# LLM Commit Classifier Refactoring

## Overview

The LLM commit classifier has been refactored from a monolithic 836-line file into a modular architecture with clear separation of concerns. This refactoring maintains 100% backward compatibility while improving maintainability, testability, and extensibility.

## Architecture Changes

### Before (Monolithic)
```
llm_commit_classifier.py (836 lines)
├── Configuration
├── Prompt generation
├── OpenAI API interaction
├── Response parsing
├── Caching
├── Cost tracking
├── Batch processing
└── Rule-based fallback
```

### After (Modular)
```
llm_commit_classifier.py (542 lines) - Main orchestrator
└── llm/
    ├── __init__.py         - Module exports
    ├── base.py            - Abstract base classes and interfaces
    ├── prompts.py         - Prompt generation and templates
    ├── openai_client.py   - OpenAI/OpenRouter implementation
    ├── response_parser.py - Response parsing and validation
    ├── cost_tracker.py    - Cost calculation and tracking
    ├── batch_processor.py - Batch processing logic
    └── cache.py          - LLM-specific caching layer
```

## Key Improvements

### 1. Separation of Concerns
Each module now has a single, well-defined responsibility:
- **base.py**: Defines interfaces for LLM providers
- **prompts.py**: Manages all prompt engineering
- **openai_client.py**: Handles OpenAI-specific API interactions
- **response_parser.py**: Parses and validates LLM responses
- **cost_tracker.py**: Tracks usage and costs
- **batch_processor.py**: Manages batch processing
- **cache.py**: Provides persistent caching

### 2. Provider Abstraction
The `BaseLLMClassifier` abstract class allows easy addition of new LLM providers:
```python
class BaseLLMClassifier(ABC):
    @abstractmethod
    def classify_commit(self, message: str, files_changed: Optional[list[str]]) -> ClassificationResult
    
    @abstractmethod
    def get_provider_name(self) -> str
    
    @abstractmethod
    def estimate_cost(self, text: str) -> float
```

### 3. Enhanced Prompt Engineering
- Multiple prompt versions for A/B testing
- Support for few-shot learning
- Template-based prompt generation
- Easy experimentation with different strategies

### 4. Improved Cost Management
- Detailed cost tracking per model
- Budget limits and warnings
- Cost export for analysis
- Support for multiple pricing models

### 5. Better Error Handling
- Graceful degradation when LLM unavailable
- Specific exception types
- Retry logic with exponential backoff
- Comprehensive fallback mechanisms

## Backward Compatibility

The refactoring maintains 100% backward compatibility:
- All original imports still work
- Original `LLMConfig` and `LLMCommitClassifier` interfaces unchanged
- Legacy `LLMPredictionCache` wrapper provided
- All existing methods and signatures preserved

## Usage Examples

### Traditional Usage (Unchanged)
```python
from gitflow_analytics.qualitative.classifiers.llm_commit_classifier import (
    LLMCommitClassifier,
    LLMConfig
)

config = LLMConfig(
    api_key="your-api-key",
    model="mistralai/mistral-7b-instruct"
)
classifier = LLMCommitClassifier(config)
result = classifier.classify_commit("fix: resolve bug")
```

### New Modular Usage
```python
from gitflow_analytics.qualitative.classifiers.llm import (
    PromptGenerator,
    PromptVersion,
    ResponseParser,
    CostTracker
)

# Use components independently
prompt_gen = PromptGenerator(PromptVersion.V4_FEWSHOT)
parser = ResponseParser()
tracker = CostTracker(daily_budget=10.0)
```

### Adding a New Provider
```python
from gitflow_analytics.qualitative.classifiers.llm.base import BaseLLMClassifier

class AnthropicClassifier(BaseLLMClassifier):
    def get_provider_name(self) -> str:
        return "anthropic"
    
    def classify_commit(self, message: str, files_changed: Optional[list[str]]) -> ClassificationResult:
        # Anthropic-specific implementation
        pass
```

## Testing

The refactoring includes comprehensive testing:
- Unit tests for each module
- Integration tests for backward compatibility
- Mock LLM responses for testing without API calls
- Performance benchmarks

## Benefits

1. **Maintainability**: Each module can be understood and modified independently
2. **Testability**: Components can be tested in isolation
3. **Extensibility**: Easy to add new LLM providers or features
4. **Reusability**: Components can be used in other parts of the system
5. **Performance**: Better caching and batch processing
6. **Cost Control**: Enhanced tracking and budget management

## Migration Guide

No migration needed! The refactoring is fully backward compatible. However, for new code, consider:

1. Using the new modular imports for better tree-shaking
2. Leveraging the provider abstraction for multi-provider support
3. Using the enhanced prompt templates for better results
4. Taking advantage of improved cost tracking features

## Future Enhancements

The modular architecture enables future improvements:
- Parallel batch processing
- Additional LLM providers (Anthropic, Cohere, local models)
- Advanced prompt optimization with automated A/B testing
- ML-based prompt selection
- Real-time cost optimization
- Distributed caching