result content. - "is_error": True or False, # Whether the code execution resulted in an error. - "result": "A String", # Required. The output of the code execution. - }, - "fileSearchResult": { # File Search result content. - "result": [ # Optional. The results of the File Search. - { # The result of the File Search. - }, - ], - }, - "functionResult": { # A function tool result content block. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "functionResult": { # A function tool result content block. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "is_error": True or False, # Whether the tool call resulted in an error. + "name": "A String", # The name of the tool that was called. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "text": "A String", # Required. The text content. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, - }, - ], + ], + }, }, - "is_error": True or False, # Whether the tool call resulted in an error. - "name": "A String", # The name of the tool that was called. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue + "googleMapsResult": { # Google Maps result content. + "result": [ # Required. The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, ], + "url": "A String", # URI reference of the place. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. }, ], }, - }, - "googleMapsResult": { # Google Maps result content. - "result": [ # Required. The results of the Google Maps. - { # The result of the Google Maps. - "places": [ # The places that were found. + "googleSearchResult": { # Google Search result content. + "is_error": True or False, # Whether the Google Search resulted in an error. + "result": [ # Required. The results of the Google Search. + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, + ], + }, + "mcpServerToolResult": { # MCPServer tool result content. + "contentList": { + "contents": [ { - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. + }, }, ], - "widget_context_token": "A String", # Resource name of the Google Maps widget context token. }, - ], - }, - "googleSearchResult": { # Google Search result content. - "is_error": True or False, # Whether the Google Search resulted in an error. - "result": [ # Required. The results of the Google Search. - { # The result of the Google Search. - "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. - }, - ], - }, - "mcpServerToolResult": { # MCPServer tool result content. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. }, ], }, }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. }, }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. - }, - }, - ], + ], + }, + "contentString": "A String", # The content of the step. A single string. }, }, ], @@ -15071,6 +15124,7 @@Method Details
"agent": "A String", # The name of the `Agent` used for generating the completion. "deep_research_config": { # Configuration for the Deep Research agent. "collaborative_planning": True or False, # Enables human-in-the-loop planning for the Deep Research agent. If set to true, the Deep Research agent will provide a research plan in its response. The agent will then proceed only if the user confirms the plan in the next turn. + "enable_bigquery_tool": True or False, # Enables bigquery tool for the Deep Research agent. "thinking_summaries": "A String", # Whether to include thought summaries in the response. "visualization": "A String", # Whether to include visualizations in the response. }, @@ -15993,6 +16047,8 @@Method Details
], }, "created": "A String", # Required. Output only. The time at which the response was created in ISO 8601 format (YYYY-MM-DDThh:mm:ssZ). + "env_id": "A String", # The environment ID for the interaction. Can be 'remote' for default environment. + "environment_id": "A String", # Output only. The environment ID for the interaction. Only populated if environment config is set in the request. "id": "A String", # Required. Output only. A unique identifier for the interaction completion. "modelInteraction": { # Interaction for generating the completion using models. # Interaction for generating the completion using models. "generation_config": { # Configuration parameters for model interactions. # Input only. Configuration parameters for the model interaction. @@ -16477,6 +16533,28 @@Method Details
}, ], "previous_interaction_id": "A String", # The ID of the previous interaction, if any. + "remoteEnvironment": { # Configuration for a custom environment. + "networkAllowlist": { # Network egress configuration for the environment. # Allow only specific domains. + "allowlist": [ # List of allowed domains and their configurations. + { # A network egress rule that controls which external domains the environment is allowed to reach. Each rule identifies a target domain and, optionally, a set of HTTP headers to inject into every matching outbound request. + "domain": "A String", # The domain pattern to match for this rule. Use an exact hostname (e.g., `github.com`), a wildcard prefix (e.g., `*.googleapis.com`), or `*` to match all domains. + "transform": { # Headers to inject into requests matching this rule. Key: header name (e.g., "Authorization"). Value: header value (e.g., "Bearer your-token"). + "a_key": "A String", + }, + }, + ], + }, + "networkMode": "A String", # Network egress mode. + "sources": [ + { # A source to be mounted into the environment. + "content": "A String", # The inline content if `type` is `INLINE`. + "encoding": "A String", # Optional encoding for inline content (e.g. `base64`). + "source": "A String", # The source of the environment. For GCS, this is the GCS path. For GitHub, this is the GitHub path. + "target": "A String", # Where the source should appear in the environment. + "type": "A String", + }, + ], + }, "responseFormatList": { "response_formats": [ { @@ -16533,8 +16611,6 @@Method Details
], }, }, - "video": { # Configuration for video output format. - }, }, ], }, @@ -16592,8 +16668,6 @@Method Details
], }, }, - "video": { # Configuration for video output format. - }, }, "response_format": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. # Enforces that the generated response is a JSON object that complies with the JSON schema specified in this field. "boolValue": True or False, # Represents a boolean value. @@ -17432,118 +17506,325 @@Method Details
}, }, "userInput": { # Input provided by the user. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "contentList": { # A list of Content. # The content of the step. An array of Content objects. + "contents": [ # The contents of the list. + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. }, - "place_citation": { # A place citation annotation. # A place citation annotation. + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "toolCall": { # Tool call content. + "codeExecutionCall": { # Code execution content. + "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. + }, + }, + "fileSearchCall": { # File Search content. + }, + "functionCall": { # A function tool call content block. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "name": "A String", # Required. The name of the tool to call. + }, + "googleMapsCall": { # Google Maps content. + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], + }, + }, + "googleSearchCall": { # Google Search content. + "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], + }, + "search_type": "A String", # The type of search grounding enabled. + }, + "id": "A String", # Required. A unique ID for this specific tool call. + "mcpServerToolCall": { # MCPServer tool call content. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "name": "A String", # Required. The name of the tool which was called. + "server_name": "A String", # Required. The name of the used MCP server. + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { # URL context content. + "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, + }, + }, + "toolResult": { # Tool result content. + "call_id": "A String", # Required. ID to match the ID from the function call block. + "codeExecutionResult": { # Code execution result content. + "is_error": True or False, # Whether the code execution resulted in an error. + "result": "A String", # Required. The output of the code execution. + }, + "fileSearchResult": { # File Search result content. + "result": [ # Optional. The results of the File Search. + { # The result of the File Search. + }, + ], + }, + "functionResult": { # A function tool result content block. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "is_error": True or False, # Whether the tool call resulted in an error. + "name": "A String", # The name of the tool that was called. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + }, + "googleMapsResult": { # Google Maps result content. + "result": [ # Required. The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { "name": "A String", # Title of the place. "place_id": "A String", # The ID of the place, in `places/{place_id}` format. "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. @@ -17555,201 +17836,246 @@Method Details
], "url": "A String", # URI reference of the place. }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. + }, + ], + }, + "googleSearchResult": { # Google Search result content. + "is_error": True or False, # Whether the Google Search resulted in an error. + "result": [ # Required. The results of the Google Search. + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, + ], + }, + "mcpServerToolResult": { # MCPServer tool result content. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. }, }, ], - "text": "A String", # Required. The text content. }, - }, - ], - }, - "toolCall": { # Tool call content. - "codeExecutionCall": { # Code execution content. - "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. - "code": "A String", # The code to be executed. - "language": "A String", # Programming language of the `code`. - }, - }, - "fileSearchCall": { # File Search content. - }, - "functionCall": { # A function tool call content block. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. }, ], }, - "name": "A String", # Required. The name of the tool to call. }, - "googleMapsCall": { # Google Maps content. - "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. - "queries": [ # The queries to be executed. - "A String", - ], - }, + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. }, - "googleSearchCall": { # Google Search content. - "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. - "queries": [ # Web search queries for the following-up web search. - "A String", - ], - }, - "search_type": "A String", # The type of search grounding enabled. - }, - "id": "A String", # Required. A unique ID for this specific tool call. - "mcpServerToolCall": { # MCPServer tool call content. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + }, + ], + }, + "contentString": "A String", # The content of the step. A single string. + }, + }, + ], + }, + "steps": [ # Required. Output only. The steps that make up the interaction. + { # A step in the interaction. + "modelOutput": { # Output generated by the model. + "content": [ + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. }, - "name": "A String", # Required. The name of the tool which was called. - "server_name": "A String", # Required. The name of the used MCP server. - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextCall": { # URL context content. - "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. - "urls": [ # The URLs to fetch. - "A String", - ], + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, }, - }, - "toolResult": { # Tool result content. - "call_id": "A String", # Required. ID to match the ID from the function call block. - "codeExecutionResult": { # Code execution result content. - "is_error": True or False, # Whether the code execution resulted in an error. - "result": "A String", # Required. The output of the code execution. - }, - "fileSearchResult": { # File Search result content. - "result": [ # Optional. The results of the File Search. - { # The result of the File Search. - }, - ], - }, - "functionResult": { # A function tool result content block. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "is_error": True or False, # Whether the tool call resulted in an error. - "name": "A String", # The name of the tool that was called. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue ], }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. }, - }, - ], - }, - }, - "googleMapsResult": { # Google Maps result content. - "result": [ # Required. The results of the Google Maps. - { # The result of the Google Maps. - "places": [ # The places that were found. - { + "place_citation": { # A place citation annotation. # A place citation annotation. "name": "A String", # Title of the place. "place_id": "A String", # The ID of the place, in `places/{place_id}` format. "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. @@ -17761,264 +18087,15 @@Method Details
], "url": "A String", # URI reference of the place. }, - ], - "widget_context_token": "A String", # Resource name of the Google Maps widget context token. - }, - ], - }, - "googleSearchResult": { # Google Search result content. - "is_error": True or False, # Whether the Google Search resulted in an error. - "result": [ # Required. The results of the Google Search. - { # The result of the Google Search. - "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. - }, - ], - }, - "mcpServerToolResult": { # MCPServer tool result content. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], - }, - }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. - }, - }, - ], - }, - }, - ], - }, - "steps": [ # Output only. The steps that make up the interaction. - { # A step in the interaction. - "modelOutput": { # Output generated by the model. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. + }, }, ], }, @@ -18702,454 +18779,457 @@Method Details
}, }, "userInput": { # Input provided by the user. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "contentList": { # A list of Content. # The content of the step. An array of Content objects. + "contents": [ # The contents of the list. + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "toolCall": { # Tool call content. + "codeExecutionCall": { # Code execution content. + "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. + }, + }, + "fileSearchCall": { # File Search content. + }, + "functionCall": { # A function tool call content block. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, }, ], - "text": "A String", # Required. The text content. }, + "name": "A String", # Required. The name of the tool to call. }, - ], - }, - "toolCall": { # Tool call content. - "codeExecutionCall": { # Code execution content. - "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. - "code": "A String", # The code to be executed. - "language": "A String", # Programming language of the `code`. + "googleMapsCall": { # Google Maps content. + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], + }, }, - }, - "fileSearchCall": { # File Search content. - }, - "functionCall": { # A function tool call content block. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "googleSearchCall": { # Google Search content. + "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], + }, + "search_type": "A String", # The type of search grounding enabled. + }, + "id": "A String", # Required. A unique ID for this specific tool call. + "mcpServerToolCall": { # MCPServer tool call content. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], + ], + }, + "name": "A String", # Required. The name of the tool which was called. + "server_name": "A String", # Required. The name of the used MCP server. }, - "name": "A String", # Required. The name of the tool to call. - }, - "googleMapsCall": { # Google Maps content. - "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. - "queries": [ # The queries to be executed. - "A String", - ], + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { # URL context content. + "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, }, }, - "googleSearchCall": { # Google Search content. - "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. - "queries": [ # Web search queries for the following-up web search. - "A String", - ], + "toolResult": { # Tool result content. + "call_id": "A String", # Required. ID to match the ID from the function call block. + "codeExecutionResult": { # Code execution result content. + "is_error": True or False, # Whether the code execution resulted in an error. + "result": "A String", # Required. The output of the code execution. }, - "search_type": "A String", # The type of search grounding enabled. - }, - "id": "A String", # Required. A unique ID for this specific tool call. - "mcpServerToolCall": { # MCPServer tool call content. