Package Methods (1.93.1)

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.DataFrame

Returns 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.

See more: vertexai.preview.log_classification_metrics

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.ExperimentRun

Start 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.Prompt

Creates 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) -> None

Deletes 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.Prompt

Creates 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.PromptVersionMetadata

Restores 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.

See more: vertexai.preview.tuning.sft.rebase_tuned_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.SupervisedTuningJob

Tunes 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.Prompt

Creates 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) -> None

Deletes the online prompt resource associated with the prompt id.

See more: vertexai.prompts._prompt_management.delete

vertexai.prompts._prompt_management.get

get(
 prompt_id: str, version_id: typing.Optional[str] = None
) -> vertexai.prompts._prompts.Prompt

Creates 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.

See more: vertexai.prompts._prompt_management.list_prompts

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.

See more: vertexai.prompts._prompt_management.list_versions

vertexai.prompts._prompt_management.restore_version

restore_version(
 prompt_id: str, version_id: str
) -> vertexai.prompts._prompt_management.PromptVersionMetadata

Restores 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.

See more: vertexai.evaluation.EvalTask.display_runs

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.EvalResult

Runs an evaluation for the EvalTask.

See more: vertexai.evaluation.EvalTask.evaluate

vertexai.evaluation.MetricPromptTemplateExamples.get_prompt_template

get_prompt_template(metric_name: str) -> str

Returns 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.

See more: vertexai.evaluation.PairwiseMetricPromptTemplate

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.PromptTemplate

Replaces 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() -> str

Returns 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.

See more: vertexai.evaluation.PointwiseMetricPromptTemplate

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.PromptTemplate

Replaces 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() -> str

Returns 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__() -> str

Returns a string representation of the PromptTemplate.

See more: vertexai.evaluation.PromptTemplate.repr

vertexai.evaluation.PromptTemplate.__str__

__str__() -> str

Returns the template string.

See more: vertexai.evaluation.PromptTemplate.str

vertexai.evaluation.PromptTemplate.assemble

assemble(**kwargs) -> vertexai.evaluation.prompt_template.PromptTemplate

Replaces only the provided variables in the template with specific values.

See more: vertexai.evaluation.PromptTemplate.assemble

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

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.

See more: vertexai.generative_models.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,
 model_config: typing.Optional[GenerationConfig.ModelConfig] = None
)

Constructs a GenerationConfig object.

See more: vertexai.generative_models.GenerationConfig

vertexai.generative_models.GenerationConfig.ModelConfig.__delattr__

__delattr__(key)

Delete the value on the given field.

See more: vertexai.generative_models.GenerationConfig.ModelConfig.delattr

vertexai.generative_models.GenerationConfig.ModelConfig.__eq__

__eq__(other)

Return True if the messages are equal, False otherwise.

See more: vertexai.generative_models.GenerationConfig.ModelConfig.eq

vertexai.generative_models.GenerationConfig.ModelConfig.__ne__

__ne__(other)

Return True if the messages are unequal, False otherwise.

See more: vertexai.generative_models.GenerationConfig.ModelConfig.ne

vertexai.generative_models.GenerationConfig.ModelConfig.__setattr__

__setattr__(key, value)

Set the value on the given field.

See more: vertexai.generative_models.GenerationConfig.ModelConfig.setattr

vertexai.generative_models.GenerationConfig.RoutingConfig.AutoRoutingMode

AutoRoutingMode(
 *,
 model_routing_preference: google.cloud.aiplatform_v1beta1.types.content.GenerationConfig.RoutingConfig.AutoRoutingMode.ModelRoutingPreference
)

vertexai.generative_models.GenerationConfig.RoutingConfig.ManualRoutingMode

ManualRoutingMode(*, model_name: str)

vertexai.generative_models.GenerativeModel.compute_tokens

compute_tokens(
 contents: ContentsType,
) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponse

vertexai.generative_models.GenerativeModel.compute_tokens_async

compute_tokens_async(
 contents: ContentsType,
) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponse

Computes 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.CountTokensResponse

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.CountTokensResponse

Counts tokens asynchronously.

See more: vertexai.generative_models.GenerativeModel.count_tokens_async

vertexai.generative_models.GenerativeModel.from_cached_content

from_cached_content(
 cached_content: typing.Union[str, CachedContent],
 *,
 generation_config: typing.Optional[GenerationConfigType] = None,
 safety_settings: typing.Optional[SafetySettingsType] = None
) -> _GenerativeModel

Creates a model from cached content.

