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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 05

Published on: May 2026

AI-INTEGRATED KNOWLEDGE INFORMATION BASE IN LIBRARY MANAGEMENT SYSTEM: PUSTAKALAYA – A RAG-ENABLED INTELLIGENT ACADEMIC LIBRARY FRAMEWORK

Varun J Bogith V Esakkimuthu P

Jagadeesh N

Department of Computer science and Engineering (Cyber Security)

Sri Venkateswaraa College of Technology (Autonomous)

Vadakal village Sriperumbudur

Article Status

Plagiarism Passed Peer Reviewed Open Access

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Abstract

Traditional library management systems in academia mainly focus on organizing resources and controlling access, offering limited capabilities for intelligent knowledge retrieval and personalized learning support. While recent studies have investigated AI-based recommendation systems and chatbots for educational assistance, many current methods depend on generic information sources or lack integration with institution-specific academic content, which diminishes their reliability and educational relevance. This paper introduces Pustakalaya, an advanced framework for library management and knowledge assistance that combines structured digital library resources with a Retrieval- Augmented Generation (RAG)-based conversational interface. The proposed method creates a curated institutional knowledge base derived from digitized textbooks, reference materials, and academic documents, which facilitates semantic retrieval and context-sensitive response generation grounded in verified sources. Unlike general- purpose chatbot systems, Pustakalaya limits its retrieval to educational materials and features adaptation based on student profiles to modify the depth of explanations and recommendation strategies in accordance with individual learning abilities and academic contexts. To enhance the effectiveness of retrieval, semantic embeddings are utilized for indexing documents and matching queries, ensuring precise content selection without depending on external web searches or large-scale generative model training. Experimental testing on controlled academic datasets demonstrates that the proposed framework offers reliable, interpretable, and curriculum-aligned responses while enabling personalized access to content with lower system complexity. By merging intelligent library management with knowledge-focused conversational engagement, Pustakalaya presents a practical and viable solution for advanced academic learning environments.

 Index Terms—Library Management System, Retrieval- Augmented Generation (RAG), Natural Language Processing, Conversational AI, Semantic Embeddings, Academic Chatbot, Knowledge Information Base, Django Framework

How to Cite this Paper

J, V., V, B. & P, E. (2026). AI-Integrated Knowledge Information Base in Library Management System: Pustakalaya – A RAG-Enabled Intelligent Academic Library Framework. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i4.704

J, Varun, et al.. "AI-Integrated Knowledge Information Base in Library Management System: Pustakalaya – A RAG-Enabled Intelligent Academic Library Framework." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.704.

J, Varun,Bogith V, and Esakkimuthu P. "AI-Integrated Knowledge Information Base in Library Management System: Pustakalaya – A RAG-Enabled Intelligent Academic Library Framework." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.704.

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Ethical Compliance & Review Process

  • All submissions are screened under plagiarism detection.
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  • Peer Review Type: Double-Blind Peer Review
  • Published on: May 13 2026
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