This package uses open source models hosted on FireworksAI to do retrieval using an agent architecture. By default, this does retrieval over Arxiv.
We will use
Mixtral8x7b-instruct-v0.1, which is shown in this blog to yield reasonable
results with function calling even though it is not fine tuned for this task: https://huggingface.co/blog/open-source-llms-as-agents
There are various great ways to run OSS models. We will use FireworksAI as an easy way to run the models. See here for more information.
FIREWORKS_API_KEY environment variable to access Fireworks.
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 retrieval-agent-fireworks
If you want to add this to an existing project, you can just run:
langchain app add retrieval-agent-fireworks
And add the following code to your
from retrieval_agent_fireworks import chain as retrieval_agent_fireworks_chain
add_routes(app, retrieval_agent_fireworks_chain, path="/retrieval-agent-fireworks")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
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:
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/retrieval-agent-fireworks/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/retrieval-agent-fireworks")