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Hilbert Quantization

PyPI version Python 3.8+ License: MIT Tests

Ultra-fast RAG system with 6x compression and competitive performance

Hilbert Quantization is a high-performance Retrieval-Augmented Generation (RAG) system that combines Hilbert curve mapping with MPEG-AI compression to deliver both speed and storage efficiency. It's designed for building efficient RAG 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 RAG Features

  • 📚 Complete RAG Pipeline: Document processing, embedding generation, and similarity search
  • ⚡ Ultra-fast retrieval: Sub-millisecond to few-millisecond search times
  • 💾 6x compression: Massive storage savings for document embeddings
  • 🎬 Video-enhanced storage: Advanced temporal coherence for better compression
  • 📊 Document validation: Comprehensive quality checks and metadata management
  • 🔧 Easy to use: Simple RAG 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

pip install hilbert-quantization

Optional Dependencies

# 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

Simple RAG System Setup

from hilbert_quantization.rag import RAGSystem, RAGConfig

# Initialize RAG system with default settings
config = RAGConfig(
    chunk_size=512,
    overlap_size=50,
    embedding_dimension=1024
)

rag_system = RAGSystem(config)

# Add documents to your RAG system
documents = [
    "Machine learning is a subset of artificial intelligence that focuses on algorithms.",
    "Natural language processing enables computers to understand human language.",
    "Computer vision allows machines to interpret and understand visual information."
]

# Process and store documents
for i, doc in enumerate(documents):
    rag_system.add_document(f"doc_{i}", doc)

# Search for relevant information
query = "What is machine learning?"
results = rag_system.search(query, max_results=3)

# Display results
for result in results:
    print(f"Document: {result.document_id}")
    print(f"Similarity: {result.similarity_score:.3f}")
    print(f"Content: {result.content}")
    print("---")

📚 RAG System Usage

Build a complete RAG system with document processing and similarity search:

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:

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:

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:

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 for detailed analysis, scaling characteristics, compression benefits, and optimization guidelines.

🚀 Quick Start Examples

# 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:

# 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:

# 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:

# 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:

# 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

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

🎬 Video Storage Documentation

🤗 HuggingFace Integration Documentation

🌊 Streaming Processing Documentation

🔧 Advanced Features

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

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

Running Tests

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 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

Made with ❤️ for the AI/ML community

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Ultra-fast similarity search with Hilbert curve quantization and MPEG-AI compression

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