# rag-pinecone-multi-query This template performs RAG using Pinecone and OpenAI with a multi-query retriever. It uses an LLM to generate multiple queries from different perspectives based on the user's input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries for answer synthesis. ## Environment Setup This template uses Pinecone as a vectorstore and requires that `PINECONE_API_KEY`, `PINECONE_ENVIRONMENT`, and `PINECONE_INDEX` are set. Set the `OPENAI_API_KEY` environment variable to access the OpenAI models. ## Usage To use this package, you should first install the LangChain CLI: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this package, do: ```shell langchain app new my-app --package rag-pinecone-multi-query ``` To add this package to an existing project, run: ```shell langchain app add rag-pinecone-multi-query ``` And add the following code to your `server.py` file: ```python from rag_pinecone_multi_query import chain as rag_pinecone_multi_query_chain add_routes(app, rag_pinecone_multi_query_chain, path="/rag-pinecone-multi-query") ``` (Optional) Now, let's 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 running locally at [http://localhost:8000](http://localhost:8000) You can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) You can access the playground at [http://127.0.0.1:8000/rag-pinecone-multi-query/playground](http://127.0.0.1:8000/rag-pinecone-multi-query/playground) To access the template from code, use: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-pinecone-multi-query") ```