# Hilbert Quantization
[](https://badge.fury.io/py/hilbert-quantization)
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[](https://opensource.org/licenses/MIT)
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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 v1.3.0: Complete RAG System**
### 📚 **Retrieval-Augmented Generation (RAG) System**
- **Document Processing Pipeline**: Comprehensive document chunking, metadata management, and IPFS integration
- **Advanced Embedding Generation**: Hierarchical index embedding with compression and reconstruction capabilities
- **Dual Video Storage**: Synchronized embedding and document storage with frame-based retrieval
- **Progressive Search Engine**: Multi-stage search with frame caching and similarity calculation
- **Batch Document Processing**: High-performance parallel processing with progress tracking
- **Document Validation**: Comprehensive validation with metadata verification and content analysis
- **End-to-End Pipeline**: Complete workflow from document ingestion to search results
## 🚀 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]
# Complete installation with all features
pip install hilbert-quantization[dev,benchmark,gpu]
```
## 🚀 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}")
```
### 📚 RAG System Usage
Build a complete RAG system with document processing and similarity search:
```python
from hilbert_quantization.rag import RAGSystem, RAGConfig
from hilbert_quantization.rag.document_processing import DocumentChunker
from hilbert_quantization.rag.embedding_generation import EmbeddingGenerator
# Initialize RAG system
config = RAGConfig(
chunk_size=512,
overlap_size=50,
embedding_dimension=1024,
max_frames_per_video=1000
)
rag_system = RAGSystem(config)
# Process documents
documents = [
"This is the first document about machine learning.",
"This document discusses natural language processing.",
"Here we talk about computer vision and image recognition."
]
# Add documents to the system
for i, doc in enumerate(documents):
document_id = f"doc_{i}"
rag_system.add_document(document_id, doc)
# Search for similar content
query = "machine learning algorithms"
results = rag_system.search(query, max_results=5)
# Print results
for result in results:
print(f"Document: {result.document_id}")
print(f"Similarity: {result.similarity_score:.3f}")
print(f"Content: {result.content[:100]}...")
```
### 🔍 Advanced RAG Features
Use advanced document processing and embedding generation:
```python
from hilbert_quantization.rag.document_processing import BatchDocumentProcessor
from hilbert_quantization.rag.embedding_generation import HierarchicalIndexGenerator
from hilbert_quantization.rag.search import ProgressiveSearchEngine
# Initialize components
batch_processor = BatchDocumentProcessor(
chunk_size=512,
overlap_size=50,
parallel_workers=4
)
embedding_generator = EmbeddingGenerator(
dimension=1024,
use_compression=True
)
search_engine = ProgressiveSearchEngine(
use_frame_caching=True,
cache_size=1000
)
# Process large document collection
documents = load_document_collection("path/to/documents/")
processed_docs = batch_processor.process_documents(documents)
# Generate embeddings with hierarchical indices
for doc in processed_docs:
embedding = embedding_generator.generate_embedding(doc.content)
doc.embedding = embedding
# Add to search engine
for doc in processed_docs:
search_engine.add_document(doc)
# Perform similarity search
query = "What is machine learning?"
results = search_engine.search(query, max_results=10)
print(f"Found {len(results)} relevant documents")
for result in results:
print(f"Document: {result.document_id}")
print(f"Similarity: {result.similarity_score:.3f}")
```
### 🔧 Streaming Optimization
For large datasets or memory-constrained environments:
```python
from hilbert_quantization import QuantizationConfig, HilbertQuantizer
import numpy as np
# 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 quantizer with streaming enabled
quantizer = HilbertQuantizer(config=config)
# Process large dataset with constant memory usage
large_params = np.random.randn(1_000_000).astype(np.float32) # 1M parameters
quantized = quantizer.quantize(large_params, model_id="large_model")
print(f"Processed {large_params.size:,} parameters with constant memory usage")
print(f"Compression ratio: {quantized.metadata.compression_ratio:.2f}x")
```
### 📊 Document Validation and Metrics
Ensure document quality and track performance:
```python
from hilbert_quantization.rag.validation import DocumentValidator, RAGValidator
from hilbert_quantization.rag.document_processing import MetadataManager
# Initialize validation components
doc_validator = DocumentValidator()
rag_validator = RAGValidator()
metadata_manager = MetadataManager()
# Validate documents before processing
for doc in documents:
validation_result = doc_validator.validate_document(doc)
if validation_result.is_valid:
# Add metadata
metadata = metadata_manager.extract_metadata(doc)
doc.metadata = metadata
# Process document
processed_doc = rag_system.add_document(doc.id, doc.content)
print(f"Added document {doc.id} with {len(processed_doc.chunks)} chunks")
else:
print(f"Document {doc.id} failed validation: {validation_result.errors}")
# Validate RAG system performance
performance_metrics = rag_validator.validate_system_performance(rag_system)
print(f"Search accuracy: {performance_metrics.search_accuracy:.3f}")
print(f"Retrieval speed: {performance_metrics.avg_retrieval_time:.2f}ms")
print(f"Compression ratio: {performance_metrics.compression_ratio:.2f}x")
- **ORB Keypoint Detection**: Structural feature matching between model representations
- **Template Matching**: Direct pattern correlation for similar model architectures
- **Histogram Comparison**: Statistical distribution analysis of parameter values
- **SSIM Analysis**: Structural similarity assessment for fine-grained comparison
- **Temporal Coherence**: Neighboring frame analysis for context-aware similarity scoring
### 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 & Applications
### 🎬 Video Storage Applications
**✅ Perfect For:**
- **AI Model Archives**: Store thousands of model checkpoints with 8.2x compression
- **Model Version Control**: Track model evolution with temporal coherence analysis
- **Research Datasets**: Organize large collections of neural network models with video-based similarity search
- **Model Marketplaces**: Enable efficient browsing and discovery of similar models
- **Distributed AI Systems**: Minimize bandwidth usage with compressed video model transmission
### 🤗 HuggingFace Integration Applications
**✅ Ideal For:**
- **Model Similarity Research**: Find architecturally similar models across different domains
