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.



11717
11718
11719
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11717

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:



11683
11684
11685
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11683

def crowding_tag
  @crowding_tag
end

#datapoint_id ⇒ String

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

Returns:

  • (String) 


11688
11689
11690
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11688

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>) 


11694
11695
11696
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11694

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:



11701
11702
11703
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11701

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:



11709
11710
11711
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11709

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:



11715
11716
11717
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11715

def sparse_embedding
  @sparse_embedding
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



11722
11723
11724
11725
11726
11727
11728
11729
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11722

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