BeanGPT: a domain-specific retrieval-augmented generation system for Phaseolus vulgaris research.

Abstract

BeanGPT is a domain-specific retrieval augmented generation system designed to support research and breeding decisions in common bean (Phaseolus vulgaris L.) by transforming natural language questions into citation-backed, verifiable answers. The platform integrates a large, curated corpus of legume-focused peer-reviewed literature with structured multi-year agronomic trial records collected across diverse environments, climate projections extending to 2090 under multiple emission scenarios, and standardized cultivar nomenclature to resolve naming inconsistencies across datasets and publications. BeanGPT combines semantic retrieval from a vector database with intent-based query routing and structured parameter extraction to direct questions to genetics, field performance analytics, or climate modules. To reduce errors that commonly occur in general-purpose language models, BeanGPT incorporates a genomic index that enables constant time membership lookup of gene and protein identifiers against authoritative resources, ensuring that molecular entities are either validated or clearly flagged as literature-derived. The system is implemented with a streaming web interface and an asynchronous backend that supports concurrent users and can generate interactive visualizations through automated Plotly code generation. Beta testing demonstrated strong retrieval relevance, low response latency, reliable gene verification, and high citation precision, indicating that domain-grounded RAG can improve accuracy and usability for Phaseolus vulgaris research.