This project teaches RAG by implementing it from scratch in JavaScript, covering document loading, text chunking, embedding generation, vector storage and retrieval, re-ranking, query preprocessing, and context-augmented generation with local LLMs. It uses node-llama-cpp for local inference and provides detailed code walkthroughs so developers fully understand each component of a RAG pipeline.