Question

You are building a customer-support RAG assistant using LangChain LCEL on Databricks. The chain retrieves context from Mosaic AI Vector Search, injects it into a prompt, and calls a Foundation Model API chat endpoint. Product wants the assistant to display the model's answer progressively as it is generated in the web chat UI, but the retrieval and prompt-assembly steps should complete before any output appears. Which approach correctly implements this behavior with a single LCEL chain?

Accepted Answer

Invoke the full chain with `.stream()`; the retriever and prompt steps run to completion first, and only the final LLM step yields incremental tokens as they are produced.

Answer

Call `.invoke()` on the chain and set `temperature=0` so the endpoint returns tokens one at a time to the UI.

Answer

Wrap the retriever in a `RunnableParallel` so it streams document chunks to the UI while the LLM generates the answer simultaneously.

Answer

Use `.batch()` on the chain with a batch size of 1, which forces the endpoint into streaming mode for the final step.