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.
This template uses Pinecone as a vectorstore and requires that
PINECONE_INDEX are set.
OPENAI_API_KEY environment variable to access the OpenAI models.
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
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. If you don't have access, you can skip this section
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:
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")