See more: vertexai.generative_models.GenerativeModel.from_cached_content

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],
]

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.ChatSession

Creates 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.Image

Loads image from image bytes.

See more: vertexai.generative_models.Image.from_bytes

vertexai.generative_models.Image.load_from_file

load_from_file(
 location: str,
) -> vertexai.generative_models._generative_models.Image

Loads image from file.

See more: vertexai.generative_models.Image.load_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.

See more: vertexai.generative_models.grounding.Retrieval

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.T

Loads 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._LanguageModel

Loads 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.ChatSession

Starts a chat session with the model.

See more: vertexai.language_models.ChatModel.start_chat

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._LanguageModelTuningJob

Tunes a model based on training data.

See more: vertexai.language_models.ChatModel.tune_model

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._LanguageModelTuningJob

Tunes 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.MultiCandidateTextGenerationResponse

Sends message to the language model and gets a response.

See more: vertexai.language_models.ChatSession.send_message

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.MultiCandidateTextGenerationResponse

Asynchronously 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.T

Loads 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._LanguageModel

Loads 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.CodeChatSession

Starts a chat session with the code chat model.

See more: vertexai.language_models.CodeChatModel.start_chat

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._LanguageModelTuningJob

Tunes a model based on training data.

See more: vertexai.language_models.CodeChatModel.tune_model

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.MultiCandidateTextGenerationResponse

Sends 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.MultiCandidateTextGenerationResponse

Asynchronously 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.BatchPredictionJob

Starts 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.T

vertexai.language_models.CodeGenerationModel.get_tuned_model

get_tuned_model(
 tuned_model_name: str,
) -> vertexai.language_models._language_models._LanguageModel

Loads 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]

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.TextGenerationResponse

Gets 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.TextGenerationResponse

Asynchronously 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._LanguageModelTuningJob

Tunes 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.BatchPredictionJob

Starts 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.CountTokensResponse

Counts 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._LanguageModel

Loads 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.T

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._TextEmbeddingModelTuningJob

Tunes 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.BatchPredictionJob

Starts 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.T

vertexai.language_models.TextGenerationModel.get_tuned_model

get_tuned_model(
 tuned_model_name: str,
) -> vertexai.language_models._language_models._LanguageModel

Loads 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]

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.MultiCandidateTextGenerationResponse

Gets 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.MultiCandidateTextGenerationResponse

Asynchronously 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._LanguageModelTuningJob

Tunes 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._LanguageModelTuningJob

Tunes 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)

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._LanguageModelTuningJob

Tunes 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)

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.CallableFunctionDeclaration

Automatically 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],
]

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,
 model_config: typing.Optional[GenerationConfig.ModelConfig] = None
)

Constructs a GenerationConfig object.

See more: vertexai.preview.generative_models.GenerationConfig

vertexai.preview.generative_models.GenerationConfig.ModelConfig.__delattr__

__delattr__(key)

Delete the value on the given field.

See more: vertexai.preview.generative_models.GenerationConfig.ModelConfig.delattr

vertexai.preview.generative_models.GenerationConfig.ModelConfig.__eq__

__eq__(other)

Return True if the messages are equal, False otherwise.

See more: vertexai.preview.generative_models.GenerationConfig.ModelConfig.eq

vertexai.preview.generative_models.GenerationConfig.ModelConfig.__ne__

__ne__(other)

Return True if the messages are unequal, False otherwise.

See more: vertexai.preview.generative_models.GenerationConfig.ModelConfig.ne

vertexai.preview.generative_models.GenerationConfig.ModelConfig.__setattr__

__setattr__(key, value)

vertexai.preview.generative_models.GenerationConfig.RoutingConfig.AutoRoutingMode

AutoRoutingMode(
 *,
 model_routing_preference: google.cloud.aiplatform_v1beta1.types.content.GenerationConfig.RoutingConfig.AutoRoutingMode.ModelRoutingPreference
)

vertexai.preview.generative_models.GenerationConfig.RoutingConfig.ManualRoutingMode

ManualRoutingMode(*, model_name: str)

vertexai.preview.generative_models.GenerativeModel.compute_tokens

compute_tokens(
 contents: ContentsType,
) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponse

vertexai.preview.generative_models.GenerativeModel.compute_tokens_async

compute_tokens_async(
 contents: ContentsType,
) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponse

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.CountTokensResponse

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.CountTokensResponse

vertexai.preview.generative_models.GenerativeModel.from_cached_content

from_cached_content(
 cached_content: typing.Union[str, CachedContent],
 *,
 generation_config: typing.Optional[GenerationConfigType] = None,
 safety_settings: typing.Optional[SafetySettingsType] = None
) -> _GenerativeModel

Creates 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],
]

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
 ],
]

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.ChatSession

Creates 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.Image

Loads 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.Image

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
)

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__() -> str

Returns a string representation of the unassembled prompt.

