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 memory-mapped index persistence to reduce RAM overhead during loading and serialization.
The project covers a broad range of capabilities including exact brute-force linear scans, batch processing for bulk similarity searches, and thread-safe concurrent index construction. It implements multiple distance metrics—such as Euclidean, Hamming, Jaccard, and Haversine for geospatial proximity—while allowing for the integration of custom user-defined metric functions.
Additional utility surfaces include vector data clustering, semantic data joining, and tools for benchmarking search performance and accuracy evaluation.
