用AI助手上百倍提升蛋白质结构投稿支持效率
RCSB PDB AI Help Desk: retrieval-augmented generation for protein structure deposition support
- 基于RAG技术构建双模型架构，精准处理投稿咨询
- 可处理8000余条投稿记录中的1.9万条消息，响应速度提升数倍
- 适合生物学家、数据管理员及结构生物学研究者使用
结构生物学家已向蛋白质数据库（PDB）提交超过24.5万条实验测定的生物大分子三维结构。全球约40%的结构数据由RCSB PDB处理，其约20名专业生物编目员面临巨大压力：2025年共收到约8,000个条目、总计约1.9万条来自投稿者的咨询消息。为此，我们开发了一个基于LangChain、pgvector（PostgreSQL）和GPT-4.1-mini的AI助手机制，采用pymupdf4llm实现保留格式的PDF提取，两阶段文档分块与最大边际相关性检索，结合主题过滤器与专用系统提示防止内部术语泄露。系统采用双大模型架构，分别用于问题简化与回答生成。部署于Kubernetes环境，提供全天候、带引用来源、流式输出的智能支持。
原文摘要 · Abstract (English)
Motivation: Structural Biologists have contributed more than 245,000 experimentally determined three-dimensional structures of biological macromolecules to the Protein Data Bank (PDB). Incoming data are validated and biocurated by ~20 expert biocurators across the wwPDB. RCSB PDB biocurators who process more than 40% of global depositions face increasing challenges in maintaining efficient Help Desk operations, with approximately 19,000 messages in approximately 8,000 entries received from depositors in 2025. Results: We developed an AI-powered Help Desk using Retrieval-Augmented Generation (RAG) built on LangChain with a pgvector store (PostgreSQL) and GPT-4.1-mini. The system employs pymupdf4llm for Markdown-preserving PDF extraction, two-stage document chunking, Maximal Marginal Relevance retrieval, a topical guardrail that filters off-topic queries, and a specialized system prompt that prevents exposure of internal terminology. A dual-LLM architecture uses separate model configurations for question condensing and response generation. Deployed in production on Kubernetes with PostgreSQL (pgvector), it provides around-the-clock depositor assistance with citation-backed, streaming responses. Availability and implementation: Freely available at https://rcsb-deposit-help.rcsb.org.
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