VT.ai

Minimal multimodal AI chat app with dynamic conversation routing

Overview

VT.ai is a VT.ai - Minimal multimodal AI chat app that provides a seamless chat interface for interacting with various Large Language Models (LLMs). It supports both cloud-based providers and local model execution through Ollama.

Key Features 🚀

• Multi-modal Interactions

  • Text and image processing capabilities
  • Real-time streaming responses
  • [Beta] Advanced Assistant features via OpenAI's Assistant API

• Flexible Model Support

  • OpenAI, Anthropic, and Google integration
  • Local model execution via Ollama
  • Dynamic parameter adjustment (temperature, top-p)

• Modern Architecture

  • Built on Chainlit for responsive UI
  • SemanticRouter for intelligent conversation routing
  • Real-time response streaming
  • Customizable model settings

Screenshots

Quick Start Guide

Prerequisites

• Python 3.7+
• (Recommended) rye for dependency management
• For local models:
  • Ollama client
  • Desired Ollama models

Installation

1. Clone the repository
2. Copy .env.example to .env and configure your API keys
3. Set up Python environment:

   python3 -m venv .venv
   source .venv/bin/activate
   pip3 install -r requirements.txt

4. Optional: Train semantic router

   python3 src/router/trainer.py

5. Launch the application:

   chainlit run src/app.py -w

Using Local Models with Ollama

# Download model
ollama pull llama3

# Start Ollama server
ollama serve

Technical Stack

• Chainlit: Frontend framework
• LiteLLM: LLM integration layer
• SemanticRouter: Conversation routing

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to branch: git push origin feature/amazing-feature
5. Open a Pull Request

License

This project is licensed under the MIT License. See LICENSE for details.

Connect

-Email: [email protected]