- 1.122.0 (latest)
- 1.121.0
- 1.120.0
- 1.119.0
- 1.118.0
- 1.117.0
- 1.95.1
- 1.94.0
- 1.93.1
- 1.92.0
- 1.91.0
- 1.90.0
- 1.89.0
- 1.88.0
- 1.87.0
- 1.86.0
- 1.85.0
- 1.84.0
- 1.83.0
- 1.82.0
- 1.81.0
- 1.80.0
- 1.79.0
- 1.78.0
- 1.77.0
- 1.76.0
- 1.75.0
- 1.74.0
- 1.73.0
- 1.72.0
- 1.71.1
- 1.70.0
- 1.69.0
- 1.68.0
- 1.67.1
- 1.66.0
- 1.65.0
- 1.63.0
- 1.62.0
- 1.60.0
- 1.59.0
Summary of entries of Methods for aiplatform.
vertexai.init
init( *, project: typing.Optional[str] = None, location: typing.Optional[str] = None, experiment: typing.Optional[str] = None, experiment_description: typing.Optional[str] = None, experiment_tensorboard: typing.Optional[ typing.Union[ str, google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard, bool, ] ] = None, staging_bucket: typing.Optional[str] = None, credentials: typing.Optional[google.auth.credentials.Credentials] = None, encryption_spec_key_name: typing.Optional[str] = None, network: typing.Optional[str] = None, service_account: typing.Optional[str] = None, api_endpoint: typing.Optional[str] = None, api_key: typing.Optional[str] = None, api_transport: typing.Optional[str] = None, request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = None )Updates common initialization parameters with provided options.
See more: vertexai.init
vertexai.preview.end_run
end_run( state: google.cloud.aiplatform_v1.types.execution.Execution.State = State.COMPLETE, )Ends the the current experiment run.
See more: vertexai.preview.end_run
vertexai.preview.get_experiment_df
get_experiment_df( experiment: typing.Optional[str] = None, *, include_time_series: bool = True ) -> pd.DataFrameReturns a Pandas DataFrame of the parameters and metrics associated with one experiment.
See more: vertexai.preview.get_experiment_df
vertexai.preview.log_classification_metrics
log_classification_metrics( *, labels: typing.Optional[typing.List[str]] = None, matrix: typing.Optional[typing.List[typing.List[int]]] = None, fpr: typing.Optional[typing.List[float]] = None, tpr: typing.Optional[typing.List[float]] = None, threshold: typing.Optional[typing.List[float]] = None, display_name: typing.Optional[str] = None ) -> ( google.cloud.aiplatform.metadata.schema.google.artifact_schema.ClassificationMetrics )Create an artifact for classification metrics and log to ExperimentRun.
vertexai.preview.log_metrics
log_metrics(metrics: typing.Dict[str, typing.Union[float, int, str]])Log single or multiple Metrics with specified key and value pairs.
See more: vertexai.preview.log_metrics
vertexai.preview.log_params
log_params(params: typing.Dict[str, typing.Union[float, int, str]])Log single or multiple parameters with specified key and value pairs.
See more: vertexai.preview.log_params
vertexai.preview.log_time_series_metrics
log_time_series_metrics( metrics: typing.Dict[str, float], step: typing.Optional[int] = None, wall_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None, )Logs time series metrics to to this Experiment Run.
See more: vertexai.preview.log_time_series_metrics
vertexai.preview.start_run
start_run( run: str, *, tensorboard: typing.Optional[ typing.Union[ google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard, str ] ] = None, resume=False ) -> google.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRunStart a run to current session.
See more: vertexai.preview.start_run
vertexai.preview.prompts.create_version
create_version( prompt: vertexai.prompts._prompts.Prompt, prompt_id: typing.Optional[str] = None, version_name: typing.Optional[str] = None, ) -> vertexai.prompts._prompts.PromptCreates a Prompt or Prompt Version in the online prompt store .
See more: vertexai.preview.prompts.create_version
vertexai.preview.prompts.delete
delete(prompt_id: str) -> NoneDeletes the online prompt resource associated with the prompt id.
See more: vertexai.preview.prompts.delete
vertexai.preview.prompts.get
get( prompt_id: str, version_id: typing.Optional[str] = None ) -> vertexai.prompts._prompts.PromptCreates a Prompt object from an online resource.
See more: vertexai.preview.prompts.get
vertexai.preview.prompts.list
list() -> list[vertexai.prompts._prompt_management.PromptMetadata]Lists all prompt resources in the online prompt store associated with the project.
See more: vertexai.preview.prompts.list
vertexai.preview.prompts.list_versions
list_versions( prompt_id: str, ) -> list[vertexai.prompts._prompt_management.PromptVersionMetadata]Returns a list of PromptVersionMetadata objects for the prompt resource.
See more: vertexai.preview.prompts.list_versions
vertexai.preview.prompts.restore_version
restore_version( prompt_id: str, version_id: str ) -> vertexai.prompts._prompt_management.PromptVersionMetadataRestores a previous version of the prompt resource and loads that version into the current Prompt object.
See more: vertexai.preview.prompts.restore_version
vertexai.preview.tuning.sft.rebase_tuned_model
rebase_tuned_model( tuned_model_ref: str, *, artifact_destination: typing.Optional[str] = None, deploy_to_same_endpoint: typing.Optional[bool] = False )Re-runs fine tuning on top of a new foundational model.
vertexai.preview.tuning.sft.train
train( *, source_model: typing.Union[str, vertexai.generative_models.GenerativeModel], train_dataset: str, validation_dataset: typing.Optional[str] = None, tuned_model_display_name: typing.Optional[str] = None, epochs: typing.Optional[int] = None, learning_rate_multiplier: typing.Optional[float] = None, adapter_size: typing.Optional[typing.Literal[1, 4, 8, 16]] = None, labels: typing.Optional[typing.Dict[str, str]] = None ) -> vertexai.tuning._supervised_tuning.SupervisedTuningJobTunes a model using supervised training.
See more: vertexai.preview.tuning.sft.train
vertexai.prompts._prompt_management.create_version
create_version( prompt: vertexai.prompts._prompts.Prompt, prompt_id: typing.Optional[str] = None, version_name: typing.Optional[str] = None, ) -> vertexai.prompts._prompts.PromptCreates a Prompt or Prompt Version in the online prompt store .
See more: vertexai.prompts._prompt_management.create_version
vertexai.prompts._prompt_management.delete
delete(prompt_id: str) -> NoneDeletes the online prompt resource associated with the prompt id.
vertexai.prompts._prompt_management.get
get( prompt_id: str, version_id: typing.Optional[str] = None ) -> vertexai.prompts._prompts.PromptCreates a Prompt object from an online resource.
See more: vertexai.prompts._prompt_management.get
vertexai.prompts._prompt_management.list_prompts
list_prompts() -> list[vertexai.prompts._prompt_management.PromptMetadata]Lists all prompt resources in the online prompt store associated with the project.
vertexai.prompts._prompt_management.list_versions
list_versions( prompt_id: str, ) -> list[vertexai.prompts._prompt_management.PromptVersionMetadata]Returns a list of PromptVersionMetadata objects for the prompt resource.
vertexai.prompts._prompt_management.restore_version
restore_version( prompt_id: str, version_id: str ) -> vertexai.prompts._prompt_management.PromptVersionMetadataRestores a previous version of the prompt resource and loads that version into the current Prompt object.
See more: vertexai.prompts._prompt_management.restore_version
vertexai.evaluation.CustomMetric
CustomMetric( name: str, metric_function: typing.Callable[ [typing.Dict[str, typing.Any]], typing.Dict[str, typing.Any] ], )Initializes the evaluation metric.
See more: vertexai.evaluation.CustomMetric
vertexai.evaluation.EvalTask
EvalTask( *, dataset: typing.Union[pd.DataFrame, str, typing.Dict[str, typing.Any]], metrics: typing.List[ typing.Union[ typing.Literal[ "exact_match", "bleu", "rouge_1", "rouge_2", "rouge_l", "rouge_l_sum", "tool_call_valid", "tool_name_match", "tool_parameter_key_match", "tool_parameter_kv_match", ], vertexai.evaluation.CustomMetric, vertexai.evaluation.metrics._base._AutomaticMetric, vertexai.evaluation.metrics._base._TranslationMetric, vertexai.evaluation.metrics.pointwise_metric.PointwiseMetric, vertexai.evaluation.metrics.pairwise_metric.PairwiseMetric, ] ], experiment: typing.Optional[str] = None, metric_column_mapping: typing.Optional[typing.Dict[str, str]] = None, output_uri_prefix: typing.Optional[str] = "" )Initializes an EvalTask.
See more: vertexai.evaluation.EvalTask
vertexai.evaluation.EvalTask.display_runs
display_runs()Displays experiment runs associated with this EvalTask.
vertexai.evaluation.EvalTask.evaluate
evaluate( *, model: typing.Optional[ typing.Union[ vertexai.generative_models.GenerativeModel, typing.Callable[[str], str] ] ] = None, prompt_template: typing.Optional[str] = None, experiment_run_name: typing.Optional[str] = None, response_column_name: typing.Optional[str] = None, baseline_model_response_column_name: typing.Optional[str] = None, evaluation_service_qps: typing.Optional[float] = None, retry_timeout: float = 120.0, output_file_name: typing.Optional[str] = None ) -> vertexai.evaluation.EvalResultRuns an evaluation for the EvalTask.
See more: vertexai.evaluation.EvalTask.evaluate
vertexai.evaluation.MetricPromptTemplateExamples.get_prompt_template
get_prompt_template(metric_name: str) -> strReturns the prompt template for the given metric name.
See more: vertexai.evaluation.MetricPromptTemplateExamples.get_prompt_template
vertexai.evaluation.MetricPromptTemplateExamples.list_example_metric_names
list_example_metric_names() -> typing.List[str]Returns a list of all metric prompt templates.
See more: vertexai.evaluation.MetricPromptTemplateExamples.list_example_metric_names
vertexai.evaluation.PairwiseMetric
PairwiseMetric( *, metric: str, metric_prompt_template: typing.Union[ vertexai.evaluation.metrics.metric_prompt_template.PairwiseMetricPromptTemplate, str, ], baseline_model: typing.Optional[ typing.Union[ vertexai.generative_models.GenerativeModel, typing.Callable[[str], str] ] ] = None )Initializes a pairwise evaluation metric.
See more: vertexai.evaluation.PairwiseMetric
vertexai.evaluation.PairwiseMetricPromptTemplate
PairwiseMetricPromptTemplate( *, criteria: typing.Dict[str, str], rating_rubric: typing.Dict[str, str], input_variables: typing.Optional[typing.List[str]] = None, instruction: typing.Optional[str] = None, metric_definition: typing.Optional[str] = None, evaluation_steps: typing.Optional[typing.Dict[str, str]] = None, few_shot_examples: typing.Optional[typing.List[str]] = None )Initializes a pairwise metric prompt template.
vertexai.evaluation.PairwiseMetricPromptTemplate.__str__
__str__()Serializes the pairwise metric prompt template to a string.
See more: vertexai.evaluation.PairwiseMetricPromptTemplate.str
vertexai.evaluation.PairwiseMetricPromptTemplate.assemble
assemble(**kwargs) -> vertexai.evaluation.prompt_template.PromptTemplateReplaces only the provided variables in the template with specific values.
See more: vertexai.evaluation.PairwiseMetricPromptTemplate.assemble
vertexai.evaluation.PairwiseMetricPromptTemplate.get_default_pairwise_evaluation_steps
get_default_pairwise_evaluation_steps() -> typing.Dict[str, str]Returns the default evaluation steps for the metric prompt template.
See more: vertexai.evaluation.PairwiseMetricPromptTemplate.get_default_pairwise_evaluation_steps
vertexai.evaluation.PairwiseMetricPromptTemplate.get_default_pairwise_instruction
get_default_pairwise_instruction() -> strReturns the default instruction for the metric prompt template.
See more: vertexai.evaluation.PairwiseMetricPromptTemplate.get_default_pairwise_instruction
vertexai.evaluation.PointwiseMetric
PointwiseMetric( *, metric: str, metric_prompt_template: typing.Union[ vertexai.evaluation.metrics.metric_prompt_template.PointwiseMetricPromptTemplate, str, ] )Initializes a pointwise evaluation metric.
