{"categories":[{"label":"Database Engines/Servers","url":"https://skillfed.io/packages/category/database-database-engines-servers"}],"enrichment":{"capability":"Voyager performs fast approximate nearest-neighbor searches on in-memory vector collections using the HNSW algorithm, enabling efficient similarity queries on embedding data.","skillfed_tags":["vector-search","embeddings","similarity-search"],"use_cases":["Powering recommendation systems by finding similar user embeddings or item embeddings in real time.","Semantic search over document embeddings to retrieve relevant results faster than exact similarity.","Duplicate detection by indexing embeddings and querying for near-neighbors to identify similar content.","Machine learning feature retrieval, retrieving nearest neighbors in embedding space for classification or clustering tasks.","Real-time personalization by querying user preference embeddings against product or content embeddings."],"what_it_does":"Voyager is a production-grade library for approximate nearest-neighbor search on vector embeddings, built on the HNSW algorithm and used at Spotify to handle hundreds of millions of queries daily. It wraps the open-source hnswlib with additional convenience features and maintains feature parity between Python and Java implementations, making it suitable for both single-machine and distributed systems that need fast similarity lookups on embedding data.\n\nThe library is designed for in-memory operation, meaning vectors are loaded into RAM for query speed. It supports multiple distance metrics (e.g., cosine, Euclidean) and is optimized for recall-accuracy tradeoffs typical of approximate search. Installation is straightforward via pip on supported platforms (Linux, macOS, Windows), with pre-built wheels for Python 3.7\u20133.12, though it does depend on numpy and compiled C++ bindings.","worth_installing":"Yes. Voyager is production-ready, actively maintained, permissively licensed, and has no known vulnerabilities. It offers a clean API for a common and performance-critical task (approximate nearest-neighbor search). Install it if you need fast vector similarity queries on embeddings; the medium install friction is offset by broad platform coverage and proven scale at Spotify."},"id":"voyager","links":{"html":"https://skillfed.io/packages/voyager","md":"https://skillfed.io/packages/voyager.md","pypi":"https://pypi.org/project/voyager/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-12-13","license_spdx":null,"license_treatment":"permissive","name":"voyager","python_support":"unspecified","summary":"Easy-to-use, fast, simple multi-platform approximate nearest-neighbor search library."},"popularity":{"monthly_downloads":117976,"position":12140,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.1.0"}