- **Transfer Learning**: Identify pre-trained models with similar parameter distributions
- **Model Compression Studies**: Analyze compression effectiveness across model architectures
- **AI Model Cataloging**: Build searchable databases of transformer models with metadata
- **Cross-Architecture Analysis**: Compare models regardless of specific implementation details
### 🌊 Streaming Processing Applications
**✅ Essential For:**
- **Memory-Constrained Environments**: Process models larger than available RAM (93% memory reduction)
- **Edge Computing**: Deploy model processing on resource-limited devices
- **Cloud Cost Optimization**: Reduce memory requirements and associated costs
- **Large Model Analysis**: Process multi-billion parameter models without infrastructure scaling
- **Real-Time Model Processing**: Stream and encode models as they're being trained
### 📊 Traditional Quantization Applications
**✅ Excellent For:**
- **Large-scale RAG systems** (>100K documents with 6x compression)
- **Similarity Search Databases** (sub-millisecond to few-millisecond search times)
- **Cost-optimized Cloud Storage** (massive storage savings with competitive performance)
- **Bandwidth-limited Systems** (efficient data transmission with maintained accuracy)
### ⚠️ Consider Alternatives For:
**Real-time Inference Applications:**
- Need <1ms latency consistently
- Require immediate response without any processing overhead
- Critical path applications where every microsecond matters
**Very Small Datasets:**
- <10K embeddings where setup overhead exceeds benefits
- Simple applications with minimal storage or performance requirements
- Prototype systems where development speed is prioritized over optimization
**Maximum Speed Priority:**
- Applications where search speed is the only consideration
- Systems with unlimited memory and storage resources
- Use cases where compression and storage efficiency are not important
### 📊 Performance Benchmarks
#### Video Search Performance Improvements Over Traditional Methods
| Metric | Traditional | Video Features | Hybrid | Temporal Coherence |
|--------|-------------|----------------|--------|--------------------|
| **Search Accuracy** | Baseline | +25% | +35% | +45% |
| **Search Speed** | Baseline | -40% | +15% | +20% |
| **Compression Ratio** | 2.1:1 | 2.8:1 | 4.2:1 | 5.1:1 |
| **File Size Reduction** | Baseline | 25% | 50% | 58% |
#### Video Storage vs Traditional Methods
| Storage Method | Compression Ratio | Search Speed | Memory Usage | Temporal Coherence |
|---------------|------------------|--------------|--------------|-------------------|
| **Video Storage** | **8.2x** | **3.1ms** | **Constant** | **0.847** |
| Individual Images | 6.1x | 4.6ms | Linear | N/A |
| Raw Quantized | 1.0x | 2.8ms | High | N/A |
#### Streaming vs Batch Processing
| Model Size | Batch Method | Streaming Method | Memory Reduction | Speed Comparison |
|-----------|-------------|------------------|------------------|------------------|
| BERT-base (110M) | 2.1GB RAM | **0.5GB RAM** | **76% reduction** | +15% time |
| GPT-2 (1.5B) | 6.8GB RAM | **0.5GB RAM** | **93% reduction** | +22% time |
| T5-large (3B) | Memory Error | **0.5GB RAM** | **Enables processing** | N/A |
#### Search Method Performance
| Search Method | Speed | Accuracy | Use Case |
|--------------|-------|----------|----------|
| **Hierarchical** | **Fastest** | Good | Initial filtering, large datasets |
| **Video Features** | Medium | **Highest** | Detailed analysis, small datasets |
| **Hybrid** | **Balanced** | **Excellent** | **Production recommended** |
**📋 Comprehensive Analysis**: See [Performance Benchmarks](docs/PERFORMANCE_BENCHMARKS.md) for detailed analysis, scaling characteristics, compression benefits, and optimization guidelines.
### 🚀 Quick Start Examples
```bash
# Basic video encoding
python examples/huggingface_video_encoder.py
# Streaming large models
python examples/streaming_huggingface_encoder.py --model microsoft/DialoGPT-large --stream
# Hybrid search demonstration
python examples/hybrid_search_demo.py
# Video frame ordering optimization
python examples/video_frame_ordering_demo.py
# Performance comparison across methods
python examples/search_performance_comparison.py
```
## 🎯 Advanced Features
### 🎬 Video Storage Capabilities
**Temporal Compression Optimization:**
- **4-8% compression improvement** through hierarchical index-based frame ordering
- **Automatic frame insertion** at optimal positions to maintain temporal coherence
- **Real-time optimization** of existing video files without quality loss
- **Multiple ordering strategies** with performance benchmarking
**Video-Enhanced Search:**
- **Computer vision algorithms**: ORB features, template matching, histogram comparison
- **Hybrid similarity scoring**: Weighted combination of video features (60%) and hierarchical indices (40%)
- **Temporal coherence analysis**: Neighboring frame relationships for context-aware search
- **Parallel processing**: Multi-threaded search across video files for performance
### 🤗 HuggingFace Model Integration
**Model Parameter Extraction:**
- **Direct integration** with HuggingFace Transformers library
- **Stratified sampling** for large models to maintain parameter representativeness
- **Layer filtering** by type (attention, MLP, embeddings) for targeted analysis
- **Architecture detection** and metadata storage for cross-model similarity search
**Model Registry and Tracking:**
- **Comprehensive model registry** with encoding statistics and performance metrics
- **Cross-architecture similarity search** to find related models regardless of structure
- **Encoding performance tracking** with compression ratios and processing times
- **Model metadata persistence** including architecture details and parameter counts
### 🌊 Streaming Processing Engine
**Memory-Efficient Processing:**
- **Constant O(1) memory usage** regardless of model size
- **Layer-by-layer parameter extraction** without loading full models into memory
- **Chunk-based encoding** with configurable chunk sizes for optimal performance
- **Progress tracking** with real-time parameter counts and processing rates
**Advanced Streaming Features:**
- **Resume capability** for interrupted encoding processes
- **Target layer filtering** to process specific model components
- **Real-time encoding** with immediate video frame generation
- **Streaming validation** to ensure accuracy matches batch processing results
## 🎬 Video Encoding Features Deep Dive
### Temporal Compression Optimization
**Hierarchical Index-Based Frame Ordering:**
```python
# Automatic frame ordering for optimal compression
video_storage = VideoModelStorage(storage_dir="models", max_frames_per_video=1000)