See more: vertexai.evaluation.PointwiseMetric
vertexai.evaluation.PointwiseMetricPromptTemplate
PointwiseMetricPromptTemplate( *, criteria: typing.Dict[str, str], rating_rubric: typing.Dict[str, str], input_variables: typing.Optional[typing.List[str]] = None, instruction: typing.Optional[str] = None, metric_definition: typing.Optional[str] = None, evaluation_steps: typing.Optional[typing.Dict[str, str]] = None, few_shot_examples: typing.Optional[typing.List[str]] = None )Initializes a pointwise metric prompt template.
vertexai.evaluation.PointwiseMetricPromptTemplate.__str__
__str__()Serializes the pointwise metric prompt template to a string.
See more: vertexai.evaluation.PointwiseMetricPromptTemplate.str
vertexai.evaluation.PointwiseMetricPromptTemplate.assemble
assemble(**kwargs) -> vertexai.evaluation.prompt_template.PromptTemplateReplaces only the provided variables in the template with specific values.
See more: vertexai.evaluation.PointwiseMetricPromptTemplate.assemble
vertexai.evaluation.PointwiseMetricPromptTemplate.get_default_pointwise_evaluation_steps
get_default_pointwise_evaluation_steps() -> typing.Dict[str, str]Returns the default evaluation steps for the metric prompt template.
See more: vertexai.evaluation.PointwiseMetricPromptTemplate.get_default_pointwise_evaluation_steps
vertexai.evaluation.PointwiseMetricPromptTemplate.get_default_pointwise_instruction
get_default_pointwise_instruction() -> strReturns the default instruction for the metric prompt template.
See more: vertexai.evaluation.PointwiseMetricPromptTemplate.get_default_pointwise_instruction
vertexai.evaluation.PromptTemplate
PromptTemplate(template: str)Initializes the PromptTemplate with a given template.
See more: vertexai.evaluation.PromptTemplate
vertexai.evaluation.PromptTemplate.__repr__
__repr__() -> strReturns a string representation of the PromptTemplate.
See more: vertexai.evaluation.PromptTemplate.repr
vertexai.evaluation.PromptTemplate.__str__
__str__() -> strReturns the template string.
See more: vertexai.evaluation.PromptTemplate.str
vertexai.evaluation.PromptTemplate.assemble
assemble(**kwargs) -> vertexai.evaluation.prompt_template.PromptTemplateReplaces only the provided variables in the template with specific values.
vertexai.evaluation.Rouge
Rouge( *, rouge_type: typing.Literal[ "rouge1", "rouge2", "rouge3", "rouge4", "rouge5", "rouge6", "rouge7", "rouge8", "rouge9", "rougeL", "rougeLsum", ], use_stemmer: bool = False, split_summaries: bool = False )Initializes the ROUGE metric.
See more: vertexai.evaluation.Rouge
vertexai.generative_models.ChatSession.send_message
Generates content.
See more: vertexai.generative_models.ChatSession.send_message
vertexai.generative_models.ChatSession.send_message_async
Generates content asynchronously.
See more: vertexai.generative_models.ChatSession.send_message_async
vertexai.generative_models.FunctionDeclaration
FunctionDeclaration( *, name: str, parameters: typing.Dict[str, typing.Any], description: typing.Optional[str] = None, response: typing.Optional[typing.Dict[str, typing.Any]] = None )Constructs a FunctionDeclaration.
vertexai.generative_models.GenerationConfig
GenerationConfig( *, temperature: typing.Optional[float] = None, top_p: typing.Optional[float] = None, top_k: typing.Optional[int] = None, candidate_count: typing.Optional[int] = None, max_output_tokens: typing.Optional[int] = None, stop_sequences: typing.Optional[typing.List[str]] = None, presence_penalty: typing.Optional[float] = None, frequency_penalty: typing.Optional[float] = None, response_mime_type: typing.Optional[str] = None, response_schema: typing.Optional[typing.Dict[str, typing.Any]] = None, seed: typing.Optional[int] = None, audio_timestamp: typing.Optional[bool] = None, routing_config: typing.Optional[RoutingConfig] = None, logprobs: typing.Optional[int] = None, response_logprobs: typing.Optional[bool] = None, response_modalities: typing.Optional[typing.List[GenerationConfig.Modality]] = None )Constructs a GenerationConfig object.
vertexai.generative_models.GenerationConfig.RoutingConfig.AutoRoutingMode
AutoRoutingMode( *, model_routing_preference: google.cloud.aiplatform_v1beta1.types.content.GenerationConfig.RoutingConfig.AutoRoutingMode.ModelRoutingPreference )AutoRouingMode constructor .
See more: vertexai.generative_models.GenerationConfig.RoutingConfig.AutoRoutingMode
vertexai.generative_models.GenerationConfig.RoutingConfig.ManualRoutingMode
ManualRoutingMode(*, model_name: str)ManualRoutingMode constructor .
See more: vertexai.generative_models.GenerationConfig.RoutingConfig.ManualRoutingMode
vertexai.generative_models.GenerativeModel.compute_tokens
compute_tokens( contents: ContentsType, ) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponseComputes tokens.
See more: vertexai.generative_models.GenerativeModel.compute_tokens
vertexai.generative_models.GenerativeModel.compute_tokens_async
compute_tokens_async( contents: ContentsType, ) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponseComputes tokens asynchronously.
See more: vertexai.generative_models.GenerativeModel.compute_tokens_async
vertexai.generative_models.GenerativeModel.count_tokens
count_tokens( contents: ContentsType, *, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None ) -> google.cloud.aiplatform_v1beta1.types.prediction_service.CountTokensResponseCounts tokens.
See more: vertexai.generative_models.GenerativeModel.count_tokens
vertexai.generative_models.GenerativeModel.count_tokens_async
count_tokens_async( contents: ContentsType, *, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None ) -> google.cloud.aiplatform_v1beta1.types.prediction_service.CountTokensResponseCounts tokens asynchronously.
See more: vertexai.generative_models.GenerativeModel.count_tokens_async
vertexai.generative_models.GenerativeModel.generate_content
generate_content( contents: ContentsType, *, generation_config: typing.Optional[GenerationConfigType] = None, safety_settings: typing.Optional[SafetySettingsType] = None, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None, tool_config: typing.Optional[ vertexai.generative_models._generative_models.ToolConfig ] = None, labels: typing.Optional[typing.Dict[str, str]] = None, stream: bool = False ) -> typing.Union[ vertexai.generative_models._generative_models.GenerationResponse, typing.Iterable[vertexai.generative_models._generative_models.GenerationResponse], ]Generates content.
See more: vertexai.generative_models.GenerativeModel.generate_content
vertexai.generative_models.GenerativeModel.generate_content_async
generate_content_async( contents: ContentsType, *, generation_config: typing.Optional[GenerationConfigType] = None, safety_settings: typing.Optional[SafetySettingsType] = None, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None, tool_config: typing.Optional[ vertexai.generative_models._generative_models.ToolConfig ] = None, labels: typing.Optional[typing.Dict[str, str]] = None, stream: bool = False ) -> typing.Union[ vertexai.generative_models._generative_models.GenerationResponse, typing.AsyncIterable[ vertexai.generative_models._generative_models.GenerationResponse ], ]Generates content asynchronously.
See more: vertexai.generative_models.GenerativeModel.generate_content_async
vertexai.generative_models.GenerativeModel.start_chat
start_chat( *, history: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Content] ] = None, response_validation: bool = True ) -> vertexai.generative_models._generative_models.ChatSessionCreates a stateful chat session.
See more: vertexai.generative_models.GenerativeModel.start_chat
vertexai.generative_models.Image.from_bytes
from_bytes(data: bytes) -> vertexai.generative_models._generative_models.ImageLoads image from image bytes.
vertexai.generative_models.Image.load_from_file
load_from_file( location: str, ) -> vertexai.generative_models._generative_models.ImageLoads image from file.
vertexai.generative_models.ResponseValidationError.with_traceback
Exception.with_traceback(tb) -- set self.traceback to tb and return self.
See more: vertexai.generative_models.ResponseValidationError.with_traceback
vertexai.generative_models.SafetySetting
SafetySetting( *, category: google.cloud.aiplatform_v1beta1.types.content.HarmCategory, threshold: google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold, method: typing.Optional[ google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockMethod ] = None )Safety settings.
See more: vertexai.generative_models.SafetySetting
vertexai.generative_models.grounding.DynamicRetrievalConfig
DynamicRetrievalConfig( mode: google.cloud.aiplatform_v1beta1.types.tool.DynamicRetrievalConfig.Mode = Mode.MODE_UNSPECIFIED, dynamic_threshold: typing.Optional[float] = None, )Initializes a DynamicRetrievalConfig.
See more: vertexai.generative_models.grounding.DynamicRetrievalConfig
vertexai.generative_models.grounding.GoogleSearchRetrieval
GoogleSearchRetrieval( dynamic_retrieval_config: typing.Optional[ vertexai.generative_models._generative_models.grounding.DynamicRetrievalConfig ] = None, )Initializes a Google Search Retrieval tool.
See more: vertexai.generative_models.grounding.GoogleSearchRetrieval
vertexai.generative_models.grounding.Retrieval
Retrieval( source: vertexai.generative_models._generative_models.grounding.VertexAISearch, disable_attribution: typing.Optional[bool] = None, )Initializes a Retrieval tool.
vertexai.generative_models.grounding.VertexAISearch
VertexAISearch( datastore: str, *, project: typing.Optional[str] = None, location: typing.Optional[str] = None )Initializes a Vertex AI Search tool.
See more: vertexai.generative_models.grounding.VertexAISearch
vertexai.language_models.ChatModel
ChatModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a LanguageModel.
See more: vertexai.language_models.ChatModel
vertexai.language_models.ChatModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.language_models.ChatModel.from_pretrained
vertexai.language_models.ChatModel.get_tuned_model
get_tuned_model( tuned_model_name: str, ) -> vertexai.language_models._language_models._LanguageModelLoads the specified tuned language model.
See more: vertexai.language_models.ChatModel.get_tuned_model
vertexai.language_models.ChatModel.list_tuned_model_names
list_tuned_model_names() -> typing.Sequence[str]Lists the names of tuned models.
See more: vertexai.language_models.ChatModel.list_tuned_model_names
vertexai.language_models.ChatModel.start_chat
start_chat( *, 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 ) -> vertexai.language_models.ChatSessionStarts a chat session with the model.
vertexai.language_models.ChatModel.tune_model
tune_model( training_data: typing.Union[str, pandas.core.frame.DataFrame], *, train_steps: typing.Optional[int] = None, learning_rate_multiplier: typing.Optional[float] = None, tuning_job_location: typing.Optional[str] = None, tuned_model_location: typing.Optional[str] = None, model_display_name: typing.Optional[str] = None, default_context: typing.Optional[str] = None, accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None, tuning_evaluation_spec: typing.Optional[ vertexai.language_models.TuningEvaluationSpec ] = None ) -> vertexai.language_models._language_models._LanguageModelTuningJobTunes a model based on training data.
vertexai.language_models.ChatModel.tune_model_rlhf
tune_model_rlhf( *, prompt_data: typing.Union[str, pandas.core.frame.DataFrame], preference_data: typing.Union[str, pandas.core.frame.DataFrame], model_display_name: typing.Optional[str] = None, prompt_sequence_length: typing.Optional[int] = None, target_sequence_length: typing.Optional[int] = None, reward_model_learning_rate_multiplier: typing.Optional[float] = None, reinforcement_learning_rate_multiplier: typing.Optional[float] = None, reward_model_train_steps: typing.Optional[int] = None, reinforcement_learning_train_steps: typing.Optional[int] = None, kl_coeff: typing.Optional[float] = None, default_context: typing.Optional[str] = None, tuning_job_location: typing.Optional[str] = None, accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None, tuning_evaluation_spec: typing.Optional[ vertexai.language_models.TuningEvaluationSpec ] = None ) -> vertexai.language_models._language_models._LanguageModelTuningJobTunes a model using reinforcement learning from human feedback.
