YugabyteDB's pluggable vector indexing architecture that separates vector search logic from the database engine, enabling integration with multiple ANN backends like USearch.
YugabyteDB's Vector LSM is a decoupled, pluggable indexing layer that separates vector search logic from the rest of the database engine, enabling easy integration with multiple ANN (Approximate Nearest Neighbor) backends.
Vector LSM works like an LSM tree built for vector indexes: vectors are first inserted into an in-memory buffer and indexed using HNSW-based libraries like USearch, then flushed to disk as immutable vector chunks once full, with searches fanning out across all in-memory and on-disk vector indexes, and results filtered using MVCC rules to ensure consistency.
YugabyteDB uses a co-partitioned vector index layout, where vector indexes are stored in the same tablets as the corresponding table rows, ensuring tight data locality and operational advantages.
In benchmarking with the Deep1B dataset, YugabyteDB successfully indexed 1 billion 96-dimensional vectors, achieving a recall of 96.56% and sub-second query latency.
Included in YugabyteDB, open-source and commercial versions available.
Loading more......