Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1IndexDatapoint

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

A datapoint of Index.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1IndexDatapoint

Returns a new instance of GoogleCloudAiplatformV1IndexDatapoint.



11932
11933
11934
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11932

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

Instance Attribute Details

#crowding_tag ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1IndexDatapointCrowdingTag

Crowding tag 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 crowdingTag

Returns:



11898
11899
11900
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11898

def crowding_tag
  @crowding_tag
end

#datapoint_id ⇒ String

Required. Unique identifier of the datapoint. Corresponds to the JSON property datapointId

Returns:

  • (String) 


11903
11904
11905
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11903

def datapoint_id
  @datapoint_id
end

#feature_vector ⇒ Array<Float>

Required. Feature embedding vector for dense index. An array of numbers with the length of [NearestNeighborSearchConfig.dimensions]. Corresponds to the JSON property featureVector

Returns:

  • (Array<Float>) 


11909
11910
11911
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11909

def feature_vector
  @feature_vector
end

#numeric_restricts ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1IndexDatapointNumericRestriction>

Optional. List of Restrict of the datapoint, used to perform "restricted searches" where boolean rule are used to filter the subset of the database eligible for matching. This uses numeric comparisons. Corresponds to the JSON property numericRestricts

Returns:



11916
11917
11918
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11916

def numeric_restricts
  @numeric_restricts
end

#restricts ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1IndexDatapointRestriction>

Optional. List of Restrict of the datapoint, used to perform "restricted searches" where boolean rule are used to filter the subset of the database eligible for matching. This uses categorical tokens. See: https://cloud.google. com/vertex-ai/docs/matching-engine/filtering Corresponds to the JSON property restricts

Returns:



11924
11925
11926
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11924

def restricts
  @restricts
end

#sparse_embedding ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1IndexDatapointSparseEmbedding

Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions. Corresponds to the JSON property sparseEmbedding

Returns:



11930
11931
11932
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11930

def sparse_embedding
  @sparse_embedding
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



11937
11938
11939
11940
11941
11942
11943
11944
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11937

def update!(**args)
  @crowding_tag = args[:crowding_tag] if args.key?(:crowding_tag)
  @datapoint_id = args[:datapoint_id] if args.key?(:datapoint_id)
  @feature_vector = args[:feature_vector] if args.key?(:feature_vector)
  @numeric_restricts = args[:numeric_restricts] if args.key?(:numeric_restricts)
  @restricts = args[:restricts] if args.key?(:restricts)
  @sparse_embedding = args[:sparse_embedding] if args.key?(:sparse_embedding)
end