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

Overview

Learn to build a fully local Cache-Augmented Generation (CAG) chatbot using Qwen3, Ollama, LangChain, and Streamlit that processes your private documents without external APIs or complex retrieval systems.

Syllabus

• Introduction to Cache-Augmented Generation (CAG)
Overview of CAG systems
Benefits of using CAG for document AI chatbots
• Understanding the Tools
Qwen3: Overview and Features
Ollama: Overview and Integration
Introduction to LangChain
Streamlit Basics for UI Development
• Setting Up the Development Environment
Installing Qwen3, Ollama, and LangChain
Configuring necessary libraries and dependencies
Setting up Streamlit for local development
• Preparing Data for CAG
Identifying and collecting private documents
Data preprocessing techniques
Troubleshooting common data issues
• Building the Local CAG System
Designing the chatbot architecture
Integrating Qwen3 and Ollama
Implementing CAG with LangChain
• Developing a Streamlit Interface
Designing a user-friendly chatbot interface
Connecting the interface to the backend system
Testing user interactions
• Fine-tuning and Optimization
Techniques for improving CAG performance
Tailoring the system for specific document types
Conducting user testing and gathering feedback
• Ensuring Data Privacy and Security
Strategies for local data storage and processing
Implementing security measures in chatbot systems
• Final Project: Building Your Own AI Chatbot
Project requirements and expectations
Presentation and demonstration of projects
• Future Trends and Advanced Topics
Consideration of emerging tools and technologies
Opportunities for further learning and development

Subjects

Computer Science