BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A

Samy Ateia, Udo Kruschwitz
12/16/2024
cs.CLcs.AI

No official code found. Best implementation: SamyAteia/BioRAGent (4 stars, Apache-2.0, last commit 2026-03-09).

Abstract

We present BioRAGent, an interactive web-based retrieval-augmented generation (RAG) system for biomedical question answering. The system uses large language models (LLMs) for query expansion, snippet extraction, and answer generation while maintaining transparency through citation links to the source documents and displaying generated queries for further editing. Building on our successful participation in the BioASQ 2024 challenge, we demonstrate how few-shot learning with LLMs can be effectively applied for a professional search setting. The system supports both direct short paragraph style responses and responses with inline citations. Our demo is available online, and the source code is publicly accessible through GitHub.

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Code Implementations(1)

SamyAteia/BioRAGentCommunity100%
Apache-2.0

A retrieval augmented generation system for showcasing generative query expansion and domain-specific search for scientific Q&A

40Shell, PythonOct 25, 20247 months agoApache-2.0

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Cite this paper

@article{ateia2024bioragent,
  title  = {BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A},
  author = {Samy Ateia and Udo Kruschwitz},
  year   = {2024},
  eprint = {2412.12358},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.12358v1}
}

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