ChatLite 🤖
A lightweight, extensible chat application framework for building AI-powered chat interfaces. ChatLite provides an easy-to-use platform for integrating various language models with web-based chat applications.
✨ Features
- 🔄 Real-time WebSocket communication
- 🎯 Multi-model support (Llama, Qwen, etc.)
- 🌐 Web search integration
- 🎨 Customizable UI with modern design
- 🔌 Plugin architecture for easy extensions
- 💬 Chat history management
- 🎭 Multiple agent types support
- 📱 Responsive design
🚀 Quick Start
Installation
pip install chatlite
Basic Usage
import chatlite
# Start a simple chat server with Llama 3.2
chatlite.local_llama3p2()
# Or use Qwen 2.5
chatlite.local_qwen2p5()
# Custom configuration
server = chatlite.create_server(
model_type="local",
model_name="llama3.2:latest",
temperature=0.7,
max_tokens=4000
)
server.run()
Pre-configured Models
ChatLite comes with several pre-configured models:
# Use different models directly
from chatlite import mistral_7b_v3, mixtral_8x7b, qwen_72b
# Start Mistral 7B server
mistral_7b_v3()
# Start Mixtral 8x7B server
mixtral_8x7b()
# Start Qwen 72B server
qwen_72b()
💻 Frontend Integration
ChatLite includes a Flutter-based frontend that can be easily customized. Here's a basic example of connecting to the ChatLite server:
final channel = WebSocketChannel.connect(
Uri.parse('ws://localhost:8143/ws/$clientId'),
);
// Send message
channel.sink.add(json.encode({
'message': 'Hello!',
'model': 'llama3.2:latest',
'system_prompt': 'You are a helpful assistant',
'agent_type': 'WebSearchAgent',
'is_websearch_chat': true
}));
// Listen for responses
channel.stream.listen(
(message) {
final data = jsonDecode(message);
if (data['type'] == 'stream') {
print(data['message']);
}
},
onError: (error) => print('Error: $error'),
onDone: () => print('Connection closed'),
);
🔧 Configuration
ChatLite supports various configuration options:
from chatlite import create_server
server = create_server(
model_type="local", # local, huggingface, etc.
model_name="llama3.2:latest",
api_key="your-api-key", # if needed
temperature=0.7, # model temperature
max_tokens=4000, # max response length
base_url="http://localhost:11434/v1", # model API endpoint
)
🧩 Available Agents
ChatLite supports different agent types for specialized tasks:
WebSearchAgent: Internet-enabled chat with web search capabilitiesRawWebSearchAgent: Direct web search results without summarizationEmailAssistantFeature: Email composition and analysisDefaultChatFeature: Standard chat functionality
Example usage:
# Client-side configuration
message_data = {
"message": "What's the latest news about AI?",
"model": "llama3.2:latest",
"agent_type": "WebSearchAgent",
"is_websearch_chat": True
}
🎨 UI Customization
The included Flutter frontend supports extensive customization:
ThemeData(
brightness: Brightness.dark,
scaffoldBackgroundColor: const Color(0xFF1C1C1E),
primaryColor: const Color(0xFF1C1C1E),
colorScheme: const ColorScheme.dark(
primary: Color(0xFFFF7762),
secondary: Color(0xFFFF7762),
),
)
📦 Project Structure
chatlite/
├── __init__.py # Main package initialization
├── core/ # Core functionality
│ ├── config.py # Configuration handling
│ ├── model_service.py # Model interaction
│ └── features/ # Feature implementations
├── ui/ # Flutter frontend
└── examples/ # Usage examples
🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
🙏 Acknowledgments
- Built with FastAPI and Flutter
- Inspired by modern chat applications
- Uses various open-source language models
1 maintainer
Metadata
1 maintainer
Release files for chatlite 0.1.13
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