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RCSB PDB AI Help Desk

Retrieval-Augmented Generation (RAG) chatbot built on LangChain with a PostgreSQL + pgvector vector store and GPT-4.1-mini that answers questions about protein-structure deposition, validation, biocuration, and wwPDB policies. Uses pymupdf4llm for Markdown-preserving PDF extraction, two-stage document chunking, Maximal Marginal Relevance retrieval, a topical guardrail, and a dual-LLM architecture with separate question-condensing and response-generation models. Deployed at https://rcsb-deposit-help.rcsb.org.

By Chithari, Vivek Reddy, Young, Jasmine Y., Persikova, Irina, Liang, Yuhe, Crichlow, Gregg V., Flatt, Justin W., Ghosh, Sutapa, Hudson, Brian P., Peisach, Ezra, Sekharan, Monica, Shao, Chenghua, Burley, Stephen K.ToolReference information

Resource information

Format
zip
Original repository
19560785
DOI
10.5281/zenodo.19560785
Credit basis
Creators in Zenodo record metadata
Access
Open files available from the original Zenodo deposit
Last checked
Nov 5, 2026
License
mit-license
Last record update
Nov 5, 2026

About this resource

Retrieval-Augmented Generation (RAG) chatbot built on LangChain with a PostgreSQL + pgvector vector store and GPT-4.1-mini that answers questions about protein-structure deposition, validation, biocuration, and wwPDB policies. Uses pymupdf4llm for Markdown-preserving PDF extraction, two-stage document chunking, Maximal Marginal Relevance retrieval, a topical guardrail, and a dual-LLM architecture with separate question-condensing and response-generation models. Deployed at https://rcsb-deposit-help.rcsb.org.