BioRAGent: natural language biomedical querying with retrieval-augmented multiagent systems

Understanding the roles of genes, phenotypes, and diseases is crucial for advancing biomedical research. However, efficient and accessible retrieval of biomedical knowledge remains a challenge due to the complexity of the relevant data. We introduce BioRAGent, an intelligent biomedical assistant that combines Tool-augmented retrieval-augmented generation (RAG) with a multiagent system. Leveraging the ability of large language models, BioRAGent facilitates natural language queries about genes, phenotypes, diseases, and their interrelationships. BioRAGent employs three specialized agents: Guide (query optimization), Retriever (data retrieval), and Reviewer (answer validation) to access authoritative biomedical databases and to generate accurate responses. We evaluate the performance of BioRAGent on a benchmark of eleven single-hop and three multi-hop tasks, demonstrating superior results compared with state-of-the-art models. User evaluations highlight the practicality and robust user experience of BioRAGent, particularly in handling complex multi-hop queries. Moreover, ablation experiments validate the contribution of each agent in improving retrieval accuracy.