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

Configuration for vector search.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig

Returns a new instance of GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig.



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7314

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:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7276

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) 


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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7284

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_v1beta1/classes.rb', line 7289

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_v1beta1/classes.rb', line 7295

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_v1beta1/classes.rb', line 7300

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_v1beta1/classes.rb', line 7305

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:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7312

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_v1beta1/classes.rb', line 7319

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