See more: vertexai.preview.prompts.Prompt.repr

vertexai.preview.prompts.Prompt.__str__

__str__() -> str

Returns 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.

See more: vertexai.preview.prompts.Prompt.assemble_contents

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.

See more: vertexai.preview.prompts.Prompt.generate_content

vertexai.preview.prompts.Prompt.get_unassembled_prompt_data

get_unassembled_prompt_data() -> PartsType

Returns 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.

See more: vertexai.preview.reasoning_engines.AG2Agent

vertexai.preview.reasoning_engines.AG2Agent.clone

clone() -> vertexai.preview.reasoning_engines.templates.ag2.AG2Agent

Returns a clone of the AG2Agent.

See more: vertexai.preview.reasoning_engines.AG2Agent.clone

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.

See more: vertexai.preview.reasoning_engines.AG2Agent.query

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.AdkApp

AdkApp(
 *,
 agent: BaseAgent,
 enable_tracing: bool = False,
 session_service_builder: typing.Optional[
 typing.Callable[[...], BaseSessionService]
 ] = None,
 artifact_service_builder: typing.Optional[
 typing.Callable[[...], BaseArtifactService]
 ] = None,
 env_vars: typing.Optional[typing.Dict[str, str]] = None
)

An ADK Application.

See more: vertexai.preview.reasoning_engines.AdkApp

vertexai.preview.reasoning_engines.AdkApp.async_stream_query

async_stream_query(
 *,
 message: typing.Union[str, typing.Dict[str, typing.Any]],
 user_id: str,
 session_id: typing.Optional[str] = None,
 **kwargs
) -> typing.AsyncIterable[typing.Dict[str, typing.Any]]

Streams responses asynchronously from the ADK application.

See more: vertexai.preview.reasoning_engines.AdkApp.async_stream_query

vertexai.preview.reasoning_engines.AdkApp.clone

clone()

Returns a clone of the ADK application.

See more: vertexai.preview.reasoning_engines.AdkApp.clone

vertexai.preview.reasoning_engines.AdkApp.create_session

create_session(
 *,
 user_id: str,
 session_id: typing.Optional[str] = None,
 state: typing.Optional[typing.Dict[str, typing.Any]] = None,
 **kwargs
)

vertexai.preview.reasoning_engines.AdkApp.delete_session

delete_session(*, user_id: str, session_id: str, **kwargs)

Deletes a session for the given user.

See more: vertexai.preview.reasoning_engines.AdkApp.delete_session

vertexai.preview.reasoning_engines.AdkApp.get_session

get_session(*, user_id: str, session_id: str, **kwargs)

Get a session for the given user.

See more: vertexai.preview.reasoning_engines.AdkApp.get_session

vertexai.preview.reasoning_engines.AdkApp.list_sessions

list_sessions(*, user_id: str, **kwargs)

List sessions for the given user.

See more: vertexai.preview.reasoning_engines.AdkApp.list_sessions

vertexai.preview.reasoning_engines.AdkApp.register_operations

register_operations() -> typing.Dict[str, typing.List[str]]

Registers the operations of the ADK application.

See more: vertexai.preview.reasoning_engines.AdkApp.register_operations

vertexai.preview.reasoning_engines.AdkApp.set_up

set_up()

Sets up the ADK application.

See more: vertexai.preview.reasoning_engines.AdkApp.set_up

vertexai.preview.reasoning_engines.AdkApp.stream_query

stream_query(
 *,
 message: typing.Union[str, typing.Dict[str, typing.Any]],
 user_id: str,
 session_id: typing.Optional[str] = None,
 **kwargs
)

Streams responses from the ADK application in response to a message.

See more: vertexai.preview.reasoning_engines.AdkApp.stream_query

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.

See more: vertexai.preview.reasoning_engines.LangchainAgent

vertexai.preview.reasoning_engines.LangchainAgent.clone

clone() -> vertexai.preview.reasoning_engines.templates.langchain.LangchainAgent

Returns 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.

