CampusQuery: An Enterprise-Grade AI Campus Assistant

CampusQuery is a sophisticated, AI-driven digital assistant designed to streamline the information retrieval process within a university ecosystem. Leveraging advanced Retrieval-Augmented Generation (RAG) architecture and Natural Language Processing (NLP), the system provides accurate, contextually relevant, and multilingual support for students, faculty, and campus visitors.

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🚀 Key Features

• RAG-Enhanced Knowledge Retrieval: Integrates vector-based search to ensure responses are grounded in verified campus documentation.
• Multilingual Processing: Seamlessly handles queries in multiple languages (English and Hindi supported out-of-the-box), enhancing institutional accessibility.
• Intent Classification: Sophisticated NLP algorithms categorize user queries to provide specialized responses across domains (Admissions, Timetables, Events, etc.).
• Real-Time Asynchronous Communication: High-performance backend providing low-latency responses through modern messaging patterns.
• Omnichannel Support: Integrated support for web-based chat and Telegram bot interfaces.

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🏗 System Architecture

The project follows a modular, decoupled architecture optimized for scalability and maintainability:

1. Retrieval-Augmented Generation (RAG) Pipeline

• Ingestion: Documents (PDFs, Text) are processed and split into semantic chunks.
• Vector Encoding: Chunks are converted into high-dimensional embeddings using Google/OpenAI models.
• Storage: Embeddings are indexed in a Pinecone vector database.
• Retrieval: User queries trigger a semantic search to fetch the most relevant knowledge context.
• Generation: The LLM (Gemini/GPT) synthesizes a final response using the retrieved context.

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🛠 Technology Stack

Backend

• Runtime: Node.js (ES Modules)
• Framework: Express.js
• AI Orchestration: LangChain, Google Generative AI (@google/genai)
• Database: MongoDB (Metadata), Pinecone (Vector Store)
• Messaging: Node Telegram Bot API

Frontend

• Framework: React.js (Vite)
• Styling: Tailwind CSS, Vanilla CSS
• Routing: React Router DOM
• State Management: React Hooks

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📂 Project Structure

CampusQuery/
├── client/                 # React Frontend Application
│   ├── src/
│   │   ├── pages/          # Chatbot and Dashboard UI Components
│   │   └── App.jsx         # Application Routing
├── controllers/            # AI and Business Logic Handlers
├── models/                 # Mongoose Database Schemas
├── routes/                 # Express API Endpoint Definitions
├── chat.js                 # Core Orchestration Layer
├── embedder.js             # Vectorization and Embedding Logic
├── index.js                # Server Entry Point
└── package.json            # Dependency Manifest

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⚙️ Installation & Setup

Prerequisites

• Node.js (v18 or higher)
• MongoDB Instance
• Pinecone Account
• Google Gemini or OpenAI API Key

1. Clone the Repository

git clone https://github.com/iamaparnaojha/CampusQuery.git
cd CampusQuery

2. Backend Installation

npm install

3. Frontend Installation

cd client
npm install
cd ..

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🔑 Environment Configuration

Create a .env file in the root directory and configure the following variables:

# Server Configuration
PORT=5000
MONGODB_URI=your_mongodb_connection_string

# AI Provider Keys
GOOGLE_API_KEY=your_gemini_api_key
OPENAI_API_KEY=your_openai_api_key

# Vector DB Configuration
PINECONE_API_KEY=your_pinecone_key
PINECONE_ENVIRONMENT=your_pinecone_env

# Application Configuration
CLIENT_URL=http://localhost:5173
SUPPORTED_LANGUAGES=en,hi
DEFAULT_LANGUAGE=en

# Telegram Integration (Optional)
TELEGRAM_BOT_TOKEN=your_bot_token

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🚀 Execution

Development Mode

To run the system in development mode with hot-reloading:

Backend:

npm run dev

Frontend:

cd client
npm run dev

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📡 API Endpoints

Method	Endpoint	Description
POST	/chat	Processes user questions and returns AI-generated responses.
POST	/api/files/upload	Uploads and processes documents for the RAG knowledge base.

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🤝 Contribution Guidelines

We welcome contributions to CampusQuery. To contribute:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/NewFeature).
3. Commit your changes (git commit -m 'Add NewFeature').
4. Push to the branch (git push origin feature/NewFeature).
5. Open a Pull Request.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.