langchain/templates/rag-pinecone-multi-query/README.md

RAG Pinecone multi query

This template performs RAG using Pinecone and OpenAI with the multi-query retriever.

This will use an LLM to generate multiple queries from different perspectives for a given user input query.

For each query, it retrieves a set of relevant documents and takes the unique union across all queries for answer synthesis.

Pinecone

This template uses Pinecone as a vectorstore and requires that PINECONE_API_KEY, PINECONE_ENVIRONMENT, and PINECONE_INDEX are set.

LLM

Be sure that OPENAI_API_KEY is set in order to the OpenAI models.

App

Example server.py:

from fastapi import FastAPI
from langserve import add_routes
from rag_pinecone_multi_query.chain import chain

app = FastAPI()

# Edit this to add the chain you want to add
add_routes(app, chain, path="rag_pinecone_multi_query")

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=8001)

Run:

python app/server.py

Check endpoint:

http://0.0.0.0:8001/docs

See rag_pinecone_multi_query.ipynb for example usage -

from langserve.client import RemoteRunnable
rag_app_pinecone = RemoteRunnable('http://0.0.0.0:8001/rag_pinecone_multi_query')
rag_app_pinecone.invoke("What are the different types of agent memory")