Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1FeatureViewIndexConfig

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1/classes.rb,
lib/google/apis/aiplatform_v1/representations.rb,
lib/google/apis/aiplatform_v1/representations.rb

Overview

Configuration for vector indexing.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1FeatureViewIndexConfig

Returns a new instance of GoogleCloudAiplatformV1FeatureViewIndexConfig.



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8283

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#brute_force_config ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1FeatureViewIndexConfigBruteForceConfig

Configuration options for using brute force search. Corresponds to the JSON property bruteForceConfig

Returns:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8245

def brute_force_config
  @brute_force_config
end

#crowding_column ⇒ String

Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService. SearchNearestEntities to diversify search results. If NearestNeighborQuery. per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it's guaranteed that no more than K entities of the same crowding attribute are returned in the response. Corresponds to the JSON property crowdingColumn

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8255

def crowding_column
  @crowding_column
end

#distance_measure_type ⇒ String

Optional. The distance measure used in nearest neighbor search. Corresponds to the JSON property distanceMeasureType

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8260

def distance_measure_type
  @distance_measure_type
end

#embedding_column ⇒ String

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. Corresponds to the JSON property embeddingColumn

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8266

def embedding_column
  @embedding_column
end

#embedding_dimension ⇒ Fixnum

Optional. The number of dimensions of the input embedding. Corresponds to the JSON property embeddingDimension

Returns:

  • (Fixnum) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8271

def embedding_dimension
  @embedding_dimension
end

#filter_columns ⇒ Array<String>

Optional. Columns of features that're used to filter vector search results. Corresponds to the JSON property filterColumns

Returns:

  • (Array<String>) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8276

def filter_columns
  @filter_columns
end

#tree_ah_config ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1FeatureViewIndexConfigTreeAhConfig

Configuration options for the tree-AH algorithm. Corresponds to the JSON property treeAhConfig

Returns:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8281

def tree_ah_config
  @tree_ah_config
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8288

def update!(**args)
  @brute_force_config = args[:brute_force_config] if args.key?(:brute_force_config)
  @crowding_column = args[:crowding_column] if args.key?(:crowding_column)
  @distance_measure_type = args[:distance_measure_type] if args.key?(:distance_measure_type)
  @embedding_column = args[:embedding_column] if args.key?(:embedding_column)
  @embedding_dimension = args[:embedding_dimension] if args.key?(:embedding_dimension)
  @filter_columns = args[:filter_columns] if args.key?(:filter_columns)
  @tree_ah_config = args[:tree_ah_config] if args.key?(:tree_ah_config)
end