How to build multi-agent RAG system with LlamaIndex?

In a tutorial by Matea Pesic, a multi-agent Retrieval-Augmented Generation (RAG) system is constructed using LlamaIndex and Memgraph, expanding upon a previous single-agent GraphRAG system to improve information retrieval for language models. The multi-agent approach enables specialization and collaboration among agents, thus allowing for more dynamic and capable data processing pipelines. The system integrates graph-based querying and tool-using agents by setting up Memgraph as a graph store, creating a Property Graph Index for structured knowledge retrieval, and implementing function agents for arithmetic and semantic tasks. It then combines these elements in an AgentWorkflow to handle complex queries, demonstrated through a scenario involving the 2023 Canadian federal budget. The tutorial concludes by highlighting the benefits of multi-agent systems in executing specialized tasks and hints at future developments involving Memgraph algorithms for even richer interactions.

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