Smart Campus 🎓

AI-Powered Academic Assistant for Intelligent Document Analysis

Smart Campus is a full-stack application designed to help students and researchers navigate complex academic materials. By leveraging Large Language Models (LLMs) and Vector Search, it allows users to index PDFs, ask questions, generate summaries, and find precise citations in seconds.

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✨ Key Features

• 📁 Intelligent PDF Indexing: Upload and process academic documents for instant retrieval.
• 📝 Multiple AI Study Modes:
  • Summarized Note: Get structural summaries of complex chapters.
  • Q&A: Generate practice questions and explanatory answers.
  • Find Pages: Exact page-number citations for facts and figures.
• 🔍 Hybrid Search: Combines literal string matching with AI-powered vector similarity (ChromaDB).
• 🚀 Parallel Intelligence: Uses a "Best of All" logic by querying multiple LLMs (Gemini, OpenAI) simultaneously.
• 🔐 Secure Auth: JWT-based user registration and login system.
• 💬 Real-time Chat: Interactive glassmorphism UI with WebSocket-driven responses.

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🛠 Tech Stack

Backend

• Framework: FastAPI (Python)
• AI Orchestration: LangChain & LangGraph
• Database: MongoDB (User data & Chat history)
• Vector Store: ChromaDB (Document indexing)
• LLMs: Google Gemini 2.0 Flash, OpenAI, DeepSeek (via OpenRouter)

Frontend

• Library: React 19 + Vite
• Styling: Vanilla CSS (Glassmorphism design system)
• Communication: WebSockets & REST API

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🏗 System Architecture

graph TD
    User((Student)) --> Frontend[React + Vite UI]
    Frontend --> Auth[JWT Authentication]
    Frontend --> Backend[FastAPI Core]
    Backend --> AI[LangChain + LangGraph]
    AI --> LLM[Gemini / OpenAI / DeepSeek]
    Backend --> DB[(MongoDB: User Data)]
    Backend --> VDB[(ChromaDB: Vector Search)]

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🚀 Getting Started

Prerequisites

• Python 3.10+
• Node.js 18+
• MongoDB (Local or Atlas)

1. Backend Setup

1. Navigate to the backend folder:

   cd backend

2. Install dependencies:

   pip install -r requirements.txt

3. Configure environment variables (create a .env file):

   MONGODB_URI=mongodb://localhost:27017
   OPENAI_API_KEY=your_key
   GEMINI_API_KEY=your_key

4. Run the API:

   python -m uvicorn main:app --reload --port 8002

2. Frontend Setup

1. Navigate to the frontend folder:

   cd frontend

2. Install packages:

   npm install

3. Start the development server:

   npm run dev

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📖 Usage Guide

1. Login/Register: Create an account to persist your chat history and notes.
2. Upload PDF: Use the folder icon in the chat to upload a textbook or paper.
3. Select Mode: Use the mode switcher (📝, ❓, 🔍) to tell the AI how to focus.
4. Search: Prefix a query with search for: to perform a literal page search.

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