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    Module language_models (1.33.1)

    Classes for working with language models.

    Classes

    ChatMessage 

      ChatMessage 
 ( 
 content 
 : 
 str 
 , 
 author 
 : 
 str 
 )

    A chat message.

    Author of the message.

    ChatSession 

      ChatSession 
 ( 
 model 
 : 
 vertexai 
 . 
 language_models 
 . 
 ChatModel 
 , 
 context 
 : 
 typing 
 . 
 Optional 
 [ 
 str 
 ] 
 = 
 None 
 , 
 examples 
 : 
 typing 
 . 
 Optional 
 [ 
 typing 
 . 
 List 
 [ 
 vertexai 
 . 
 language_models 
 . 
 InputOutputTextPair 
 ] 
 ] 
 = 
 None 
 , 
 max_output_tokens 
 : 
 typing 
 . 
 Optional 
 [ 
 int 
 ] 
 = 
 None 
 , 
 temperature 
 : 
 typing 
 . 
 Optional 
 [ 
 float 
 ] 
 = 
 None 
 , 
 top_k 
 : 
 typing 
 . 
 Optional 
 [ 
 int 
 ] 
 = 
 None 
 , 
 top_p 
 : 
 typing 
 . 
 Optional 
 [ 
 float 
 ] 
 = 
 None 
 , 
 message_history 
 : 
 typing 
 . 
 Optional 
 [ 
 typing 
 . 
 List 
 [ 
 vertexai 
 . 
 language_models 
 . 
 ChatMessage 
 ] 
 ] 
 = 
 None 
 , 
 stop_sequences 
 : 
 typing 
 . 
 Optional 
 [ 
 typing 
 . 
 List 
 [ 
 str 
 ]] 
 = 
 None 
 , 
 )

    ChatSession represents a chat session with a language model.

    Within a chat session, the model keeps context and remembers the previous conversation.

    CodeChatSession 

      CodeChatSession 
 ( 
 model 
 : 
 vertexai 
 . 
 language_models 
 . 
 CodeChatModel 
 , 
 context 
 : 
 typing 
 . 
 Optional 
 [ 
 str 
 ] 
 = 
 None 
 , 
 max_output_tokens 
 : 
 typing 
 . 
 Optional 
 [ 
 int 
 ] 
 = 
 None 
 , 
 temperature 
 : 
 typing 
 . 
 Optional 
 [ 
 float 
 ] 
 = 
 None 
 , 
 message_history 
 : 
 typing 
 . 
 Optional 
 [ 
 typing 
 . 
 List 
 [ 
 vertexai 
 . 
 language_models 
 . 
 ChatMessage 
 ] 
 ] 
 = 
 None 
 , 
 stop_sequences 
 : 
 typing 
 . 
 Optional 
 [ 
 typing 
 . 
 List 
 [ 
 str 
 ]] 
 = 
 None 
 , 
 )

    CodeChatSession represents a chat session with code chat language model.

    Within a code chat session, the model keeps context and remembers the previous converstion.

    EvaluationClassificationMetric 

      EvaluationClassificationMetric 
 ( 
 label_name 
 : 
 typing 
 . 
 Optional 
 [ 
 str 
 ] 
 = 
 None 
 , 
 auPrc 
 : 
 typing 
 . 
 Optional 
 [ 
 float 
 ] 
 = 
 None 
 , 
 auRoc 
 : 
 typing 
 . 
 Optional 
 [ 
 float 
 ] 
 = 
 None 
 , 
 logLoss 
 : 
 typing 
 . 
 Optional 
 [ 
 float 
 ] 
 = 
 None 
 , 
 confidenceMetrics 
 : 
 typing 
 . 
 Optional 
 [ 
 typing 
 . 
 List 
 [ 
 typing 
 . 
 Dict 
 [ 
 str 
 , 
 typing 
 . 
 Any 
 ]] 
 ] 
 = 
 None 
 , 
 confusionMatrix 
 : 
 typing 
 . 
 Optional 
 [ 
 typing 
 . 
 Dict 
 [ 
 str 
 , 
 typing 
 . 
 Any 
 ]] 
 = 
 None 
 , 
 )

    The evaluation metric response for classification metrics.

    Parameters
    Name
    Description
    label_name
    str

    Optional. The name of the label associated with the metrics. This is only returned when only_summary_metrics=False is passed to evaluate().

    auPrc
    float

    Optional. The area under the precision recall curve.

    auRoc
    float

    Optional. The area under the receiver operating characteristic curve.

    logLoss
    float

    Optional. Logarithmic loss.

    confidenceMetrics
    List[Dict[str, Any]]

    Optional. This is only returned when only_summary_metrics=False is passed to evaluate().

    confusionMatrix
    Dict[str, Any]

    Optional. This is only returned when only_summary_metrics=False is passed to evaluate().

    EvaluationMetric 

      EvaluationMetric 
 ( 
 bleu 
 : 
 typing 
 . 
 Optional 
 [ 
 float 
 ] 
 = 
 None 
 , 
 rougeLSum 
 : 
 typing 
 . 
 Optional 
 [ 
 float 
 ] 
 = 
 None 
 )

    The evaluation metric response.

    Parameters
    Name
    Description
    bleu
    float

    Optional. BLEU (Bilingual evauation understudy). Scores based on sacrebleu implementation.

    rougeLSum
    float

    Optional. ROUGE-L (Longest Common Subsequence) scoring at summary level.

