I Built a Multi-Agent RAG System 🤯 | Smart AI That Reads Docs (AutoGen Python)

This tutorial demonstrates building a multi-agent RAG (Retrieval-Augmented Generation) system using AutoGen in Python. The system enables intelligent agents to read, process, and reason over documents by combining retrieval capabilities with generative AI. The project showcases how to architect multiple specialized agents that collaborate to answer questions based on document content, making it ideal for document-heavy workflows and knowledge management applications.

Key Points

• •Multi-agent architecture allows specialized agents to handle different tasks (retrieval, reasoning, synthesis) in parallel
• •RAG (Retrieval-Augmented Generation) combines document retrieval with LLM generation for accurate, source-grounded responses
• •AutoGen framework simplifies agent orchestration and inter-agent communication patterns
• •Document preprocessing and vectorization enable semantic search across large knowledge bases
• •Agent collaboration through message passing allows complex workflows like question answering with source attribution
• •Implement retrieval agents to fetch relevant documents and reasoning agents to synthesize answers
• •Use conversation patterns between agents to enable iterative refinement and validation of responses
• •RAG systems reduce hallucination by grounding responses in actual document content

Workflow Diagram

Concepts

Artifacts (1)

https://github.com/dearnidhi/Autogen-Agents-Course.git