TL;DR
A collection of Jupyter notebooks for step-by-step learning of RAG from basic principles to advanced techniques.
Key features
Implement the core steps of the RAG pipeline—indexing, retrieval, and generation—from scratch
Practice various retrieval techniques (simple retrieval, compressed retrieval, self-querying, etc.)
Provide real code examples using LangChain
Maximize learning effectiveness by integrating with video lectures
When to use it
When you want to systematically learn the concepts and implementation of RAG for the first time
When you want to learn how to build RAG applications using LangChain
When you want to practice various variations and optimization techniques of retrieval-augmented generation