Howie/samples 4

sdk/ai/azure-ai-projects/CHANGELOG.md

@@ -60,6 +60,7 @@ Breaking changes in beta classes:
   * Added Hosted Agent code-upload samples `sample_create_hosted_agent_from_code.py` and `sample_create_hosted_agent_from_code_async.py`, demonstrating uploading a code package (zip) as a new hosted agent version.
   * The Hosted Agent creation sample also demonstrates assigning the hosted agent managed identity the Azure AI User RBAC role on the backing Azure AI account.
   * Updated the other Hosted Agent samples to reuse an existing Hosted Agent as a prerequisite, instead of creating a new hosted agent version in each sample.
+  * Added Routines samples `sample_routines_crud.py` demonstring CRUD operations and `sample_routines_with_timer_trigger.py` trigger a routine by a timer.
 * Added Toolbox tool-search sample `sample_toolboxes_with_search_preview.py` and `sample_toolboxes_with_search_preview_async.py`, demonstrating creating a Toolbox version with `ToolboxSearchPreviewTool` and invoking `MCPTool`.
 * Added `.beta.models` samples under `samples/models/`:
   * `sample_models_basic.py` — synchronous end-to-end registration via the `create` helper (uses `azcopy`), followed by `get`, `list_versions`, `list`, `get_credentials`, `update`, and `delete`.

sdk/ai/azure-ai-projects/assets.json

@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "python",
   "TagPrefix": "python/ai/azure-ai-projects",
-  "Tag": "python/ai/azure-ai-projects_449a9f8e06"
+  "Tag": "python/ai/azure-ai-projects_11a3786ca2"
 }

sdk/ai/azure-ai-projects/samples/evaluations/sample_agent_trace_evaluation_smart_filter.py

@@ -127,7 +127,7 @@
         "start_time": start_time,
         "end_time": end_time,
         "max_traces": args.max_traces,
-        "filter_strategy": "smart_filtering"
+        "filter_strategy": "smart_filtering",
     }
 
     if args.agent_id:
@@ -173,4 +173,4 @@
         print(f"\n✗ Evaluation run failed: {run.error}")
 
     client.evals.delete(eval_id=eval_object.id)
-    print("Evaluation deleted")
+    print("Evaluation deleted")

sdk/ai/azure-ai-projects/samples/evaluations/sample_scheduled_agent_traces_evaluation_smart_filter.py

@@ -43,7 +43,7 @@
     Schedule,
     RecurrenceTrigger,
     DailyRecurrenceSchedule,
-    EvaluationScheduleTask
+    EvaluationScheduleTask,
 )
 import time
 
@@ -198,6 +198,7 @@ def assign_rbac():  # pylint: disable=too-many-statements
                 print("An unexpected error occurred. Please check the error details above.")
                 raise
 
+
 def schedule_trace_evaluation():
     endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
     model_deployment_name = os.environ["FOUNDRY_MODEL_NAME"]
@@ -284,7 +285,7 @@ def schedule_trace_evaluation():
             "start_time": start_time,
             "end_time": end_time,
             "max_traces": args.max_traces,
-            "filter_strategy": "smart_filtering"
+            "filter_strategy": "smart_filtering",
         }
 
         if args.agent_id:
@@ -304,7 +305,7 @@ def schedule_trace_evaluation():
         eval_run_object = {
             "eval_id": eval_object.id,
             "name": "trace_eval_with_smart_filter",
-            "data_source": data_source
+            "data_source": data_source,
         }
 
         print("Eval Run:")
@@ -335,5 +336,6 @@ def schedule_trace_evaluation():
         client.evals.delete(eval_id=eval_object.id)
         print("Evaluation deleted")
 
