Class: Google::Cloud::Datastore::V1::FindNearest

Inherits:
Object
  • Object
show all
Extended by:
Protobuf::MessageExts::ClassMethods
Includes:
Protobuf::MessageExts
Defined in:
proto_docs/google/datastore/v1/query.rb

Overview

Nearest Neighbors search config. The ordering provided by FindNearest supersedes the order_by stage. If multiple documents have the same vector distance, the returned document order is not guaranteed to be stable between queries.

Defined Under Namespace

Modules: DistanceMeasure

Instance Attribute Summary collapse

Instance Attribute Details

#distance_measure ⇒ ::Google::Cloud::Datastore::V1::FindNearest::DistanceMeasure

Returns Required. The Distance Measure to use, required.

Returns:



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# File 'proto_docs/google/datastore/v1/query.rb', line 512

class FindNearest
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The distance measure to use when comparing vectors.
  module DistanceMeasure
    # Should not be set.
    DISTANCE_MEASURE_UNSPECIFIED = 0

    # Measures the EUCLIDEAN distance between the vectors. See
    # [Euclidean](https://en.wikipedia.org/wiki/Euclidean_distance) to learn
    # more. The resulting distance decreases the more similar two vectors are.
    EUCLIDEAN = 1

    # COSINE distance compares vectors based on the angle between them, which
    # allows you to measure similarity that isn't based on the vectors
    # magnitude. We recommend using DOT_PRODUCT with unit normalized vectors
    # instead of COSINE distance, which is mathematically equivalent with
    # better performance. See [Cosine
    # Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) to learn
    # more about COSINE similarity and COSINE distance. The resulting COSINE
    # distance decreases the more similar two vectors are.
    COSINE = 2

    # Similar to cosine but is affected by the magnitude of the vectors. See
    # [Dot Product](https://en.wikipedia.org/wiki/Dot_product) to learn more.
    # The resulting distance increases the more similar two vectors are.
    DOT_PRODUCT = 3
  end
end

#distance_result_property ⇒ ::String

Returns Optional. Optional name of the field to output the result of the vector distance calculation. Must conform to [entity property][google.datastore.v1.Entity.properties] limitations.

Returns:

  • (::String) —

    Optional. Optional name of the field to output the result of the vector distance calculation. Must conform to [entity property][google.datastore.v1.Entity.properties] limitations.



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# File 'proto_docs/google/datastore/v1/query.rb', line 512

class FindNearest
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The distance measure to use when comparing vectors.
  module DistanceMeasure
    # Should not be set.
    DISTANCE_MEASURE_UNSPECIFIED = 0

    # Measures the EUCLIDEAN distance between the vectors. See
    # [Euclidean](https://en.wikipedia.org/wiki/Euclidean_distance) to learn
    # more. The resulting distance decreases the more similar two vectors are.
    EUCLIDEAN = 1

    # COSINE distance compares vectors based on the angle between them, which
    # allows you to measure similarity that isn't based on the vectors
    # magnitude. We recommend using DOT_PRODUCT with unit normalized vectors
    # instead of COSINE distance, which is mathematically equivalent with
    # better performance. See [Cosine
    # Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) to learn
    # more about COSINE similarity and COSINE distance. The resulting COSINE
    # distance decreases the more similar two vectors are.
    COSINE = 2

    # Similar to cosine but is affected by the magnitude of the vectors. See
    # [Dot Product](https://en.wikipedia.org/wiki/Dot_product) to learn more.
    # The resulting distance increases the more similar two vectors are.
    DOT_PRODUCT = 3
  end
end

#distance_threshold ⇒ ::Google::Protobuf::DoubleValue

Returns Optional. Option to specify a threshold for which no less similar documents will be returned. The behavior of the specified distance_measure will affect the meaning of the distance threshold. Since DOT_PRODUCT distances increase when the vectors are more similar, the comparison is inverted.

For EUCLIDEAN, COSINE: WHERE distance <= distance_threshold For DOT_PRODUCT: WHERE distance >= distance_threshold.

Returns:

  • (::Google::Protobuf::DoubleValue) —

    Optional. Option to specify a threshold for which no less similar documents will be returned. The behavior of the specified distance_measure will affect the meaning of the distance threshold. Since DOT_PRODUCT distances increase when the vectors are more similar, the comparison is inverted.