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, + "fileSearchResult": { # File Search result content. + "result": [ # Optional. The results of the File Search. + { # The result of the File Search. }, ], }, - "name": "A String", # Required. The name of the tool which was called. - "server_name": "A String", # Required. The name of the used MCP server. - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextCall": { # URL context content. - "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. - "urls": [ # The URLs to fetch. - "A String", - ], - }, - }, - }, - "toolResult": { # Tool result content. - "call_id": "A String", # Required. ID to match the ID from the function call block. - "codeExecutionResult": { # Code execution result content. - "is_error": True or False, # Whether the code execution resulted in an error. - "result": "A String", # Required. The output of the code execution. - }, - "fileSearchResult": { # File Search result content. - "result": [ # Optional. The results of the File Search. - { # The result of the File Search. - }, - ], - }, - "functionResult": { # A function tool result content block. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "functionResult": { # A function tool result content block. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "is_error": True or False, # Whether the tool call resulted in an error. - "name": "A String", # The name of the tool that was called. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue ], + "text": "A String", # Required. The text content. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], - }, - }, - "googleMapsResult": { # Google Maps result content. - "result": [ # Required. The results of the Google Maps. - { # The result of the Google Maps. - "places": [ # The places that were found. - { - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. + ], + }, + "is_error": True or False, # Whether the tool call resulted in an error. + "name": "A String", # The name of the tool that was called. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "url": "A String", # URI reference of the place. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, ], - "widget_context_token": "A String", # Resource name of the Google Maps widget context token. - }, - ], - }, - "googleSearchResult": { # Google Search result content. - "is_error": True or False, # Whether the Google Search resulted in an error. - "result": [ # Required. The results of the Google Search. - { # The result of the Google Search. - "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. }, - ], - }, - "mcpServerToolResult": { # MCPServer tool result content. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + }, + "googleMapsResult": { # Google Maps result content. + "result": [ # Required. The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. + }, + ], + }, + "googleSearchResult": { # Google Search result content. + "is_error": True or False, # Whether the Google Search resulted in an error. + "result": [ # Required. The results of the Google Search. + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, + ], + }, + "mcpServerToolResult": { # MCPServer tool result content. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. }, ], }, }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. }, }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. - }, - }, - ], + ], + }, + "contentString": "A String", # The content of the step. A single string. }, }, ], @@ -19733,6 +19813,7 @@Method Details
"agent": "A String", # The name of the `Agent` used for generating the completion. "deep_research_config": { # Configuration for the Deep Research agent. "collaborative_planning": True or False, # Enables human-in-the-loop planning for the Deep Research agent. If set to true, the Deep Research agent will provide a research plan in its response. The agent will then proceed only if the user confirms the plan in the next turn. + "enable_bigquery_tool": True or False, # Enables bigquery tool for the Deep Research agent. "thinking_summaries": "A String", # Whether to include thought summaries in the response. "visualization": "A String", # Whether to include visualizations in the response. }, @@ -20655,6 +20736,8 @@Method Details
], }, "created": "A String", # Required. Output only. The time at which the response was created in ISO 8601 format (YYYY-MM-DDThh:mm:ssZ). + "env_id": "A String", # The environment ID for the interaction. Can be 'remote' for default environment. + "environment_id": "A String", # Output only. The environment ID for the interaction. Only populated if environment config is set in the request. "id": "A String", # Required. Output only. A unique identifier for the interaction completion. "modelInteraction": { # Interaction for generating the completion using models. # Interaction for generating the completion using models. "generation_config": { # Configuration parameters for model interactions. # Input only. Configuration parameters for the model interaction. @@ -21139,6 +21222,28 @@Method Details
}, ], "previous_interaction_id": "A String", # The ID of the previous interaction, if any. + "remoteEnvironment": { # Configuration for a custom environment. + "networkAllowlist": { # Network egress configuration for the environment. # Allow only specific domains. + "allowlist": [ # List of allowed domains and their configurations. + { # A network egress rule that controls which external domains the environment is allowed to reach. Each rule identifies a target domain and, optionally, a set of HTTP headers to inject into every matching outbound request. + "domain": "A String", # The domain pattern to match for this rule. Use an exact hostname (e.g., `github.com`), a wildcard prefix (e.g., `*.googleapis.com`), or `*` to match all domains. + "transform": { # Headers to inject into requests matching this rule. Key: header name (e.g., "Authorization"). Value: header value (e.g., "Bearer your-token"). + "a_key": "A String", + }, + }, + ], + }, + "networkMode": "A String", # Network egress mode. + "sources": [ + { # A source to be mounted into the environment. + "content": "A String", # The inline content if `type` is `INLINE`. + "encoding": "A String", # Optional encoding for inline content (e.g. `base64`). + "source": "A String", # The source of the environment. For GCS, this is the GCS path. For GitHub, this is the GitHub path. + "target": "A String", # Where the source should appear in the environment. + "type": "A String", + }, + ], + }, "responseFormatList": { "response_formats": [ { @@ -21195,8 +21300,6 @@Method Details
], }, }, - "video": { # Configuration for video output format. - }, }, ], }, @@ -21254,8 +21357,6 @@Method Details
], }, }, - "video": { # Configuration for video output format. - }, }, "response_format": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. # Enforces that the generated response is a JSON object that complies with the JSON schema specified in this field. "boolValue": True or False, # Represents a boolean value. @@ -22094,459 +22195,462 @@Method Details
}, }, "userInput": { # Input provided by the user. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "contentList": { # A list of Content. # The content of the step. An array of Content objects. + "contents": [ # The contents of the list. + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "toolCall": { # Tool call content. + "codeExecutionCall": { # Code execution content. + "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. }, }, - ], - }, - "toolCall": { # Tool call content. - "codeExecutionCall": { # Code execution content. - "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. - "code": "A String", # The code to be executed. - "language": "A String", # Programming language of the `code`. + "fileSearchCall": { # File Search content. }, - }, - "fileSearchCall": { # File Search content. - }, - "functionCall": { # A function tool call content block. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "functionCall": { # A function tool call content block. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], + ], + }, + "name": "A String", # Required. The name of the tool to call. }, - "name": "A String", # Required. The name of the tool to call. - }, - "googleMapsCall": { # Google Maps content. - "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. - "queries": [ # The queries to be executed. - "A String", - ], + "googleMapsCall": { # Google Maps content. + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], + }, }, - }, - "googleSearchCall": { # Google Search content. - "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. - "queries": [ # Web search queries for the following-up web search. - "A String", - ], + "googleSearchCall": { # Google Search content. + "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], + }, + "search_type": "A String", # The type of search grounding enabled. }, - "search_type": "A String", # The type of search grounding enabled. - }, - "id": "A String", # Required. A unique ID for this specific tool call. - "mcpServerToolCall": { # MCPServer tool call content. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "id": "A String", # Required. A unique ID for this specific tool call. + "mcpServerToolCall": { # MCPServer tool call content. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], + ], + }, + "name": "A String", # Required. The name of the tool which was called. + "server_name": "A String", # Required. The name of the used MCP server. + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { # URL context content. + "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, }, - "name": "A String", # Required. The name of the tool which was called. - "server_name": "A String", # Required. The name of the used MCP server. }, - "signature": "A String", # A signature hash for backend validation. - "urlContextCall": { # URL context content. - "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. - "urls": [ # The URLs to fetch. - "A String", + "toolResult": { # Tool result content. + "call_id": "A String", # Required. ID to match the ID from the function call block. + "codeExecutionResult": { # Code execution result content. + "is_error": True or False, # Whether the code execution resulted in an error. + "result": "A String", # Required. The output of the code execution. + }, + "fileSearchResult": { # File Search result content. + "result": [ # Optional. The results of the File Search. + { # The result of the File Search. + }, ], }, - }, - }, - "toolResult": { # Tool result content. - "call_id": "A String", # Required. ID to match the ID from the function call block. - "codeExecutionResult": { # Code execution result content. - "is_error": True or False, # Whether the code execution resulted in an error. - "result": "A String", # Required. The output of the code execution. - }, - "fileSearchResult": { # File Search result content. - "result": [ # Optional. The results of the File Search. - { # The result of the File Search. - }, - ], - }, - "functionResult": { # A function tool result content block. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "functionResult": { # A function tool result content block. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "is_error": True or False, # Whether the tool call resulted in an error. + "name": "A String", # The name of the tool that was called. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "text": "A String", # Required. The text content. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, - }, - ], + ], + }, }, - "is_error": True or False, # Whether the tool call resulted in an error. - "name": "A String", # The name of the tool that was called. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue + "googleMapsResult": { # Google Maps result content. + "result": [ # Required. The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, ], + "url": "A String", # URI reference of the place. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. }, ], }, - }, - "googleMapsResult": { # Google Maps result content. - "result": [ # Required. The results of the Google Maps. - { # The result of the Google Maps. - "places": [ # The places that were found. + "googleSearchResult": { # Google Search result content. + "is_error": True or False, # Whether the Google Search resulted in an error. + "result": [ # Required. The results of the Google Search. + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, + ], + }, + "mcpServerToolResult": { # MCPServer tool result content. + "contentList": { + "contents": [ { - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. + }, }, ], - "widget_context_token": "A String", # Resource name of the Google Maps widget context token. }, - ], - }, - "googleSearchResult": { # Google Search result content. - "is_error": True or False, # Whether the Google Search resulted in an error. - "result": [ # Required. The results of the Google Search. - { # The result of the Google Search. - "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. - }, - ], - }, - "mcpServerToolResult": { # MCPServer tool result content. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. }, ], }, }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. }, }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. - }, - }, - ], + ], + }, + "contentString": "A String", # The content of the step. A single string. }, }, ], }, - "steps": [ # Output only. The steps that make up the interaction. + "steps": [ # Required. Output only. The steps that make up the interaction. { # A step in the interaction. "modelOutput": { # Output generated by the model. "content": [ @@ -23364,454 +23468,457 @@Method Details
}, }, "userInput": { # Input provided by the user. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "contentList": { # A list of Content. # The content of the step. An array of Content objects. + "contents": [ # The contents of the list. + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "toolCall": { # Tool call content. + "codeExecutionCall": { # Code execution content. + "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. + }, + }, + "fileSearchCall": { # File Search content. + }, + "functionCall": { # A function tool call content block. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, }, ], - "text": "A String", # Required. The text content. }, + "name": "A String", # Required. The name of the tool to call. }, - ], - }, - "toolCall": { # Tool call content. - "codeExecutionCall": { # Code execution content. - "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. - "code": "A String", # The code to be executed. - "language": "A String", # Programming language of the `code`. + "googleMapsCall": { # Google Maps content. + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], + }, }, - }, - "fileSearchCall": { # File Search content. - }, - "functionCall": { # A function tool call content block. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "googleSearchCall": { # Google Search content. + "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], + }, + "search_type": "A String", # The type of search grounding enabled. + }, + "id": "A String", # Required. A unique ID for this specific tool call. + "mcpServerToolCall": { # MCPServer tool call content. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], + ], + }, + "name": "A String", # Required. The name of the tool which was called. + "server_name": "A String", # Required. The name of the used MCP server. }, - "name": "A String", # Required. The name of the tool to call. - }, - "googleMapsCall": { # Google Maps content. - "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. - "queries": [ # The queries to be executed. - "A String", - ], + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { # URL context content. + "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, }, }, - "googleSearchCall": { # Google Search content. - "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. - "queries": [ # Web search queries for the following-up web search. - "A String", - ], + "toolResult": { # Tool result content. + "call_id": "A String", # Required. ID to match the ID from the function call block. + "codeExecutionResult": { # Code execution result content. + "is_error": True or False, # Whether the code execution resulted in an error. + "result": "A String", # Required. The output of the code execution. }, - "search_type": "A String", # The type of search grounding enabled. - }, - "id": "A String", # Required. A unique ID for this specific tool call. - "mcpServerToolCall": { # MCPServer tool call content. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, + "fileSearchResult": { # File Search result content. + "result": [ # Optional. The results of the File Search. + { # The result of the File Search. }, ], }, - "name": "A String", # Required. The name of the tool which was called. - "server_name": "A String", # Required. The name of the used MCP server. - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextCall": { # URL context content. - "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. - "urls": [ # The URLs to fetch. - "A String", - ], - }, - }, - }, - "toolResult": { # Tool result content. - "call_id": "A String", # Required. ID to match the ID from the function call block. - "codeExecutionResult": { # Code execution result content. - "is_error": True or False, # Whether the code execution resulted in an error. - "result": "A String", # Required. The output of the code execution. - }, - "fileSearchResult": { # File Search result content. - "result": [ # Optional. The results of the File Search. - { # The result of the File Search. - }, - ], - }, - "functionResult": { # A function tool result content block. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "functionResult": { # A function tool result content block. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "is_error": True or False, # Whether the tool call resulted in an error. + "name": "A String", # The name of the tool that was called. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "text": "A String", # Required. The text content. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, - }, - ], + ], + }, }, - "is_error": True or False, # Whether the tool call resulted in an error. - "name": "A String", # The name of the tool that was called. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue + "googleMapsResult": { # Google Maps result content. + "result": [ # Required. The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, ], + "url": "A String", # URI reference of the place. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. }, ], }, - }, - "googleMapsResult": { # Google Maps result content. - "result": [ # Required. The results of the Google Maps. - { # The result of the Google Maps. - "places": [ # The places that were found. + "googleSearchResult": { # Google Search result content. + "is_error": True or False, # Whether the Google Search resulted in an error. + "result": [ # Required. The results of the Google Search. + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, + ], + }, + "mcpServerToolResult": { # MCPServer tool result content. + "contentList": { + "contents": [ { - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. + }, }, ], - "widget_context_token": "A String", # Resource name of the Google Maps widget context token. }, - ], - }, - "googleSearchResult": { # Google Search result content. - "is_error": True or False, # Whether the Google Search resulted in an error. - "result": [ # Required. The results of the Google Search. - { # The result of the Google Search. - "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. - }, - ], - }, - "mcpServerToolResult": { # MCPServer tool result content. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. }, ], }, }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. }, }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. - }, - }, - ], + ], + }, + "contentString": "A String", # The content of the step. A single string. }, }, ], @@ -24395,6 +24502,7 @@Method Details
