Leveraging Retrieval-Augmented Generation in Local Library Systems: The BiblioGPT Prototype

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dc.contributor.authorSantra,Patit Paban
dc.contributor.authorKashyap, Rupsikha
dc.contributor.authorMahata,Anima
dc.contributor.authorRath, Durga Sankar
dc.contributor.authorSk, Md. Ajimuddin
dc.date.accessioned2025-12-03T06:58:53Z
dc.date.available2025-12-03T06:58:53Z
dc.date.issued2025-12-02
dc.description14th International CALIBER 2025, Sri Venkateswara University, Tirupati, Andhra Pradesh, November 17-19, 2025
dc.description.abstractIn today's digital age, library users often seek concept-oriented information that keyword searching cannot easily pro-vide, particularly when dealing with complex classification schemes such as the Dewey Decimal Classification (DDC) or the Library of Congress Classification (LCC). This work introduces BiblioGPT, a locally deployed conversational search application designed to fill the gap by providing natural language queries over structured library knowledge. The architecture combines the open-source Mistral language model with a Retrieval-Augmented Generation (RAG) pipeline through the WARC-GPT framework, which ingests and semantically processes WARC (Web ARChive) files as searchable content utilizing vector embedding (Chroma) and Groq-based inference. An easy-to-use interface enables smooth interaction, pulling contextually appropriate and accurate responses. Two case studies, a theoretical and a prac-tical one, demonstrate the prototype's capability to correctly interpret and answer questions, including assigning the proper DDC numbers to book titles. Although trained on a smaller dataset than the popular cloud models, BiblioGPT preserved stable performance while protecting user privacy by being deployed locally. The results confirm BiblioGPT's promise as a privacy-protecting, scalable solution that reinvents library system interaction, transforming from inflexi-ble keyword searching to flexible, smarter, and natural language-supported information retrieval. This paper describes a visionary strategy for digital library services, setting BiblioGPT as a model for future domain-specific AI-based li-brary software
dc.identifier.isbn9789381232149
dc.identifier.urihttps://ir.inflibnet.ac.in/handle/1944/2521
dc.language.isoen
dc.publisherINFLIBNET Centre Gandhinagar
dc.subjectBiblioGPT
dc.subjectDewey Decimal Classification
dc.subjectGenerative Pretrained Transformer
dc.subjectLarge Language Model
dc.subjectLibrary of Congress Classification
dc.subjectMistral
dc.subjectOllama
dc.subjectRetrieval Augmented Generation
dc.subjectWeb ARChive
dc.titleLeveraging Retrieval-Augmented Generation in Local Library Systems: The BiblioGPT Prototype
dc.typeArticle

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