Rag Based Personal AI Chatbot for College Information System

Abstract

Retrieval-Augmented Generation (RAG) systems have transformed chatbots from pattern-based to knowledge-grounded conversational agents. While traditional large language models (LLMs) like GPT and Claude generate fluent responses, they often hallucinate facts. RAG mitigates this by fusing semantic retrieval from external knowledge with generation. This paper presents UniFlow-RAG, an advanced, multimodal, visual-flow driven chatbot architecture built using LangChain, LangFlow, and a backend stack of OpenAI GPT, Google Gemini, Meta LLaMA, and Claude 3. The system enables modular chatbot development with document QA, domain-specialized agents, and visual orchestration.

Introduction

College students often struggle to access academic information such as syllabi, exam rules, notices, and deadlines because these are scattered across PDFs, websites, and circulars. Traditional rule-based chatbots were limited in handling such queries, and LLM-only chatbots risk hallucinating incorrect answers without access to up-to-date documents.

This project implements a Retrieval Augmented Generation (RAG) chatbot tailored for college use. Key documents are collected, cleaned, chunked, and stored in a vector database. When a student asks a question, the system retrieves relevant document chunks and passes them to a language model (e.g., GPT, Gemini, Claude) to generate accurate, document-grounded answers. The workflow is designed with LangChain and LangFlow, and the interface is built using React and TypeScript for easy student interaction.

Testing showed the chatbot achieves ~90% accuracy, ~2-second response time, 85% context retention, and a low hallucination rate (<8%). It provides a reliable, fast, and centralized platform for academic information, reducing confusion and improving accessibility for students.

Conclusion

This paper introduced a practical AI chatbot specifically designed for college students, leveraging Retrieval Augmented Generation along with LangChain and LangFlow. The system allows students to quickly access accurate information from real sources such as syllabi, academic calendars, and official websites. By reducing confusion, saving time, and maintaining consistency in academic communication, it provides a reliable support tool for students. Future enhancements will include support for voice input, regional languages, and seamless integration with college ERP systems. The modular architecture ensures that the system can be easily adapted for use in other educational institutions with minimal adjustments, making it a scalable and versatile solution for improving academic information access.

References

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Copyright

Copyright © 2025 Vishal Yadav , Aayushkumar Yadav , Satyam Yadav, Mrs. Anvita Birje, Mrs. Megha Gupta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.