+
 if __name__ == "__main__":
     main()

sdk/ai/azure-ai-projects/samples/hosted_agents/sample_routines_crud.py

@@ -0,0 +1,111 @@
+# pylint: disable=line-too-long,useless-suppression
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    This sample demonstrates how to perform CRUD operations on Routines
+    using the synchronous AIProjectClient.
+
+    It creates a routine bound to an existing hosted agent, retrieves it,
+    toggles its `enabled` state via `disable` / `enable`, lists routines,
+    and finally deletes it. A `CustomRoutineTrigger` is used to keep the
+    sample self-contained (no GitHub or schedule resources required).
+
+    Routines are currently a preview feature. In the Python SDK, you access
+    these operations via `project_client.beta.routines`.
+
+USAGE:
+    python sample_routines_crud.py
+
+    Before running the sample:
+
+    pip install "azure-ai-projects>=2.2.0" python-dotenv
+
+    Set these environment variables with your own values:
+    1) FOUNDRY_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
+       page of your Microsoft Foundry portal.
+    2) FOUNDRY_HOSTED_AGENT_NAME - The name of an existing Hosted Agent to invoke
+       when the routine fires.
+"""
+
+import os
+
+from dotenv import load_dotenv
+
+from azure.core.exceptions import ResourceNotFoundError
+from azure.identity import DefaultAzureCredential
+
+from azure.ai.projects import AIProjectClient
+from azure.ai.projects.models import (
+    CustomRoutineTrigger,
+    InvokeAgentResponsesApiRoutineAction,
+    Routine,
+    RoutineTrigger,
+)
+
+load_dotenv()
+
+endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+agent_name = os.environ["FOUNDRY_HOSTED_AGENT_NAME"]
+
+
+def print_routine_state(routine: Routine) -> None:
+    print(f"  - routine `{routine.name}` enabled={routine.enabled} description={routine.description!r}")
+
+
+with (
+    DefaultAzureCredential() as credential,
+    AIProjectClient(endpoint=endpoint, credential=credential, allow_preview=True) as project_client,
+):
+
+    routine_name = "sample-routine"
+
+    try:
+        project_client.beta.routines.delete(routine_name)
+        print(f"Routine `{routine_name}` deleted")
+    except ResourceNotFoundError:
+        pass
+
+    triggers: dict[str, RoutineTrigger] = {
+        "manual": CustomRoutineTrigger(
+            provider="sample-provider",
+            event_name="sample-event",
+            parameters={"source": "sample_routines_crud"},
+        ),
+    }
+
+    action = InvokeAgentResponsesApiRoutineAction(agent_name=agent_name)
+
+    created = project_client.beta.routines.create_or_update(
+        routine_name,
+        description="Routine created by the azure-ai-projects sample.",
+        enabled=True,
+        triggers=triggers,
+        action=action,
+    )
+    print(f"Created routine: {created.name} enabled={created.enabled}")
+
+    disabled = project_client.beta.routines.disable(routine_name)
+    print(f"Disabled routine: {disabled.name} enabled={disabled.enabled}")
+
+    fetched = project_client.beta.routines.get(routine_name)
+    print("Retrieved routine after disable:")
+    print_routine_state(fetched)
+
+    enabled = project_client.beta.routines.enable(routine_name)
+    print(f"Enabled routine: {enabled.name} enabled={enabled.enabled}")
+
+    fetched = project_client.beta.routines.get(routine_name)
+    print("Retrieved routine after enable:")
+    print_routine_state(fetched)
+
+    routines = list(project_client.beta.routines.list())
+    print(f"Found {len(routines)} routine(s):")
+    for item in routines:
+        print(f"  - {item.name} enabled={item.enabled}")
+
+    project_client.beta.routines.delete(routine_name)
+    print("Routine deleted")

sdk/ai/azure-ai-projects/samples/hosted_agents/sample_routines_with_timer_trigger.py

