Fast vector similarity using Rust and Python

The Fast Vector Similarity Library is a highly efficient tool designed for tasks like data analysis, machine learning, and statistics that require measuring the relationship between vectors. Written in Rust, it offers high-performance computing and can be easily integrated with Python. The library implements several popular similarity measures, such as Spearman’s Rank-Order Correlation and Hoeffding’s D Measure. It also supports bootstrapping, providing robust similarity computation by resampling the dataset. The library leverages parallel computing and vectorized operations for optimal performance. Python bindings are included for seamless integration. The code example demonstrates how to apply the library to text embedding vectors and analyze large datasets. It is compatible with high-dimensional embeddings commonly used in modern language models. The library’s comprehensive suite of features and inclusion of the bootstrapping technique make it a powerful tool for computing similarity measures.

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