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This template performs RAG using Ollama and OpenAI with a multi-query retriever.

The multi-query retriever is an example of query transformation, generating multiple queries from different perspectives based on the user's input query.

For each query, it retrieves a set of relevant documents and takes the unique union across all queries for answer synthesis.

We use a private, local LLM for the narrow task of query generation to avoid excessive calls to a larger LLM API.

See an example trace for Ollama LLM performing the query expansion here.

But we use OpenAI for the more challenging task of answer syntesis (full trace example here).

Environment Setup​

To set up the environment, you need to download Ollama.

Follow the instructions here.

You can choose the desired LLM with Ollama.

This template uses zephyr, which can be accessed using ollama pull zephyr.

There are many other options available here.

Set the OPENAI_API_KEY environment variable to access the OpenAI models.


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

pip install -U langchain-cli

To create a new LangChain project and install this package, do:

langchain app new my-app --package rag-ollama-multi-query

To add this package to an existing project, run:

langchain app add rag-ollama-multi-query

And add the following code to your file:

from rag_ollama_multi_query import chain as rag_ollama_multi_query_chain

add_routes(app, rag_ollama_multi_query_chain, path="/rag-ollama-multi-query")

(Optional) Now, let's 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 running locally at http://localhost:8000

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

To access the template from code, use:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-ollama-multi-query")

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