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VT.ai

Multimodal AI Platform with Dynamic Routing & Assistant Framework

🚀 Features

Multi-Provider AI Orchestration

✅ Supported Platforms:

• OpenAI (GPT-4o, DALL-E 3, Whisper, TTS)
• Anthropic (Claude 3.5 models)
• Google Gemini (Vision & Language)
• Local Models via Ollama (Llama3, Phi-3, Mistral, etc.)
• Groq (Llama3-70B, Mixtral)
• Cohere (Command R+)
• OpenRouter

✨ Core Capabilities:

• Dynamic conversation routing with SemanticRouter
• Multi-modal interactions (Text/Image/Audio)
• Assistant framework with code interpretation
• Real-time response streaming
• Cross-provider model switching
• Local model support with Ollama integration

📦 Installation

Prerequisites

• Python 3.11+
• Ollama (for local models) - Install Guide

git clone https://github.com/vinhnx/VT.ai.git
cd VT.ai

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# .venv\Scripts\activate  # Windows

pip install -r requirements.txt
cp .env.example .env

🔧 Configuration

Populate .env with your API keys:

OPENAI_API_KEY=sk-your-key
GEMINI_API_KEY=your-gemini-key
COHERE_API_KEY=your-cohere-key
ANTHROPIC_API_KEY=your-claude-key

# Local Models
OLLAMA_HOST=http://localhost:11434

🖥️ Usage

Start Application

# Train semantic router (recommended)
python src/router/trainer.py

# Launch interface
chainlit run src/app.py -w

Key Commands

Shortcut	Action
Ctrl+/	Switch model provider
Ctrl+,	Open settings
Ctrl+L	Clear conversation history

🧩 Chat Profiles

Standard Chat Mode

• Multi-LLM conversations
• Dynamic model switching
• Image generation & analysis
• Audio transcription

Assistant Mode (Beta)

# Example assistant capabilities
async def solve_math_problem(problem: str):
    assistant = MinoAssistant()
    return await assistant.solve(problem)

• Code interpreter for complex calculations
• File attachments (PDF/CSV/Images)
• Persistent conversation threads
• Custom tool integrations

🏗️ Project Structure

VT.ai/
├── src/
│   ├── assistants/       # Custom AI assistant implementations
│   ├── router/           # Semantic routing configuration
│   ├── utils/            # Helper functions & configs
│   └── app.py            # Main application entrypoint
├── public/               # Static assets
├── requirements.txt      # Python dependencies
└── .env.example          # Environment template

🌐 Supported Models

Category	Models
Chat	GPT-4o, Claude 3.5, Gemini 1.5, Llama3-70B, Mixtral 8x7B
Vision	GPT-4o, Gemini 1.5 Pro, Llama3.2 Vision
Image Gen	DALL-E 3
TTS	OpenAI TTS-1, TTS-1-HD
Local	Llama3, Phi-3, Mistral, Deepseek R1 series

🤝 Contributing

Development Setup

# Install development tools
pip install -r requirements-dev.txt

# Run tests
pytest tests/

# Format code
black .

Contribution Guidelines

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Add Type hints for new functions
4. Update documentation
5. Open Pull Request

📄 License

MIT License - See LICENSE for full text.

🌟 Acknowledgements

• Inspired by Chainlit for chat interface
• Powered by LiteLLM for model abstraction
• Semantic routing via SemanticRouter