Cosine Distance. Defined as 1 - cosine similarity.
We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead
of COSINE distance. Our algorithms have been more optimized for
DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is
mathematically equivalent to COSINE distance and results in the same
ranking.
DotProductDistance
Dot Product Distance. Defined as a negative of the dot product.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-03-21 UTC."],[[["This page provides versioned documentation for the `DistanceMeasureType` enum within the Google Cloud AI Platform v1 API, specifically for the `.NET` framework."],["The latest version documented is 3.22.0, while the oldest documented version is 1.0.0, showcasing a range of historical versions for reference."],["The `DistanceMeasureType` enum offers four field options: `CosineDistance`, `DotProductDistance`, `SquaredL2Distance`, and `Unspecified`, each representing a different method for measuring distance in nearest neighbor searches."],["The documentation includes assembly information, and namespace details for the `DistanceMeasureType` enum, helping developers understand the context of this element."],["The content highlights that using `DOT_PRODUCT_DISTANCE` with `UNIT_L2_NORM` is recommended over `COSINE_DISTANCE` for optimized algorithm performance."]]],[]]
