Cloud AI Platform v1beta1 API - Namespace Google.Cloud.AIPlatform.V1Beta1 (1.0.0-beta05)

Version 1.0.0-beta05

• 1.0.0-beta12 (latest)
• 1.0.0-beta11

Classes

ActiveLearningConfig

Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy.

AddContextArtifactsAndExecutionsRequest

Request message for [MetadataService.AddContextArtifactsAndExecutions][google.cloud.aiplatform.v1beta1.MetadataService.AddContextArtifactsAndExecutions].

AddContextArtifactsAndExecutionsResponse

Response message for [MetadataService.AddContextArtifactsAndExecutions][google.cloud.aiplatform.v1beta1.MetadataService.AddContextArtifactsAndExecutions].

AddContextChildrenRequest

Request message for [MetadataService.AddContextChildren][google.cloud.aiplatform.v1beta1.MetadataService.AddContextChildren].

AddContextChildrenResponse

Response message for [MetadataService.AddContextChildren][google.cloud.aiplatform.v1beta1.MetadataService.AddContextChildren].

AddExecutionEventsRequest

Request message for [MetadataService.AddExecutionEvents][google.cloud.aiplatform.v1beta1.MetadataService.AddExecutionEvents].

AddExecutionEventsResponse

Response message for [MetadataService.AddExecutionEvents][google.cloud.aiplatform.v1beta1.MetadataService.AddExecutionEvents].

AddTrialMeasurementRequest

Request message for [VizierService.AddTrialMeasurement][google.cloud.aiplatform.v1beta1.VizierService.AddTrialMeasurement].

AnnotatedDatasetName

Resource name for the AnnotatedDataset resource.

Annotation

Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.

AnnotationName

Resource name for the Annotation resource.

AnnotationSpec

Identifies a concept with which DataItems may be annotated with.

AnnotationSpecName

Resource name for the AnnotationSpec resource.

ApiAuth

The generic reusable api auth config.

ApiAuth.Types

Container for nested types declared in the ApiAuth message type.

ApiAuth.Types.ApiKeyConfig

The API secret.

Artifact

Instance of a general artifact.

Artifact.Types

Container for nested types declared in the Artifact message type.

ArtifactName

Resource name for the Artifact resource.

ArtifactTypeSchema

The definition of a artifact type in MLMD.

AssignNotebookRuntimeOperationMetadata

Metadata information for [NotebookService.AssignNotebookRuntime][google.cloud.aiplatform.v1beta1.NotebookService.AssignNotebookRuntime].

AssignNotebookRuntimeRequest

Request message for [NotebookService.AssignNotebookRuntime][google.cloud.aiplatform.v1beta1.NotebookService.AssignNotebookRuntime].

Attribution

Attribution that explains a particular prediction output.

AuthConfig

Auth configuration to run the extension.

AuthConfig.Types

Container for nested types declared in the AuthConfig message type.

AuthConfig.Types.ApiKeyConfig

Config for authentication with API key.

AuthConfig.Types.GoogleServiceAccountConfig

Config for Google Service Account Authentication.

AuthConfig.Types.HttpBasicAuthConfig

Config for HTTP Basic Authentication.

AuthConfig.Types.OauthConfig

Config for user oauth.

AuthConfig.Types.OidcConfig

Config for user OIDC auth.

AutoMLDatasetName

Resource name for the AutoMLDataset resource.

AutoMLModelName

Resource name for the AutoMLModel resource.

AutomaticResources

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines.

AutoscalingMetricSpec

The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.

AvroSource

The storage details for Avro input content.

BatchCancelPipelineJobsOperationMetadata

Runtime operation information for [PipelineService.BatchCancelPipelineJobs][google.cloud.aiplatform.v1beta1.PipelineService.BatchCancelPipelineJobs].

BatchCancelPipelineJobsRequest

Request message for [PipelineService.BatchCancelPipelineJobs][google.cloud.aiplatform.v1beta1.PipelineService.BatchCancelPipelineJobs].

BatchCancelPipelineJobsResponse

Response message for [PipelineService.BatchCancelPipelineJobs][google.cloud.aiplatform.v1beta1.PipelineService.BatchCancelPipelineJobs].

BatchCreateFeaturesOperationMetadata

Details of operations that perform batch create Features.

BatchCreateFeaturesRequest

Request message for [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.BatchCreateFeatures].

BatchCreateFeaturesResponse

Response message for [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.BatchCreateFeatures].

BatchCreateTensorboardRunsRequest

Request message for [TensorboardService.BatchCreateTensorboardRuns][google.cloud.aiplatform.v1beta1.TensorboardService.BatchCreateTensorboardRuns].

BatchCreateTensorboardRunsResponse

Response message for [TensorboardService.BatchCreateTensorboardRuns][google.cloud.aiplatform.v1beta1.TensorboardService.BatchCreateTensorboardRuns].

BatchCreateTensorboardTimeSeriesRequest

Request message for [TensorboardService.BatchCreateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.BatchCreateTensorboardTimeSeries].

BatchCreateTensorboardTimeSeriesResponse

Response message for [TensorboardService.BatchCreateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.BatchCreateTensorboardTimeSeries].

BatchDedicatedResources

A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.

BatchDeletePipelineJobsRequest

Request message for [PipelineService.BatchDeletePipelineJobs][google.cloud.aiplatform.v1beta1.PipelineService.BatchDeletePipelineJobs].

BatchDeletePipelineJobsResponse

Response message for [PipelineService.BatchDeletePipelineJobs][google.cloud.aiplatform.v1beta1.PipelineService.BatchDeletePipelineJobs].

BatchImportEvaluatedAnnotationsRequest

Request message for [ModelService.BatchImportEvaluatedAnnotations][google.cloud.aiplatform.v1beta1.ModelService.BatchImportEvaluatedAnnotations]

BatchImportEvaluatedAnnotationsResponse

Response message for [ModelService.BatchImportEvaluatedAnnotations][google.cloud.aiplatform.v1beta1.ModelService.BatchImportEvaluatedAnnotations]

BatchImportModelEvaluationSlicesRequest

Request message for [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.BatchImportModelEvaluationSlices]

BatchImportModelEvaluationSlicesResponse

Response message for [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.BatchImportModelEvaluationSlices]

BatchMigrateResourcesOperationMetadata

Runtime operation information for [MigrationService.BatchMigrateResources][google.cloud.aiplatform.v1beta1.MigrationService.BatchMigrateResources].

BatchMigrateResourcesOperationMetadata.Types

Container for nested types declared in the BatchMigrateResourcesOperationMetadata message type.

BatchMigrateResourcesOperationMetadata.Types.PartialResult

Represents a partial result in batch migration operation for one [MigrateResourceRequest][google.cloud.aiplatform.v1beta1.MigrateResourceRequest].

BatchMigrateResourcesRequest

Request message for [MigrationService.BatchMigrateResources][google.cloud.aiplatform.v1beta1.MigrationService.BatchMigrateResources].

BatchMigrateResourcesResponse

Response message for [MigrationService.BatchMigrateResources][google.cloud.aiplatform.v1beta1.MigrationService.BatchMigrateResources].

BatchPredictionJob

A job that uses a [Model][google.cloud.aiplatform.v1beta1.BatchPredictionJob.model] to produce predictions on multiple [input instances][google.cloud.aiplatform.v1beta1.BatchPredictionJob.input_config]. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

BatchPredictionJob.Types

Container for nested types declared in the BatchPredictionJob message type.

BatchPredictionJob.Types.InputConfig

Configures the input to [BatchPredictionJob][google.cloud.aiplatform.v1beta1.BatchPredictionJob]. See [Model.supported_input_storage_formats][google.cloud.aiplatform.v1beta1.Model.supported_input_storage_formats] for Model's supported input formats, and how instances should be expressed via any of them.

BatchPredictionJob.Types.InstanceConfig

Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.

BatchPredictionJob.Types.OutputConfig

Configures the output of [BatchPredictionJob][google.cloud.aiplatform.v1beta1.BatchPredictionJob]. See [Model.supported_output_storage_formats][google.cloud.aiplatform.v1beta1.Model.supported_output_storage_formats] for supported output formats, and how predictions are expressed via any of them.

BatchPredictionJob.Types.OutputInfo

Further describes this job's output. Supplements [output_config][google.cloud.aiplatform.v1beta1.BatchPredictionJob.output_config].

BatchPredictionJobName

Resource name for the BatchPredictionJob resource.

BatchReadFeatureValuesOperationMetadata

Details of operations that batch reads Feature values.

BatchReadFeatureValuesRequest

Request message for [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.BatchReadFeatureValues].

BatchReadFeatureValuesRequest.Types

Container for nested types declared in the BatchReadFeatureValuesRequest message type.

BatchReadFeatureValuesRequest.Types.EntityTypeSpec

Selects Features of an EntityType to read values of and specifies read settings.

BatchReadFeatureValuesRequest.Types.PassThroughField

Describe pass-through fields in read_instance source.

BatchReadFeatureValuesResponse

Response message for [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.BatchReadFeatureValues].

BatchReadTensorboardTimeSeriesDataRequest

Request message for [TensorboardService.BatchReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.BatchReadTensorboardTimeSeriesData].

BatchReadTensorboardTimeSeriesDataResponse

Response message for [TensorboardService.BatchReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.BatchReadTensorboardTimeSeriesData].

BigQueryDestination

The BigQuery location for the output content.

BigQuerySource

The BigQuery location for the input content.

BleuInput

Input for bleu metric.

BleuInstance

Spec for bleu instance.

BleuMetricValue

Bleu metric value for an instance.

BleuResults

Results for bleu metric.

BleuSpec

Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1.

Blob

Content blob.

It's preferred to send as [text][google.cloud.aiplatform.v1beta1.Part.text] directly rather than raw bytes.

BlurBaselineConfig

Config for blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

BoolArray

A list of boolean values.

CachedContent

A resource used in LLM queries for users to explicitly specify what to cache and how to cache.

CachedContent.Types

Container for nested types declared in the CachedContent message type.

CachedContent.Types.UsageMetadata

Metadata on the usage of the cached content.

CachedContentName

Resource name for the CachedContent resource.

CancelBatchPredictionJobRequest

Request message for [JobService.CancelBatchPredictionJob][google.cloud.aiplatform.v1beta1.JobService.CancelBatchPredictionJob].

CancelCustomJobRequest

Request message for [JobService.CancelCustomJob][google.cloud.aiplatform.v1beta1.JobService.CancelCustomJob].

CancelDataLabelingJobRequest

Request message for [JobService.CancelDataLabelingJob][google.cloud.aiplatform.v1beta1.JobService.CancelDataLabelingJob].

CancelHyperparameterTuningJobRequest

Request message for [JobService.CancelHyperparameterTuningJob][google.cloud.aiplatform.v1beta1.JobService.CancelHyperparameterTuningJob].

CancelNasJobRequest

Request message for [JobService.CancelNasJob][google.cloud.aiplatform.v1beta1.JobService.CancelNasJob].

CancelPipelineJobRequest

Request message for [PipelineService.CancelPipelineJob][google.cloud.aiplatform.v1beta1.PipelineService.CancelPipelineJob].

CancelTrainingPipelineRequest

Request message for [PipelineService.CancelTrainingPipeline][google.cloud.aiplatform.v1beta1.PipelineService.CancelTrainingPipeline].

CancelTuningJobRequest

Request message for [GenAiTuningService.CancelTuningJob][google.cloud.aiplatform.v1beta1.GenAiTuningService.CancelTuningJob].

Candidate

A response candidate generated from the model.

Candidate.Types

Container for nested types declared in the Candidate message type.

ChatCompletionsRequest

Request message for [PredictionService.ChatCompletions]

CheckTrialEarlyStoppingStateMetatdata

This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.

CheckTrialEarlyStoppingStateRequest

Request message for [VizierService.CheckTrialEarlyStoppingState][google.cloud.aiplatform.v1beta1.VizierService.CheckTrialEarlyStoppingState].

CheckTrialEarlyStoppingStateResponse

Response message for [VizierService.CheckTrialEarlyStoppingState][google.cloud.aiplatform.v1beta1.VizierService.CheckTrialEarlyStoppingState].

Citation

Source attributions for content.

CitationMetadata

A collection of source attributions for a piece of content.

CoherenceInput

Input for coherence metric.

CoherenceInstance

Spec for coherence instance.

CoherenceResult

Spec for coherence result.

CoherenceSpec

Spec for coherence score metric.

CompleteTrialRequest

Request message for [VizierService.CompleteTrial][google.cloud.aiplatform.v1beta1.VizierService.CompleteTrial].

CompletionStats

Success and error statistics of processing multiple entities (for example, DataItems or structured data rows) in batch.

ComputeTokensRequest

Request message for ComputeTokens RPC call.

ComputeTokensResponse

Response message for ComputeTokens RPC call.

ContainerRegistryDestination

The Container Registry location for the container image.

ContainerSpec

The spec of a Container.

Content

The base structured datatype containing multi-part content of a message.

A Content includes a role field designating the producer of the Content and a parts field containing multi-part data that contains the content of the message turn.

Context

Instance of a general context.

ContextName

Resource name for the Context resource.

CopyModelOperationMetadata

Details of [ModelService.CopyModel][google.cloud.aiplatform.v1beta1.ModelService.CopyModel] operation.

CopyModelRequest

Request message for [ModelService.CopyModel][google.cloud.aiplatform.v1beta1.ModelService.CopyModel].

CopyModelResponse

Response message of [ModelService.CopyModel][google.cloud.aiplatform.v1beta1.ModelService.CopyModel] operation.

CorpusStatus

RagCorpus status.

CorpusStatus.Types

Container for nested types declared in the CorpusStatus message type.

CountTokensRequest

Request message for [PredictionService.CountTokens][google.cloud.aiplatform.v1beta1.PredictionService.CountTokens].

CountTokensResponse

Response message for [PredictionService.CountTokens][google.cloud.aiplatform.v1beta1.PredictionService.CountTokens].

CreateArtifactRequest

Request message for [MetadataService.CreateArtifact][google.cloud.aiplatform.v1beta1.MetadataService.CreateArtifact].

CreateBatchPredictionJobRequest

Request message for [JobService.CreateBatchPredictionJob][google.cloud.aiplatform.v1beta1.JobService.CreateBatchPredictionJob].

CreateCachedContentRequest

Request message for [GenAiCacheService.CreateCachedContent][google.cloud.aiplatform.v1beta1.GenAiCacheService.CreateCachedContent].

CreateContextRequest

Request message for [MetadataService.CreateContext][google.cloud.aiplatform.v1beta1.MetadataService.CreateContext].

CreateCustomJobRequest

Request message for [JobService.CreateCustomJob][google.cloud.aiplatform.v1beta1.JobService.CreateCustomJob].

CreateDataLabelingJobRequest

Request message for [JobService.CreateDataLabelingJob][google.cloud.aiplatform.v1beta1.JobService.CreateDataLabelingJob].

CreateDatasetOperationMetadata

Runtime operation information for [DatasetService.CreateDataset][google.cloud.aiplatform.v1beta1.DatasetService.CreateDataset].

CreateDatasetRequest

Request message for [DatasetService.CreateDataset][google.cloud.aiplatform.v1beta1.DatasetService.CreateDataset].

CreateDatasetVersionOperationMetadata

Runtime operation information for [DatasetService.CreateDatasetVersion][google.cloud.aiplatform.v1beta1.DatasetService.CreateDatasetVersion].

CreateDatasetVersionRequest

Request message for [DatasetService.CreateDatasetVersion][google.cloud.aiplatform.v1beta1.DatasetService.CreateDatasetVersion].

CreateDeploymentResourcePoolOperationMetadata

Runtime operation information for CreateDeploymentResourcePool method.

CreateDeploymentResourcePoolRequest

Request message for CreateDeploymentResourcePool method.

CreateEndpointOperationMetadata

Runtime operation information for [EndpointService.CreateEndpoint][google.cloud.aiplatform.v1beta1.EndpointService.CreateEndpoint].

CreateEndpointRequest

Request message for [EndpointService.CreateEndpoint][google.cloud.aiplatform.v1beta1.EndpointService.CreateEndpoint].

CreateEntityTypeOperationMetadata

Details of operations that perform create EntityType.

CreateEntityTypeRequest

Request message for [FeaturestoreService.CreateEntityType][google.cloud.aiplatform.v1beta1.FeaturestoreService.CreateEntityType].

CreateExecutionRequest

Request message for [MetadataService.CreateExecution][google.cloud.aiplatform.v1beta1.MetadataService.CreateExecution].

CreateFeatureGroupOperationMetadata

Details of operations that perform create FeatureGroup.

CreateFeatureGroupRequest

Request message for [FeatureRegistryService.CreateFeatureGroup][google.cloud.aiplatform.v1beta1.FeatureRegistryService.CreateFeatureGroup].

CreateFeatureOnlineStoreOperationMetadata

Details of operations that perform create FeatureOnlineStore.

CreateFeatureOnlineStoreRequest

Request message for [FeatureOnlineStoreAdminService.CreateFeatureOnlineStore][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.CreateFeatureOnlineStore].

CreateFeatureOperationMetadata

Details of operations that perform create Feature.

CreateFeatureRequest

Request message for [FeaturestoreService.CreateFeature][google.cloud.aiplatform.v1beta1.FeaturestoreService.CreateFeature]. Request message for [FeatureRegistryService.CreateFeature][google.cloud.aiplatform.v1beta1.FeatureRegistryService.CreateFeature].

CreateFeatureViewOperationMetadata

Details of operations that perform create FeatureView.

CreateFeatureViewRequest

Request message for [FeatureOnlineStoreAdminService.CreateFeatureView][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.CreateFeatureView].

CreateFeaturestoreOperationMetadata

Details of operations that perform create Featurestore.

CreateFeaturestoreRequest

Request message for [FeaturestoreService.CreateFeaturestore][google.cloud.aiplatform.v1beta1.FeaturestoreService.CreateFeaturestore].

CreateHyperparameterTuningJobRequest

Request message for [JobService.CreateHyperparameterTuningJob][google.cloud.aiplatform.v1beta1.JobService.CreateHyperparameterTuningJob].

CreateIndexEndpointOperationMetadata

Runtime operation information for [IndexEndpointService.CreateIndexEndpoint][google.cloud.aiplatform.v1beta1.IndexEndpointService.CreateIndexEndpoint].

CreateIndexEndpointRequest

Request message for [IndexEndpointService.CreateIndexEndpoint][google.cloud.aiplatform.v1beta1.IndexEndpointService.CreateIndexEndpoint].

CreateIndexOperationMetadata

Runtime operation information for [IndexService.CreateIndex][google.cloud.aiplatform.v1beta1.IndexService.CreateIndex].

CreateIndexRequest

Request message for [IndexService.CreateIndex][google.cloud.aiplatform.v1beta1.IndexService.CreateIndex].

CreateMetadataSchemaRequest

Request message for [MetadataService.CreateMetadataSchema][google.cloud.aiplatform.v1beta1.MetadataService.CreateMetadataSchema].

CreateMetadataStoreOperationMetadata

Details of operations that perform [MetadataService.CreateMetadataStore][google.cloud.aiplatform.v1beta1.MetadataService.CreateMetadataStore].

CreateMetadataStoreRequest

Request message for [MetadataService.CreateMetadataStore][google.cloud.aiplatform.v1beta1.MetadataService.CreateMetadataStore].

CreateModelDeploymentMonitoringJobRequest

Request message for [JobService.CreateModelDeploymentMonitoringJob][google.cloud.aiplatform.v1beta1.JobService.CreateModelDeploymentMonitoringJob].

CreateModelMonitorOperationMetadata

Runtime operation information for [ModelMonitoringService.CreateModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.CreateModelMonitor].

CreateModelMonitorRequest

Request message for [ModelMonitoringService.CreateModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.CreateModelMonitor].

CreateModelMonitoringJobRequest

Request message for [ModelMonitoringService.CreateModelMonitoringJob][google.cloud.aiplatform.v1beta1.ModelMonitoringService.CreateModelMonitoringJob].

CreateNasJobRequest

Request message for [JobService.CreateNasJob][google.cloud.aiplatform.v1beta1.JobService.CreateNasJob].

CreateNotebookExecutionJobOperationMetadata

Metadata information for [NotebookService.CreateNotebookExecutionJob][google.cloud.aiplatform.v1beta1.NotebookService.CreateNotebookExecutionJob].

CreateNotebookExecutionJobRequest

Request message for [NotebookService.CreateNotebookExecutionJob]

CreateNotebookRuntimeTemplateOperationMetadata

Metadata information for [NotebookService.CreateNotebookRuntimeTemplate][google.cloud.aiplatform.v1beta1.NotebookService.CreateNotebookRuntimeTemplate].

CreateNotebookRuntimeTemplateRequest

Request message for [NotebookService.CreateNotebookRuntimeTemplate][google.cloud.aiplatform.v1beta1.NotebookService.CreateNotebookRuntimeTemplate].

CreatePersistentResourceOperationMetadata

Details of operations that perform create PersistentResource.

CreatePersistentResourceRequest

Request message for [PersistentResourceService.CreatePersistentResource][google.cloud.aiplatform.v1beta1.PersistentResourceService.CreatePersistentResource].

CreatePipelineJobRequest

Request message for [PipelineService.CreatePipelineJob][google.cloud.aiplatform.v1beta1.PipelineService.CreatePipelineJob].

CreateRagCorpusOperationMetadata

Runtime operation information for [VertexRagDataService.CreateRagCorpus][google.cloud.aiplatform.v1beta1.VertexRagDataService.CreateRagCorpus].

CreateRagCorpusRequest

Request message for [VertexRagDataService.CreateRagCorpus][google.cloud.aiplatform.v1beta1.VertexRagDataService.CreateRagCorpus].

CreateReasoningEngineOperationMetadata

Details of [ReasoningEngineService.CreateReasoningEngine][google.cloud.aiplatform.v1beta1.ReasoningEngineService.CreateReasoningEngine] operation.

CreateReasoningEngineRequest

Request message for [ReasoningEngineService.CreateReasoningEngine][google.cloud.aiplatform.v1beta1.ReasoningEngineService.CreateReasoningEngine].

CreateRegistryFeatureOperationMetadata

Details of operations that perform create FeatureGroup.

CreateScheduleRequest

Request message for [ScheduleService.CreateSchedule][google.cloud.aiplatform.v1beta1.ScheduleService.CreateSchedule].

CreateSpecialistPoolOperationMetadata

Runtime operation information for [SpecialistPoolService.CreateSpecialistPool][google.cloud.aiplatform.v1beta1.SpecialistPoolService.CreateSpecialistPool].

CreateSpecialistPoolRequest

Request message for [SpecialistPoolService.CreateSpecialistPool][google.cloud.aiplatform.v1beta1.SpecialistPoolService.CreateSpecialistPool].

CreateStudyRequest

Request message for [VizierService.CreateStudy][google.cloud.aiplatform.v1beta1.VizierService.CreateStudy].

CreateTensorboardExperimentRequest

Request message for [TensorboardService.CreateTensorboardExperiment][google.cloud.aiplatform.v1beta1.TensorboardService.CreateTensorboardExperiment].

CreateTensorboardOperationMetadata

Details of operations that perform create Tensorboard.

CreateTensorboardRequest

Request message for [TensorboardService.CreateTensorboard][google.cloud.aiplatform.v1beta1.TensorboardService.CreateTensorboard].

CreateTensorboardRunRequest

Request message for [TensorboardService.CreateTensorboardRun][google.cloud.aiplatform.v1beta1.TensorboardService.CreateTensorboardRun].

CreateTensorboardTimeSeriesRequest

Request message for [TensorboardService.CreateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.CreateTensorboardTimeSeries].

CreateTrainingPipelineRequest

Request message for [PipelineService.CreateTrainingPipeline][google.cloud.aiplatform.v1beta1.PipelineService.CreateTrainingPipeline].

CreateTrialRequest

Request message for [VizierService.CreateTrial][google.cloud.aiplatform.v1beta1.VizierService.CreateTrial].

CreateTuningJobRequest

Request message for [GenAiTuningService.CreateTuningJob][google.cloud.aiplatform.v1beta1.GenAiTuningService.CreateTuningJob].

CsvDestination

The storage details for CSV output content.

CsvSource

The storage details for CSV input content.

CustomJob

Represents a job that runs custom workloads such as a Docker container or a Python package. A CustomJob can have multiple worker pools and each worker pool can have its own machine and input spec. A CustomJob will be cleaned up once the job enters terminal state (failed or succeeded).

CustomJobName

Resource name for the CustomJob resource.

CustomJobSpec

Represents the spec of a CustomJob.

DataItem

A piece of data in a Dataset. Could be an image, a video, a document or plain text.

DataItemName

Resource name for the DataItem resource.

DataItemView

A container for a single DataItem and Annotations on it.

DataLabelingDatasetName

Resource name for the DataLabelingDataset resource.

DataLabelingJob

DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:

DataLabelingJobName

Resource name for the DataLabelingJob resource.

Dataset

A collection of DataItems and Annotations on them.

DatasetDistribution

Distribution computed over a tuning dataset.

DatasetDistribution.Types

Container for nested types declared in the DatasetDistribution message type.

DatasetDistribution.Types.DistributionBucket

Dataset bucket used to create a histogram for the distribution given a population of values.

DatasetName

Resource name for the Dataset resource.

DatasetService

The service that manages Vertex AI Dataset and its child resources.

DatasetService.DatasetServiceBase

Base class for server-side implementations of DatasetService

DatasetService.DatasetServiceClient

Client for DatasetService

DatasetServiceClient

DatasetService client wrapper, for convenient use.

DatasetServiceClientBuilder

Builder class for DatasetServiceClient to provide simple configuration of credentials, endpoint etc.

DatasetServiceClientImpl

DatasetService client wrapper implementation, for convenient use.

DatasetServiceSettings

Settings for DatasetServiceClient instances.

DatasetStats

Statistics computed over a tuning dataset.

DatasetVersion

Describes the dataset version.

DatasetVersionName

Resource name for the DatasetVersion resource.

DedicatedResources

A description of resources that are dedicated to a DeployedModel, and that need a higher degree of manual configuration.

DeleteArtifactRequest

Request message for [MetadataService.DeleteArtifact][google.cloud.aiplatform.v1beta1.MetadataService.DeleteArtifact].

DeleteBatchPredictionJobRequest

Request message for [JobService.DeleteBatchPredictionJob][google.cloud.aiplatform.v1beta1.JobService.DeleteBatchPredictionJob].

DeleteCachedContentRequest

Request message for [GenAiCacheService.DeleteCachedContent][google.cloud.aiplatform.v1beta1.GenAiCacheService.DeleteCachedContent].

DeleteContextRequest

Request message for [MetadataService.DeleteContext][google.cloud.aiplatform.v1beta1.MetadataService.DeleteContext].

DeleteCustomJobRequest

Request message for [JobService.DeleteCustomJob][google.cloud.aiplatform.v1beta1.JobService.DeleteCustomJob].

DeleteDataLabelingJobRequest

Request message for [JobService.DeleteDataLabelingJob][google.cloud.aiplatform.v1beta1.JobService.DeleteDataLabelingJob].

DeleteDatasetRequest

Request message for [DatasetService.DeleteDataset][google.cloud.aiplatform.v1beta1.DatasetService.DeleteDataset].

DeleteDatasetVersionRequest

Request message for [DatasetService.DeleteDatasetVersion][google.cloud.aiplatform.v1beta1.DatasetService.DeleteDatasetVersion].

DeleteDeploymentResourcePoolRequest

Request message for DeleteDeploymentResourcePool method.

DeleteEndpointRequest

Request message for [EndpointService.DeleteEndpoint][google.cloud.aiplatform.v1beta1.EndpointService.DeleteEndpoint].

DeleteEntityTypeRequest

Request message for [FeaturestoreService.DeleteEntityTypes][].

DeleteExecutionRequest

Request message for [MetadataService.DeleteExecution][google.cloud.aiplatform.v1beta1.MetadataService.DeleteExecution].

DeleteExtensionRequest

Request message for [ExtensionRegistryService.DeleteExtension][google.cloud.aiplatform.v1beta1.ExtensionRegistryService.DeleteExtension].

DeleteFeatureGroupRequest

Request message for [FeatureRegistryService.DeleteFeatureGroup][google.cloud.aiplatform.v1beta1.FeatureRegistryService.DeleteFeatureGroup].

DeleteFeatureOnlineStoreRequest

Request message for [FeatureOnlineStoreAdminService.DeleteFeatureOnlineStore][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.DeleteFeatureOnlineStore].

DeleteFeatureRequest

Request message for [FeaturestoreService.DeleteFeature][google.cloud.aiplatform.v1beta1.FeaturestoreService.DeleteFeature]. Request message for [FeatureRegistryService.DeleteFeature][google.cloud.aiplatform.v1beta1.FeatureRegistryService.DeleteFeature].

DeleteFeatureValuesOperationMetadata

Details of operations that delete Feature values.

DeleteFeatureValuesRequest

Request message for [FeaturestoreService.DeleteFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.DeleteFeatureValues].

DeleteFeatureValuesRequest.Types

Container for nested types declared in the DeleteFeatureValuesRequest message type.

DeleteFeatureValuesRequest.Types.SelectEntity

Message to select entity. If an entity id is selected, all the feature values corresponding to the entity id will be deleted, including the entityId.

DeleteFeatureValuesRequest.Types.SelectTimeRangeAndFeature

Message to select time range and feature. Values of the selected feature generated within an inclusive time range will be deleted. Using this option permanently deletes the feature values from the specified feature IDs within the specified time range. This might include data from the online storage. If you want to retain any deleted historical data in the online storage, you must re-ingest it.

DeleteFeatureValuesResponse

Response message for [FeaturestoreService.DeleteFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.DeleteFeatureValues].

DeleteFeatureValuesResponse.Types

Container for nested types declared in the DeleteFeatureValuesResponse message type.

DeleteFeatureValuesResponse.Types.SelectEntity

Response message if the request uses the SelectEntity option.

DeleteFeatureValuesResponse.Types.SelectTimeRangeAndFeature

Response message if the request uses the SelectTimeRangeAndFeature option.

DeleteFeatureViewRequest

Request message for [FeatureOnlineStoreAdminService.DeleteFeatureViews][].

DeleteFeaturestoreRequest

Request message for [FeaturestoreService.DeleteFeaturestore][google.cloud.aiplatform.v1beta1.FeaturestoreService.DeleteFeaturestore].

DeleteHyperparameterTuningJobRequest

Request message for [JobService.DeleteHyperparameterTuningJob][google.cloud.aiplatform.v1beta1.JobService.DeleteHyperparameterTuningJob].

DeleteIndexEndpointRequest

Request message for [IndexEndpointService.DeleteIndexEndpoint][google.cloud.aiplatform.v1beta1.IndexEndpointService.DeleteIndexEndpoint].

DeleteIndexRequest

Request message for [IndexService.DeleteIndex][google.cloud.aiplatform.v1beta1.IndexService.DeleteIndex].

DeleteMetadataStoreOperationMetadata

Details of operations that perform [MetadataService.DeleteMetadataStore][google.cloud.aiplatform.v1beta1.MetadataService.DeleteMetadataStore].

DeleteMetadataStoreRequest

Request message for [MetadataService.DeleteMetadataStore][google.cloud.aiplatform.v1beta1.MetadataService.DeleteMetadataStore].

DeleteModelDeploymentMonitoringJobRequest

Request message for [JobService.DeleteModelDeploymentMonitoringJob][google.cloud.aiplatform.v1beta1.JobService.DeleteModelDeploymentMonitoringJob].

DeleteModelMonitorRequest

Request message for [ModelMonitoringService.DeleteModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.DeleteModelMonitor].

DeleteModelMonitoringJobRequest

Request message for [ModelMonitoringService.DeleteModelMonitoringJob][google.cloud.aiplatform.v1beta1.ModelMonitoringService.DeleteModelMonitoringJob].

DeleteModelRequest

Request message for [ModelService.DeleteModel][google.cloud.aiplatform.v1beta1.ModelService.DeleteModel].

DeleteModelVersionRequest

Request message for [ModelService.DeleteModelVersion][google.cloud.aiplatform.v1beta1.ModelService.DeleteModelVersion].

DeleteNasJobRequest

Request message for [JobService.DeleteNasJob][google.cloud.aiplatform.v1beta1.JobService.DeleteNasJob].

DeleteNotebookExecutionJobRequest

Request message for [NotebookService.DeleteNotebookExecutionJob]

DeleteNotebookRuntimeRequest

Request message for [NotebookService.DeleteNotebookRuntime][google.cloud.aiplatform.v1beta1.NotebookService.DeleteNotebookRuntime].

DeleteNotebookRuntimeTemplateRequest

Request message for [NotebookService.DeleteNotebookRuntimeTemplate][google.cloud.aiplatform.v1beta1.NotebookService.DeleteNotebookRuntimeTemplate].

DeleteOperationMetadata

Details of operations that perform deletes of any entities.

DeletePersistentResourceRequest

Request message for [PersistentResourceService.DeletePersistentResource][google.cloud.aiplatform.v1beta1.PersistentResourceService.DeletePersistentResource].

DeletePipelineJobRequest

Request message for [PipelineService.DeletePipelineJob][google.cloud.aiplatform.v1beta1.PipelineService.DeletePipelineJob].

DeleteRagCorpusRequest

Request message for [VertexRagDataService.DeleteRagCorpus][google.cloud.aiplatform.v1beta1.VertexRagDataService.DeleteRagCorpus].

DeleteRagFileRequest

Request message for [VertexRagDataService.DeleteRagFile][google.cloud.aiplatform.v1beta1.VertexRagDataService.DeleteRagFile].

DeleteReasoningEngineRequest

Request message for [ReasoningEngineService.DeleteReasoningEngine][google.cloud.aiplatform.v1beta1.ReasoningEngineService.DeleteReasoningEngine].

DeleteSavedQueryRequest

Request message for [DatasetService.DeleteSavedQuery][google.cloud.aiplatform.v1beta1.DatasetService.DeleteSavedQuery].

DeleteScheduleRequest

Request message for [ScheduleService.DeleteSchedule][google.cloud.aiplatform.v1beta1.ScheduleService.DeleteSchedule].

DeleteSpecialistPoolRequest

Request message for [SpecialistPoolService.DeleteSpecialistPool][google.cloud.aiplatform.v1beta1.SpecialistPoolService.DeleteSpecialistPool].

DeleteStudyRequest

Request message for [VizierService.DeleteStudy][google.cloud.aiplatform.v1beta1.VizierService.DeleteStudy].

DeleteTensorboardExperimentRequest

Request message for [TensorboardService.DeleteTensorboardExperiment][google.cloud.aiplatform.v1beta1.TensorboardService.DeleteTensorboardExperiment].

DeleteTensorboardRequest

Request message for [TensorboardService.DeleteTensorboard][google.cloud.aiplatform.v1beta1.TensorboardService.DeleteTensorboard].

DeleteTensorboardRunRequest

Request message for [TensorboardService.DeleteTensorboardRun][google.cloud.aiplatform.v1beta1.TensorboardService.DeleteTensorboardRun].

DeleteTensorboardTimeSeriesRequest

Request message for [TensorboardService.DeleteTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.DeleteTensorboardTimeSeries].

DeleteTrainingPipelineRequest

Request message for [PipelineService.DeleteTrainingPipeline][google.cloud.aiplatform.v1beta1.PipelineService.DeleteTrainingPipeline].

DeleteTrialRequest

Request message for [VizierService.DeleteTrial][google.cloud.aiplatform.v1beta1.VizierService.DeleteTrial].

DeployIndexOperationMetadata

Runtime operation information for [IndexEndpointService.DeployIndex][google.cloud.aiplatform.v1beta1.IndexEndpointService.DeployIndex].

DeployIndexRequest

Request message for [IndexEndpointService.DeployIndex][google.cloud.aiplatform.v1beta1.IndexEndpointService.DeployIndex].

DeployIndexResponse

Response message for [IndexEndpointService.DeployIndex][google.cloud.aiplatform.v1beta1.IndexEndpointService.DeployIndex].

DeployModelOperationMetadata

Runtime operation information for [EndpointService.DeployModel][google.cloud.aiplatform.v1beta1.EndpointService.DeployModel].

DeployModelRequest

Request message for [EndpointService.DeployModel][google.cloud.aiplatform.v1beta1.EndpointService.DeployModel].

DeployModelResponse

Response message for [EndpointService.DeployModel][google.cloud.aiplatform.v1beta1.EndpointService.DeployModel].

DeployedIndex

A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.

DeployedIndexAuthConfig

Used to set up the auth on the DeployedIndex's private endpoint.

DeployedIndexAuthConfig.Types

Container for nested types declared in the DeployedIndexAuthConfig message type.

DeployedIndexAuthConfig.Types.AuthProvider

Configuration for an authentication provider, including support for JSON Web Token (JWT).

DeployedIndexRef

Points to a DeployedIndex.

DeployedModel

A deployment of a Model. Endpoints contain one or more DeployedModels.

DeployedModelRef

Points to a DeployedModel.

DeploymentResourcePool

A description of resources that can be shared by multiple DeployedModels, whose underlying specification consists of a DedicatedResources.

DeploymentResourcePoolName

Resource name for the DeploymentResourcePool resource.

DeploymentResourcePoolService

A service that manages the DeploymentResourcePool resource.

DeploymentResourcePoolService.DeploymentResourcePoolServiceBase

Base class for server-side implementations of DeploymentResourcePoolService

DeploymentResourcePoolService.DeploymentResourcePoolServiceClient

Client for DeploymentResourcePoolService

DeploymentResourcePoolServiceClient

DeploymentResourcePoolService client wrapper, for convenient use.

DeploymentResourcePoolServiceClientBuilder

Builder class for DeploymentResourcePoolServiceClient to provide simple configuration of credentials, endpoint etc.

DeploymentResourcePoolServiceClientImpl

DeploymentResourcePoolService client wrapper implementation, for convenient use.

DeploymentResourcePoolServiceSettings

Settings for DeploymentResourcePoolServiceClient instances.

DestinationFeatureSetting

DirectPredictRequest

Request message for [PredictionService.DirectPredict][google.cloud.aiplatform.v1beta1.PredictionService.DirectPredict].

DirectPredictResponse

Response message for [PredictionService.DirectPredict][google.cloud.aiplatform.v1beta1.PredictionService.DirectPredict].

DirectRawPredictRequest

Request message for [PredictionService.DirectRawPredict][google.cloud.aiplatform.v1beta1.PredictionService.DirectRawPredict].

DirectRawPredictResponse

Response message for [PredictionService.DirectRawPredict][google.cloud.aiplatform.v1beta1.PredictionService.DirectRawPredict].

DirectUploadSource

The input content is encapsulated and uploaded in the request.

DiskSpec

Represents the spec of disk options.

DistillationDataStats

Statistics computed for datasets used for distillation.

DistillationHyperParameters

Hyperparameters for Distillation.

DistillationSpec

Tuning Spec for Distillation.

DoubleArray

A list of double values.

DynamicRetrievalConfig

Describes the options to customize dynamic retrieval.

DynamicRetrievalConfig.Types

Container for nested types declared in the DynamicRetrievalConfig message type.

EncryptionSpec

Represents a customer-managed encryption key spec that can be applied to a top-level resource.

Endpoint

Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

EndpointName

Resource name for the Endpoint resource.

EndpointService

A service for managing Vertex AI's Endpoints.

EndpointService.EndpointServiceBase

Base class for server-side implementations of EndpointService

EndpointService.EndpointServiceClient

Client for EndpointService

EndpointServiceClient

EndpointService client wrapper, for convenient use.

EndpointServiceClientBuilder

Builder class for EndpointServiceClient to provide simple configuration of credentials, endpoint etc.

EndpointServiceClientImpl

EndpointService client wrapper implementation, for convenient use.

EndpointServiceSettings

Settings for EndpointServiceClient instances.

EntityIdSelector

Selector for entityId. Getting ids from the given source.

EntityType

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

EntityTypeName

Resource name for the EntityType resource.

EnvVar

Represents an environment variable present in a Container or Python Module.

ErrorAnalysisAnnotation

Model error analysis for each annotation.

ErrorAnalysisAnnotation.Types

Container for nested types declared in the ErrorAnalysisAnnotation message type.

ErrorAnalysisAnnotation.Types.AttributedItem

Attributed items for a given annotation, typically representing neighbors from the training sets constrained by the query type.

EvaluateInstancesRequest

Request message for EvaluationService.EvaluateInstances.

EvaluateInstancesResponse

Response message for EvaluationService.EvaluateInstances.

EvaluatedAnnotation

True positive, false positive, or false negative.

EvaluatedAnnotation is only available under ModelEvaluationSlice with slice of annotationSpec dimension.

EvaluatedAnnotation.Types

Container for nested types declared in the EvaluatedAnnotation message type.

EvaluatedAnnotationExplanation

Explanation result of the prediction produced by the Model.

EvaluationService

Vertex AI Online Evaluation Service.

EvaluationService.EvaluationServiceBase

Base class for server-side implementations of EvaluationService

EvaluationService.EvaluationServiceClient

Client for EvaluationService

EvaluationServiceClient

EvaluationService client wrapper, for convenient use.

EvaluationServiceClientBuilder

Builder class for EvaluationServiceClient to provide simple configuration of credentials, endpoint etc.

EvaluationServiceClientImpl

EvaluationService client wrapper implementation, for convenient use.

EvaluationServiceSettings

Settings for EvaluationServiceClient instances.

Event

An edge describing the relationship between an Artifact and an Execution in a lineage graph.

Event.Types

Container for nested types declared in the Event message type.

ExactMatchInput

Input for exact match metric.

ExactMatchInstance

Spec for exact match instance.

ExactMatchMetricValue

Exact match metric value for an instance.

ExactMatchResults

Results for exact match metric.

ExactMatchSpec

Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0.

Examples

Example-based explainability that returns the nearest neighbors from the provided dataset.

Examples.Types

Container for nested types declared in the Examples message type.

Examples.Types.ExampleGcsSource

The Cloud Storage input instances.

Examples.Types.ExampleGcsSource.Types

Container for nested types declared in the ExampleGcsSource message type.

ExamplesOverride

Overrides for example-based explanations.

ExamplesOverride.Types

Container for nested types declared in the ExamplesOverride message type.

ExamplesRestrictionsNamespace

Restrictions namespace for example-based explanations overrides.

ExecuteExtensionRequest

Request message for [ExtensionExecutionService.ExecuteExtension][google.cloud.aiplatform.v1beta1.ExtensionExecutionService.ExecuteExtension].

ExecuteExtensionResponse

Response message for [ExtensionExecutionService.ExecuteExtension][google.cloud.aiplatform.v1beta1.ExtensionExecutionService.ExecuteExtension].

Execution

Instance of a general execution.

Execution.Types

Container for nested types declared in the Execution message type.

ExecutionName

Resource name for the Execution resource.

ExplainRequest

Request message for [PredictionService.Explain][google.cloud.aiplatform.v1beta1.PredictionService.Explain].

ExplainResponse

Response message for [PredictionService.Explain][google.cloud.aiplatform.v1beta1.PredictionService.Explain].

ExplainResponse.Types

Container for nested types declared in the ExplainResponse message type.

ExplainResponse.Types.ConcurrentExplanation

This message is a wrapper grouping Concurrent Explanations.

Explanation

Explanation of a prediction (provided in [PredictResponse.predictions][google.cloud.aiplatform.v1beta1.PredictResponse.predictions]) produced by the Model on a given [instance][google.cloud.aiplatform.v1beta1.ExplainRequest.instances].

ExplanationMetadata

Metadata describing the Model's input and output for explanation.

ExplanationMetadata.Types

Container for nested types declared in the ExplanationMetadata message type.

ExplanationMetadata.Types.InputMetadata

Metadata of the input of a feature.

Fields other than [InputMetadata.input_baselines][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.input_baselines] are applicable only for Models that are using Vertex AI-provided images for Tensorflow.

ExplanationMetadata.Types.InputMetadata.Types

Container for nested types declared in the InputMetadata message type.

ExplanationMetadata.Types.InputMetadata.Types.FeatureValueDomain

Domain details of the input feature value. Provides numeric information about the feature, such as its range (min, max). If the feature has been pre-processed, for example with z-scoring, then it provides information about how to recover the original feature. For example, if the input feature is an image and it has been pre-processed to obtain 0-mean and stddev = 1 values, then original_mean, and original_stddev refer to the mean and stddev of the original feature (e.g. image tensor) from which input feature (with mean = 0 and stddev = 1) was obtained.

ExplanationMetadata.Types.InputMetadata.Types.Visualization

Visualization configurations for image explanation.

ExplanationMetadata.Types.InputMetadata.Types.Visualization.Types

Container for nested types declared in the Visualization message type.

ExplanationMetadata.Types.OutputMetadata

Metadata of the prediction output to be explained.

ExplanationMetadataOverride

The [ExplanationMetadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata] entries that can be overridden at [online explanation][google.cloud.aiplatform.v1beta1.PredictionService.Explain] time.

ExplanationMetadataOverride.Types

Container for nested types declared in the ExplanationMetadataOverride message type.

ExplanationMetadataOverride.Types.InputMetadataOverride

The [input metadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata] entries to be overridden.

ExplanationParameters

Parameters to configure explaining for Model's predictions.

ExplanationSpec

Specification of Model explanation.

ExplanationSpecOverride

The [ExplanationSpec][google.cloud.aiplatform.v1beta1.ExplanationSpec] entries that can be overridden at [online explanation][google.cloud.aiplatform.v1beta1.PredictionService.Explain] time.

ExportDataConfig

Describes what part of the Dataset is to be exported, the destination of the export and how to export.

ExportDataOperationMetadata

Runtime operation information for [DatasetService.ExportData][google.cloud.aiplatform.v1beta1.DatasetService.ExportData].

ExportDataRequest

Request message for [DatasetService.ExportData][google.cloud.aiplatform.v1beta1.DatasetService.ExportData].

ExportDataResponse

Response message for [DatasetService.ExportData][google.cloud.aiplatform.v1beta1.DatasetService.ExportData].

ExportFeatureValuesOperationMetadata

Details of operations that exports Features values.

ExportFeatureValuesRequest

Request message for [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.ExportFeatureValues].

ExportFeatureValuesRequest.Types

Container for nested types declared in the ExportFeatureValuesRequest message type.

ExportFeatureValuesRequest.Types.FullExport

Describes exporting all historical Feature values of all entities of the EntityType between [start_time, end_time].

ExportFeatureValuesRequest.Types.SnapshotExport

Describes exporting the latest Feature values of all entities of the EntityType between [start_time, snapshot_time].

ExportFeatureValuesResponse

Response message for [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.ExportFeatureValues].

ExportFractionSplit

Assigns the input data to training, validation, and test sets as per the given fractions. Any of training_fraction, validation_fraction and test_fraction may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test.

ExportModelOperationMetadata

Details of [ModelService.ExportModel][google.cloud.aiplatform.v1beta1.ModelService.ExportModel] operation.

ExportModelOperationMetadata.Types

Container for nested types declared in the ExportModelOperationMetadata message type.

ExportModelOperationMetadata.Types.OutputInfo

Further describes the output of the ExportModel. Supplements [ExportModelRequest.OutputConfig][google.cloud.aiplatform.v1beta1.ExportModelRequest.OutputConfig].

ExportModelRequest

Request message for [ModelService.ExportModel][google.cloud.aiplatform.v1beta1.ModelService.ExportModel].

ExportModelRequest.Types

Container for nested types declared in the ExportModelRequest message type.

ExportModelRequest.Types.OutputConfig

Output configuration for the Model export.

ExportModelResponse

Response message of [ModelService.ExportModel][google.cloud.aiplatform.v1beta1.ModelService.ExportModel] operation.

ExportTensorboardTimeSeriesDataRequest

Request message for [TensorboardService.ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.ExportTensorboardTimeSeriesData].

ExportTensorboardTimeSeriesDataResponse

Response message for [TensorboardService.ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.ExportTensorboardTimeSeriesData].

Extension

Extensions are tools for large language models to access external data, run computations, etc.

ExtensionExecutionService

A service for Extension execution.

ExtensionExecutionService.ExtensionExecutionServiceBase

Base class for server-side implementations of ExtensionExecutionService

ExtensionExecutionService.ExtensionExecutionServiceClient

Client for ExtensionExecutionService

ExtensionExecutionServiceClient

ExtensionExecutionService client wrapper, for convenient use.

ExtensionExecutionServiceClientBuilder

Builder class for ExtensionExecutionServiceClient to provide simple configuration of credentials, endpoint etc.

ExtensionExecutionServiceClientImpl

ExtensionExecutionService client wrapper implementation, for convenient use.

ExtensionExecutionServiceSettings

Settings for ExtensionExecutionServiceClient instances.

ExtensionManifest

Manifest spec of an Extension needed for runtime execution.

ExtensionManifest.Types

Container for nested types declared in the ExtensionManifest message type.

ExtensionManifest.Types.ApiSpec

The API specification shown to the LLM.

ExtensionName

Resource name for the Extension resource.

ExtensionOperation

Operation of an extension.

ExtensionPrivateServiceConnectConfig

PrivateExtensionConfig configuration for the extension.

ExtensionRegistryService

A service for managing Vertex AI's Extension registry.

ExtensionRegistryService.ExtensionRegistryServiceBase

Base class for server-side implementations of ExtensionRegistryService

ExtensionRegistryService.ExtensionRegistryServiceClient

Client for ExtensionRegistryService

ExtensionRegistryServiceClient

ExtensionRegistryService client wrapper, for convenient use.

ExtensionRegistryServiceClientBuilder

Builder class for ExtensionRegistryServiceClient to provide simple configuration of credentials, endpoint etc.

ExtensionRegistryServiceClientImpl

ExtensionRegistryService client wrapper implementation, for convenient use.

ExtensionRegistryServiceSettings

Settings for ExtensionRegistryServiceClient instances.

Feature

Feature Metadata information. For example, color is a feature that describes an apple.

Feature.Types

Container for nested types declared in the Feature message type.

Feature.Types.MonitoringStatsAnomaly

A list of historical [SnapshotAnalysis][google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig.SnapshotAnalysis] or [ImportFeaturesAnalysis][google.cloud.aiplatform.v1beta1.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis] stats requested by user, sorted by [FeatureStatsAnomaly.start_time][google.cloud.aiplatform.v1beta1.FeatureStatsAnomaly.start_time] descending.

Feature.Types.MonitoringStatsAnomaly.Types

Container for nested types declared in the MonitoringStatsAnomaly message type.

FeatureGroup

Vertex AI Feature Group.

FeatureGroup.Types

Container for nested types declared in the FeatureGroup message type.

FeatureGroup.Types.BigQuery

Input source type for BigQuery Tables and Views.

FeatureGroup.Types.BigQuery.Types

Container for nested types declared in the BigQuery message type.

FeatureGroup.Types.BigQuery.Types.TimeSeries

FeatureGroupName

Resource name for the FeatureGroup resource.

FeatureName

Resource name for the Feature resource.

FeatureNoiseSigma

Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients.

FeatureNoiseSigma.Types

Container for nested types declared in the FeatureNoiseSigma message type.

FeatureNoiseSigma.Types.NoiseSigmaForFeature

Noise sigma for a single feature.

FeatureOnlineStore

Vertex AI Feature Online Store provides a centralized repository for serving ML features and embedding indexes at low latency. The Feature Online Store is a top-level container.

FeatureOnlineStore.Types

Container for nested types declared in the FeatureOnlineStore message type.

FeatureOnlineStore.Types.Bigtable

FeatureOnlineStore.Types.Bigtable.Types

Container for nested types declared in the Bigtable message type.

FeatureOnlineStore.Types.Bigtable.Types.AutoScaling

FeatureOnlineStore.Types.DedicatedServingEndpoint

The dedicated serving endpoint for this FeatureOnlineStore. Only need to set when you choose Optimized storage type. Public endpoint is provisioned by default.

FeatureOnlineStore.Types.EmbeddingManagement

Deprecated: This sub message is no longer needed anymore and embedding management is automatically enabled when specifying Optimized storage type. Contains settings for embedding management.

FeatureOnlineStore.Types.Optimized

Optimized storage type

FeatureOnlineStoreAdminService

The service that handles CRUD and List for resources for FeatureOnlineStore.

FeatureOnlineStoreAdminService.FeatureOnlineStoreAdminServiceBase

Base class for server-side implementations of FeatureOnlineStoreAdminService

FeatureOnlineStoreAdminService.FeatureOnlineStoreAdminServiceClient

Client for FeatureOnlineStoreAdminService

FeatureOnlineStoreAdminServiceClient

FeatureOnlineStoreAdminService client wrapper, for convenient use.

FeatureOnlineStoreAdminServiceClientBuilder

Builder class for FeatureOnlineStoreAdminServiceClient to provide simple configuration of credentials, endpoint etc.

FeatureOnlineStoreAdminServiceClientImpl

FeatureOnlineStoreAdminService client wrapper implementation, for convenient use.

FeatureOnlineStoreAdminServiceSettings

Settings for FeatureOnlineStoreAdminServiceClient instances.

FeatureOnlineStoreName

Resource name for the FeatureOnlineStore resource.

FeatureOnlineStoreService

A service for fetching feature values from the online store.

FeatureOnlineStoreService.FeatureOnlineStoreServiceBase

Base class for server-side implementations of FeatureOnlineStoreService

FeatureOnlineStoreService.FeatureOnlineStoreServiceClient

Client for FeatureOnlineStoreService

FeatureOnlineStoreServiceClient

FeatureOnlineStoreService client wrapper, for convenient use.

