Building a Local CAG System with Qwen3, Ollama and LangChain for Private Document AI Chatbots

This tutorial demonstrates how to build a Cache-Augmented Generation (CAG) chatbot that operates completely locally using Qwen3, Ollama, LangChain, and Streamlit. Learn to create a private knowledge base AI system called CogVault CAG that directly feeds documents (PDFs, URLs) into the LLM's context without relying on external APIs or complex retrieval systems. The video begins with a demo, explains the concept of CAG, compares it with RAG to help choose the right approach, and walks through the entire implementation process including project structure, minimal CAG application with prompt caching, document loading, chatbot development with streaming capabilities, and UI creation with Streamlit. Follow along to test the system with PDF files and understand when to use CAG versus traditional RAG approaches. Additional resources include links to Qwen3, relevant research papers, and full source code access through MLExpert Pro.