30, April 2026

IndiraGPT: College Website Chatbot System

Author(s): 1. Prasad Munde, 2. Vaibhav kulkarni, 3. Monika Patil

Authors Affiliations:

  1. Student, Department of Artificial intelligence and Data science Engineering, Indira College of Engineering, Pune, India;
  2.  Student, Department of Artificial intelligence and Data science Engineering, Indira College of Engineering, Pune, India;
  3. Professor, Department of Artificial intelligence and Data science Engineering, Indira College of Engineering, Pune, India;

DOIs:10.2015/IJIRMF/202604027     |     Paper ID: IJIRMF202604027


Abstract
Keywords
Cite this Article/Paper as
References

Abstract

Contemporary educational institutions have difficulties in providing direct and reliable access to information over the use of conventional Internet interfaces. In review, IndiraGPT is proposed, which makes use of Retrieval Augmentation and Generative Models for effective data dissemination in academic institutions. The framework makes it possible to retrieve dependent facts and produce responses based on the context, thus ensuring accurate and well-timed communication. The proposed architecture enables nonstop information retrieval, multi-flip dialogs, and query understanding. Empirical results indicate higher accuracy, less effort-intensive tasks, and extensive user interaction compared to conventional frameworks.

Chatbot System, Retrieval Augmented Generation, Natural Language Processing, Large Language Model, Semantic Retrieval

Prasad Munde, Vaibhav kulkarni, 3. Monika Patil (2026); IndiraGPT: College Website Chatbot System, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-12, Issue-4, Available on –   https://www.ijirmf.com/

  1. Hussain A., et al., A Survey on Chatbots and Conversational Agents, 2020.
  2. Adamopoulou E., Moussiades L., An Overview of Chatbot Technology, 2020.
  3. Karpukhin V., et al., Dense Passage Retrieval for Open-Domain Question Answering, 2020.
  4. Johnson J., Douze M., Jegou H., Billion-scale Similarity Search with FAISS, 2017.

Download Full Paper

Download PDF No. of Downloads:1 | No. of Views: 17