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This template is used for conversational retrieval, which is one of the most popular LLM use-cases.

It passes both a conversation history and retrieved documents into an LLM for synthesis.

Environment Setup​

This template uses Pinecone as a vectorstore and requires that PINECONE_API_KEY, PINECONE_ENVIRONMENT, and PINECONE_INDEX are set.

Set the OPENAI_API_KEY environment variable to access the OpenAI models.


To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package rag-conversation

If you want to add this to an existing project, you can just run:

langchain app add rag-conversation

And add the following code to your file:

from rag_conversation import chain as rag_conversation_chain

add_routes(app, rag_conversation_chain, path="/rag-conversation")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at We can access the playground at

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-conversation")

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