Reimagining Academic Library Services through Local RAG: Concepts and Frameworks
DOI:
https://doi.org/10.63880/jlii.v1i2.49Keywords:
Retrieval-Augmented Generation, Local RAG, Artificial Intelligence, Open-source LLM, information retrieval, academic library, conversational AI, library technologyAbstract
Purpose: This study examines Local Retrieval-Augmented Generation as an emerging artificial intelligence framework for strengthening information services in academic libraries. It addresses the limitations of traditional keyword-based retrieval systems and cloud-based language models, particularly issues related to semantic accuracy, unreliable responses, data privacy, and institutional dependency. The study aims to conceptualize Local Retrieval-Augmented Generation for library contexts and to outline its potential role in modernizing academic information services while preserving institutional control.
Methodology: The study adopts a conceptual and analytical research design based on a critical review of recent literature and practical implementations related to artificial intelligence, language models, and retrieval systems in libraries. A structured conceptual architecture of Local Retrieval-Augmented Generation is developed, followed by the formulation of a six-phase deployment framework designed specifically for academic library environments.
Findings: The analysis indicates that Local Retrieval-Augmented Generation enhances information accessibility through conversational interfaces and improves retrieval accuracy by grounding responses in locally curated institutional documents. It supports complete data sovereignty, offers cost-effective deployment options, and allows high levels of system customization. However, implementation challenges include technical complexity, infrastructure and computational demands, the need for staff training, and ethical and governance considerations.
Implications: The study concludes that Local Retrieval-Augmented Generation is a viable and strategic solution for academic libraries seeking to modernize services while maintaining autonomy over data and systems. Successful adoption requires systematic planning, capacity building, and sustained institutional commitment to ethical and responsible artificial intelligence governance.
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