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Scalar and Binary Quantization for Pgvector Vector Search and Storage (2024)

Jonathan Katz· ·16 min read · 0 reactions · 0 comments · 13 views
#postgresql#vector-database#quantization#ai-ml#data-compression
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The article discusses scalar and binary quantization techniques in pgvector 0.7.0 to reduce storage and memory usage for high-dimensional vectors in PostgreSQL. These methods compress vector data by reducing dimension precision or converting values to binary, enabling more efficient indexing and search at the cost of potential accuracy tradeoffs. The upcoming release supports 2-byte floats and bit vectors, allowing larger vector dimensions and improved scalability for AI/ML workloads.

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Jonathan Katz · Jonathan Katz
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HomePostsTalksAboutScalar and Binary Quantization for Pgvector Vector Search and Storage Tue, Apr 9, 2024 21-minute readWhile many AI/ML embedding models generate vectors that provide large amounts of information by using high dimensionality, this can come at the cost of using more memory for searches and more overall storage. Both of these can have an impact on the cost and performance of a system that’s storing vectors, including when using PostgreSQL with the pgvector for these use cases.When I talk about vector search in PostgreSQL, I have a slide that I like to call “no shortcuts without tradeoffs” that calls out the different challenges around searching vectors in a database.

Excerpt limited to ~120 words for fair-use compliance. The full article is at Jonathan Katz.

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