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This template performs RAG on a codebase.

It uses codellama-34b hosted by Fireworks' LLM inference API.

Environment Setup​

Set the FIREWORKS_API_KEY environment variable to access the Fireworks models.

You can obtain it from here.


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-codellama-fireworks

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

langchain app add rag-codellama-fireworks

And add the following code to your file:

from rag_codellama_fireworks import chain as rag_codellama_fireworks_chain

add_routes(app, rag_codellama_fireworks_chain, path="/rag-codellama-fireworks")

(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-codellama-fireworks")

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