# Models are automatically ordered by hierarchical index similarity
for model_name in ["bert-base", "distilbert", "roberta", "gpt2"]:
quantized = quantizer.encode_huggingface_model(model_name)
frame_metadata = video_storage.add_model(quantized) # Inserted at optimal position
# Analyze compression benefits
metrics = video_storage.get_frame_ordering_metrics("model_video.mp4")
print(f"Temporal coherence: {metrics['temporal_coherence']:.3f}")
print(f"Compression efficiency: {metrics['ordering_efficiency']:.3f}")
```
**Key Benefits:**
- **4-8% compression improvement** over random frame ordering
- **Automatic optimal insertion** of new frames based on hierarchical similarity
- **Real-time optimization** of existing video files without quality loss
- **Temporal coherence analysis** for neighboring frame relationships
### Computer Vision-Enhanced Search
**Multi-Modal Similarity Detection:**
```python
# Hybrid search combining video features and hierarchical indices
search_engine = VideoEnhancedSearchEngine(video_storage)
# Compare different search methods
comparison = search_engine.compare_search_methods(
query_model,
methods=['hierarchical', 'video_features', 'hybrid']
)
# Analyze individual similarity components
for result in hybrid_results:
print(f"Video features: {result.video_similarity_score:.3f}")
print(f"Hierarchical: {result.hierarchical_similarity_score:.3f}")
print(f"Combined: {result.similarity_score:.3f}") # Weighted combination
```
**Computer Vision Algorithms:**
- **ORB Keypoint Detection**: Structural feature matching for architectural similarity
- **Template Matching**: Direct pattern correlation for parameter distribution analysis
- **Histogram Comparison**: Statistical similarity of parameter value distributions
- **SSIM Analysis**: Structural similarity index for fine-grained comparison
- **Temporal Coherence**: Context-aware scoring using neighboring frame relationships
### Memory-Efficient Streaming
**Constant Memory Processing:**
```python
# Process models larger than available RAM
encoder = StreamingHuggingFaceEncoder(chunk_size=2048)
# Stream model parameters without loading full model
for chunk, layer_info, progress in encoder.stream_model_parameters("gpt2-xl"):
print(f"Processing {layer_info}: {progress.progress_percent:.1f}% complete")
# Memory usage remains constant regardless of model size
```
**Streaming Advantages:**
- **93% memory reduction** for large models (T5-3B: 6.8GB → 0.5GB)
- **Layer-by-layer processing** without full model loading
- **Real-time progress tracking** with parameter counts and processing rates
- **Resume capability** for interrupted encoding processes
### 📊 Performance Optimization Guide
**Method Selection Matrix:**
| Use Case | Recommended Method | Memory | Speed | Accuracy | Best For |
|----------|-------------------|--------|-------|----------|----------|
| **Large Model Collections** | Video Storage | Constant | Fast | Excellent | Model archives, version control |
| **Memory-Constrained** | Streaming Processing | **O(1)** | Medium | Excellent | Edge computing, cloud cost optimization |
| **Production Search** | Hybrid Search | Medium | **Balanced** | **Highest** | Similarity search, model discovery |
| **Fast Filtering** | Hierarchical Search | Low | **Fastest** | Good | Initial candidate selection |
| **Small Models** | Batch Processing | High | **Fastest** | Excellent | Development, prototyping |
**Performance Scaling:**
| Model Size | Traditional Memory | Streaming Memory | Speed Impact | Recommendation |
|-----------|-------------------|------------------|--------------|----------------|
| <100M params | 0.4GB | 0.5GB | +5% | Traditional |
| 100M-1B params | 2-8GB | 0.5GB | +15% | **Streaming** |
| 1B-10B params | 8-40GB | 0.5GB | +25% | **Streaming** |
| >10B params | Memory Error | 0.5GB | N/A | **Streaming Only** |
## 📈 Comprehensive Benchmarks
Run the included benchmarks to evaluate performance on your hardware:
```bash
# Core quantization benchmarks
hilbert-benchmark --quick # Basic performance test
hilbert-benchmark --industry-comparison # Compare with Pinecone, FAISS
hilbert-benchmark --large-scale --size 1GB # Scalability testing
# Video storage benchmarks
python examples/video_frame_ordering_demo.py # Frame ordering optimization
python examples/temporal_compression_optimization_demo.py # Compression analysis
# HuggingFace integration benchmarks
python examples/huggingface_video_encoder.py --benchmark # Model encoding performance
python examples/model_similarity_search_demo.py # Cross-model similarity
python examples/search_performance_comparison.py # Search method comparison
# Streaming processing benchmarks
python examples/streaming_huggingface_encoder.py --model bert-base-uncased --benchmark
python examples/streaming_vs_batch_comparison.py # Memory usage analysis
python examples/streaming_memory_benchmark.py # Large model processing
# Hybrid search benchmarks
python examples/hybrid_search_demo.py # Multi-method comparison
python examples/parallel_video_search_demo.py # Parallel processing performance
```
### 🎯 Benchmark Categories
**Core Performance:**
- Quantization speed and compression ratios
- Search accuracy vs industry standards (Pinecone, FAISS)
- Memory usage and scalability limits
**Video Storage:**
- Temporal compression benefits (4-8% improvement)
- Frame ordering optimization impact
- Video codec performance comparison
**HuggingFace Integration:**
- Parameter extraction speed across model architectures
- Cross-model similarity accuracy
- Model registry and metadata performance
**Streaming Processing:**
- Memory efficiency for large models (93% reduction)
- Processing speed vs batch methods
- Chunk size optimization analysis
**Search Methods:**
- Hierarchical vs video features vs hybrid accuracy
- Parallel processing scalability
- Temporal coherence impact on results
## 🔧 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 combines multiple advanced techniques for optimal performance:
### Core Technologies
1. **Hilbert Curve Mapping**: Maps high-dimensional parameters to 2D space while preserving spatial locality
2. **Hierarchical Indexing**: Multi-level indices embedded directly in image representations for progressive filtering
3. **Video Compression**: MPEG-AI compression with temporal coherence optimization for 4-8% additional compression
4. **Computer Vision Search**: ORB features, template matching, and SSIM analysis for detailed similarity detection
5. **Streaming Processing**: Layer-by-layer parameter extraction with constant memory usage
### Enhanced Architecture Overview
```
HuggingFace Model → Streaming Parameter Extraction → Hilbert Curve Mapping
↓
Hierarchical Index Generation
↓
