IT Support Agent System

A comprehensive IT support ticketing system with AI-powered assistance, built with Flask and designed for easy deployment and configuration.

🚀 Features

Core Functionality

• Ticket Management: Create, view, update, and track IT support tickets
• AI Assistant: Intelligent chatbot that provides policy-based guidance
• Multi-LLM Support: Easily switch between OpenAI, OpenRouter, or other providers
• Policy-Based Responses: AI cites official IT policies and procedures
• Real-time Updates: Live status updates and notifications
• Search & Filtering: Find tickets by status, priority, or content
• Responsive Design: Works on desktop, tablet, and mobile devices

AI Agent Capabilities

• Password reset guidance with approval workflows
• Software installation request processing
• Network troubleshooting step-by-step instructions
• Security policy explanations
• Automatic ticket creation suggestions
• Transparent reasoning with policy citations

Admin Features

• Easy LLM provider configuration via .env file
• Comprehensive logging and audit trails
• Ticket statistics and reporting
• Policy document management
• No-code configuration for most settings

📋 Requirements

• Python 3.8+
• Flask 3.0+
• SQLite (included with Python)
• Internet connection for AI features
• Modern web browser

🛠️ Installation

1. Clone or Download the Project

git clone <repository-url>
cd ChimeraHackathon

2. Create Virtual Environment

# Windows
python -m venv venv
venv\Scripts\activate

# Linux/Mac
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment

Copy the example environment file and configure your settings:

# Create .env file in backend directory
cd backend
copy .env.example .env  # Windows
cp .env.example .env    # Linux/Mac

Edit the .env file:

# Choose your LLM provider: openrouter, openai, gemini
LLM_PROVIDER=openrouter

# Add your API key for the chosen provider
OPENROUTER_API_KEY=your_openrouter_key_here
OPENAI_API_KEY=your_openai_key_here
GEMINI_API_KEY=your_gemini_key_here

5. Run the Application

cd backend
python app.py

The application will start at http://localhost:5000

🔧 Configuration

Switching LLM Providers

To switch between AI providers, simply edit the .env file:

For OpenRouter (Free tier available):

LLM_PROVIDER=openrouter
OPENROUTER_API_KEY=sk-or-v1-your-key-here

For OpenAI:

LLM_PROVIDER=openai
OPENAI_API_KEY=sk-your-openai-key-here

For Gemini:

LLM_PROVIDER=gemini
GEMINI_API_KEY=your-gemini-key-here

No code changes required! Just restart the application.

Adding New Policies

1. Create a new .txt file in backend/policies/
2. Add your policy content in plain text
3. Restart the application
4. The AI agent will automatically include the new policy in its knowledge base

Customizing the Interface

• Logo/Branding: Edit backend/templates/base.html
• Colors/Styling: Modify backend/static/style.css
• Additional Pages: Create new templates in backend/templates/

📖 Usage Guide

For Employees

1. Creating a Ticket:

   • Click "New Ticket" from the dashboard
   • Fill in your name and describe the issue
   • Select appropriate priority level
   • Submit the ticket

2. Using the AI Assistant:

   • Click "AI Assistant" from the menu
   • Type your IT question in plain English
   • Follow the step-by-step guidance provided
   • Create a ticket if suggested by the AI

3. Tracking Tickets:

   • View all tickets in the "Tickets" section
   • Click on any ticket to see detailed information
   • Track status changes and resolution progress

For IT Staff

1. Managing Tickets:

   • View all tickets with filtering and search
   • Update ticket status (New → In Progress → Resolved → Closed)
   • Assign tickets to team members
   • Add resolution notes

2. Monitoring System:

   • Dashboard shows real-time statistics
   • Search tickets by employee, content, or status
   • View AI interaction logs for transparency

For Administrators

1. LLM Configuration:

   • Edit .env file to change AI provider
   • No coding required for basic configuration
   • Restart application to apply changes

