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 2025-03-31 UTC."],[[["This documentation details the `DistanceMeasureType` enum within the Vertex AI v1beta1 API, specifically found in the `Google.Cloud.AIPlatform.V1Beta1.FeatureView.Types.IndexConfig.Types` namespace."],["The `DistanceMeasureType` enum is used to specify the distance metric applied during nearest neighbor searches, with options including `CosineDistance`, `DotProductDistance`, and `SquaredL2Distance`."],["The `DotProductDistance` in combination with `UNIT_L2_NORM` is the suggested alternative for `CosineDistance` to ensure optimal performance because the algorithms are optimized for this combination."],["The enum also contains an `Unspecified` field, indicating that it should not be explicitly set by the user."],["This documentation covers the latest beta version (1.0.0-beta21) of the relevant library, as well as a prior version, (1.0.0-beta20), providing historical context and allowing for comparison."]]],[]]
