Tutorial: Build a RAG pipeline with LangChain, OpenAI and Pinecone

This tutorial contains everything you need to build production-ready Retrieval Augmented Generation (RAG) pipelines on your own data.

Whether you're working with a corporate knowledge base, personal blog, or ticketing system, you'll learn how to create an AI-powered chat interface that provides accurate answers with citations.

Complete Example Code

The full source code for this tutorial is available in the companion repository on GitHub. This repository contains a complete, working example that you can clone and run locally.

Try It Yourself

See the complete working demo at /chat. This tutorial walks you through building this exact same experience:

Why Premium if the repos are public?

The GitHub repos are a reference. Premium is a production path:

• Comprehensive step by step walkthrough
• Patterns for OpenAI/Pinecone + index lifecycle
• License + lifetime updates

Table of contents

• System Architecture
• Phase 1: Data processing
  • Step 1: Load and configure the data processing notebook
  • Step 2: Clone the data source
  • Step 3: Install dependencies
  • Step 4: Loading blog posts into memory
  • Step 5: Loading your Open AI and Pinecone API keys into the environment
  • Step 6: Creating a Pinecone index
  • Step 7: Creating a vectorstore with LangChain
  • Understanding Document Chunking
• Phase 2: Application development
  • Step 1. Create the supporting services
    • The context service
    • The embeddings service
    • The Pinecone service
  • Step 2. Create the chat API
  • Step 3. Create the user interface
• Phase 3: Deployment
• Troubleshooting Matrix
• Production Checklist
• Additional Resources

System Architecture

How does the RAG pipeline work? (Click to expand)

Let's understand the complete system we'll be creating:

This is a Retrieval Augmented Generation (RAG) pipeline that allows users to chat with your content. Here's how it works:

1. When a user asks a question, their query is converted to a vector (embedding)
2. This vector is used to search your Pinecone database for similar content
3. The most relevant content is retrieved and injected into the LLM's prompt, the LLM generates a response based on your content, and the response is streamed back to the user along with citations