Overview
Compression Ratio Optimization refers to techniques for finding the optimal balance between memory usage and accuracy when compressing vectors or text in AI systems.
Approaches
Vector Quantization
- Binary quantization: 32x compression
- Scalar quantization: 4x compression
- Product quantization: 8-16x compression
- Hybrid approaches: Variable compression
Text Compression
- Observer-based: 5-40x compression (Mastra)
- Summarization: 10-20x compression
- Extraction: 3-10x compression
Key Considerations
- Accuracy Impact: How much quality is lost
- Compute Overhead: Cost of compression/decompression
- Latency: Real-time vs batch compression
- Reversibility: Can original be recovered
Adaptive Strategies
- Dynamic compression based on content importance
- Progressive compression over time
- Context-aware compression rates
- Quality-guided compression thresholds
Real-World Performance
Mastra's Observational Memory achieves 5-40x compression while maintaining 95% accuracy on LongMemEval, demonstrating that aggressive compression is possible without significant quality loss.
Tools and Frameworks
- Qdrant: Multiple quantization options
- pgvectorscale: Statistical binary quantization
- Mastra: Observer-Reflector compression
- CSRv2: Ultra-sparse embeddings
Pricing
Techniques and patterns, various implementations available.