FeatureOnlineStoreServiceClient.StreamingFetchFeatureValuesStream

Bidirectional streaming methods for StreamingFetchFeatureValues(CallSettings, BidirectionalStreamingSettings).

FeatureOnlineStoreServiceClientBuilder

Builder class for FeatureOnlineStoreServiceClient to provide simple configuration of credentials, endpoint etc.

FeatureOnlineStoreServiceClientImpl

FeatureOnlineStoreService client wrapper implementation, for convenient use.

FeatureOnlineStoreServiceSettings

Settings for FeatureOnlineStoreServiceClient instances.

FeatureRegistryService

The service that handles CRUD and List for resources for FeatureRegistry.

FeatureRegistryService.FeatureRegistryServiceBase

Base class for server-side implementations of FeatureRegistryService

FeatureRegistryService.FeatureRegistryServiceClient

Client for FeatureRegistryService

FeatureRegistryServiceClient

FeatureRegistryService client wrapper, for convenient use.

FeatureRegistryServiceClientBuilder

Builder class for FeatureRegistryServiceClient to provide simple configuration of credentials, endpoint etc.

FeatureRegistryServiceClientImpl

FeatureRegistryService client wrapper implementation, for convenient use.

FeatureRegistryServiceSettings

Settings for FeatureRegistryServiceClient instances.

FeatureSelector

Selector for Features of an EntityType.

FeatureStatsAnomaly

Stats and Anomaly generated at specific timestamp for specific Feature. The start_time and end_time are used to define the time range of the dataset that current stats belongs to, e.g. prediction traffic is bucketed into prediction datasets by time window. If the Dataset is not defined by time window, start_time = end_time. Timestamp of the stats and anomalies always refers to end_time. Raw stats and anomalies are stored in stats_uri or anomaly_uri in the tensorflow defined protos. Field data_stats contains almost identical information with the raw stats in Vertex AI defined proto, for UI to display.

FeatureValue

Value for a feature.

FeatureValue.Types

Container for nested types declared in the FeatureValue message type.

FeatureValue.Types.Metadata

Metadata of feature value.

FeatureValueDestination

A destination location for Feature values and format.

FeatureValueList

Container for list of values.

FeatureView

FeatureView is representation of values that the FeatureOnlineStore will serve based on its syncConfig.

FeatureView.Types

Container for nested types declared in the FeatureView message type.

FeatureView.Types.BigQuerySource

FeatureView.Types.FeatureRegistrySource

A Feature Registry source for features that need to be synced to Online Store.

FeatureView.Types.FeatureRegistrySource.Types

Container for nested types declared in the FeatureRegistrySource message type.

FeatureView.Types.FeatureRegistrySource.Types.FeatureGroup

Features belonging to a single feature group that will be synced to Online Store.

FeatureView.Types.IndexConfig

Configuration for vector indexing.

FeatureView.Types.IndexConfig.Types

Container for nested types declared in the IndexConfig message type.

FeatureView.Types.IndexConfig.Types.BruteForceConfig

Configuration options for using brute force search.

FeatureView.Types.IndexConfig.Types.TreeAHConfig

Configuration options for the tree-AH algorithm.

FeatureView.Types.SyncConfig

Configuration for Sync. Only one option is set.

FeatureView.Types.VectorSearchConfig

Deprecated. Use [IndexConfig][google.cloud.aiplatform.v1beta1.FeatureView.IndexConfig] instead.

FeatureView.Types.VectorSearchConfig.Types

Container for nested types declared in the VectorSearchConfig message type.

FeatureView.Types.VectorSearchConfig.Types.BruteForceConfig

FeatureView.Types.VectorSearchConfig.Types.TreeAHConfig

FeatureView.Types.VertexRagSource

A Vertex Rag source for features that need to be synced to Online Store.

FeatureViewDataKey

Lookup key for a feature view.

FeatureViewDataKey.Types

Container for nested types declared in the FeatureViewDataKey message type.

FeatureViewDataKey.Types.CompositeKey

ID that is comprised from several parts (columns).

FeatureViewName

Resource name for the FeatureView resource.

FeatureViewSync

FeatureViewSync is a representation of sync operation which copies data from data source to Feature View in Online Store.

FeatureViewSync.Types

Container for nested types declared in the FeatureViewSync message type.

FeatureViewSync.Types.SyncSummary

Summary from the Sync job. For continuous syncs, the summary is updated periodically. For batch syncs, it gets updated on completion of the sync.

FeatureViewSyncName

Resource name for the FeatureViewSync resource.

Featurestore

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Featurestore.Types

Container for nested types declared in the Featurestore message type.

Featurestore.Types.OnlineServingConfig

OnlineServingConfig specifies the details for provisioning online serving resources.

Featurestore.Types.OnlineServingConfig.Types

Container for nested types declared in the OnlineServingConfig message type.

Featurestore.Types.OnlineServingConfig.Types.Scaling

Online serving scaling configuration. If min_node_count and max_node_count are set to the same value, the cluster will be configured with the fixed number of node (no auto-scaling).

FeaturestoreMonitoringConfig

Configuration of how features in Featurestore are monitored.

FeaturestoreMonitoringConfig.Types

Container for nested types declared in the FeaturestoreMonitoringConfig message type.

FeaturestoreMonitoringConfig.Types.ImportFeaturesAnalysis

Configuration of the Featurestore's ImportFeature Analysis Based Monitoring. This type of analysis generates statistics for values of each Feature imported by every [ImportFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.ImportFeatureValues] operation.

FeaturestoreMonitoringConfig.Types.ImportFeaturesAnalysis.Types

Container for nested types declared in the ImportFeaturesAnalysis message type.

FeaturestoreMonitoringConfig.Types.SnapshotAnalysis

Configuration of the Featurestore's Snapshot Analysis Based Monitoring. This type of analysis generates statistics for each Feature based on a snapshot of the latest feature value of each entities every monitoring_interval.

FeaturestoreMonitoringConfig.Types.ThresholdConfig

The config for Featurestore Monitoring threshold.

FeaturestoreName

Resource name for the Featurestore resource.

FeaturestoreOnlineServingService

A service for serving online feature values.

FeaturestoreOnlineServingService.FeaturestoreOnlineServingServiceBase

Base class for server-side implementations of FeaturestoreOnlineServingService

FeaturestoreOnlineServingService.FeaturestoreOnlineServingServiceClient

Client for FeaturestoreOnlineServingService

FeaturestoreOnlineServingServiceClient

FeaturestoreOnlineServingService client wrapper, for convenient use.

FeaturestoreOnlineServingServiceClient.StreamingReadFeatureValuesStream

Server streaming methods for StreamingReadFeatureValues(StreamingReadFeatureValuesRequest, CallSettings).

FeaturestoreOnlineServingServiceClientBuilder

Builder class for FeaturestoreOnlineServingServiceClient to provide simple configuration of credentials, endpoint etc.

FeaturestoreOnlineServingServiceClientImpl

FeaturestoreOnlineServingService client wrapper implementation, for convenient use.

FeaturestoreOnlineServingServiceSettings

Settings for FeaturestoreOnlineServingServiceClient instances.

FeaturestoreService

The service that handles CRUD and List for resources for Featurestore.

FeaturestoreService.FeaturestoreServiceBase

Base class for server-side implementations of FeaturestoreService

FeaturestoreService.FeaturestoreServiceClient

Client for FeaturestoreService

FeaturestoreServiceClient

FeaturestoreService client wrapper, for convenient use.

FeaturestoreServiceClientBuilder

Builder class for FeaturestoreServiceClient to provide simple configuration of credentials, endpoint etc.

FeaturestoreServiceClientImpl

FeaturestoreService client wrapper implementation, for convenient use.

FeaturestoreServiceSettings

Settings for FeaturestoreServiceClient instances.

FetchFeatureValuesRequest

Request message for [FeatureOnlineStoreService.FetchFeatureValues][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreService.FetchFeatureValues]. All the features under the requested feature view will be returned.

FetchFeatureValuesRequest.Types

Container for nested types declared in the FetchFeatureValuesRequest message type.

FetchFeatureValuesResponse

Response message for [FeatureOnlineStoreService.FetchFeatureValues][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreService.FetchFeatureValues]

FetchFeatureValuesResponse.Types

Container for nested types declared in the FetchFeatureValuesResponse message type.

FetchFeatureValuesResponse.Types.FeatureNameValuePairList

Response structure in the format of key (feature name) and (feature) value pair.

FetchFeatureValuesResponse.Types.FeatureNameValuePairList.Types

Container for nested types declared in the FeatureNameValuePairList message type.

FetchFeatureValuesResponse.Types.FeatureNameValuePairList.Types.FeatureNameValuePair

Feature name & value pair.

FileData

URI based data.

FileStatus

RagFile status.

FileStatus.Types

Container for nested types declared in the FileStatus message type.

FilterSplit

Assigns input data to training, validation, and test sets based on the given filters, data pieces not matched by any filter are ignored. Currently only supported for Datasets containing DataItems. If any of the filters in this message are to match nothing, then they can be set as '-' (the minus sign).

Supported only for unstructured Datasets.

FindNeighborsRequest

The request message for [MatchService.FindNeighbors][google.cloud.aiplatform.v1beta1.MatchService.FindNeighbors].

FindNeighborsRequest.Types

Container for nested types declared in the FindNeighborsRequest message type.

FindNeighborsRequest.Types.Query

A query to find a number of the nearest neighbors (most similar vectors) of a vector.

FindNeighborsRequest.Types.Query.Types

Container for nested types declared in the Query message type.

FindNeighborsRequest.Types.Query.Types.RRF

Parameters for RRF algorithm that combines search results.

FindNeighborsResponse

The response message for [MatchService.FindNeighbors][google.cloud.aiplatform.v1beta1.MatchService.FindNeighbors].

FindNeighborsResponse.Types

Container for nested types declared in the FindNeighborsResponse message type.

FindNeighborsResponse.Types.NearestNeighbors

Nearest neighbors for one query.

FindNeighborsResponse.Types.Neighbor

A neighbor of the query vector.

FluencyInput

Input for fluency metric.

FluencyInstance

Spec for fluency instance.

FluencyResult

Spec for fluency result.

FluencySpec

Spec for fluency score metric.

FractionSplit

Assigns the input data to training, validation, and test sets as per the given fractions. Any of training_fraction, validation_fraction and test_fraction may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test.

FulfillmentInput

Input for fulfillment metric.

FulfillmentInstance

Spec for fulfillment instance.

FulfillmentResult

Spec for fulfillment result.

FulfillmentSpec

Spec for fulfillment metric.

FunctionCall

A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values.

FunctionCallingConfig

Function calling config.

FunctionCallingConfig.Types

Container for nested types declared in the FunctionCallingConfig message type.

FunctionDeclaration

Structured representation of a function declaration as defined by the OpenAPI 3.0 specification. Included in this declaration are the function name and parameters. This FunctionDeclaration is a representation of a block of code that can be used as a Tool by the model and executed by the client.

FunctionResponse

The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction.

GcsDestination

The Google Cloud Storage location where the output is to be written to.

GcsSource

The Google Cloud Storage location for the input content.

GenAiCacheService

Service for managing Vertex AI's CachedContent resource.

GenAiCacheService.GenAiCacheServiceBase

Base class for server-side implementations of GenAiCacheService

GenAiCacheService.GenAiCacheServiceClient

Client for GenAiCacheService

GenAiCacheServiceClient

GenAiCacheService client wrapper, for convenient use.

GenAiCacheServiceClientBuilder

Builder class for GenAiCacheServiceClient to provide simple configuration of credentials, endpoint etc.

GenAiCacheServiceClientImpl

GenAiCacheService client wrapper implementation, for convenient use.

GenAiCacheServiceSettings

Settings for GenAiCacheServiceClient instances.

GenAiTuningService

A service for creating and managing GenAI Tuning Jobs.

GenAiTuningService.GenAiTuningServiceBase

Base class for server-side implementations of GenAiTuningService

GenAiTuningService.GenAiTuningServiceClient

Client for GenAiTuningService

GenAiTuningServiceClient

GenAiTuningService client wrapper, for convenient use.

GenAiTuningServiceClientBuilder

Builder class for GenAiTuningServiceClient to provide simple configuration of credentials, endpoint etc.

GenAiTuningServiceClientImpl

GenAiTuningService client wrapper implementation, for convenient use.

GenAiTuningServiceSettings

Settings for GenAiTuningServiceClient instances.

GenerateContentRequest

Request message for [PredictionService.GenerateContent].

GenerateContentResponse

Response message for [PredictionService.GenerateContent].

GenerateContentResponse.Types

Container for nested types declared in the GenerateContentResponse message type.

GenerateContentResponse.Types.PromptFeedback

Content filter results for a prompt sent in the request.

GenerateContentResponse.Types.PromptFeedback.Types

Container for nested types declared in the PromptFeedback message type.

GenerateContentResponse.Types.UsageMetadata

Usage metadata about response(s).

GenerateVideoResponse

Generate video response.

GenerationConfig

Generation config.

GenerationConfig.Types

Container for nested types declared in the GenerationConfig message type.

GenerationConfig.Types.RoutingConfig

The configuration for routing the request to a specific model.

GenerationConfig.Types.RoutingConfig.Types

Container for nested types declared in the RoutingConfig message type.

GenerationConfig.Types.RoutingConfig.Types.AutoRoutingMode

When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference.

GenerationConfig.Types.RoutingConfig.Types.AutoRoutingMode.Types

Container for nested types declared in the AutoRoutingMode message type.

GenerationConfig.Types.RoutingConfig.Types.ManualRoutingMode

When manual routing is set, the specified model will be used directly.

GenericOperationMetadata

Generic Metadata shared by all operations.

GenieSource

Contains information about the source of the models generated from Generative AI Studio.

GetAnnotationSpecRequest

Request message for [DatasetService.GetAnnotationSpec][google.cloud.aiplatform.v1beta1.DatasetService.GetAnnotationSpec].

GetArtifactRequest

Request message for [MetadataService.GetArtifact][google.cloud.aiplatform.v1beta1.MetadataService.GetArtifact].

GetBatchPredictionJobRequest

Request message for [JobService.GetBatchPredictionJob][google.cloud.aiplatform.v1beta1.JobService.GetBatchPredictionJob].

GetCachedContentRequest

Request message for [GenAiCacheService.GetCachedContent][google.cloud.aiplatform.v1beta1.GenAiCacheService.GetCachedContent].

GetContextRequest

Request message for [MetadataService.GetContext][google.cloud.aiplatform.v1beta1.MetadataService.GetContext].

GetCustomJobRequest

Request message for [JobService.GetCustomJob][google.cloud.aiplatform.v1beta1.JobService.GetCustomJob].

GetDataLabelingJobRequest

Request message for [JobService.GetDataLabelingJob][google.cloud.aiplatform.v1beta1.JobService.GetDataLabelingJob].

GetDatasetRequest

Request message for [DatasetService.GetDataset][google.cloud.aiplatform.v1beta1.DatasetService.GetDataset].

GetDatasetVersionRequest

Request message for [DatasetService.GetDatasetVersion][google.cloud.aiplatform.v1beta1.DatasetService.GetDatasetVersion].

GetDeploymentResourcePoolRequest

Request message for GetDeploymentResourcePool method.

GetEndpointRequest

Request message for [EndpointService.GetEndpoint][google.cloud.aiplatform.v1beta1.EndpointService.GetEndpoint]

GetEntityTypeRequest

Request message for [FeaturestoreService.GetEntityType][google.cloud.aiplatform.v1beta1.FeaturestoreService.GetEntityType].

GetExecutionRequest

Request message for [MetadataService.GetExecution][google.cloud.aiplatform.v1beta1.MetadataService.GetExecution].

GetExtensionRequest

Request message for [ExtensionRegistryService.GetExtension][google.cloud.aiplatform.v1beta1.ExtensionRegistryService.GetExtension].

GetFeatureGroupRequest

Request message for [FeatureRegistryService.GetFeatureGroup][google.cloud.aiplatform.v1beta1.FeatureRegistryService.GetFeatureGroup].

GetFeatureOnlineStoreRequest

Request message for [FeatureOnlineStoreAdminService.GetFeatureOnlineStore][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.GetFeatureOnlineStore].

GetFeatureRequest

Request message for [FeaturestoreService.GetFeature][google.cloud.aiplatform.v1beta1.FeaturestoreService.GetFeature]. Request message for [FeatureRegistryService.GetFeature][google.cloud.aiplatform.v1beta1.FeatureRegistryService.GetFeature].

GetFeatureViewRequest

Request message for [FeatureOnlineStoreAdminService.GetFeatureView][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.GetFeatureView].

GetFeatureViewSyncRequest

Request message for [FeatureOnlineStoreAdminService.GetFeatureViewSync][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.GetFeatureViewSync].

GetFeaturestoreRequest

Request message for [FeaturestoreService.GetFeaturestore][google.cloud.aiplatform.v1beta1.FeaturestoreService.GetFeaturestore].

GetHyperparameterTuningJobRequest

Request message for [JobService.GetHyperparameterTuningJob][google.cloud.aiplatform.v1beta1.JobService.GetHyperparameterTuningJob].

GetIndexEndpointRequest

Request message for [IndexEndpointService.GetIndexEndpoint][google.cloud.aiplatform.v1beta1.IndexEndpointService.GetIndexEndpoint]

GetIndexRequest

Request message for [IndexService.GetIndex][google.cloud.aiplatform.v1beta1.IndexService.GetIndex]

GetMetadataSchemaRequest

Request message for [MetadataService.GetMetadataSchema][google.cloud.aiplatform.v1beta1.MetadataService.GetMetadataSchema].

GetMetadataStoreRequest

Request message for [MetadataService.GetMetadataStore][google.cloud.aiplatform.v1beta1.MetadataService.GetMetadataStore].

GetModelDeploymentMonitoringJobRequest

Request message for [JobService.GetModelDeploymentMonitoringJob][google.cloud.aiplatform.v1beta1.JobService.GetModelDeploymentMonitoringJob].

GetModelEvaluationRequest

Request message for [ModelService.GetModelEvaluation][google.cloud.aiplatform.v1beta1.ModelService.GetModelEvaluation].

GetModelEvaluationSliceRequest

Request message for [ModelService.GetModelEvaluationSlice][google.cloud.aiplatform.v1beta1.ModelService.GetModelEvaluationSlice].

GetModelMonitorRequest

Request message for [ModelMonitoringService.GetModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.GetModelMonitor].

GetModelMonitoringJobRequest

Request message for [ModelMonitoringService.GetModelMonitoringJob][google.cloud.aiplatform.v1beta1.ModelMonitoringService.GetModelMonitoringJob].

GetModelRequest

Request message for [ModelService.GetModel][google.cloud.aiplatform.v1beta1.ModelService.GetModel].

GetNasJobRequest

Request message for [JobService.GetNasJob][google.cloud.aiplatform.v1beta1.JobService.GetNasJob].

GetNasTrialDetailRequest

Request message for [JobService.GetNasTrialDetail][google.cloud.aiplatform.v1beta1.JobService.GetNasTrialDetail].

GetNotebookExecutionJobRequest

Request message for [NotebookService.GetNotebookExecutionJob]

GetNotebookRuntimeRequest

Request message for [NotebookService.GetNotebookRuntime][google.cloud.aiplatform.v1beta1.NotebookService.GetNotebookRuntime]

GetNotebookRuntimeTemplateRequest

Request message for [NotebookService.GetNotebookRuntimeTemplate][google.cloud.aiplatform.v1beta1.NotebookService.GetNotebookRuntimeTemplate]

GetPersistentResourceRequest

Request message for [PersistentResourceService.GetPersistentResource][google.cloud.aiplatform.v1beta1.PersistentResourceService.GetPersistentResource].

GetPipelineJobRequest

Request message for [PipelineService.GetPipelineJob][google.cloud.aiplatform.v1beta1.PipelineService.GetPipelineJob].

GetPublisherModelRequest

Request message for [ModelGardenService.GetPublisherModel][google.cloud.aiplatform.v1beta1.ModelGardenService.GetPublisherModel]

GetRagCorpusRequest

Request message for [VertexRagDataService.GetRagCorpus][google.cloud.aiplatform.v1beta1.VertexRagDataService.GetRagCorpus]

GetRagFileRequest

Request message for [VertexRagDataService.GetRagFile][google.cloud.aiplatform.v1beta1.VertexRagDataService.GetRagFile]

GetReasoningEngineRequest

Request message for [ReasoningEngineService.GetReasoningEngine][google.cloud.aiplatform.v1beta1.ReasoningEngineService.GetReasoningEngine].

GetScheduleRequest

Request message for [ScheduleService.GetSchedule][google.cloud.aiplatform.v1beta1.ScheduleService.GetSchedule].

GetSpecialistPoolRequest

Request message for [SpecialistPoolService.GetSpecialistPool][google.cloud.aiplatform.v1beta1.SpecialistPoolService.GetSpecialistPool].

GetStudyRequest

Request message for [VizierService.GetStudy][google.cloud.aiplatform.v1beta1.VizierService.GetStudy].

GetTensorboardExperimentRequest

Request message for [TensorboardService.GetTensorboardExperiment][google.cloud.aiplatform.v1beta1.TensorboardService.GetTensorboardExperiment].

GetTensorboardRequest

Request message for [TensorboardService.GetTensorboard][google.cloud.aiplatform.v1beta1.TensorboardService.GetTensorboard].

GetTensorboardRunRequest

Request message for [TensorboardService.GetTensorboardRun][google.cloud.aiplatform.v1beta1.TensorboardService.GetTensorboardRun].

GetTensorboardTimeSeriesRequest

Request message for [TensorboardService.GetTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.GetTensorboardTimeSeries].

GetTrainingPipelineRequest

Request message for [PipelineService.GetTrainingPipeline][google.cloud.aiplatform.v1beta1.PipelineService.GetTrainingPipeline].

GetTrialRequest

Request message for [VizierService.GetTrial][google.cloud.aiplatform.v1beta1.VizierService.GetTrial].

GetTuningJobRequest

Request message for [GenAiTuningService.GetTuningJob][google.cloud.aiplatform.v1beta1.GenAiTuningService.GetTuningJob].

GoogleDriveSource

The Google Drive location for the input content.

GoogleDriveSource.Types

Container for nested types declared in the GoogleDriveSource message type.

GoogleDriveSource.Types.ResourceId

The type and ID of the Google Drive resource.

GoogleDriveSource.Types.ResourceId.Types

Container for nested types declared in the ResourceId message type.

GoogleSearchRetrieval

Tool to retrieve public web data for grounding, powered by Google.

GroundednessInput

Input for groundedness metric.

GroundednessInstance

Spec for groundedness instance.

GroundednessResult

Spec for groundedness result.

GroundednessSpec

Spec for groundedness metric.

GroundingChunk

Grounding chunk.

GroundingChunk.Types

Container for nested types declared in the GroundingChunk message type.

GroundingChunk.Types.RetrievedContext

Chunk from context retrieved by the retrieval tools.

GroundingChunk.Types.Web

Chunk from the web.

GroundingMetadata

Metadata returned to client when grounding is enabled.

GroundingSupport

Grounding support.

HyperparameterTuningJob

Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification.

HyperparameterTuningJobName

Resource name for the HyperparameterTuningJob resource.

IdMatcher

Matcher for Features of an EntityType by Feature ID.

ImportDataConfig

Describes the location from where we import data into a Dataset, together with the labels that will be applied to the DataItems and the Annotations.

ImportDataOperationMetadata

Runtime operation information for [DatasetService.ImportData][google.cloud.aiplatform.v1beta1.DatasetService.ImportData].

ImportDataRequest

Request message for [DatasetService.ImportData][google.cloud.aiplatform.v1beta1.DatasetService.ImportData].

ImportDataResponse

Response message for [DatasetService.ImportData][google.cloud.aiplatform.v1beta1.DatasetService.ImportData].

ImportExtensionOperationMetadata

Details of [ExtensionRegistryService.ImportExtension][google.cloud.aiplatform.v1beta1.ExtensionRegistryService.ImportExtension] operation.

ImportExtensionRequest

Request message for [ExtensionRegistryService.ImportExtension][google.cloud.aiplatform.v1beta1.ExtensionRegistryService.ImportExtension].

ImportFeatureValuesOperationMetadata

Details of operations that perform import Feature values.

ImportFeatureValuesRequest

Request message for [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.ImportFeatureValues].

ImportFeatureValuesRequest.Types

Container for nested types declared in the ImportFeatureValuesRequest message type.

ImportFeatureValuesRequest.Types.FeatureSpec

Defines the Feature value(s) to import.

ImportFeatureValuesResponse

Response message for [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.ImportFeatureValues].

ImportModelEvaluationRequest

Request message for [ModelService.ImportModelEvaluation][google.cloud.aiplatform.v1beta1.ModelService.ImportModelEvaluation]

ImportRagFilesConfig

Config for importing RagFiles.

ImportRagFilesOperationMetadata

Runtime operation information for [VertexRagDataService.ImportRagFiles][google.cloud.aiplatform.v1beta1.VertexRagDataService.ImportRagFiles].

ImportRagFilesRequest

Request message for [VertexRagDataService.ImportRagFiles][google.cloud.aiplatform.v1beta1.VertexRagDataService.ImportRagFiles].

ImportRagFilesResponse

Response message for [VertexRagDataService.ImportRagFiles][google.cloud.aiplatform.v1beta1.VertexRagDataService.ImportRagFiles].

Index

A representation of a collection of database items organized in a way that allows for approximate nearest neighbor (a.k.a ANN) algorithms search.

Index.Types

Container for nested types declared in the Index message type.

IndexDatapoint

A datapoint of Index.

IndexDatapoint.Types

Container for nested types declared in the IndexDatapoint message type.

IndexDatapoint.Types.CrowdingTag

Crowding tag is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

IndexDatapoint.Types.NumericRestriction

This field allows restricts to be based on numeric comparisons rather than categorical tokens.

IndexDatapoint.Types.NumericRestriction.Types

Container for nested types declared in the NumericRestriction message type.

IndexDatapoint.Types.Restriction

Restriction of a datapoint which describe its attributes(tokens) from each of several attribute categories(namespaces).

IndexDatapoint.Types.SparseEmbedding

Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions.

IndexEndpoint

Indexes are deployed into it. An IndexEndpoint can have multiple DeployedIndexes.

IndexEndpointName

Resource name for the IndexEndpoint resource.

IndexEndpointService

A service for managing Vertex AI's IndexEndpoints.

IndexEndpointService.IndexEndpointServiceBase

Base class for server-side implementations of IndexEndpointService

IndexEndpointService.IndexEndpointServiceClient

Client for IndexEndpointService

IndexEndpointServiceClient

IndexEndpointService client wrapper, for convenient use.

IndexEndpointServiceClientBuilder

Builder class for IndexEndpointServiceClient to provide simple configuration of credentials, endpoint etc.

