{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"Fast approximate nearest neighbor search using the HNSW algorithm with Python bindings, supporting incremental index construction, updates, and deletions on vector data.","skillfed_tags":["vector-search","approximate-nearest-neighbor","hnsw-algorithm"],"use_cases":["Build a semantic search engine over document embeddings to find similar texts or documents by vector similarity.","Implement real-time product recommendation by indexing product embeddings and querying for nearest neighbors to a user's preference vector.","Create a reverse image search system by indexing image feature vectors and finding visually similar images.","Deduplicate large datasets by indexing all embeddings and finding near-duplicate vectors within a distance threshold.","Support incremental machine learning pipelines where new training examples are continuously added to an index for online similarity lookup."],"what_it_does":"Hnswlib is a Python binding to a header-only C++ implementation of the Hierarchical Navigable Small World (HNSW) algorithm for fast approximate nearest neighbor search. It lets you build an index of high-dimensional vectors and query them efficiently to find the k nearest neighbors, with support for three distance metrics: squared L2, inner product, and cosine distance. The library is designed for incremental workflows\u2014you can add, update, and delete vectors after index creation, mark elements as deleted without rebuilding, and save/load indexes to disk.\n\nThe main dependency is numpy for array handling. Installation requires a C++ compiler because the package compiles a native extension at install time. Once built, it offers thread-safe batch operations for both insertion and querying, with tunable parameters (M and ef_construction) to trade off memory footprint and construction speed against query accuracy. It is commonly used in machine learning and information retrieval pipelines where you need to find similar embeddings or vectors quickly without exhaustive search.","worth_installing":"Yes, with conditions. Hnswlib is actively maintained, has no known vulnerabilities, and is popular (571766 monthly downloads). Install it if you need fast approximate nearest neighbor search and can handle the C++ compilation requirement. However, verify the license before use in proprietary contexts, and be aware that the last release was 985 days ago\u2014check whether the current version meets your needs or if you need to build from the active repository."},"id":"hnswlib","links":{"html":"https://skillfed.io/packages/hnswlib","md":"https://skillfed.io/packages/hnswlib.md","pypi":"https://pypi.org/project/hnswlib/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2023-12-03","license_spdx":null,"license_treatment":"unclear","name":"hnswlib","python_support":"unspecified","summary":"hnswlib"},"popularity":{"monthly_downloads":571766,"position":5948,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.0"}