What is RAG? The Complete Tutorial - From Scratch to Deployed API on Production | LangChain & Ollama
Автор: Venelin Valkov
Загружено: 2025-07-19
Просмотров: 1407
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.
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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
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