GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig constructor

const GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig({ Input<Map<String, dynamic>>? bruteForceConfig, Input<String>? crowdingColumn, Input<GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigDistanceMeasureType>? distanceMeasureType, Input<String>? embeddingColumn, Input<int>? embeddingDimension, Input<List<String>>? filterColumns, Input<GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfig>? treeAhConfig, })

Creates a new GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig. bruteForceConfig Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search. crowdingColumn Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute. distanceMeasureType Optional. The distance measure used in nearest neighbor search. embeddingColumn Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search. embeddingDimension Optional. The number of dimensions of the input embedding. filterColumns Optional. Columns of features that're used to filter vector search results. treeAhConfig Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

Implementation

const GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig({
  this.bruteForceConfig,
  this.crowdingColumn,
  this.distanceMeasureType,
  this.embeddingColumn,
  this.embeddingDimension,
  this.filterColumns,
  this.treeAhConfig,
});