Production RAG with LangChain & Vector Databases

Master the transition from simple prototypes to production-grade RAG systems by addressing the critical scaling, debugging, and security challenges that standard tutorials often ignore.

We just posted a comprehensive course on the freeCodeCamp.org YouTube channel that covers the entire RAG pipeline—from vector database optimization and observability to advanced agentic and multimodal architectures. You will learn to make sure your AI applications are robust, secure, and ready for deployment. Paulo Dichone created this course.

Here are the sections in the course:

Intro

Full RAG Overview

Development Environment Setup

Document Loader - Overview

Document Processing Pipeline - RAG Indexing Pipeline

Embedding Dimensions - Deep Dive

Hands-on - Create a Vector DB Using Chroma

Similarity Search with Scores

Building a Basic RAG System

Debugging RAG Systems

Hybrid Search

Token Budgeting

Observability - Introduction

LangSmith Setup

RAG Optimization

Scaling RAG Systems

The Real Costs of Vector Search

Production Hosting

Supabase and PGVector - Set up and Introduction

Three Pillars of Production Visibility

Production Project

Set up the Security Layer

Set up the LangGraph Agent and the FastAPI API - Testing and LangSmith Observability Dashboard

Test the Security Layer

Security Checklist

Advanced RAG Topics - Long Context Models vs RAG

Contextual Retrieval

Late Chunking vs Early Chunking

Agentic RAG - Self-Correcting Retrieval

GraphRAG - Multi-hop Reasoning

Multimodal RAG - ColPali - Vision-Based Document RAG

Summary - Advanced RAG (Current State)

RAG Evolution - Overview

Outro

Watch the full course on the freeCodeCamp.org YouTube channel (8-hour watch).