Rethinking Library OPAC: From Siloed Retrieval to AI and ML-Driven Discovery Using FastAPI and MySQL
Date of this Version
2026
Document Type
Article
Citation
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Webliogrphy
https://uvicorn.dev/ (Accessed on 11th January 2026)
https://www.sqlalchemy.org/ (Accessed on 11th January 2026)
https://www.mysql.com/ (Accessed on 11th January 2026)
https://packaging.python.org/en/latest/ (Accessed on 11th January 2026)
https://fastapi.tiangolo.com/ (Accessed on 11th January 2026)
https://dev.mysql.com/ (Accessed on 11th January 2026)
https://developer.api.oclc.org/fast-api (Accessed on 11th January 2026)
Abstract
The paper explores integration methods between MySQL and FastAPI to develop dynamic Online Public Access Catalog services including functionalities for modern libraries and information centers. It addresses evolving user expectations by replacing rigid keyword-based search models with adaptive, intelligent discovery experiences that prioritize search relevance. The goal involves transforming traditional library retrieval tools into contextual discovery platforms that effectively understand specific user intent and complex needs. The prototype was created on Ubuntu in a customized Python environment. It uses FastAPI to manage asynchronous API calls and MySQL to store bibliographic data. SQLAlchemy abstracts the database layer, and Pydantic validates data. The AI system includes an English/non-English multilingual chatbox for reliable academic workflow support. Search is transformed by the logic of algorithms from “String Match” to “Conceptual Discovery,” making it possible for predictive analysis. This system can support queries in English, Hindi, and Bengali. It will also enable the integration of the database (with an API) with the cloud archive. The method of this system to modernize the library, reduce cost, improve accessibility, and enhance users' engagement is very supportive to the librarians and staff of the Information Centre.