"agent": "A String", # The name of the `Agent` used for generating the completion. "deep_research_config": { # Configuration for the Deep Research agent. "collaborative_planning": True or False, # Enables human-in-the-loop planning for the Deep Research agent. If set to true, the Deep Research agent will provide a research plan in its response. The agent will then proceed only if the user confirms the plan in the next turn. + "enable_bigquery_tool": True or False, # Enables bigquery tool for the Deep Research agent. "thinking_summaries": "A String", # Whether to include thought summaries in the response. "visualization": "A String", # Whether to include visualizations in the response. }, @@ -25317,6 +25425,8 @@Method Details
], }, "created": "A String", # Required. Output only. The time at which the response was created in ISO 8601 format (YYYY-MM-DDThh:mm:ssZ). + "env_id": "A String", # The environment ID for the interaction. Can be 'remote' for default environment. + "environment_id": "A String", # Output only. The environment ID for the interaction. Only populated if environment config is set in the request. "id": "A String", # Required. Output only. A unique identifier for the interaction completion. "modelInteraction": { # Interaction for generating the completion using models. # Interaction for generating the completion using models. "generation_config": { # Configuration parameters for model interactions. # Input only. Configuration parameters for the model interaction. @@ -25801,6 +25911,28 @@Method Details
}, ], "previous_interaction_id": "A String", # The ID of the previous interaction, if any. + "remoteEnvironment": { # Configuration for a custom environment. + "networkAllowlist": { # Network egress configuration for the environment. # Allow only specific domains. + "allowlist": [ # List of allowed domains and their configurations. + { # A network egress rule that controls which external domains the environment is allowed to reach. Each rule identifies a target domain and, optionally, a set of HTTP headers to inject into every matching outbound request. + "domain": "A String", # The domain pattern to match for this rule. Use an exact hostname (e.g., `github.com`), a wildcard prefix (e.g., `*.googleapis.com`), or `*` to match all domains. + "transform": { # Headers to inject into requests matching this rule. Key: header name (e.g., "Authorization"). Value: header value (e.g., "Bearer your-token"). + "a_key": "A String", + }, + }, + ], + }, + "networkMode": "A String", # Network egress mode. + "sources": [ + { # A source to be mounted into the environment. + "content": "A String", # The inline content if `type` is `INLINE`. + "encoding": "A String", # Optional encoding for inline content (e.g. `base64`). + "source": "A String", # The source of the environment. For GCS, this is the GCS path. For GitHub, this is the GitHub path. + "target": "A String", # Where the source should appear in the environment. + "type": "A String", + }, + ], + }, "responseFormatList": { "response_formats": [ { @@ -25857,8 +25989,6 @@Method Details
], }, }, - "video": { # Configuration for video output format. - }, }, ], }, @@ -25916,8 +26046,6 @@Method Details
], }, }, - "video": { # Configuration for video output format. - }, }, "response_format": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. # Enforces that the generated response is a JSON object that complies with the JSON schema specified in this field. "boolValue": True or False, # Represents a boolean value. @@ -26756,118 +26884,325 @@Method Details
}, }, "userInput": { # Input provided by the user. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "contentList": { # A list of Content. # The content of the step. An array of Content objects. + "contents": [ # The contents of the list. + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "toolCall": { # Tool call content. + "codeExecutionCall": { # Code execution content. + "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. }, }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. + "fileSearchCall": { # File Search content. + }, + "functionCall": { # A function tool call content block. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + "name": "A String", # Required. The name of the tool to call. + }, + "googleMapsCall": { # Google Maps content. + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], + }, + }, + "googleSearchCall": { # Google Search content. + "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], + }, + "search_type": "A String", # The type of search grounding enabled. + }, + "id": "A String", # Required. A unique ID for this specific tool call. + "mcpServerToolCall": { # MCPServer tool call content. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "name": "A String", # Required. The name of the tool which was called. + "server_name": "A String", # Required. The name of the used MCP server. + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { # URL context content. + "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, + }, + }, + "toolResult": { # Tool result content. + "call_id": "A String", # Required. ID to match the ID from the function call block. + "codeExecutionResult": { # Code execution result content. + "is_error": True or False, # Whether the code execution resulted in an error. + "result": "A String", # Required. The output of the code execution. + }, + "fileSearchResult": { # File Search result content. + "result": [ # Optional. The results of the File Search. + { # The result of the File Search. + }, + ], + }, + "functionResult": { # A function tool result content block. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "is_error": True or False, # Whether the tool call resulted in an error. + "name": "A String", # The name of the tool that was called. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue ], }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "place_citation": { # A place citation annotation. # A place citation annotation. + }, + ], + }, + }, + "googleMapsResult": { # Google Maps result content. + "result": [ # Required. The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { "name": "A String", # Title of the place. "place_id": "A String", # The ID of the place, in `places/{place_id}` format. "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. @@ -26879,450 +27214,246 @@Method Details
], "url": "A String", # URI reference of the place. }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. + }, + ], + }, + "googleSearchResult": { # Google Search result content. + "is_error": True or False, # Whether the Google Search resulted in an error. + "result": [ # Required. The results of the Google Search. + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, + ], + }, + "mcpServerToolResult": { # MCPServer tool result content. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. }, }, ], - "text": "A String", # Required. The text content. }, - }, - ], - }, - "toolCall": { # Tool call content. - "codeExecutionCall": { # Code execution content. - "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. - "code": "A String", # The code to be executed. - "language": "A String", # Programming language of the `code`. - }, - }, - "fileSearchCall": { # File Search content. - }, - "functionCall": { # A function tool call content block. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. }, ], }, - "name": "A String", # Required. The name of the tool to call. }, - "googleMapsCall": { # Google Maps content. - "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. - "queries": [ # The queries to be executed. - "A String", - ], - }, - }, - "googleSearchCall": { # Google Search content. - "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. - "queries": [ # Web search queries for the following-up web search. - "A String", - ], - }, - "search_type": "A String", # The type of search grounding enabled. + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. }, - "id": "A String", # Required. A unique ID for this specific tool call. - "mcpServerToolCall": { # MCPServer tool call content. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + }, + ], + }, + "contentString": "A String", # The content of the step. A single string. + }, + }, + ], + }, + "steps": [ # Required. Output only. The steps that make up the interaction. + { # A step in the interaction. + "modelOutput": { # Output generated by the model. + "content": [ + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. }, - "name": "A String", # Required. The name of the tool which was called. - "server_name": "A String", # Required. The name of the used MCP server. - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextCall": { # URL context content. - "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. - "urls": [ # The URLs to fetch. - "A String", - ], + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, }, - }, - "toolResult": { # Tool result content. - "call_id": "A String", # Required. ID to match the ID from the function call block. - "codeExecutionResult": { # Code execution result content. - "is_error": True or False, # Whether the code execution resulted in an error. - "result": "A String", # Required. The output of the code execution. - }, - "fileSearchResult": { # File Search result content. - "result": [ # Optional. The results of the File Search. - { # The result of the File Search. - }, - ], - }, - "functionResult": { # A function tool result content block. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "is_error": True or False, # Whether the tool call resulted in an error. - "name": "A String", # The name of the tool that was called. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue ], }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. }, - }, - ], - }, - }, - "googleMapsResult": { # Google Maps result content. - "result": [ # Required. The results of the Google Maps. - { # The result of the Google Maps. - "places": [ # The places that were found. - { - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - ], - "widget_context_token": "A String", # Resource name of the Google Maps widget context token. - }, - ], - }, - "googleSearchResult": { # Google Search result content. - "is_error": True or False, # Whether the Google Search resulted in an error. - "result": [ # Required. The results of the Google Search. - { # The result of the Google Search. - "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. - }, - ], - }, - "mcpServerToolResult": { # MCPServer tool result content. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], - }, - }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. - }, - }, - ], - }, - }, - ], - }, - "steps": [ # Output only. The steps that make up the interaction. - { # A step in the interaction. - "modelOutput": { # Output generated by the model. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, - ], - }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. + "place_citation": { # A place citation annotation. # A place citation annotation. "name": "A String", # Title of the place. "place_id": "A String", # The ID of the place, in `places/{place_id}` format. "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. @@ -28026,454 +28157,457 @@Method Details
}, }, "userInput": { # Input provided by the user. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "contentList": { # A list of Content. # The content of the step. An array of Content objects. + "contents": [ # The contents of the list. + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. - }, - }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + ], + "text": "A String", # Required. The text content. + }, + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "toolCall": { # Tool call content. + "codeExecutionCall": { # Code execution content. + "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. + }, + }, + "fileSearchCall": { # File Search content. + }, + "functionCall": { # A function tool call content block. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, }, ], - "text": "A String", # Required. The text content. }, + "name": "A String", # Required. The name of the tool to call. }, - ], - }, - "toolCall": { # Tool call content. - "codeExecutionCall": { # Code execution content. - "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. - "code": "A String", # The code to be executed. - "language": "A String", # Programming language of the `code`. + "googleMapsCall": { # Google Maps content. + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], + }, }, - }, - "fileSearchCall": { # File Search content. - }, - "functionCall": { # A function tool call content block. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "googleSearchCall": { # Google Search content. + "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], + }, + "search_type": "A String", # The type of search grounding enabled. + }, + "id": "A String", # Required. A unique ID for this specific tool call. + "mcpServerToolCall": { # MCPServer tool call content. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], + ], + }, + "name": "A String", # Required. The name of the tool which was called. + "server_name": "A String", # Required. The name of the used MCP server. }, - "name": "A String", # Required. The name of the tool to call. - }, - "googleMapsCall": { # Google Maps content. - "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. - "queries": [ # The queries to be executed. - "A String", - ], + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { # URL context content. + "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, }, }, - "googleSearchCall": { # Google Search content. - "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. - "queries": [ # Web search queries for the following-up web search. - "A String", - ], + "toolResult": { # Tool result content. + "call_id": "A String", # Required. ID to match the ID from the function call block. + "codeExecutionResult": { # Code execution result content. + "is_error": True or False, # Whether the code execution resulted in an error. + "result": "A String", # Required. The output of the code execution. }, - "search_type": "A String", # The type of search grounding enabled. - }, - "id": "A String", # Required. A unique ID for this specific tool call. - "mcpServerToolCall": { # MCPServer tool call content. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, + "fileSearchResult": { # File Search result content. + "result": [ # Optional. The results of the File Search. + { # The result of the File Search. }, ], }, - "name": "A String", # Required. The name of the tool which was called. - "server_name": "A String", # Required. The name of the used MCP server. - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextCall": { # URL context content. - "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. - "urls": [ # The URLs to fetch. - "A String", - ], - }, - }, - }, - "toolResult": { # Tool result content. - "call_id": "A String", # Required. ID to match the ID from the function call block. - "codeExecutionResult": { # Code execution result content. - "is_error": True or False, # Whether the code execution resulted in an error. - "result": "A String", # Required. The output of the code execution. - }, - "fileSearchResult": { # File Search result content. - "result": [ # Optional. The results of the File Search. - { # The result of the File Search. - }, - ], - }, - "functionResult": { # A function tool result content block. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "functionResult": { # A function tool result content block. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "is_error": True or False, # Whether the tool call resulted in an error. - "name": "A String", # The name of the tool that was called. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue ], + "text": "A String", # Required. The text content. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], - }, - }, - "googleMapsResult": { # Google Maps result content. - "result": [ # Required. The results of the Google Maps. - { # The result of the Google Maps. - "places": [ # The places that were found. - { - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. + ], + }, + "is_error": True or False, # Whether the tool call resulted in an error. + "name": "A String", # The name of the tool that was called. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "url": "A String", # URI reference of the place. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, ], - "widget_context_token": "A String", # Resource name of the Google Maps widget context token. - }, - ], - }, - "googleSearchResult": { # Google Search result content. - "is_error": True or False, # Whether the Google Search resulted in an error. - "result": [ # Required. The results of the Google Search. - { # The result of the Google Search. - "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. }, - ], - }, - "mcpServerToolResult": { # MCPServer tool result content. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + }, + "googleMapsResult": { # Google Maps result content. + "result": [ # Required. The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. + }, + ], + }, + "googleSearchResult": { # Google Search result content. + "is_error": True or False, # Whether the Google Search resulted in an error. + "result": [ # Required. The results of the Google Search. + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, + ], + }, + "mcpServerToolResult": { # MCPServer tool result content. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. }, ], }, }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. }, }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. - }, - }, - ], + ], + }, + "contentString": "A String", # The content of the step. A single string. }, }, ], @@ -28976,108 +29110,428 @@Method Details
# Object with schema name: GenaiValue ], }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. + }, + ], + }, + }, + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. + }, + }, + ], + }, + "contentString": "A String", # The content of the turn. A single string. + "role": "A String", # The originator of this turn. Must be user for input or model for model output. + }, + ], + }, + "updated": "A String", # Required. Output only. The time at which the response was last updated in ISO 8601 format (YYYY-MM-DDThh:mm:ssZ). + "usage": { # Statistics on the interaction request's token usage. # Output only. Statistics on the interaction request's token usage. + "cached_tokens_by_modality": [ # A breakdown of cached token usage by modality. + { # The token count for a single response modality. + "modality": "A String", # The modality associated with the token count. + "tokens": 42, # Number of tokens for the modality. + }, + ], + "grounding_tool_count": [ # Grounding tool count. + { # The number of grounding tool counts. + "count": 42, # The number of grounding tool counts. + "type": "A String", # The grounding tool type associated with the count. + }, + ], + "input_tokens_by_modality": [ # A breakdown of input token usage by modality. + { # The token count for a single response modality. + "modality": "A String", # The modality associated with the token count. + "tokens": 42, # Number of tokens for the modality. + }, + ], + "output_tokens_by_modality": [ # A breakdown of output token usage by modality. + { # The token count for a single response modality. + "modality": "A String", # The modality associated with the token count. + "tokens": 42, # Number of tokens for the modality. + }, + ], + "tool_use_tokens_by_modality": [ # A breakdown of tool-use token usage by modality. + { # The token count for a single response modality. + "modality": "A String", # The modality associated with the token count. + "tokens": 42, # Number of tokens for the modality. + }, + ], + "total_cached_tokens": 42, # Number of tokens in the cached part of the prompt (the cached content). + "total_input_tokens": 42, # Number of tokens in the prompt (context). + "total_output_tokens": 42, # Total number of tokens across all the generated responses. + "total_thought_tokens": 42, # Number of tokens of thoughts for thinking models. + "total_tokens": 42, # Total token count for the interaction request (prompt + responses + other internal tokens). + "total_tool_use_tokens": 42, # Number of tokens present in tool-use prompt(s). + }, + }, + }, + "interactionStatusUpdate": { # The interaction status data, used for interaction.status_update events. + "interaction_id": "A String", + "status": "A String", + }, + "metadata": { # Optional metadata accompanying ANY streamed event. + "usage": { # Statistics on the interaction request's token usage. + "cached_tokens_by_modality": [ # A breakdown of cached token usage by modality. + { # The token count for a single response modality. + "modality": "A String", # The modality associated with the token count. + "tokens": 42, # Number of tokens for the modality. + }, + ], + "grounding_tool_count": [ # Grounding tool count. + { # The number of grounding tool counts. + "count": 42, # The number of grounding tool counts. + "type": "A String", # The grounding tool type associated with the count. + }, + ], + "input_tokens_by_modality": [ # A breakdown of input token usage by modality. + { # The token count for a single response modality. + "modality": "A String", # The modality associated with the token count. + "tokens": 42, # Number of tokens for the modality. + }, + ], + "output_tokens_by_modality": [ # A breakdown of output token usage by modality. + { # The token count for a single response modality. + "modality": "A String", # The modality associated with the token count. + "tokens": 42, # Number of tokens for the modality. + }, + ], + "tool_use_tokens_by_modality": [ # A breakdown of tool-use token usage by modality. + { # The token count for a single response modality. + "modality": "A String", # The modality associated with the token count. + "tokens": 42, # Number of tokens for the modality. + }, + ], + "total_cached_tokens": 42, # Number of tokens in the cached part of the prompt (the cached content). + "total_input_tokens": 42, # Number of tokens in the prompt (context). + "total_output_tokens": 42, # Total number of tokens across all the generated responses. + "total_thought_tokens": 42, # Number of tokens of thoughts for thinking models. + "total_tokens": 42, # Total token count for the interaction request (prompt + responses + other internal tokens). + "total_tool_use_tokens": 42, # Number of tokens present in tool-use prompt(s). + }, + }, + "stepDelta": { # The step delta data, used for step.delta events. + "delta": { + "argumentsDelta": { + "arguments": "A String", + }, + "audio": { + "channels": 42, # The number of audio channels. + "data": "A String", + "mime_type": "A String", + "rate": 42, # Deprecated. Use sample_rate instead. The value is ignored. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", + }, + "document": { + "data": "A String", + "mime_type": "A String", + "uri": "A String", + }, + "functionResult": { + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, + }, + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "is_error": True or False, + "name": "A String", + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + }, + "image": { + "data": "A String", + "mime_type": "A String", + "resolution": "A String", # The resolution of the media. + "uri": "A String", + }, + "serverToolCall": { + "codeExecutionCall": { + "arguments": { # The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. + }, + }, + "fileSearchCall": { + }, + "googleMapsCall": { + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], + }, + }, + "googleSearchCall": { + "arguments": { # The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], + }, + }, + "mcpServerToolCall": { + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "name": "A String", + "server_name": "A String", + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { + "arguments": { # The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, + }, + }, + "serverToolResult": { + "codeExecutionResult": { + "is_error": True or False, + "result": "A String", + }, + "fileSearchResult": { + "result": [ + { # The result of the File Search. + }, + ], + }, + "googleMapsResult": { + "result": [ # The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. + }, + ], + }, + "googleSearchResult": { + "is_error": True or False, + "result": [ + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, + ], + }, + "mcpServerToolResult": { + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], - }, - }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. - }, + ], + "text": "A String", # Required. The text content. }, - ], - }, - "contentString": "A String", # The content of the turn. A single string. - "role": "A String", # The originator of this turn. Must be user for input or model for model output. - }, - ], - }, - "updated": "A String", # Required. Output only. The time at which the response was last updated in ISO 8601 format (YYYY-MM-DDThh:mm:ssZ). - "usage": { # Statistics on the interaction request's token usage. # Output only. Statistics on the interaction request's token usage. - "cached_tokens_by_modality": [ # A breakdown of cached token usage by modality. - { # The token count for a single response modality. - "modality": "A String", # The modality associated with the token count. - "tokens": 42, # Number of tokens for the modality. - }, - ], - "grounding_tool_count": [ # Grounding tool count. - { # The number of grounding tool counts. - "count": 42, # The number of grounding tool counts. - "type": "A String", # The grounding tool type associated with the count. - }, - ], - "input_tokens_by_modality": [ # A breakdown of input token usage by modality. - { # The token count for a single response modality. - "modality": "A String", # The modality associated with the token count. - "tokens": 42, # Number of tokens for the modality. - }, - ], - "output_tokens_by_modality": [ # A breakdown of output token usage by modality. - { # The token count for a single response modality. - "modality": "A String", # The modality associated with the token count. - "tokens": 42, # Number of tokens for the modality. + }, + ], }, - ], - "tool_use_tokens_by_modality": [ # A breakdown of tool-use token usage by modality. - { # The token count for a single response modality. - "modality": "A String", # The modality associated with the token count. - "tokens": 42, # Number of tokens for the modality. + "name": "A String", + "server_name": "A String", + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], }, - ], - "total_cached_tokens": 42, # Number of tokens in the cached part of the prompt (the cached content). - "total_input_tokens": 42, # Number of tokens in the prompt (context). - "total_output_tokens": 42, # Total number of tokens across all the generated responses. - "total_thought_tokens": 42, # Number of tokens of thoughts for thinking models. - "total_tokens": 42, # Total token count for the interaction request (prompt + responses + other internal tokens). - "total_tool_use_tokens": 42, # Number of tokens present in tool-use prompt(s). - }, - }, - }, - "interactionStatusUpdate": { # The interaction status data, used for interaction.status_update events. - "interaction_id": "A String", - "status": "A String", - }, - "stepDelta": { # The step delta data, used for step.delta events. - "delta": { - "argumentsDelta": { - "partial_arguments": "A String", - }, - "audio": { - "channels": 42, # The number of audio channels. - "data": "A String", - "mime_type": "A String", - "rate": 42, # Deprecated. Use sample_rate instead. The value is ignored. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", - }, - "document": { - "data": "A String", - "mime_type": "A String", - "uri": "A String", - }, - "image": { - "data": "A String", - "mime_type": "A String", - "resolution": "A String", # The resolution of the media. - "uri": "A String", + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { + "is_error": True or False, + "result": [ + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. + }, + ], + }, }, "text": { "text": "A String", @@ -29983,495 +30437,498 @@Method Details
"url": "A String", # The URL. }, }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result step. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. + }, + ], + }, + }, + "userInput": { # Input provided by the user. + "contentList": { # A list of Content. # The content of the step. An array of Content objects. + "contents": [ # The contents of the list. + { # The content of the response. + "audio": { # An audio content block. + "channels": 42, # The number of audio channels. + "data": "A String", # The audio content. + "mime_type": "A String", # The mime type of the audio. + "sample_rate": 42, # The sample rate of the audio. + "uri": "A String", # The URI of the audio. + }, + "document": { # A document content block. + "data": "A String", # The document content. + "mime_type": "A String", # The mime type of the document. + "uri": "A String", # The URI of the document. + }, + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. + }, }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, + ], + "text": "A String", # Required. The text content. }, - ], - }, - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result step. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], - }, - }, - "userInput": { # Input provided by the user. - "content": [ - { # The content of the response. - "audio": { # An audio content block. - "channels": 42, # The number of audio channels. - "data": "A String", # The audio content. - "mime_type": "A String", # The mime type of the audio. - "sample_rate": 42, # The sample rate of the audio. - "uri": "A String", # The URI of the audio. - }, - "document": { # A document content block. - "data": "A String", # The document content. - "mime_type": "A String", # The mime type of the document. - "uri": "A String", # The URI of the document. - }, - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue + "thought": { # A thought content block. + "signature": "A String", # Signature to match the backend source to be part of the generation. + "summary": [ # A summary of the thought. + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, + }, ], }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, + ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, }, ], + "text": "A String", # Required. The text content. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + ], + }, + "toolCall": { # Tool call content. + "codeExecutionCall": { # Code execution content. + "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. + "code": "A String", # The code to be executed. + "language": "A String", # Programming language of the `code`. }, }, - ], - "text": "A String", # Required. The text content. - }, - "thought": { # A thought content block. - "signature": "A String", # Signature to match the backend source to be part of the generation. - "summary": [ # A summary of the thought. - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, - }, + "fileSearchCall": { # File Search content. + }, + "functionCall": { # A function tool call content block. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue ], }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, }, ], - "text": "A String", # Required. The text content. + }, + "name": "A String", # Required. The name of the tool to call. + }, + "googleMapsCall": { # Google Maps content. + "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. + "queries": [ # The queries to be executed. + "A String", + ], }, }, - ], - }, - "toolCall": { # Tool call content. - "codeExecutionCall": { # Code execution content. - "arguments": { # The arguments to pass to the code execution. # Required. The arguments to pass to the code execution. - "code": "A String", # The code to be executed. - "language": "A String", # Programming language of the `code`. + "googleSearchCall": { # Google Search content. + "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. + "queries": [ # Web search queries for the following-up web search. + "A String", + ], + }, + "search_type": "A String", # The type of search grounding enabled. }, - }, - "fileSearchCall": { # File Search content. - }, - "functionCall": { # A function tool call content block. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The arguments to pass to the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "id": "A String", # Required. A unique ID for this specific tool call. + "mcpServerToolCall": { # MCPServer tool call content. + "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - }, - ], + ], + }, + "name": "A String", # Required. The name of the tool which was called. + "server_name": "A String", # Required. The name of the used MCP server. }, - "name": "A String", # Required. The name of the tool to call. - }, - "googleMapsCall": { # Google Maps content. - "arguments": { # The arguments to pass to the Google Maps tool. # The arguments to pass to the Google Maps tool. - "queries": [ # The queries to be executed. - "A String", - ], + "signature": "A String", # A signature hash for backend validation. + "urlContextCall": { # URL context content. + "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. + "urls": [ # The URLs to fetch. + "A String", + ], + }, }, }, - "googleSearchCall": { # Google Search content. - "arguments": { # The arguments to pass to Google Search. # Required. The arguments to pass to Google Search. - "queries": [ # Web search queries for the following-up web search. - "A String", - ], + "toolResult": { # Tool result content. + "call_id": "A String", # Required. ID to match the ID from the function call block. + "codeExecutionResult": { # Code execution result content. + "is_error": True or False, # Whether the code execution resulted in an error. + "result": "A String", # Required. The output of the code execution. }, - "search_type": "A String", # The type of search grounding enabled. - }, - "id": "A String", # Required. A unique ID for this specific tool call. - "mcpServerToolCall": { # MCPServer tool call content. - "arguments": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # Required. The JSON object of arguments for the function. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], - }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, + "fileSearchResult": { # File Search result content. + "result": [ # Optional. The results of the File Search. + { # The result of the File Search. }, ], }, - "name": "A String", # Required. The name of the tool which was called. - "server_name": "A String", # Required. The name of the used MCP server. - }, - "signature": "A String", # A signature hash for backend validation. - "urlContextCall": { # URL context content. - "arguments": { # The arguments to pass to the URL context. # Required. The arguments to pass to the URL context. - "urls": [ # The URLs to fetch. - "A String", - ], - }, - }, - }, - "toolResult": { # Tool result content. - "call_id": "A String", # Required. ID to match the ID from the function call block. - "codeExecutionResult": { # Code execution result content. - "is_error": True or False, # Whether the code execution resulted in an error. - "result": "A String", # Required. The output of the code execution. - }, - "fileSearchResult": { # File Search result content. - "result": [ # Optional. The results of the File Search. - { # The result of the File Search. - }, - ], - }, - "functionResult": { # A function tool result content block. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "functionResult": { # A function tool result content block. + "contentList": { + "contents": [ + { + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, + ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "is_error": True or False, # Whether the tool call resulted in an error. + "name": "A String", # The name of the tool that was called. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], }, - ], - "text": "A String", # Required. The text content. + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. + }, }, - }, - ], + ], + }, }, - "is_error": True or False, # Whether the tool call resulted in an error. - "name": "A String", # The name of the tool that was called. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue + "googleMapsResult": { # Google Maps result content. + "result": [ # Required. The results of the Google Maps. + { # The result of the Google Maps. + "places": [ # The places that were found. + { + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. + }, ], + "url": "A String", # URI reference of the place. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. - }, + ], + "widget_context_token": "A String", # Resource name of the Google Maps widget context token. }, ], }, - }, - "googleMapsResult": { # Google Maps result content. - "result": [ # Required. The results of the Google Maps. - { # The result of the Google Maps. - "places": [ # The places that were found. + "googleSearchResult": { # Google Search result content. + "is_error": True or False, # Whether the Google Search resulted in an error. + "result": [ # Required. The results of the Google Search. + { # The result of the Google Search. + "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. + }, + ], + }, + "mcpServerToolResult": { # MCPServer tool result content. + "contentList": { + "contents": [ { - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - ], - "widget_context_token": "A String", # Resource name of the Google Maps widget context token. - }, - ], - }, - "googleSearchResult": { # Google Search result content. - "is_error": True or False, # Whether the Google Search resulted in an error. - "result": [ # Required. The results of the Google Search. - { # The result of the Google Search. - "search_suggestions": "A String", # Web content snippet that can be embedded in a web page or an app webview. - }, - ], - }, - "mcpServerToolResult": { # MCPServer tool result content. - "contentList": { - "contents": [ - { - "image": { # An image content block. - "data": "A String", # The image content. - "mime_type": "A String", # The mime type of the image. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the image. - }, - "text": { # A text content block. - "annotations": [ # Citation information for model-generated content. - { # Citation information for model-generated content. - "end_index": 42, # End of the attributed segment, exclusive. - "file_citation": { # A file citation annotation. # A file citation annotation. - "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue - ], + "image": { # An image content block. + "data": "A String", # The image content. + "mime_type": "A String", # The mime type of the image. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the image. + }, + "text": { # A text content block. + "annotations": [ # Citation information for model-generated content. + { # Citation information for model-generated content. + "end_index": 42, # End of the attributed segment, exclusive. + "file_citation": { # A file citation annotation. # A file citation annotation. + "custom_metadata": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. # User provided metadata about the retrieved context. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + "document_uri": "A String", # The URI of the file. + "file_name": "A String", # The name of the file. + "media_id": "A String", # Media ID in-case of image citations, if applicable. + "page_number": 42, # Page number of the cited document, if applicable. + "source": "A String", # Source attributed for a portion of the text. + }, + "place_citation": { # A place citation annotation. # A place citation annotation. + "name": "A String", # Title of the place. + "place_id": "A String", # The ID of the place, in `places/{place_id}` format. + "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. + { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. + "review_id": "A String", # The ID of the review snippet. + "title": "A String", # Title of the review. + "url": "A String", # A link that corresponds to the user review on Google Maps. }, ], + "url": "A String", # URI reference of the place. + }, + "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. + "url_citation": { # A URL citation annotation. # A URL citation annotation. + "title": "A String", # The title of the URL. + "url": "A String", # The URL. }, - "document_uri": "A String", # The URI of the file. - "file_name": "A String", # The name of the file. - "media_id": "A String", # Media ID in-case of image citations, if applicable. - "page_number": 42, # Page number of the cited document, if applicable. - "source": "A String", # Source attributed for a portion of the text. - }, - "place_citation": { # A place citation annotation. # A place citation annotation. - "name": "A String", # Title of the place. - "place_id": "A String", # The ID of the place, in `places/{place_id}` format. - "review_snippets": [ # Snippets of reviews that are used to generate answers about the features of a given place in Google Maps. - { # Encapsulates a snippet of a user review that answers a question about the features of a specific place in Google Maps. - "review_id": "A String", # The ID of the review snippet. - "title": "A String", # Title of the review. - "url": "A String", # A link that corresponds to the user review on Google Maps. - }, - ], - "url": "A String", # URI reference of the place. - }, - "start_index": 42, # Start of segment of the response that is attributed to this source. Index indicates the start of the segment, measured in bytes. - "url_citation": { # A URL citation annotation. # A URL citation annotation. - "title": "A String", # The title of the URL. - "url": "A String", # The URL. }, - }, - ], - "text": "A String", # Required. The text content. - }, - }, - ], - }, - "name": "A String", # Name of the tool which is called for this specific tool call. - "server_name": "A String", # The name of the used MCP server. - "stringResult": "A String", - "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. - "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. - { # Represents a single field in a struct. - "name": "A String", - "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. - "boolValue": True or False, # Represents a boolean value. - "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. - "values": [ # Repeated field of dynamically typed values. - # Object with schema name: GenaiValue ], + "text": "A String", # Required. The text content. + }, + }, + ], + }, + "name": "A String", # Name of the tool which is called for this specific tool call. + "server_name": "A String", # The name of the used MCP server. + "stringResult": "A String", + "structResult": { # `Struct` represents a structured data value, consisting of fields which map to dynamically typed values. + "fields": [ # Dynamically typed fields. List instead of map because LLMs are sensitive to ordering, and we want to give users full control. + { # Represents a single field in a struct. + "name": "A String", + "value": { # `Value` represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant indicates an error. + "boolValue": True or False, # Represents a boolean value. + "listValue": { # `ListValue` is a wrapper around a repeated field of values. # Represents a repeated `Value`. + "values": [ # Repeated field of dynamically typed values. + # Object with schema name: GenaiValue + ], + }, + "nullValue": "A String", # Represents a null value. + "numberValue": 3.14, # Represents a double value. + "stringValue": "A String", # Represents a string value. + "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, - "nullValue": "A String", # Represents a null value. - "numberValue": 3.14, # Represents a double value. - "stringValue": "A String", # Represents a string value. - "structValue": # Object with schema name: GenaiStruct # Represents a structured value. }, + ], + }, + }, + "signature": "A String", # A signature hash for backend validation. + "urlContextResult": { # URL context result content. + "is_error": True or False, # Whether the URL context resulted in an error. + "result": [ # Required. The results of the URL context. + { # The result of the URL context. + "status": "A String", # The status of the URL retrieval. + "url": "A String", # The URL that was fetched. }, ], }, }, - "signature": "A String", # A signature hash for backend validation. - "urlContextResult": { # URL context result content. - "is_error": True or False, # Whether the URL context resulted in an error. - "result": [ # Required. The results of the URL context. - { # The result of the URL context. - "status": "A String", # The status of the URL retrieval. - "url": "A String", # The URL that was fetched. - }, - ], + "video": { # A video content block. + "data": "A String", # The video content. + "mime_type": "A String", # The mime type of the video. + "resolution": "A String", # The resolution of the media. + "uri": "A String", # The URI of the video. }, }, - "video": { # A video content block. - "data": "A String", # The video content. - "mime_type": "A String", # The mime type of the video. - "resolution": "A String", # The resolution of the media. - "uri": "A String", # The URI of the video. - }, - }, - ], + ], + }, + "contentString": "A String", # The content of the step. A single string. }, }, }, diff --git a/docs/dyn/aiplatform_v1beta1.projects.html b/docs/dyn/aiplatform_v1beta1.projects.html index 3256588629..14644521fc 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.html +++ b/docs/dyn/aiplatform_v1beta1.projects.html @@ -125,7 +125,8 @@Method Details
An object of the form: { # This message contains configs of a publisher model. - "loggingConfig": { # Configuration for logging request-response to a BigQuery table. # The prediction request/response logging config. + "dataSharingEnabledProvider": "A String", # Optional. The model provider (publisher) for which the customer has enabled data sharing. For publisher models that are configured to require data sharing, a prediction request is only allowed when the model's publisher matches this provider. Otherwise, the request is rejected. + "loggingConfig": { # Configuration for logging request-response to a BigQuery table. # Optional. The prediction request/response logging config. "bigqueryDestination": { # The BigQuery location for the output content. # BigQuery table for logging. If only given a project, a new dataset will be created with name `logging__` where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores). If no table name is given, a new table will be created with name `request_response_logging` "outputUri": "A String", # Required. BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: `bq://projectId` or `bq://projectId.bqDatasetId` or `bq://projectId.bqDatasetId.bqTableId`. }, @@ -169,7 +170,8 @@Method Details
{ # Request message for EndpointService.SetPublisherModelConfig. "publisherModelConfig": { # This message contains configs of a publisher model. # Required. The publisher model config. - "loggingConfig": { # Configuration for logging request-response to a BigQuery table. # The prediction request/response logging config. + "dataSharingEnabledProvider": "A String", # Optional. The model provider (publisher) for which the customer has enabled data sharing. For publisher models that are configured to require data sharing, a prediction request is only allowed when the model's publisher matches this provider. Otherwise, the request is rejected. + "loggingConfig": { # Configuration for logging request-response to a BigQuery table. # Optional. The prediction request/response logging config. "bigqueryDestination": { # The BigQuery location for the output content. # BigQuery table for logging. If only given a project, a new dataset will be created with name `logging__` where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores). If no table name is given, a new table will be created with name `request_response_logging` "outputUri": "A String", # Required. BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: `bq://projectId` or `bq://projectId.bqDatasetId` or `bq://projectId.bqDatasetId.bqTableId`. }, diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.agents.html b/docs/dyn/aiplatform_v1beta1.projects.locations.agents.html index 84acb4cdd9..e273abf416 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.agents.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.agents.html @@ -84,22 +84,22 @@Instance Methods
Close httplib2 connections.
-
create(parent, body=None, x__xgafv=None)Creates a new Agent in a given location.
+Creates an agent.
-Deletes the specific Agent.
+Deletes an agent.
-Gets details of the specific Agent.
+Retrieves an agent.
-
list(parent, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)Lists Agents in a given location.
+Lists agents in a location.
Retrieves the next page of results.
-
patch(name, body=None, updateMask=None, x__xgafv=None)Updates the specific Agent.
+Updates an agent.
