Class: Google::Cloud::AIPlatform::V1::EmbedContentRequest

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

Overview

Request message for PredictionService.EmbedContent.

Defined Under Namespace

Modules: EmbeddingTaskType

Instance Attribute Summary collapse

Instance Attribute Details

#auto_truncate ⇒ ::Boolean

Returns Optional. Whether to silently truncate the input content if it's longer than the maximum sequence length.

Returns:

  • (::Boolean) —

    Optional. Whether to silently truncate the input content if it's longer than the maximum sequence length.



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# File 'proto_docs/google/cloud/aiplatform/v1/prediction_service.rb', line 723

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

  # Represents a downstream task the embeddings will be used for.
  module EmbeddingTaskType
    # Unset value, which will default to one of the other enum values.
    UNSPECIFIED = 0

    # Specifies the given text is a query in a search/retrieval setting.
    RETRIEVAL_QUERY = 2

    # Specifies the given text is a document from the corpus being searched.
    RETRIEVAL_DOCUMENT = 3

    # Specifies the given text will be used for STS.
    SEMANTIC_SIMILARITY = 4

    # Specifies that the given text will be classified.
    CLASSIFICATION = 5

    # Specifies that the embeddings will be used for clustering.
    CLUSTERING = 6

    # Specifies that the embeddings will be used for question answering.
    QUESTION_ANSWERING = 7

    # Specifies that the embeddings will be used for fact verification.
    FACT_VERIFICATION = 8

    # Specifies that the embeddings will be used for code retrieval.
    CODE_RETRIEVAL_QUERY = 9
  end
end

#content ⇒ ::Google::Cloud::AIPlatform::V1::Content

Returns Required. Input content to be embedded. Required.

Returns:



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# File 'proto_docs/google/cloud/aiplatform/v1/prediction_service.rb', line 723

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

  # Represents a downstream task the embeddings will be used for.
  module EmbeddingTaskType
    # Unset value, which will default to one of the other enum values.
    UNSPECIFIED = 0

    # Specifies the given text is a query in a search/retrieval setting.
    RETRIEVAL_QUERY = 2

    # Specifies the given text is a document from the corpus being searched.
    RETRIEVAL_DOCUMENT = 3

    # Specifies the given text will be used for STS.
    SEMANTIC_SIMILARITY = 4

    # Specifies that the given text will be classified.
    CLASSIFICATION = 5

    # Specifies that the embeddings will be used for clustering.
    CLUSTERING = 6

    # Specifies that the embeddings will be used for question answering.
    QUESTION_ANSWERING = 7

    # Specifies that the embeddings will be used for fact verification.
    FACT_VERIFICATION = 8

    # Specifies that the embeddings will be used for code retrieval.
    CODE_RETRIEVAL_QUERY = 9
  end
end

#model ⇒ ::String

Returns Required. The name of the publisher model requested to serve the prediction. Format: projects/{project}/locations/{location}/publishers/*/models/*.

Returns:

  • (::String) —

    Required. The name of the publisher model requested to serve the prediction. Format: projects/{project}/locations/{location}/publishers/*/models/*



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# File 'proto_docs/google/cloud/aiplatform/v1/prediction_service.rb', line 723

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

  # Represents a downstream task the embeddings will be used for.
  module EmbeddingTaskType
    # Unset value, which will default to one of the other enum values.
    UNSPECIFIED = 0

    # Specifies the given text is a query in a search/retrieval setting.
    RETRIEVAL_QUERY = 2

    # Specifies the given text is a document from the corpus being searched.
    RETRIEVAL_DOCUMENT = 3

    # Specifies the given text will be used for STS.
    SEMANTIC_SIMILARITY = 4

    # Specifies that the given text will be classified.
    CLASSIFICATION = 5

    # Specifies that the embeddings will be used for clustering.
    CLUSTERING = 6

    # Specifies that the embeddings will be used for question answering.
    QUESTION_ANSWERING = 7

    # Specifies that the embeddings will be used for fact verification.
    FACT_VERIFICATION = 8

    # Specifies that the embeddings will be used for code retrieval.
    CODE_RETRIEVAL_QUERY = 9
  end
end

#output_dimensionality ⇒ ::Integer

Returns Optional. Optional reduced dimension for the output embedding. If set, excessive values in the output embedding are truncated from the end.

