Research and Development of a Hybrid Retrieval-Augmented Generation (RAG) Question-Answering System for the Health Statistical Yearbook

Corresponding author: liyazi, li.yazi@imicams.ac.cn

DOI: 10.12201/bmr.202604.00082

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  Abstract: Objective: To simplify and democratize queries against the Health Statistical Yearbook, we integrate the LangChain framework with a large language model and a local knowledge base, allowing users to interrogate the yearbook in plain language and markedly lowering the threshold for effective use.

  Key words: large; language model, knowledge; graph, retrieval-augmented; generation, question-answering; system, China; Health Statistical; Yearbook

  Submit time: 10 April 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2026-01-12	10.12201/bmr.202604.00082V1	Download

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