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.



11298
11299
11300
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11298

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:



11264
11265
11266
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11264

def crowding_tag
  @crowding_tag
end

#datapoint_id ⇒ String

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

Returns:

  • (String) 


11269
11270
11271
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11269

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>) 


11275
11276
11277
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11275

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:



11282
11283
11284
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11282

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:



11290
11291
11292
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11290

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:



11296
11297
11298
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11296

def sparse_embedding
  @sparse_embedding
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



11303
11304
11305
11306
11307
11308
11309
11310
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 11303

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