IndexEndpointServiceClientImpl

IndexEndpointService client wrapper implementation, for convenient use.

IndexEndpointServiceSettings

Settings for IndexEndpointServiceClient instances.

IndexName

Resource name for the Index resource.

IndexPrivateEndpoints

IndexPrivateEndpoints proto is used to provide paths for users to send requests via private endpoints (e.g. private service access, private service connect). To send request via private service access, use match_grpc_address. To send request via private service connect, use service_attachment.

IndexService

A service for creating and managing Vertex AI's Index resources.

IndexService.IndexServiceBase

Base class for server-side implementations of IndexService

IndexService.IndexServiceClient

Client for IndexService

IndexServiceClient

IndexService client wrapper, for convenient use.

IndexServiceClientBuilder

Builder class for IndexServiceClient to provide simple configuration of credentials, endpoint etc.

IndexServiceClientImpl

IndexService client wrapper implementation, for convenient use.

IndexServiceSettings

Settings for IndexServiceClient instances.

IndexStats

Stats of the Index.

InputDataConfig

Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model.

Int64Array

A list of int64 values.

IntegratedGradientsAttribution

An attribution method that computes the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365

JiraSource

The Jira source for the ImportRagFilesRequest.

JiraSource.Types

Container for nested types declared in the JiraSource message type.

JiraSource.Types.JiraQueries

JiraQueries contains the Jira queries and corresponding authentication.

JobService

A service for creating and managing Vertex AI's jobs.

JobService.JobServiceBase

Base class for server-side implementations of JobService

JobService.JobServiceClient

Client for JobService

JobServiceClient

JobService client wrapper, for convenient use.

JobServiceClientBuilder

Builder class for JobServiceClient to provide simple configuration of credentials, endpoint etc.

JobServiceClientImpl

JobService client wrapper implementation, for convenient use.

JobServiceSettings

Settings for JobServiceClient instances.

LargeModelReference

Contains information about the Large Model.

LineageSubgraph

A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes.

ListAnnotationsRequest

Request message for [DatasetService.ListAnnotations][google.cloud.aiplatform.v1beta1.DatasetService.ListAnnotations].

ListAnnotationsResponse

Response message for [DatasetService.ListAnnotations][google.cloud.aiplatform.v1beta1.DatasetService.ListAnnotations].

ListArtifactsRequest

Request message for [MetadataService.ListArtifacts][google.cloud.aiplatform.v1beta1.MetadataService.ListArtifacts].

ListArtifactsResponse

Response message for [MetadataService.ListArtifacts][google.cloud.aiplatform.v1beta1.MetadataService.ListArtifacts].

ListBatchPredictionJobsRequest

Request message for [JobService.ListBatchPredictionJobs][google.cloud.aiplatform.v1beta1.JobService.ListBatchPredictionJobs].

ListBatchPredictionJobsResponse

Response message for [JobService.ListBatchPredictionJobs][google.cloud.aiplatform.v1beta1.JobService.ListBatchPredictionJobs]

ListCachedContentsRequest

Request to list CachedContents.

ListCachedContentsResponse

Response with a list of CachedContents.

ListContextsRequest

Request message for [MetadataService.ListContexts][google.cloud.aiplatform.v1beta1.MetadataService.ListContexts]

ListContextsResponse

Response message for [MetadataService.ListContexts][google.cloud.aiplatform.v1beta1.MetadataService.ListContexts].

ListCustomJobsRequest

Request message for [JobService.ListCustomJobs][google.cloud.aiplatform.v1beta1.JobService.ListCustomJobs].

ListCustomJobsResponse

Response message for [JobService.ListCustomJobs][google.cloud.aiplatform.v1beta1.JobService.ListCustomJobs]

ListDataItemsRequest

Request message for [DatasetService.ListDataItems][google.cloud.aiplatform.v1beta1.DatasetService.ListDataItems].

ListDataItemsResponse

Response message for [DatasetService.ListDataItems][google.cloud.aiplatform.v1beta1.DatasetService.ListDataItems].

ListDataLabelingJobsRequest

Request message for [JobService.ListDataLabelingJobs][google.cloud.aiplatform.v1beta1.JobService.ListDataLabelingJobs].

ListDataLabelingJobsResponse

Response message for [JobService.ListDataLabelingJobs][google.cloud.aiplatform.v1beta1.JobService.ListDataLabelingJobs].

ListDatasetVersionsRequest

Request message for [DatasetService.ListDatasetVersions][google.cloud.aiplatform.v1beta1.DatasetService.ListDatasetVersions].

ListDatasetVersionsResponse

Response message for [DatasetService.ListDatasetVersions][google.cloud.aiplatform.v1beta1.DatasetService.ListDatasetVersions].

ListDatasetsRequest

Request message for [DatasetService.ListDatasets][google.cloud.aiplatform.v1beta1.DatasetService.ListDatasets].

ListDatasetsResponse

Response message for [DatasetService.ListDatasets][google.cloud.aiplatform.v1beta1.DatasetService.ListDatasets].

ListDeploymentResourcePoolsRequest

Request message for ListDeploymentResourcePools method.

ListDeploymentResourcePoolsResponse

Response message for ListDeploymentResourcePools method.

ListEndpointsRequest

Request message for [EndpointService.ListEndpoints][google.cloud.aiplatform.v1beta1.EndpointService.ListEndpoints].

ListEndpointsResponse

Response message for [EndpointService.ListEndpoints][google.cloud.aiplatform.v1beta1.EndpointService.ListEndpoints].

ListEntityTypesRequest

Request message for [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListEntityTypes].

ListEntityTypesResponse

Response message for [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListEntityTypes].

ListExecutionsRequest

Request message for [MetadataService.ListExecutions][google.cloud.aiplatform.v1beta1.MetadataService.ListExecutions].

ListExecutionsResponse

Response message for [MetadataService.ListExecutions][google.cloud.aiplatform.v1beta1.MetadataService.ListExecutions].

ListExtensionsRequest

Request message for [ExtensionRegistryService.ListExtensions][google.cloud.aiplatform.v1beta1.ExtensionRegistryService.ListExtensions].

ListExtensionsResponse

Response message for [ExtensionRegistryService.ListExtensions][google.cloud.aiplatform.v1beta1.ExtensionRegistryService.ListExtensions]

ListFeatureGroupsRequest

Request message for [FeatureRegistryService.ListFeatureGroups][google.cloud.aiplatform.v1beta1.FeatureRegistryService.ListFeatureGroups].

ListFeatureGroupsResponse

Response message for [FeatureRegistryService.ListFeatureGroups][google.cloud.aiplatform.v1beta1.FeatureRegistryService.ListFeatureGroups].

ListFeatureOnlineStoresRequest

Request message for [FeatureOnlineStoreAdminService.ListFeatureOnlineStores][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.ListFeatureOnlineStores].

ListFeatureOnlineStoresResponse

Response message for [FeatureOnlineStoreAdminService.ListFeatureOnlineStores][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.ListFeatureOnlineStores].

ListFeatureViewSyncsRequest

Request message for [FeatureOnlineStoreAdminService.ListFeatureViewSyncs][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.ListFeatureViewSyncs].

ListFeatureViewSyncsResponse

Response message for [FeatureOnlineStoreAdminService.ListFeatureViewSyncs][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.ListFeatureViewSyncs].

ListFeatureViewsRequest

Request message for [FeatureOnlineStoreAdminService.ListFeatureViews][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.ListFeatureViews].

ListFeatureViewsResponse

Response message for [FeatureOnlineStoreAdminService.ListFeatureViews][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.ListFeatureViews].

ListFeaturesRequest

Request message for [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeatures]. Request message for [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1beta1.FeatureRegistryService.ListFeatures].

ListFeaturesResponse

Response message for [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeatures]. Response message for [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1beta1.FeatureRegistryService.ListFeatures].

ListFeaturestoresRequest

Request message for [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeaturestores].

ListFeaturestoresResponse

Response message for [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeaturestores].

ListHyperparameterTuningJobsRequest

Request message for [JobService.ListHyperparameterTuningJobs][google.cloud.aiplatform.v1beta1.JobService.ListHyperparameterTuningJobs].

ListHyperparameterTuningJobsResponse

Response message for [JobService.ListHyperparameterTuningJobs][google.cloud.aiplatform.v1beta1.JobService.ListHyperparameterTuningJobs]

ListIndexEndpointsRequest

Request message for [IndexEndpointService.ListIndexEndpoints][google.cloud.aiplatform.v1beta1.IndexEndpointService.ListIndexEndpoints].

ListIndexEndpointsResponse

Response message for [IndexEndpointService.ListIndexEndpoints][google.cloud.aiplatform.v1beta1.IndexEndpointService.ListIndexEndpoints].

ListIndexesRequest

Request message for [IndexService.ListIndexes][google.cloud.aiplatform.v1beta1.IndexService.ListIndexes].

ListIndexesResponse

Response message for [IndexService.ListIndexes][google.cloud.aiplatform.v1beta1.IndexService.ListIndexes].

ListMetadataSchemasRequest

Request message for [MetadataService.ListMetadataSchemas][google.cloud.aiplatform.v1beta1.MetadataService.ListMetadataSchemas].

ListMetadataSchemasResponse

Response message for [MetadataService.ListMetadataSchemas][google.cloud.aiplatform.v1beta1.MetadataService.ListMetadataSchemas].

ListMetadataStoresRequest

Request message for [MetadataService.ListMetadataStores][google.cloud.aiplatform.v1beta1.MetadataService.ListMetadataStores].

ListMetadataStoresResponse

Response message for [MetadataService.ListMetadataStores][google.cloud.aiplatform.v1beta1.MetadataService.ListMetadataStores].

ListModelDeploymentMonitoringJobsRequest

Request message for [JobService.ListModelDeploymentMonitoringJobs][google.cloud.aiplatform.v1beta1.JobService.ListModelDeploymentMonitoringJobs].

ListModelDeploymentMonitoringJobsResponse

Response message for [JobService.ListModelDeploymentMonitoringJobs][google.cloud.aiplatform.v1beta1.JobService.ListModelDeploymentMonitoringJobs].

ListModelEvaluationSlicesRequest

Request message for [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluationSlices].

ListModelEvaluationSlicesResponse

Response message for [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluationSlices].

ListModelEvaluationsRequest

Request message for [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluations].

ListModelEvaluationsResponse

Response message for [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluations].

ListModelMonitoringJobsRequest

Request message for [ModelMonitoringService.ListModelMonitoringJobs][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitoringJobs].

ListModelMonitoringJobsResponse

Response message for [ModelMonitoringService.ListModelMonitoringJobs][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitoringJobs].

ListModelMonitorsRequest

Request message for [ModelMonitoringService.ListModelMonitors][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitors].

ListModelMonitorsResponse

Response message for [ModelMonitoringService.ListModelMonitors][google.cloud.aiplatform.v1beta1.ModelMonitoringService.ListModelMonitors]

ListModelVersionsRequest

Request message for [ModelService.ListModelVersions][google.cloud.aiplatform.v1beta1.ModelService.ListModelVersions].

ListModelVersionsResponse

Response message for [ModelService.ListModelVersions][google.cloud.aiplatform.v1beta1.ModelService.ListModelVersions]

ListModelsRequest

Request message for [ModelService.ListModels][google.cloud.aiplatform.v1beta1.ModelService.ListModels].

ListModelsResponse

Response message for [ModelService.ListModels][google.cloud.aiplatform.v1beta1.ModelService.ListModels]

ListNasJobsRequest

Request message for [JobService.ListNasJobs][google.cloud.aiplatform.v1beta1.JobService.ListNasJobs].

ListNasJobsResponse

Response message for [JobService.ListNasJobs][google.cloud.aiplatform.v1beta1.JobService.ListNasJobs]

ListNasTrialDetailsRequest

Request message for [JobService.ListNasTrialDetails][google.cloud.aiplatform.v1beta1.JobService.ListNasTrialDetails].

ListNasTrialDetailsResponse

Response message for [JobService.ListNasTrialDetails][google.cloud.aiplatform.v1beta1.JobService.ListNasTrialDetails]

ListNotebookExecutionJobsRequest

Request message for [NotebookService.ListNotebookExecutionJobs]

ListNotebookExecutionJobsResponse

Response message for [NotebookService.CreateNotebookExecutionJob]

ListNotebookRuntimeTemplatesRequest

Request message for [NotebookService.ListNotebookRuntimeTemplates][google.cloud.aiplatform.v1beta1.NotebookService.ListNotebookRuntimeTemplates].

ListNotebookRuntimeTemplatesResponse

Response message for [NotebookService.ListNotebookRuntimeTemplates][google.cloud.aiplatform.v1beta1.NotebookService.ListNotebookRuntimeTemplates].

ListNotebookRuntimesRequest

Request message for [NotebookService.ListNotebookRuntimes][google.cloud.aiplatform.v1beta1.NotebookService.ListNotebookRuntimes].

ListNotebookRuntimesResponse

Response message for [NotebookService.ListNotebookRuntimes][google.cloud.aiplatform.v1beta1.NotebookService.ListNotebookRuntimes].

ListOptimalTrialsRequest

Request message for [VizierService.ListOptimalTrials][google.cloud.aiplatform.v1beta1.VizierService.ListOptimalTrials].

ListOptimalTrialsResponse

Response message for [VizierService.ListOptimalTrials][google.cloud.aiplatform.v1beta1.VizierService.ListOptimalTrials].

ListPersistentResourcesRequest

Request message for [PersistentResourceService.ListPersistentResource][].

ListPersistentResourcesResponse

Response message for [PersistentResourceService.ListPersistentResources][google.cloud.aiplatform.v1beta1.PersistentResourceService.ListPersistentResources]

ListPipelineJobsRequest

Request message for [PipelineService.ListPipelineJobs][google.cloud.aiplatform.v1beta1.PipelineService.ListPipelineJobs].

ListPipelineJobsResponse

Response message for [PipelineService.ListPipelineJobs][google.cloud.aiplatform.v1beta1.PipelineService.ListPipelineJobs]

ListPublisherModelsRequest

Request message for [ModelGardenService.ListPublisherModels][google.cloud.aiplatform.v1beta1.ModelGardenService.ListPublisherModels].

ListPublisherModelsResponse

Response message for [ModelGardenService.ListPublisherModels][google.cloud.aiplatform.v1beta1.ModelGardenService.ListPublisherModels].

ListRagCorporaRequest

Request message for [VertexRagDataService.ListRagCorpora][google.cloud.aiplatform.v1beta1.VertexRagDataService.ListRagCorpora].

ListRagCorporaResponse

Response message for [VertexRagDataService.ListRagCorpora][google.cloud.aiplatform.v1beta1.VertexRagDataService.ListRagCorpora].

ListRagFilesRequest

Request message for [VertexRagDataService.ListRagFiles][google.cloud.aiplatform.v1beta1.VertexRagDataService.ListRagFiles].

ListRagFilesResponse

Response message for [VertexRagDataService.ListRagFiles][google.cloud.aiplatform.v1beta1.VertexRagDataService.ListRagFiles].

ListReasoningEnginesRequest

Request message for [ReasoningEngineService.ListReasoningEngines][google.cloud.aiplatform.v1beta1.ReasoningEngineService.ListReasoningEngines].

ListReasoningEnginesResponse

Response message for [ReasoningEngineService.ListReasoningEngines][google.cloud.aiplatform.v1beta1.ReasoningEngineService.ListReasoningEngines]

ListSavedQueriesRequest

Request message for [DatasetService.ListSavedQueries][google.cloud.aiplatform.v1beta1.DatasetService.ListSavedQueries].

ListSavedQueriesResponse

Response message for [DatasetService.ListSavedQueries][google.cloud.aiplatform.v1beta1.DatasetService.ListSavedQueries].

ListSchedulesRequest

Request message for [ScheduleService.ListSchedules][google.cloud.aiplatform.v1beta1.ScheduleService.ListSchedules].

ListSchedulesResponse

Response message for [ScheduleService.ListSchedules][google.cloud.aiplatform.v1beta1.ScheduleService.ListSchedules]

ListSpecialistPoolsRequest

Request message for [SpecialistPoolService.ListSpecialistPools][google.cloud.aiplatform.v1beta1.SpecialistPoolService.ListSpecialistPools].

ListSpecialistPoolsResponse

Response message for [SpecialistPoolService.ListSpecialistPools][google.cloud.aiplatform.v1beta1.SpecialistPoolService.ListSpecialistPools].

ListStudiesRequest

Request message for [VizierService.ListStudies][google.cloud.aiplatform.v1beta1.VizierService.ListStudies].

ListStudiesResponse

Response message for [VizierService.ListStudies][google.cloud.aiplatform.v1beta1.VizierService.ListStudies].

ListTensorboardExperimentsRequest

Request message for [TensorboardService.ListTensorboardExperiments][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardExperiments].

ListTensorboardExperimentsResponse

Response message for [TensorboardService.ListTensorboardExperiments][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardExperiments].

ListTensorboardRunsRequest

Request message for [TensorboardService.ListTensorboardRuns][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardRuns].

ListTensorboardRunsResponse

Response message for [TensorboardService.ListTensorboardRuns][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardRuns].

ListTensorboardTimeSeriesRequest

Request message for [TensorboardService.ListTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardTimeSeries].

ListTensorboardTimeSeriesResponse

Response message for [TensorboardService.ListTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardTimeSeries].

ListTensorboardsRequest

Request message for [TensorboardService.ListTensorboards][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboards].

ListTensorboardsResponse

Response message for [TensorboardService.ListTensorboards][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboards].

ListTrainingPipelinesRequest

Request message for [PipelineService.ListTrainingPipelines][google.cloud.aiplatform.v1beta1.PipelineService.ListTrainingPipelines].

ListTrainingPipelinesResponse

Response message for [PipelineService.ListTrainingPipelines][google.cloud.aiplatform.v1beta1.PipelineService.ListTrainingPipelines]

ListTrialsRequest

Request message for [VizierService.ListTrials][google.cloud.aiplatform.v1beta1.VizierService.ListTrials].

ListTrialsResponse

Response message for [VizierService.ListTrials][google.cloud.aiplatform.v1beta1.VizierService.ListTrials].

ListTuningJobsRequest

Request message for [GenAiTuningService.ListTuningJobs][google.cloud.aiplatform.v1beta1.GenAiTuningService.ListTuningJobs].

ListTuningJobsResponse

Response message for [GenAiTuningService.ListTuningJobs][google.cloud.aiplatform.v1beta1.GenAiTuningService.ListTuningJobs]

LlmUtilityService

Service for LLM related utility functions.

LlmUtilityService.LlmUtilityServiceBase

Base class for server-side implementations of LlmUtilityService

LlmUtilityService.LlmUtilityServiceClient

Client for LlmUtilityService

LlmUtilityServiceClient

LlmUtilityService client wrapper, for convenient use.

LlmUtilityServiceClientBuilder

Builder class for LlmUtilityServiceClient to provide simple configuration of credentials, endpoint etc.

LlmUtilityServiceClientImpl

LlmUtilityService client wrapper implementation, for convenient use.

LlmUtilityServiceSettings

Settings for LlmUtilityServiceClient instances.

LogprobsResult

Logprobs Result

LogprobsResult.Types

Container for nested types declared in the LogprobsResult message type.

LogprobsResult.Types.Candidate

Candidate for the logprobs token and score.

LogprobsResult.Types.TopCandidates

Candidates with top log probabilities at each decoding step.

LookupStudyRequest

Request message for [VizierService.LookupStudy][google.cloud.aiplatform.v1beta1.VizierService.LookupStudy].

MachineSpec

Specification of a single machine.

ManualBatchTuningParameters

Manual batch tuning parameters.

MatchService

MatchService is a Google managed service for efficient vector similarity search at scale.

MatchService.MatchServiceBase

Base class for server-side implementations of MatchService

MatchService.MatchServiceClient

Client for MatchService

MatchServiceClient

MatchService client wrapper, for convenient use.

MatchServiceClientBuilder

Builder class for MatchServiceClient to provide simple configuration of credentials, endpoint etc.

MatchServiceClientImpl

MatchService client wrapper implementation, for convenient use.

MatchServiceSettings

Settings for MatchServiceClient instances.

Measurement

A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.

Measurement.Types

Container for nested types declared in the Measurement message type.

Measurement.Types.Metric

A message representing a metric in the measurement.

MergeVersionAliasesRequest

Request message for [ModelService.MergeVersionAliases][google.cloud.aiplatform.v1beta1.ModelService.MergeVersionAliases].

MetadataSchema

Instance of a general MetadataSchema.

MetadataSchema.Types

Container for nested types declared in the MetadataSchema message type.

MetadataSchemaName

Resource name for the MetadataSchema resource.

MetadataService

Service for reading and writing metadata entries.

MetadataService.MetadataServiceBase

Base class for server-side implementations of MetadataService

MetadataService.MetadataServiceClient

Client for MetadataService

MetadataServiceClient

MetadataService client wrapper, for convenient use.

MetadataServiceClientBuilder

Builder class for MetadataServiceClient to provide simple configuration of credentials, endpoint etc.

MetadataServiceClientImpl

MetadataService client wrapper implementation, for convenient use.

MetadataServiceSettings

Settings for MetadataServiceClient instances.

MetadataStore

Instance of a metadata store. Contains a set of metadata that can be queried.

MetadataStore.Types

Container for nested types declared in the MetadataStore message type.

MetadataStore.Types.DataplexConfig

Represents Dataplex integration settings.

MetadataStore.Types.MetadataStoreState

Represents state information for a MetadataStore.

MetadataStoreName

Resource name for the MetadataStore resource.

MigratableResource

Represents one resource that exists in automl.googleapis.com, datalabeling.googleapis.com or ml.googleapis.com.

MigratableResource.Types

Container for nested types declared in the MigratableResource message type.

MigratableResource.Types.AutomlDataset

Represents one Dataset in automl.googleapis.com.

MigratableResource.Types.AutomlModel

Represents one Model in automl.googleapis.com.

MigratableResource.Types.DataLabelingDataset

Represents one Dataset in datalabeling.googleapis.com.

MigratableResource.Types.DataLabelingDataset.Types

Container for nested types declared in the DataLabelingDataset message type.

MigratableResource.Types.DataLabelingDataset.Types.DataLabelingAnnotatedDataset

Represents one AnnotatedDataset in datalabeling.googleapis.com.

MigratableResource.Types.MlEngineModelVersion

Represents one model Version in ml.googleapis.com.

MigrateResourceRequest

Config of migrating one resource from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

MigrateResourceRequest.Types

Container for nested types declared in the MigrateResourceRequest message type.

MigrateResourceRequest.Types.MigrateAutomlDatasetConfig

Config for migrating Dataset in automl.googleapis.com to Vertex AI's Dataset.

MigrateResourceRequest.Types.MigrateAutomlModelConfig

Config for migrating Model in automl.googleapis.com to Vertex AI's Model.

MigrateResourceRequest.Types.MigrateDataLabelingDatasetConfig

Config for migrating Dataset in datalabeling.googleapis.com to Vertex AI's Dataset.

MigrateResourceRequest.Types.MigrateDataLabelingDatasetConfig.Types

Container for nested types declared in the MigrateDataLabelingDatasetConfig message type.

MigrateResourceRequest.Types.MigrateDataLabelingDatasetConfig.Types.MigrateDataLabelingAnnotatedDatasetConfig

Config for migrating AnnotatedDataset in datalabeling.googleapis.com to Vertex AI's SavedQuery.

MigrateResourceRequest.Types.MigrateMlEngineModelVersionConfig

Config for migrating version in ml.googleapis.com to Vertex AI's Model.

MigrateResourceResponse

Describes a successfully migrated resource.

MigrationService

A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

MigrationService.MigrationServiceBase

Base class for server-side implementations of MigrationService

MigrationService.MigrationServiceClient

Client for MigrationService

MigrationServiceClient

MigrationService client wrapper, for convenient use.

MigrationServiceClientBuilder

Builder class for MigrationServiceClient to provide simple configuration of credentials, endpoint etc.

MigrationServiceClientImpl

MigrationService client wrapper implementation, for convenient use.

MigrationServiceSettings

Settings for MigrationServiceClient instances.

Model

A trained machine learning Model.

Model.Types

Container for nested types declared in the Model message type.

Model.Types.BaseModelSource

User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

Model.Types.ExportFormat

Represents export format supported by the Model. All formats export to Google Cloud Storage.

Model.Types.ExportFormat.Types

Container for nested types declared in the ExportFormat message type.

Model.Types.OriginalModelInfo

Contains information about the original Model if this Model is a copy.

ModelContainerSpec

Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification.

ModelDeploymentMonitoringBigQueryTable

ModelDeploymentMonitoringBigQueryTable specifies the BigQuery table name as well as some information of the logs stored in this table.

ModelDeploymentMonitoringBigQueryTable.Types

Container for nested types declared in the ModelDeploymentMonitoringBigQueryTable message type.

ModelDeploymentMonitoringJob

Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

ModelDeploymentMonitoringJob.Types

Container for nested types declared in the ModelDeploymentMonitoringJob message type.

ModelDeploymentMonitoringJob.Types.LatestMonitoringPipelineMetadata

All metadata of most recent monitoring pipelines.

ModelDeploymentMonitoringJobName

Resource name for the ModelDeploymentMonitoringJob resource.

ModelDeploymentMonitoringObjectiveConfig

ModelDeploymentMonitoringObjectiveConfig contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig.

ModelDeploymentMonitoringScheduleConfig

The config for scheduling monitoring job.

ModelEvaluation

A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data.

ModelEvaluation.Types

Container for nested types declared in the ModelEvaluation message type.

ModelEvaluation.Types.BiasConfig

Configuration for bias detection.

ModelEvaluation.Types.ModelEvaluationExplanationSpec

ModelEvaluationName

Resource name for the ModelEvaluation resource.

ModelEvaluationSlice

A collection of metrics calculated by comparing Model's predictions on a slice of the test data against ground truth annotations.

ModelEvaluationSlice.Types

Container for nested types declared in the ModelEvaluationSlice message type.

ModelEvaluationSlice.Types.Slice

Definition of a slice.

ModelEvaluationSlice.Types.Slice.Types

Container for nested types declared in the Slice message type.

ModelEvaluationSlice.Types.Slice.Types.SliceSpec

Specification for how the data should be sliced.

ModelEvaluationSlice.Types.Slice.Types.SliceSpec.Types

Container for nested types declared in the SliceSpec message type.

ModelEvaluationSlice.Types.Slice.Types.SliceSpec.Types.Range

A range of values for slice(s). low is inclusive, high is exclusive.

ModelEvaluationSlice.Types.Slice.Types.SliceSpec.Types.SliceConfig

Specification message containing the config for this SliceSpec. When kind is selected as value and/or range, only a single slice will be computed. When all_values is present, a separate slice will be computed for each possible label/value for the corresponding key in config. Examples, with feature zip_code with values 12345, 23334, 88888 and feature country with values "US", "Canada", "Mexico" in the dataset:

Example 1:

{
  "zip_code": { "value": { "float_value": 12345.0 } }
}

A single slice for any data with zip_code 12345 in the dataset.

