Prospects of Retrieval Augmented Generation (RAG) for Academic Library Search and Retrieval

• Published in: Information technology and libraries Vol. 44; no. 2; pp. 1 - 15
• Main Authors: Bevara, Ravi Varma Kumar, Lund, Brady D., Mannuru, Nishith Reddy, Karedla, Sai Pranathi, Mohammed, Yara, Kolapudi, Sai Tulasi, Mannuru, Aashrith
• Format: Journal Article
• Language: English
• Published: Chicago American Library Association 01.06.2025; American Library Association: Library and Information Technology Association
• Subjects: Academic libraries; Academic Standards; Accuracy; Architecture; Computer architecture; Computer Science; Copyright law; Copyrights; Credibility; Database Management Systems; Database middleware; Educational Needs; Ethics; Feedback (Response); Humanities and Social Sciences; Indexing; Indexing (Content analysis); Influence of Technology; Information Needs; Information Retrieval; Interdisciplinary Approach; Knowledge bases (artificial intelligence); Language models (Artificial intelligence); Large language models; Libraries; Library and information sciences; Library collections; Library Materials; Library Networks; Library Services; Metadata; Middleware; Opportunities; Query processing; Real time; Research Needs; Researchers; Retrieval; Searching; Semantics; University and college libraries; User experience; Video Technology; Academic Libraries; Library Technology; Semantic Search; Library Innovation; 检索增强生成; User Experience; Information Systems; Information Retrieval; Retrieval Augmented Generation (RAG); Large Language Models; Digital Libraries; génération augmentée de récupération
• ISSN: 0730-9295; 2163-5226
• DOI: 10.5860/ital.v44i2.17361

This paper examines the integration of retrieval-augmented generation (RAG) systems within academic library environments, focusing on their potential to transform traditional search and retrieval mechanisms. RAG combines the natural language understanding capabilities of large language models with structured retrieval from verified knowledge bases, offering a novel approach to academic information discovery. The study analyzes the technical requirements for implementing RAG in library systems, including embedding pipelines, vector databases, and middleware architecture for integration with existing library infrastructure. We explore how RAG systems can enhance search precision through semantic indexing, real-time query processing, and contextual understanding while maintaining compliance with data privacy and copyright regulations. The research highlights RAG’s ability to improve user experience through personalized research assistance, conversational interfaces, and multimodal content integration. Critical considerations including ethical implications, copyright compliance, and system transparency are addressed. Our findings indicate that while RAG presents significant opportunities for advancing academic library services, successful implementation requires careful attention to technical architecture, data protection, and user trust. The study concludes that RAG integration holds promise for revolutionizing academic library services while emphasizing the need for continued research in areas of scalability, ethical compliance, and cost-effective implementation.