The Ultimate Local RAG Stack: EmbeddingGemma + SQLite-vec + Ollama

Build a complete, 100% private Retrieval-Augmented Generation (RAG) stack that runs entirely on your local machine. This tutorial provides a step-by-step guide to creating a powerful, offline AI system using a modern, efficient, and entirely free open-source stack. This guide is for developers who want full control over their data and architecture, eliminating reliance on third-party APIs, fees, and privacy trade-offs. We will engineer a production-quality local knowledge base and query engine from scratch. 📂 **Full Project Code on GitHub:** https://github.com/LLM-Implementation/private-rag-embeddinggemma ✅ **System Architecture & Key Components:** * **Embeddings Model:** Google's EmbeddingGemma-300m for state-of-the-art on-device performance. We'll implement 256-dimension truncation for a 3x performance boost with minimal quality loss. * **Vector Database:** SQLite-vec, a high-performance extension for SQLite that enables fast, local vector similarity search without a separate database server. * **Language Model (LLM):** Qwen3-4B, a powerful and efficient model served locally via Ollama. * **Data Pipeline:** A Python script using BeautifulSoup for web scraping, followed by a custom token-aware chunking function aligned with the embedding model's tokenizer for optimal retrieval accuracy. 🕒 **Chapters:** 00:00 - Final System Demo & Architecture 00:38 - Stack Overview: EmbeddingGemma, SQLite-vec, Ollama 01:21 - Step 1: Environment Setup & Configuration 02:18 - Step 2: Scrape Documentation into a Local Knowledge Base 03:16 - Step 3: Initialize EmbeddingGemma Model (256-dim) 04:57 - Step 4: Configure SQLite-vec Virtual Table 05:47 - Step 5: Implement Smart Token-Based Chunking 07:15 - Step 6: Store & Index Embeddings 07:41 - Step 7: Build the RAG Query & Prompting Function 08:50 - Recap & Performance Benefits --- 🔗 **Resources:** • EmbeddingGemma Model Card: [Hugging Face Link] • SQLite-vec GitHub & Docs: [Link] • Ollama: [Link] This project demonstrate