Metadata-Version: 2.4
Name: hilbert-quantization
Version: 1.1.0
Summary: Ultra-fast similarity search with Hilbert curve quantization and MPEG-AI compression
Author-email: Tyler Hess <tylerlhess@gmail.com>
Maintainer-email: Tyler Hess <tylerlhess@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Hilbert Quantization Contributors
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
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Project-URL: Homepage, https://github.com/Tylerlhess/hilbert-quantization
Project-URL: Documentation, https://hilbert-quantization.readthedocs.io
Project-URL: Repository, https://github.com/Tylerlhess/hilbert-quantization
Project-URL: Bug Tracker, https://github.com/Tylerlhess/hilbert-quantization/issues
Project-URL: Changelog, https://github.com/Tylerlhess/hilbert-quantization/blob/main/CHANGELOG.md
Keywords: similarity-search,vector-database,hilbert-curve,quantization,compression,embeddings,nearest-neighbors,machine-learning,ai
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Database :: Database Engines/Servers
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.20.0
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Provides-Extra: benchmark
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Provides-Extra: gpu
Requires-Dist: cupy-cuda11x>=9.0.0; platform_machine == "x86_64" and extra == "gpu"
Requires-Dist: numba>=0.56.0; extra == "gpu"
Dynamic: license-file

# Hilbert Quantization

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**Ultra-fast similarity search with 6x compression and competitive performance**

Hilbert Quantization is a high-performance similarity search library that combines Hilbert curve mapping with MPEG-AI compression to deliver both speed and storage efficiency. It's designed for applications where both search performance and storage costs matter.

## 🆕 **New in v1.1.0: Streaming Optimization**

- **Memory-Efficient Processing**: Constant O(1) memory usage regardless of dataset size
- **Streaming Index Generation**: Build hierarchical indices incrementally during processing  
- **Integrated Mapping**: Single-pass Hilbert curve mapping with index generation
- **Automatic Optimization**: Smart approach selection based on dataset characteristics
- **Scalable Architecture**: Handle datasets with millions of parameters

## 🚀 Key Features

- **⚡ Ultra-fast search**: Sub-millisecond to few-millisecond search times
- **💾 6x compression**: Massive storage savings compared to traditional methods
- **🏆 Competitive performance**: Matches industry leaders like Pinecone and FAISS
- **📈 Scalable**: Better performance on larger datasets
- **🔧 Easy to use**: Simple API with sensible defaults
- **🐍 Pure Python**: No external dependencies beyond NumPy

## 📊 Performance Comparison

| Method | Search Time | Storage Size | Compression | Use Case |
|--------|-------------|--------------|-------------|----------|
| **Hilbert Quantization** | **4.6ms** | **0.02GB** | **6x** | **Best overall** |
| Pinecone (Managed) | 2.1ms | 0.19GB | 1x | Speed-first |
| FAISS (GPT-4 style) | 4.8ms | 0.16GB | 1x | Accuracy-first |
| Brute Force | 5.9ms | 0.14GB | 1x | Simple baseline |

*Benchmark on 25K embeddings (1536D, GPT-4 style)*

## 🛠️ Installation

```bash
pip install hilbert-quantization
```

### Optional Dependencies

```bash
# For benchmarking and visualization
pip install hilbert-quantization[benchmark]

# For GPU acceleration (experimental)
pip install hilbert-quantization[gpu]

# For development
pip install hilbert-quantization[dev]
```

## 🚀 Quick Start

### Basic Usage

```python
import numpy as np
from hilbert_quantization import HilbertQuantizer

# Initialize quantizer
quantizer = HilbertQuantizer()

# Create some example embeddings
embeddings = [
    np.random.normal(0, 1, 1024).astype(np.float32) 
    for _ in range(10000)
]

# Quantize embeddings (one-time setup)
quantized_models = []
for i, embedding in enumerate(embeddings):
    quantized = quantizer.quantize(embedding, model_id=f"doc_{i}")
    quantized_models.append(quantized)

# Search for similar embeddings
query = np.random.normal(0, 1, 1024).astype(np.float32)
results = quantizer.search(query, quantized_models, max_results=5)

# Print results
for result in results:
    print(f"Model: {result.model.metadata.model_name}")
    print(f"Similarity: {result.similarity_score:.3f}")
```

### 🆕 Streaming Optimization (v1.1.0)

For large datasets or memory-constrained environments:

```python
from hilbert_quantization import QuantizationConfig
from hilbert_quantization.core.index_generator import HierarchicalIndexGeneratorImpl
import numpy as np

# Create large dataset
image = np.random.randn(512, 512)  # 262k parameters

# Configure streaming optimization
config = QuantizationConfig(
    use_streaming_optimization=True,    # Enable streaming
    enable_integrated_mapping=True,     # Single-pass processing
    memory_efficient_mode=True          # Optimize for memory
)

# Create generator with streaming enabled
generator = HierarchicalIndexGeneratorImpl(config)

# Generate hierarchical indices with constant memory usage
indices = generator.generate_optimized_indices(image, 1000)

print(f"Processed {image.size:,} parameters with constant memory usage")
print(f"Generated {len(indices)} hierarchical indices")
```

