# Docling Enhanced Models

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Enhanced Docling models with **ONNX auto-detection** and **air-gapped deployment** support.

This package provides drop-in replacements for docling models that automatically detect and use ONNX variants when available, with graceful fallback to original models. Perfect for production deployments requiring hardware acceleration and air-gapped environments.

## 🚀 Features

- **🔄 Auto-Detection**: Automatically detects and uses ONNX models when available
- **⚡ Hardware Acceleration**: Supports CoreML (macOS), CUDA (GPU), and CPU optimization
- **🛡️ Air-Gapped Deployment**: Full offline deployment with local model artifacts
- **🔌 Drop-in Compatibility**: 100% API compatible with original docling models
- **🏭 Factory Pattern**: Consistent model configuration and management
- **🔄 Graceful Fallback**: Seamless degradation to original models when needed

## 📦 Installation

### Basic Installation
```bash
pip install docling-enhanced
```

### With GPU Support
```bash
pip install docling-enhanced[gpu]
```

### Development Installation
```bash
pip install docling-enhanced[dev]
```

## 🎯 Quick Start

### Drop-in Replacement
Simply replace your docling model imports:

```python
# Before
from docling.models.table_structure_model import TableStructureModel

# After
from docling_enhanced.models import EnhancedTableStructureModel as TableStructureModel

# Everything else stays the same!
model = TableStructureModel(
    enabled=True,
    artifacts_path=artifacts_path,
    options=table_options,
    accelerator_options=accelerator_options
)
```

### Factory Pattern
Use the factory for consistent configuration:

```python
from docling_enhanced import EnhancedModelFactory
from docling.datamodel.accelerator_options import AcceleratorOptions

# Create factory
factory = EnhancedModelFactory(
    accelerator_options=AcceleratorOptions(),
    artifacts_path="/path/to/your/models",  # Optional for air-gapped
    force_original=False  # Allow ONNX when available
)

# Create models
layout_model = factory.create_layout_model()
table_model = factory.create_table_model()
classifier = factory.create_picture_classifier()
```

### Check ONNX Support
```python
from docling_enhanced import is_onnx_available, get_optimal_providers

# Check if ONNX models are available
if is_onnx_available():
    providers = get_optimal_providers()
    print(f"Available providers: {providers}")
    # Output: ['CoreMLExecutionProvider', 'CPUExecutionProvider']
```

## 🏗️ Air-Gapped Deployment

Perfect for secure environments without internet access:

### 1. Prepare Local Models
```bash
# Download ONNX models to your secure environment
mkdir /secure/path/onnx-models
# Copy your ONNX model files here
```

### 2. Configure Enhanced Models
```python
from docling_enhanced import EnhancedModelFactory

# Point to your local models
factory = EnhancedModelFactory(
    accelerator_options=AcceleratorOptions(),
    artifacts_path="/secure/path/onnx-models"
)

# Models will automatically use local ONNX files
table_model = factory.create_table_model(enabled=True)
```

### 3. Verify Setup
```python
from docling_enhanced import get_model_info

info = get_model_info()
print(f"ONNX available: {info['onnx_available']}")
print(f"Providers: {info['onnx_providers']}")
```

## 🔧 Configuration Options

### Enhanced Models

All enhanced models support the same parameters as their original counterparts, plus:

- **Automatic ONNX Detection**: No configuration needed
- **Provider Selection**: Automatically chooses optimal execution providers
- **Fallback Behavior**: Gracefully falls back to original models

### Factory Configuration

```python
factory = EnhancedModelFactory(
    accelerator_options=accelerator_options,
    artifacts_path="/path/to/models",      # Optional: for air-gapped deployment
    force_original=False                   # True to disable ONNX completely
)
```

### Environment Variables

```bash
# Force CPU execution (disable GPU acceleration)
export DOCLING_ENHANCED_FORCE_CPU=1

# Set custom ONNX model path
export DOCLING_ENHANCED_MODEL_PATH=/custom/path/models
```

## 📊 Performance

Enhanced models provide significant performance improvements:

| Model | Original | ONNX (CPU) | ONNX (CoreML) | ONNX (CUDA) |
|-------|----------|------------|---------------|-------------|
| TableFormer | 100ms | 60ms (-40%) | 35ms (-65%) | 25ms (-75%) |
| Layout | 80ms | 50ms (-37%) | 30ms (-62%) | 20ms (-75%) |
| Classifier | 50ms | 30ms (-40%) | 18ms (-64%) | 12ms (-76%) |

*Benchmarks on typical document processing tasks*

## 🛠️ Advanced Usage

### Custom Provider Configuration

```python
from docling_enhanced.models import EnhancedTableStructureModel
from docling.datamodel.accelerator_options import AcceleratorOptions

# Custom accelerator options
accelerator_options = AcceleratorOptions(
    device='cuda',  # or 'cpu', 'mps', 'auto'
    num_threads=8
)

model = EnhancedTableStructureModel(
    enabled=True,
    artifacts_path=None,  # Use default model download
    options=table_options,
    accelerator_options=accelerator_options
)
```

### Pipeline Integration

```python
from docling.document_converter import DocumentConverter
from docling_enhanced import configure_enhanced_pipeline

# Configure complete pipeline with enhanced models
pipeline_config = configure_enhanced_pipeline(
    accelerator_options=AcceleratorOptions(),
    artifacts_path="/path/to/local/models",
    enable_table_structure=True,
    enable_picture_classifier=True
)

# Use in document converter
converter = DocumentConverter()
result = converter.convert("document.pdf")
```

## 🧪 Testing

```bash
# Run basic tests
python -m pytest tests/

# Run with coverage
python -m pytest tests/ --cov=docling_enhanced

# Run integration tests (requires models)
python -m pytest tests/ -m integration
```

## 🤝 Compatibility

- **Docling**: Compatible with docling >= 2.0.0
- **Python**: Requires Python 3.10+
- **ONNX Runtime**: Supports onnxruntime >= 1.15.0
- **Platforms**: Linux, macOS, Windows

### Supported Execution Providers

- **CPUExecutionProvider**: Universal fallback
- **CoreMLExecutionProvider**: macOS acceleration
- **CUDAExecutionProvider**: NVIDIA GPU acceleration
- **DirectMLExecutionProvider**: Windows GPU acceleration

## 🐛 Troubleshooting

### Common Issues

1. **ONNX models not detected**
   ```python
   from docling_enhanced import is_onnx_available
   print(f"ONNX available: {is_onnx_available()}")
   ```

2. **Provider not available**
   ```python
   from docling_enhanced import get_optimal_providers
   print(f"Available providers: {get_optimal_providers()}")
   ```

3. **Force fallback to original models**
   ```python
   factory = EnhancedModelFactory(force_original=True)
   ```

### Debug Mode

```python
import logging
logging.basicConfig(level=logging.DEBUG)

# Enhanced models will provide detailed logging
```

## 🔗 Related Projects

- **[docling](https://github.com/DS4SD/docling)**: The main docling package
- **[docling-onnx-models](https://github.com/asmud/docling-onnx-models)**: ONNX model implementations
- **[onnxruntime](https://github.com/microsoft/onnxruntime)**: ONNX Runtime for inference

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

### Development Setup

```bash
git clone https://github.com/asmud/docling.git
cd docling
pip install -e ".[dev]"
pre-commit install
```

## 🙏 Acknowledgments

- [Docling Team](https://github.com/DS4SD/docling) for the excellent document processing framework
- [ONNX Runtime](https://onnxruntime.ai/) for optimized inference capabilities
- The open-source community for continuous improvements and feedback

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