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.
Cite
@article{arxiv.2604.22800,
title = {RCSB PDB AI Help Desk: retrieval-augmented generation for protein structure deposition support},
author = {Vivek Reddy Chithari and Jasmine Y. Young and Irina Persikova and Yuhe Liang and Gregg V. Crichlow and Justin W. Flatt and Sutapa Ghosh and Brian P. Hudson and Ezra Peisach and Monica Sekharan and Chenghua Shao and Stephen K. Burley},
journal= {arXiv preprint arXiv:2604.22800},
year = {2026}
}