TechNova University AI Chat Assistant

A modern AI-powered chat interface for TechNova University that provides instant answers to questions about the university's programs, campuses, and services. The application uses RAG (Retrieval-Augmented Generation) to provide accurate and context-aware responses based on the university's documentation.

Features

• 🤖 AI-powered chat interface with natural language understanding
• 📚 RAG-based responses using university documentation
• 🎓 Information about programs, campuses, and services
• 📊 Real-time chat statistics and analytics
• 🔒 Session management for continuous conversations
• 📱 Responsive design for all devices
• 🎨 Modern and intuitive user interface

Tech Stack

Backend

• Python 3.8+
• Flask: Web framework
• LangChain: AI/ML framework
• Ollama: Local LLM server
• SQLite: Database for storing chat statistics
• Flask-CORS: Cross-origin resource sharing

Frontend

• HTML5/CSS3: Modern web interface
• JavaScript: Dynamic interactions
• Chart.js: Statistics visualization

Prerequisites

• Python 3.8 or higher
• Ollama installed and running locally
• Git (for cloning the repository)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/python-rag-ollama.git
cd python-rag-ollama

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the required packages:

pip install -r requirements.txt

4. Start the Ollama server:

ollama serve

5. In a new terminal, start the Flask application:

python app.py

The application will be available at http://localhost:5001

Usage

1. Open your browser and navigate to http://localhost:5001
2. Enter your name to start chatting
3. Ask questions about TechNova University, such as:
   • Available programs and degrees
   • Campus locations and facilities
   • Admission requirements
   • Student services
   • Research opportunities

Backoffice Dashboard

Access the backoffice dashboard at http://localhost:5001/backoffice to view:

• Real-time chat statistics
• Popular questions
• System performance metrics

Project Structure

python-rag-ollama/
├── app.py              # Main Flask application
├── database.py         # Database operations
├── vector.py          # Vector store and retrieval
├── static/            # Static files
│   ├── styles.css     # CSS styles
│   └── logo.png       # University logo
├── templates/         # HTML templates
├── requirements.txt   # Python dependencies
└── README.md         # Project documentation

Development

Running Tests

python -m pytest tests/

Code Style

The project follows PEP 8 style guidelines. Use a linter to ensure code quality:

flake8 .

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Acknowledgments

• TechNova University for providing the documentation
• Ollama team for the local LLM server
• LangChain team for the RAG framework

Support

For support, please open an issue in the GitHub repository or contact the development team.

Roadmap

• Add user authentication
• Implement chat history export
• Add support for multiple languages
• Integrate with university's student portal
• Add voice input/output capabilities