vibebash 1.0.1

Natural language to shell commands with AI - Interactive CLI tool

VibeBash - Offline AI Shell Assistant

VibeBash is a completely offline natural language to shell command translator powered by local AI models through Ollama. No API keys, no internet connection required - everything runs locally on your machine.

🌟 Features

• 🔒 100% Offline: All processing happens locally via Ollama
• 🤖 AI-Powered: Convert natural language to shell commands
• 🚀 Fast: Local models mean no network latency
• 🔐 Private: Your commands never leave your machine
• 🎯 Accurate: Supports reasoning models like deepcoder
• ⚡ Interactive: Review, modify, or skip commands before execution
• 📝 Smart History: Learn from previous command patterns
• 🛠️ Command Validation: Checks command availability and provides installation hints

🎯 Supported Models

VibeBash works with any Ollama-compatible model:

• deepcoder - Reasoning model with think tokens (recommended)
• llama3.2 - Fast and capable general model
• codellama - Code-focused model
• mistral - Efficient and accurate
• gemma - Google's lightweight model

📦 Installation

Requirements

1. Python 3.8+
2. Ollama - Install from ollama.ai

Quick Install with pipx (Recommended)

pipx install vibebash

Install from Source

git clone https://github.com/yourusername/vibebash.git
cd vibebash
pip install -e .

Development Setup

./scripts/setup-dev.sh

🚀 Quick Start

1. Start Ollama:

   ollama serve
   

2. Pull a model (if you don't have one):

   ollama pull deepcoder    # Reasoning model (recommended)
   # or
   ollama pull llama3.2     # Fast general model
   

3. Run VibeBash:

   vibebash "list all Python files in this directory"
   

4. First time setup - VibeBash will ask for:

   • Ollama base URL (default: http://localhost:11434)
   • Default model name (e.g., deepcoder, llama3.2)

💡 Usage Examples

Basic Commands

vibebash "show disk usage"
vibebash "find large files over 100MB"
vibebash "kill process on port 8080"

With Specific Models

vibebash --model-name deepcoder "optimize this Python script"
vibebash --model-name llama3.2 "compress all images in this folder"

Interactive Mode

vibebash
# Then type your request when prompted

View History

vibebash --history

Reconfigure

vibebash --config

🔧 Configuration

VibeBash stores configuration in ~/.vibebash_config.json:

{
  "ollama_base_url": "http://localhost:11434",
  "default_model_name": "deepcoder"
}

Custom Ollama Setup

If running Ollama on a different host or port:

vibebash --config
# Enter custom URL like: http://my-server:11434

🧪 Testing

Run All Tests

make test-ollama

Quick Core Tests

python3 -m pytest tests/unit/test_models.py tests/unit/test_command_availability.py -v

Test with Your Model

make test-deepcoder  # Test with deepcoder model

📁 Project Structure

vibebash/
├── vibebash/           # Main package
│   ├── main.py         # Core VibeBash class
│   └── __init__.py     # Package initialization
├── tests/              # Test suite
│   ├── unit/           # Unit tests
│   └── integration/    # Integration tests
├── scripts/            # Setup and utility scripts
│   ├── quick-start.sh  # Quick setup
│   └── setup-dev.sh    # Development setup
└── docs/               # Documentation

🛠️ Development

Setup Development Environment

./scripts/setup-dev.sh

Run Tests

make test           # All tests
make test-unit      # Unit tests only
make coverage       # With coverage report

Code Quality

make lint           # Linting
make format         # Code formatting
make type-check     # Type checking

Build Package

make build

🤖 Model Recommendations

For Reasoning Tasks (Recommended)

• deepcoder: Best for complex command generation with step-by-step thinking
• codellama: Good for code-related tasks

For Speed

• llama3.2: Fast and lightweight, good balance of speed and accuracy
• gemma: Very fast, good for simple commands

Memory Usage

• Small models: llama3.2 (3B), gemma (2B-7B)
• Large models: deepcoder (14B), codellama (13B+)

🔍 How It Works

1. Input: You provide a natural language description
2. Processing: Local Ollama model converts it to shell commands
3. Validation: VibeBash checks command availability
4. Review: You can approve, modify, or skip each command
5. Execution: Commands run in your local shell
6. Learning: History helps improve future suggestions

🛡️ Privacy & Security

• No Data Transmission: Everything stays on your machine
• No API Keys: No external services required
• Command Review: You control what gets executed
• Safe Defaults: Built-in protections against dangerous commands

📚 Advanced Usage

Custom Prompts

VibeBash includes system context about your environment:

• Current directory
• Available commands
• Operating system
• Recent command history

Batch Operations

vibebash "create backup of all config files and compress them"
vibebash "find and remove all temporary files older than 7 days"

Error Recovery

When commands fail, VibeBash can suggest alternatives:

vibebash "install package X"
# If package manager not found, suggests alternatives for your OS

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Run tests: make test-ollama
4. Submit a pull request

📜 License

MIT License - see LICENSE file.

🔗 Links

• Ollama: https://ollama.ai
• Models: https://ollama.ai/library
• Issues: https://github.com/yourusername/vibebash/issues

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VibeBash: Because your shell should understand you, not the other way around. 🎯