See more: vertexai.preview.reasoning_engines.LanggraphAgent

vertexai.preview.reasoning_engines.LanggraphAgent.clone

clone() -> vertexai.preview.reasoning_engines.templates.langgraph.LanggraphAgent

Returns 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.LlamaIndexQueryPipelineAgent

LlamaIndexQueryPipelineAgent(
 model: str,
 *,
 system_instruction: typing.Optional[str] = None,
 prompt: typing.Optional[QueryComponent] = None,
 model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
 model_builder: typing.Optional[typing.Callable[[...], FunctionCallingLLM]] = None,
 retriever_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
 retriever_builder: typing.Optional[typing.Callable[[...], QueryComponent]] = None,
 response_synthesizer_kwargs: typing.Optional[
 typing.Mapping[str, typing.Any]
 ] = None,
 response_synthesizer_builder: typing.Optional[
 typing.Callable[[...], QueryComponent]
 ] = None,
 runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
 runnable_builder: typing.Optional[typing.Callable[[...], QueryPipeline]] = None,
 enable_tracing: bool = False
)

Initializes the LlamaIndexQueryPipelineAgent.

See more: vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent

vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.clone

clone() -> (
 vertexai.preview.reasoning_engines.templates.llama_index.LlamaIndexQueryPipelineAgent
)

Returns a clone of the LlamaIndexQueryPipelineAgent.

See more: vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.clone

vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.query

query(
 input: typing.Union[str, typing.Mapping[str, typing.Any]], **kwargs: typing.Any
) -> typing.Union[
 str,
 typing.Dict[str, typing.Any],
 typing.Sequence[typing.Union[str, typing.Dict[str, typing.Any]]],
]

Queries the Agent with the given input and config.

See more: vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.query

vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.set_up

set_up()

Sets up the agent for execution of queries at runtime.

See more: vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.set_up

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.ReasoningEngine

Creates a new ReasoningEngine.

See more: vertexai.preview.reasoning_engines.ReasoningEngine.create

vertexai.preview.reasoning_engines.ReasoningEngine.delete

delete(sync: bool = True) -> None

Deletes 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.ReasoningEngine

Updates 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.TuningJob

Refreshed the tuning job from the service.

See more: vertexai.preview.tuning.TuningJob.refresh

vertexai.preview.tuning.TuningJob.to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

See more: vertexai.preview.tuning.TuningJob.to_dict

vertexai.preview.tuning.sft.SupervisedTuningJob.list

list(
 filter: typing.Optional[str] = None,
) -> typing.List[vertexai.tuning._tuning.TuningJob]

vertexai.preview.tuning.sft.SupervisedTuningJob.refresh

refresh() -> vertexai.tuning._tuning.TuningJob

Refreshed 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.

See more: vertexai.preview.vision_models.GeneratedImage

vertexai.preview.vision_models.GeneratedImage.load_from_file

load_from_file(location: str) -> vertexai.preview.vision_models.GeneratedImage

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()

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.

See more: vertexai.preview.vision_models.GeneratedMask

vertexai.preview.vision_models.GeneratedMask.load_from_file

load_from_file(location: str) -> vertexai.vision_models.Image

Loads 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.

See more: vertexai.preview.vision_models.GeneratedMask.save

vertexai.preview.vision_models.GeneratedMask.show

show()

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.Image

Loads 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.

See more: vertexai.preview.vision_models.Image.save

vertexai.preview.vision_models.Image.show

show()

Shows the image.

See more: vertexai.preview.vision_models.Image.show

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.T

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.ImageGenerationResponse

Edits 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.T

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.ImageGenerationResponse

Generates 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.Image

vertexai.preview.vision_models.ImageGenerationResponse.__getitem__

__getitem__(idx: int) -> vertexai.preview.vision_models.GeneratedImage

Gets 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.

See more: vertexai.preview.vision_models.ImageQnAModel

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.T

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.T

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.ImageSegmentationResponse

vertexai.preview.vision_models.ImageSegmentationResponse.__getitem__

__getitem__(idx: int) -> vertexai.preview.vision_models.GeneratedMask

Gets 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.

See more: vertexai.preview.vision_models.ImageTextModel

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.T

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.

See more: vertexai.preview.vision_models.MaskReferenceImage

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.T

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.MultiModalEmbeddingResponse

Gets 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.

