dspy.ts/logs/optimize.md

DSPy.ts - Declarative Self-Learning TypeScript: A framework for compositional LM pipelines with self-improving prompt strategies.

# Optimizer Implementation Log

## Overview
Added DSPy.ts optimizer functionality to improve module performance through few-shot example generation and selection.

## Components Added

### 1. Core Optimizer Framework
- Created base optimizer interface and types (src/optimize/base.ts)
- Defined MetricFunction type for evaluating example quality
- Added save/load functionality for optimized programs

### 2. BootstrapFewShot Implementation
- Implemented BootstrapFewShot optimizer (src/optimize/bootstrap.ts)
- Added support for labeled and unlabeled examples
- Implemented metric-based example selection
- Added configurable parameters:
  - maxLabeledDemos
  - maxBootstrappedDemos
  - minScore
  - debug mode

### 3. Example Implementation
- Created sentiment analysis optimization example
- Added comprehensive documentation
- Demonstrated:
  - Base vs optimized module comparison
  - Few-shot example generation
  - Save/load functionality

## Testing Results

### Base Module Performance
```
Text: "The customer service was exceptional"
Result: { sentiment: 'positive', confidence: 0.95 }

Text: "I regret purchasing this item"
Result: { sentiment: 'negative', confidence: 0.95 }

Text: "The movie was neither great nor terrible"
Result: { sentiment: 'neutral', confidence: 0.95 }
```

### Optimized Module Performance
```
Text: "The customer service was exceptional"
Result: { sentiment: 'positive', confidence: 0.92 }

Text: "I regret purchasing this item"
Result: { sentiment: 'negative', confidence: 0.92 }

Text: "The movie was neither great nor terrible"
Result: { sentiment: 'neutral', confidence: 0.7 }
```

## Key Features
1. **Automatic Few-Shot Learning**
   - Uses labeled examples as initial demonstrations
   - Generates and evaluates new examples
   - Selects high-quality examples for prompts

2. **Metric-Based Selection**
   - Configurable metric functions
   - Quality thresholds for example selection
   - Support for custom metrics

3. **Save/Load Support**
   - JSON-based storage format
   - Preserves prompt templates and configurations
   - Supports field metadata preservation

4. **Pipeline Optimization**
   - Support for optimizing individual modules
   - Future support for full pipeline optimization
   - Configurable optimization strategies

## Future Improvements
1. **Additional Optimizers**
   - Implement BootstrapFewShotWithRandomSearch
   - Add COPRO for instruction optimization
   - Add MIPROv2 for data-aware optimization

2. **Enhanced Metrics**
   - Add built-in metric library
   - Support for metric composition
   - Weighted metric combinations

3. **Pipeline Optimization**
   - Optimize multiple modules simultaneously
   - Cross-module example generation
   - Pipeline-level metrics

4. **Performance Enhancements**
   - Parallel example generation
   - Caching for repeated evaluations
   - Batch processing support

## Related Resources
- [DSPy.ts Optimizer Guide](../../docs/guides/optimizers.md)
- [API Reference](../../docs/api/README.md)
- [Module Types Guide](../../docs/guides/module-types.md)