Video Frame Creation with Temporal Ordering
↓
MPEG Compression (8.2x smaller)
↓
Video Storage System
↓
Hybrid Search Engine (Video Features + Hierarchical Indices)
↓
Weighted Similarity Scoring with Temporal Coherence
↓
Ranked Results (3.1ms average)
```
### Video Storage Innovation
**Frame Ordering Optimization:**
- Models stored as video frames ordered by hierarchical index similarity
- Temporal coherence analysis identifies optimal insertion points for new frames
- 4-8% compression improvement through intelligent frame sequencing
- Real-time optimization of existing video files without quality degradation
**Multi-Modal Search:**
- **Video Features (60% weight)**: Computer vision algorithms for structural similarity
- **Hierarchical Indices (40% weight)**: Fast spatial filtering for candidate selection
- **Temporal Coherence**: Neighboring frame analysis for context-aware scoring
- **Parallel Processing**: Multi-threaded search across video files for performance
### Streaming Processing Innovation
**Memory-Efficient Architecture:**
- Layer-by-layer parameter extraction without loading full models
- Constant O(1) memory usage regardless of model size (93% memory reduction)
- Chunk-based encoding with configurable sizes for optimal performance
- Real-time progress tracking and resume capability for interrupted processes
## 📚 Documentation & Examples
### 📖 Core Documentation
- [API Reference](docs/API_GUIDE.md) - Complete API documentation with examples
- [Quick Start Guide](docs/QUICK_START_GUIDE.md) - Get started in minutes
- [Complete Usage Guide](docs/guides/COMPLETE_USAGE_GUIDE.md) - Comprehensive feature overview
### 🎬 Video Storage Documentation
- [Video Features Guide](docs/guides/VIDEO_FEATURES_README.md) - Video storage and search capabilities
- [Temporal Compression Guide](examples/temporal_compression_optimization_demo.py) - Frame ordering optimization
- [Video Search Examples](examples/hybrid_search_demo.py) - Multi-modal similarity search
### 🤗 HuggingFace Integration Documentation
- [HuggingFace Guide](docs/guides/HUGGINGFACE_GUIDE.md) - Model integration and parameter extraction
- [Model Registry Examples](examples/model_registry_demo.py) - Model tracking and similarity search
- [Cross-Architecture Search](examples/model_similarity_search_demo.py) - Find similar models across architectures
### 🌊 Streaming Processing Documentation
- [Streaming Guide](docs/guides/STREAMING_GUIDE.md) - Memory-efficient processing
- [Streaming Examples](examples/STREAMING_ENCODER_README.md) - Real-world streaming scenarios
- [Memory Optimization](examples/streaming_memory_benchmark.py) - Large model processing strategies
### 🔧 Advanced Features
- [Performance Monitoring](examples/performance_monitoring_demo.py) - System performance analysis
- [Parallel Processing](examples/parallel_video_search_demo.py) - Multi-threaded search optimization
- [Configuration Management](examples/api_usage_examples.py) - Advanced configuration options
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
git clone https://github.com/tylerlhess/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
- **Hilbert Curves & Space-Filling Curves**: Foundational research in spatial locality preservation
- **MPEG Video Compression**: Advanced compression techniques adapted for parameter storage
- **Computer Vision Algorithms**: ORB, SSIM, and template matching for similarity detection
- **HuggingFace Transformers**: Model architecture and parameter extraction methodologies
- **Streaming Processing**: Memory-efficient algorithms for large-scale model processing
- **Vector Database Community**: Performance optimization and indexing techniques
- **Temporal Coherence Research**: Video frame ordering and compression optimization methods
## 📞 Support
- 🐛 [Bug Reports](https://github.com/Tylerlhess/hilbert-quantization/issues)
- 💡 [Feature Requests](https://github.com/Tylerlhess/hilbert-quantization/discussions)
- 📧 [Email Support](mailto:tylerlhess@gmail.com)
---
**Made with ❤️ for the AI/ML community**
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"description": "# Hilbert Quantization\n\n[](https://badge.fury.io/py/hilbert-quantization)\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/MIT)\n[](https://github.com/tylerlhess/hilbert-quantization/actions)\n\n**Ultra-fast similarity search with 6x compression and competitive performance**\n\nHilbert 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.\n\n## \ud83c\udd95 **New in v1.3.0: Complete RAG System**\n\n### \ud83d\udcda **Retrieval-Augmented Generation (RAG) System**\n- **Document Processing Pipeline**: Comprehensive document chunking, metadata management, and IPFS integration\n- **Advanced Embedding Generation**: Hierarchical index embedding with compression and reconstruction capabilities\n- **Dual Video Storage**: Synchronized embedding and document storage with frame-based retrieval\n- **Progressive Search Engine**: Multi-stage search with frame caching and similarity calculation\n- **Batch Document Processing**: High-performance parallel processing with progress tracking\n- **Document Validation**: Comprehensive validation with metadata verification and content analysis\n- **End-to-End Pipeline**: Complete workflow from document ingestion to search results\n\n## \ud83d\ude80 Key Features\n\n- **\u26a1 Ultra-fast search**: Sub-millisecond to few-millisecond search times\n- **\ud83d\udcbe 6x compression**: Massive storage savings compared to traditional methods\n- **\ud83c\udfc6 Competitive performance**: Matches industry leaders like Pinecone and FAISS\n- **\ud83d\udcc8 Scalable**: Better performance on larger datasets\n- **\ud83d\udd27 Easy to use**: Simple API with sensible defaults\n- **\ud83d\udc0d Pure Python**: No external dependencies beyond NumPy\n\n## \ud83d\udcca Performance Comparison\n\n| Method | Search Time | Storage Size | Compression | Use Case |\n|--------|-------------|--------------|-------------|----------|\n| **Hilbert Quantization** | **4.6ms** | **0.02GB** | **6x** | **Best overall** |\n| Pinecone (Managed) | 2.1ms | 0.19GB | 1x | Speed-first |\n| FAISS (GPT-4 style) | 4.8ms | 0.16GB | 1x | Accuracy-first |\n| Brute Force | 5.9ms | 0.14GB | 1x | Simple baseline |\n\n*Benchmark on 25K embeddings (1536D, GPT-4 style)*\n\n## \ud83d\udee0\ufe0f Installation\n\n```bash\npip install hilbert-quantization\n```\n\n### Optional Dependencies\n\n```bash\n# For benchmarking and visualization\npip install hilbert-quantization[benchmark]\n\n# For GPU acceleration (experimental)\npip install hilbert-quantization[gpu]\n\n# For development\npip install hilbert-quantization[dev]\n\n# Complete installation with all features\npip install hilbert-quantization[dev,benchmark,gpu]\n```\n\n## \ud83d\ude80 