RAG From Scratch is an educational open-source project designed to teach developers how retrieval-augmented generation systems work by building them step by step. Instead of relying on complex frameworks or cloud services, the repository demonstrates the entire RAG pipeline using transparent and minimal implementations. The project walks through key concepts such as generating embeddings, building vector databases, retrieving relevant documents, and integrating the retrieved context into language model prompts. Each example is written with detailed explanations so that developers can understand the internal mechanics of semantic search and context-aware language generation. The repository emphasizes learning through direct implementation, allowing users to see how each component of the RAG architecture functions independently.

Features

  • Step-by-step implementation of retrieval-augmented generation pipelines
  • Examples demonstrating embeddings and semantic vector search
  • Local vector database implementation for document retrieval
  • Context integration techniques for grounding language model responses
  • Re-ranking and normalization methods to improve retrieval accuracy
  • Educational code walkthroughs explaining every component of the system

Project Samples

Project Activity

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License

MIT License

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Additional Project Details

Programming Language

JavaScript

Related Categories

JavaScript Large Language Models (LLM), JavaScript Semantic Search Tool

Registered

2026-03-09