This repository takes a clear, hands-on approach to Retrieval-Augmented Generation (RAG), breaking down advanced techniques into straightforward, understandable implementations. Instead of relying on frameworks like LangChain or FAISS, everything here is built using familiar Python libraries openai, numpy, matplotlib, and a few others.

The goal is simple: provide code that is readable, modifiable, and educational. By focusing on the fundamentals, this project helps demystify RAG and makes it easier to understand how it really works.