Example 2:

{
  "zip_code": { "range": { "low": 12345, "high": 20000 } }
}

A single slice containing data where the zip_codes between 12345 and 20000 For this example, data with the zip_code of 12345 will be in this slice.

Example 3:

{
  "zip_code": { "range": { "low": 10000, "high": 20000 } },
  "country": { "value": { "string_value": "US" } }
}

A single slice containing data where the zip_codes between 10000 and 20000 has the country "US". For this example, data with the zip_code of 12345 and country "US" will be in this slice.

Example 4:

{ "country": {"all_values": { "value": true } } }

Three slices are computed, one for each unique country in the dataset.

Example 5:

{
  "country": { "all_values": { "value": true } },
  "zip_code": { "value": { "float_value": 12345.0 } }
}

Three slices are computed, one for each unique country in the dataset where the zip_code is also 12345. For this example, data with zip_code 12345 and country "US" will be in one slice, zip_code 12345 and country "Canada" in another slice, and zip_code 12345 and country "Mexico" in another slice, totaling 3 slices.

ModelEvaluationSlice.Types.Slice.Types.SliceSpec.Types.Value

Single value that supports strings and floats.

ModelEvaluationSliceName

Resource name for the ModelEvaluationSlice resource.

ModelExplanation

Aggregated explanation metrics for a Model over a set of instances.

ModelGardenService

The interface of Model Garden Service.

ModelGardenService.ModelGardenServiceBase

Base class for server-side implementations of ModelGardenService

ModelGardenService.ModelGardenServiceClient

Client for ModelGardenService

ModelGardenServiceClient

ModelGardenService client wrapper, for convenient use.

ModelGardenServiceClientBuilder

Builder class for ModelGardenServiceClient to provide simple configuration of credentials, endpoint etc.

ModelGardenServiceClientImpl

ModelGardenService client wrapper implementation, for convenient use.

ModelGardenServiceSettings

Settings for ModelGardenServiceClient instances.

ModelGardenSource

Contains information about the source of the models generated from Model Garden.

ModelMonitor

Vertex AI Model Monitoring Service serves as a central hub for the analysis and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.

ModelMonitor.Types

Container for nested types declared in the ModelMonitor message type.

ModelMonitor.Types.ModelMonitoringTarget

The monitoring target refers to the entity that is subject to analysis. e.g. Vertex AI Model version.

ModelMonitor.Types.ModelMonitoringTarget.Types

Container for nested types declared in the ModelMonitoringTarget message type.

ModelMonitor.Types.ModelMonitoringTarget.Types.VertexModelSource

Model in Vertex AI Model Registry.

ModelMonitorName

Resource name for the ModelMonitor resource.

ModelMonitoringAlert

Represents a single monitoring alert. This is currently used in the SearchModelMonitoringAlerts api, thus the alert wrapped in this message belongs to the resource asked in the request.

ModelMonitoringAlertCondition

Monitoring alert triggered condition.

ModelMonitoringAlertConfig

The alert config for model monitoring.

ModelMonitoringAlertConfig.Types

Container for nested types declared in the ModelMonitoringAlertConfig message type.

ModelMonitoringAlertConfig.Types.EmailAlertConfig

The config for email alert.

ModelMonitoringAnomaly

Represents a single model monitoring anomaly.

ModelMonitoringAnomaly.Types

Container for nested types declared in the ModelMonitoringAnomaly message type.

ModelMonitoringAnomaly.Types.TabularAnomaly

Tabular anomaly details.

ModelMonitoringConfig

The model monitoring configuration used for Batch Prediction Job.

ModelMonitoringInput

Model monitoring data input spec.

ModelMonitoringInput.Types

Container for nested types declared in the ModelMonitoringInput message type.

ModelMonitoringInput.Types.BatchPredictionOutput

Data from Vertex AI Batch prediction job output.

ModelMonitoringInput.Types.ModelMonitoringDataset

Input dataset spec.

ModelMonitoringInput.Types.ModelMonitoringDataset.Types

Container for nested types declared in the ModelMonitoringDataset message type.

ModelMonitoringInput.Types.ModelMonitoringDataset.Types.ModelMonitoringBigQuerySource

Dataset spec for data sotred in BigQuery.

ModelMonitoringInput.Types.ModelMonitoringDataset.Types.ModelMonitoringGcsSource

Dataset spec for data stored in Google Cloud Storage.

ModelMonitoringInput.Types.ModelMonitoringDataset.Types.ModelMonitoringGcsSource.Types

Container for nested types declared in the ModelMonitoringGcsSource message type.

ModelMonitoringInput.Types.TimeOffset

Time offset setting.

ModelMonitoringInput.Types.VertexEndpointLogs

Data from Vertex AI Endpoint request response logging.

ModelMonitoringJob

Represents a model monitoring job that analyze dataset using different monitoring algorithm.

ModelMonitoringJobExecutionDetail

Represent the execution details of the job.

ModelMonitoringJobExecutionDetail.Types

Container for nested types declared in the ModelMonitoringJobExecutionDetail message type.

ModelMonitoringJobExecutionDetail.Types.ProcessedDataset

Processed dataset information.

ModelMonitoringJobName

Resource name for the ModelMonitoringJob resource.

ModelMonitoringNotificationSpec

Notification spec(email, notification channel) for model monitoring statistics/alerts.

ModelMonitoringNotificationSpec.Types

Container for nested types declared in the ModelMonitoringNotificationSpec message type.

ModelMonitoringNotificationSpec.Types.EmailConfig

The config for email alerts.

ModelMonitoringNotificationSpec.Types.NotificationChannelConfig

Google Cloud Notification Channel config.

ModelMonitoringObjectiveConfig

The objective configuration for model monitoring, including the information needed to detect anomalies for one particular model.

ModelMonitoringObjectiveConfig.Types

Container for nested types declared in the ModelMonitoringObjectiveConfig message type.

ModelMonitoringObjectiveConfig.Types.ExplanationConfig

The config for integrating with Vertex Explainable AI. Only applicable if the Model has explanation_spec populated.

ModelMonitoringObjectiveConfig.Types.ExplanationConfig.Types

Container for nested types declared in the ExplanationConfig message type.

ModelMonitoringObjectiveConfig.Types.ExplanationConfig.Types.ExplanationBaseline

Output from [BatchPredictionJob][google.cloud.aiplatform.v1beta1.BatchPredictionJob] for Model Monitoring baseline dataset, which can be used to generate baseline attribution scores.

ModelMonitoringObjectiveConfig.Types.ExplanationConfig.Types.ExplanationBaseline.Types

Container for nested types declared in the ExplanationBaseline message type.

ModelMonitoringObjectiveConfig.Types.PredictionDriftDetectionConfig

The config for Prediction data drift detection.

ModelMonitoringObjectiveConfig.Types.TrainingDataset

Training Dataset information.

ModelMonitoringObjectiveConfig.Types.TrainingPredictionSkewDetectionConfig

The config for Training & Prediction data skew detection. It specifies the training dataset sources and the skew detection parameters.

ModelMonitoringObjectiveSpec

Monitoring objectives spec.

ModelMonitoringObjectiveSpec.Types

Container for nested types declared in the ModelMonitoringObjectiveSpec message type.

ModelMonitoringObjectiveSpec.Types.DataDriftSpec

Data drift monitoring spec. Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period.

ModelMonitoringObjectiveSpec.Types.FeatureAttributionSpec

Feature attribution monitoring spec.

ModelMonitoringObjectiveSpec.Types.TabularObjective

Tabular monitoring objective.

ModelMonitoringOutputSpec

Specification for the export destination of monitoring results, including metrics, logs, etc.

ModelMonitoringSchema

The Model Monitoring Schema definition.

ModelMonitoringSchema.Types

Container for nested types declared in the ModelMonitoringSchema message type.

ModelMonitoringSchema.Types.FieldSchema

Schema field definition.

ModelMonitoringService

A service for creating and managing Vertex AI Model moitoring. This includes ModelMonitor resources, ModelMonitoringJob resources.

ModelMonitoringService.ModelMonitoringServiceBase

Base class for server-side implementations of ModelMonitoringService

ModelMonitoringService.ModelMonitoringServiceClient

Client for ModelMonitoringService

ModelMonitoringServiceClient

ModelMonitoringService client wrapper, for convenient use.

ModelMonitoringServiceClientBuilder

Builder class for ModelMonitoringServiceClient to provide simple configuration of credentials, endpoint etc.

ModelMonitoringServiceClientImpl

ModelMonitoringService client wrapper implementation, for convenient use.

ModelMonitoringServiceSettings

Settings for ModelMonitoringServiceClient instances.

ModelMonitoringSpec

Monitoring monitoring job spec. It outlines the specifications for monitoring objectives, notifications, and result exports.

ModelMonitoringStats

Represents the collection of statistics for a metric.

ModelMonitoringStatsAnomalies

Statistics and anomalies generated by Model Monitoring.

ModelMonitoringStatsAnomalies.Types

Container for nested types declared in the ModelMonitoringStatsAnomalies message type.

ModelMonitoringStatsAnomalies.Types.FeatureHistoricStatsAnomalies

Historical Stats (and Anomalies) for a specific Feature.

ModelMonitoringStatsDataPoint

Represents a single statistics data point.

ModelMonitoringStatsDataPoint.Types

Container for nested types declared in the ModelMonitoringStatsDataPoint message type.

ModelMonitoringStatsDataPoint.Types.TypedValue

Typed value of the statistics.

ModelMonitoringStatsDataPoint.Types.TypedValue.Types

Container for nested types declared in the TypedValue message type.

ModelMonitoringStatsDataPoint.Types.TypedValue.Types.DistributionDataValue

Summary statistics for a population of values.

ModelMonitoringTabularStats

A collection of data points that describes the time-varying values of a tabular metric.

ModelName

Resource name for the Model resource.

ModelService

A service for managing Vertex AI's machine learning Models.

ModelService.ModelServiceBase

Base class for server-side implementations of ModelService

ModelService.ModelServiceClient

Client for ModelService

ModelServiceClient

ModelService client wrapper, for convenient use.

ModelServiceClientBuilder

Builder class for ModelServiceClient to provide simple configuration of credentials, endpoint etc.

ModelServiceClientImpl

ModelService client wrapper implementation, for convenient use.

ModelServiceSettings

Settings for ModelServiceClient instances.

ModelSourceInfo

Detail description of the source information of the model.

ModelSourceInfo.Types

Container for nested types declared in the ModelSourceInfo message type.

MutateDeployedIndexOperationMetadata

Runtime operation information for [IndexEndpointService.MutateDeployedIndex][google.cloud.aiplatform.v1beta1.IndexEndpointService.MutateDeployedIndex].

MutateDeployedIndexRequest

Request message for [IndexEndpointService.MutateDeployedIndex][google.cloud.aiplatform.v1beta1.IndexEndpointService.MutateDeployedIndex].

MutateDeployedIndexResponse

Response message for [IndexEndpointService.MutateDeployedIndex][google.cloud.aiplatform.v1beta1.IndexEndpointService.MutateDeployedIndex].

MutateDeployedModelOperationMetadata

Runtime operation information for [EndpointService.MutateDeployedModel][google.cloud.aiplatform.v1beta1.EndpointService.MutateDeployedModel].

MutateDeployedModelRequest

Request message for [EndpointService.MutateDeployedModel][google.cloud.aiplatform.v1beta1.EndpointService.MutateDeployedModel].

MutateDeployedModelResponse

Response message for [EndpointService.MutateDeployedModel][google.cloud.aiplatform.v1beta1.EndpointService.MutateDeployedModel].

NasJob

Represents a Neural Architecture Search (NAS) job.

NasJobName

Resource name for the NasJob resource.

NasJobOutput

Represents a uCAIP NasJob output.

NasJobOutput.Types

Container for nested types declared in the NasJobOutput message type.

NasJobOutput.Types.MultiTrialJobOutput

The output of a multi-trial Neural Architecture Search (NAS) jobs.

NasJobSpec

Represents the spec of a NasJob.

NasJobSpec.Types

Container for nested types declared in the NasJobSpec message type.

NasJobSpec.Types.MultiTrialAlgorithmSpec

The spec of multi-trial Neural Architecture Search (NAS).

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types

Container for nested types declared in the MultiTrialAlgorithmSpec message type.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.MetricSpec

Represents a metric to optimize.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.MetricSpec.Types

Container for nested types declared in the MetricSpec message type.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.SearchTrialSpec

Represent spec for search trials.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.TrainTrialSpec

Represent spec for train trials.

NasTrial

Represents a uCAIP NasJob trial.

NasTrial.Types

Container for nested types declared in the NasTrial message type.

NasTrialDetail

Represents a NasTrial details along with its parameters. If there is a corresponding train NasTrial, the train NasTrial is also returned.

NasTrialDetailName

Resource name for the NasTrialDetail resource.

NearestNeighborQuery

A query to find a number of similar entities.

NearestNeighborQuery.Types

Container for nested types declared in the NearestNeighborQuery message type.

NearestNeighborQuery.Types.Embedding

The embedding vector.

NearestNeighborQuery.Types.NumericFilter

Numeric filter is used to search a subset of the entities by using boolean rules on numeric columns. For example: Database Point 0: {name: “a” value_int: 42} {name: “b” value_float: 1.0} Database Point 1: {name: “a” value_int: 10} {name: “b” value_float: 2.0} Database Point 2: {name: “a” value_int: -1} {name: “b” value_float: 3.0} Query: {name: “a” value_int: 12 operator: LESS} // Matches Point 1, 2 {name: “b” value_float: 2.0 operator: EQUAL} // Matches Point 1

NearestNeighborQuery.Types.NumericFilter.Types

Container for nested types declared in the NumericFilter message type.

NearestNeighborQuery.Types.Parameters

Parameters that can be overrided in each query to tune query latency and recall.

NearestNeighborQuery.Types.StringFilter

String filter is used to search a subset of the entities by using boolean rules on string columns. For example: if a query specifies string filter with 'name = color, allow_tokens = {red, blue}, deny_tokens = {purple}',' then that query will match entities that are red or blue, but if those points are also purple, then they will be excluded even if they are red/blue. Only string filter is supported for now, numeric filter will be supported in the near future.

NearestNeighborSearchOperationMetadata

Runtime operation metadata with regard to Matching Engine Index.

NearestNeighborSearchOperationMetadata.Types

Container for nested types declared in the NearestNeighborSearchOperationMetadata message type.

NearestNeighborSearchOperationMetadata.Types.ContentValidationStats

NearestNeighborSearchOperationMetadata.Types.RecordError

NearestNeighborSearchOperationMetadata.Types.RecordError.Types

Container for nested types declared in the RecordError message type.

NearestNeighbors

Nearest neighbors for one query.

NearestNeighbors.Types

Container for nested types declared in the NearestNeighbors message type.

NearestNeighbors.Types.Neighbor

A neighbor of the query vector.

Neighbor

Neighbors for example-based explanations.

NetworkAttachmentName

Resource name for the NetworkAttachment resource.

NetworkName

Resource name for the Network resource.

NetworkSpec

Network spec.

NfsMount

Represents a mount configuration for Network File System (NFS) to mount.

NotebookEucConfig

The euc configuration of NotebookRuntimeTemplate.

NotebookExecutionJob

NotebookExecutionJob represents an instance of a notebook execution.

NotebookExecutionJob.Types

Container for nested types declared in the NotebookExecutionJob message type.

NotebookExecutionJob.Types.DataformRepositorySource

The Dataform Repository containing the input notebook.

NotebookExecutionJob.Types.DirectNotebookSource

The content of the input notebook in ipynb format.

NotebookExecutionJob.Types.GcsNotebookSource

The Cloud Storage uri for the input notebook.

NotebookExecutionJobName

Resource name for the NotebookExecutionJob resource.

NotebookIdleShutdownConfig

The idle shutdown configuration of NotebookRuntimeTemplate, which contains the idle_timeout as required field.

NotebookRuntime

A runtime is a virtual machine allocated to a particular user for a particular Notebook file on temporary basis with lifetime limited to 24 hours.

NotebookRuntime.Types

Container for nested types declared in the NotebookRuntime message type.

NotebookRuntimeName

Resource name for the NotebookRuntime resource.

NotebookRuntimeTemplate

A template that specifies runtime configurations such as machine type, runtime version, network configurations, etc. Multiple runtimes can be created from a runtime template.

NotebookRuntimeTemplateName

Resource name for the NotebookRuntimeTemplate resource.

NotebookRuntimeTemplateRef

Points to a NotebookRuntimeTemplateRef.

NotebookService

The interface for Vertex Notebook service (a.k.a. Colab on Workbench).

NotebookService.NotebookServiceBase

Base class for server-side implementations of NotebookService

NotebookService.NotebookServiceClient

Client for NotebookService

NotebookServiceClient

NotebookService client wrapper, for convenient use.

NotebookServiceClientBuilder

Builder class for NotebookServiceClient to provide simple configuration of credentials, endpoint etc.

NotebookServiceClientImpl

NotebookService client wrapper implementation, for convenient use.

NotebookServiceSettings

Settings for NotebookServiceClient instances.

NotificationChannelName

Resource name for the NotificationChannel resource.

OpenApiSchema

Schema is used to define the format of input/output data. Represents a select subset of an OpenAPI 3.0 schema object. More fields may be added in the future as needed.

PSCAutomationConfig

PSC config that is used to automatically create forwarding rule via ServiceConnectionMap.

PairwiseMetricInput

Input for pairwise metric.

PairwiseMetricInstance

Pairwise metric instance. Usually one instance corresponds to one row in an evaluation dataset.

PairwiseMetricResult

Spec for pairwise metric result.

PairwiseMetricSpec

Spec for pairwise metric.

PairwiseQuestionAnsweringQualityInput

Input for pairwise question answering quality metric.

PairwiseQuestionAnsweringQualityInstance

Spec for pairwise question answering quality instance.

PairwiseQuestionAnsweringQualityResult

Spec for pairwise question answering quality result.

PairwiseQuestionAnsweringQualitySpec

Spec for pairwise question answering quality score metric.

PairwiseSummarizationQualityInput

Input for pairwise summarization quality metric.

PairwiseSummarizationQualityInstance

Spec for pairwise summarization quality instance.

PairwiseSummarizationQualityResult

Spec for pairwise summarization quality result.

PairwiseSummarizationQualitySpec

Spec for pairwise summarization quality score metric.

Part

A datatype containing media that is part of a multi-part Content message.

A Part consists of data which has an associated datatype. A Part can only contain one of the accepted types in Part.data.

A Part must have a fixed IANA MIME type identifying the type and subtype of the media if inline_data or file_data field is filled with raw bytes.

PauseModelDeploymentMonitoringJobRequest

Request message for [JobService.PauseModelDeploymentMonitoringJob][google.cloud.aiplatform.v1beta1.JobService.PauseModelDeploymentMonitoringJob].

PauseScheduleRequest

Request message for [ScheduleService.PauseSchedule][google.cloud.aiplatform.v1beta1.ScheduleService.PauseSchedule].

PersistentDiskSpec

Represents the spec of [persistent disk][https://cloud.google.com/compute/docs/disks/persistent-disks] options.

PersistentResource

Represents long-lasting resources that are dedicated to users to runs custom workloads. A PersistentResource can have multiple node pools and each node pool can have its own machine spec.

PersistentResource.Types

Container for nested types declared in the PersistentResource message type.

PersistentResourceName

Resource name for the PersistentResource resource.

PersistentResourceService

A service for managing Vertex AI's machine learning PersistentResource.

PersistentResourceService.PersistentResourceServiceBase

Base class for server-side implementations of PersistentResourceService

PersistentResourceService.PersistentResourceServiceClient

Client for PersistentResourceService

PersistentResourceServiceClient

PersistentResourceService client wrapper, for convenient use.

PersistentResourceServiceClientBuilder

Builder class for PersistentResourceServiceClient to provide simple configuration of credentials, endpoint etc.

PersistentResourceServiceClientImpl

PersistentResourceService client wrapper implementation, for convenient use.

PersistentResourceServiceSettings

Settings for PersistentResourceServiceClient instances.

PipelineJob

An instance of a machine learning PipelineJob.

PipelineJob.Types

Container for nested types declared in the PipelineJob message type.

PipelineJob.Types.RuntimeConfig

The runtime config of a PipelineJob.

PipelineJob.Types.RuntimeConfig.Types

Container for nested types declared in the RuntimeConfig message type.

PipelineJob.Types.RuntimeConfig.Types.InputArtifact

The type of an input artifact.

PipelineJobDetail

The runtime detail of PipelineJob.

PipelineJobName

Resource name for the PipelineJob resource.

PipelineService

A service for creating and managing Vertex AI's pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Vertex AI Pipelines).

PipelineService.PipelineServiceBase

Base class for server-side implementations of PipelineService

PipelineService.PipelineServiceClient

Client for PipelineService

PipelineServiceClient

PipelineService client wrapper, for convenient use.

PipelineServiceClientBuilder

Builder class for PipelineServiceClient to provide simple configuration of credentials, endpoint etc.

PipelineServiceClientImpl

PipelineService client wrapper implementation, for convenient use.

PipelineServiceSettings

Settings for PipelineServiceClient instances.

PipelineTaskDetail

The runtime detail of a task execution.

PipelineTaskDetail.Types

Container for nested types declared in the PipelineTaskDetail message type.

PipelineTaskDetail.Types.ArtifactList

A list of artifact metadata.

PipelineTaskDetail.Types.PipelineTaskStatus

A single record of the task status.

PipelineTaskExecutorDetail

The runtime detail of a pipeline executor.

PipelineTaskExecutorDetail.Types

Container for nested types declared in the PipelineTaskExecutorDetail message type.

PipelineTaskExecutorDetail.Types.ContainerDetail

The detail of a container execution. It contains the job names of the lifecycle of a container execution.

PipelineTaskExecutorDetail.Types.CustomJobDetail

The detailed info for a custom job executor.

PipelineTaskRerunConfig

User provided rerun config to submit a rerun pipelinejob. This includes

1. Which task to rerun
2. User override input parameters and artifacts.

PipelineTaskRerunConfig.Types

Container for nested types declared in the PipelineTaskRerunConfig message type.

PipelineTaskRerunConfig.Types.ArtifactList

A list of artifact metadata.

PipelineTaskRerunConfig.Types.Inputs

Runtime inputs data of the task.

PipelineTemplateMetadata

Pipeline template metadata if [PipelineJob.template_uri][google.cloud.aiplatform.v1beta1.PipelineJob.template_uri] is from supported template registry. Currently, the only supported registry is Artifact Registry.

PointwiseMetricInput

Input for pointwise metric.

PointwiseMetricInstance

Pointwise metric instance. Usually one instance corresponds to one row in an evaluation dataset.

PointwiseMetricResult

Spec for pointwise metric result.

PointwiseMetricSpec

Spec for pointwise metric.

Port

Represents a network port in a container.

PredefinedSplit

Assigns input data to training, validation, and test sets based on the value of a provided key.

Supported only for tabular Datasets.

PredictLongRunningMetadata

Metadata for PredictLongRunning long running operations.

PredictLongRunningResponse

Response message for [PredictionService.PredictLongRunning]

PredictRequest

Request message for [PredictionService.Predict][google.cloud.aiplatform.v1beta1.PredictionService.Predict].

PredictRequestResponseLoggingConfig

Configuration for logging request-response to a BigQuery table.

PredictResponse

Response message for [PredictionService.Predict][google.cloud.aiplatform.v1beta1.PredictionService.Predict].

PredictSchemata

Contains the schemata used in Model's predictions and explanations via [PredictionService.Predict][google.cloud.aiplatform.v1beta1.PredictionService.Predict], [PredictionService.Explain][google.cloud.aiplatform.v1beta1.PredictionService.Explain] and [BatchPredictionJob][google.cloud.aiplatform.v1beta1.BatchPredictionJob].

PredictionService

A service for online predictions and explanations.

PredictionService.PredictionServiceBase

Base class for server-side implementations of PredictionService

PredictionService.PredictionServiceClient

Client for PredictionService

PredictionServiceClient

PredictionService client wrapper, for convenient use.

PredictionServiceClient.ChatCompletionsStream

Server streaming methods for ChatCompletions(ChatCompletionsRequest, CallSettings).

PredictionServiceClient.ServerStreamingPredictStream

Server streaming methods for ServerStreamingPredict(StreamingPredictRequest, CallSettings).

PredictionServiceClient.StreamDirectPredictStream

Bidirectional streaming methods for StreamDirectPredict(CallSettings, BidirectionalStreamingSettings).

PredictionServiceClient.StreamDirectRawPredictStream

Bidirectional streaming methods for StreamDirectRawPredict(CallSettings, BidirectionalStreamingSettings).

PredictionServiceClient.StreamGenerateContentStream

Server streaming methods for StreamGenerateContent(GenerateContentRequest, CallSettings).

PredictionServiceClient.StreamRawPredictStream

Server streaming methods for StreamRawPredict(StreamRawPredictRequest, CallSettings).

PredictionServiceClient.StreamingPredictStream

Bidirectional streaming methods for StreamingPredict(CallSettings, BidirectionalStreamingSettings).

PredictionServiceClient.StreamingRawPredictStream

Bidirectional streaming methods for StreamingRawPredict(CallSettings, BidirectionalStreamingSettings).

PredictionServiceClientBuilder

Builder class for PredictionServiceClient to provide simple configuration of credentials, endpoint etc.

PredictionServiceClientImpl

PredictionService client wrapper implementation, for convenient use.

PredictionServiceSettings

Settings for PredictionServiceClient instances.

Presets

Preset configuration for example-based explanations

Presets.Types

Container for nested types declared in the Presets message type.

PrivateEndpoints

PrivateEndpoints proto is used to provide paths for users to send requests privately. To send request via private service access, use predict_http_uri, explain_http_uri or health_http_uri. To send request via private service connect, use service_attachment.

PrivateServiceConnectConfig

Represents configuration for private service connect.

Probe

Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic.

Probe.Types

Container for nested types declared in the Probe message type.

Probe.Types.ExecAction

ExecAction specifies a command to execute.

PscAutomatedEndpoints

PscAutomatedEndpoints defines the output of the forwarding rule automatically created by each PscAutomationConfig.

PscInterfaceConfig

Configuration for PSC-I.

PublisherModel

A Model Garden Publisher Model.

PublisherModel.Types

Container for nested types declared in the PublisherModel message type.

PublisherModel.Types.CallToAction

Actions could take on this Publisher Model.

PublisherModel.Types.CallToAction.Types

Container for nested types declared in the CallToAction message type.

PublisherModel.Types.CallToAction.Types.Deploy

Model metadata that is needed for UploadModel or DeployModel/CreateEndpoint requests.

PublisherModel.Types.CallToAction.Types.Deploy.Types

Container for nested types declared in the Deploy message type.

PublisherModel.Types.CallToAction.Types.Deploy.Types.DeployMetadata

Metadata information about the deployment for managing deployment config.

PublisherModel.Types.CallToAction.Types.DeployGke

Configurations for PublisherModel GKE deployment

PublisherModel.Types.CallToAction.Types.OpenFineTuningPipelines

Open fine tuning pipelines.

PublisherModel.Types.CallToAction.Types.OpenNotebooks

Open notebooks.

PublisherModel.Types.CallToAction.Types.RegionalResourceReferences

The regional resource name or the URI. Key is region, e.g., us-central1, europe-west2, global, etc..

PublisherModel.Types.CallToAction.Types.ViewRestApi

Rest API docs.

PublisherModel.Types.Documentation

A named piece of documentation.

PublisherModel.Types.Parent

The information about the parent of a model.

PublisherModel.Types.ResourceReference

Reference to a resource.

PublisherModelName

Resource name for the PublisherModel resource.

PurgeArtifactsMetadata

Details of operations that perform [MetadataService.PurgeArtifacts][google.cloud.aiplatform.v1beta1.MetadataService.PurgeArtifacts].

PurgeArtifactsRequest

Request message for [MetadataService.PurgeArtifacts][google.cloud.aiplatform.v1beta1.MetadataService.PurgeArtifacts].

PurgeArtifactsResponse

Response message for [MetadataService.PurgeArtifacts][google.cloud.aiplatform.v1beta1.MetadataService.PurgeArtifacts].

PurgeContextsMetadata

Details of operations that perform [MetadataService.PurgeContexts][google.cloud.aiplatform.v1beta1.MetadataService.PurgeContexts].

PurgeContextsRequest

Request message for [MetadataService.PurgeContexts][google.cloud.aiplatform.v1beta1.MetadataService.PurgeContexts].

PurgeContextsResponse

Response message for [MetadataService.PurgeContexts][google.cloud.aiplatform.v1beta1.MetadataService.PurgeContexts].

PurgeExecutionsMetadata

Details of operations that perform [MetadataService.PurgeExecutions][google.cloud.aiplatform.v1beta1.MetadataService.PurgeExecutions].

PurgeExecutionsRequest

Request message for [MetadataService.PurgeExecutions][google.cloud.aiplatform.v1beta1.MetadataService.PurgeExecutions].

PurgeExecutionsResponse

Response message for [MetadataService.PurgeExecutions][google.cloud.aiplatform.v1beta1.MetadataService.PurgeExecutions].

PythonPackageSpec

The spec of a Python packaged code.

QueryArtifactLineageSubgraphRequest

Request message for [MetadataService.QueryArtifactLineageSubgraph][google.cloud.aiplatform.v1beta1.MetadataService.QueryArtifactLineageSubgraph].

QueryContextLineageSubgraphRequest

Request message for [MetadataService.QueryContextLineageSubgraph][google.cloud.aiplatform.v1beta1.MetadataService.QueryContextLineageSubgraph].

QueryDeployedModelsRequest

Request message for QueryDeployedModels method.

QueryDeployedModelsResponse

Response message for QueryDeployedModels method.

QueryExecutionInputsAndOutputsRequest

Request message for [MetadataService.QueryExecutionInputsAndOutputs][google.cloud.aiplatform.v1beta1.MetadataService.QueryExecutionInputsAndOutputs].

QueryExtensionRequest

Request message for [ExtensionExecutionService.QueryExtension][google.cloud.aiplatform.v1beta1.ExtensionExecutionService.QueryExtension].

QueryExtensionResponse

Response message for [ExtensionExecutionService.QueryExtension][google.cloud.aiplatform.v1beta1.ExtensionExecutionService.QueryExtension].

QueryReasoningEngineRequest

Request message for [ReasoningEngineExecutionService.Query][].

QueryReasoningEngineResponse

Response message for [ReasoningEngineExecutionService.Query][]

QuestionAnsweringCorrectnessInput

Input for question answering correctness metric.

QuestionAnsweringCorrectnessInstance

Spec for question answering correctness instance.

QuestionAnsweringCorrectnessResult

Spec for question answering correctness result.

QuestionAnsweringCorrectnessSpec

Spec for question answering correctness metric.

QuestionAnsweringHelpfulnessInput

Input for question answering helpfulness metric.

QuestionAnsweringHelpfulnessInstance

Spec for question answering helpfulness instance.

QuestionAnsweringHelpfulnessResult

Spec for question answering helpfulness result.

QuestionAnsweringHelpfulnessSpec

Spec for question answering helpfulness metric.

QuestionAnsweringQualityInput

Input for question answering quality metric.

QuestionAnsweringQualityInstance

Spec for question answering quality instance.

QuestionAnsweringQualityResult

Spec for question answering quality result.

QuestionAnsweringQualitySpec

Spec for question answering quality score metric.

QuestionAnsweringRelevanceInput

Input for question answering relevance metric.

QuestionAnsweringRelevanceInstance

Spec for question answering relevance instance.

QuestionAnsweringRelevanceResult

Spec for question answering relevance result.

QuestionAnsweringRelevanceSpec

Spec for question answering relevance metric.

RagContexts

Relevant contexts for one query.

RagContexts.Types

Container for nested types declared in the RagContexts message type.

RagContexts.Types.Context

A context of the query.

RagCorpus

A RagCorpus is a RagFile container and a project can have multiple RagCorpora.

RagCorpusName

Resource name for the RagCorpus resource.

RagEmbeddingModelConfig

Config for the embedding model to use for RAG.

RagEmbeddingModelConfig.Types

Container for nested types declared in the RagEmbeddingModelConfig message type.

RagEmbeddingModelConfig.Types.HybridSearchConfig

Config for hybrid search.

RagEmbeddingModelConfig.Types.SparseEmbeddingConfig

Configuration for sparse emebdding generation.

RagEmbeddingModelConfig.Types.SparseEmbeddingConfig.Types

Container for nested types declared in the SparseEmbeddingConfig message type.

RagEmbeddingModelConfig.Types.SparseEmbeddingConfig.Types.Bm25

Message for BM25 parameters.

RagEmbeddingModelConfig.Types.VertexPredictionEndpoint

Config representing a model hosted on Vertex Prediction Endpoint.

RagFile

A RagFile contains user data for chunking, embedding and indexing.

RagFile.Types

Container for nested types declared in the RagFile message type.

RagFileChunkingConfig

Specifies the size and overlap of chunks for RagFiles.

RagFileName

Resource name for the RagFile resource.

RagFileParsingConfig

Specifies the parsing config for RagFiles.

RagQuery

A query to retrieve relevant contexts.

RagQuery.Types

Container for nested types declared in the RagQuery message type.

RagQuery.Types.Ranking

Configurations for hybrid search results ranking.

RagVectorDbConfig

Config for the Vector DB to use for RAG.

RagVectorDbConfig.Types

Container for nested types declared in the RagVectorDbConfig message type.

RagVectorDbConfig.Types.Pinecone

The config for the Pinecone.

RagVectorDbConfig.Types.RagManagedDb

The config for the default RAG-managed Vector DB.

RagVectorDbConfig.Types.VertexFeatureStore

The config for the Vertex Feature Store.

RagVectorDbConfig.Types.VertexVectorSearch

The config for the Vertex Vector Search.

RagVectorDbConfig.Types.Weaviate

The config for the Weaviate.

RawPredictRequest

Request message for [PredictionService.RawPredict][google.cloud.aiplatform.v1beta1.PredictionService.RawPredict].

RayLogsSpec

Configuration for the Ray OSS Logs.

RayMetricSpec

Configuration for the Ray metrics.

RaySpec

Configuration information for the Ray cluster. For experimental launch, Ray cluster creation and Persistent cluster creation are 1:1 mapping: We will provision all the nodes within the Persistent cluster as Ray nodes.

ReadFeatureValuesRequest

Request message for [FeaturestoreOnlineServingService.ReadFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreOnlineServingService.ReadFeatureValues].

ReadFeatureValuesResponse

Response message for [FeaturestoreOnlineServingService.ReadFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreOnlineServingService.ReadFeatureValues].

ReadFeatureValuesResponse.Types

Container for nested types declared in the ReadFeatureValuesResponse message type.

ReadFeatureValuesResponse.Types.EntityView

Entity view with Feature values.

ReadFeatureValuesResponse.Types.EntityView.Types

Container for nested types declared in the EntityView message type.

ReadFeatureValuesResponse.Types.EntityView.Types.Data

Container to hold value(s), successive in time, for one Feature from the request.

ReadFeatureValuesResponse.Types.FeatureDescriptor

Metadata for requested Features.

ReadFeatureValuesResponse.Types.Header

Response header with metadata for the requested [ReadFeatureValuesRequest.entity_type][google.cloud.aiplatform.v1beta1.ReadFeatureValuesRequest.entity_type] and Features.

ReadIndexDatapointsRequest

The request message for [MatchService.ReadIndexDatapoints][google.cloud.aiplatform.v1beta1.MatchService.ReadIndexDatapoints].

ReadIndexDatapointsResponse

The response message for [MatchService.ReadIndexDatapoints][google.cloud.aiplatform.v1beta1.MatchService.ReadIndexDatapoints].

ReadTensorboardBlobDataRequest

Request message for [TensorboardService.ReadTensorboardBlobData][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardBlobData].

ReadTensorboardBlobDataResponse

Response message for [TensorboardService.ReadTensorboardBlobData][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardBlobData].

ReadTensorboardSizeRequest

Request message for [TensorboardService.ReadTensorboardSize][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardSize].

ReadTensorboardSizeResponse

Response message for [TensorboardService.ReadTensorboardSize][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardSize].

ReadTensorboardTimeSeriesDataRequest

Request message for [TensorboardService.ReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardTimeSeriesData].

ReadTensorboardTimeSeriesDataResponse

Response message for [TensorboardService.ReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardTimeSeriesData].

ReadTensorboardUsageRequest

Request message for [TensorboardService.ReadTensorboardUsage][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardUsage].

ReadTensorboardUsageResponse

Response message for [TensorboardService.ReadTensorboardUsage][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardUsage].

ReadTensorboardUsageResponse.Types

Container for nested types declared in the ReadTensorboardUsageResponse message type.

ReadTensorboardUsageResponse.Types.PerMonthUsageData

Per month usage data

ReadTensorboardUsageResponse.Types.PerUserUsageData

Per user usage data.

ReasoningEngine

ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.

ReasoningEngineExecutionService

A service for executing queries on Reasoning Engine.

ReasoningEngineExecutionService.ReasoningEngineExecutionServiceBase

Base class for server-side implementations of ReasoningEngineExecutionService

ReasoningEngineExecutionService.ReasoningEngineExecutionServiceClient

Client for ReasoningEngineExecutionService

ReasoningEngineExecutionServiceClient

ReasoningEngineExecutionService client wrapper, for convenient use.

ReasoningEngineExecutionServiceClientBuilder

Builder class for ReasoningEngineExecutionServiceClient to provide simple configuration of credentials, endpoint etc.

ReasoningEngineExecutionServiceClientImpl

ReasoningEngineExecutionService client wrapper implementation, for convenient use.

ReasoningEngineExecutionServiceSettings

Settings for ReasoningEngineExecutionServiceClient instances.

ReasoningEngineName

Resource name for the ReasoningEngine resource.

ReasoningEngineService

A service for managing Vertex AI's Reasoning Engines.

ReasoningEngineService.ReasoningEngineServiceBase

Base class for server-side implementations of ReasoningEngineService

ReasoningEngineService.ReasoningEngineServiceClient

Client for ReasoningEngineService

ReasoningEngineServiceClient

ReasoningEngineService client wrapper, for convenient use.

ReasoningEngineServiceClientBuilder

Builder class for ReasoningEngineServiceClient to provide simple configuration of credentials, endpoint etc.

ReasoningEngineServiceClientImpl

ReasoningEngineService client wrapper implementation, for convenient use.

ReasoningEngineServiceSettings

Settings for ReasoningEngineServiceClient instances.

ReasoningEngineSpec

ReasoningEngine configurations

ReasoningEngineSpec.Types

Container for nested types declared in the ReasoningEngineSpec message type.

ReasoningEngineSpec.Types.PackageSpec

User provided package spec like pickled object and package requirements.

RebaseTunedModelOperationMetadata

Runtime operation information for [GenAiTuningService.RebaseTunedModel][google.cloud.aiplatform.v1beta1.GenAiTuningService.RebaseTunedModel].

RebaseTunedModelRequest

Request message for [GenAiTuningService.RebaseTunedModel][google.cloud.aiplatform.v1beta1.GenAiTuningService.RebaseTunedModel].

RebootPersistentResourceOperationMetadata

Details of operations that perform reboot PersistentResource.

RebootPersistentResourceRequest

Request message for [PersistentResourceService.RebootPersistentResource][google.cloud.aiplatform.v1beta1.PersistentResourceService.RebootPersistentResource].

RemoveContextChildrenRequest

Request message for [MetadataService.DeleteContextChildrenRequest][].

RemoveContextChildrenResponse

Response message for [MetadataService.RemoveContextChildren][google.cloud.aiplatform.v1beta1.MetadataService.RemoveContextChildren].

RemoveDatapointsRequest

Request message for [IndexService.RemoveDatapoints][google.cloud.aiplatform.v1beta1.IndexService.RemoveDatapoints]

RemoveDatapointsResponse

Response message for [IndexService.RemoveDatapoints][google.cloud.aiplatform.v1beta1.IndexService.RemoveDatapoints]

ReservationAffinity

A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity.

ReservationAffinity.Types

Container for nested types declared in the ReservationAffinity message type.

ReservationName

Resource name for the Reservation resource.

ResourcePool

Represents the spec of a group of resources of the same type, for example machine type, disk, and accelerators, in a PersistentResource.

ResourcePool.Types

Container for nested types declared in the ResourcePool message type.

ResourcePool.Types.AutoscalingSpec

The min/max number of replicas allowed if enabling autoscaling

ResourceRuntime

Persistent Cluster runtime information as output

ResourceRuntimeSpec

Configuration for the runtime on a PersistentResource instance, including but not limited to:

• Service accounts used to run the workloads.
• Whether to make it a dedicated Ray Cluster.

ResourcesConsumed

Statistics information about resource consumption.

RestoreDatasetVersionOperationMetadata

Runtime operation information for [DatasetService.RestoreDatasetVersion][google.cloud.aiplatform.v1beta1.DatasetService.RestoreDatasetVersion].

RestoreDatasetVersionRequest

Request message for [DatasetService.RestoreDatasetVersion][google.cloud.aiplatform.v1beta1.DatasetService.RestoreDatasetVersion].

ResumeModelDeploymentMonitoringJobRequest

Request message for [JobService.ResumeModelDeploymentMonitoringJob][google.cloud.aiplatform.v1beta1.JobService.ResumeModelDeploymentMonitoringJob].

ResumeScheduleRequest

Request message for [ScheduleService.ResumeSchedule][google.cloud.aiplatform.v1beta1.ScheduleService.ResumeSchedule].

Retrieval

Defines a retrieval tool that model can call to access external knowledge.

RetrievalMetadata

Metadata related to retrieval in the grounding flow.

RetrieveContextsRequest

Request message for [VertexRagService.RetrieveContexts][google.cloud.aiplatform.v1beta1.VertexRagService.RetrieveContexts].

RetrieveContextsRequest.Types

Container for nested types declared in the RetrieveContextsRequest message type.

RetrieveContextsRequest.Types.VertexRagStore

The data source for Vertex RagStore.

RetrieveContextsRequest.Types.VertexRagStore.Types

Container for nested types declared in the VertexRagStore message type.

RetrieveContextsRequest.Types.VertexRagStore.Types.RagResource

The definition of the Rag resource.

RetrieveContextsResponse

Response message for [VertexRagService.RetrieveContexts][google.cloud.aiplatform.v1beta1.VertexRagService.RetrieveContexts].

RougeInput

Input for rouge metric.

RougeInstance

Spec for rouge instance.

RougeMetricValue

Rouge metric value for an instance.

RougeResults

Results for rouge metric.

RougeSpec

Spec for rouge score metric - calculates the recall of n-grams in prediction as compared to reference - returns a score ranging between 0 and 1.

RuntimeArtifact

The definition of a runtime artifact.

RuntimeConfig

Runtime configuration to run the extension.

RuntimeConfig.Types

Container for nested types declared in the RuntimeConfig message type.

RuntimeConfig.Types.CodeInterpreterRuntimeConfig

RuntimeConfig.Types.VertexAISearchRuntimeConfig

SafetyInput

Input for safety metric.

SafetyInstance

Spec for safety instance.

SafetyRating

Safety rating corresponding to the generated content.

SafetyRating.Types

Container for nested types declared in the SafetyRating message type.

SafetyResult

Spec for safety result.

SafetySetting

Safety settings.

SafetySetting.Types

Container for nested types declared in the SafetySetting message type.

SafetySpec

Spec for safety metric.

SampleConfig

Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.

SampleConfig.Types

Container for nested types declared in the SampleConfig message type.

SampledShapleyAttribution

An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.

SamplingStrategy

Sampling Strategy for logging, can be for both training and prediction dataset.

SamplingStrategy.Types

Container for nested types declared in the SamplingStrategy message type.

SamplingStrategy.Types.RandomSampleConfig

Requests are randomly selected.

SavedQuery

A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.

SavedQueryName

Resource name for the SavedQuery resource.

Scalar

One point viewable on a scalar metric plot.

Schedule

An instance of a Schedule periodically schedules runs to make API calls based on user specified time specification and API request type.

Schedule.Types

Container for nested types declared in the Schedule message type.

Schedule.Types.RunResponse

Status of a scheduled run.

ScheduleName

Resource name for the Schedule resource.

ScheduleService

A service for creating and managing Vertex AI's Schedule resources to periodically launch shceudled runs to make API calls.

ScheduleService.ScheduleServiceBase

Base class for server-side implementations of ScheduleService

ScheduleService.ScheduleServiceClient

Client for ScheduleService

ScheduleServiceClient

ScheduleService client wrapper, for convenient use.

ScheduleServiceClientBuilder

Builder class for ScheduleServiceClient to provide simple configuration of credentials, endpoint etc.

ScheduleServiceClientImpl

ScheduleService client wrapper implementation, for convenient use.

ScheduleServiceSettings

Settings for ScheduleServiceClient instances.

Scheduling

All parameters related to queuing and scheduling of custom jobs.

Scheduling.Types

Container for nested types declared in the Scheduling message type.

SearchDataItemsRequest

Request message for [DatasetService.SearchDataItems][google.cloud.aiplatform.v1beta1.DatasetService.SearchDataItems].

SearchDataItemsRequest.Types

Container for nested types declared in the SearchDataItemsRequest message type.

SearchDataItemsRequest.Types.OrderByAnnotation

Expression that allows ranking results based on annotation's property.

SearchDataItemsResponse

Response message for [DatasetService.SearchDataItems][google.cloud.aiplatform.v1beta1.DatasetService.SearchDataItems].

SearchEntryPoint

Google search entry point.

SearchFeaturesRequest

Request message for [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.SearchFeatures].

SearchFeaturesResponse

Response message for [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1beta1.FeaturestoreService.SearchFeatures].

SearchMigratableResourcesRequest

Request message for [MigrationService.SearchMigratableResources][google.cloud.aiplatform.v1beta1.MigrationService.SearchMigratableResources].

SearchMigratableResourcesResponse

Response message for [MigrationService.SearchMigratableResources][google.cloud.aiplatform.v1beta1.MigrationService.SearchMigratableResources].

SearchModelDeploymentMonitoringStatsAnomaliesRequest

Request message for [JobService.SearchModelDeploymentMonitoringStatsAnomalies][google.cloud.aiplatform.v1beta1.JobService.SearchModelDeploymentMonitoringStatsAnomalies].

SearchModelDeploymentMonitoringStatsAnomaliesRequest.Types

Container for nested types declared in the SearchModelDeploymentMonitoringStatsAnomaliesRequest message type.

SearchModelDeploymentMonitoringStatsAnomaliesRequest.Types.StatsAnomaliesObjective

Stats requested for specific objective.

SearchModelDeploymentMonitoringStatsAnomaliesResponse

Response message for [JobService.SearchModelDeploymentMonitoringStatsAnomalies][google.cloud.aiplatform.v1beta1.JobService.SearchModelDeploymentMonitoringStatsAnomalies].

SearchModelMonitoringAlertsRequest

Request message for [ModelMonitoringService.SearchModelMonitoringAlerts][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringAlerts].

SearchModelMonitoringAlertsResponse

Response message for [ModelMonitoringService.SearchModelMonitoringAlerts][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringAlerts].

SearchModelMonitoringStatsFilter

Filter for searching ModelMonitoringStats.

SearchModelMonitoringStatsFilter.Types

Container for nested types declared in the SearchModelMonitoringStatsFilter message type.

SearchModelMonitoringStatsFilter.Types.TabularStatsFilter

Tabular statistics filter.

SearchModelMonitoringStatsRequest

Request message for [ModelMonitoringService.SearchModelMonitoringStats][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringStats].

SearchModelMonitoringStatsResponse

Response message for [ModelMonitoringService.SearchModelMonitoringStats][google.cloud.aiplatform.v1beta1.ModelMonitoringService.SearchModelMonitoringStats].

SearchNearestEntitiesRequest

The request message for [FeatureOnlineStoreService.SearchNearestEntities][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreService.SearchNearestEntities].

SearchNearestEntitiesResponse

Response message for [FeatureOnlineStoreService.SearchNearestEntities][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreService.SearchNearestEntities]

SecretVersionName

Resource name for the SecretVersion resource.

Segment

Segment of the content.

ServiceAccountSpec

Configuration for the use of custom service account to run the workloads.

ServiceName

Resource name for the Service resource.

SharePointSources

The SharePointSources to pass to ImportRagFiles.

SharePointSources.Types

Container for nested types declared in the SharePointSources message type.

SharePointSources.Types.SharePointSource

An individual SharePointSource.

ShieldedVmConfig

A set of Shielded Instance options. See Images using supported Shielded VM features.

SlackSource

The Slack source for the ImportRagFilesRequest.

SlackSource.Types

Container for nested types declared in the SlackSource message type.

SlackSource.Types.SlackChannels

SlackChannels contains the Slack channels and corresponding access token.

SlackSource.Types.SlackChannels.Types

Container for nested types declared in the SlackChannels message type.

SlackSource.Types.SlackChannels.Types.SlackChannel

SlackChannel contains the Slack channel ID and the time range to import.

SmoothGradConfig

Config for SmoothGrad approximation of gradients.

When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

SpecialistPool

SpecialistPool represents customers' own workforce to work on their data labeling jobs. It includes a group of specialist managers and workers. Managers are responsible for managing the workers in this pool as well as customers' data labeling jobs associated with this pool. Customers create specialist pool as well as start data labeling jobs on Cloud, managers and workers handle the jobs using CrowdCompute console.

SpecialistPoolName

Resource name for the SpecialistPool resource.

SpecialistPoolService

A service for creating and managing Customer SpecialistPools. When customers start Data Labeling jobs, they can reuse/create Specialist Pools to bring their own Specialists to label the data. Customers can add/remove Managers for the Specialist Pool on Cloud console, then Managers will get email notifications to manage Specialists and tasks on CrowdCompute console.

SpecialistPoolService.SpecialistPoolServiceBase

Base class for server-side implementations of SpecialistPoolService

SpecialistPoolService.SpecialistPoolServiceClient

Client for SpecialistPoolService

SpecialistPoolServiceClient

SpecialistPoolService client wrapper, for convenient use.

SpecialistPoolServiceClientBuilder

Builder class for SpecialistPoolServiceClient to provide simple configuration of credentials, endpoint etc.

SpecialistPoolServiceClientImpl

SpecialistPoolService client wrapper implementation, for convenient use.

SpecialistPoolServiceSettings

Settings for SpecialistPoolServiceClient instances.

StartNotebookRuntimeOperationMetadata

Metadata information for [NotebookService.StartNotebookRuntime][google.cloud.aiplatform.v1beta1.NotebookService.StartNotebookRuntime].

StartNotebookRuntimeRequest

Request message for [NotebookService.StartNotebookRuntime][google.cloud.aiplatform.v1beta1.NotebookService.StartNotebookRuntime].

StartNotebookRuntimeResponse

Response message for [NotebookService.StartNotebookRuntime][google.cloud.aiplatform.v1beta1.NotebookService.StartNotebookRuntime].

StopTrialRequest

Request message for [VizierService.StopTrial][google.cloud.aiplatform.v1beta1.VizierService.StopTrial].

StratifiedSplit

Assigns input data to the training, validation, and test sets so that the distribution of values found in the categorical column (as specified by the key field) is mirrored within each split. The fraction values determine the relative sizes of the splits.

For example, if the specified column has three values, with 50% of the rows having value "A", 25% value "B", and 25% value "C", and the split fractions are specified as 80/10/10, then the training set will constitute 80% of the training data, with about 50% of the training set rows having the value "A" for the specified column, about 25% having the value "B", and about 25% having the value "C".

Only the top 500 occurring values are used; any values not in the top 500 values are randomly assigned to a split. If less than three rows contain a specific value, those rows are randomly assigned.

Supported only for tabular Datasets.

StreamDirectPredictRequest

Request message for [PredictionService.StreamDirectPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamDirectPredict].

The first message must contain [endpoint][google.cloud.aiplatform.v1beta1.StreamDirectPredictRequest.endpoint] field and optionally [input][]. The subsequent messages must contain [input][].

StreamDirectPredictResponse

Response message for [PredictionService.StreamDirectPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamDirectPredict].

StreamDirectRawPredictRequest

Request message for [PredictionService.StreamDirectRawPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamDirectRawPredict].

The first message must contain [endpoint][google.cloud.aiplatform.v1beta1.StreamDirectRawPredictRequest.endpoint] and [method_name][google.cloud.aiplatform.v1beta1.StreamDirectRawPredictRequest.method_name] fields and optionally [input][google.cloud.aiplatform.v1beta1.StreamDirectRawPredictRequest.input]. The subsequent messages must contain [input][google.cloud.aiplatform.v1beta1.StreamDirectRawPredictRequest.input]. [method_name][google.cloud.aiplatform.v1beta1.StreamDirectRawPredictRequest.method_name] in the subsequent messages have no effect.

StreamDirectRawPredictResponse

Response message for [PredictionService.StreamDirectRawPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamDirectRawPredict].

StreamRawPredictRequest

Request message for [PredictionService.StreamRawPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamRawPredict].

StreamingFetchFeatureValuesRequest

Request message for [FeatureOnlineStoreService.StreamingFetchFeatureValues][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreService.StreamingFetchFeatureValues]. For the entities requested, all features under the requested feature view will be returned.

