Awesome

<p align="center"> <img src="./public/logo_dark.png" height="200" alt="icon" /> </p> <h1 align="center">VT.ai</h1> <p align="center"> <em>Minimal multimodal AI chat app with dynamic conversation routing</em> </p> <p align="center"> <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a> <a href="https://github.com/vinhnx"><img src="https://img.shields.io/github/stars/vinhnx?style=social" alt="GitHub Stars"></a> <a href="https://news.ycombinator.com/user?id=vinhnx"><img src="https://img.shields.io/hackernews/user-karma/vinhnx?style=social" alt="HackerNews Karma"></a> <a href="https://twitter.com/vinhnx"><img src="https://img.shields.io/twitter/follow/vinhnx?style=social" alt="Twitter Follow"></a> <a href="https://twitter.com/vtdotai"><img src="https://img.shields.io/twitter/follow/vtdotai?style=social" alt="VT.ai Twitter"></a> </p>

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

Multi LLM Providers Multi-modal Conversation

Quick Start Guide

Prerequisites

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

Installation

Clone the repository
Copy .env.example to .env and configure your API keys

Set up Python environment:

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

Optional: Train semantic router
```
python3 src/router/trainer.py
```
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

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

Release Information

Check our releases page for version history and updates.

License

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

Connect

Twitter: @vinhnx
Project Updates: @vtdotai