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.



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20229

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:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20190

def crowding_tag
  @crowding_tag
end

#datapoint_id ⇒ String

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

Returns:

  • (String) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20195

def datapoint_id
  @datapoint_id
end

#embedding_metadata ⇒ Hash<String,Object>

Optional. The key-value map of additional metadata for the datapoint. Corresponds to the JSON property embeddingMetadata

Returns:

  • (Hash<String,Object>) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20200

def embedding_metadata
  @embedding_metadata
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>) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20206

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:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20213

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:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20221

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:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20227

def sparse_embedding
  @sparse_embedding
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 20234

def update!(**args)
  @crowding_tag = args[:crowding_tag] if args.key?(:crowding_tag)
  @datapoint_id = args[:datapoint_id] if args.key?(:datapoint_id)
  @embedding_metadata = args[:embedding_metadata] if args.key?(:embedding_metadata)
  @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