In Memory Vector Indexing With Approximate Nearest Neighbor

via “hnsw-based approximate nearest neighbor search with configurable recall-latency tradeoff”

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Unique: Implements HNSW with native support for multiple distance metrics (L2, cosine, dot product, Manhattan) and integrates graph construction into segment lifecycle management, allowing incremental index building during segment optimization rather than requiring full re-indexing on updates

vs others: Faster approximate search than IVF-based methods for high-dimensional vectors (>100D) and supports dynamic insertion without full index rebuild, unlike traditional HNSW implementations that require offline construction