See more: vertexai.language_models.ChatModel.tune_model_rlhf
vertexai.language_models.ChatSession.send_message
send_message( message: str, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, top_k: typing.Optional[int] = None, top_p: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None, candidate_count: typing.Optional[int] = None, grounding_source: typing.Optional[ typing.Union[ vertexai.language_models._language_models.WebSearch, vertexai.language_models._language_models.VertexAISearch, vertexai.language_models._language_models.InlineContext, ] ] = None ) -> vertexai.language_models.MultiCandidateTextGenerationResponseSends message to the language model and gets a response.
vertexai.language_models.ChatSession.send_message_async
send_message_async( message: str, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, top_k: typing.Optional[int] = None, top_p: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None, candidate_count: typing.Optional[int] = None, grounding_source: typing.Optional[ typing.Union[ vertexai.language_models._language_models.WebSearch, vertexai.language_models._language_models.VertexAISearch, vertexai.language_models._language_models.InlineContext, ] ] = None ) -> vertexai.language_models.MultiCandidateTextGenerationResponseAsynchronously sends message to the language model and gets a response.
See more: vertexai.language_models.ChatSession.send_message_async
vertexai.language_models.ChatSession.send_message_streaming
send_message_streaming( message: str, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, top_k: typing.Optional[int] = None, top_p: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None ) -> typing.Iterator[vertexai.language_models.TextGenerationResponse]Sends message to the language model and gets a streamed response.
See more: vertexai.language_models.ChatSession.send_message_streaming
vertexai.language_models.ChatSession.send_message_streaming_async
send_message_streaming_async( message: str, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, top_k: typing.Optional[int] = None, top_p: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None ) -> typing.AsyncIterator[vertexai.language_models.TextGenerationResponse]Asynchronously sends message to the language model and gets a streamed response.
See more: vertexai.language_models.ChatSession.send_message_streaming_async
vertexai.language_models.CodeChatModel
CodeChatModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a LanguageModel.
See more: vertexai.language_models.CodeChatModel
vertexai.language_models.CodeChatModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.language_models.CodeChatModel.from_pretrained
vertexai.language_models.CodeChatModel.get_tuned_model
get_tuned_model( tuned_model_name: str, ) -> vertexai.language_models._language_models._LanguageModelLoads the specified tuned language model.
See more: vertexai.language_models.CodeChatModel.get_tuned_model
vertexai.language_models.CodeChatModel.list_tuned_model_names
list_tuned_model_names() -> typing.Sequence[str]Lists the names of tuned models.
See more: vertexai.language_models.CodeChatModel.list_tuned_model_names
vertexai.language_models.CodeChatModel.start_chat
start_chat( *, 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 ) -> vertexai.language_models.CodeChatSessionStarts a chat session with the code chat model.
vertexai.language_models.CodeChatModel.tune_model
tune_model( training_data: typing.Union[str, pandas.core.frame.DataFrame], *, train_steps: typing.Optional[int] = None, learning_rate_multiplier: typing.Optional[float] = None, tuning_job_location: typing.Optional[str] = None, tuned_model_location: typing.Optional[str] = None, model_display_name: typing.Optional[str] = None, default_context: typing.Optional[str] = None, accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None, tuning_evaluation_spec: typing.Optional[ vertexai.language_models.TuningEvaluationSpec ] = None ) -> vertexai.language_models._language_models._LanguageModelTuningJobTunes a model based on training data.
vertexai.language_models.CodeChatSession.send_message
send_message( message: str, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None, candidate_count: typing.Optional[int] = None ) -> vertexai.language_models.MultiCandidateTextGenerationResponseSends message to the code chat model and gets a response.
See more: vertexai.language_models.CodeChatSession.send_message
vertexai.language_models.CodeChatSession.send_message_async
send_message_async( message: str, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, candidate_count: typing.Optional[int] = None ) -> vertexai.language_models.MultiCandidateTextGenerationResponseAsynchronously sends message to the code chat model and gets a response.
See more: vertexai.language_models.CodeChatSession.send_message_async
vertexai.language_models.CodeChatSession.send_message_streaming
send_message_streaming( message: str, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None ) -> typing.Iterator[vertexai.language_models.TextGenerationResponse]Sends message to the language model and gets a streamed response.
See more: vertexai.language_models.CodeChatSession.send_message_streaming
vertexai.language_models.CodeChatSession.send_message_streaming_async
send_message_streaming_async( message: str, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None ) -> typing.AsyncIterator[vertexai.language_models.TextGenerationResponse]Asynchronously sends message to the language model and gets a streamed response.
See more: vertexai.language_models.CodeChatSession.send_message_streaming_async
vertexai.language_models.CodeGenerationModel.batch_predict
batch_predict( *, dataset: typing.Union[str, typing.List[str]], destination_uri_prefix: str, model_parameters: typing.Optional[typing.Dict] = None ) -> google.cloud.aiplatform.jobs.BatchPredictionJobStarts a batch prediction job with the model.
See more: vertexai.language_models.CodeGenerationModel.batch_predict
vertexai.language_models.CodeGenerationModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.language_models.CodeGenerationModel.from_pretrained
vertexai.language_models.CodeGenerationModel.get_tuned_model
get_tuned_model( tuned_model_name: str, ) -> vertexai.language_models._language_models._LanguageModelLoads the specified tuned language model.
See more: vertexai.language_models.CodeGenerationModel.get_tuned_model
vertexai.language_models.CodeGenerationModel.list_tuned_model_names
list_tuned_model_names() -> typing.Sequence[str]Lists the names of tuned models.
See more: vertexai.language_models.CodeGenerationModel.list_tuned_model_names
vertexai.language_models.CodeGenerationModel.predict
predict( prefix: str, suffix: typing.Optional[str] = None, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None, candidate_count: typing.Optional[int] = None ) -> vertexai.language_models.TextGenerationResponseGets model response for a single prompt.
See more: vertexai.language_models.CodeGenerationModel.predict
vertexai.language_models.CodeGenerationModel.predict_async
predict_async( prefix: str, suffix: typing.Optional[str] = None, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None, candidate_count: typing.Optional[int] = None ) -> vertexai.language_models.TextGenerationResponseAsynchronously gets model response for a single prompt.
See more: vertexai.language_models.CodeGenerationModel.predict_async
vertexai.language_models.CodeGenerationModel.predict_streaming
predict_streaming( prefix: str, suffix: typing.Optional[str] = None, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None ) -> typing.Iterator[vertexai.language_models.TextGenerationResponse]Predicts the code based on previous code.
See more: vertexai.language_models.CodeGenerationModel.predict_streaming
vertexai.language_models.CodeGenerationModel.predict_streaming_async
predict_streaming_async( prefix: str, suffix: typing.Optional[str] = None, *, max_output_tokens: typing.Optional[int] = None, temperature: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None ) -> typing.AsyncIterator[vertexai.language_models.TextGenerationResponse]Asynchronously predicts the code based on previous code.
See more: vertexai.language_models.CodeGenerationModel.predict_streaming_async
vertexai.language_models.CodeGenerationModel.tune_model
tune_model( training_data: typing.Union[str, pandas.core.frame.DataFrame], *, train_steps: typing.Optional[int] = None, learning_rate_multiplier: typing.Optional[float] = None, tuning_job_location: typing.Optional[str] = None, tuned_model_location: typing.Optional[str] = None, model_display_name: typing.Optional[str] = None, tuning_evaluation_spec: typing.Optional[ vertexai.language_models.TuningEvaluationSpec ] = None, accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None, max_context_length: typing.Optional[str] = None ) -> vertexai.language_models._language_models._LanguageModelTuningJobTunes a model based on training data.
See more: vertexai.language_models.CodeGenerationModel.tune_model
vertexai.language_models.TextEmbeddingModel.batch_predict
batch_predict( *, dataset: typing.Union[str, typing.List[str]], destination_uri_prefix: str, model_parameters: typing.Optional[typing.Dict] = None ) -> google.cloud.aiplatform.jobs.BatchPredictionJobStarts a batch prediction job with the model.
See more: vertexai.language_models.TextEmbeddingModel.batch_predict
vertexai.language_models.TextEmbeddingModel.count_tokens
count_tokens( prompts: typing.List[str], ) -> vertexai.preview.language_models.CountTokensResponseCounts the tokens and billable characters for a given prompt.
See more: vertexai.language_models.TextEmbeddingModel.count_tokens
vertexai.language_models.TextEmbeddingModel.deploy_tuned_model
deploy_tuned_model( tuned_model_name: str, machine_type: typing.Optional[str] = None, accelerator: typing.Optional[str] = None, accelerator_count: typing.Optional[int] = None, ) -> vertexai.language_models._language_models._LanguageModelLoads the specified tuned language model.
See more: vertexai.language_models.TextEmbeddingModel.deploy_tuned_model
vertexai.language_models.TextEmbeddingModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.language_models.TextEmbeddingModel.from_pretrained
vertexai.language_models.TextEmbeddingModel.get_embeddings
get_embeddings( texts: typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]], *, auto_truncate: bool = True, output_dimensionality: typing.Optional[int] = None ) -> typing.List[vertexai.language_models.TextEmbedding]Calculates embeddings for the given texts.
See more: vertexai.language_models.TextEmbeddingModel.get_embeddings
vertexai.language_models.TextEmbeddingModel.get_embeddings_async
get_embeddings_async( texts: typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]], *, auto_truncate: bool = True, output_dimensionality: typing.Optional[int] = None ) -> typing.List[vertexai.language_models.TextEmbedding]Asynchronously calculates embeddings for the given texts.
See more: vertexai.language_models.TextEmbeddingModel.get_embeddings_async
vertexai.language_models.TextEmbeddingModel.get_tuned_model
get_tuned_model(*args, **kwargs)Loads the specified tuned language model.
See more: vertexai.language_models.TextEmbeddingModel.get_tuned_model
vertexai.language_models.TextEmbeddingModel.list_tuned_model_names
list_tuned_model_names() -> typing.Sequence[str]Lists the names of tuned models.
See more: vertexai.language_models.TextEmbeddingModel.list_tuned_model_names
vertexai.language_models.TextEmbeddingModel.tune_model
tune_model( *, training_data: typing.Optional[str] = None, corpus_data: typing.Optional[str] = None, queries_data: typing.Optional[str] = None, test_data: typing.Optional[str] = None, validation_data: typing.Optional[str] = None, batch_size: typing.Optional[int] = None, train_steps: typing.Optional[int] = None, tuned_model_location: typing.Optional[str] = None, model_display_name: typing.Optional[str] = None, task_type: typing.Optional[str] = None, machine_type: typing.Optional[str] = None, accelerator: typing.Optional[str] = None, accelerator_count: typing.Optional[int] = None, output_dimensionality: typing.Optional[int] = None, learning_rate_multiplier: typing.Optional[float] = None ) -> vertexai.language_models._language_models._TextEmbeddingModelTuningJobTunes a model based on training data.
See more: vertexai.language_models.TextEmbeddingModel.tune_model
vertexai.language_models.TextGenerationModel.batch_predict
batch_predict( *, dataset: typing.Union[str, typing.List[str]], destination_uri_prefix: str, model_parameters: typing.Optional[typing.Dict] = None ) -> google.cloud.aiplatform.jobs.BatchPredictionJobStarts a batch prediction job with the model.
See more: vertexai.language_models.TextGenerationModel.batch_predict
vertexai.language_models.TextGenerationModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.language_models.TextGenerationModel.from_pretrained
vertexai.language_models.TextGenerationModel.get_tuned_model
get_tuned_model( tuned_model_name: str, ) -> vertexai.language_models._language_models._LanguageModelLoads the specified tuned language model.
See more: vertexai.language_models.TextGenerationModel.get_tuned_model
vertexai.language_models.TextGenerationModel.list_tuned_model_names
list_tuned_model_names() -> typing.Sequence[str]Lists the names of tuned models.
See more: vertexai.language_models.TextGenerationModel.list_tuned_model_names
vertexai.language_models.TextGenerationModel.predict
predict( prompt: str, *, max_output_tokens: typing.Optional[int] = 128, temperature: typing.Optional[float] = None, top_k: typing.Optional[int] = None, top_p: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None, candidate_count: typing.Optional[int] = None, grounding_source: typing.Optional[ typing.Union[ vertexai.language_models._language_models.WebSearch, vertexai.language_models._language_models.VertexAISearch, vertexai.language_models._language_models.InlineContext, ] ] = None, logprobs: typing.Optional[int] = None, presence_penalty: typing.Optional[float] = None, frequency_penalty: typing.Optional[float] = None, logit_bias: typing.Optional[typing.Dict[str, float]] = None, seed: typing.Optional[int] = None ) -> vertexai.language_models.MultiCandidateTextGenerationResponseGets model response for a single prompt.
