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:

pip install -U langchain-cli

To create a new LangChain project and install this package, do:

langchain app new my-app --package rag-pinecone-multi-query

To add this package to an existing project, run:

langchain app add rag-pinecone-multi-query

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

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. You can sign up for LangSmith 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 running locally at http://localhost:8000

You can see all templates at http://127.0.0.1:8000/docs You can access the playground at http://127.0.0.1:8000/rag-pinecone-multi-query/playground

To access the template from code, use:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-pinecone-multi-query")