@@ -0,0 +1,136 @@
+# pylint: disable=line-too-long,useless-suppression
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    This sample demonstrates how to create a Routine that fires automatically
+    from a one-shot timer trigger, then record the resulting runs by polling
+    `list_runs(...)` using the synchronous AIProjectClient.
+
+    The routine is bound to an existing hosted agent and scheduled to fire a
+    short time in the future. The sample then polls the run history until a
+    terminal phase is reached (or a deadline elapses), printing each observed
+    transition. The routine is deleted at the end of the sample.
+
+    Routines are currently a preview feature. In the Python SDK, you access
+    these operations via `project_client.beta.routines`.
+
+USAGE:
+    python sample_routines_with_timer_trigger.py
+
+    Before running the sample:
+
+    pip install "azure-ai-projects>=2.2.0" python-dotenv
+
+    Set these environment variables with your own values:
+    1) FOUNDRY_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
+       page of your Microsoft Foundry portal.
+    2) FOUNDRY_HOSTED_AGENT_NAME - The name of an existing Hosted Agent to invoke
+       when the routine timer fires.
+"""
+
+import datetime
+import json
+import os
+import time
+
+from dotenv import load_dotenv
+
+from azure.core.exceptions import ResourceNotFoundError
+from azure.core.settings import settings
+
+settings.tracing_implementation = "opentelemetry"
+from opentelemetry import trace
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import SimpleSpanProcessor, ConsoleSpanExporter
+from azure.monitor.opentelemetry import configure_azure_monitor
+from azure.ai.projects.telemetry import AIProjectInstrumentor
+
+from azure.identity import DefaultAzureCredential
+
+from azure.ai.projects import AIProjectClient
+from azure.ai.projects.models import (
+    InvokeAgentResponsesApiRoutineAction,
+    RoutineRun,
+    RoutineRunPhase,
+    TimerRoutineTrigger,
+)
+
+load_dotenv()
+
+# Console exporter: spans printed to stdout as they finish.
+tracer_provider = TracerProvider()
+tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))
+trace.set_tracer_provider(tracer_provider)
+tracer = trace.get_tracer(__name__)
+AIProjectInstrumentor().instrument()
+
+endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+agent_name = os.environ["FOUNDRY_HOSTED_AGENT_NAME"]
+
+
+with (
+    DefaultAzureCredential() as credential,
+    AIProjectClient(endpoint=endpoint, credential=credential, allow_preview=True) as project_client,
+):
+    # Azure Monitor exporter: same spans also sent to the Application Insights
+    # resource attached to the Foundry project, viewable in the "Tracing" tab
+    # on ai.azure.com.
+    configure_azure_monitor(connection_string=project_client.telemetry.get_application_insights_connection_string())
+
+    routine_name = "sample-routine-timer"
+
+    try:
+        project_client.beta.routines.delete(routine_name)
+        print(f"Routine `{routine_name}` deleted")
+    except ResourceNotFoundError:
+        pass
+
+    fire_at = datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(seconds=20)
+    created = project_client.beta.routines.create_or_update(
+        routine_name,
+        description="Routine used by the timer-trigger sample.",
+        enabled=True,
+        triggers={"once": TimerRoutineTrigger(at=fire_at)},
+        action=InvokeAgentResponsesApiRoutineAction(agent_name=agent_name),
+    )
+    print(f"Created routine: {created.name} enabled={created.enabled} fire_at={fire_at.isoformat()}")
+
+    terminal_phases = {RoutineRunPhase.COMPLETED, RoutineRunPhase.FAILED}
+    seen_phases: dict[str, RoutineRunPhase] = {}
+    final_run: RoutineRun | None = None
+
+    deadline = time.monotonic() + 180
+    while time.monotonic() < deadline:
+        runs = list(project_client.beta.routines.list_runs(routine_name, limit=20, order="desc"))
+        for run in runs:
+            if run.id is None:
+                continue
+            if seen_phases.get(run.id) == run.phase:
+                continue
+            seen_phases[run.id] = run.phase  # type: ignore[assignment]
+            print(
+                f"  - run_id={run.id} phase={run.phase} status={run.status} "
+                f"trigger_type={run.trigger_type} triggered_at={run.triggered_at} ended_at={run.ended_at}"
+            )
+            if str(run.status).lower() == "finished":
+                final_run = run
+
+        if final_run is not None:
+            break
+        time.sleep(5)
+
+    if final_run:
+        print("Final run:")
+        print(json.dumps(final_run.as_dict(), indent=2, default=str))
+        # Note: retrieving the response body produced by a routine-dispatched
+        # run via `openai_client.responses.retrieve(final_run.response_id)` is
+        # not yet supported by the service for this scenario.
+    else:
+        print("Timer did not produce a terminal run within the deadline.")
+
+    project_client.beta.routines.delete(routine_name)
+    print("Routine deleted")