See more: vertexai.language_models.TextGenerationModel.predict
vertexai.language_models.TextGenerationModel.predict_async
predict_async( prompt: str, *, max_output_tokens: typing.Optional[int] = 128, temperature: typing.Optional[float] = None, top_k: typing.Optional[int] = None, top_p: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None, candidate_count: typing.Optional[int] = None, grounding_source: typing.Optional[ typing.Union[ vertexai.language_models._language_models.WebSearch, vertexai.language_models._language_models.VertexAISearch, vertexai.language_models._language_models.InlineContext, ] ] = None, logprobs: typing.Optional[int] = None, presence_penalty: typing.Optional[float] = None, frequency_penalty: typing.Optional[float] = None, logit_bias: typing.Optional[typing.Dict[str, float]] = None, seed: typing.Optional[int] = None ) -> vertexai.language_models.MultiCandidateTextGenerationResponseAsynchronously gets model response for a single prompt.
See more: vertexai.language_models.TextGenerationModel.predict_async
vertexai.language_models.TextGenerationModel.predict_streaming
predict_streaming( prompt: str, *, max_output_tokens: int = 128, temperature: typing.Optional[float] = None, top_k: typing.Optional[int] = None, top_p: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None, logprobs: typing.Optional[int] = None, presence_penalty: typing.Optional[float] = None, frequency_penalty: typing.Optional[float] = None, logit_bias: typing.Optional[typing.Dict[str, float]] = None, seed: typing.Optional[int] = None ) -> typing.Iterator[vertexai.language_models.TextGenerationResponse]Gets a streaming model response for a single prompt.
See more: vertexai.language_models.TextGenerationModel.predict_streaming
vertexai.language_models.TextGenerationModel.predict_streaming_async
predict_streaming_async( prompt: str, *, max_output_tokens: int = 128, temperature: typing.Optional[float] = None, top_k: typing.Optional[int] = None, top_p: typing.Optional[float] = None, stop_sequences: typing.Optional[typing.List[str]] = None, logprobs: typing.Optional[int] = None, presence_penalty: typing.Optional[float] = None, frequency_penalty: typing.Optional[float] = None, logit_bias: typing.Optional[typing.Dict[str, float]] = None, seed: typing.Optional[int] = None ) -> typing.AsyncIterator[vertexai.language_models.TextGenerationResponse]Asynchronously gets a streaming model response for a single prompt.
See more: vertexai.language_models.TextGenerationModel.predict_streaming_async
vertexai.language_models.TextGenerationModel.tune_model
tune_model( training_data: typing.Union[str, pandas.core.frame.DataFrame], *, train_steps: typing.Optional[int] = None, learning_rate_multiplier: typing.Optional[float] = None, tuning_job_location: typing.Optional[str] = None, tuned_model_location: typing.Optional[str] = None, model_display_name: typing.Optional[str] = None, tuning_evaluation_spec: typing.Optional[ vertexai.language_models.TuningEvaluationSpec ] = None, accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None, max_context_length: typing.Optional[str] = None ) -> vertexai.language_models._language_models._LanguageModelTuningJobTunes a model based on training data.
See more: vertexai.language_models.TextGenerationModel.tune_model
vertexai.language_models.TextGenerationModel.tune_model_rlhf
tune_model_rlhf( *, prompt_data: typing.Union[str, pandas.core.frame.DataFrame], preference_data: typing.Union[str, pandas.core.frame.DataFrame], model_display_name: typing.Optional[str] = None, prompt_sequence_length: typing.Optional[int] = None, target_sequence_length: typing.Optional[int] = None, reward_model_learning_rate_multiplier: typing.Optional[float] = None, reinforcement_learning_rate_multiplier: typing.Optional[float] = None, reward_model_train_steps: typing.Optional[int] = None, reinforcement_learning_train_steps: typing.Optional[int] = None, kl_coeff: typing.Optional[float] = None, default_context: typing.Optional[str] = None, tuning_job_location: typing.Optional[str] = None, accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None, tuning_evaluation_spec: typing.Optional[ vertexai.language_models.TuningEvaluationSpec ] = None ) -> vertexai.language_models._language_models._LanguageModelTuningJobTunes a model using reinforcement learning from human feedback.
See more: vertexai.language_models.TextGenerationModel.tune_model_rlhf
vertexai.language_models._language_models._TunableModelMixin
_TunableModelMixin(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a LanguageModel.
See more: vertexai.language_models._language_models._TunableModelMixin
vertexai.language_models._language_models._TunableModelMixin.tune_model
tune_model( training_data: typing.Union[str, pandas.core.frame.DataFrame], *, corpus_data: typing.Optional[str] = None, queries_data: typing.Optional[str] = None, test_data: typing.Optional[str] = None, validation_data: typing.Optional[str] = None, batch_size: typing.Optional[int] = None, train_steps: typing.Optional[int] = None, learning_rate: typing.Optional[float] = None, learning_rate_multiplier: typing.Optional[float] = None, tuning_job_location: typing.Optional[str] = None, tuned_model_location: typing.Optional[str] = None, model_display_name: typing.Optional[str] = None, tuning_evaluation_spec: typing.Optional[ vertexai.language_models.TuningEvaluationSpec ] = None, default_context: typing.Optional[str] = None, task_type: typing.Optional[str] = None, machine_type: typing.Optional[str] = None, accelerator: typing.Optional[str] = None, accelerator_count: typing.Optional[int] = None, accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None, max_context_length: typing.Optional[str] = None, output_dimensionality: typing.Optional[int] = None ) -> vertexai.language_models._language_models._LanguageModelTuningJobTunes a model based on training data.
See more: vertexai.language_models._language_models._TunableModelMixin.tune_model
vertexai.preview.generative_models.AutomaticFunctionCallingResponder
AutomaticFunctionCallingResponder(max_automatic_function_calls: int = 1)Initializes the responder.
See more: vertexai.preview.generative_models.AutomaticFunctionCallingResponder
vertexai.preview.generative_models.CallableFunctionDeclaration
CallableFunctionDeclaration( name: str, function: typing.Callable[[...], typing.Any], parameters: typing.Dict[str, typing.Any], description: typing.Optional[str] = None, )Constructs a FunctionDeclaration.
See more: vertexai.preview.generative_models.CallableFunctionDeclaration
vertexai.preview.generative_models.CallableFunctionDeclaration.from_func
from_func( func: typing.Callable[[...], typing.Any], ) -> vertexai.generative_models._generative_models.CallableFunctionDeclarationAutomatically creates a CallableFunctionDeclaration from a Python function.
See more: vertexai.preview.generative_models.CallableFunctionDeclaration.from_func
vertexai.preview.generative_models.ChatSession.send_message
send_message( content: PartsType, *, generation_config: typing.Optional[GenerationConfigType] = None, safety_settings: typing.Optional[SafetySettingsType] = None, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None, labels: typing.Optional[typing.Dict[str, str]] = None, stream: bool = False ) -> typing.Union[ vertexai.generative_models._generative_models.GenerationResponse, typing.Iterable[vertexai.generative_models._generative_models.GenerationResponse], ]Generates content.
See more: vertexai.preview.generative_models.ChatSession.send_message
vertexai.preview.generative_models.ChatSession.send_message_async
send_message_async( content: PartsType, *, generation_config: typing.Optional[GenerationConfigType] = None, safety_settings: typing.Optional[SafetySettingsType] = None, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None, labels: typing.Optional[typing.Dict[str, str]] = None, stream: bool = False ) -> typing.Union[ typing.Awaitable[vertexai.generative_models._generative_models.GenerationResponse], typing.Awaitable[ typing.AsyncIterable[ vertexai.generative_models._generative_models.GenerationResponse ] ], ]Generates content asynchronously.
See more: vertexai.preview.generative_models.ChatSession.send_message_async
vertexai.preview.generative_models.FunctionDeclaration
FunctionDeclaration( *, name: str, parameters: typing.Dict[str, typing.Any], description: typing.Optional[str] = None, response: typing.Optional[typing.Dict[str, typing.Any]] = None )Constructs a FunctionDeclaration.
See more: vertexai.preview.generative_models.FunctionDeclaration
vertexai.preview.generative_models.GenerationConfig
GenerationConfig( *, temperature: typing.Optional[float] = None, top_p: typing.Optional[float] = None, top_k: typing.Optional[int] = None, candidate_count: typing.Optional[int] = None, max_output_tokens: typing.Optional[int] = None, stop_sequences: typing.Optional[typing.List[str]] = None, presence_penalty: typing.Optional[float] = None, frequency_penalty: typing.Optional[float] = None, response_mime_type: typing.Optional[str] = None, response_schema: typing.Optional[typing.Dict[str, typing.Any]] = None, seed: typing.Optional[int] = None, audio_timestamp: typing.Optional[bool] = None, routing_config: typing.Optional[RoutingConfig] = None, logprobs: typing.Optional[int] = None, response_logprobs: typing.Optional[bool] = None, response_modalities: typing.Optional[typing.List[GenerationConfig.Modality]] = None )Constructs a GenerationConfig object.
See more: vertexai.preview.generative_models.GenerationConfig
vertexai.preview.generative_models.GenerationConfig.RoutingConfig.AutoRoutingMode
AutoRoutingMode( *, model_routing_preference: google.cloud.aiplatform_v1beta1.types.content.GenerationConfig.RoutingConfig.AutoRoutingMode.ModelRoutingPreference )AutoRouingMode constructor .
See more: vertexai.preview.generative_models.GenerationConfig.RoutingConfig.AutoRoutingMode
vertexai.preview.generative_models.GenerationConfig.RoutingConfig.ManualRoutingMode
ManualRoutingMode(*, model_name: str)ManualRoutingMode constructor .
See more: vertexai.preview.generative_models.GenerationConfig.RoutingConfig.ManualRoutingMode
vertexai.preview.generative_models.GenerativeModel.compute_tokens
compute_tokens( contents: ContentsType, ) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponseComputes tokens.
See more: vertexai.preview.generative_models.GenerativeModel.compute_tokens
vertexai.preview.generative_models.GenerativeModel.compute_tokens_async
compute_tokens_async( contents: ContentsType, ) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponseComputes tokens asynchronously.
See more: vertexai.preview.generative_models.GenerativeModel.compute_tokens_async
vertexai.preview.generative_models.GenerativeModel.count_tokens
count_tokens( contents: ContentsType, *, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None ) -> google.cloud.aiplatform_v1beta1.types.prediction_service.CountTokensResponseCounts tokens.
See more: vertexai.preview.generative_models.GenerativeModel.count_tokens
vertexai.preview.generative_models.GenerativeModel.count_tokens_async
count_tokens_async( contents: ContentsType, *, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None ) -> google.cloud.aiplatform_v1beta1.types.prediction_service.CountTokensResponseCounts tokens asynchronously.
See more: vertexai.preview.generative_models.GenerativeModel.count_tokens_async
vertexai.preview.generative_models.GenerativeModel.from_cached_content
from_cached_content( cached_content: typing.Union[str, caching.CachedContent], *, generation_config: typing.Optional[GenerationConfigType] = None, safety_settings: typing.Optional[SafetySettingsType] = None ) -> _GenerativeModelCreates a model from cached content.
See more: vertexai.preview.generative_models.GenerativeModel.from_cached_content
vertexai.preview.generative_models.GenerativeModel.generate_content
generate_content( contents: ContentsType, *, generation_config: typing.Optional[GenerationConfigType] = None, safety_settings: typing.Optional[SafetySettingsType] = None, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None, tool_config: typing.Optional[ vertexai.generative_models._generative_models.ToolConfig ] = None, labels: typing.Optional[typing.Dict[str, str]] = None, stream: bool = False ) -> typing.Union[ vertexai.generative_models._generative_models.GenerationResponse, typing.Iterable[vertexai.generative_models._generative_models.GenerationResponse], ]Generates content.
See more: vertexai.preview.generative_models.GenerativeModel.generate_content
vertexai.preview.generative_models.GenerativeModel.generate_content_async
generate_content_async( contents: ContentsType, *, generation_config: typing.Optional[GenerationConfigType] = None, safety_settings: typing.Optional[SafetySettingsType] = None, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None, tool_config: typing.Optional[ vertexai.generative_models._generative_models.ToolConfig ] = None, labels: typing.Optional[typing.Dict[str, str]] = None, stream: bool = False ) -> typing.Union[ vertexai.generative_models._generative_models.GenerationResponse, typing.AsyncIterable[ vertexai.generative_models._generative_models.GenerationResponse ], ]Generates content asynchronously.
