Retrieval Augmented Generation (RAG) with Langchain: A Complete Tutorial

This comprehensive tutorial guides you through building Retrieval Augmented Generation (RAG) systems using LangChain. We cover everything from setting up your environment with environment variables and working with chat models (including Ollama), to the core components of RAG: loading and splitting documents, creating embeddings, storing them in vector databases, and using retrievers. The video culminates in practical, full RAG examples, including a basic RAG pipeline, a web-based RAG application, and an extended web RAG implementation, demonstrating how to connect your LLM to external knowledge sources like the web for more accurate and informed responses. Learn how to empower your large language models with real-world data and reduce hallucinations using practical, hands-on examples. #machinelearning #ai #langchain Code: https://github.com/KodySimpson/rag-langchain Join the Community! - https://rebrand.ly/discordlink Want to Support the Channel? - Become a Member: https://www.youtube.com/KodySimpson/join - https://buymeacoffee.com/kodysimpson My Socials: Github: https://github.com/KodySimpson Instagram: https://www.instagram.com/kody_a_simpson/ Twitter: https://twitter.com/kodysimp Blog: https://simpson.hashnode.dev/ Timestamps: 0:00:00 - Introduction 0:06:42 - Environment Setup 0:12:05 - Getting an OpenAI Key 0:14:00 - Environment Variables 0:18:01 - Chat Models 0:32:16 - Using Ollama 0:36:42 - Document Loaders 0:47:24 - Splitting 1:01:16 - Embeddings & Vector Stores 1:22:18 - Retrievers 1:28:42 - Full RAG Example 1:40:39 - Web RAG App 1:58:20 - Adding File Uploading 2:09:16 - Outro More Videos coming soon. Leave a comment for any future video suggestions.