3 repositorios
Low-level SIMD implementations for calculating mathematical distances between vectors.
Distinct from SIMD-Accelerated Arithmetic: Specifically targets distance metrics for vector search rather than general arithmetic operations.
Explore 3 awesome GitHub repositories matching data & databases · Vector Distance Kernels. Refine with filters or upvote what's useful.
USearch is a high-performance vector similarity search engine and approximate nearest neighbor index designed for dense embeddings. It functions as a low-level vector database core and high-dimensional vector indexer, providing the primitives necessary to store and retrieve vectors across massive datasets. The engine distinguishes itself through hardware-level SIMD acceleration for distance kernels and a proximity-graph indexing system that enables fast retrieval across billions of vectors. It supports multi-precision vector quantization to balance memory usage and accuracy, and utilizes memo
Ships hardware-level SIMD acceleration for Euclidean, cosine, and Hamming distance kernels.
RapidFuzz is a C++ accelerated Python library providing high-performance string comparison and similarity calculations. It functions as a fuzzy string matching toolkit used to quantify the difference between text sequences through Levenshtein distance and other edit distance metrics. The library focuses on scalable approximate text matching, enabling the identification and ranking of similar strings within large datasets. It provides specialized utilities for finding the best matches in a collection and generating pairwise similarity matrices. The project covers a broad surface of text proce
Employs SIMD vector distance kernels to process multiple characters simultaneously for accelerated string distance calculations.
TensorFlow Similarity is a Python framework designed for training neural networks to learn high-dimensional vector representations and perform similarity-based retrieval. It provides a comprehensive toolkit for metric learning, enabling the development of systems that group similar items together in vector space and identify them through distance-based comparisons. The library distinguishes itself by integrating specialized training techniques, such as contrastive and triplet-based learning, with robust data management tools that ensure stable model convergence. It supports self-supervised re
Calculates mathematical separation between embedding vectors using high-speed linear algebra operations for efficient similarity comparisons.