VectorSearchRetrieverTool Openai integration#39
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leonbi-db merged 15 commits intodatabricks:mainfrom Jan 14, 2025
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leonbi100
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Jan 10, 2025
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| from databricks_openai.vector_search_retriever_tool import VectorSearchRetrieverTool | |||
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| # Expose all integrations to users under databricks-langchain | |||
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I'm assuming this is supposed to be databricks-openai?
| None, | ||
| description="The name of the text column to use for the embeddings. " | ||
| "Required for direct-access index or delta-sync index with " | ||
| "self-managed embeddings. Used for direct access indexes or " |
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nit: do we need the description to be duplicated (required for ... used for ...)?
| ) -> List[Dict[str, Any]]: | ||
| """ | ||
| Execute the VectorSearchIndex tool calls from the ChatCompletions response that correspond to the | ||
| self.tool VectorSearchRetrieverToolInput and attach the retrieved documents into toll call messages. |
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nit: typo
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| self.tool VectorSearchRetrieverToolInput and attach the retrieved documents into toll call messages. | |
| self.tool VectorSearchRetrieverToolInput and attach the retrieved documents into tool call messages. |
| embedding_model_name=self_managed_embeddings_test.embedding_model_name, | ||
| openai_client=self_managed_embeddings_test.open_ai_client, | ||
| ) | ||
| assert response is not None |
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Could we add a little more validation on the response?
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What does this PR do?
This PR introduces the databricks-openai python package with the VectorSearchRetrieverTool. The VectorSearchRetrieverTool allows for easy integration between OpenAI chat completions tool calling and Databricks VectorSearch. This tool supports the 3 types of VectorSearch Indexes. See testing below for usage details.
Delta Sync Index with Databricks managed embeddings:
Delta Sync Index with self managed embeddings:
Direct Access Index: