Production RAG with LangChain & Vector Databases – Full Course

freeCodeCamp.org · Beginner ·🔍 RAG & Vector Search ·1mo ago

Skills: RAG Basics90%Vector Stores80%LLMOps60%

Key Takeaways

Builds a production-ready RAG system using LangChain and vector databases

Original Description

Learn to build, debug, optimize, and scale RAG systems for production. 🚀 Free Production AI Starter Kit: https://bit.ly/production-ai-pack This course teaches what tutorials skip: why 90% of RAG projects fail and how to fix them. 💻 Code, Parts 1-5: https://github.com/pdichone/production-course-main-code 💻 Code, Part 6: https://github.com/pdichone/fcc-production-rag-part-6 Paulo's channel: @vincibits ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp ⭐️ Chapters ⭐️ 0:00:00 Intro 0:01:44 Full RAG Overview 0:08:27 Development Environment Setup 0:15:35 Document Loader - Overview 0:28:27 Document Processing Pipeline - RAG Indexing Pipeline 0:48:12 Embedding Dimensions - Deep Dive 1:01:05 Hands-on - Create a Vector DB Using Chroma 1:17:48 Similarity Search with Scores 1:24:32 Building a Basic RAG System 1:33:16 Debugging RAG Systems 1:53:46 Hybrid Search 1:13:49 Token Budgeting 2:21:10 Observability - Introduction 2:29:56 LangSmith Setup 2:37:56 RAG Optimization 3:12:58 Scaling RAG Systems 3:23:35 The Real Costs of Vector Search 3:33:17 Production Hosting 3:36:00 Supabase and PGVector - Set up and Introduction 4:04:41 Three Pillars of Production Visibility 4:16:11 Production Project 4:34:36 Set up the Security Layer 4:16:11 Set up the LangGraph Agent and the FastAPI API - Testing and LangSmith Observability Dashboard 5:27:46 Test the Security Layer 5:41:36 Security Checklist 6:06:09 Advanced RAG Topics - Long Context Models vs RAG 6:14:29 Contextual Retrieval 6:24:26 Late Chunking vs Early Chunking 6:42:04 Agentic RAG - Self-Correcting Retrieval 7:04:45 GraphRAG - Multi-hop Reasoning 7:24:28 Multimodal RAG - ColPali - Vision-Based Document RAG 7:34:45 Summary - Advanced RAG (Current State) 7:37:02 RAG Evolution - Overview 7:38:35 Outro 🎉 Thanks to our Champion and Sponsor supporters: 👾 @omerhattapoglu1158 👾 @goddardtan 👾 @akihayashi6629 👾 @kikilogsin

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Chapters (34)

Intro

1:44 Full RAG Overview

8:27 Development Environment Setup

15:35 Document Loader - Overview

28:27 Document Processing Pipeline - RAG Indexing Pipeline

48:12 Embedding Dimensions - Deep Dive

1:01:05 Hands-on - Create a Vector DB Using Chroma

1:17:48 Similarity Search with Scores

1:24:32 Building a Basic RAG System

1:33:16 Debugging RAG Systems

1:53:46 Hybrid Search

1:13:49 Token Budgeting

2:21:10 Observability - Introduction

2:29:56 LangSmith Setup

2:37:56 RAG Optimization

3:12:58 Scaling RAG Systems

3:23:35 The Real Costs of Vector Search

3:33:17 Production Hosting

3:36:00 Supabase and PGVector - Set up and Introduction

4:04:41 Three Pillars of Production Visibility

4:16:11 Production Project

4:34:36 Set up the Security Layer

4:16:11 Set up the LangGraph Agent and the FastAPI API - Testing and LangSmith Observabi

5:27:46 Test the Security Layer

5:41:36 Security Checklist

6:06:09 Advanced RAG Topics - Long Context Models vs RAG

6:14:29 Contextual Retrieval

6:24:26 Late Chunking vs Early Chunking

6:42:04 Agentic RAG - Self-Correcting Retrieval

7:04:45 GraphRAG - Multi-hop Reasoning

7:24:28 Multimodal RAG - ColPali - Vision-Based Document RAG

7:34:45 Summary - Advanced RAG (Current State)

7:37:02 RAG Evolution - Overview

7:38:35 Outro

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