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Setup

# Fork and clone the repository
git clone https://github.com/your-username/GA.git
cd GA

# Install with development dependencies
pip install -e ".[dev]"

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Project Structure

GA/
├── docs/                         # Documentation
├── src/generalanalysis/
│   ├── adversarial_candidate_generator/  # Prompt generation
│   ├── jailbreaks/                       # Jailbreak methods
│   ├── boiler_room/                      # Model interface
│   ├── loss/                             # Loss functions
│   ├── data_utils/                       # Data utilities
│   ├── utils/                            # Common utilities
│   └── training/                         # Training utilities
└── pyproject.toml                # Package configuration

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Workflow

  1. Create a branch: git checkout -b feature/your-feature
  2. Implement your changes
  3. Submit a pull request

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Adding a New Jailbreak Method

# src/generalanalysis/jailbreaks/your_method/
from generalanalysis.jailbreaks.base import JailbreakMethod
from typing import List, Dict, Any

class YourJailbreakMethod(JailbreakMethod):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        # Initialize your method-specific attributes
        
    def optimize(self, goals: List[str], **kwargs) -> Dict[str, Any]:
        # Implement optimization logic
        results = {}
        for goal in goals:
            # Your custom implementation here
            generated_prompts = ["Prompt 1", "Prompt 2"]
            results[goal] = generated_prompts
        return results

# Register in src/generalanalysis/jailbreaks/__init__.py

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Adding a New Generator

# src/generalanalysis/adversarial_candidate_generator/
from generalanalysis.adversarial_candidate_generator.base import AdversarialCandidateGenerator
from generalanalysis.jailbreaks import JailbreakMethod
from typing import List, Dict, Any

class YourGenerator(AdversarialCandidateGenerator):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        # Initialize your generator-specific attributes
        
    def generate_candidates(self, jailbreak_method_instance: JailbreakMethod, **kwargs) -> List[str]:
        # Implement generation logic
        candidates = []
        # Your custom implementation here
        return candidates

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Code Style

  • Follow PEP 8 guidelines
  • Classes: CamelCase
  • Functions/methods: snake_case
  • Line length: 88 characters max
  • Docstrings: Google style
def function(arg1, arg2):
    """Short summary.
    
    Args:
        arg1: Description
        arg2: Description
        
    Returns:
        Description of return value
    """

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