Now that we have covered the theoretical foundations of Retrieval-Augmented Generation (RAG) and Pinecone, let's implement RAG in Python step by step.

What We’ll Cover in This Section

1. Installing the required libraries
2. Initializing Pinecone
3. Generating embeddings for documents using OpenAI
4. Storing embeddings in Pinecone
5. Performing a similarity search in Pinecone
6. Using retrieved context for LLM-powered responses
7. Optimizing the RAG pipeline

Step 1: Install Required Libraries

We need pinecone-client, openai, and langchain for handling embeddings, vector storage, and retrieval.

pip install openai pinecone-client langchain tiktoken

Step 2: Initialize Pinecone

First, we need to set up Pinecone and create an index to store our embeddings.

import os
from pinecone import Pinecone, ServerlessSpec

pc = Pinecone(api_key="********-****-****-****-************")

index_name = "quickstart"

pc.create_index(
    name=index_name,
    dimension=2, # Replace with your model dimensions
    metric="cosine", # Replace with your model metric
    spec=ServerlessSpec(
        cloud="aws",
        region="us-east-1"
    ) 
)

Explanation:

• Pinecone is initialized using an API key.
• We check if an index named "rag-index" exists; if not, we create one.