# 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: ```shell export AZURE_OPENAI_ENDPOINT=... export AZURE_OPENAI_API_VERSION=... export AZURE_OPENAI_API_KEY=... ``` ## Usage To use this package, you should first have the LangChain CLI installed: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package retrieval-agent ``` If you want to add this to an existing project, you can just run: ```shell langchain app add retrieval-agent ``` And add the following code to your `server.py` file: ```python 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](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) We can access the playground at [http://127.0.0.1:8000/retrieval-agent/playground](http://127.0.0.1:8000/retrieval-agent/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/retrieval-agent") ```