2. Policy Management:

   • Add new policy files to backend/policies/
   • Edit existing policies as needed
   • AI automatically uses updated policies

🏗️ Architecture

Project Structure

ChimeraHackathon/
├── backend/
│   ├── app.py              # Main Flask application
│   ├── database.py         # Database operations
│   ├── ticket.py          # Ticket management logic
│   ├── llm_client.py      # AI agent integration
│   ├── .env               # Configuration file
│   ├── policies/          # IT policy documents
│   │   ├── password_reset.txt
│   │   ├── software_install.txt
│   │   └── network_issues.txt
│   ├── templates/         # HTML templates
│   │   ├── base.html
│   │   ├── index.html
│   │   ├── agent.html
│   │   ├── tickets.html
│   │   └── create_ticket.html
│   └── static/           # CSS, JavaScript, images
│       ├── style.css
│       └── script.js
├── requirements.txt       # Python dependencies
├── README.md             # This file
└── venv/                # Virtual environment

Key Components

1. Flask Web Server (app.py):

   • Routes for web pages and API endpoints
   • Handles HTTP requests and responses
   • Coordinates between frontend and backend

2. Database Layer (database.py):

   • SQLite database for ticket storage
   • CRUD operations with proper error handling
   • Automatic logging of all changes

3. Business Logic (ticket.py):

   • Ticket lifecycle management
   • Validation and business rules
   • Statistics and reporting

4. AI Integration (llm_client.py):

   • Multi-provider LLM support
   • Policy document loading and searching
   • Response generation with citations

5. Frontend (Templates + Static files):

   • Responsive web interface
   • Real-time updates with JavaScript
   • Modern UI with Bootstrap

🔍 API Endpoints

Tickets API

• POST /api/tickets - Create new ticket
• GET /api/tickets - Get all tickets (with filters)
• PUT /api/tickets/<id> - Update ticket
• GET /api/tickets/<id> - Get specific ticket

AI Agent API

• POST /api/agent/ask - Ask AI assistant a question

System API

• GET /api/stats - Get system statistics

🐛 Troubleshooting

Common Issues

Application won't start:

• Check that you're in the backend directory
• Ensure virtual environment is activated
• Verify all dependencies are installed

AI agent not working:

• Check your API key in .env file
• Verify the LLM provider is set correctly
• Check internet connection
• Look at console logs for error messages

Database errors:

• Delete it_support.db to reset database
• Check file permissions in the directory
• Ensure SQLite is available (included with Python)

Templates not loading:

• Verify you're running from backend directory
• Check that templates folder exists
• Restart the application

Getting Help

1. Check the logs: Look at the console output when running the app
2. Create a test ticket: Use the system to create a simple ticket
3. Test AI separately: Try the AI assistant with a simple question
4. Check configuration: Verify your .env file settings

🚧 Development

Running in Development Mode

cd backend
export FLASK_ENV=development  # Linux/Mac
set FLASK_ENV=development     # Windows
python app.py

Adding New Features

1. New API endpoints: Add routes to app.py
2. Database changes: Modify database.py schema
3. New templates: Create HTML files in templates/
4. Styling: Edit static/style.css
5. Client-side logic: Modify static/script.js

Testing

Basic testing can be done by:

1. Creating test tickets through the web interface
2. Asking the AI assistant various questions
3. Testing different ticket statuses and updates
4. Verifying policy citations are working

📈 Production Deployment

For production use:

1. Use a production WSGI server (Gunicorn, uWSGI)
2. Set up a reverse proxy (Nginx, Apache)
3. Use environment variables for sensitive configuration
4. Set up proper logging and monitoring
5. Regular backups of the SQLite database
6. SSL/HTTPS for secure communication

🤝 Contributing

To contribute to this project:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

📄 License

This project is provided as-is for educational and demonstration purposes.

🙋 Support

For support or questions:

1. Check this README for common solutions
2. Create a ticket using the system itself
3. Ask the AI assistant for help with IT questions
4. Contact the development team

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Built with ❤️ for the IT support community