A complete step-by-step guide to building a fully private, local RAG (Retrieval-Augmented Generation) system using JavaScript, Node.js, and React — with no cloud dependencies. The stack uses Ollama for local LLM inference (Mistral 7B) and embeddings (nomic-embed-text), ChromaDB via Docker for vector storage, LangChain for the pipeline, and a React frontend with drag-and-drop upload and streaming chat. Covers document ingestion (PDF, Markdown, text), chunking strategy, local embedding generation, vector similarity search, SSE-based response streaming, prompt engineering for local models, performance tuning, and security/privacy hardening including network isolation verification.
Table of contents
How to Build a Private Local RAG SystemTable of ContentsWhy Go Local with RAG?How Local RAG Works: Core ConceptsSetting Up the Local AI InfrastructureBuilding the Document Ingestion PipelineVector Storage with ChromaDBThe RAG Query Engine: Tying It TogetherBuilding the React FrontendImplementation Checklist and Performance TuningSecurity and Privacy ConsiderationsNext StepsSort: