langchain/templates/rag-pinecone-rerank/README.md

rag-pinecone-rerank

This template performs RAG using Pinecone and OpenAI along with Cohere to perform re-ranking on returned documents.

Re-ranking provides a way to rank retrieved documents using specified filters or criteria.

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.

Set the COHERE_API_KEY environment variable to access the Cohere ReRank.

Usage

To use this package, you should first have the LangChain CLI installed:

pip install -U "langchain-cli[serve]"

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package rag-pinecone-rerank

If you want to add this to an existing project, you can just run:

langchain app add rag-pinecone-rerank

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

from rag_pinecone_rerank import chain as rag_pinecone_rerank_chain

add_routes(app, rag_pinecone_rerank_chain, path="/rag-pinecone-rerank")

(Optional) Let's now 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_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 is running locally at http://localhost:8000

We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/rag-pinecone-rerank/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

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