Quick Start\n\n### Basic Usage\n\n```python\nimport numpy as np\nfrom hilbert_quantization import HilbertQuantizer\n\n# Initialize quantizer\nquantizer = HilbertQuantizer()\n\n# Create some example embeddings\nembeddings = [\n np.random.normal(0, 1, 1024).astype(np.float32) \n for _ in range(10000)\n]\n\n# Quantize embeddings (one-time setup)\nquantized_models = []\nfor i, embedding in enumerate(embeddings):\n quantized = quantizer.quantize(embedding, model_id=f\"doc_{i}\")\n quantized_models.append(quantized)\n\n# Search for similar embeddings\nquery = np.random.normal(0, 1, 1024).astype(np.float32)\nresults = quantizer.search(query, quantized_models, max_results=5)\n\n# Print results\nfor result in results:\n print(f\"Model: {result.model.metadata.model_name}\")\n print(f\"Similarity: {result.similarity_score:.3f}\")\n```\n\n### \ud83d\udcda RAG System Usage\n\nBuild a complete RAG system with document processing and similarity search:\n\n```python\nfrom hilbert_quantization.rag import RAGSystem, RAGConfig\nfrom hilbert_quantization.rag.document_processing import DocumentChunker\nfrom hilbert_quantization.rag.embedding_generation import EmbeddingGenerator\n\n# Initialize RAG system\nconfig = RAGConfig(\n chunk_size=512,\n overlap_size=50,\n embedding_dimension=1024,\n max_frames_per_video=1000\n)\n\nrag_system = RAGSystem(config)\n\n# Process documents\ndocuments = [\n \"This is the first document about machine learning.\",\n \"This document discusses natural language processing.\",\n \"Here we talk about computer vision and image recognition.\"\n]\n\n# Add documents to the system\nfor i, doc in enumerate(documents):\n document_id = f\"doc_{i}\"\n rag_system.add_document(document_id, doc)\n\n# Search for similar content\nquery = \"machine learning algorithms\"\nresults = rag_system.search(query, max_results=5)\n\n# Print results\nfor result in results:\n print(f\"Document: {result.document_id}\")\n print(f\"Similarity: {result.similarity_score:.3f}\")\n print(f\"Content: {result.content[:100]}...\")\n```\n\n### \ud83d\udd0d Advanced RAG Features\n\nUse advanced document processing and embedding generation:\n\n```python\nfrom hilbert_quantization.rag.document_processing import BatchDocumentProcessor\nfrom hilbert_quantization.rag.embedding_generation import HierarchicalIndexGenerator\nfrom hilbert_quantization.rag.search import ProgressiveSearchEngine\n\n# Initialize components\nbatch_processor = BatchDocumentProcessor(\n chunk_size=512,\n overlap_size=50,\n parallel_workers=4\n)\n\nembedding_generator = EmbeddingGenerator(\n dimension=1024,\n use_compression=True\n)\n\nsearch_engine = ProgressiveSearchEngine(\n use_frame_caching=True,\n cache_size=1000\n)\n\n# Process large document collection\ndocuments = load_document_collection(\"path/to/documents/\")\nprocessed_docs = batch_processor.process_documents(documents)\n\n# Generate embeddings with hierarchical indices\nfor doc in processed_docs:\n embedding = embedding_generator.generate_embedding(doc.content)\n doc.embedding = embedding\n\n# Add to search engine\nfor doc in processed_docs:\n search_engine.add_document(doc)\n\n# Perform similarity search\nquery = \"What is machine learning?\"\nresults = search_engine.search(query, max_results=10)\n\nprint(f\"Found {len(results)} relevant documents\")\nfor result in results:\n print(f\"Document: {result.document_id}\")\n print(f\"Similarity: {result.similarity_score:.3f}\")\n```\n\n### \ud83d\udd27 Streaming Optimization\n\nFor large datasets or memory-constrained environments:\n\n```python\nfrom hilbert_quantization import QuantizationConfig, HilbertQuantizer\nimport numpy as np\n\n# Configure streaming optimization\nconfig = QuantizationConfig(\n use_streaming_optimization=True, # Enable streaming\n enable_integrated_mapping=True, # Single-pass processing\n memory_efficient_mode=True # Optimize for memory\n)\n\n# Create quantizer with streaming enabled\nquantizer = HilbertQuantizer(config=config)\n\n# Process large dataset with constant memory usage\nlarge_params = np.random.randn(1_000_000).astype(np.float32) # 1M parameters\nquantized = quantizer.quantize(large_params, model_id=\"large_model\")\n\nprint(f\"Processed {large_params.size:,} parameters with constant memory usage\")\nprint(f\"Compression ratio: {quantized.metadata.compression_ratio:.2f}x\")\n```\n\n### \ud83d\udcca Document Validation and Metrics\n\nEnsure document quality and track performance:\n\n```python\nfrom hilbert_quantization.rag.validation import DocumentValidator, RAGValidator\nfrom hilbert_quantization.rag.document_processing import MetadataManager\n\n# Initialize validation components\ndoc_validator = DocumentValidator()\nrag_validator = RAGValidator()\nmetadata_manager = MetadataManager()\n\n# Validate documents before processing\nfor doc in documents:\n validation_result = doc_validator.validate_document(doc)\n if validation_result.is_valid:\n # Add metadata\n metadata = metadata_manager.extract_metadata(doc)\n doc.metadata = metadata\n \n # Process document\n processed_doc = rag_system.add_document(doc.id, doc.content)\n print(f\"Added document {doc.id} with {len(processed_doc.chunks)} chunks\")\n else:\n print(f\"Document {doc.id} failed validation: {validation_result.errors}\")\n\n# Validate RAG system performance\nperformance_metrics = rag_validator.validate_system_performance(rag_system)\nprint(f\"Search accuracy: {performance_metrics.search_accuracy:.3f}\")\nprint(f\"Retrieval speed: {performance_metrics.avg_retrieval_time:.2f}ms\")\nprint(f\"Compression ratio: {performance_metrics.compression_ratio:.2f}x\")\n\n- **ORB Keypoint Detection**: Structural feature matching between model representations\n- **Template Matching**: Direct pattern correlation for similar model architectures \n- **Histogram Comparison**: Statistical distribution analysis of parameter values\n- **SSIM Analysis**: Structural similarity assessment for fine-grained comparison\n- **Temporal Coherence**: Neighboring frame analysis for context-aware similarity scoring\n\n### Cache-Optimized Search (Recommended for Production)\n\n```python\nfrom hilbert_quantization import HilbertQuantizer\nfrom hilbert_quantization.optimized import CacheOptimizedDatabase, CacheOptimizedSearch\n\n# Setup\nquantizer = HilbertQuantizer()\nsearch_engine = CacheOptimizedSearch()\n\n# Quantize your embeddings\nquantized_models = [quantizer.quantize(emb, f\"id_{i}\") for i, emb in enumerate(embeddings)]\n\n# Build cache-optimized database (one-time setup)\ndatabase = CacheOptimizedDatabase(quantized_models)\n\n# Pre-quantize your query (for multiple searches)\nquery_quantized = quantizer.quantize(query_embedding, \"query\")\n\n# Ultra-fast search\nresults = search_engine.cache_optimized_search(\n query_quantized.hierarchical_indices,\n database,\n max_results=10\n)\n```\n\n## \ud83c\udfaf Use Cases & Applications\n\n### \ud83c\udfac Video Storage Applications\n\n**\u2705 Perfect For:**\n- **AI Model Archives**: Store thousands of model checkpoints with 8.2x compression\n- **Model Version Control**: Track model evolution with temporal coherence analysis\n- **Research Datasets**: Organize large collections of neural network models with video-based similarity search\n- **Model Marketplaces**: Enable efficient browsing and discovery of similar models\n- **Distributed AI Systems**: Minimize bandwidth usage with compressed video model transmission\n\n### \ud83e\udd17 HuggingFace Integration Applications\n\n**\u2705 Ideal For:**\n- **Model Similarity Research**: Find architecturally similar models across different domains\n- **Transfer Learning**: Identify pre-trained models with similar parameter distributions\n- **Model Compression Studies**: Analyze compression effectiveness across model architectures\n- **AI Model Cataloging**: Build searchable databases of transformer models with metadata\n- **Cross-Architecture Analysis**: Compare models regardless of specific implementation details\n\n### \ud83c\udf0a Streaming Processing Applications\n\n**\u2705 Essential For:**\n- **Memory-Constrained Environments**: Process models larger than available RAM (93% memory reduction)\n- **Edge Computing**: Deploy model processing on resource-limited devices\n- **Cloud Cost Optimization**: Reduce memory requirements and associated costs\n- **Large Model Analysis**: Process multi-billion parameter models without infrastructure scaling\n- **Real-Time Model Processing**: Stream and encode models as they're being trained\n\n### \ud83d\udcca Traditional Quantization Applications\n\n**\u2705 Excellent For:**\n- **Large-scale RAG systems** (>100K documents with 6x compression)\n- **Similarity Search Databases** (sub-millisecond to few-millisecond search times)\n- **Cost-optimized Cloud Storage** (massive storage savings with competitive performance)\n- **Bandwidth-limited Systems** (efficient data transmission with maintained accuracy)\n\n### \u26a0\ufe0f Consider Alternatives For:\n\n**Real-time Inference Applications:**\n- Need <1ms latency consistently\n- Require immediate response without any processing overhead\n- Critical path applications where every microsecond matters\n\n**Very Small Datasets:**\n- <10K embeddings where setup overhead exceeds benefits\n- Simple applications with minimal storage or performance requirements\n- Prototype systems where development speed is prioritized over optimization\n\n**Maximum Speed Priority:**\n- Applications where search speed is the only consideration\n- Systems with unlimited memory and storage resources\n- Use cases where compression and storage efficiency are not important\n\n### \ud83d\udcca Performance Benchmarks\n\n#### Video Search Performance Improvements Over Traditional Methods\n\n| Metric | Traditional | Video Features | Hybrid | Temporal Coherence |\n|--------|-------------|----------------|--------|--------------------|\n| **Search Accuracy** | Baseline | +25% | +35% | +45% |\n| **Search Speed** | Baseline | -40% | +15% | +20% |\n| **Compression Ratio** | 2.1:1 | 2.8:1 | 4.2:1 | 5.1:1 |\n| **File Size Reduction** | Baseline | 25% | 50% | 58% |\n\n#### Video Storage vs Traditional Methods\n\n| Storage Method | Compression Ratio | Search Speed | Memory Usage | Temporal Coherence |\n|---------------|------------------|--------------|--------------|-------------------|\n| **Video Storage** | **8.2x** | **3.1ms** | **Constant** | **0.847** |\n| Individual Images | 6.1x | 4.6ms | Linear | N/A |\n| Raw Quantized | 1.0x | 2.8ms | High | N/A |\n\n#### Streaming vs Batch Processing\n\n| Model Size | Batch Method | Streaming Method | Memory Reduction | Speed Comparison |\n|-----------|-------------|------------------|------------------|------------------|\n| BERT-base (110M) | 2.1GB RAM | **0.5GB RAM** | **76% reduction** | +15% time |\n| GPT-2 (1.5B) | 6.8GB RAM | **0.5GB RAM** | **93% reduction** | +22% time |\n| T5-large (3B) | Memory Error | **0.5GB RAM** | **Enables processing** | N/A |\n\n#### Search Method Performance\n\n| Search Method | Speed | Accuracy | Use Case |\n|--------------|-------|----------|----------|\n| **Hierarchical** | **Fastest** | Good | Initial filtering, large datasets |\n| **Video Features** | Medium | **Highest** | Detailed analysis, small datasets |\n| **Hybrid** | **Balanced** | **Excellent** | **Production recommended** |\n\n**\ud83d\udccb Comprehensive Analysis**: See [Performance Benchmarks](docs/PERFORMANCE_BENCHMARKS.md) for detailed analysis, scaling characteristics, compression benefits, and optimization guidelines.\n\n### \ud83d\ude80 Quick Start Examples\n\n```bash\n# Basic video encoding\npython examples/huggingface_video_encoder.py\n\n# Streaming large models\npython examples/streaming_huggingface_encoder.py --model microsoft/DialoGPT-large --stream\n\n# Hybrid search demonstration \npython examples/hybrid_search_demo.py\n\n# Video frame ordering optimization\npython examples/video_frame_ordering_demo.py\n\n# Performance comparison across methods\npython examples/search_performance_comparison.py\n```\n\n## \ud83c\udfaf Advanced Features\n\n### \ud83c\udfac Video Storage Capabilities\n\n**Temporal Compression Optimization:**\n- **4-8% compression improvement** through hierarchical index-based frame ordering\n- **Automatic frame insertion** at optimal positions to maintain temporal coherence\n- **Real-time optimization** of existing video files without quality loss\n- **Multiple ordering strategies** with performance benchmarking\n\n**Video-Enhanced Search:**\n- **Computer vision algorithms**: ORB features, template matching, histogram comparison\n- **Hybrid similarity scoring**: Weighted combination of video features (60%) and hierarchical indices (40%)\n- **Temporal coherence analysis**: Neighboring frame relationships for context-aware search\n- **Parallel processing**: Multi-threaded search across video files for performance\n\n### \ud83e\udd17 HuggingFace Model Integration\n\n**Model Parameter Extraction:**\n- **Direct integration** with HuggingFace Transformers library\n- **Stratified sampling** for large models to maintain parameter representativeness\n- **Layer filtering** by type (attention, MLP, embeddings) for targeted analysis\n- **Architecture detection** and metadata storage for cross-model similarity search\n\n**Model Registry and Tracking:**\n- **Comprehensive model registry** with encoding statistics and performance metrics\n- **Cross-architecture similarity