Stop importing RAG, start writing it

What it does This repository is a progressive, example-driven course for building Retrieval-Augmented Generation in plain Node.js. It walks through every unglamorous stage of the pipeline—text chunking, local embedding generation, vector storage, query preprocessing, and context augmentation—using node-llama-cpp and no cloud APIs. By the end you have a working RAG system and, more importantly, a precise understanding of why each stage exists.

The interesting bit Instead of treating RAG as a framework configuration problem, the project treats it as a series of discrete programming exercises. You build a naive keyword-search version in under 70 lines, then swap in real vector stores, hybrid search, and query rewriting only after you understand the mechanics. The src/ directory contains a surprisingly modular little library—swappable vector stores, retrievers, and chains—but the README makes clear the code is there to be read, not just imported.

Key highlights

• Runs fully offline using local LLMs via node-llama-cpp
• Progressive learning path from a 70-line naive simulation up to multi-query retrieval with reciprocal rank fusion
• Reusable src/ components including in-memory, LanceDB, and Qdrant vector stores, plus hybrid and reranker retrievers
• Explicit coverage of the tedious but critical bits: embedding normalization, stopword removal, query rewriting, and chunk boundary strategies
• Every example ships with paired concept explanations and line-by-line code walkthroughs

Caveats

• The README is largely a table of contents with minimal detail on testing, performance, or production readiness; the library code is explicitly educational scaffolding.
• Several sections appear truncated in the source (e.g., the query rewriting description cuts off mid-sentence), so the documentation may still be a work in progress.

Verdict Grab this if you are tired of treating LangChain like a black box and want to know exactly how retrieval scoring and prompt augmentation work. Look elsewhere if you need a hardened, production-grade framework with SLAs.