Question

A Generative AI Engineer is building a RAG chain with LangChain on Databricks. Business stakeholders require that every generated answer include a list of the source documents that were retrieved and passed to the LLM, so end users can verify claims. The engineer is composing the chain using LCEL (LangChain Expression Language) with a Databricks Vector Search retriever and a Foundation Model API chat model. What is the most appropriate way to satisfy this requirement while keeping the generated answer and the sources in a single structured output?

Accepted Answer

Use RunnableParallel to run the retriever and pass through the question, then structure the output so that the retrieved documents flow alongside the LLM-generated answer into a final dictionary containing both 'answer' and 'context' keys.

Answer

Increase the retriever's k value so more documents are retrieved, and instruct the LLM in the prompt to copy every retrieved chunk verbatim into its answer text.

Answer

Enable inference tables on the model serving endpoint so the retrieved documents are automatically appended to each response returned to the client.

Answer

Store the retrieved documents in a separate Delta table keyed by request timestamp so users can join them to the answer afterward.