Method Details
close()@@ -108,7 +108,7 @@Method Details
create(parent, body=None, x__xgafv=None)-Creates a new Agent in a given location. +Creates an agent. Args: parent: string, Required. The resource name of the location to create the agent in. Format: `projects/{project}/locations/{location}`. (required) @@ -116,28 +116,28 @@Method Details
The object takes the form of: { # A Vertex agent contains instructions and configurations for the LLM to execute a certain task. - "base_agent": "A String", # Required. The base agent of the agent. Supported values: - "antigravity-preview-05-2026" - "base_environment": "", # Optional. The environment config of the agent. Valid types are: - string value for environment_id, or 'remote' for default - struct value for EnvironmentConfig. - "created": "A String", # Output only. Timestamp when the agent was created. + "base_agent": "A String", # Required. The base agent for the agent. Supported values: * `antigravity-preview-05-2026` + "base_environment": "", # Optional. The base environment configuration for the agent. Valid types: * A string value for the environment ID, or `remote` for the default. * A struct value for the `environment_config`. + "created": "A String", # Output only. The time the agent was created. "description": "A String", # Optional. The description of the agent. - "id": "A String", # Immutable. The ID to use for agent, which will become the final component of the agent resource name. If not provided, Vertex AI will generate a value for this ID. This value may be up to 63 characters, and valid characters are `[a-z0-9-]`. The first character must be a letter, the last character must be a letter or number. - "metadata": { # Optional. The metadata of the agent. + "id": "A String", # Immutable. The user-specified ID for the agent. This ID becomes the final component of the agent resource name. If not provided, Vertex AI will generate a value for this ID. The ID can be up to 63 characters and must match the regular expression `[a-z]([a-z0-9-]{0,61}[a-z0-9])?`. + "metadata": { # Optional. The metadata for the agent. "a_key": "A String", }, - "name": "A String", # Identifier. The resource name of the Agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. - "object": "A String", # Output only. The object type of this resource. Always set to "agent" in this case. - "system_instruction": "A String", # Optional. The detailed struction that the agent should follow. The instruction is passed to LLM as system instruction. - "tools": [ # Optional. A list of tools that are available for the agent during the process of execucting the task. - { # A tool provides a list of actions that are available for the Agent during the process of executing the task. - "headers": { # Optional. Headers for the MCP server (e.g., authentication). Only applicable when `type` is "mcp". + "name": "A String", # Identifier. The resource name of the agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. + "object": "A String", # Output only. The object type of the resource. For agents, the value is `agent`. + "system_instruction": "A String", # Optional. The instructions for the agent to follow. These instructions are passed to the LLM as a system instruction. + "tools": [ # Optional. The tools available to the agent. + { # A tool provides a list of actions available to the Agent during the process of executing a task. + "headers": { # Optional. The headers for the MCP server, such as for authentication. Only applicable when `type` is `mcp_server`. "a_key": "A String", }, - "name": "A String", # Optional. The name of the MCP server. Only applicable when `type` is "mcp". - "type": "A String", # Required. Type of the tool. Supported types: - "code_execution" - "filesystem" - "google_search" - "mcp" - "url_context" - "url": "A String", # Optional. The full URL for the MCP server endpoint. Only applicable when `type` is "mcp". + "name": "A String", # Optional. The name of the MCP server. Only applicable when `type` is `mcp_server`. + "type": "A String", # Required. The type of the tool. Supported types: * `code_execution` * `filesystem` * `google_search` * `mcp_server` * `url_context` + "url": "A String", # Optional. The URL for the MCP server endpoint. Only applicable when `type` is `mcp_server`. }, ], - "updated": "A String", # Output only. Timestamp when the agent was last updated. + "updated": "A String", # Output only. The time the agent was last updated. } x__xgafv: string, V1 error format. @@ -171,10 +171,10 @@Method Details
delete(name, x__xgafv=None)-Deletes the specific Agent. +Deletes an agent. Args: - name: string, Required. The resource name of the agent. Format: `projects/{project}/locations/{location}/agents/{agent}` (required) + name: string, Required. The resource name of the agent to delete. Format: `projects/{project}/locations/{location}/agents/{agent}`. (required) x__xgafv: string, V1 error format. Allowed values 1 - v1 error format @@ -206,10 +206,10 @@Method Details
get(name, x__xgafv=None)-Gets details of the specific Agent. +Retrieves an agent. Args: - name: string, Required. The resource name of the agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. (required) + name: string, Required. The resource name of the agent to retrieve. Format: `projects/{project}/locations/{location}/agents/{agent}`. (required) x__xgafv: string, V1 error format. Allowed values 1 - v1 error format @@ -219,40 +219,40 @@Method Details
An object of the form: { # A Vertex agent contains instructions and configurations for the LLM to execute a certain task. - "base_agent": "A String", # Required. The base agent of the agent. Supported values: - "antigravity-preview-05-2026" - "base_environment": "", # Optional. The environment config of the agent. Valid types are: - string value for environment_id, or 'remote' for default - struct value for EnvironmentConfig. - "created": "A String", # Output only. Timestamp when the agent was created. + "base_agent": "A String", # Required. The base agent for the agent. Supported values: * `antigravity-preview-05-2026` + "base_environment": "", # Optional. The base environment configuration for the agent. Valid types: * A string value for the environment ID, or `remote` for the default. * A struct value for the `environment_config`. + "created": "A String", # Output only. The time the agent was created. "description": "A String", # Optional. The description of the agent. - "id": "A String", # Immutable. The ID to use for agent, which will become the final component of the agent resource name. If not provided, Vertex AI will generate a value for this ID. This value may be up to 63 characters, and valid characters are `[a-z0-9-]`. The first character must be a letter, the last character must be a letter or number. - "metadata": { # Optional. The metadata of the agent. + "id": "A String", # Immutable. The user-specified ID for the agent. This ID becomes the final component of the agent resource name. If not provided, Vertex AI will generate a value for this ID. The ID can be up to 63 characters and must match the regular expression `[a-z]([a-z0-9-]{0,61}[a-z0-9])?`. + "metadata": { # Optional. The metadata for the agent. "a_key": "A String", }, - "name": "A String", # Identifier. The resource name of the Agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. - "object": "A String", # Output only. The object type of this resource. Always set to "agent" in this case. - "system_instruction": "A String", # Optional. The detailed struction that the agent should follow. The instruction is passed to LLM as system instruction. - "tools": [ # Optional. A list of tools that are available for the agent during the process of execucting the task. - { # A tool provides a list of actions that are available for the Agent during the process of executing the task. - "headers": { # Optional. Headers for the MCP server (e.g., authentication). Only applicable when `type` is "mcp". + "name": "A String", # Identifier. The resource name of the agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. + "object": "A String", # Output only. The object type of the resource. For agents, the value is `agent`. + "system_instruction": "A String", # Optional. The instructions for the agent to follow. These instructions are passed to the LLM as a system instruction. + "tools": [ # Optional. The tools available to the agent. + { # A tool provides a list of actions available to the Agent during the process of executing a task. + "headers": { # Optional. The headers for the MCP server, such as for authentication. Only applicable when `type` is `mcp_server`. "a_key": "A String", }, - "name": "A String", # Optional. The name of the MCP server. Only applicable when `type` is "mcp". - "type": "A String", # Required. Type of the tool. Supported types: - "code_execution" - "filesystem" - "google_search" - "mcp" - "url_context" - "url": "A String", # Optional. The full URL for the MCP server endpoint. Only applicable when `type` is "mcp". + "name": "A String", # Optional. The name of the MCP server. Only applicable when `type` is `mcp_server`. + "type": "A String", # Required. The type of the tool. Supported types: * `code_execution` * `filesystem` * `google_search` * `mcp_server` * `url_context` + "url": "A String", # Optional. The URL for the MCP server endpoint. Only applicable when `type` is `mcp_server`. }, ], - "updated": "A String", # Output only. Timestamp when the agent was last updated. + "updated": "A String", # Output only. The time the agent was last updated. }@@ -308,39 +308,39 @@list(parent, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)-Lists Agents in a given location. +Lists agents in a location. Args: - parent: string, Required. The resource name of the location to list agents from. Format: `projects/{project}/locations/{location} (required) - orderBy: string, Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `create_time` * `update_time` Example: `create_time desc`. - pageSize: integer, Optional. The maximum number of agents to return. The service may return fewer than this value. If unspecified, at most 100 agents will be returned. - pageToken: string, Optional. The next_page_token value returned from a previous list AgentService.ListAgents call. + parent: string, Required. The resource name of the location to list agents from. Format: `projects/{project}/locations/{location}`. (required) + orderBy: string, Optional. A comma-separated list of fields to order by. Supported fields: * `create_time` * `update_time` Use `desc` after a field name for descending order. Example: `create_time desc`. + pageSize: integer, Optional. The maximum number of agents to return. The service may return fewer than this value. The maximum page size is 100; values above 100 will be coerced to 100. If unspecified, the default page size is 10. + pageToken: string, Optional. A page token, received from a previous AgentService.ListAgents call. Provide this to retrieve the subsequent page. x__xgafv: string, V1 error format. Allowed values 1 - v1 error format @@ -262,33 +262,33 @@Method Details
An object of the form: { # Response message for AgentService.ListAgents. - "agents": [ # A list of agents matching the request. + "agents": [ # The agents matching the request. { # A Vertex agent contains instructions and configurations for the LLM to execute a certain task. - "base_agent": "A String", # Required. The base agent of the agent. Supported values: - "antigravity-preview-05-2026" - "base_environment": "", # Optional. The environment config of the agent. Valid types are: - string value for environment_id, or 'remote' for default - struct value for EnvironmentConfig. - "created": "A String", # Output only. Timestamp when the agent was created. + "base_agent": "A String", # Required. The base agent for the agent. Supported values: * `antigravity-preview-05-2026` + "base_environment": "", # Optional. The base environment configuration for the agent. Valid types: * A string value for the environment ID, or `remote` for the default. * A struct value for the `environment_config`. + "created": "A String", # Output only. The time the agent was created. "description": "A String", # Optional. The description of the agent. - "id": "A String", # Immutable. The ID to use for agent, which will become the final component of the agent resource name. If not provided, Vertex AI will generate a value for this ID. This value may be up to 63 characters, and valid characters are `[a-z0-9-]`. The first character must be a letter, the last character must be a letter or number. - "metadata": { # Optional. The metadata of the agent. + "id": "A String", # Immutable. The user-specified ID for the agent. This ID becomes the final component of the agent resource name. If not provided, Vertex AI will generate a value for this ID. The ID can be up to 63 characters and must match the regular expression `[a-z]([a-z0-9-]{0,61}[a-z0-9])?`. + "metadata": { # Optional. The metadata for the agent. "a_key": "A String", }, - "name": "A String", # Identifier. The resource name of the Agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. - "object": "A String", # Output only. The object type of this resource. Always set to "agent" in this case. - "system_instruction": "A String", # Optional. The detailed struction that the agent should follow. The instruction is passed to LLM as system instruction. - "tools": [ # Optional. A list of tools that are available for the agent during the process of execucting the task. - { # A tool provides a list of actions that are available for the Agent during the process of executing the task. - "headers": { # Optional. Headers for the MCP server (e.g., authentication). Only applicable when `type` is "mcp". + "name": "A String", # Identifier. The resource name of the agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. + "object": "A String", # Output only. The object type of the resource. For agents, the value is `agent`. + "system_instruction": "A String", # Optional. The instructions for the agent to follow. These instructions are passed to the LLM as a system instruction. + "tools": [ # Optional. The tools available to the agent. + { # A tool provides a list of actions available to the Agent during the process of executing a task. + "headers": { # Optional. The headers for the MCP server, such as for authentication. Only applicable when `type` is `mcp_server`. "a_key": "A String", }, - "name": "A String", # Optional. The name of the MCP server. Only applicable when `type` is "mcp". - "type": "A String", # Required. Type of the tool. Supported types: - "code_execution" - "filesystem" - "google_search" - "mcp" - "url_context" - "url": "A String", # Optional. The full URL for the MCP server endpoint. Only applicable when `type` is "mcp". + "name": "A String", # Optional. The name of the MCP server. Only applicable when `type` is `mcp_server`. + "type": "A String", # Required. The type of the tool. Supported types: * `code_execution` * `filesystem` * `google_search` * `mcp_server` * `url_context` + "url": "A String", # Optional. The URL for the MCP server endpoint. Only applicable when `type` is `mcp_server`. }, ], - "updated": "A String", # Output only. Timestamp when the agent was last updated. + "updated": "A String", # Output only. The time the agent was last updated. }, ], - "nextPageToken": "A String", # A token, which can be sent as ListAgentsRequest.page_token to retrieve the next page. Absence of this field indicates there are no subsequent pages. + "nextPageToken": "A String", # A token to retrieve the next page of results. Pass this value as ListAgentsRequest.page_token in a subsequent call. }Method Details
diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.batchPredictionJobs.html b/docs/dyn/aiplatform_v1beta1.projects.locations.batchPredictionJobs.html index abb8ec40a4..eb564ab078 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.batchPredictionJobs.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.batchPredictionJobs.html @@ -147,10 +147,10 @@patch(name, body=None, updateMask=None, x__xgafv=None)-Updates the specific Agent. +Updates an agent. Args: - name: string, Identifier. The resource name of the Agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. (required) + name: string, Identifier. The resource name of the agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. (required) body: object, The request body. The object takes the form of: { # A Vertex agent contains instructions and configurations for the LLM to execute a certain task. - "base_agent": "A String", # Required. The base agent of the agent. Supported values: - "antigravity-preview-05-2026" - "base_environment": "", # Optional. The environment config of the agent. Valid types are: - string value for environment_id, or 'remote' for default - struct value for EnvironmentConfig. - "created": "A String", # Output only. Timestamp when the agent was created. + "base_agent": "A String", # Required. The base agent for the agent. Supported values: * `antigravity-preview-05-2026` + "base_environment": "", # Optional. The base environment configuration for the agent. Valid types: * A string value for the environment ID, or `remote` for the default. * A struct value for the `environment_config`. + "created": "A String", # Output only. The time the agent was created. "description": "A String", # Optional. The description of the agent. - "id": "A String", # Immutable. The ID to use for agent, which will become the final component of the agent resource name. If not provided, Vertex AI will generate a value for this ID. This value may be up to 63 characters, and valid characters are `[a-z0-9-]`. The first character must be a letter, the last character must be a letter or number. - "metadata": { # Optional. The metadata of the agent. + "id": "A String", # Immutable. The user-specified ID for the agent. This ID becomes the final component of the agent resource name. If not provided, Vertex AI will generate a value for this ID. The ID can be up to 63 characters and must match the regular expression `[a-z]([a-z0-9-]{0,61}[a-z0-9])?`. + "metadata": { # Optional. The metadata for the agent. "a_key": "A String", }, - "name": "A String", # Identifier. The resource name of the Agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. - "object": "A String", # Output only. The object type of this resource. Always set to "agent" in this case. - "system_instruction": "A String", # Optional. The detailed struction that the agent should follow. The instruction is passed to LLM as system instruction. - "tools": [ # Optional. A list of tools that are available for the agent during the process of execucting the task. - { # A tool provides a list of actions that are available for the Agent during the process of executing the task. - "headers": { # Optional. Headers for the MCP server (e.g., authentication). Only applicable when `type` is "mcp". + "name": "A String", # Identifier. The resource name of the agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. + "object": "A String", # Output only. The object type of the resource. For agents, the value is `agent`. + "system_instruction": "A String", # Optional. The instructions for the agent to follow. These instructions are passed to the LLM as a system instruction. + "tools": [ # Optional. The tools available to the agent. + { # A tool provides a list of actions available to the Agent during the process of executing a task. + "headers": { # Optional. The headers for the MCP server, such as for authentication. Only applicable when `type` is `mcp_server`. "a_key": "A String", }, - "name": "A String", # Optional. The name of the MCP server. Only applicable when `type` is "mcp". - "type": "A String", # Required. Type of the tool. Supported types: - "code_execution" - "filesystem" - "google_search" - "mcp" - "url_context" - "url": "A String", # Optional. The full URL for the MCP server endpoint. Only applicable when `type` is "mcp". + "name": "A String", # Optional. The name of the MCP server. Only applicable when `type` is `mcp_server`. + "type": "A String", # Required. The type of the tool. Supported types: * `code_execution` * `filesystem` * `google_search` * `mcp_server` * `url_context` + "url": "A String", # Optional. The URL for the MCP server endpoint. Only applicable when `type` is `mcp_server`. }, ], - "updated": "A String", # Output only. Timestamp when the agent was last updated. + "updated": "A String", # Output only. The time the agent was last updated. } - updateMask: string, Optional. Field mask is used to control which fields get updated. If the mask is not present, all fields will be updated. + updateMask: string, Optional. The list of fields to update. If not present, all fields are updated. x__xgafv: string, V1 error format. Allowed values 1 - v1 error format @@ -350,28 +350,28 @@Method Details
An object of the form: { # A Vertex agent contains instructions and configurations for the LLM to execute a certain task. - "base_agent": "A String", # Required. The base agent of the agent. Supported values: - "antigravity-preview-05-2026" - "base_environment": "", # Optional. The environment config of the agent. Valid types are: - string value for environment_id, or 'remote' for default - struct value for EnvironmentConfig. - "created": "A String", # Output only. Timestamp when the agent was created. + "base_agent": "A String", # Required. The base agent for the agent. Supported values: * `antigravity-preview-05-2026` + "base_environment": "", # Optional. The base environment configuration for the agent. Valid types: * A string value for the environment ID, or `remote` for the default. * A struct value for the `environment_config`. + "created": "A String", # Output only. The time the agent was created. "description": "A String", # Optional. The description of the agent. - "id": "A String", # Immutable. The ID to use for agent, which will become the final component of the agent resource name. If not provided, Vertex AI will generate a value for this ID. This value may be up to 63 characters, and valid characters are `[a-z0-9-]`. The first character must be a letter, the last character must be a letter or number. - "metadata": { # Optional. The metadata of the agent. + "id": "A String", # Immutable. The user-specified ID for the agent. This ID becomes the final component of the agent resource name. If not provided, Vertex AI will generate a value for this ID. The ID can be up to 63 characters and must match the regular expression `[a-z]([a-z0-9-]{0,61}[a-z0-9])?`. + "metadata": { # Optional. The metadata for the agent. "a_key": "A String", }, - "name": "A String", # Identifier. The resource name of the Agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. - "object": "A String", # Output only. The object type of this resource. Always set to "agent" in this case. - "system_instruction": "A String", # Optional. The detailed struction that the agent should follow. The instruction is passed to LLM as system instruction. - "tools": [ # Optional. A list of tools that are available for the agent during the process of execucting the task. - { # A tool provides a list of actions that are available for the Agent during the process of executing the task. - "headers": { # Optional. Headers for the MCP server (e.g., authentication). Only applicable when `type` is "mcp". + "name": "A String", # Identifier. The resource name of the agent. Format: `projects/{project}/locations/{location}/agents/{agent}`. + "object": "A String", # Output only. The object type of the resource. For agents, the value is `agent`. + "system_instruction": "A String", # Optional. The instructions for the agent to follow. These instructions are passed to the LLM as a system instruction. + "tools": [ # Optional. The tools available to the agent. + { # A tool provides a list of actions available to the Agent during the process of executing a task. + "headers": { # Optional. The headers for the MCP server, such as for authentication. Only applicable when `type` is `mcp_server`. "a_key": "A String", }, - "name": "A String", # Optional. The name of the MCP server. Only applicable when `type` is "mcp". - "type": "A String", # Required. Type of the tool. Supported types: - "code_execution" - "filesystem" - "google_search" - "mcp" - "url_context" - "url": "A String", # Optional. The full URL for the MCP server endpoint. Only applicable when `type` is "mcp". + "name": "A String", # Optional. The name of the MCP server. Only applicable when `type` is `mcp_server`. + "type": "A String", # Required. The type of the tool. Supported types: * `code_execution` * `filesystem` * `google_search` * `mcp_server` * `url_context` + "url": "A String", # Optional. The URL for the MCP server endpoint. Only applicable when `type` is `mcp_server`. }, ], - "updated": "A String", # Output only. Timestamp when the agent was last updated. + "updated": "A String", # Output only. The time the agent was last updated. }Method Details
"maxRuntimeDuration": "A String", # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days. }, "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -164,7 +164,7 @@Method Details
}, "maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10. "spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms). - "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count + "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count }, "disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true. "displayName": "A String", # Required. The user-defined name of this BatchPredictionJob. @@ -652,10 +652,10 @@Method Details
"maxRuntimeDuration": "A String", # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days. }, "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -669,7 +669,7 @@Method Details
}, "maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10. "spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms). - "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count + "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count }, "disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true. "displayName": "A String", # Required. The user-defined name of this BatchPredictionJob. @@ -1199,10 +1199,10 @@Method Details
"maxRuntimeDuration": "A String", # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days. }, "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -1216,7 +1216,7 @@Method Details
}, "maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10. "spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms). - "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count + "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count }, "disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true. "displayName": "A String", # Required. The user-defined name of this BatchPredictionJob. @@ -1717,10 +1717,10 @@Method Details
"maxRuntimeDuration": "A String", # The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days. }, "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -1734,7 +1734,7 @@Method Details
}, "maxReplicaCount": 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10. "spot": True or False, # Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms). - "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count + "startingReplicaCount": 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count }, "disableContainerLogging": True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true. "displayName": "A String", # Required. The user-defined name of this BatchPredictionJob. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.cachedContents.html b/docs/dyn/aiplatform_v1beta1.projects.locations.cachedContents.html index 7c49d837c6..5222efde2e 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.cachedContents.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.cachedContents.html @@ -305,6 +305,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -727,6 +733,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -1174,6 +1186,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -1607,6 +1625,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -2048,6 +2072,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -2471,6 +2501,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.customJobs.html b/docs/dyn/aiplatform_v1beta1.projects.locations.customJobs.html index ad12ff9dc9..3fcc7b8fb2 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.customJobs.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.customJobs.html @@ -221,10 +221,10 @@Method Details
}, ], "machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -367,10 +367,10 @@Method Details
}, ], "machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -555,10 +555,10 @@Method Details
}, ], "machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -714,10 +714,10 @@Method Details
}, ], "machineSpec": { # Specification of a single machine. # Optional. Immutable. The specification of a single machine. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html b/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html index 1e2efb0af9..9a94bab942 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html @@ -483,6 +483,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -1066,6 +1072,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.deploymentResourcePools.html b/docs/dyn/aiplatform_v1beta1.projects.locations.deploymentResourcePools.html index e46379f4e2..041f7ed066 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.deploymentResourcePools.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.deploymentResourcePools.html @@ -139,10 +139,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -154,7 +154,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -270,10 +270,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -285,7 +285,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -340,10 +340,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -355,7 +355,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -418,10 +418,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -433,7 +433,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -508,8 +508,8 @@Method Details
], "deployedModels": [ # DEPRECATED Use deployed_model_refs instead. { # A deployment of a Model. Endpoints contain one or more DeployedModels. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, "checkpointId": "A String", # The checkpoint id of the model. @@ -529,10 +529,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -544,7 +544,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html b/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html index 7bd79425a3..a7765bb4fe 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html @@ -613,6 +613,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -861,8 +867,8 @@Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon. "deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. { # A deployment of a Model. Endpoints contain one or more DeployedModels. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, "checkpointId": "A String", # The checkpoint id of the model. @@ -882,10 +888,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -897,7 +903,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -1203,8 +1209,8 @@Method Details
{ # Request message for EndpointService.DeployModel. "deployedModel": { # A deployment of a Model. Endpoints contain one or more DeployedModels. # Required. The DeployedModel to be created within the Endpoint. Note that Endpoint.traffic_split must be updated for the DeployedModel to start receiving traffic, either as part of this call, or via EndpointService.UpdateEndpoint. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, "checkpointId": "A String", # The checkpoint id of the model. @@ -1224,10 +1230,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -1239,7 +1245,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -2302,6 +2308,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -2831,8 +2843,8 @@Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon. "deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. { # A deployment of a Model. Endpoints contain one or more DeployedModels. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, "checkpointId": "A String", # The checkpoint id of the model. @@ -2852,10 +2864,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -2867,7 +2879,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -3163,8 +3175,8 @@Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon. "deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. { # A deployment of a Model. Endpoints contain one or more DeployedModels. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, "checkpointId": "A String", # The checkpoint id of the model. @@ -3184,10 +3196,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -3199,7 +3211,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -3458,8 +3470,8 @@Method Details
{ # Request message for EndpointService.MutateDeployedModel. "deployedModel": { # A deployment of a Model. Endpoints contain one or more DeployedModels. # Required. The DeployedModel to be mutated within the Endpoint. Only the following fields can be mutated: * `min_replica_count` in either DedicatedResources or AutomaticResources * `max_replica_count` in either DedicatedResources or AutomaticResources * `required_replica_count` in DedicatedResources * autoscaling_metric_specs * `disable_container_logging` (v1 only) * `enable_container_logging` (v1beta1 only) * `scale_to_zero_spec` in DedicatedResources (v1beta1 only) * `initial_replica_count` in DedicatedResources (v1beta1 only) - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, "checkpointId": "A String", # The checkpoint id of the model. @@ -3479,10 +3491,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -3494,7 +3506,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -3716,8 +3728,8 @@Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon. "deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. { # A deployment of a Model. Endpoints contain one or more DeployedModels. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, "checkpointId": "A String", # The checkpoint id of the model. @@ -3737,10 +3749,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -3752,7 +3764,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -4000,8 +4012,8 @@Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon. "deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. { # A deployment of a Model. Endpoints contain one or more DeployedModels. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, "checkpointId": "A String", # The checkpoint id of the model. @@ -4021,10 +4033,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -4036,7 +4048,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -4967,6 +4979,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -5599,8 +5617,8 @@Method Details
"dedicatedEndpointEnabled": True or False, # If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon. "deployedModels": [ # Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. { # A deployment of a Model. Endpoints contain one or more DeployedModels. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, "checkpointId": "A String", # The checkpoint id of the model. @@ -5620,10 +5638,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -5635,7 +5653,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationItems.html b/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationItems.html index 029be95727..3cfdae478a 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationItems.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationItems.html @@ -154,6 +154,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -384,6 +390,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -799,6 +811,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -1029,6 +1047,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -1443,6 +1467,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -1673,6 +1703,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -2134,6 +2170,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -2364,6 +2406,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -2779,6 +2827,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -3009,6 +3063,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -3423,6 +3483,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -3653,6 +3719,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -4114,6 +4186,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -4344,6 +4422,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -4759,6 +4843,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -4989,6 +5079,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -5403,6 +5499,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -5633,6 +5735,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -6094,6 +6202,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -6324,6 +6438,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -6739,6 +6859,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -6969,6 +7095,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -7383,6 +7515,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -7613,6 +7751,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -8116,6 +8260,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -8346,6 +8496,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -8761,6 +8917,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -8991,6 +9153,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -9405,6 +9573,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -9635,6 +9809,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -10096,6 +10276,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -10326,6 +10512,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -10741,6 +10933,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -10971,6 +11169,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -11385,6 +11589,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -11615,6 +11825,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -12089,6 +12305,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -12319,6 +12541,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -12734,6 +12962,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -12964,6 +13198,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -13378,6 +13618,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -13608,6 +13854,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -14069,6 +14321,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -14299,6 +14557,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -14714,6 +14978,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -14944,6 +15214,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -15358,6 +15634,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -15588,6 +15870,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationRuns.html b/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationRuns.html index a9c4764dbf..745357588c 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationRuns.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationRuns.html @@ -1395,6 +1395,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -1625,6 +1631,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -2040,6 +2052,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -2270,6 +2288,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -2684,6 +2708,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -2914,6 +2944,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -3443,6 +3479,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -3809,6 +3851,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -5411,6 +5459,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -5641,6 +5695,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -6056,6 +6116,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -6286,6 +6352,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -6700,6 +6772,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -6930,6 +7008,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -7459,6 +7543,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -7825,6 +7915,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -9469,6 +9565,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -9699,6 +9801,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -10114,6 +10222,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -10344,6 +10458,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -10758,6 +10878,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -10988,6 +11114,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -11517,6 +11649,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -11883,6 +12021,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -13498,6 +13642,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -13728,6 +13878,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -14143,6 +14299,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -14373,6 +14535,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -14787,6 +14955,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -15017,6 +15191,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -15546,6 +15726,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -15912,6 +16098,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationSets.html b/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationSets.html index 6ef0cdef36..c6a627010c 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationSets.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.evaluationSets.html @@ -141,6 +141,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -395,6 +401,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -691,6 +703,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -958,6 +976,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -1231,6 +1255,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -1486,6 +1516,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.featureOnlineStores.featureViews.html b/docs/dyn/aiplatform_v1beta1.projects.locations.featureOnlineStores.featureViews.html index 3ff9b94001..868b8dae6b 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.featureOnlineStores.featureViews.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.featureOnlineStores.featureViews.html @@ -189,8 +189,8 @@Method Details
}, "name": "A String", # Identifier. Name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}` "optimizedConfig": { # Configuration for FeatureViews created in Optimized FeatureOnlineStore. # Optional. Configuration for FeatureView created under Optimized FeatureOnlineStore. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, }, @@ -604,8 +604,8 @@Method Details
}, "name": "A String", # Identifier. Name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}` "optimizedConfig": { # Configuration for FeatureViews created in Optimized FeatureOnlineStore. # Optional. Configuration for FeatureView created under Optimized FeatureOnlineStore. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, }, @@ -736,8 +736,8 @@Method Details
}, "name": "A String", # Identifier. Name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}` "optimizedConfig": { # Configuration for FeatureViews created in Optimized FeatureOnlineStore. # Optional. Configuration for FeatureView created under Optimized FeatureOnlineStore. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, }, @@ -839,8 +839,8 @@Method Details
}, "name": "A String", # Identifier. Name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}` "optimizedConfig": { # Configuration for FeatureViews created in Optimized FeatureOnlineStore. # Optional. Configuration for FeatureView created under Optimized FeatureOnlineStore. - "automaticResources": { # A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000. - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. + "automaticResources": { # A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines. # Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000. + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number. "minReplicaCount": 42, # Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. }, }, diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.html b/docs/dyn/aiplatform_v1beta1.projects.locations.html index d22572260c..0441795c9c 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.html @@ -384,6 +384,9 @@Instance Methods
getRagEngineConfig(name, x__xgafv=None)Gets a RagEngineConfig.