Returns:

  • (::Integer) —

    Optional. Optional reduced dimension for the output embedding. If set, excessive values in the output embedding are truncated from the end.



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# File 'proto_docs/google/cloud/aiplatform/v1/prediction_service.rb', line 723

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

  # Represents a downstream task the embeddings will be used for.
  module EmbeddingTaskType
    # Unset value, which will default to one of the other enum values.
    UNSPECIFIED = 0

    # Specifies the given text is a query in a search/retrieval setting.
    RETRIEVAL_QUERY = 2

    # Specifies the given text is a document from the corpus being searched.
    RETRIEVAL_DOCUMENT = 3

    # Specifies the given text will be used for STS.
    SEMANTIC_SIMILARITY = 4

    # Specifies that the given text will be classified.
    CLASSIFICATION = 5

    # Specifies that the embeddings will be used for clustering.
    CLUSTERING = 6

    # Specifies that the embeddings will be used for question answering.
    QUESTION_ANSWERING = 7

    # Specifies that the embeddings will be used for fact verification.
    FACT_VERIFICATION = 8

    # Specifies that the embeddings will be used for code retrieval.
    CODE_RETRIEVAL_QUERY = 9
  end
end

#task_type ⇒ ::Google::Cloud::AIPlatform::V1::EmbedContentRequest::EmbeddingTaskType

Returns Optional. The task type of the embedding.

Returns:



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# File 'proto_docs/google/cloud/aiplatform/v1/prediction_service.rb', line 723

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

  # Represents a downstream task the embeddings will be used for.
  module EmbeddingTaskType
    # Unset value, which will default to one of the other enum values.
    UNSPECIFIED = 0

    # Specifies the given text is a query in a search/retrieval setting.
    RETRIEVAL_QUERY = 2

    # Specifies the given text is a document from the corpus being searched.
    RETRIEVAL_DOCUMENT = 3

    # Specifies the given text will be used for STS.
    SEMANTIC_SIMILARITY = 4

    # Specifies that the given text will be classified.
    CLASSIFICATION = 5

    # Specifies that the embeddings will be used for clustering.
    CLUSTERING = 6

    # Specifies that the embeddings will be used for question answering.
    QUESTION_ANSWERING = 7

    # Specifies that the embeddings will be used for fact verification.
    FACT_VERIFICATION = 8

    # Specifies that the embeddings will be used for code retrieval.
    CODE_RETRIEVAL_QUERY = 9
  end
end

#title ⇒ ::String

Returns Optional. An optional title for the text.

Returns:

  • (::String) —

    Optional. An optional title for the text.



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# File 'proto_docs/google/cloud/aiplatform/v1/prediction_service.rb', line 723

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

  # Represents a downstream task the embeddings will be used for.
  module EmbeddingTaskType
    # Unset value, which will default to one of the other enum values.
    UNSPECIFIED = 0

    # Specifies the given text is a query in a search/retrieval setting.
    RETRIEVAL_QUERY = 2

    # Specifies the given text is a document from the corpus being searched.
    RETRIEVAL_DOCUMENT = 3

    # Specifies the given text will be used for STS.
    SEMANTIC_SIMILARITY = 4

    # Specifies that the given text will be classified.
    CLASSIFICATION = 5

    # Specifies that the embeddings will be used for clustering.
    CLUSTERING = 6

    # Specifies that the embeddings will be used for question answering.
    QUESTION_ANSWERING = 7

    # Specifies that the embeddings will be used for fact verification.
    FACT_VERIFICATION = 8

    # Specifies that the embeddings will be used for code retrieval.
    CODE_RETRIEVAL_QUERY = 9
  end
end