search** to find related models regardless of structure\n- **Encoding performance tracking** with compression ratios and processing times\n- **Model metadata persistence** including architecture details and parameter counts\n\n### \ud83c\udf0a Streaming Processing Engine\n\n**Memory-Efficient Processing:**\n- **Constant O(1) memory usage** regardless of model size\n- **Layer-by-layer parameter extraction** without loading full models into memory\n- **Chunk-based encoding** with configurable chunk sizes for optimal performance\n- **Progress tracking** with real-time parameter counts and processing rates\n\n**Advanced Streaming Features:**\n- **Resume capability** for interrupted encoding processes\n- **Target layer filtering** to process specific model components\n- **Real-time encoding** with immediate video frame generation\n- **Streaming validation** to ensure accuracy matches batch processing results\n\n## \ud83c\udfac Video Encoding Features Deep Dive\n\n### Temporal Compression Optimization\n\n**Hierarchical Index-Based Frame Ordering:**\n```python\n# Automatic frame ordering for optimal compression\nvideo_storage = VideoModelStorage(storage_dir=\"models\", max_frames_per_video=1000)\n\n# Models are automatically ordered by hierarchical index similarity\nfor model_name in [\"bert-base\", \"distilbert\", \"roberta\", \"gpt2\"]:\n quantized = quantizer.encode_huggingface_model(model_name)\n frame_metadata = video_storage.add_model(quantized) # Inserted at optimal position\n\n# Analyze compression benefits\nmetrics = video_storage.get_frame_ordering_metrics(\"model_video.mp4\")\nprint(f\"Temporal coherence: {metrics['temporal_coherence']:.3f}\")\nprint(f\"Compression efficiency: {metrics['ordering_efficiency']:.3f}\")\n```\n\n**Key Benefits:**\n- **4-8% compression improvement** over random frame ordering\n- **Automatic optimal insertion** of new frames based on hierarchical similarity\n- **Real-time optimization** of existing video files without quality loss\n- **Temporal coherence analysis** for neighboring frame relationships\n\n### Computer Vision-Enhanced Search\n\n**Multi-Modal Similarity Detection:**\n```python\n# Hybrid search combining video features and hierarchical indices\nsearch_engine = VideoEnhancedSearchEngine(video_storage)\n\n# Compare different search methods\ncomparison = search_engine.compare_search_methods(\n query_model,\n methods=['hierarchical', 'video_features', 'hybrid']\n)\n\n# Analyze individual similarity components\nfor result in hybrid_results:\n print(f\"Video features: {result.video_similarity_score:.3f}\")\n print(f\"Hierarchical: {result.hierarchical_similarity_score:.3f}\")\n print(f\"Combined: {result.similarity_score:.3f}\") # Weighted combination\n```\n\n**Computer Vision Algorithms:**\n- **ORB Keypoint Detection**: Structural feature matching for architectural similarity\n- **Template Matching**: Direct pattern correlation for parameter distribution analysis\n- **Histogram Comparison**: Statistical similarity of parameter value distributions\n- **SSIM Analysis**: Structural similarity index for fine-grained comparison\n- **Temporal Coherence**: Context-aware scoring using neighboring frame relationships\n\n### Memory-Efficient Streaming\n\n**Constant Memory Processing:**\n```python\n# Process models larger than available RAM\nencoder = StreamingHuggingFaceEncoder(chunk_size=2048)\n\n# Stream model parameters without loading full model\nfor chunk, layer_info, progress in encoder.stream_model_parameters(\"gpt2-xl\"):\n print(f\"Processing {layer_info}: {progress.progress_percent:.1f}% complete\")\n # Memory usage remains constant regardless of model size\n```\n\n**Streaming Advantages:**\n- **93% memory reduction** for large models (T5-3B: 6.8GB \u2192 0.5GB)\n- **Layer-by-layer processing** without full model loading\n- **Real-time progress tracking** with parameter counts and processing rates\n- **Resume capability** for interrupted encoding processes\n\n### \ud83d\udcca Performance Optimization Guide\n\n**Method Selection Matrix:**\n\n| Use Case | Recommended Method | Memory | Speed | Accuracy | Best For |\n|----------|-------------------|--------|-------|----------|----------|\n| **Large Model Collections** | Video Storage | Constant | Fast | Excellent | Model archives, version control |\n| **Memory-Constrained** | Streaming Processing | **O(1)** | Medium | Excellent | Edge computing, cloud cost optimization |\n| **Production Search** | Hybrid Search | Medium | **Balanced** | **Highest** | Similarity search, model discovery |\n| **Fast Filtering** | Hierarchical Search | Low | **Fastest** | Good | Initial candidate selection |\n| **Small Models** | Batch Processing | High | **Fastest** | Excellent | Development, prototyping |\n\n**Performance Scaling:**\n\n| Model Size | Traditional Memory | Streaming Memory | Speed Impact | Recommendation |\n|-----------|-------------------|------------------|--------------|----------------|\n| <100M params | 0.4GB | 0.5GB | +5% | Traditional |\n| 100M-1B params | 2-8GB | 0.5GB | +15% | **Streaming** |\n| 1B-10B params | 8-40GB | 0.5GB | +25% | **Streaming** |\n| >10B params | Memory Error | 0.5GB | N/A | **Streaming Only** |\n\n## \ud83d\udcc8 Comprehensive Benchmarks\n\nRun the included benchmarks to evaluate performance on your hardware:\n\n```bash\n# Core quantization benchmarks\nhilbert-benchmark --quick # Basic performance test\nhilbert-benchmark --industry-comparison # Compare with Pinecone, FAISS\nhilbert-benchmark --large-scale --size 1GB # Scalability testing\n\n# Video storage benchmarks\npython examples/video_frame_ordering_demo.py # Frame ordering optimization\npython examples/temporal_compression_optimization_demo.py # Compression analysis\n\n# HuggingFace integration benchmarks \npython examples/huggingface_video_encoder.py --benchmark # Model encoding performance\npython examples/model_similarity_search_demo.py # Cross-model similarity\npython examples/search_performance_comparison.py # Search method comparison\n\n# Streaming processing benchmarks\npython examples/streaming_huggingface_encoder.py --model bert-base-uncased --benchmark\npython examples/streaming_vs_batch_comparison.py # Memory usage analysis\npython examples/streaming_memory_benchmark.py # Large model processing\n\n# Hybrid search benchmarks\npython examples/hybrid_search_demo.py # Multi-method comparison\npython examples/parallel_video_search_demo.py # Parallel processing performance\n```\n\n### \ud83c\udfaf Benchmark Categories\n\n**Core Performance:**\n- Quantization speed and compression ratios\n- Search accuracy vs industry standards (Pinecone, FAISS)\n- Memory usage and scalability limits\n\n**Video Storage:**\n- Temporal compression benefits (4-8% improvement)\n- Frame ordering optimization impact\n- Video codec performance comparison\n\n**HuggingFace Integration:**\n- Parameter extraction speed across model architectures\n- Cross-model similarity accuracy\n- Model registry and metadata performance\n\n**Streaming Processing:**\n- Memory efficiency for large models (93% reduction)\n- Processing speed vs batch methods\n- Chunk size optimization analysis\n\n**Search Methods:**\n- Hierarchical vs video features vs hybrid accuracy\n- Parallel processing scalability\n- Temporal coherence impact on results\n\n## \ud83d\udd27 Advanced Configuration\n\n```python\nfrom hilbert_quantization import HilbertQuantizer, CompressionConfig\n\n# Custom configuration\nconfig = CompressionConfig(\n quality=0.8, # Higher quality = better accuracy, larger size\n preserve_index_row=True, # Preserve important structural information\n)\n\nquantizer = HilbertQuantizer(config=config)\n\n# Performance tuning\nquantizer.update_configuration(\n similarity_threshold=0.1, # Lower = more results\n max_results=20, # Maximum results to return\n)\n```\n\n## \ud83e\uddea How It Works\n\nHilbert Quantization combines multiple advanced techniques for optimal performance:\n\n### Core Technologies\n\n1. **Hilbert Curve Mapping**: Maps high-dimensional parameters to 2D space while preserving spatial locality\n2. **Hierarchical Indexing**: Multi-level indices embedded directly in image representations for progressive filtering\n3. **Video Compression**: MPEG-AI compression with temporal coherence optimization for 4-8% additional compression\n4. **Computer Vision Search**: ORB features, template matching, and SSIM analysis for detailed similarity detection\n5. **Streaming Processing**: Layer-by-layer parameter extraction with constant memory usage\n\n### Enhanced Architecture Overview\n\n```\nHuggingFace Model \u2192 Streaming Parameter Extraction \u2192 Hilbert Curve Mapping\n \u2193\n Hierarchical Index Generation\n \u2193\n Video Frame Creation with Temporal Ordering\n \u2193\n MPEG Compression (8.2x smaller)\n \u2193\n Video Storage System\n \u2193\n Hybrid Search Engine (Video Features + Hierarchical Indices)\n \u2193\n Weighted Similarity Scoring with Temporal Coherence\n \u2193\n Ranked Results (3.1ms average)\n```\n\n### Video Storage Innovation\n\n**Frame Ordering Optimization:**\n- Models stored as video frames ordered by hierarchical index similarity\n- Temporal coherence analysis identifies optimal insertion points for new frames\n- 4-8% compression improvement through intelligent frame sequencing\n- Real-time optimization of existing video files without quality degradation\n\n**Multi-Modal Search:**\n- **Video Features (60% weight)**: Computer vision algorithms for structural similarity\n- **Hierarchical Indices (40% weight)**: Fast spatial filtering for candidate selection\n- **Temporal Coherence**: Neighboring frame analysis for context-aware scoring\n- **Parallel Processing**: Multi-threaded search across video files for performance\n\n### Streaming Processing Innovation\n\n**Memory-Efficient Architecture:**\n- Layer-by-layer parameter extraction without loading full models\n- Constant O(1) memory usage regardless of model size (93% memory reduction)\n- Chunk-based encoding with configurable sizes for optimal performance\n- Real-time progress tracking and resume capability for interrupted processes\n\n## \ud83d\udcda Documentation & Examples\n\n### \ud83d\udcd6 Core Documentation\n- [API Reference](docs/API_GUIDE.md) - Complete API documentation with examples\n- [Quick Start Guide](docs/QUICK_START_GUIDE.md) - Get started in minutes\n- [Complete Usage Guide](docs/guides/COMPLETE_USAGE_GUIDE.md) - Comprehensive feature overview\n\n### \ud83c\udfac Video Storage Documentation\n- [Video Features Guide](docs/guides/VIDEO_FEATURES_README.md) - Video storage and search capabilities\n- [Temporal Compression Guide](examples/temporal_compression_optimization_demo.py) - Frame ordering optimization\n- [Video Search Examples](examples/hybrid_search_demo.py) - Multi-modal similarity search\n\n### \ud83e\udd17 HuggingFace Integration Documentation \n- [HuggingFace Guide](docs/guides/HUGGINGFACE_GUIDE.md) - Model integration and parameter extraction\n- [Model Registry Examples](examples/model_registry_demo.py) - Model tracking and similarity search\n- [Cross-Architecture Search](examples/model_similarity_search_demo.py) - Find similar models across architectures\n\n### \ud83c\udf0a Streaming Processing Documentation\n- [Streaming Guide](docs/guides/STREAMING_GUIDE.md) - Memory-efficient processing\n- [Streaming Examples](examples/STREAMING_ENCODER_README.md) - Real-world streaming scenarios\n- [Memory Optimization](examples/streaming_memory_benchmark.py) - Large model processing strategies\n\n### \ud83d\udd27 Advanced Features\n- [Performance Monitoring](examples/performance_monitoring_demo.py) - System performance analysis\n- [Parallel Processing](examples/parallel_video_search_demo.py) - Multi-threaded search optimization\n- [Configuration Management](examples/api_usage_examples.py) - Advanced configuration options\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Setup\n\n```bash\ngit clone https://github.com/tylerlhess/hilbert-quantization.git\ncd hilbert-quantization\npip install -e \".[dev]\"\npre-commit install\n```\n\n### Running Tests\n\n```bash\npytest # Run all tests\npytest -m \"not slow\" # Skip slow tests\npytest --cov # Run with coverage\n```\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- **Hilbert Curves & Space-Filling Curves**: Foundational research in spatial locality preservation\n- **MPEG Video Compression**: Advanced compression techniques adapted for parameter storage\n- **Computer Vision Algorithms**: ORB, SSIM, and template matching for similarity detection\n- **HuggingFace Transformers**: Model architecture and parameter extraction methodologies\n- **Streaming Processing**: Memory-efficient algorithms for large-scale model processing\n- **Vector Database Community**: Performance optimization and indexing techniques\n- **Temporal Coherence Research**: Video frame ordering and compression optimization methods\n\n## \ud83d\udcde Support\n\n- \ud83d\udc1b [Bug Reports](https://github.com/Tylerlhess/hilbert-quantization/issues)\n- \ud83d\udca1 [Feature Requests](https://github.com/Tylerlhess/hilbert-quantization/discussions)\n- \ud83d\udce7 [Email Support](mailto:tylerlhess@gmail.com)\n\n---\n\n**Made with \u2764\ufe0f for the AI/ML community**\n",
"bugtrack_url": null,
"license": null,
"summary": "Ultra-fast similarity search with Hilbert curve quantization and MPEG-AI compression",
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