Build a multi-agent RAG system with Granite locally

This tutorial demonstrates building a multi-agent Retrieval-Augmented Generation (RAG) system locally, bypassing the need for expensive large language models (LLMs). Agentic RAG uses AI agents to plan and execute tasks, enhancing RAG's scalability and optimization. A multi-agent system, using a small, efficient model like Granite 3.2, offers superior performance compared to single agents. The system employs a modular architecture with specialized agents: a Planner, Research Assistant, Summarizer, Critic, Reflection Agent, and Report Generator. Each agent performs a specific role, contributing to efficient task completion and improved accuracy. AutoGen (AG2) orchestrates workflow and decision-making, alongside Ollama for local LLM serving and Open WebUI for user interaction. All components are open-source, prioritizing privacy and cost-effectiveness. The system utilizes tools like vector databases for document search and SearXNG for web searches. Detailed setup instructions are available on the project's GitHub repository. This approach creates a powerful and privacy-conscious AI system accessible on a personal laptop.