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169 changes: 79 additions & 90 deletions .agents/skills/author-recipes-and-cookbooks/SKILL.md

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1 change: 0 additions & 1 deletion api/content-markdown.ts
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Expand Up @@ -233,7 +233,6 @@ function readCookbookMarkdown(rootDir: string, slug: string): string {
goal,
intro: goal ? undefined : readCookbookIntro(rootDir, slug),
recipes: recipeInputs,
mode: "agent",
});
}

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21 changes: 0 additions & 21 deletions content/examples/rag-chat/content.md

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216 changes: 0 additions & 216 deletions content/recipes/ai-chat-model-serving/content.md

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2 changes: 0 additions & 2 deletions content/recipes/ai-chat-model-serving/goal.md
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Build a streaming AI chat experience in a Databricks App using Vercel AI SDK with Databricks Model Serving and OpenAI-compatible endpoints.

When done, you will have:

- A real-time streaming chat interface in your Databricks App
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1 change: 0 additions & 1 deletion content/recipes/ai-chat-model-serving/prerequisites.md
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Expand Up @@ -5,6 +5,5 @@ Complete these prerequisite templates first:

Then verify these Databricks workspace features are enabled. If any check fails, ask your workspace admin to enable the feature.

- **Databricks CLI authenticated.** Run `databricks auth profiles` and confirm at least one profile shows `Valid: YES`. If none do, authenticate with `databricks auth login --host <workspace-url> --profile <PROFILE>`.
- **An OpenAI-compatible chat endpoint in Model Serving.** Run `databricks serving-endpoints list --profile <PROFILE>` and confirm at least one OpenAI-compatible chat endpoint is listed (e.g. `databricks-gpt-5-4-mini`, `databricks-meta-llama-3-3-70b-instruct`, or `databricks-claude-sonnet-4`). Endpoint availability varies by workspace and region; note the one you plan to set as `DATABRICKS_ENDPOINT`.
- **Databricks Apps enabled.** Run `databricks apps list --profile <PROFILE>` and confirm the command succeeds (an empty list is fine). A permission or `not enabled` error means Apps is not available to this identity in this workspace.
63 changes: 0 additions & 63 deletions content/recipes/embeddings-generation/content.md

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2 changes: 0 additions & 2 deletions content/recipes/embeddings-generation/goal.md
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Generate text embeddings from a Databricks AI Gateway endpoint using the Databricks SDK.

When done, you will have:

- A configured embedding endpoint in your app environment
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1 change: 0 additions & 1 deletion content/recipes/embeddings-generation/prerequisites.md
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Verify these Databricks workspace features are enabled before starting. If any check fails, ask your workspace admin to enable the feature.

- **Databricks CLI authenticated.** Run `databricks auth profiles` and confirm at least one profile shows `Valid: YES`. If none do, authenticate with `databricks auth login --host <workspace-url> --profile <PROFILE>`.
- **An embedding endpoint in Model Serving.** Run `databricks serving-endpoints list --profile <PROFILE>` and confirm at least one embedding endpoint is listed (for example `databricks-gte-large-en` or `databricks-bge-large-en`, both 1024-dimension). Endpoint availability varies by workspace; note the endpoint name you plan to set as `DATABRICKS_EMBEDDING_ENDPOINT`.
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