Introducing ft-Q: Improving Vector Compression with Feature-Level Quantization

ft-Quantization (ft-Q) is a novel approach to quantization that aims to improve vector compression by performing quantization at the feature level. This method addresses the limitations of traditional quantization by considering individual feature distributions, resulting in better data representation and reduced error rates. The implementation is open-source and can be especially useful in scenarios involving processed embeddings.