See more: vertexai.preview.vision_models.RawReferenceImage

vertexai.preview.vision_models.ReferenceImage

ReferenceImage(
 reference_id,
 image: typing.Optional[
 typing.Union[bytes, vertexai.vision_models.Image, str]
 ] = None,
)

Creates a ReferenceImage object.

See more: vertexai.preview.vision_models.ReferenceImage

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.Video

Loads 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.

See more: vertexai.preview.vision_models.Video.save

vertexai.preview.vision_models.VideoEmbedding

VideoEmbedding(
 start_offset_sec: int, end_offset_sec: int, embedding: typing.List[float]
)

Creates a VideoEmbedding object.

See more: vertexai.preview.vision_models.VideoEmbedding

vertexai.preview.vision_models.VideoSegmentConfig

VideoSegmentConfig(
 start_offset_sec: int = 0, end_offset_sec: int = 120, interval_sec: int = 16
)

Creates a VideoSegmentConfig object.

See more: vertexai.preview.vision_models.VideoSegmentConfig

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.T

vertexai.preview.vision_models.WatermarkVerificationModel.verify_image

verify_image(
 image: vertexai.vision_models.Image,
) -> vertexai.preview.vision_models.WatermarkVerificationResponse

Verifies 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__() -> str

Returns a string representation of the unassembled prompt.

See more: vertexai.prompts.prompts.Prompt._repr

vertexai.prompts._prompts.Prompt.__str__

__str__() -> str

Returns 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.

See more: vertexai.prompts._prompts.Prompt.generate_content

vertexai.prompts._prompts.Prompt.get_unassembled_prompt_data

get_unassembled_prompt_data() -> PartsType

Returns 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.ModelMonitor

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.Schedule

Creates 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) -> None

Force 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) -> None

vertexai.resources.preview.ml_monitoring.ModelMonitor.delete_schedule

delete_schedule(schedule_name: str) -> None

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.ModelMonitoringJob

vertexai.resources.preview.ml_monitoring.ModelMonitor.get_schedule

get_schedule(
 schedule_name: str,
) -> google.cloud.aiplatform_v1beta1.types.schedule.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_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_schedules

vertexai.resources.preview.ml_monitoring.ModelMonitor.pause_schedule

pause_schedule(schedule_name: str) -> None

vertexai.resources.preview.ml_monitoring.ModelMonitor.resume_schedule

resume_schedule(schedule_name: str) -> None

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.ModelMonitoringJob

Creates 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]

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_stats

vertexai.resources.preview.ml_monitoring.ModelMonitor.show_feature_attribution_drift_stats

show_feature_attribution_drift_stats(model_monitoring_job_name: str) -> None

The 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) -> None

The 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) -> None

The 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.ModelMonitor

Updates 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.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.ModelMonitoringJob

Creates a new ModelMonitoringJob.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.create

vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.delete

delete() -> None

Deletes an Model Monitoring Job.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.delete

vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.done

done() -> bool

Method 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) -> str

Transform 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.GeneratedImage

vertexai.vision_models.GeneratedImage.save

save(location: str, include_generation_parameters: bool = True)

Saves image to a file.

See more: vertexai.vision_models.GeneratedImage.save

vertexai.vision_models.GeneratedImage.show

show()

Shows the image.

See more: vertexai.vision_models.GeneratedImage.show

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.Image

Loads image from local file or Google Cloud Storage.

See more: vertexai.vision_models.Image.load_from_file

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.

See more: vertexai.vision_models.ImageCaptioningModel

vertexai.vision_models.ImageCaptioningModel.from_pretrained

from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.T

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.

See more: vertexai.vision_models.ImageGenerationModel

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.ImageGenerationResponse

Edits 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.T

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.ImageGenerationResponse

Generates 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.Image

vertexai.vision_models.ImageGenerationResponse.__getitem__

__getitem__(idx: int) -> vertexai.preview.vision_models.GeneratedImage

Gets 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.

See more: vertexai.vision_models.ImageQnAModel.ask_question

vertexai.vision_models.ImageQnAModel.from_pretrained

from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.T

Loads 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.T

Loads 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.

See more: vertexai.vision_models.MultiModalEmbeddingModel

vertexai.vision_models.MultiModalEmbeddingModel.from_pretrained

from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.T

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.MultiModalEmbeddingResponse

Gets 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.Video

Loads video from local file or Google Cloud Storage.

See more: vertexai.vision_models.Video.load_from_file

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.

See more: vertexai.vision_models.VideoSegmentConfig

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Last updated 2025年10月30日 UTC.