https://github.com/rstudio-tech/multi-ai-chat-app
https://github.com/rstudio-tech/multi-ai-chat-app
agent ai assistant chatbot llama mutli-modal openai python
Last synced: 11 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/rstudio-tech/multi-ai-chat-app
- Owner: rstudio-tech
- License: mit
- Created: 2024-12-16T04:32:29.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-16T04:41:19.000Z (over 1 year ago)
- Last Synced: 2025-07-02T17:55:28.696Z (11 months ago)
- Topics: agent, ai, assistant, chatbot, llama, mutli-modal, openai, python
- Language: Python
- Homepage:
- Size: 722 KB
- Stars: 15
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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](https://github.com/ollama/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](https://ollama.com/download) client
- Desired [Ollama models](https://ollama.com/library)
### Installation
1. Clone the repository
2. Copy `.env.example` to `.env` and configure your API keys
3. Set up Python environment:
```bash
python3 -m venv .venv
source .venv/bin/activate
pip3 install -r requirements.txt
```
4. Optional: Train semantic router
```bash
python3 src/router/trainer.py
```
5. Launch the application:
```bash
chainlit run src/app.py -w
```
### Using Local Models with Ollama
```bash
# Download model
ollama pull llama3
# Start Ollama server
ollama serve
```
## Technical Stack
- **[Chainlit](https://github.com/Chainlit/chainlit)**: Frontend framework
- **[LiteLLM](https://github.com/BerriAI/litellm)**: LLM integration layer
- **[SemanticRouter](https://github.com/aurelio-labs/semantic-router)**: 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](LICENSE) for details.
## Connect
-Email: sunstar962090@gmail.com