Can multi-agent RAG improve online learning?
Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System
February 7, 2025
https://arxiv.org/pdf/2502.03948This paper introduces a multi-agent system designed to improve online learning by intelligently gathering information from various online sources like YouTube, GitHub, and documentation websites. Each agent specializes in a specific resource type, using GPT-40 for semantic understanding and ChromaDB for storage and retrieval. The system then synthesizes the collected information to answer user queries, aiming to provide a more efficient and integrated learning experience. Key aspects for LLM-based multi-agent systems include the specialized agent design, the use of GPT-40 for embeddings and semantic search, and the integration of diverse online resources.