How I Built a Local-First AI Stack for Document Q&A Without OpenAI

How I Built a Local-First AI Stack for Document Q&A Without OpenAI 📚🤖 A multi-service monorepo with llama.cpp , Qdrant, Python FastAPI services, React, Node and MCP support for AI agents. You’ve probably seen buzzwords like RAG, vector database, embeddings, MCP, and local LLMs everywhere. This article is meant to make those terms feel concrete by showing how they fit together in a real project. What You’ll See in This Project 👀 Local-first RAG architecture Document PDF ingestion and chunking pipeline Embedding generation using sentence-transformers Vector search with Qdrant Local LLM inference with llama.cpp Python backend microservices built with FastAPI React frontend for document upload and chat Optional ML layer for security and query analysis MCP integration so AI agents can use the system as tools Table of contents 🧭 1. Introduction 2. What Is a Local AI Stack 3. Why Build AI Without OpenAI 4. Use Cases for Local AI 5. Key Concepts Behind the System 6. High Level Architecture 7.