See more: vertexai.preview.generative_models.GenerativeModel.generate_content_async
vertexai.preview.generative_models.GenerativeModel.start_chat
start_chat( *, history: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Content] ] = None, response_validation: bool = True, responder: typing.Optional[ vertexai.generative_models._generative_models.AutomaticFunctionCallingResponder ] = None ) -> vertexai.generative_models._generative_models.ChatSessionCreates a stateful chat session.
See more: vertexai.preview.generative_models.GenerativeModel.start_chat
vertexai.preview.generative_models.Image.from_bytes
from_bytes(data: bytes) -> vertexai.generative_models._generative_models.ImageLoads image from image bytes.
See more: vertexai.preview.generative_models.Image.from_bytes
vertexai.preview.generative_models.Image.load_from_file
load_from_file( location: str, ) -> vertexai.generative_models._generative_models.ImageLoads image from file.
See more: vertexai.preview.generative_models.Image.load_from_file
vertexai.preview.generative_models.ResponseBlockedError.with_traceback
Exception.with_traceback(tb) -- set self.traceback to tb and return self.
See more: vertexai.preview.generative_models.ResponseBlockedError.with_traceback
vertexai.preview.generative_models.ResponseValidationError.with_traceback
Exception.with_traceback(tb) -- set self.traceback to tb and return self.
See more: vertexai.preview.generative_models.ResponseValidationError.with_traceback
vertexai.preview.generative_models.SafetySetting
SafetySetting( *, category: google.cloud.aiplatform_v1beta1.types.content.HarmCategory, threshold: google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold, method: typing.Optional[ google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockMethod ] = None )Safety settings.
vertexai.preview.prompts.Prompt
Prompt( prompt_data: typing.Optional[PartsType] = None, *, variables: typing.Optional[typing.List[typing.Dict[str, PartsType]]] = None, prompt_name: typing.Optional[str] = None, generation_config: typing.Optional[ vertexai.generative_models._generative_models.GenerationConfig ] = None, model_name: typing.Optional[str] = None, safety_settings: typing.Optional[ vertexai.generative_models._generative_models.SafetySetting ] = None, system_instruction: typing.Optional[PartsType] = None, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None, tool_config: typing.Optional[ vertexai.generative_models._generative_models.ToolConfig ] = None )Initializes the Prompt with a given prompt, and variables.
See more: vertexai.preview.prompts.Prompt
vertexai.preview.prompts.Prompt.__repr__
__repr__() -> strReturns a string representation of the unassembled prompt.
See more: vertexai.preview.prompts.Prompt.repr
vertexai.preview.prompts.Prompt.__str__
__str__() -> strReturns the prompt data as a string, without any variables replaced.
See more: vertexai.preview.prompts.Prompt.str
vertexai.preview.prompts.Prompt.assemble_contents
assemble_contents( **variables_dict: PartsType, ) -> typing.List[vertexai.generative_models._generative_models.Content]Returns the prompt data, as a List[Content], assembled with variables if applicable.
vertexai.preview.prompts.Prompt.generate_content
generate_content( contents: ContentsType, *, generation_config: typing.Optional[GenerationConfigType] = None, safety_settings: typing.Optional[SafetySettingsType] = None, model_name: typing.Optional[str] = None, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None, tool_config: typing.Optional[ vertexai.generative_models._generative_models.ToolConfig ] = None, stream: bool = False, system_instruction: typing.Optional[PartsType] = None ) -> typing.Union[ vertexai.generative_models._generative_models.GenerationResponse, typing.Iterable[vertexai.generative_models._generative_models.GenerationResponse], ]Generates content using the saved Prompt configs.
vertexai.preview.prompts.Prompt.get_unassembled_prompt_data
get_unassembled_prompt_data() -> PartsTypeReturns the prompt data, without any variables replaced.
See more: vertexai.preview.prompts.Prompt.get_unassembled_prompt_data
vertexai.preview.reasoning_engines.AG2Agent
AG2Agent( model: str, runnable_name: str, *, api_type: typing.Optional[str] = None, llm_config: typing.Optional[typing.Mapping[str, typing.Any]] = None, system_instruction: typing.Optional[str] = None, runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, runnable_builder: typing.Optional[typing.Callable[[...], ConversableAgent]] = None, tools: typing.Optional[typing.Sequence[typing.Callable[[...], typing.Any]]] = None, enable_tracing: bool = False )Initializes the AG2 Agent.
vertexai.preview.reasoning_engines.AG2Agent.clone
clone() -> vertexai.preview.reasoning_engines.templates.ag2.AG2AgentReturns a clone of the AG2Agent.
vertexai.preview.reasoning_engines.AG2Agent.query
query( *, input: typing.Union[str, typing.Mapping[str, typing.Any]], max_turns: typing.Optional[int] = None, **kwargs: typing.Any ) -> typing.Dict[str, typing.Any]Queries the Agent with the given input.
vertexai.preview.reasoning_engines.AG2Agent.set_up
set_up()Sets up the agent for execution of queries at runtime.
See more: vertexai.preview.reasoning_engines.AG2Agent.set_up
vertexai.preview.reasoning_engines.LangchainAgent
LangchainAgent( model: str, *, system_instruction: typing.Optional[str] = None, prompt: typing.Optional[RunnableSerializable] = None, tools: typing.Optional[typing.Sequence[_ToolLike]] = None, output_parser: typing.Optional[RunnableSerializable] = None, chat_history: typing.Optional[GetSessionHistoryCallable] = None, model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, model_tool_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, agent_executor_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, model_builder: typing.Optional[typing.Callable] = None, runnable_builder: typing.Optional[typing.Callable] = None, enable_tracing: bool = False )Initializes the LangchainAgent.
vertexai.preview.reasoning_engines.LangchainAgent.clone
clone() -> vertexai.preview.reasoning_engines.templates.langchain.LangchainAgentReturns a clone of the LangchainAgent.
See more: vertexai.preview.reasoning_engines.LangchainAgent.clone
vertexai.preview.reasoning_engines.LangchainAgent.query
query( *, input: typing.Union[str, typing.Mapping[str, typing.Any]], config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any ) -> typing.Dict[str, typing.Any]Queries the Agent with the given input and config.
See more: vertexai.preview.reasoning_engines.LangchainAgent.query
vertexai.preview.reasoning_engines.LangchainAgent.set_up
set_up()Sets up the agent for execution of queries at runtime.
See more: vertexai.preview.reasoning_engines.LangchainAgent.set_up
vertexai.preview.reasoning_engines.LangchainAgent.stream_query
stream_query( *, input: typing.Union[str, typing.Mapping[str, typing.Any]], config: typing.Optional[RunnableConfig] = None, **kwargs ) -> typing.Iterable[typing.Any]Stream queries the Agent with the given input and config.
See more: vertexai.preview.reasoning_engines.LangchainAgent.stream_query
vertexai.preview.reasoning_engines.LanggraphAgent
LanggraphAgent( model: str, *, tools: typing.Optional[typing.Sequence[_ToolLike]] = None, model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, model_tool_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, model_builder: typing.Optional[typing.Callable[[...], BaseLanguageModel]] = None, runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, runnable_builder: typing.Optional[ typing.Callable[[...], RunnableSerializable] ] = None, checkpointer_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, checkpointer_builder: typing.Optional[ typing.Callable[[...], BaseCheckpointSaver] ] = None, enable_tracing: bool = False )Initializes the LangGraph Agent.
vertexai.preview.reasoning_engines.LanggraphAgent.clone
clone() -> vertexai.preview.reasoning_engines.templates.langgraph.LanggraphAgentReturns a clone of the LanggraphAgent.
See more: vertexai.preview.reasoning_engines.LanggraphAgent.clone
vertexai.preview.reasoning_engines.LanggraphAgent.get_state
get_state( config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any ) -> typing.Dict[str, typing.Any]Gets the current state of the Agent.
See more: vertexai.preview.reasoning_engines.LanggraphAgent.get_state
vertexai.preview.reasoning_engines.LanggraphAgent.get_state_history
get_state_history( config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any ) -> typing.Iterable[typing.Any]Gets the state history of the Agent.
See more: vertexai.preview.reasoning_engines.LanggraphAgent.get_state_history
vertexai.preview.reasoning_engines.LanggraphAgent.query
query( *, input: typing.Union[str, typing.Mapping[str, typing.Any]], config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any ) -> typing.Dict[str, typing.Any]Queries the Agent with the given input and config.
See more: vertexai.preview.reasoning_engines.LanggraphAgent.query
vertexai.preview.reasoning_engines.LanggraphAgent.register_operations
register_operations() -> typing.Mapping[str, typing.Sequence[str]]Registers the operations of the Agent.
See more: vertexai.preview.reasoning_engines.LanggraphAgent.register_operations
vertexai.preview.reasoning_engines.LanggraphAgent.set_up
set_up()Sets up the agent for execution of queries at runtime.
See more: vertexai.preview.reasoning_engines.LanggraphAgent.set_up
vertexai.preview.reasoning_engines.LanggraphAgent.stream_query
stream_query( *, input: typing.Union[str, typing.Mapping[str, typing.Any]], config: typing.Optional[RunnableConfig] = None, **kwargs ) -> typing.Iterable[typing.Any]Stream queries the Agent with the given input and config.
See more: vertexai.preview.reasoning_engines.LanggraphAgent.stream_query
vertexai.preview.reasoning_engines.LanggraphAgent.update_state
update_state( config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any ) -> typing.Dict[str, typing.Any]Updates the state of the Agent.
See more: vertexai.preview.reasoning_engines.LanggraphAgent.update_state
vertexai.preview.reasoning_engines.Queryable.query
query(**kwargs)Runs the Reasoning Engine to serve the user query.
See more: vertexai.preview.reasoning_engines.Queryable.query
vertexai.preview.reasoning_engines.ReasoningEngine
ReasoningEngine(reasoning_engine_name: str)Retrieves a Reasoning Engine resource.
See more: vertexai.preview.reasoning_engines.ReasoningEngine
vertexai.preview.reasoning_engines.ReasoningEngine.create
create( reasoning_engine: typing.Union[ vertexai.reasoning_engines._reasoning_engines.Queryable, vertexai.reasoning_engines._reasoning_engines.OperationRegistrable, ], *, requirements: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None, reasoning_engine_name: typing.Optional[str] = None, display_name: typing.Optional[str] = None, description: typing.Optional[str] = None, gcs_dir_name: str = "reasoning_engine", sys_version: typing.Optional[str] = None, extra_packages: typing.Optional[typing.Sequence[str]] = None ) -> vertexai.reasoning_engines._reasoning_engines.ReasoningEngineCreates a new ReasoningEngine.
See more: vertexai.preview.reasoning_engines.ReasoningEngine.create
vertexai.preview.reasoning_engines.ReasoningEngine.delete
delete(sync: bool = True) -> NoneDeletes this Vertex AI resource.
See more: vertexai.preview.reasoning_engines.ReasoningEngine.delete
vertexai.preview.reasoning_engines.ReasoningEngine.list
list( filter: typing.Optional[str] = None, order_by: typing.Optional[str] = None, project: typing.Optional[str] = None, location: typing.Optional[str] = None, credentials: typing.Optional[google.auth.credentials.Credentials] = None, parent: typing.Optional[str] = None, ) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]List all instances of this Vertex AI Resource.
See more: vertexai.preview.reasoning_engines.ReasoningEngine.list
vertexai.preview.reasoning_engines.ReasoningEngine.operation_schemas
operation_schemas() -> typing.Sequence[typing.Dict[str, typing.Any]]Returns the (Open)API schemas for the Reasoning Engine.
See more: vertexai.preview.reasoning_engines.ReasoningEngine.operation_schemas
vertexai.preview.reasoning_engines.ReasoningEngine.to_dict
to_dict() -> typing.Dict[str, typing.Any]Returns the resource proto as a dictionary.
See more: vertexai.preview.reasoning_engines.ReasoningEngine.to_dict
vertexai.preview.reasoning_engines.ReasoningEngine.update
update( *, reasoning_engine: typing.Optional[ typing.Union[ vertexai.reasoning_engines._reasoning_engines.Queryable, vertexai.reasoning_engines._reasoning_engines.OperationRegistrable, ] ] = None, requirements: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None, display_name: typing.Optional[str] = None, description: typing.Optional[str] = None, gcs_dir_name: str = "reasoning_engine", sys_version: typing.Optional[str] = None, extra_packages: typing.Optional[typing.Sequence[str]] = None ) -> vertexai.reasoning_engines._reasoning_engines.ReasoningEngineUpdates an existing ReasoningEngine.
