wehub-resource-sync
54 lines
1.9 KiB
YAML
54 lines
1.9 KiB
YAML
# Agent with Unity Catalog tools — demonstrates UC function calling.
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#
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# This agent uses tools backed by Databricks SQL functions, including
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# the built-in `ai_query` function.
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#
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# Usage:
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# export DATABRICKS_CONFIG_PROFILE=<your-profile>
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# export DATABRICKS_WAREHOUSE_ID=<your-sql-warehouse-id>
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#
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# python -m omnigent tests/resources/examples/agent_with_uc_tools.yaml \
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# --prompt "Use ask_llm to summarize: Databricks Unity Catalog is great"
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#
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# The runner executes UC functions via the SQL Statement Execution API.
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# The warehouse ID is read from the DATABRICKS_WAREHOUSE_ID env var at
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# execution time (or can be set per-tool via warehouse_id: in the YAML).
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name: uc_tool_agent
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prompt: |
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You are a helpful assistant with access to Databricks AI functions.
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When the user asks you to summarize or analyze text, use the ask_llm tool.
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Always use 'databricks-claude-sonnet-4-6' as the endpoint.
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executor:
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harness: openai-agents
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model: databricks-gpt-5-4-mini
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auth:
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type: databricks
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profile: oss
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tools:
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# Built-in ai_query function — calls a model serving endpoint.
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# Works on any workspace with model serving enabled (pro/serverless SQL warehouse).
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# warehouse_id is resolved from DATABRICKS_WAREHOUSE_ID env var at runtime.
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ask_llm:
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type: function
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description: "Query a Databricks-hosted LLM via ai_query. Pass the endpoint name and a prompt string."
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catalog_path: ai_query
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parameters:
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type: object
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properties:
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endpoint:
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type: string
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description: "Model serving endpoint name, e.g. 'databricks-claude-sonnet-4-6'."
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prompt:
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type: string
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description: "The prompt to send to the model."
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required: [endpoint, prompt]
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# To add a custom UC function, uncomment and replace with your own:
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classify_text:
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type: function
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description: "Classify the sentiment of the given text."
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catalog_path: my_catalog.my_schema.classify_sentiment
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