RSET Campus Assistant

An AI-powered campus assistant for Rajagiri School of Engineering and Technology (RSET). Students can ask questions about admissions, courses, schedules, and more through a chat interface.

Architecture

• Frontend (frontend/): React + Vite chat interface
• Backend (backend/): FastAPI server with LangChain multi-agent system
  • RAG Agent: Answers questions using FAISS vector database + TinyLlama LLM
  • Schedule Agent: Returns upcoming events from JSON schedule
  • Notification Agent: Sends reminders/alerts
  • Router Agent: Routes questions to the correct agent (keyword matching + LLM fallback)

Quick Start

Prerequisites

• Node.js v18+ (for frontend)
• Python 3.10+ (for backend)
• Ollama with qwen model pulled (ollama pull qwen)

1. Build the Vector Database

cd backend
pip install -r requirements.txt
python rag/build_vector_db.py

2. Start the Backend

cd backend
uvicorn server:app --reload --port 8000

3. Start the Frontend

cd frontend
npm install
npm run dev

Open http://localhost:5173 in your browser.

Team

Member	Component
Member 1	Chatbot Interface (Frontend)
Member 2	RAG Query System
Member 3	Data Preparation
Member 4	Schedule System
Member 5	Notification System
Member 6	Multi-Agent Router
Member 7	Backend Integration + Deployment