See more: vertexai.preview.reasoning_engines.ReasoningEngine.update
vertexai.preview.reasoning_engines.ReasoningEngine.wait
wait()Helper method that blocks until all futures are complete.
See more: vertexai.preview.reasoning_engines.ReasoningEngine.wait
vertexai.preview.tuning.TuningJob
TuningJob(tuning_job_name: str)Initializes class with project, location, and api_client.
See more: vertexai.preview.tuning.TuningJob
vertexai.preview.tuning.TuningJob.list
list( filter: typing.Optional[str] = None, ) -> typing.List[vertexai.tuning._tuning.TuningJob]Lists TuningJobs.
See more: vertexai.preview.tuning.TuningJob.list
vertexai.preview.tuning.TuningJob.refresh
refresh() -> vertexai.tuning._tuning.TuningJobRefreshed the tuning job from the service.
vertexai.preview.tuning.TuningJob.to_dict
to_dict() -> typing.Dict[str, typing.Any]Returns the resource proto as a dictionary.
vertexai.preview.tuning.sft.SupervisedTuningJob.list
list( filter: typing.Optional[str] = None, ) -> typing.List[vertexai.tuning._tuning.TuningJob]Lists TuningJobs.
See more: vertexai.preview.tuning.sft.SupervisedTuningJob.list
vertexai.preview.tuning.sft.SupervisedTuningJob.refresh
refresh() -> vertexai.tuning._tuning.TuningJobRefreshed the tuning job from the service.
See more: vertexai.preview.tuning.sft.SupervisedTuningJob.refresh
vertexai.preview.tuning.sft.SupervisedTuningJob.to_dict
to_dict() -> typing.Dict[str, typing.Any]Returns the resource proto as a dictionary.
See more: vertexai.preview.tuning.sft.SupervisedTuningJob.to_dict
vertexai.preview.vision_models.ControlReferenceImage
ControlReferenceImage( reference_id, image: typing.Optional[ typing.Union[bytes, vertexai.vision_models.Image, str] ] = None, control_type: typing.Optional[ typing.Literal["default", "scribble", "face_mesh", "canny"] ] = None, enable_control_image_computation: typing.Optional[bool] = False, )Creates a ControlReferenceImage object.
See more: vertexai.preview.vision_models.ControlReferenceImage
vertexai.preview.vision_models.GeneratedImage
GeneratedImage( image_bytes: typing.Optional[bytes], generation_parameters: typing.Dict[str, typing.Any], gcs_uri: typing.Optional[str] = None, )Creates a GeneratedImage object.
vertexai.preview.vision_models.GeneratedImage.load_from_file
load_from_file(location: str) -> vertexai.preview.vision_models.GeneratedImageLoads image from file.
See more: vertexai.preview.vision_models.GeneratedImage.load_from_file
vertexai.preview.vision_models.GeneratedImage.save
save(location: str, include_generation_parameters: bool = True)Saves image to a file.
See more: vertexai.preview.vision_models.GeneratedImage.save
vertexai.preview.vision_models.GeneratedImage.show
show()Shows the image.
See more: vertexai.preview.vision_models.GeneratedImage.show
vertexai.preview.vision_models.GeneratedMask
GeneratedMask( image_bytes: typing.Optional[bytes], gcs_uri: typing.Optional[str] = None, labels: typing.Optional[ typing.List[vertexai.preview.vision_models.EntityLabel] ] = None, )Creates a GeneratedMask object.
vertexai.preview.vision_models.GeneratedMask.load_from_file
load_from_file(location: str) -> vertexai.vision_models.ImageLoads image from local file or Google Cloud Storage.
See more: vertexai.preview.vision_models.GeneratedMask.load_from_file
vertexai.preview.vision_models.GeneratedMask.save
save(location: str)Saves image to a file.
vertexai.preview.vision_models.GeneratedMask.show
show()Shows the image.
vertexai.preview.vision_models.Image
Image( image_bytes: typing.Optional[bytes] = None, gcs_uri: typing.Optional[str] = None )Creates an Image object.
See more: vertexai.preview.vision_models.Image
vertexai.preview.vision_models.Image.load_from_file
load_from_file(location: str) -> vertexai.vision_models.ImageLoads image from local file or Google Cloud Storage.
See more: vertexai.preview.vision_models.Image.load_from_file
vertexai.preview.vision_models.Image.save
save(location: str)Saves image to a file.
vertexai.preview.vision_models.Image.show
show()Shows the image.
vertexai.preview.vision_models.ImageCaptioningModel
ImageCaptioningModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a _ModelGardenModel.
See more: vertexai.preview.vision_models.ImageCaptioningModel
vertexai.preview.vision_models.ImageCaptioningModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.preview.vision_models.ImageCaptioningModel.from_pretrained
vertexai.preview.vision_models.ImageCaptioningModel.get_captions
get_captions( image: vertexai.vision_models.Image, *, number_of_results: int = 1, language: str = "en", output_gcs_uri: typing.Optional[str] = None ) -> typing.List[str]Generates captions for a given image.
See more: vertexai.preview.vision_models.ImageCaptioningModel.get_captions
vertexai.preview.vision_models.ImageGenerationModel
ImageGenerationModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a _ModelGardenModel.
See more: vertexai.preview.vision_models.ImageGenerationModel
vertexai.preview.vision_models.ImageGenerationModel.edit_image
edit_image( *, prompt: str, base_image: typing.Optional[vertexai.vision_models.Image] = None, mask: typing.Optional[vertexai.vision_models.Image] = None, reference_images: typing.Optional[ typing.List[vertexai.vision_models.ReferenceImage] ] = None, negative_prompt: typing.Optional[str] = None, number_of_images: int = 1, guidance_scale: typing.Optional[float] = None, edit_mode: typing.Optional[ typing.Literal[ "inpainting-insert", "inpainting-remove", "outpainting", "product-image" ] ] = None, mask_mode: typing.Optional[ typing.Literal["background", "foreground", "semantic"] ] = None, segmentation_classes: typing.Optional[typing.List[str]] = None, mask_dilation: typing.Optional[float] = None, product_position: typing.Optional[typing.Literal["fixed", "reposition"]] = None, output_mime_type: typing.Optional[typing.Literal["image/png", "image/jpeg"]] = None, compression_quality: typing.Optional[float] = None, language: typing.Optional[str] = None, seed: typing.Optional[int] = None, output_gcs_uri: typing.Optional[str] = None, safety_filter_level: typing.Optional[ typing.Literal["block_most", "block_some", "block_few", "block_fewest"] ] = None, person_generation: typing.Optional[ typing.Literal["dont_allow", "allow_adult", "allow_all"] ] = None ) -> vertexai.preview.vision_models.ImageGenerationResponseEdits an existing image based on text prompt.
See more: vertexai.preview.vision_models.ImageGenerationModel.edit_image
vertexai.preview.vision_models.ImageGenerationModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.preview.vision_models.ImageGenerationModel.from_pretrained
vertexai.preview.vision_models.ImageGenerationModel.generate_images
generate_images( prompt: str, *, negative_prompt: typing.Optional[str] = None, number_of_images: int = 1, aspect_ratio: typing.Optional[ typing.Literal["1:1", "9:16", "16:9", "4:3", "3:4"] ] = None, guidance_scale: typing.Optional[float] = None, language: typing.Optional[str] = None, seed: typing.Optional[int] = None, output_gcs_uri: typing.Optional[str] = None, add_watermark: typing.Optional[bool] = True, safety_filter_level: typing.Optional[ typing.Literal["block_most", "block_some", "block_few", "block_fewest"] ] = None, person_generation: typing.Optional[ typing.Literal["dont_allow", "allow_adult", "allow_all"] ] = None ) -> vertexai.preview.vision_models.ImageGenerationResponseGenerates images from text prompt.
See more: vertexai.preview.vision_models.ImageGenerationModel.generate_images
vertexai.preview.vision_models.ImageGenerationModel.upscale_image
upscale_image( image: typing.Union[ vertexai.vision_models.Image, vertexai.preview.vision_models.GeneratedImage ], new_size: typing.Optional[int] = 2048, upscale_factor: typing.Optional[typing.Literal["x2", "x4"]] = None, output_mime_type: typing.Optional[ typing.Literal["image/png", "image/jpeg"] ] = "image/png", output_compression_quality: typing.Optional[int] = None, output_gcs_uri: typing.Optional[str] = None, ) -> vertexai.vision_models.ImageUpscales an image.
See more: vertexai.preview.vision_models.ImageGenerationModel.upscale_image
vertexai.preview.vision_models.ImageGenerationResponse.__getitem__
__getitem__(idx: int) -> vertexai.preview.vision_models.GeneratedImageGets the generated image by index.
See more: vertexai.preview.vision_models.ImageGenerationResponse.getitem
vertexai.preview.vision_models.ImageGenerationResponse.__iter__
__iter__() -> typing.Iterator[vertexai.preview.vision_models.GeneratedImage]Iterates through the generated images.
See more: vertexai.preview.vision_models.ImageGenerationResponse.iter
vertexai.preview.vision_models.ImageQnAModel
ImageQnAModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a _ModelGardenModel.
vertexai.preview.vision_models.ImageQnAModel.ask_question
ask_question( image: vertexai.vision_models.Image, question: str, *, number_of_results: int = 1 ) -> typing.List[str]Answers questions about an image.
See more: vertexai.preview.vision_models.ImageQnAModel.ask_question
vertexai.preview.vision_models.ImageQnAModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.preview.vision_models.ImageQnAModel.from_pretrained
vertexai.preview.vision_models.ImageSegmentationModel
ImageSegmentationModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a _ModelGardenModel.
See more: vertexai.preview.vision_models.ImageSegmentationModel
vertexai.preview.vision_models.ImageSegmentationModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.preview.vision_models.ImageSegmentationModel.from_pretrained
vertexai.preview.vision_models.ImageSegmentationModel.segment_image
segment_image( base_image: vertexai.vision_models.Image, prompt: typing.Optional[str] = None, scribble: typing.Optional[vertexai.preview.vision_models.Scribble] = None, mode: typing.Literal[ "foreground", "background", "semantic", "prompt", "interactive" ] = "foreground", max_predictions: typing.Optional[int] = None, confidence_threshold: typing.Optional[float] = 0.1, mask_dilation: typing.Optional[float] = None, binary_color_threshold: typing.Optional[float] = None, ) -> vertexai.preview.vision_models.ImageSegmentationResponseSegments an image.
See more: vertexai.preview.vision_models.ImageSegmentationModel.segment_image
vertexai.preview.vision_models.ImageSegmentationResponse.__getitem__
__getitem__(idx: int) -> vertexai.preview.vision_models.GeneratedMaskGets the generated masks by index.
See more: vertexai.preview.vision_models.ImageSegmentationResponse.getitem
vertexai.preview.vision_models.ImageSegmentationResponse.__iter__
__iter__() -> typing.Iterator[vertexai.preview.vision_models.GeneratedMask]Iterates through the generated masks.
See more: vertexai.preview.vision_models.ImageSegmentationResponse.iter
vertexai.preview.vision_models.ImageTextModel
ImageTextModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a _ModelGardenModel.
vertexai.preview.vision_models.ImageTextModel.ask_question
ask_question( image: vertexai.vision_models.Image, question: str, *, number_of_results: int = 1 ) -> typing.List[str]Answers questions about an image.
See more: vertexai.preview.vision_models.ImageTextModel.ask_question
vertexai.preview.vision_models.ImageTextModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.preview.vision_models.ImageTextModel.from_pretrained
vertexai.preview.vision_models.ImageTextModel.get_captions
get_captions( image: vertexai.vision_models.Image, *, number_of_results: int = 1, language: str = "en", output_gcs_uri: typing.Optional[str] = None ) -> typing.List[str]Generates captions for a given image.
See more: vertexai.preview.vision_models.ImageTextModel.get_captions
vertexai.preview.vision_models.MaskReferenceImage
MaskReferenceImage( reference_id, image: typing.Optional[ typing.Union[bytes, vertexai.vision_models.Image, str] ] = None, mask_mode: typing.Optional[ typing.Literal[ "default", "user_provided", "background", "foreground", "semantic" ] ] = None, dilation: typing.Optional[float] = None, segmentation_classes: typing.Optional[typing.List[int]] = None, )Creates a MaskReferenceImage object.
vertexai.preview.vision_models.MultiModalEmbeddingModel
MultiModalEmbeddingModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a _ModelGardenModel.
