- 1.63.0 (latest)
- 1.62.0
- 1.60.0
- 1.59.0
- 1.58.0
- 1.57.0
- 1.56.0
- 1.55.0
- 1.54.1
- 1.53.0
- 1.52.0
- 1.51.0
- 1.50.0
- 1.49.0
- 1.48.0
- 1.47.0
- 1.46.0
- 1.45.0
- 1.44.0
- 1.43.0
- 1.39.0
- 1.38.1
- 1.37.0
- 1.36.4
- 1.35.0
- 1.34.0
- 1.33.1
- 1.32.0
- 1.31.1
- 1.30.1
- 1.29.0
- 1.28.1
- 1.27.1
- 1.26.1
- 1.25.0
- 1.24.1
- 1.23.0
- 1.22.1
- 1.21.0
- 1.20.0
- 1.19.1
- 1.18.3
- 1.17.1
- 1.16.1
- 1.15.1
- 1.14.0
- 1.13.1
- 1.12.1
- 1.11.0
- 1.10.0
- 1.9.0
- 1.8.1
- 1.7.1
- 1.6.2
- 1.5.0
- 1.4.3
- 1.3.0
- 1.2.0
- 1.1.1
- 1.0.1
- 0.9.0
- 0.8.0
- 0.7.1
- 0.6.0
- 0.5.1
- 0.4.0
- 0.3.1
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._language_models.ChatModel,
context: typing.Optional[str] = None,
examples: typing.Optional[
typing.List[vertexai.language_models._language_models.InputOutputTextPair]
] = None,
max_output_tokens: typing.Optional[int] = 128,
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._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._language_models.CodeChatModel,
max_output_tokens: int = 128,
temperature: typing.Optional[float] = None,
message_history: typing.Optional[
typing.List[vertexai.language_models._language_models.ChatMessage]
] = 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 |
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 |
confusionMatrix |
Dict[str, Any]
Optional. This is only returned when |
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 |
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: vertexai.language_models._language_models.TextEmbeddingStatistics,
_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.