What is RAG? The Complete Tutorial - From Scratch to Deployed API on Production | LangChain & Ollama

Ever wondered how to make an LLM an expert on YOUR private documents? The answer is Retrieval-Augmented Generation (RAG). While stuffing context works for small files, it's slow, expensive, and fails at scale. RAG is the industry-standard solution. In this complete, step-by-step tutorial, you will learn the fundamentals of RAG by building a system from the ground up. We'll start with first principles using Python and Scikit-learn, refactor our system with LangChain, wrap it in a streaming FastAPI, and finally deploy it as a production-ready Docker container. PDF from the video: https://cdn.prod.website-files.com/602da0632e0ff07c4548b93b/62578b9caead2d3e47282088_Customer%20Complaint%20Policy.pdf AI Bootcamp: https://www.mlexpert.io/ LinkedIn: https://www.linkedin.com/in/venelin-valkov/ Follow me on X: https://twitter.com/venelin_valkov Discord: https://discord.gg/UaNPxVD6tv Subscribe: http://bit.ly/venelin-subscribe GitHub repository: https://github.com/curiousily/AI-Bootcamp 👍 Don't Forget to Like, Comment, and Subscribe for More Tutorials! 00:00 - What is RAG? 05:58 - Project setup and dependencies 07:04 - Build a retriever 10:52 - Simple RAG 13:25 - Chat with PDF file 15:31 - Tracing and observability with MLflow 19:00 - RAG Rest API with FastAPI 24:02 - Docker container and compose 25:56 - Deploy to production 28:36 - Conclusion Join this channel to get access to the perks and support my work: https://www.youtube.com/channel/UCoW_WzQNJVAjxo4osNAxd_g/join