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.



10114
10115
10116
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 10114

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:



10080
10081
10082
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 10080

def crowding_tag
  @crowding_tag
end

#datapoint_id ⇒ String

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

Returns:

  • (String) 


10085
10086
10087
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 10085

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>) 


10091
10092
10093
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 10091

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:



10098
10099
10100
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 10098

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:



10106
10107
10108
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 10106

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:



10112
10113
10114
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 10112

def sparse_embedding
  @sparse_embedding
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



10119
10120
10121
10122
10123
10124
10125
10126
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 10119

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