StreamingFetchFeatureValuesResponse

Response message for [FeatureOnlineStoreService.StreamingFetchFeatureValues][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreService.StreamingFetchFeatureValues].

StreamingPredictRequest

Request message for [PredictionService.StreamingPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamingPredict].

The first message must contain [endpoint][google.cloud.aiplatform.v1beta1.StreamingPredictRequest.endpoint] field and optionally [input][]. The subsequent messages must contain [input][].

StreamingPredictResponse

Response message for [PredictionService.StreamingPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamingPredict].

StreamingRawPredictRequest

Request message for [PredictionService.StreamingRawPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamingRawPredict].

The first message must contain [endpoint][google.cloud.aiplatform.v1beta1.StreamingRawPredictRequest.endpoint] and [method_name][google.cloud.aiplatform.v1beta1.StreamingRawPredictRequest.method_name] fields and optionally [input][google.cloud.aiplatform.v1beta1.StreamingRawPredictRequest.input]. The subsequent messages must contain [input][google.cloud.aiplatform.v1beta1.StreamingRawPredictRequest.input]. [method_name][google.cloud.aiplatform.v1beta1.StreamingRawPredictRequest.method_name] in the subsequent messages have no effect.

StreamingRawPredictResponse

Response message for [PredictionService.StreamingRawPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamingRawPredict].

StreamingReadFeatureValuesRequest

Request message for [FeaturestoreOnlineServingService.StreamingFeatureValuesRead][].

StringArray

A list of string values.

StructFieldValue

One field of a Struct (or object) type feature value.

StructValue

Struct (or object) type feature value.

Study

A message representing a Study.

Study.Types

Container for nested types declared in the Study message type.

StudyName

Resource name for the Study resource.

StudySpec

Represents specification of a Study.

StudySpec.Types

Container for nested types declared in the StudySpec message type.

StudySpec.Types.ConvexAutomatedStoppingSpec

Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model.

StudySpec.Types.ConvexStopConfig

Configuration for ConvexStopPolicy.

StudySpec.Types.DecayCurveAutomatedStoppingSpec

The decay curve automated stopping rule builds a Gaussian Process Regressor to predict the final objective value of a Trial based on the already completed Trials and the intermediate measurements of the current Trial. Early stopping is requested for the current Trial if there is very low probability to exceed the optimal value found so far.

StudySpec.Types.MedianAutomatedStoppingSpec

The median automated stopping rule stops a pending Trial if the Trial's best objective_value is strictly below the median 'performance' of all completed Trials reported up to the Trial's last measurement. Currently, 'performance' refers to the running average of the objective values reported by the Trial in each measurement.

StudySpec.Types.MetricSpec

Represents a metric to optimize.

StudySpec.Types.MetricSpec.Types

Container for nested types declared in the MetricSpec message type.

StudySpec.Types.MetricSpec.Types.SafetyMetricConfig

Used in safe optimization to specify threshold levels and risk tolerance.

StudySpec.Types.ParameterSpec

Represents a single parameter to optimize.

StudySpec.Types.ParameterSpec.Types

Container for nested types declared in the ParameterSpec message type.

StudySpec.Types.ParameterSpec.Types.CategoricalValueSpec

Value specification for a parameter in CATEGORICAL type.

StudySpec.Types.ParameterSpec.Types.ConditionalParameterSpec

Represents a parameter spec with condition from its parent parameter.

StudySpec.Types.ParameterSpec.Types.ConditionalParameterSpec.Types

Container for nested types declared in the ConditionalParameterSpec message type.

StudySpec.Types.ParameterSpec.Types.ConditionalParameterSpec.Types.CategoricalValueCondition

Represents the spec to match categorical values from parent parameter.

StudySpec.Types.ParameterSpec.Types.ConditionalParameterSpec.Types.DiscreteValueCondition

Represents the spec to match discrete values from parent parameter.

StudySpec.Types.ParameterSpec.Types.ConditionalParameterSpec.Types.IntValueCondition

Represents the spec to match integer values from parent parameter.

StudySpec.Types.ParameterSpec.Types.DiscreteValueSpec

Value specification for a parameter in DISCRETE type.

StudySpec.Types.ParameterSpec.Types.DoubleValueSpec

Value specification for a parameter in DOUBLE type.

StudySpec.Types.ParameterSpec.Types.IntegerValueSpec

Value specification for a parameter in INTEGER type.

StudySpec.Types.StudyStoppingConfig

The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection.

StudySpec.Types.TransferLearningConfig

This contains flag for manually disabling transfer learning for a study. The names of prior studies being used for transfer learning (if any) are also listed here.

StudyTimeConstraint

Time-based Constraint for Study

SubnetworkName

Resource name for the Subnetwork resource.

SuggestTrialsMetadata

Details of operations that perform Trials suggestion.

SuggestTrialsRequest

Request message for [VizierService.SuggestTrials][google.cloud.aiplatform.v1beta1.VizierService.SuggestTrials].

SuggestTrialsResponse

Response message for [VizierService.SuggestTrials][google.cloud.aiplatform.v1beta1.VizierService.SuggestTrials].

SummarizationHelpfulnessInput

Input for summarization helpfulness metric.

SummarizationHelpfulnessInstance

Spec for summarization helpfulness instance.

SummarizationHelpfulnessResult

Spec for summarization helpfulness result.

SummarizationHelpfulnessSpec

Spec for summarization helpfulness score metric.

SummarizationQualityInput

Input for summarization quality metric.

SummarizationQualityInstance

Spec for summarization quality instance.

SummarizationQualityResult

Spec for summarization quality result.

SummarizationQualitySpec

Spec for summarization quality score metric.

SummarizationVerbosityInput

Input for summarization verbosity metric.

SummarizationVerbosityInstance

Spec for summarization verbosity instance.

SummarizationVerbosityResult

Spec for summarization verbosity result.

SummarizationVerbositySpec

Spec for summarization verbosity score metric.

SupervisedHyperParameters

Hyperparameters for SFT.

SupervisedHyperParameters.Types

Container for nested types declared in the SupervisedHyperParameters message type.

SupervisedTuningDataStats

Tuning data statistics for Supervised Tuning.

SupervisedTuningDatasetDistribution

Dataset distribution for Supervised Tuning.

SupervisedTuningDatasetDistribution.Types

Container for nested types declared in the SupervisedTuningDatasetDistribution message type.

SupervisedTuningDatasetDistribution.Types.DatasetBucket

Dataset bucket used to create a histogram for the distribution given a population of values.

SupervisedTuningSpec

Tuning Spec for Supervised Tuning for first party models.

SyncFeatureViewRequest

Request message for [FeatureOnlineStoreAdminService.SyncFeatureView][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.SyncFeatureView].

SyncFeatureViewResponse

Response message for [FeatureOnlineStoreAdminService.SyncFeatureView][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.SyncFeatureView].

TFRecordDestination

The storage details for TFRecord output content.

Tensor

A tensor value type.

Tensor.Types

Container for nested types declared in the Tensor message type.

Tensorboard

Tensorboard is a physical database that stores users' training metrics. A default Tensorboard is provided in each region of a Google Cloud project. If needed users can also create extra Tensorboards in their projects.

TensorboardBlob

One blob (e.g, image, graph) viewable on a blob metric plot.

TensorboardBlobSequence

One point viewable on a blob metric plot, but mostly just a wrapper message to work around repeated fields can't be used directly within oneof fields.

TensorboardExperiment

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

TensorboardExperimentName

Resource name for the TensorboardExperiment resource.

TensorboardName

Resource name for the Tensorboard resource.

TensorboardRun

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

TensorboardRunName

Resource name for the TensorboardRun resource.

TensorboardService

TensorboardService

TensorboardService.TensorboardServiceBase

Base class for server-side implementations of TensorboardService

TensorboardService.TensorboardServiceClient

Client for TensorboardService

TensorboardServiceClient

TensorboardService client wrapper, for convenient use.

TensorboardServiceClient.ReadTensorboardBlobDataStream

Server streaming methods for ReadTensorboardBlobData(ReadTensorboardBlobDataRequest, CallSettings).

TensorboardServiceClientBuilder

Builder class for TensorboardServiceClient to provide simple configuration of credentials, endpoint etc.

TensorboardServiceClientImpl

TensorboardService client wrapper implementation, for convenient use.

TensorboardServiceSettings

Settings for TensorboardServiceClient instances.

TensorboardTensor

One point viewable on a tensor metric plot.

TensorboardTimeSeries

TensorboardTimeSeries maps to times series produced in training runs

TensorboardTimeSeries.Types

Container for nested types declared in the TensorboardTimeSeries message type.

TensorboardTimeSeries.Types.Metadata

Describes metadata for a TensorboardTimeSeries.

TensorboardTimeSeriesName

Resource name for the TensorboardTimeSeries resource.

ThresholdConfig

The config for feature monitoring threshold.

TimeSeriesData

All the data stored in a TensorboardTimeSeries.

TimeSeriesDataPoint

A TensorboardTimeSeries data point.

TimestampSplit

Assigns input data to training, validation, and test sets based on a provided timestamps. The youngest data pieces are assigned to training set, next to validation set, and the oldest to the test set.

Supported only for tabular Datasets.

TokensInfo

Tokens info with a list of tokens and the corresponding list of token ids.

Tool

Tool details that the model may use to generate response.

A Tool is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. A Tool object should contain exactly one type of Tool (e.g FunctionDeclaration, Retrieval or GoogleSearchRetrieval).

ToolCallValidInput

Input for tool call valid metric.

ToolCallValidInstance

Spec for tool call valid instance.

ToolCallValidMetricValue

Tool call valid metric value for an instance.

ToolCallValidResults

Results for tool call valid metric.

ToolCallValidSpec

Spec for tool call valid metric.

ToolConfig

Tool config. This config is shared for all tools provided in the request.

ToolNameMatchInput

Input for tool name match metric.

ToolNameMatchInstance

Spec for tool name match instance.

ToolNameMatchMetricValue

Tool name match metric value for an instance.

ToolNameMatchResults

Results for tool name match metric.

ToolNameMatchSpec

Spec for tool name match metric.

ToolParameterKVMatchInput

Input for tool parameter key value match metric.

ToolParameterKVMatchInstance

Spec for tool parameter key value match instance.

ToolParameterKVMatchMetricValue

Tool parameter key value match metric value for an instance.

ToolParameterKVMatchResults

Results for tool parameter key value match metric.

ToolParameterKVMatchSpec

Spec for tool parameter key value match metric.

ToolParameterKeyMatchInput

Input for tool parameter key match metric.

ToolParameterKeyMatchInstance

Spec for tool parameter key match instance.

ToolParameterKeyMatchMetricValue

Tool parameter key match metric value for an instance.

ToolParameterKeyMatchResults

Results for tool parameter key match metric.

ToolParameterKeyMatchSpec

Spec for tool parameter key match metric.

ToolUseExample

A single example of the tool usage.

ToolUseExample.Types

Container for nested types declared in the ToolUseExample message type.

ToolUseExample.Types.ExtensionOperation

Identifies one operation of the extension.

TrainingConfig

CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.

TrainingPipeline

The TrainingPipeline orchestrates tasks associated with training a Model. It always executes the training task, and optionally may also export data from Vertex AI's Dataset which becomes the training input, [upload][google.cloud.aiplatform.v1beta1.ModelService.UploadModel] the Model to Vertex AI, and evaluate the Model.

TrainingPipelineName

Resource name for the TrainingPipeline resource.

Trial

A message representing a Trial. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.

Trial.Types

Container for nested types declared in the Trial message type.

Trial.Types.Parameter

A message representing a parameter to be tuned.

TrialContext

TrialName

Resource name for the Trial resource.

TunedModel

The Model Registry Model and Online Prediction Endpoint assiociated with this [TuningJob][google.cloud.aiplatform.v1.TuningJob].

TunedModelRef

TunedModel Reference for legacy model migration.

TuningDataStats

The tuning data statistic values for [TuningJob][google.cloud.aiplatform.v1.TuningJob].

TuningJob

Represents a TuningJob that runs with Google owned models.

TuningJobName

Resource name for the TuningJob resource.

UndeployIndexOperationMetadata

Runtime operation information for [IndexEndpointService.UndeployIndex][google.cloud.aiplatform.v1beta1.IndexEndpointService.UndeployIndex].

UndeployIndexRequest

Request message for [IndexEndpointService.UndeployIndex][google.cloud.aiplatform.v1beta1.IndexEndpointService.UndeployIndex].

UndeployIndexResponse

Response message for [IndexEndpointService.UndeployIndex][google.cloud.aiplatform.v1beta1.IndexEndpointService.UndeployIndex].

UndeployModelOperationMetadata

Runtime operation information for [EndpointService.UndeployModel][google.cloud.aiplatform.v1beta1.EndpointService.UndeployModel].

UndeployModelRequest

Request message for [EndpointService.UndeployModel][google.cloud.aiplatform.v1beta1.EndpointService.UndeployModel].

UndeployModelResponse

Response message for [EndpointService.UndeployModel][google.cloud.aiplatform.v1beta1.EndpointService.UndeployModel].

UnmanagedContainerModel

Contains model information necessary to perform batch prediction without requiring a full model import.

UpdateArtifactRequest

Request message for [MetadataService.UpdateArtifact][google.cloud.aiplatform.v1beta1.MetadataService.UpdateArtifact].

UpdateCachedContentRequest

Request message for [GenAiCacheService.UpdateCachedContent][google.cloud.aiplatform.v1beta1.GenAiCacheService.UpdateCachedContent]. Only expire_time or ttl can be updated.

UpdateContextRequest

Request message for [MetadataService.UpdateContext][google.cloud.aiplatform.v1beta1.MetadataService.UpdateContext].

UpdateDatasetRequest

Request message for [DatasetService.UpdateDataset][google.cloud.aiplatform.v1beta1.DatasetService.UpdateDataset].

UpdateDatasetVersionRequest

Request message for [DatasetService.UpdateDatasetVersion][google.cloud.aiplatform.v1beta1.DatasetService.UpdateDatasetVersion].

UpdateDeploymentResourcePoolOperationMetadata

Runtime operation information for UpdateDeploymentResourcePool method.

UpdateDeploymentResourcePoolRequest

Request message for UpdateDeploymentResourcePool method.

UpdateEndpointRequest

Request message for [EndpointService.UpdateEndpoint][google.cloud.aiplatform.v1beta1.EndpointService.UpdateEndpoint].

UpdateEntityTypeRequest

Request message for [FeaturestoreService.UpdateEntityType][google.cloud.aiplatform.v1beta1.FeaturestoreService.UpdateEntityType].

UpdateExecutionRequest

Request message for [MetadataService.UpdateExecution][google.cloud.aiplatform.v1beta1.MetadataService.UpdateExecution].

UpdateExplanationDatasetOperationMetadata

Runtime operation information for [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1beta1.ModelService.UpdateExplanationDataset].

UpdateExplanationDatasetRequest

Request message for [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1beta1.ModelService.UpdateExplanationDataset].

UpdateExplanationDatasetResponse

Response message of [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1beta1.ModelService.UpdateExplanationDataset] operation.

UpdateExtensionRequest

Request message for [ExtensionRegistryService.UpdateExtension][google.cloud.aiplatform.v1beta1.ExtensionRegistryService.UpdateExtension].

UpdateFeatureGroupOperationMetadata

Details of operations that perform update FeatureGroup.

UpdateFeatureGroupRequest

Request message for [FeatureRegistryService.UpdateFeatureGroup][google.cloud.aiplatform.v1beta1.FeatureRegistryService.UpdateFeatureGroup].

UpdateFeatureOnlineStoreOperationMetadata

Details of operations that perform update FeatureOnlineStore.

UpdateFeatureOnlineStoreRequest

Request message for [FeatureOnlineStoreAdminService.UpdateFeatureOnlineStore][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.UpdateFeatureOnlineStore].

UpdateFeatureOperationMetadata

Details of operations that perform update Feature.

UpdateFeatureRequest

Request message for [FeaturestoreService.UpdateFeature][google.cloud.aiplatform.v1beta1.FeaturestoreService.UpdateFeature]. Request message for [FeatureRegistryService.UpdateFeature][google.cloud.aiplatform.v1beta1.FeatureRegistryService.UpdateFeature].

UpdateFeatureViewOperationMetadata

Details of operations that perform update FeatureView.

UpdateFeatureViewRequest

Request message for [FeatureOnlineStoreAdminService.UpdateFeatureView][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreAdminService.UpdateFeatureView].

UpdateFeaturestoreOperationMetadata

Details of operations that perform update Featurestore.

UpdateFeaturestoreRequest

Request message for [FeaturestoreService.UpdateFeaturestore][google.cloud.aiplatform.v1beta1.FeaturestoreService.UpdateFeaturestore].

UpdateIndexEndpointRequest

Request message for [IndexEndpointService.UpdateIndexEndpoint][google.cloud.aiplatform.v1beta1.IndexEndpointService.UpdateIndexEndpoint].

UpdateIndexOperationMetadata

Runtime operation information for [IndexService.UpdateIndex][google.cloud.aiplatform.v1beta1.IndexService.UpdateIndex].

UpdateIndexRequest

Request message for [IndexService.UpdateIndex][google.cloud.aiplatform.v1beta1.IndexService.UpdateIndex].

UpdateModelDeploymentMonitoringJobOperationMetadata

Runtime operation information for [JobService.UpdateModelDeploymentMonitoringJob][google.cloud.aiplatform.v1beta1.JobService.UpdateModelDeploymentMonitoringJob].

UpdateModelDeploymentMonitoringJobRequest

Request message for [JobService.UpdateModelDeploymentMonitoringJob][google.cloud.aiplatform.v1beta1.JobService.UpdateModelDeploymentMonitoringJob].

UpdateModelMonitorOperationMetadata

Runtime operation information for [ModelMonitoringService.UpdateModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.UpdateModelMonitor].

UpdateModelMonitorRequest

Request message for [ModelMonitoringService.UpdateModelMonitor][google.cloud.aiplatform.v1beta1.ModelMonitoringService.UpdateModelMonitor].

UpdateModelRequest

Request message for [ModelService.UpdateModel][google.cloud.aiplatform.v1beta1.ModelService.UpdateModel].

UpdateNotebookRuntimeTemplateRequest

Request message for [NotebookService.UpdateNotebookRuntimeTemplate][google.cloud.aiplatform.v1beta1.NotebookService.UpdateNotebookRuntimeTemplate].

UpdatePersistentResourceOperationMetadata

Details of operations that perform update PersistentResource.

UpdatePersistentResourceRequest

Request message for UpdatePersistentResource method.

UpdateRagCorpusOperationMetadata

Runtime operation information for [VertexRagDataService.UpdateRagCorpus][google.cloud.aiplatform.v1beta1.VertexRagDataService.UpdateRagCorpus].

UpdateRagCorpusRequest

Request message for [VertexRagDataService.UpdateRagCorpus][google.cloud.aiplatform.v1beta1.VertexRagDataService.UpdateRagCorpus].

UpdateReasoningEngineOperationMetadata

Details of [ReasoningEngineService.UpdateReasoningEngine][google.cloud.aiplatform.v1beta1.ReasoningEngineService.UpdateReasoningEngine] operation.

UpdateReasoningEngineRequest

Request message for [ReasoningEngineService.UpdateReasoningEngine][google.cloud.aiplatform.v1beta1.ReasoningEngineService.UpdateReasoningEngine].

UpdateScheduleRequest

Request message for [ScheduleService.UpdateSchedule][google.cloud.aiplatform.v1beta1.ScheduleService.UpdateSchedule].

UpdateSpecialistPoolOperationMetadata

Runtime operation metadata for [SpecialistPoolService.UpdateSpecialistPool][google.cloud.aiplatform.v1beta1.SpecialistPoolService.UpdateSpecialistPool].

UpdateSpecialistPoolRequest

Request message for [SpecialistPoolService.UpdateSpecialistPool][google.cloud.aiplatform.v1beta1.SpecialistPoolService.UpdateSpecialistPool].

UpdateTensorboardExperimentRequest

Request message for [TensorboardService.UpdateTensorboardExperiment][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboardExperiment].

UpdateTensorboardOperationMetadata

Details of operations that perform update Tensorboard.

UpdateTensorboardRequest

Request message for [TensorboardService.UpdateTensorboard][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboard].

UpdateTensorboardRunRequest

Request message for [TensorboardService.UpdateTensorboardRun][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboardRun].

UpdateTensorboardTimeSeriesRequest

Request message for [TensorboardService.UpdateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboardTimeSeries].

UpgradeNotebookRuntimeOperationMetadata

Metadata information for [NotebookService.UpgradeNotebookRuntime][google.cloud.aiplatform.v1beta1.NotebookService.UpgradeNotebookRuntime].

UpgradeNotebookRuntimeRequest

Request message for [NotebookService.UpgradeNotebookRuntime][google.cloud.aiplatform.v1beta1.NotebookService.UpgradeNotebookRuntime].

UpgradeNotebookRuntimeResponse

Response message for [NotebookService.UpgradeNotebookRuntime][google.cloud.aiplatform.v1beta1.NotebookService.UpgradeNotebookRuntime].

UploadModelOperationMetadata

Details of [ModelService.UploadModel][google.cloud.aiplatform.v1beta1.ModelService.UploadModel] operation.

UploadModelRequest

Request message for [ModelService.UploadModel][google.cloud.aiplatform.v1beta1.ModelService.UploadModel].

UploadModelResponse

Response message of [ModelService.UploadModel][google.cloud.aiplatform.v1beta1.ModelService.UploadModel] operation.

UploadRagFileConfig

Config for uploading RagFile.

UploadRagFileRequest

Request message for [VertexRagDataService.UploadRagFile][google.cloud.aiplatform.v1beta1.VertexRagDataService.UploadRagFile].

UploadRagFileResponse

Response message for [VertexRagDataService.UploadRagFile][google.cloud.aiplatform.v1beta1.VertexRagDataService.UploadRagFile].

UpsertDatapointsRequest

Request message for [IndexService.UpsertDatapoints][google.cloud.aiplatform.v1beta1.IndexService.UpsertDatapoints]

UpsertDatapointsResponse

Response message for [IndexService.UpsertDatapoints][google.cloud.aiplatform.v1beta1.IndexService.UpsertDatapoints]

UserActionReference

References an API call. It contains more information about long running operation and Jobs that are triggered by the API call.

Value

Value is the value of the field.

VersionName

Resource name for the Version resource.

VertexAISearch

Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation

VertexRagDataService

A service for managing user data for RAG.

VertexRagDataService.VertexRagDataServiceBase

Base class for server-side implementations of VertexRagDataService

VertexRagDataService.VertexRagDataServiceClient

Client for VertexRagDataService

VertexRagDataServiceClient

VertexRagDataService client wrapper, for convenient use.

VertexRagDataServiceClientBuilder

Builder class for VertexRagDataServiceClient to provide simple configuration of credentials, endpoint etc.

VertexRagDataServiceClientImpl

VertexRagDataService client wrapper implementation, for convenient use.

VertexRagDataServiceSettings

Settings for VertexRagDataServiceClient instances.

VertexRagService

A service for retrieving relevant contexts.

VertexRagService.VertexRagServiceBase

Base class for server-side implementations of VertexRagService

VertexRagService.VertexRagServiceClient

Client for VertexRagService

VertexRagServiceClient

VertexRagService client wrapper, for convenient use.

VertexRagServiceClientBuilder

Builder class for VertexRagServiceClient to provide simple configuration of credentials, endpoint etc.

VertexRagServiceClientImpl

VertexRagService client wrapper implementation, for convenient use.

VertexRagServiceSettings

Settings for VertexRagServiceClient instances.

VertexRagStore

Retrieve from Vertex RAG Store for grounding.

VertexRagStore.Types

Container for nested types declared in the VertexRagStore message type.

VertexRagStore.Types.RagResource

The definition of the Rag resource.

VideoMetadata

Metadata describes the input video content.

VizierService

Vertex AI Vizier API.

Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

VizierService.VizierServiceBase

Base class for server-side implementations of VizierService

VizierService.VizierServiceClient

Client for VizierService

VizierServiceClient

VizierService client wrapper, for convenient use.

VizierServiceClientBuilder

Builder class for VizierServiceClient to provide simple configuration of credentials, endpoint etc.

VizierServiceClientImpl

VizierService client wrapper implementation, for convenient use.

VizierServiceSettings

Settings for VizierServiceClient instances.

WorkerPoolSpec

Represents the spec of a worker pool in a job.

WriteFeatureValuesPayload

Contains Feature values to be written for a specific entity.

WriteFeatureValuesRequest

Request message for [FeaturestoreOnlineServingService.WriteFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreOnlineServingService.WriteFeatureValues].

WriteFeatureValuesResponse

Response message for [FeaturestoreOnlineServingService.WriteFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreOnlineServingService.WriteFeatureValues].

WriteTensorboardExperimentDataRequest

Request message for [TensorboardService.WriteTensorboardExperimentData][google.cloud.aiplatform.v1beta1.TensorboardService.WriteTensorboardExperimentData].

WriteTensorboardExperimentDataResponse

Response message for [TensorboardService.WriteTensorboardExperimentData][google.cloud.aiplatform.v1beta1.TensorboardService.WriteTensorboardExperimentData].

WriteTensorboardRunDataRequest

Request message for [TensorboardService.WriteTensorboardRunData][google.cloud.aiplatform.v1beta1.TensorboardService.WriteTensorboardRunData].

WriteTensorboardRunDataResponse

Response message for [TensorboardService.WriteTensorboardRunData][google.cloud.aiplatform.v1beta1.TensorboardService.WriteTensorboardRunData].

XraiAttribution

An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825

Supported only by image Models.

Enums

AcceleratorType

Represents a hardware accelerator type.

ActiveLearningConfig.HumanLabelingBudgetOneofCase

Enum of possible cases for the "human_labeling_budget" oneof.

AnnotatedDatasetName.ResourceNameType

The possible contents of AnnotatedDatasetName.

AnnotationName.ResourceNameType

The possible contents of AnnotationName.

AnnotationSpecName.ResourceNameType

The possible contents of AnnotationSpecName.

ApiAuth.AuthConfigOneofCase

Enum of possible cases for the "auth_config" oneof.

Artifact.Types.State

Describes the state of the Artifact.

ArtifactName.ResourceNameType

The possible contents of ArtifactName.

ArtifactTypeSchema.KindOneofCase

Enum of possible cases for the "kind" oneof.

AuthConfig.AuthConfigOneofCase

Enum of possible cases for the "auth_config" oneof.

AuthConfig.Types.OauthConfig.OauthConfigOneofCase

Enum of possible cases for the "oauth_config" oneof.

AuthConfig.Types.OidcConfig.OidcConfigOneofCase

Enum of possible cases for the "oidc_config" oneof.

AuthType

Type of Auth.

AutoMLDatasetName.ResourceNameType

The possible contents of AutoMLDatasetName.

