{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"},{"label":"Database Engines/Servers","url":"https://skillfed.io/packages/category/database-database-engines-servers"},{"label":"Clustering","url":"https://skillfed.io/packages/category/system-clustering"}],"enrichment":{"capability":"USearch provides approximate nearest-neighbor vector search using HNSW indexing, supporting custom metrics, multiple data types, and on-disk index serving without loading into RAM.","skillfed_tags":["vector-search","approximate-nearest-neighbors","embeddings"],"use_cases":["Building semantic search systems that find similar embeddings from language models or vision models in large document or image collections.","Implementing recommendation engines that match user embeddings to candidate item embeddings in real time.","Clustering millions of vectors for data analysis, with support for on-disk indexes to reduce memory costs.","Genomics and chemistry applications using binary Tanimoto and Sorensen coefficients for molecular similarity.","Hybrid search combining vector similarity with custom filtering predicates or external data structures."],"what_it_does":"USearch is a vector search engine that builds approximate nearest-neighbor indexes using the HNSW algorithm. It is designed as a lightweight, single-file C++11 library with Python bindings and support for custom distance metrics. The package lets you index vectors, search for similar vectors efficiently, and optionally serve large indexes from disk without loading them entirely into memory.\n\nTypical usage involves creating an Index with a specified dimensionality and metric, adding vectors with keys, and then searching for nearest neighbors. It supports various data types (f32, f16, bf16, i8, and others) for memory efficiency, works across Linux, macOS, and Windows, and integrates with numpy. The runtime dependencies are numpy, tqdm, and numkong.","worth_installing":"Yes. USearch is production-stable (Development Status 5), actively maintained, has no known vulnerabilities, and offers a lightweight alternative to heavier vector search libraries. The permissive Apache-2.0 license and broad platform support (Linux, macOS, Windows) make it suitable for most projects. Install friction is moderate due to compiled wheels, but pre-built binaries are available for current Python versions (3.10+). Verify that the numkong dependency is intentional before deploying."},"id":"usearch","links":{"html":"https://skillfed.io/packages/usearch","md":"https://skillfed.io/packages/usearch.md","pypi":"https://pypi.org/project/usearch/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-10","license_spdx":null,"license_treatment":"permissive","name":"usearch","python_support":"supports_current","summary":"Smaller & Faster Single-File Vector Search Engine from Unum"},"popularity":{"monthly_downloads":486107,"position":6392,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.26.0"}