See more: vertexai.preview.vision_models.MultiModalEmbeddingModel
vertexai.preview.vision_models.MultiModalEmbeddingModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.preview.vision_models.MultiModalEmbeddingModel.from_pretrained
vertexai.preview.vision_models.MultiModalEmbeddingModel.get_embeddings
get_embeddings( image: typing.Optional[vertexai.vision_models.Image] = None, video: typing.Optional[vertexai.vision_models.Video] = None, contextual_text: typing.Optional[str] = None, dimension: typing.Optional[int] = None, video_segment_config: typing.Optional[ vertexai.vision_models.VideoSegmentConfig ] = None, ) -> vertexai.vision_models.MultiModalEmbeddingResponseGets embedding vectors from the provided image.
See more: vertexai.preview.vision_models.MultiModalEmbeddingModel.get_embeddings
vertexai.preview.vision_models.RawReferenceImage
RawReferenceImage( reference_id, image: typing.Optional[ typing.Union[bytes, vertexai.vision_models.Image, str] ] = None, )Creates a ReferenceImage object.
vertexai.preview.vision_models.ReferenceImage
ReferenceImage( reference_id, image: typing.Optional[ typing.Union[bytes, vertexai.vision_models.Image, str] ] = None, )Creates a ReferenceImage object.
vertexai.preview.vision_models.Scribble
Scribble(image_bytes: typing.Optional[bytes], gcs_uri: typing.Optional[str] = None)Creates a Scribble object.
See more: vertexai.preview.vision_models.Scribble
vertexai.preview.vision_models.StyleReferenceImage
StyleReferenceImage( reference_id, image: typing.Optional[ typing.Union[bytes, vertexai.vision_models.Image, str] ] = None, style_description: typing.Optional[str] = None, )Creates a StyleReferenceImage object.
See more: vertexai.preview.vision_models.StyleReferenceImage
vertexai.preview.vision_models.SubjectReferenceImage
SubjectReferenceImage( reference_id, image: typing.Optional[ typing.Union[bytes, vertexai.vision_models.Image, str] ] = None, subject_description: typing.Optional[str] = None, subject_type: typing.Optional[ typing.Literal["default", "person", "animal", "product"] ] = None, )Creates a SubjectReferenceImage object.
See more: vertexai.preview.vision_models.SubjectReferenceImage
vertexai.preview.vision_models.Video
Video( video_bytes: typing.Optional[bytes] = None, gcs_uri: typing.Optional[str] = None )Creates a Video object.
See more: vertexai.preview.vision_models.Video
vertexai.preview.vision_models.Video.load_from_file
load_from_file(location: str) -> vertexai.vision_models.VideoLoads video from local file or Google Cloud Storage.
See more: vertexai.preview.vision_models.Video.load_from_file
vertexai.preview.vision_models.Video.save
save(location: str)Saves video to a file.
vertexai.preview.vision_models.VideoEmbedding
VideoEmbedding( start_offset_sec: int, end_offset_sec: int, embedding: typing.List[float] )Creates a VideoEmbedding object.
vertexai.preview.vision_models.VideoSegmentConfig
VideoSegmentConfig( start_offset_sec: int = 0, end_offset_sec: int = 120, interval_sec: int = 16 )Creates a VideoSegmentConfig object.
vertexai.preview.vision_models.WatermarkVerificationModel
WatermarkVerificationModel( model_id: str, endpoint_name: typing.Optional[str] = None )Creates a _ModelGardenModel.
See more: vertexai.preview.vision_models.WatermarkVerificationModel
vertexai.preview.vision_models.WatermarkVerificationModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.preview.vision_models.WatermarkVerificationModel.from_pretrained
vertexai.preview.vision_models.WatermarkVerificationModel.verify_image
verify_image( image: vertexai.vision_models.Image, ) -> vertexai.preview.vision_models.WatermarkVerificationResponseVerifies the watermark of an image.
See more: vertexai.preview.vision_models.WatermarkVerificationModel.verify_image
vertexai.prompts._prompts.Prompt
Prompt( prompt_data: typing.Optional[PartsType] = None, *, variables: typing.Optional[typing.List[typing.Dict[str, PartsType]]] = None, prompt_name: typing.Optional[str] = None, generation_config: typing.Optional[ vertexai.generative_models._generative_models.GenerationConfig ] = None, model_name: typing.Optional[str] = None, safety_settings: typing.Optional[ vertexai.generative_models._generative_models.SafetySetting ] = None, system_instruction: typing.Optional[PartsType] = None, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None, tool_config: typing.Optional[ vertexai.generative_models._generative_models.ToolConfig ] = None )Initializes the Prompt with a given prompt, and variables.
See more: vertexai.prompts._prompts.Prompt
vertexai.prompts._prompts.Prompt.__repr__
__repr__() -> strReturns a string representation of the unassembled prompt.
See more: vertexai.prompts.prompts.Prompt._repr
vertexai.prompts._prompts.Prompt.__str__
__str__() -> strReturns the prompt data as a string, without any variables replaced.
See more: vertexai.prompts.prompts.Prompt._str
vertexai.prompts._prompts.Prompt.assemble_contents
assemble_contents( **variables_dict: PartsType, ) -> typing.List[vertexai.generative_models._generative_models.Content]Returns the prompt data, as a List[Content], assembled with variables if applicable.
See more: vertexai.prompts._prompts.Prompt.assemble_contents
vertexai.prompts._prompts.Prompt.generate_content
generate_content( contents: ContentsType, *, generation_config: typing.Optional[GenerationConfigType] = None, safety_settings: typing.Optional[SafetySettingsType] = None, model_name: typing.Optional[str] = None, tools: typing.Optional[ typing.List[vertexai.generative_models._generative_models.Tool] ] = None, tool_config: typing.Optional[ vertexai.generative_models._generative_models.ToolConfig ] = None, stream: bool = False, system_instruction: typing.Optional[PartsType] = None ) -> typing.Union[ vertexai.generative_models._generative_models.GenerationResponse, typing.Iterable[vertexai.generative_models._generative_models.GenerationResponse], ]Generates content using the saved Prompt configs.
vertexai.prompts._prompts.Prompt.get_unassembled_prompt_data
get_unassembled_prompt_data() -> PartsTypeReturns the prompt data, without any variables replaced.
See more: vertexai.prompts._prompts.Prompt.get_unassembled_prompt_data
vertexai.resources.preview.ml_monitoring.ModelMonitor
ModelMonitor( model_monitor_name: str, project: typing.Optional[str] = None, location: typing.Optional[str] = None, credentials: typing.Optional[google.auth.credentials.Credentials] = None, )Initializes class with project, location, and api_client.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor
vertexai.resources.preview.ml_monitoring.ModelMonitor.create
create( model_name: str, model_version_id: str, training_dataset: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput ] = None, display_name: typing.Optional[str] = None, model_monitoring_schema: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.schema.ModelMonitoringSchema ] = None, tabular_objective_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective ] = None, output_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec ] = None, notification_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec ] = None, explanation_spec: typing.Optional[ google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec ] = None, project: typing.Optional[str] = None, location: typing.Optional[str] = None, credentials: typing.Optional[google.auth.credentials.Credentials] = None, model_monitor_id: typing.Optional[str] = None, ) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitorCreates a new ModelMonitor.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.create
vertexai.resources.preview.ml_monitoring.ModelMonitor.create_schedule
create_schedule( cron: str, target_dataset: vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput, display_name: typing.Optional[str] = None, model_monitoring_job_display_name: typing.Optional[str] = None, start_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None, end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None, tabular_objective_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective ] = None, baseline_dataset: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput ] = None, output_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec ] = None, notification_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec ] = None, explanation_spec: typing.Optional[ google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec ] = None, ) -> google.cloud.aiplatform_v1beta1.types.schedule.ScheduleCreates a new Scheduled run for model monitoring job.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.create_schedule
vertexai.resources.preview.ml_monitoring.ModelMonitor.delete
delete(force: bool = False, sync: bool = True) -> NoneForce delete the model monitor.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.delete
vertexai.resources.preview.ml_monitoring.ModelMonitor.delete_model_monitoring_job
delete_model_monitoring_job(model_monitoring_job_name: str) -> NoneDelete a model monitoring job.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.delete_model_monitoring_job
vertexai.resources.preview.ml_monitoring.ModelMonitor.delete_schedule
delete_schedule(schedule_name: str) -> NoneDeletes an existing Schedule.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.delete_schedule
vertexai.resources.preview.ml_monitoring.ModelMonitor.get_model_monitoring_job
get_model_monitoring_job( model_monitoring_job_name: str, ) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitoringJobGet the specified ModelMonitoringJob.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.get_model_monitoring_job
vertexai.resources.preview.ml_monitoring.ModelMonitor.get_schedule
get_schedule( schedule_name: str, ) -> google.cloud.aiplatform_v1beta1.types.schedule.ScheduleGets an existing Schedule.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.get_schedule
vertexai.resources.preview.ml_monitoring.ModelMonitor.get_schema
get_schema() -> ( google.cloud.aiplatform_v1beta1.types.model_monitor.ModelMonitoringSchema )Get the schema of the model monitor.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.get_schema
vertexai.resources.preview.ml_monitoring.ModelMonitor.list
list( filter: typing.Optional[str] = None, order_by: typing.Optional[str] = None, project: typing.Optional[str] = None, location: typing.Optional[str] = None, credentials: typing.Optional[google.auth.credentials.Credentials] = None, parent: typing.Optional[str] = None, ) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]List all instances of this Vertex AI Resource.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.list
vertexai.resources.preview.ml_monitoring.ModelMonitor.list_jobs
list_jobs( page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None ) -> ListJobsResponse.list_jobsList ModelMonitoringJobs.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.list_jobs
vertexai.resources.preview.ml_monitoring.ModelMonitor.list_schedules
list_schedules( filter: typing.Optional[str] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, ) -> ListSchedulesResponse.list_schedulesList Schedules.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.list_schedules
vertexai.resources.preview.ml_monitoring.ModelMonitor.pause_schedule
pause_schedule(schedule_name: str) -> NonePauses an existing Schedule.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.pause_schedule
vertexai.resources.preview.ml_monitoring.ModelMonitor.resume_schedule
resume_schedule(schedule_name: str) -> NoneResumes an existing Schedule.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.resume_schedule
vertexai.resources.preview.ml_monitoring.ModelMonitor.run
run( target_dataset: vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput, display_name: typing.Optional[str] = None, model_monitoring_job_id: typing.Optional[str] = None, sync: typing.Optional[bool] = False, tabular_objective_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective ] = None, baseline_dataset: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput ] = None, output_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec ] = None, notification_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec ] = None, explanation_spec: typing.Optional[ google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec ] = None, ) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitoringJobCreates a new ModelMonitoringJob.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.run
vertexai.resources.preview.ml_monitoring.ModelMonitor.search_alerts
search_alerts( stats_name: typing.Optional[str] = None, objective_type: typing.Optional[str] = None, model_monitoring_job_name: typing.Optional[str] = None, start_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None, end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, ) -> typing.Dict[str, typing.Any]Search ModelMonitoringAlerts.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.search_alerts
vertexai.resources.preview.ml_monitoring.ModelMonitor.search_metrics
search_metrics( stats_name: typing.Optional[str] = None, objective_type: typing.Optional[str] = None, model_monitoring_job_name: typing.Optional[str] = None, schedule_name: typing.Optional[str] = None, algorithm: typing.Optional[str] = None, start_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None, end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, ) -> MetricsSearchResponse.monitoring_statsSearch ModelMonitoringStats.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.search_metrics
vertexai.resources.preview.ml_monitoring.ModelMonitor.show_feature_attribution_drift_stats
show_feature_attribution_drift_stats(model_monitoring_job_name: str) -> NoneThe method to visualize the feature attribution drift result from a model monitoring job as a histogram chart and a table.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.show_feature_attribution_drift_stats
vertexai.resources.preview.ml_monitoring.ModelMonitor.show_feature_drift_stats
show_feature_drift_stats(model_monitoring_job_name: str) -> NoneThe method to visualize the feature drift result from a model monitoring job as a histogram chart and a table.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.show_feature_drift_stats
vertexai.resources.preview.ml_monitoring.ModelMonitor.show_output_drift_stats
show_output_drift_stats(model_monitoring_job_name: str) -> NoneThe method to visualize the prediction output drift result from a model monitoring job as a histogram chart and a table.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.show_output_drift_stats
vertexai.resources.preview.ml_monitoring.ModelMonitor.to_dict
to_dict() -> typing.Dict[str, typing.Any]Returns the resource proto as a dictionary.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.to_dict
vertexai.resources.preview.ml_monitoring.ModelMonitor.update
update( display_name: typing.Optional[str] = None, training_dataset: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput ] = None, model_monitoring_schema: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.schema.ModelMonitoringSchema ] = None, tabular_objective_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective ] = None, output_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec ] = None, notification_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec ] = None, explanation_spec: typing.Optional[ google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec ] = None, ) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitorUpdates an existing ModelMonitor.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.update
vertexai.resources.preview.ml_monitoring.ModelMonitor.update_schedule
update_schedule( schedule_name: str, display_name: typing.Optional[str] = None, model_monitoring_job_display_name: typing.Optional[str] = None, cron: typing.Optional[str] = None, baseline_dataset: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput ] = None, target_dataset: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput ] = None, tabular_objective_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective ] = None, output_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec ] = None, notification_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec ] = None, explanation_spec: typing.Optional[ google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec ] = None, end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None, ) -> google.cloud.aiplatform_v1beta1.types.schedule.ScheduleUpdates an existing Schedule.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.update_schedule
vertexai.resources.preview.ml_monitoring.ModelMonitor.wait
wait()Helper method that blocks until all futures are complete.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.wait
vertexai.resources.preview.ml_monitoring.ModelMonitoringJob
ModelMonitoringJob( model_monitoring_job_name: str, model_monitor_id: typing.Optional[str] = None, project: typing.Optional[str] = None, location: typing.Optional[str] = None, credentials: typing.Optional[google.auth.credentials.Credentials] = None, )Initializes class with project, location, and api_client.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob
vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.create
create( model_monitor_name: typing.Optional[str] = None, target_dataset: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput ] = None, display_name: typing.Optional[str] = None, model_monitoring_job_id: typing.Optional[str] = None, project: typing.Optional[str] = None, location: typing.Optional[str] = None, credentials: typing.Optional[google.auth.credentials.Credentials] = None, baseline_dataset: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput ] = None, tabular_objective_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective ] = None, output_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec ] = None, notification_spec: typing.Optional[ vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec ] = None, explanation_spec: typing.Optional[ google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec ] = None, sync: bool = False, ) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitoringJobCreates a new ModelMonitoringJob.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.create
vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.delete
delete() -> NoneDeletes an Model Monitoring Job.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.delete
vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.done
done() -> boolMethod indicating whether a job has completed.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.done
vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.list
list( filter: typing.Optional[str] = None, order_by: typing.Optional[str] = None, project: typing.Optional[str] = None, location: typing.Optional[str] = None, credentials: typing.Optional[google.auth.credentials.Credentials] = None, parent: typing.Optional[str] = None, ) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]List all instances of this Vertex AI Resource.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.list
vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.to_dict
to_dict() -> typing.Dict[str, typing.Any]Returns the resource proto as a dictionary.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.to_dict
vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.wait
wait()Helper method that blocks until all futures are complete.
