Free AI Docker 🐬📦

Empower AI with more System Permissions through an the Middleware Container for Advanced Automation. Serves as the Middleware Container between large AI models and applications.

Technical Route

• Technical Route 1: Programmatic Parsing of AI-Returned Content and Command Execution 🛠️💻
• Technical Route 2: Training an AI Model to Parse and Execute Commands Independently 🤖📚

Warning🚨🚨🚨

• Do Not Expose Your Docker Interfaces 🔒: Exposing your Docker interfaces can lead to the risk of malicious attacks by unauthorized users. It's crucial to secure your Docker environment to prevent potential threats.
• Project in Early Stage ⚠️: This project is currently in its early development phase, and security measures are still being improved. There is a possibility that attackers could bypass Docker to target the host system. We recommend using this project in a secure environment until further security enhancements are implemented.
• Resource Consumption Warning 💻: Enabling full automation for the AI can result in significant resource consumption. Please proceed with caution when enabling this feature, and use it judiciously based on your system's capacity.

✨ Features

• 🤖 AI Integration: Seamlessly integrate with various AI models (Qwen, OpenAI, Gemini)
• 🔄 Auto-Execution: Automatically parse and execute commands from AI responses
• 📁 File Management: Create and manage files based on AI instructions
• 🖥️ Terminal Integration: Real-time command execution feedback
• 🔍 Error Handling: Automatic error detection and retry mechanism
• 💾 Persistent Storage: Save chat history and configurations

🛠️ Tech Stack

• Frontend:

  • Vue 3
  • TypeScript
  • Element Plus
  • Pinia
  • Vite

• Backend:

  • Node.js
  • Express
  • Socket.IO
  • TypeScript

🚀 Getting Started

Prerequisites

• Node.js (v18.0.0 or higher)
• npm (v9.0.0 or higher)
• PowerShell (v5.1 or higher)
• Git

Quick Install

1. Clone the repository

git clone https://github.com/supersuperbruce/free-ai-docker.git
cd free-ai-docker

2. Install all dependencies using requirements.txt

# Install backend dependencies
cd backend
npm install $(cat requirements.txt | grep "Backend" -A 10 | grep -v "Backend" | grep -v "^$" | tr '\n' ' ')

# Install frontend dependencies
cd ../frontend
npm install $(cat ../requirements.txt | grep "Frontend" -A 10 | grep -v "Frontend" | grep -v "^$" | tr '\n' ' ')

Manual Installation

1. Install backend dependencies

cd backend
npm install [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] @types/[email protected] @types/[email protected] @types/[email protected]

2. Install frontend dependencies

cd frontend
npm install [email protected] [email protected] @vitejs/[email protected] [email protected] @vue/[email protected]

Development Setup

1. Start backend server

cd backend
npm run dev
# Server will start on http://localhost:3000

2. Start frontend development server

cd frontend
npm run dev
# Frontend will be available on http://localhost:5173

3. Access the application

• Open your browser and navigate to http://localhost:5173
• Configure your AI provider settings in the configuration panel

Configuration

Configure the following in the settings panel:

• API Key: Your AI provider's API key
• API URL: The API endpoint URL
• Model: AI model name
• Save Path: Directory path for file operations

Supported AI Providers

• Qwen

  • URL: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation
  • Models: qwen-turbo, qwen-plus, qwen-max

• OpenAI

  • URL: https://api.openai.com/v1/chat/completions
  • Models: gpt-3.5-turbo, gpt-4

• Gemini

  • URL: https://generativelanguage.googleapis.com/v1/models/gemini-pro:generateContent
  • Models: gemini-pro

• Ollama

  • URL: http://localhost:11434/api/chat
  • Models:
    • llama2
    • codellama
    • mistral
    • neural-chat
    • starling-lm
  • Features:
    • Local deployment
    • No API key required
    • Multiple model support
    • Low latency responses
    • Custom model loading

🔧 Configuration

1. Get your API key from the AI provider
2. Configure the API settings in the configuration panel:
   • API Key
   • API URL
   • Model Name
   • Save Path

💡 Usage

1. Configure API Settings

   • Enter your API credentials
   • Set the save path for generated files

2. Start Chatting

   • Type your request in the chat input
   • AI will respond and automatically execute commands
   • View execution results in the terminal panel

3. Error Handling

   • System automatically detects execution errors
   • AI provides alternative solutions
   • Retry mechanism ensures task completion

🌟 Key Features Explained

Automatic Command Execution

The system automatically detects and executes:

• Shell commands
• Python scripts
• Batch files
• File operations

Real-time Terminal

• View command execution in real-time
• See detailed error messages
• Track operation progress

Error Recovery

• Automatic error detection
• AI-powered error resolution
• Smart retry mechanism

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Thanks to all contributors
• Inspired by the AI development community
• Built with ❤️ using Vue.js and Node.js

📞 Contact

Project Link: https://github.com/supersuperbruce/free-ai-docker

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