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- Initial commit oss-tool-retrieval-agent - README update - lint - lock - format imports - Rename to retrieval-agent-fireworks - cr <!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes if applicable, - **Dependencies:** any dependencies required for this change, - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> --------- Co-authored-by: Taqi Jaffri <tjaffri@docugami.com> |
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retrieval-agent-fireworks
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
Environment Setup
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.
Set the FIREWORKS_API_KEY environment variable to access Fireworks.
Usage
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 server.py file:
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_TRACING_V2=true
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 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")