    EvaluationQuestionAnsweringSpec 

      EvaluationQuestionAnsweringSpec 
 ( 
 ground_truth_data 
 : 
 typing 
 . 
 Union 
 [ 
 typing 
 . 
 List 
 [ 
 str 
 ], 
 str 
 , 
 pandas 
 . 
 DataFrame 
 ], 
 task_name 
 : 
 str 
 = 
 "question-answering" 
 , 
 )

    Spec for question answering model evaluation tasks.

    EvaluationTextClassificationSpec 

      EvaluationTextClassificationSpec 
 ( 
 ground_truth_data 
 : 
 typing 
 . 
 Union 
 [ 
 typing 
 . 
 List 
 [ 
 str 
 ], 
 str 
 , 
 pandas 
 . 
 DataFrame 
 ], 
 target_column_name 
 : 
 str 
 , 
 class_names 
 : 
 typing 
 . 
 List 
 [ 
 str 
 ], 
 )

    Spec for text classification model evaluation tasks.

    Parameters
    Name
    Description
    target_column_name
    str

    Required. The label column in the dataset provided in ground_truth_data . Required when task_name='text-classification'.

    class_names
    List[str]

    Required. A list of all possible label names in your dataset. Required when task_name='text-classification'.

    EvaluationTextGenerationSpec 

      EvaluationTextGenerationSpec 
 ( 
 ground_truth_data 
 : 
 typing 
 . 
 Union 
 [ 
 typing 
 . 
 List 
 [ 
 str 
 ], 
 str 
 , 
 pandas 
 . 
 DataFrame 
 ] 
 )

    Spec for text generation model evaluation tasks.

    EvaluationTextSummarizationSpec 

      EvaluationTextSummarizationSpec 
 ( 
 ground_truth_data 
 : 
 typing 
 . 
 Union 
 [ 
 typing 
 . 
 List 
 [ 
 str 
 ], 
 str 
 , 
 pandas 
 . 
 DataFrame 
 ], 
 task_name 
 : 
 str 
 = 
 "summarization" 
 , 
 )

    Spec for text summarization model evaluation tasks.

    InputOutputTextPair 

      InputOutputTextPair 
 ( 
 input_text 
 : 
 str 
 , 
 output_text 
 : 
 str 
 )

    InputOutputTextPair represents a pair of input and output texts.

    TextEmbedding 

      TextEmbedding 
 ( 
 values 
 : 
 typing 
 . 
 List 
 [ 
 float 
 ], 
 statistics 
 : 
 typing 
 . 
 Optional 
 [ 
 vertexai 
 . 
 language_models 
 . 
 TextEmbeddingStatistics 
 ] 
 = 
 None 
 , 
 _prediction_response 
 : 
 typing 
 . 
 Optional 
 [ 
 google 
 . 
 cloud 
 . 
 aiplatform 
 . 
 models 
 . 
 Prediction 
 ] 
 = 
 None 
 , 
 )

    Text embedding vector and statistics.

    TextEmbeddingInput 

      TextEmbeddingInput 
 ( 
 text 
 : 
 str 
 , 
 task_type 
 : 
 typing 
 . 
 Optional 
 [ 
 str 
 ] 
 = 
 None 
 , 
 title 
 : 
 typing 
 . 
 Optional 
 [ 
 str 
 ] 
 = 
 None 
 , 
 )

    Structural text embedding input.

    The name of the downstream task the embeddings will be used for. Valid values: RETRIEVAL_QUERY Specifies the given text is a query in a search/retrieval setting. RETRIEVAL_DOCUMENT Specifies the given text is a document from the corpus being searched. SEMANTIC_SIMILARITY Specifies the given text will be used for STS. CLASSIFICATION Specifies that the given text will be classified. CLUSTERING Specifies that the embeddings will be used for clustering.

    TextGenerationResponse 

      TextGenerationResponse 
 ( 
 text 
 : 
 str 
 , 
 _prediction_response 
 : 
 typing 
 . 
 Any 
 , 
 is_blocked 
 : 
 bool 
 = 
 False 
 , 
 safety_attributes 
 : 
 typing 
 . 
 Dict 
 [ 
 str 
 , 
 float 
 ] 
 = 
< factory 
> )

    TextGenerationResponse represents a response of a language model. .. attribute:: text

    The generated text

    Scores for safety attributes. Learn more about the safety attributes here: https://cloud.google.com/vertex-ai/docs/generative-ai/learn/responsible-ai#safety_attribute_descriptions

    TuningEvaluationSpec 

      TuningEvaluationSpec 
 ( 
 evaluation_data 
 : 
 str 
 , 
 evaluation_interval 
 : 
 typing 
 . 
 Optional 
 [ 
 int 
 ] 
 = 
 None 
 , 
 enable_early_stopping 
 : 
 typing 
 . 
 Optional 
 [ 
 bool 
 ] 
 = 
 None 
 , 
 tensorboard 
 : 
 typing 
 . 
 Optional 
 [ 
 typing 
 . 
 Union 
 [ 
 google 
 . 
 cloud 
 . 
 aiplatform 
 . 
 tensorboard 
 . 
 tensorboard_resource 
 . 
 Tensorboard 
 , 
 str 
 ] 
 ] 
 = 
 None 
 , 
 )

    Specification for model evaluation to perform during tuning.

    The evaluation will run at every evaluation_interval tuning steps. Default: 20.

    Vertex Tensorboard where to write the evaluation metrics. The Tensorboard must be in the same location as the tuning job.
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