    For EUCLIDEAN, COSINE: WHERE distance <= distance_threshold For DOT_PRODUCT: WHERE distance >= distance_threshold



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# File 'proto_docs/google/datastore/v1/query.rb', line 512

class FindNearest
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The distance measure to use when comparing vectors.
  module DistanceMeasure
    # Should not be set.
    DISTANCE_MEASURE_UNSPECIFIED = 0

    # Measures the EUCLIDEAN distance between the vectors. See
    # [Euclidean](https://en.wikipedia.org/wiki/Euclidean_distance) to learn
    # more. The resulting distance decreases the more similar two vectors are.
    EUCLIDEAN = 1

    # COSINE distance compares vectors based on the angle between them, which
    # allows you to measure similarity that isn't based on the vectors
    # magnitude. We recommend using DOT_PRODUCT with unit normalized vectors
    # instead of COSINE distance, which is mathematically equivalent with
    # better performance. See [Cosine
    # Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) to learn
    # more about COSINE similarity and COSINE distance. The resulting COSINE
    # distance decreases the more similar two vectors are.
    COSINE = 2

    # Similar to cosine but is affected by the magnitude of the vectors. See
    # [Dot Product](https://en.wikipedia.org/wiki/Dot_product) to learn more.
    # The resulting distance increases the more similar two vectors are.
    DOT_PRODUCT = 3
  end
end

#limit ⇒ ::Google::Protobuf::Int32Value

Returns Required. The number of nearest neighbors to return. Must be a positive integer of no more than 100.

Returns:



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# File 'proto_docs/google/datastore/v1/query.rb', line 512

class FindNearest
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The distance measure to use when comparing vectors.
  module DistanceMeasure
    # Should not be set.
    DISTANCE_MEASURE_UNSPECIFIED = 0

    # Measures the EUCLIDEAN distance between the vectors. See
    # [Euclidean](https://en.wikipedia.org/wiki/Euclidean_distance) to learn
    # more. The resulting distance decreases the more similar two vectors are.
    EUCLIDEAN = 1

    # COSINE distance compares vectors based on the angle between them, which
    # allows you to measure similarity that isn't based on the vectors
    # magnitude. We recommend using DOT_PRODUCT with unit normalized vectors
    # instead of COSINE distance, which is mathematically equivalent with
    # better performance. See [Cosine
    # Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) to learn
    # more about COSINE similarity and COSINE distance. The resulting COSINE
    # distance decreases the more similar two vectors are.
    COSINE = 2

    # Similar to cosine but is affected by the magnitude of the vectors. See
    # [Dot Product](https://en.wikipedia.org/wiki/Dot_product) to learn more.
    # The resulting distance increases the more similar two vectors are.
    DOT_PRODUCT = 3
  end
end

#query_vector ⇒ ::Google::Cloud::Datastore::V1::Value

Returns Required. The query vector that we are searching on. Must be a vector of no more than 2048 dimensions.

Returns:



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# File 'proto_docs/google/datastore/v1/query.rb', line 512

class FindNearest
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The distance measure to use when comparing vectors.
  module DistanceMeasure
    # Should not be set.
    DISTANCE_MEASURE_UNSPECIFIED = 0

    # Measures the EUCLIDEAN distance between the vectors. See
    # [Euclidean](https://en.wikipedia.org/wiki/Euclidean_distance) to learn
    # more. The resulting distance decreases the more similar two vectors are.
    EUCLIDEAN = 1

    # COSINE distance compares vectors based on the angle between them, which
    # allows you to measure similarity that isn't based on the vectors
    # magnitude. We recommend using DOT_PRODUCT with unit normalized vectors
    # instead of COSINE distance, which is mathematically equivalent with
    # better performance. See [Cosine
    # Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) to learn
    # more about COSINE similarity and COSINE distance. The resulting COSINE
    # distance decreases the more similar two vectors are.
    COSINE = 2

    # Similar to cosine but is affected by the magnitude of the vectors. See
    # [Dot Product](https://en.wikipedia.org/wiki/Dot_product) to learn more.
    # The resulting distance increases the more similar two vectors are.
    DOT_PRODUCT = 3
  end
end

#vector_property ⇒ ::Google::Cloud::Datastore::V1::PropertyReference

Required. An indexed vector property to search upon. Only documents which contain vectors whose dimensionality match the query_vector can be returned.

Returns:



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# File 'proto_docs/google/datastore/v1/query.rb', line 512

class FindNearest
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The distance measure to use when comparing vectors.
  module DistanceMeasure
    # Should not be set.
    DISTANCE_MEASURE_UNSPECIFIED = 0

    # Measures the EUCLIDEAN distance between the vectors. See
    # [Euclidean](https://en.wikipedia.org/wiki/Euclidean_distance) to learn
    # more. The resulting distance decreases the more similar two vectors are.
    EUCLIDEAN = 1

    # COSINE distance compares vectors based on the angle between them, which
    # allows you to measure similarity that isn't based on the vectors
    # magnitude. We recommend using DOT_PRODUCT with unit normalized vectors
    # instead of COSINE distance, which is mathematically equivalent with
    # better performance. See [Cosine
    # Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) to learn
    # more about COSINE similarity and COSINE distance. The resulting COSINE
    # distance decreases the more similar two vectors are.
    COSINE = 2

    # Similar to cosine but is affected by the magnitude of the vectors. See
    # [Dot Product](https://en.wikipedia.org/wiki/Dot_product) to learn more.
    # The resulting distance increases the more similar two vectors are.
    DOT_PRODUCT = 3
  end
end