AutoMLModelName.ResourceNameType

The possible contents of AutoMLModelName.

BatchMigrateResourcesOperationMetadata.Types.PartialResult.ResultOneofCase

Enum of possible cases for the "result" oneof.

BatchPredictionJob.Types.InputConfig.SourceOneofCase

Enum of possible cases for the "source" oneof.

BatchPredictionJob.Types.OutputConfig.DestinationOneofCase

Enum of possible cases for the "destination" oneof.

BatchPredictionJob.Types.OutputInfo.OutputLocationOneofCase

Enum of possible cases for the "output_location" oneof.

BatchPredictionJobName.ResourceNameType

The possible contents of BatchPredictionJobName.

BatchReadFeatureValuesRequest.ReadOptionOneofCase

Enum of possible cases for the "read_option" oneof.

CachedContent.ExpirationOneofCase

Enum of possible cases for the "expiration" oneof.

CachedContentName.ResourceNameType

The possible contents of CachedContentName.

Candidate.Types.FinishReason

The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.

ContextName.ResourceNameType

The possible contents of ContextName.

CopyModelRequest.DestinationModelOneofCase

Enum of possible cases for the "destination_model" oneof.

CorpusStatus.Types.State

RagCorpus life state.

CustomJobName.ResourceNameType

The possible contents of CustomJobName.

DataItemName.ResourceNameType

The possible contents of DataItemName.

DataLabelingDatasetName.ResourceNameType

The possible contents of DataLabelingDatasetName.

DataLabelingJobName.ResourceNameType

The possible contents of DataLabelingJobName.

DatasetName.ResourceNameType

The possible contents of DatasetName.

DatasetVersionName.ResourceNameType

The possible contents of DatasetVersionName.

DeleteFeatureValuesRequest.DeleteOptionOneofCase

Enum of possible cases for the "DeleteOption" oneof.

DeleteFeatureValuesResponse.ResponseOneofCase

Enum of possible cases for the "response" oneof.

DeployedModel.PredictionResourcesOneofCase

Enum of possible cases for the "prediction_resources" oneof.

DeploymentResourcePoolName.ResourceNameType

The possible contents of DeploymentResourcePoolName.

DistillationSpec.TeacherModelOneofCase

Enum of possible cases for the "teacher_model" oneof.

DynamicRetrievalConfig.Types.Mode

The mode of the predictor to be used in dynamic retrieval.

EndpointName.ResourceNameType

The possible contents of EndpointName.

EntityIdSelector.EntityIdsSourceOneofCase

Enum of possible cases for the "EntityIdsSource" oneof.

EntityTypeName.ResourceNameType

The possible contents of EntityTypeName.

ErrorAnalysisAnnotation.Types.QueryType

The query type used for finding the attributed items.

EvaluateInstancesRequest.MetricInputsOneofCase

Enum of possible cases for the "metric_inputs" oneof.

EvaluateInstancesResponse.EvaluationResultsOneofCase

Enum of possible cases for the "evaluation_results" oneof.

EvaluatedAnnotation.Types.EvaluatedAnnotationType

Describes the type of the EvaluatedAnnotation. The type is determined

Event.Types.Type

Describes whether an Event's Artifact is the Execution's input or output.

Examples.ConfigOneofCase

Enum of possible cases for the "config" oneof.

Examples.SourceOneofCase

Enum of possible cases for the "source" oneof.

Examples.Types.ExampleGcsSource.Types.DataFormat

The format of the input example instances.

ExamplesOverride.Types.DataFormat

Data format enum.

Execution.Types.State

Describes the state of the Execution.

ExecutionName.ResourceNameType

The possible contents of ExecutionName.

ExplanationMetadata.Types.InputMetadata.Types.Encoding

Defines how a feature is encoded. Defaults to IDENTITY.

ExplanationMetadata.Types.InputMetadata.Types.Visualization.Types.ColorMap

The color scheme used for highlighting areas.

ExplanationMetadata.Types.InputMetadata.Types.Visualization.Types.OverlayType

How the original image is displayed in the visualization.

ExplanationMetadata.Types.InputMetadata.Types.Visualization.Types.Polarity

Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.

ExplanationMetadata.Types.InputMetadata.Types.Visualization.Types.Type

Type of the image visualization. Only applicable to [Integrated Gradients attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.integrated_gradients_attribution].

ExplanationMetadata.Types.OutputMetadata.DisplayNameMappingOneofCase

Enum of possible cases for the "display_name_mapping" oneof.

ExplanationParameters.MethodOneofCase

Enum of possible cases for the "method" oneof.

ExportDataConfig.DestinationOneofCase

Enum of possible cases for the "destination" oneof.

ExportDataConfig.SplitOneofCase

Enum of possible cases for the "split" oneof.

ExportFeatureValuesRequest.ModeOneofCase

Enum of possible cases for the "mode" oneof.

ExtensionManifest.Types.ApiSpec.ApiSpecOneofCase

Enum of possible cases for the "api_spec" oneof.

ExtensionName.ResourceNameType

The possible contents of ExtensionName.

Feature.Types.MonitoringStatsAnomaly.Types.Objective

If the objective in the request is both Import Feature Analysis and Snapshot Analysis, this objective could be one of them. Otherwise, this objective should be the same as the objective in the request.

Feature.Types.ValueType

Only applicable for Vertex AI Legacy Feature Store. An enum representing the value type of a feature.

FeatureGroup.SourceOneofCase

Enum of possible cases for the "source" oneof.

FeatureGroupName.ResourceNameType

The possible contents of FeatureGroupName.

FeatureName.ResourceNameType

The possible contents of FeatureName.

FeatureOnlineStore.StorageTypeOneofCase

Enum of possible cases for the "storage_type" oneof.

FeatureOnlineStore.Types.State

Possible states a featureOnlineStore can have.

FeatureOnlineStoreName.ResourceNameType

The possible contents of FeatureOnlineStoreName.

FeatureValue.ValueOneofCase

Enum of possible cases for the "value" oneof.

FeatureValueDestination.DestinationOneofCase

Enum of possible cases for the "destination" oneof.

FeatureView.SourceOneofCase

Enum of possible cases for the "source" oneof.

FeatureView.Types.IndexConfig.AlgorithmConfigOneofCase

Enum of possible cases for the "algorithm_config" oneof.

FeatureView.Types.IndexConfig.Types.DistanceMeasureType

The distance measure used in nearest neighbor search.

FeatureView.Types.ServiceAgentType

Service agent type used during data sync.

FeatureView.Types.VectorSearchConfig.AlgorithmConfigOneofCase

Enum of possible cases for the "algorithm_config" oneof.

FeatureView.Types.VectorSearchConfig.Types.DistanceMeasureType

FeatureViewDataFormat

Format of the data in the Feature View.

FeatureViewDataKey.KeyOneofOneofCase

Enum of possible cases for the "key_oneof" oneof.

FeatureViewName.ResourceNameType

The possible contents of FeatureViewName.

FeatureViewSyncName.ResourceNameType

The possible contents of FeatureViewSyncName.

Featurestore.Types.State

Possible states a featurestore can have.

FeaturestoreMonitoringConfig.Types.ImportFeaturesAnalysis.Types.Baseline

Defines the baseline to do anomaly detection for feature values imported by each [ImportFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreService.ImportFeatureValues] operation.

FeaturestoreMonitoringConfig.Types.ImportFeaturesAnalysis.Types.State

The state defines whether to enable ImportFeature analysis.

FeaturestoreMonitoringConfig.Types.ThresholdConfig.ThresholdOneofCase

Enum of possible cases for the "threshold" oneof.

FeaturestoreName.ResourceNameType

The possible contents of FeaturestoreName.

FetchFeatureValuesRequest.EntityIdOneofCase

Enum of possible cases for the "entity_id" oneof.

FetchFeatureValuesRequest.Types.Format

Format of the response data.

FetchFeatureValuesResponse.FormatOneofCase

Enum of possible cases for the "format" oneof.

FetchFeatureValuesResponse.Types.FeatureNameValuePairList.Types.FeatureNameValuePair.DataOneofCase

Enum of possible cases for the "data" oneof.

FileStatus.Types.State

RagFile state.

FindNeighborsRequest.Types.Query.RankingOneofCase

Enum of possible cases for the "ranking" oneof.

FunctionCallingConfig.Types.Mode

Function calling mode.

GenerateContentResponse.Types.PromptFeedback.Types.BlockedReason

Blocked reason enumeration.

GenerationConfig.Types.RoutingConfig.RoutingConfigOneofCase

Enum of possible cases for the "routing_config" oneof.

GenerationConfig.Types.RoutingConfig.Types.AutoRoutingMode.Types.ModelRoutingPreference

The model routing preference.

GoogleDriveSource.Types.ResourceId.Types.ResourceType

The type of the Google Drive resource.

GroundingChunk.ChunkTypeOneofCase

Enum of possible cases for the "chunk_type" oneof.

HarmCategory

Harm categories that will block the content.

HttpElementLocation

Enum of location an HTTP element can be.

HyperparameterTuningJobName.ResourceNameType

The possible contents of HyperparameterTuningJobName.

ImportDataConfig.SourceOneofCase

Enum of possible cases for the "source" oneof.

ImportFeatureValuesRequest.FeatureTimeSourceOneofCase

Enum of possible cases for the "feature_time_source" oneof.

ImportFeatureValuesRequest.SourceOneofCase

Enum of possible cases for the "source" oneof.

ImportRagFilesConfig.ImportSourceOneofCase

Enum of possible cases for the "import_source" oneof.

ImportRagFilesConfig.PartialFailureSinkOneofCase

Enum of possible cases for the "partial_failure_sink" oneof.

ImportRagFilesResponse.PartialFailureSinkOneofCase

Enum of possible cases for the "partial_failure_sink" oneof.

Index.Types.IndexUpdateMethod

The update method of an Index.

IndexDatapoint.Types.NumericRestriction.Types.Operator

Which comparison operator to use. Should be specified for queries only; specifying this for a datapoint is an error.

Datapoints for which Operator is true relative to the query's Value field will be allowlisted.

IndexDatapoint.Types.NumericRestriction.ValueOneofCase

Enum of possible cases for the "Value" oneof.

IndexEndpointName.ResourceNameType

The possible contents of IndexEndpointName.

IndexName.ResourceNameType

The possible contents of IndexName.

InputDataConfig.DestinationOneofCase

Enum of possible cases for the "destination" oneof.

InputDataConfig.SplitOneofCase

Enum of possible cases for the "split" oneof.

JobState

Describes the state of a job.

MetadataSchema.Types.MetadataSchemaType

Describes the type of the MetadataSchema.

MetadataSchemaName.ResourceNameType

The possible contents of MetadataSchemaName.

MetadataStoreName.ResourceNameType

The possible contents of MetadataStoreName.

MigratableResource.ResourceOneofCase

Enum of possible cases for the "resource" oneof.

MigrateResourceRequest.RequestOneofCase

Enum of possible cases for the "request" oneof.

MigrateResourceResponse.MigratedResourceOneofCase

Enum of possible cases for the "migrated_resource" oneof.

Model.Types.BaseModelSource.SourceOneofCase

Enum of possible cases for the "source" oneof.

Model.Types.DeploymentResourcesType

Identifies a type of Model's prediction resources.

Model.Types.ExportFormat.Types.ExportableContent

The Model content that can be exported.

ModelDeploymentMonitoringBigQueryTable.Types.LogSource

Indicates where does the log come from.

ModelDeploymentMonitoringBigQueryTable.Types.LogType

Indicates what type of traffic does the log belong to.

ModelDeploymentMonitoringJob.Types.MonitoringScheduleState

The state to Specify the monitoring pipeline.

ModelDeploymentMonitoringJobName.ResourceNameType

The possible contents of ModelDeploymentMonitoringJobName.

ModelDeploymentMonitoringObjectiveType

The Model Monitoring Objective types.

ModelEvaluationName.ResourceNameType

The possible contents of ModelEvaluationName.

ModelEvaluationSlice.Types.Slice.Types.SliceSpec.Types.SliceConfig.KindOneofCase

Enum of possible cases for the "kind" oneof.

ModelEvaluationSlice.Types.Slice.Types.SliceSpec.Types.Value.KindOneofCase

Enum of possible cases for the "kind" oneof.

ModelEvaluationSliceName.ResourceNameType

The possible contents of ModelEvaluationSliceName.

ModelMonitor.DefaultObjectiveOneofCase

Enum of possible cases for the "default_objective" oneof.

ModelMonitor.Types.ModelMonitoringTarget.SourceOneofCase

Enum of possible cases for the "source" oneof.

ModelMonitorName.ResourceNameType

The possible contents of ModelMonitorName.

ModelMonitoringAlertCondition.ConditionOneofCase

Enum of possible cases for the "condition" oneof.

ModelMonitoringAlertConfig.AlertOneofCase

Enum of possible cases for the "alert" oneof.

ModelMonitoringAnomaly.AnomalyOneofCase

Enum of possible cases for the "anomaly" oneof.

ModelMonitoringInput.DatasetOneofCase

Enum of possible cases for the "dataset" oneof.

ModelMonitoringInput.TimeSpecOneofCase

Enum of possible cases for the "time_spec" oneof.

ModelMonitoringInput.Types.ModelMonitoringDataset.DataLocationOneofCase

Enum of possible cases for the "data_location" oneof.

ModelMonitoringInput.Types.ModelMonitoringDataset.Types.ModelMonitoringBigQuerySource.ConnectionOneofCase

Enum of possible cases for the "connection" oneof.

ModelMonitoringInput.Types.ModelMonitoringDataset.Types.ModelMonitoringGcsSource.Types.DataFormat

Supported data format.

ModelMonitoringJobName.ResourceNameType

The possible contents of ModelMonitoringJobName.

ModelMonitoringObjectiveConfig.Types.ExplanationConfig.Types.ExplanationBaseline.DestinationOneofCase

Enum of possible cases for the "destination" oneof.

ModelMonitoringObjectiveConfig.Types.ExplanationConfig.Types.ExplanationBaseline.Types.PredictionFormat

The storage format of the predictions generated BatchPrediction job.

ModelMonitoringObjectiveConfig.Types.TrainingDataset.DataSourceOneofCase

Enum of possible cases for the "data_source" oneof.

ModelMonitoringObjectiveSpec.ObjectiveOneofCase

Enum of possible cases for the "objective" oneof.

ModelMonitoringStats.StatsOneofCase

Enum of possible cases for the "stats" oneof.

ModelMonitoringStatsDataPoint.Types.TypedValue.ValueOneofCase

Enum of possible cases for the "value" oneof.

ModelName.ResourceNameType

The possible contents of ModelName.

ModelSourceInfo.Types.ModelSourceType

Source of the model. Different from objective field, this ModelSourceType enum indicates the source from which the model was accessed or obtained, whereas the objective indicates the overall aim or function of this model.

NasJobName.ResourceNameType

The possible contents of NasJobName.

NasJobOutput.OutputOneofCase

Enum of possible cases for the "output" oneof.

NasJobSpec.NasAlgorithmSpecOneofCase

Enum of possible cases for the "nas_algorithm_spec" oneof.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.MetricSpec.Types.GoalType

The available types of optimization goals.

NasJobSpec.Types.MultiTrialAlgorithmSpec.Types.MultiTrialAlgorithm

The available types of multi-trial algorithms.

NasTrial.Types.State

Describes a NasTrial state.

NasTrialDetailName.ResourceNameType

The possible contents of NasTrialDetailName.

NearestNeighborQuery.InstanceOneofCase

Enum of possible cases for the "instance" oneof.

NearestNeighborQuery.Types.NumericFilter.Types.Operator

Datapoints for which Operator is true relative to the query’s Value field will be allowlisted.

NearestNeighborQuery.Types.NumericFilter.ValueOneofCase

Enum of possible cases for the "Value" oneof.

NearestNeighborSearchOperationMetadata.Types.RecordError.Types.RecordErrorType

NetworkAttachmentName.ResourceNameType

The possible contents of NetworkAttachmentName.

NetworkName.ResourceNameType

The possible contents of NetworkName.

NotebookExecutionJob.EnvironmentSpecOneofCase

Enum of possible cases for the "environment_spec" oneof.

NotebookExecutionJob.ExecutionIdentityOneofCase

Enum of possible cases for the "execution_identity" oneof.

NotebookExecutionJob.ExecutionSinkOneofCase

Enum of possible cases for the "execution_sink" oneof.

NotebookExecutionJob.NotebookSourceOneofCase

Enum of possible cases for the "notebook_source" oneof.

NotebookExecutionJobName.ResourceNameType

The possible contents of NotebookExecutionJobName.

NotebookExecutionJobView

Views for Get/List NotebookExecutionJob

NotebookRuntime.Types.HealthState

The substate of the NotebookRuntime to display health information.

NotebookRuntime.Types.RuntimeState

The substate of the NotebookRuntime to display state of runtime. The resource of NotebookRuntime is in ACTIVE state for these sub state.

NotebookRuntimeName.ResourceNameType

The possible contents of NotebookRuntimeName.

NotebookRuntimeTemplateName.ResourceNameType

The possible contents of NotebookRuntimeTemplateName.

NotebookRuntimeType

Represents a notebook runtime type.

NotificationChannelName.ResourceNameType

The possible contents of NotificationChannelName.

PairwiseChoice

Pairwise prediction autorater preference.

PairwiseMetricInstance.InstanceOneofCase

Enum of possible cases for the "instance" oneof.

Part.DataOneofCase

Enum of possible cases for the "data" oneof.

Part.MetadataOneofCase

Enum of possible cases for the "metadata" oneof.

PersistentResource.Types.State

Describes the PersistentResource state.

PersistentResourceName.ResourceNameType

The possible contents of PersistentResourceName.

PipelineFailurePolicy

Represents the failure policy of a pipeline. Currently, the default of a pipeline is that the pipeline will continue to run until no more tasks can be executed, also known as PIPELINE_FAILURE_POLICY_FAIL_SLOW. However, if a pipeline is set to PIPELINE_FAILURE_POLICY_FAIL_FAST, it will stop scheduling any new tasks when a task has failed. Any scheduled tasks will continue to completion.

PipelineJob.Types.RuntimeConfig.Types.InputArtifact.KindOneofCase

Enum of possible cases for the "kind" oneof.

PipelineJobName.ResourceNameType

The possible contents of PipelineJobName.

PipelineState

Describes the state of a pipeline.

PipelineTaskDetail.Types.State

Specifies state of TaskExecution

PipelineTaskExecutorDetail.DetailsOneofCase

Enum of possible cases for the "details" oneof.

PointwiseMetricInstance.InstanceOneofCase

Enum of possible cases for the "instance" oneof.

PredictLongRunningResponse.ResponseOneofCase

Enum of possible cases for the "response" oneof.

Presets.Types.Modality

Preset option controlling parameters for different modalities

Presets.Types.Query

Preset option controlling parameters for query speed-precision trade-off

Probe.ProbeTypeOneofCase

Enum of possible cases for the "probe_type" oneof.

PublisherModel.Types.CallToAction.Types.Deploy.PredictionResourcesOneofCase

Enum of possible cases for the "prediction_resources" oneof.

PublisherModel.Types.LaunchStage

An enum representing the launch stage of a PublisherModel.

PublisherModel.Types.OpenSourceCategory

An enum representing the open source category of a PublisherModel.

PublisherModel.Types.ResourceReference.ReferenceOneofCase

Enum of possible cases for the "reference" oneof.

PublisherModel.Types.VersionState

An enum representing the state of the PublicModelVersion.

PublisherModelName.ResourceNameType

The possible contents of PublisherModelName.

PublisherModelView

View enumeration of PublisherModel.

RagCorpusName.ResourceNameType

The possible contents of RagCorpusName.

RagEmbeddingModelConfig.ModelConfigOneofCase

Enum of possible cases for the "model_config" oneof.

RagEmbeddingModelConfig.Types.SparseEmbeddingConfig.ModelOneofCase

Enum of possible cases for the "model" oneof.

RagFile.RagFileSourceOneofCase

Enum of possible cases for the "rag_file_source" oneof.

RagFile.Types.RagFileType

The type of the RagFile.

RagFileName.ResourceNameType

The possible contents of RagFileName.

RagQuery.QueryOneofCase

Enum of possible cases for the "query" oneof.

RagVectorDbConfig.VectorDbOneofCase

Enum of possible cases for the "vector_db" oneof.

ReadFeatureValuesResponse.Types.EntityView.Types.Data.DataOneofCase

Enum of possible cases for the "data" oneof.

ReasoningEngineName.ResourceNameType

The possible contents of ReasoningEngineName.

ReservationAffinity.Types.Type

Identifies a type of reservation affinity.

ReservationName.ResourceNameType

The possible contents of ReservationName.

Retrieval.SourceOneofCase

Enum of possible cases for the "source" oneof.

RetrieveContextsRequest.DataSourceOneofCase

Enum of possible cases for the "data_source" oneof.

RuntimeConfig.GoogleFirstPartyExtensionConfigOneofCase

Enum of possible cases for the "GoogleFirstPartyExtensionConfig" oneof.

SafetyRating.Types.HarmProbability

Harm probability levels in the content.

SafetyRating.Types.HarmSeverity

Harm severity levels.

SafetySetting.Types.HarmBlockMethod

Probability vs severity.

SafetySetting.Types.HarmBlockThreshold

Probability based thresholds levels for blocking.

SampleConfig.FollowingBatchSampleSizeOneofCase

Enum of possible cases for the "following_batch_sample_size" oneof.

SampleConfig.InitialBatchSampleSizeOneofCase

Enum of possible cases for the "initial_batch_sample_size" oneof.

SampleConfig.Types.SampleStrategy

Sample strategy decides which subset of DataItems should be selected for human labeling in every batch.

SavedQueryName.ResourceNameType

The possible contents of SavedQueryName.

Schedule.RequestOneofCase

Enum of possible cases for the "request" oneof.

Schedule.TimeSpecificationOneofCase

Enum of possible cases for the "time_specification" oneof.

Schedule.Types.State

Possible state of the schedule.

ScheduleName.ResourceNameType

The possible contents of ScheduleName.

Scheduling.Types.Strategy

Optional. This determines which type of scheduling strategy to use. Right now users have two options such as STANDARD which will use regular on demand resources to schedule the job, the other is SPOT which would leverage spot resources alongwith regular resources to schedule the job.

SearchDataItemsRequest.OrderOneofCase

Enum of possible cases for the "order" oneof.

SearchModelMonitoringStatsFilter.FilterOneofCase

Enum of possible cases for the "filter" oneof.

SecretVersionName.ResourceNameType

The possible contents of SecretVersionName.

ServiceName.ResourceNameType

The possible contents of ServiceName.

SharePointSources.Types.SharePointSource.DriveSourceOneofCase

Enum of possible cases for the "drive_source" oneof.

SharePointSources.Types.SharePointSource.FolderSourceOneofCase

Enum of possible cases for the "folder_source" oneof.

SmoothGradConfig.GradientNoiseSigmaOneofCase

Enum of possible cases for the "GradientNoiseSigma" oneof.

SpecialistPoolName.ResourceNameType

The possible contents of SpecialistPoolName.

Study.Types.State

Describes the Study state.

StudyName.ResourceNameType

The possible contents of StudyName.

StudySpec.AutomatedStoppingSpecOneofCase

Enum of possible cases for the "automated_stopping_spec" oneof.

StudySpec.Types.Algorithm

The available search algorithms for the Study.

StudySpec.Types.MeasurementSelectionType

This indicates which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. Choose this based on two considerations: A) Do you expect your measurements to monotonically improve? If so, choose LAST_MEASUREMENT. On the other hand, if you're in a situation where your system can "over-train" and you expect the performance to get better for a while but then start declining, choose BEST_MEASUREMENT. B) Are your measurements significantly noisy and/or irreproducible? If so, BEST_MEASUREMENT will tend to be over-optimistic, and it may be better to choose LAST_MEASUREMENT. If both or neither of (A) and (B) apply, it doesn't matter which selection type is chosen.

StudySpec.Types.MetricSpec.Types.GoalType

The available types of optimization goals.

StudySpec.Types.ObservationNoise

Describes the noise level of the repeated observations.

"Noisy" means that the repeated observations with the same Trial parameters may lead to different metric evaluations.

StudySpec.Types.ParameterSpec.ParameterValueSpecOneofCase

Enum of possible cases for the "parameter_value_spec" oneof.

StudySpec.Types.ParameterSpec.Types.ConditionalParameterSpec.ParentValueConditionOneofCase

Enum of possible cases for the "parent_value_condition" oneof.

StudySpec.Types.ParameterSpec.Types.ScaleType

The type of scaling that should be applied to this parameter.

StudyTimeConstraint.ConstraintOneofCase

Enum of possible cases for the "constraint" oneof.

SubnetworkName.ResourceNameType

The possible contents of SubnetworkName.

SupervisedHyperParameters.Types.AdapterSize

Supported adapter sizes for tuning.

Tensor.Types.DataType

Data type of the tensor.

TensorboardExperimentName.ResourceNameType

The possible contents of TensorboardExperimentName.

TensorboardName.ResourceNameType

The possible contents of TensorboardName.

TensorboardRunName.ResourceNameType

The possible contents of TensorboardRunName.

TensorboardTimeSeries.Types.ValueType

An enum representing the value type of a TensorboardTimeSeries.

TensorboardTimeSeriesName.ResourceNameType

The possible contents of TensorboardTimeSeriesName.

ThresholdConfig.ThresholdOneofCase

Enum of possible cases for the "threshold" oneof.

TimeSeriesDataPoint.ValueOneofCase

Enum of possible cases for the "value" oneof.

ToolUseExample.TargetOneofCase

Enum of possible cases for the "Target" oneof.

TrainingPipelineName.ResourceNameType

The possible contents of TrainingPipelineName.

Trial.Types.State

Describes a Trial state.

TrialName.ResourceNameType

The possible contents of TrialName.

TunedModelRef.TunedModelRefOneofCase

Enum of possible cases for the "tuned_model_ref" oneof.

TuningDataStats.TuningDataStatsOneofCase

Enum of possible cases for the "tuning_data_stats" oneof.

TuningJob.SourceModelOneofCase

Enum of possible cases for the "source_model" oneof.

TuningJob.TuningSpecOneofCase

Enum of possible cases for the "tuning_spec" oneof.

TuningJobName.ResourceNameType

The possible contents of TuningJobName.

Type

Type contains the list of OpenAPI data types as defined by https://swagger.io/docs/specification/data-models/data-types/

UploadRagFileResponse.ResultOneofCase

Enum of possible cases for the "result" oneof.

UserActionReference.ReferenceOneofCase

Enum of possible cases for the "reference" oneof.

Value.ValueOneofCase

Enum of possible cases for the "value" oneof.

VersionName.ResourceNameType

The possible contents of VersionName.

WorkerPoolSpec.TaskOneofCase

Enum of possible cases for the "task" oneof.