Development of a multi-agent chatbot for user query resolution for UTAR

Heng, Thee Yong (2025) Development of a multi-agent chatbot for user query resolution for UTAR. Final Year Project, UTAR.

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Abstract

Universities generate vast amount of information daily, including programme details, course structures, schedules, policies, and procedures. This information is often distributed across multiple sources such as university websites, portals, and PDF documents, making it difficult for students, staff, prospective applicants, and parents to quickly access accurate and up-to date details. At Universiti Tunku Abdul Rahman (UTAR), this challenge highlights the need for a unified and intelligent information access system. To address this, the project proposes and develops a multi-agent Retrieval-Augmented Generation (RAG) chatbot designed specifically for UTAR. The chatbot architecture employs specialized agents namely Admissions Agent, Finance Agent, and Examinations Agent each connected to its own vector database containing structured knowledge extracted from official university sources. Data ingestion is automated through a web scraping and PDF download module that handles inconsistencies such as broken SSL certificates on UTAR domains, ensuring reliable and up to-date knowledge collection. The system integrates OpenAI API service as the base large language model (LLM), with LangChain for orchestration, Chroma as the vector database, Flask for backend development, and React for the frontend user interface deployed on Firebase. The backend is deployed on Render to support scalability, concurrency, and real-time availability. Evaluation was carried out using technical performance testing alongside user experience testing through a structured Google Form survey. The results show that the chatbot delivers accurate and contextually relevant answers within an acceptable response time, while user feedback indicates strong satisfaction with ease of use, usefulness, and willingness to reuse the system. Open-ended responses also highlighted areas for improvement, such as expanding departmental coverage and tighter integration into UTAR’s official website. By enabling 24/7 access to official university knowledge sources, the chatbot improves information accessibility and user satisfaction, demonstrating the feasibility of applying multi-agent RAG architectures in the higher education context. Future enhancements will focus on integration with UTAR’s official platforms, and extending the system to additional departments and more.

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