Class: Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1beta1/classes.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb

Overview

Deprecated. Use IndexConfig instead.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig

Returns a new instance of GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig.



8915
8916
8917
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 8915

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

Instance Attribute Details

#brute_force_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfig

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. Corresponds to the JSON property bruteForceConfig

Returns:



8877
8878
8879
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 8877

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 nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute. Corresponds to the JSON property crowdingColumn

Returns:

  • (String) 


8885
8886
8887
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 8885

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) 


8890
8891
8892
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 8890

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) 


8896
8897
8898
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 8896

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) 


8901
8902
8903
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 8901

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>) 


8906
8907
8908
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 8906

def filter_columns
  @filter_columns
end

#tree_ah_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAhConfig

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 Corresponds to the JSON property treeAhConfig

Returns:



8913
8914
8915
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 8913

def tree_ah_config
  @tree_ah_config
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



8920
8921
8922
8923
8924
8925
8926
8927
8928
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 8920

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