Question

A Generative AI Engineer has already created and populated a Databricks Vector Search index for a customer support RAG application. Using LangChain, they need to expose this index as a component that can be plugged directly into an LCEL retrieval chain so the chain can call it with a natural-language query string and receive relevant document chunks. Which approach correctly integrates the existing Vector Search index into the LangChain pipeline?

Accepted Answer

Instantiate DatabricksVectorSearch pointing at the index, then call its .as_retriever() method to obtain a retriever object usable in the chain.

Answer

Wrap the index endpoint URL in a LangChain PromptTemplate so the query string is formatted before being sent to the vector store.

Answer

Pass the raw index name to ChatDatabricks so the foundation model queries the vectors during generation.

Answer

Register the index as a LangChain OutputParser that converts the model response into retrieved documents.