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This project is a C++ vector similarity engine and implementation of the Hierarchical Navigable Small World algorithm. It provides a header-only library for performing approximate nearest neighbor searches in high-dimensional spaces, alongside Python bindings that expose these indexing and search capabilities to data science environments.
The main features of nmslib/hnsw are: Vector Similarity Search, Low-Latency Vector Retrieval, Approximate Nearest Neighbor Search, Proximity Graph Indexes, Vector Indexing, HNSW Indexes, Vector Distance Metrics, Disk-Persistent Data Structures.
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hnswlib is a header-only C++ library and vector indexing engine designed for high-dimensional approximate nearest neighbor search. It organizes large collections of embeddings into a searchable graph structure to enable rapid proximity queries and distance calculations. The system utilizes Hierarchical Navigable Small World graphs to achieve fast vector similarity search. It distinguishes itself by allowing the definition of custom distance metrics and similarity functions to adapt calculations to specific data requirements. The engine covers the full indexing lifecycle, including incrementa
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