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Tiger Data's release of pg_vectorsearch introduces the PRISM index, a partition-based alternative to the graph-heavy HNSW approach used by standard pgvector implementations. By utilizing data partitioning and compression within PostgreSQL, this extension significantly reduces memory requirements and accelerates index build times for large-scale vector datasets without requiring separate database infrastructure.
Open Source pg_vectorsearch Brings More Scalable Vector Search to PostgreSQL
Tiger Data has released pg_vectorsearch, an open source PostgreSQL extension for vector similarity search, currently in alpha. Its PRISM index uses data partitioning and compression instead of the graph-based approach used by pgvector’s HNSW index, reducing memory requirements and index build times. By Renato Losio
Tiger Data has released pg_vectorsearch, an open source PostgreSQL extension for vector similarity search, currently in alpha. Its PRISM index uses data partitioning and compression instead of the graph-based approach used by pgvector’s HNSW index, reducing memory requirements and index build times. According to the authors, pg_vectorsearch enables faster, more memory-efficient vector search directly in PostgreSQL, reducing the infrastructure required to handle large vector datasets. The extension supports existing pgvector columns, allowing applications to perform vector search…
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