RAG Pinecone Cohere Re-rank

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

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

Pinecone

This connects to a hosted Pinecone vectorstore.

Be sure that you have set a few env variables in chain.py:

PINECONE_API_KEY
PINECONE_ENV
PINECONE_INDEX

LLM

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

Cohere

Be sure that COHERE_API_KEY is set in order to the ReRank endpoint.

Environment variables

You need to define the following environment variables

OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
COHERE_API_KEY=<YOUR_COHERE_API_KEY>
PINECONE_API_KEY=<YOUR_PINECONE_API_KEY>
PINECONE_ENVIRONMENT=<YOUR_PINECONE_INDEX>
PINECONE_INDEX=<YOUR_PINECONE_INDEX>