VT.ai

VT.ai

VT.ai - Minimal multimodal AI chat app with dynamic conversation routing

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VT.ai is a multimodal AI platform that offers dynamic conversation routing with SemanticRouter, multi-modal interactions (text/image/audio), an assistant framework with code interpretation, real-time response streaming, cross-provider model switching, and local model support with Ollama integration. It supports various AI providers such as OpenAI, Anthropic, Google Gemini, Groq, Cohere, and OpenRouter, providing a wide range of core capabilities for AI orchestration.

README:


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VT.ai

Multimodal AI Platform with Dynamic Routing & Assistant Framework

Open in GitHub Codespaces License: MIT Twitter Follow

πŸš€ Features

Multi-Provider AI Orchestration

βœ… 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

πŸ“¦ Installation

Prerequisites

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

πŸ”§ 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

πŸ–₯️ 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

πŸ—οΈ Project Structure

VT.ai/
β”œβ”€β”€ src/
β”‚   β”œβ”€β”€ assistants/       # Custom AI assistant implementations
β”‚   β”œβ”€β”€ router/           # Semantic routing configuration
β”‚   β”œβ”€β”€ utils/            # Helper functions & configs
β”‚   └── app.py            # Main application entrypoint
β”œβ”€β”€ public/               # Static assets
β”œβ”€β”€ 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

πŸ“„ License

MIT License - See LICENSE for full text.

🌟 Acknowledgements

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