AiFeatureOnlineStoreFeatureviewVectorSearchConfig constructor

const AiFeatureOnlineStoreFeatureviewVectorSearchConfig({ Input<Map<String, dynamic>?>? bruteForceConfig, Input<String?>? crowdingColumn, Input<String?>? distanceMeasureType, required Input<String> embeddingColumn, Input<int?>? embeddingDimension, Input<List<String>?>? filterColumns, Input<AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfig?>? treeAhConfig, })

Creates a new AiFeatureOnlineStoreFeatureviewVectorSearchConfig. bruteForceConfig 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 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 crowdingAttribute. distanceMeasureType The distance measure used in nearest neighbor search. embeddingColumn Column of embedding. This column contains the source data to create index for vector search. embeddingDimension The number of dimensions of the input embedding. filterColumns Columns of features that are used to filter vector search results. treeAhConfig 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 AiFeatureOnlineStoreFeatureviewVectorSearchConfig({
  this.bruteForceConfig,
  this.crowdingColumn,
  this.distanceMeasureType,
  required this.embeddingColumn,
  this.embeddingDimension,
  this.filterColumns,
  this.treeAhConfig,
});