++
+getSemanticGovernancePolicyEngine(name, x__xgafv=None)Gets a SemanticGovernancePolicyEngine. A SemanticGovernancePolicyEngine is a singleton resource that is created when its parent is created, and deleted when its parent is deleted. This method retrieves the current state of the Semantic Governance Policy Engine.
list(name, extraLocationTypes=None, filter=None, pageSize=None, pageToken=None, x__xgafv=None)Lists information about the supported locations for this service. This method lists locations based on the resource scope provided in the ListLocationsRequest.name field: * **Global locations**: If `name` is empty, the method lists the public locations available to all projects. * **Project-specific locations**: If `name` follows the format `projects/{project}`, the method lists locations visible to that specific project. This includes public, private, or other project-specific locations enabled for the project. For gRPC and client library implementations, the resource name is passed as the `name` field. For direct service calls, the resource name is incorporated into the request path based on the specific service implementation and version.
@@ -399,6 +402,9 @@Instance Methods
updateRagEngineConfig(name, body=None, x__xgafv=None)Updates a RagEngineConfig.
++
+updateSemanticGovernancePolicyEngine(name, body=None, updateMask=None, x__xgafv=None)Updates a SemanticGovernancePolicyEngine. This method performs an upsert operation. If the SemanticGovernancePolicyEngine resource does not exist, it will be created. Otherwise, it will be updated.
Method Details
+askContexts(parent, body=None, x__xgafv=None)@@ -452,6 +458,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -748,6 +760,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -1393,10 +1411,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -1408,7 +1426,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -1641,10 +1659,10 @@Method Details
}, "initialReplicaCount": 42, # Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count. "machineSpec": { # Specification of a single machine. # Required. Immutable. The specification of a single machine being used. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -1656,7 +1674,7 @@Method Details
}, "tpuTopology": "A String", # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). }, - "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). + "maxReplicaCount": 42, # Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). "minReplicaCount": 42, # Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. "requiredReplicaCount": 42, # Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. "scaleToZeroSpec": { # Specification for scale-to-zero feature. # Optional. Specification for scale-to-zero feature. @@ -2216,7 +2234,7 @@Method Details
The object takes the form of: { # Request message for EvaluationService.EvaluateInstances. - "autoraterConfig": { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Autorater config used for evaluation. + "autoraterConfig": { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Autorater config used for evaluation. Not applicable for predefined metrics (PredefinedMetricSpec); the server uses its own model configuration for predefined metrics and this field is ignored. "autoraterModel": "A String", # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}` "flipEnabled": True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias. "generationConfig": { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs. @@ -2513,6 +2531,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -2832,6 +2856,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -3224,6 +3254,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -3454,6 +3490,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -3776,6 +3818,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -4006,6 +4054,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -6199,6 +6253,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -6728,6 +6788,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -6958,6 +7024,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -7373,6 +7445,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -7603,6 +7681,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -8017,6 +8101,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -8247,6 +8337,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -8923,6 +9019,12 @@Method Details
"A String", ], }, + "exaAiSearch": { # ExaAiSearch tool type. A tool that uses the Exa.ai search engine for grounding. # Optional. Uses Exa.ai to search for information to answer user queries. The search results will be grounded on Exa.ai and presented to the model for response generation + "apiKey": "A String", # Required. The API key for ExaAiSearch. + "customConfigs": { # Optional. This field can be used to pass any parameter from the Exa.ai Search API. + "a_key": "", # Properties of the object. + }, + }, "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client. "behavior": "A String", # Optional. Specifies the function Behavior. If not specified, the system keeps the current function call behavior. This field is currently only supported by the BidiGenerateContent method. @@ -9228,6 +9330,43 @@Method Details
}++getSemanticGovernancePolicyEngine(name, x__xgafv=None)+Gets a SemanticGovernancePolicyEngine. A SemanticGovernancePolicyEngine is a singleton resource that is created when its parent is created, and deleted when its parent is deleted. This method retrieves the current state of the Semantic Governance Policy Engine. + +Args: + name: string, Required. The resource name of the SemanticGovernancePolicyEngine to retrieve. Format: projects/{project}/locations/{location}/semanticGovernancePolicyEngine (required) + x__xgafv: string, V1 error format. + Allowed values + 1 - v1 error format + 2 - v2 error format + +Returns: + An object of the form: + + { # Define a singleton SemanticGovernancePolicyEngine resource under a project and location. + "createTime": "A String", # Output only. Timestamp when this SemanticGovernancePolicyEngine was created. + "gatewayConfigs": { # Optional. Configurations for gateways. The keys are user-defined names for each gateway. + "a_key": { # Configuration for a single gateway. + "dnsRecord": "A String", # Output only. The fully qualified record name of the created A-record in Cloud DNS. + "dnsZoneName": "A String", # Optional. FQDN of the private DNS zone to create DNS record set for PSC endpoint. + "ipAddress": "A String", # Output only. The private IP address of the PSC endpoint. + "network": "A String", # Optional. The URI of the network resource where PSC-E will be provisioned. if not provided `default` network will be used. Format: projects/{project}/global/networks/{network} + "pscEndpoint": "A String", # Output only. The self-link or name of the Private Service Connect endpoint forwarding rule. + "state": "A String", # Output only. The state of the Gateway configuration. + "subnetwork": "A String", # Optional. The URI of the subnetwork resource where PSC-E will be provisioned. if not provided `default` subnet will be used from the same {location} Format: projects/{project}/regions/{region}/subnetworks/{subnetwork} + }, + }, + "ipAddress": "A String", # Output only. The private IPv4 address of the PSC endpoint. + "name": "A String", # Identifier. The resource name of the SemanticGovernancePolicyEngine. Format: projects/{project}/locations/{location}/semanticGovernancePolicyEngine + "pscForwardingRule": "A String", # Output only. The URI of the PSC endpoint resource created in customer project. Format: projects/{project}/regions/{region}/forwardingRules/{forwarding_rule} + "pscServiceAttachment": "A String", # Output only. URI of the PSC attachment resource provided by SGP. Format: projects/{project}/regions/{region}/serviceAttachments/{service_attachment} + "state": "A String", # Output only. The state of the SemanticGovernancePolicyEngine. + "targetProject": "A String", # Optional. The project where network, subnetwork and dns resources are located. If not provided, it is assumed to be the same project of SemanticGovernancePolicyEngine. This field is for Shared VPC scenarios, where network resources may be in a target project and SGP engine in a service project. + "updateTime": "A String", # Output only. Timestamp when this SemanticGovernancePolicyEngine was last updated. +}++list(name, extraLocationTypes=None, filter=None, pageSize=None, pageToken=None, x__xgafv=None)Lists information about the supported locations for this service. This method lists locations based on the resource scope provided in the ListLocationsRequest.name field: * **Global locations**: If `name` is empty, the method lists the public locations available to all projects. * **Project-specific locations**: If `name` follows the format `projects/{project}`, the method lists locations visible to that specific project. This includes public, private, or other project-specific locations enabled for the project. For gRPC and client library implementations, the resource name is passed as the `name` field. For direct service calls, the resource name is incorporated into the request path based on the specific service implementation and version. @@ -9434,10 +9573,10 @@Method Details
}, }, "machineSpec": { # Specification of a single machine. # Output only. The machine spec. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -9582,10 +9721,10 @@Method Details
}, }, "machineSpec": { # Specification of a single machine. # Output only. The machine spec. - "acceleratorCount": 42, # The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. + "acceleratorCount": 42, # The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. "acceleratorType": "A String", # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. "gpuPartitionSize": "A String", # Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. + "machineType": "A String", # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. "minGpuDriverVersion": "A String", # Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure. "multihostGpuNodeCount": 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments. "reservationAffinity": { # A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation. @@ -9749,4 +9888,65 @@Method Details
}++updateSemanticGovernancePolicyEngine(name, body=None, updateMask=None, x__xgafv=None)+Updates a SemanticGovernancePolicyEngine. This method performs an upsert operation. If the SemanticGovernancePolicyEngine resource does not exist, it will be created. Otherwise, it will be updated. + +Args: + name: string, Identifier. The resource name of the SemanticGovernancePolicyEngine. Format: projects/{project}/locations/{location}/semanticGovernancePolicyEngine (required) + body: object, The request body. + The object takes the form of: + +{ # Define a singleton SemanticGovernancePolicyEngine resource under a project and location. + "createTime": "A String", # Output only. Timestamp when this SemanticGovernancePolicyEngine was created. + "gatewayConfigs": { # Optional. Configurations for gateways. The keys are user-defined names for each gateway. + "a_key": { # Configuration for a single gateway. + "dnsRecord": "A String", # Output only. The fully qualified record name of the created A-record in Cloud DNS. + "dnsZoneName": "A String", # Optional. FQDN of the private DNS zone to create DNS record set for PSC endpoint. + "ipAddress": "A String", # Output only. The private IP address of the PSC endpoint. + "network": "A String", # Optional. The URI of the network resource where PSC-E will be provisioned. if not provided `default` network will be used. Format: projects/{project}/global/networks/{network} + "pscEndpoint": "A String", # Output only. The self-link or name of the Private Service Connect endpoint forwarding rule. + "state": "A String", # Output only. The state of the Gateway configuration. + "subnetwork": "A String", # Optional. The URI of the subnetwork resource where PSC-E will be provisioned. if not provided `default` subnet will be used from the same {location} Format: projects/{project}/regions/{region}/subnetworks/{subnetwork} + }, + }, + "ipAddress": "A String", # Output only. The private IPv4 address of the PSC endpoint. + "name": "A String", # Identifier. The resource name of the SemanticGovernancePolicyEngine. Format: projects/{project}/locations/{location}/semanticGovernancePolicyEngine + "pscForwardingRule": "A String", # Output only. The URI of the PSC endpoint resource created in customer project. Format: projects/{project}/regions/{region}/forwardingRules/{forwarding_rule} + "pscServiceAttachment": "A String", # Output only. URI of the PSC attachment resource provided by SGP. Format: projects/{project}/regions/{region}/serviceAttachments/{service_attachment} + "state": "A String", # Output only. The state of the SemanticGovernancePolicyEngine. + "targetProject": "A String", # Optional. The project where network, subnetwork and dns resources are located. If not provided, it is assumed to be the same project of SemanticGovernancePolicyEngine. This field is for Shared VPC scenarios, where network resources may be in a target project and SGP engine in a service project. + "updateTime": "A String", # Output only. Timestamp when this SemanticGovernancePolicyEngine was last updated. +} + + updateMask: string, Optional. Specifies the fields to be overwritten in the SemanticGovernancePolicyEngine resource by the update. The fields specified in the update_mask are relative to the resource itself. If no update_mask is provided, all fields are overwritten. + x__xgafv: string, V1 error format. + Allowed values + 1 - v1 error format + 2 - v2 error format + +Returns: + An object of the form: + + { # This resource represents a long-running operation that is the result of a network API call. + "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available. + "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation. + "code": 42, # The status code, which should be an enum value of google.rpc.Code. + "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use. + { + "a_key": "", # Properties of the object. Contains field @type with type URL. + }, + ], + "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. + }, + "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. + "a_key": "", # Properties of the object. Contains field @type with type URL. + }, + "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`. + "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`. + "a_key": "", # Properties of the object. Contains field @type with type URL. + }, +}+