Question

A Generative AI Engineer is building a RAG chain in LangChain on Databricks. Documents are already indexed in a Databricks Vector Search Delta Sync index backed by a Foundation Model API embedding endpoint. The engineer wants the chain to retrieve relevant chunks at query time and pass them to a chat model, using the least custom code and the officially supported integration. Which approach should the engineer use to wire the vector index into the LangChain chain?

Accepted Answer

Instantiate DatabricksVectorSearch with the index name and call its as_retriever() method, then compose it into a retrieval chain with the chat model.

Answer

Manually query the index with the Vector Search Python SDK inside a custom RunnableLambda, embedding the query with a separate SentenceTransformers model each time.

Answer

Load all chunks from the underlying Delta table into an in-memory FAISS vector store at chain startup and retrieve from FAISS.

Answer

Use the SQL connector to run a similarity query against the Delta table directly and parse the returned rows as documents in a custom retriever.