Techniques for optimizing the trade-off between memory usage and accuracy in vector quantization, achieving 5-40x compression in systems like Mastra's Observational Memory.
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Compression Ratio Optimization
Compression Ratio Optimization refers to techniques for finding the optimal balance between memory usage and accuracy when compressing vectors or text in AI systems.
Mastra's Observational Memory achieves 5-40x compression while maintaining 95% accuracy on LongMemEval, demonstrating that aggressive compression is possible without significant quality loss.
Techniques and patterns, various implementations available.
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