# rag-weaviate This template performs RAG with Weaviate. ## Environment Setup Set the `OPENAI_API_KEY` environment variable to access the OpenAI models. Also, ensure the following environment variables are set: * `WEAVIATE_ENVIRONMENT` * `WEAVIATE_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 rag-weaviate ``` If you want to add this to an existing project, you can just run: ```shell langchain app add rag-weaviate ``` And add the following code to your `server.py` file: ```python from rag_weaviate import chain as rag_weaviate_chain add_routes(app, rag_weaviate_chain, path="/rag-weaviate") ``` (Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith [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/rag-weaviate/playground](http://127.0.0.1:8000/rag-weaviate/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-weaviate") ```