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retrieval-agent

This package uses Azure OpenAI to do retrieval using an agent architecture. By default, this does retrieval over Arxiv.

Environment Setup​

Since we are using Azure OpenAI, we will need to set the following environment variables:

export AZURE_OPENAI_API_BASE=...
export AZURE_OPENAI_API_VERSION=...
export AZURE_OPENAI_API_KEY=...
export AZURE_OPENAI_DEPLOYMENT_NAME=...

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

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

langchain app add retrieval-agent

And add the following code to your server.py file:

from retrieval_agent import chain as retrieval_agent_chain

add_routes(app, retrieval_agent_chain, path="/retrieval-agent")

(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/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/retrieval-agent")