See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.wait
vertexai.resources.preview.ml_monitoring.spec.ModelMonitoringSchema.to_json
to_json(output_dir: typing.Optional[str] = None) -> strTransform ModelMonitoringSchema to json format.
See more: vertexai.resources.preview.ml_monitoring.spec.ModelMonitoringSchema.to_json
vertexai.vision_models.GeneratedImage
GeneratedImage( image_bytes: typing.Optional[bytes], generation_parameters: typing.Dict[str, typing.Any], gcs_uri: typing.Optional[str] = None, )Creates a GeneratedImage object.
See more: vertexai.vision_models.GeneratedImage
vertexai.vision_models.GeneratedImage.load_from_file
load_from_file(location: str) -> vertexai.preview.vision_models.GeneratedImageLoads image from file.
See more: vertexai.vision_models.GeneratedImage.load_from_file
vertexai.vision_models.GeneratedImage.save
save(location: str, include_generation_parameters: bool = True)Saves image to a file.
vertexai.vision_models.GeneratedImage.show
show()Shows the image.
vertexai.vision_models.Image
Image( image_bytes: typing.Optional[bytes] = None, gcs_uri: typing.Optional[str] = None )Creates an Image object.
See more: vertexai.vision_models.Image
vertexai.vision_models.Image.load_from_file
load_from_file(location: str) -> vertexai.vision_models.ImageLoads image from local file or Google Cloud Storage.
vertexai.vision_models.Image.save
save(location: str)Saves image to a file.
See more: vertexai.vision_models.Image.save
vertexai.vision_models.Image.show
show()Shows the image.
See more: vertexai.vision_models.Image.show
vertexai.vision_models.ImageCaptioningModel
ImageCaptioningModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a _ModelGardenModel.
vertexai.vision_models.ImageCaptioningModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.vision_models.ImageCaptioningModel.from_pretrained
vertexai.vision_models.ImageCaptioningModel.get_captions
get_captions( image: vertexai.vision_models.Image, *, number_of_results: int = 1, language: str = "en", output_gcs_uri: typing.Optional[str] = None ) -> typing.List[str]Generates captions for a given image.
See more: vertexai.vision_models.ImageCaptioningModel.get_captions
vertexai.vision_models.ImageGenerationModel
ImageGenerationModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a _ModelGardenModel.
vertexai.vision_models.ImageGenerationModel.edit_image
edit_image( *, prompt: str, base_image: typing.Optional[vertexai.vision_models.Image] = None, mask: typing.Optional[vertexai.vision_models.Image] = None, reference_images: typing.Optional[ typing.List[vertexai.vision_models.ReferenceImage] ] = None, negative_prompt: typing.Optional[str] = None, number_of_images: int = 1, guidance_scale: typing.Optional[float] = None, edit_mode: typing.Optional[ typing.Literal[ "inpainting-insert", "inpainting-remove", "outpainting", "product-image" ] ] = None, mask_mode: typing.Optional[ typing.Literal["background", "foreground", "semantic"] ] = None, segmentation_classes: typing.Optional[typing.List[str]] = None, mask_dilation: typing.Optional[float] = None, product_position: typing.Optional[typing.Literal["fixed", "reposition"]] = None, output_mime_type: typing.Optional[typing.Literal["image/png", "image/jpeg"]] = None, compression_quality: typing.Optional[float] = None, language: typing.Optional[str] = None, seed: typing.Optional[int] = None, output_gcs_uri: typing.Optional[str] = None, safety_filter_level: typing.Optional[ typing.Literal["block_most", "block_some", "block_few", "block_fewest"] ] = None, person_generation: typing.Optional[ typing.Literal["dont_allow", "allow_adult", "allow_all"] ] = None ) -> vertexai.preview.vision_models.ImageGenerationResponseEdits an existing image based on text prompt.
See more: vertexai.vision_models.ImageGenerationModel.edit_image
vertexai.vision_models.ImageGenerationModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.vision_models.ImageGenerationModel.from_pretrained
vertexai.vision_models.ImageGenerationModel.generate_images
generate_images( prompt: str, *, negative_prompt: typing.Optional[str] = None, number_of_images: int = 1, aspect_ratio: typing.Optional[ typing.Literal["1:1", "9:16", "16:9", "4:3", "3:4"] ] = None, guidance_scale: typing.Optional[float] = None, language: typing.Optional[str] = None, seed: typing.Optional[int] = None, output_gcs_uri: typing.Optional[str] = None, add_watermark: typing.Optional[bool] = True, safety_filter_level: typing.Optional[ typing.Literal["block_most", "block_some", "block_few", "block_fewest"] ] = None, person_generation: typing.Optional[ typing.Literal["dont_allow", "allow_adult", "allow_all"] ] = None ) -> vertexai.preview.vision_models.ImageGenerationResponseGenerates images from text prompt.
See more: vertexai.vision_models.ImageGenerationModel.generate_images
vertexai.vision_models.ImageGenerationModel.upscale_image
upscale_image( image: typing.Union[ vertexai.vision_models.Image, vertexai.preview.vision_models.GeneratedImage ], new_size: typing.Optional[int] = 2048, upscale_factor: typing.Optional[typing.Literal["x2", "x4"]] = None, output_mime_type: typing.Optional[ typing.Literal["image/png", "image/jpeg"] ] = "image/png", output_compression_quality: typing.Optional[int] = None, output_gcs_uri: typing.Optional[str] = None, ) -> vertexai.vision_models.ImageUpscales an image.
See more: vertexai.vision_models.ImageGenerationModel.upscale_image
vertexai.vision_models.ImageGenerationResponse.__getitem__
__getitem__(idx: int) -> vertexai.preview.vision_models.GeneratedImageGets the generated image by index.
See more: vertexai.vision_models.ImageGenerationResponse.getitem
vertexai.vision_models.ImageGenerationResponse.__iter__
__iter__() -> typing.Iterator[vertexai.preview.vision_models.GeneratedImage]Iterates through the generated images.
See more: vertexai.vision_models.ImageGenerationResponse.iter
vertexai.vision_models.ImageQnAModel
ImageQnAModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a _ModelGardenModel.
See more: vertexai.vision_models.ImageQnAModel
vertexai.vision_models.ImageQnAModel.ask_question
ask_question( image: vertexai.vision_models.Image, question: str, *, number_of_results: int = 1 ) -> typing.List[str]Answers questions about an image.
vertexai.vision_models.ImageQnAModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.vision_models.ImageQnAModel.from_pretrained
vertexai.vision_models.ImageTextModel
ImageTextModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a _ModelGardenModel.
See more: vertexai.vision_models.ImageTextModel
vertexai.vision_models.ImageTextModel.ask_question
ask_question( image: vertexai.vision_models.Image, question: str, *, number_of_results: int = 1 ) -> typing.List[str]Answers questions about an image.
See more: vertexai.vision_models.ImageTextModel.ask_question
vertexai.vision_models.ImageTextModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.vision_models.ImageTextModel.from_pretrained
vertexai.vision_models.ImageTextModel.get_captions
get_captions( image: vertexai.vision_models.Image, *, number_of_results: int = 1, language: str = "en", output_gcs_uri: typing.Optional[str] = None ) -> typing.List[str]Generates captions for a given image.
See more: vertexai.vision_models.ImageTextModel.get_captions
vertexai.vision_models.MultiModalEmbeddingModel
MultiModalEmbeddingModel(model_id: str, endpoint_name: typing.Optional[str] = None)Creates a _ModelGardenModel.
vertexai.vision_models.MultiModalEmbeddingModel.from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
See more: vertexai.vision_models.MultiModalEmbeddingModel.from_pretrained
vertexai.vision_models.MultiModalEmbeddingModel.get_embeddings
get_embeddings( image: typing.Optional[vertexai.vision_models.Image] = None, video: typing.Optional[vertexai.vision_models.Video] = None, contextual_text: typing.Optional[str] = None, dimension: typing.Optional[int] = None, video_segment_config: typing.Optional[ vertexai.vision_models.VideoSegmentConfig ] = None, ) -> vertexai.vision_models.MultiModalEmbeddingResponseGets embedding vectors from the provided image.
See more: vertexai.vision_models.MultiModalEmbeddingModel.get_embeddings
vertexai.vision_models.Video
Video( video_bytes: typing.Optional[bytes] = None, gcs_uri: typing.Optional[str] = None )Creates a Video object.
See more: vertexai.vision_models.Video
vertexai.vision_models.Video.load_from_file
load_from_file(location: str) -> vertexai.vision_models.VideoLoads video from local file or Google Cloud Storage.
vertexai.vision_models.Video.save
save(location: str)Saves video to a file.
See more: vertexai.vision_models.Video.save
vertexai.vision_models.VideoEmbedding
VideoEmbedding( start_offset_sec: int, end_offset_sec: int, embedding: typing.List[float] )Creates a VideoEmbedding object.
See more: vertexai.vision_models.VideoEmbedding
vertexai.vision_models.VideoSegmentConfig
VideoSegmentConfig( start_offset_sec: int = 0, end_offset_sec: int = 120, interval_sec: int = 16 )Creates a VideoSegmentConfig object.