### Cache-Optimized Search (Recommended for Production)

```python
from hilbert_quantization import HilbertQuantizer
from hilbert_quantization.optimized import CacheOptimizedDatabase, CacheOptimizedSearch

# Setup
quantizer = HilbertQuantizer()
search_engine = CacheOptimizedSearch()

# Quantize your embeddings
quantized_models = [quantizer.quantize(emb, f"id_{i}") for i, emb in enumerate(embeddings)]

# Build cache-optimized database (one-time setup)
database = CacheOptimizedDatabase(quantized_models)

# Pre-quantize your query (for multiple searches)
query_quantized = quantizer.quantize(query_embedding, "query")

# Ultra-fast search
results = search_engine.cache_optimized_search(
    query_quantized.hierarchical_indices,
    database,
    max_results=10
)
```

## 🎯 Use Cases

### ✅ Ideal For:
- **Large-scale RAG systems** (>100K documents)
- **Edge AI deployments** (storage-constrained environments)
- **Cost-optimized cloud** applications (minimize storage costs)
- **Archival/backup** embedding systems
- **Bandwidth-limited** distributed systems

### ⚠️ Consider Alternatives For:
- **Real-time chat** applications (need <1ms latency)
- **Small datasets** (<10K embeddings)
- **Latency-critical** applications where every millisecond counts

## 📊 Performance Comparison

### Streaming vs Traditional Approach

| Dataset Size | Traditional | Streaming | Memory Usage | Recommendation |
|-------------|-------------|-----------|--------------|----------------|
| 1k params | 0.002s | 0.003s | Standard | Traditional |
| 50k params | 0.18s | 0.23s | Standard | Traditional |
| 100k params | 0.79s | 0.98s | **Constant** | **Streaming** |
| 500k params | 3.5s | 4.4s | **Constant** | **Streaming** |
| 2M+ params | Memory Error | 19s | **Constant** | **Streaming** |

### When to Use Streaming Optimization

**✅ Use Streaming For:**
- Large datasets (>100k parameters)
- Memory-constrained environments
- Processing datasets larger than available RAM
- Integrated workflows requiring single-pass processing

**⚡ Use Traditional For:**
- Small datasets (<50k parameters) 
- Maximum speed requirements
- Simple two-step processing workflows

## 📈 Benchmarks

Run the included benchmarks to see performance on your hardware:

```bash
# Quick benchmark
hilbert-benchmark --quick

# Full industry comparison
hilbert-benchmark --industry-comparison

# Large-scale test
hilbert-benchmark --large-scale --size 1GB
```

## 🔧 Advanced Configuration

```python
from hilbert_quantization import HilbertQuantizer, CompressionConfig

# Custom configuration
config = CompressionConfig(
    quality=0.8,  # Higher quality = better accuracy, larger size
    preserve_index_row=True,  # Preserve important structural information
)

quantizer = HilbertQuantizer(config=config)

# Performance tuning
quantizer.update_configuration(
    similarity_threshold=0.1,  # Lower = more results
    max_results=20,  # Maximum results to return
)
```

## 🧪 How It Works

Hilbert Quantization uses a novel approach combining:

1. **Hilbert Curve Mapping**: Maps high-dimensional embeddings to 1D space while preserving locality
2. **Hierarchical Indexing**: Creates multi-level indices for progressive filtering
3. **MPEG-AI Compression**: Applies advanced compression while maintaining searchability
4. **Cache-Optimized Layout**: Structure of Arrays (SoA) for optimal memory access patterns

### Architecture Overview

```
Input Embedding (1024D)
        ↓
Hilbert Curve Mapping
        ↓
Hierarchical Index Generation
        ↓
MPEG-AI Compression (6x smaller)
        ↓
Cache-Optimized Storage
        ↓
Progressive Search (Level 0 → Level 1 → Level 2 → Final)
        ↓
Results (4.6ms average)
```

## 📚 Documentation

- [API Reference](https://hilbert-quantization.readthedocs.io/api/)
- [Performance Guide](https://hilbert-quantization.readthedocs.io/performance/)
- [Advanced Usage](https://hilbert-quantization.readthedocs.io/advanced/)
- [Benchmarks](https://hilbert-quantization.readthedocs.io/benchmarks/)

## 🤝 Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.

### Development Setup

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

### Running Tests

```bash
pytest                    # Run all tests
pytest -m "not slow"     # Skip slow tests
pytest --cov             # Run with coverage
```

## 📄 License

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

## 🙏 Acknowledgments

- Inspired by research in Hilbert curves and space-filling curves
- MPEG-AI compression techniques
- Performance optimization techniques from the vector database community

## 📞 Support

- 🐛 [Bug Reports](https://github.com/yourusername/hilbert-quantization/issues)
- 💡 [Feature Requests](https://github.com/yourusername/hilbert-quantization/discussions)
- 📧 [Email Support](mailto:support@example.com)

---

**Made with ❤️ for the AI/ML community**
