View in EDS

Developing a multimodal library intelligent Q&A chatbot using retrieval-augmented generation (RAG) technology.

Saved in:
Bibliographic Details
Title: Developing a multimodal library intelligent Q&A chatbot using retrieval-augmented generation (RAG) technology.
Authors: Lin, Zhuoran1 (AUTHOR) 1124230606@qq.com, Li, Hongjun1 (AUTHOR) lihj@cau.edu.cn, Du, Jing1 (AUTHOR) tsg001@cau.edu.cn
Source: Electronic Library. 2026, Vol. 44 Issue 5, p1067-1083. 17p.
Subject Terms: *Library reference services, *Artificial intelligence, *Academic libraries, *Benchmarking (Management), *Library public services, *Information retrieval, *Digital libraries, Research funding, Knowledge management, Natural language processing, Conceptual structures, Chatbots, Time, User interfaces
Geographic Terms: China
Abstract: Purpose: This study aims to enhance the user experience for patrons and improve library operation efficiency through implementation of an artificial intelligence (AI)-driven Q&A chatbot. Design/methodology/approach: Using RAG technology, this study developed Nana, a multimodal intelligent library chatbot based on open-source pretrained language models. Following multiple rounds of iteration and debugging to optimize its local documents retrieval and summarization capabilities, Nana's performance was compared with four other library chatbots via four key metrics. Findings: The results demonstrate that the authors' chatbot performs exceptionally well in evaluations, effectively addressing common library reference queries and optimizing librarians' time by promptly answering frequently asked questions. In addition, the authors' chatbot exhibits robust performance in understanding and responding to queries in atypical scenarios. As a multimodal chatbot, it is distinguished by its unique capability to process image data and user-uploaded documents, significantly enhancing its utility and user-friendliness. Practical implications: This research offers a practical roadmap for libraries seeking to upgrade their intelligent services. The RAG-based multimodal chatbot can efficiently handle user inquiries by retrieving local resources accurately. By reducing the burden of routine Q&A on library staff and improving service accessibility, it serves as a scalable framework for digital libraries (DL) aiming to modernize the user experience. Originality/value: This study advances smart digital libraries by developing a multimodal RAG-based Q&A chatbot, thereby addressing functional gaps in existing systems. Unlike conventional text-only chatbots, it features domain-adapted RAG optimization to boost the accuracy of retrieving library-specific local resources, plus integrated multimodal interaction (text, document, visual) for diverse user needs. Validated against four benchmarks, this study offers both a high-performance conversational solution and a replicable framework for domain-specific multimodal RAG chatbots in information services. [ABSTRACT FROM AUTHOR]
Copyright of Electronic Library is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
FullText Text:
  Availability: 0
Header DbId: ehh
DbLabel: Education Research Complete
An: 197197445
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Developing a multimodal library intelligent Q&A chatbot using retrieval-augmented generation (RAG) technology.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Lin%2C+Zhuoran%22">Lin, Zhuoran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 1124230606@qq.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Hongjun%22">Li, Hongjun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lihj@cau.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Du%2C+Jing%22">Du, Jing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tsg001@cau.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Electronic+Library%22">Electronic Library</searchLink>. 2026, Vol. 44 Issue 5, p1067-1083. 17p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Library+reference+services%22">Library reference services</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+libraries%22">Academic libraries</searchLink><br />*<searchLink fieldCode="DE" term="%22Benchmarking+%28Management%29%22">Benchmarking (Management)</searchLink><br />*<searchLink fieldCode="DE" term="%22Library+public+services%22">Library public services</searchLink><br />*<searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br />*<searchLink fieldCode="DE" term="%22Digital+libraries%22">Digital libraries</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+management%22">Knowledge management</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Conceptual+structures%22">Conceptual structures</searchLink><br /><searchLink fieldCode="DE" term="%22Chatbots%22">Chatbots</searchLink><br /><searchLink fieldCode="DE" term="%22Time%22">Time</searchLink><br /><searchLink fieldCode="DE" term="%22User+interfaces%22">User interfaces</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: This study aims to enhance the user experience for patrons and improve library operation efficiency through implementation of an artificial intelligence (AI)-driven Q&A chatbot. Design/methodology/approach: Using RAG technology, this study developed Nana, a multimodal intelligent library chatbot based on open-source pretrained language models. Following multiple rounds of iteration and debugging to optimize its local documents retrieval and summarization capabilities, Nana's performance was compared with four other library chatbots via four key metrics. Findings: The results demonstrate that the authors' chatbot performs exceptionally well in evaluations, effectively addressing common library reference queries and optimizing librarians' time by promptly answering frequently asked questions. In addition, the authors' chatbot exhibits robust performance in understanding and responding to queries in atypical scenarios. As a multimodal chatbot, it is distinguished by its unique capability to process image data and user-uploaded documents, significantly enhancing its utility and user-friendliness. Practical implications: This research offers a practical roadmap for libraries seeking to upgrade their intelligent services. The RAG-based multimodal chatbot can efficiently handle user inquiries by retrieving local resources accurately. By reducing the burden of routine Q&A on library staff and improving service accessibility, it serves as a scalable framework for digital libraries (DL) aiming to modernize the user experience. Originality/value: This study advances smart digital libraries by developing a multimodal RAG-based Q&A chatbot, thereby addressing functional gaps in existing systems. Unlike conventional text-only chatbots, it features domain-adapted RAG optimization to boost the accuracy of retrieving library-specific local resources, plus integrated multimodal interaction (text, document, visual) for diverse user needs. Validated against four benchmarks, this study offers both a high-performance conversational solution and a replicable framework for domain-specific multimodal RAG chatbots in information services. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Electronic Library is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=197197445
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1108/EL-11-2025-0438
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 1067
    Subjects:
      – SubjectFull: Library reference services
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Academic libraries
        Type: general
      – SubjectFull: Benchmarking (Management)
        Type: general
      – SubjectFull: Library public services
        Type: general
      – SubjectFull: Information retrieval
        Type: general
      – SubjectFull: Digital libraries
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Knowledge management
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Conceptual structures
        Type: general
      – SubjectFull: Chatbots
        Type: general
      – SubjectFull: Time
        Type: general
      – SubjectFull: User interfaces
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Developing a multimodal library intelligent Q&A chatbot using retrieval-augmented generation (RAG) technology.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Lin, Zhuoran
      – PersonEntity:
          Name:
            NameFull: Li, Hongjun
      – PersonEntity:
          Name:
            NameFull: Du, Jing
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: 2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 02640473
          Numbering:
            – Type: volume
              Value: 44
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Electronic Library
              Type: main
ResultId 1