dspy.ts/logs/onnx.md

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

# ONNX Example Implementation Log

## Overview
Added example demonstrating ONNX model integration with DSPy.ts for sentiment analysis.

## Changes Made

### 1. Core Implementation (examples/onnx/index.ts)
- Created SentimentModule class extending Module
- Implemented text to feature vector conversion
- Added ONNX model integration with proper error handling
- Set up pipeline-based processing
- Added resource cleanup

### 2. Test Coverage (tests/examples/onnx.spec.ts)
- Added comprehensive test suite
- Mocked ONNX model and file system
- Covered all key functionality:
  - Model initialization
  - Sentiment analysis
  - Feature vector generation
  - Error handling
  - Pipeline execution
  - Resource cleanup

### 3. Documentation (examples/onnx/README.md)
- Added setup instructions
- Included usage examples
- Documented configuration options
- Provided advanced usage patterns

## Technical Details

### Model Output Format
```typescript
{
  label: {
    cpuData: { 0: string },
    dataLocation: "cpu",
    type: "int64",
    dims: [1],
    size: 1
  },
  probabilities: {
    cpuData: [number, number],
    dataLocation: "cpu",
    type: "float32",
    dims: [1, 2],
    size: 2
  }
}
```

### Feature Vector Generation
- Text is split into words
- Words are matched against feature vocabulary
- Occurrences are counted into fixed-size vector
- Vector size matches model input requirements (11 features)

### Error Handling
- Input validation
- Model initialization errors
- Inference errors
- Resource cleanup errors
- Pipeline execution errors

## Testing Results
- All tests passing
- Coverage for key functionality
- Proper error cases tested
- Pipeline integration verified

## Next Steps
- Consider adding more advanced text preprocessing
- Add support for custom vocabularies
- Implement batched inference
- Add performance benchmarks