pguso/rag-from-scratch resource

Health v2 · maintenance only

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Adoption not part of the score

1607 stars · 193 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

An educational repository that teaches Retrieval-Augmented Generation (RAG) by building it step by step from scratch in JavaScript, using local LLMs via node-llama-cpp with no cloud APIs. It covers the full RAG pipeline including embeddings, vector stores, retrieval, re-ranking, and query rewriting with explained code walkthroughs.

Use cases

• learn how RAG works by building it from scratch
• understand how embeddings and vector search work
• build a local RAG chatbot without cloud APIs
• implement a RAG pipeline in Node.js
• learn query rewriting and re-ranking for retrieval
• run LLM-powered retrieval locally with node-llama-cpp
• study a minimal end-to-end RAG example

When to choose

• you want to deeply understand RAG internals rather than use a framework
• you prefer local LLMs and no external API dependencies
• you are a JavaScript/Node.js developer learning AI concepts
• you want progressive, well-explained tutorial examples

When to avoid

• you need a production-ready RAG framework or library
• you want managed cloud vector databases or hosted LLM APIs
• you work primarily in Python with LangChain/LlamaIndex ecosystems
• you need scalable, optimized retrieval for large document collections

Facets

learning-resource · maturity active

rag llm-inference machine-learning nlp search-engine developer-tools large-language-models artificial-intelligence tutorials education cross-platform rag-from-scratch local-llm embeddings vector-search node-llama-cpp educational-project hands-on-tutorial no-cloud-apis retrieval-augmented-generation natural-language-processing nodejs javascript

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