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Vector Search Optimization via KMeans, Voronoi Cells and Inverted File Index (aka “Cell-Probing”)

Vector Search Optimization via KMeans, Voronoi Cells and Inverted File Index (aka “Cell-Probing”)

If you're working with vectors in Azure SQL and want to optimize your vector search performance, this article is worth your attention. Using KMeans Clustering, Voronoi Cells, and Inverted File Index, the technique known as "Cell-Probing" can help improve your vector search.

The article starts by highlighting how vectors can be stored in Azure SQL and the basics of calculating cosine distance and dot product using T-SQL. It then delves into the concept of KMeans Clustering and how it can be used to cluster similar vectors together. The Voronoi Cells method is then employed to divide the space around each cluster into cells. These cells are then indexed using inverted files to speed up the search process.

Overall, this technique can help with achieving optimal vector search performance, saving time and resources along the way.

The post Vector Search Optimization via KMeans, Voronoi Cells and Inverted File Index (aka "Cell-Probing") appeared first on Azure SQL Devs' Corner.

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