"""Generated message classes for aiplatform version v1. Train high-quality custom machine learning models with minimal machine learning expertise and effort. """ # NOTE: This file is autogenerated and should not be edited by hand. from __future__ import absolute_import from apitools.base.protorpclite import messages as _messages from apitools.base.py import encoding from apitools.base.py import extra_types package = 'aiplatform' class AiplatformMediaUploadRequest(_messages.Message): r"""A AiplatformMediaUploadRequest object. Fields: googleCloudAiplatformV1UploadRagFileRequest: A GoogleCloudAiplatformV1UploadRagFileRequest resource to be passed as the request body. parent: Required. The name of the RagCorpus resource into which to upload the file. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}` """ googleCloudAiplatformV1UploadRagFileRequest = _messages.MessageField('GoogleCloudAiplatformV1UploadRagFileRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsAugmentPromptRequest(_messages.Message): r"""A AiplatformProjectsLocationsAugmentPromptRequest object. Fields: googleCloudAiplatformV1AugmentPromptRequest: A GoogleCloudAiplatformV1AugmentPromptRequest resource to be passed as the request body. parent: Required. The resource name of the Location from which to augment prompt. The users must have permission to make a call in the project. Format: `projects/{project}/locations/{location}`. """ googleCloudAiplatformV1AugmentPromptRequest = _messages.MessageField('GoogleCloudAiplatformV1AugmentPromptRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsBatchPredictionJobsCancelRequest(_messages.Message): r"""A AiplatformProjectsLocationsBatchPredictionJobsCancelRequest object. Fields: googleCloudAiplatformV1CancelBatchPredictionJobRequest: A GoogleCloudAiplatformV1CancelBatchPredictionJobRequest resource to be passed as the request body. name: Required. The name of the BatchPredictionJob to cancel. Format: `pro jects/{project}/locations/{location}/batchPredictionJobs/{batch_predicti on_job}` """ googleCloudAiplatformV1CancelBatchPredictionJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelBatchPredictionJobRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsBatchPredictionJobsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsBatchPredictionJobsCreateRequest object. Fields: googleCloudAiplatformV1BatchPredictionJob: A GoogleCloudAiplatformV1BatchPredictionJob resource to be passed as the request body. parent: Required. The resource name of the Location to create the BatchPredictionJob in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1BatchPredictionJob = _messages.MessageField('GoogleCloudAiplatformV1BatchPredictionJob', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsBatchPredictionJobsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsBatchPredictionJobsDeleteRequest object. Fields: name: Required. The name of the BatchPredictionJob resource to be deleted. Format: `projects/{project}/locations/{location}/batchPredictionJobs/{ba tch_prediction_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsBatchPredictionJobsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsBatchPredictionJobsGetRequest object. Fields: name: Required. The name of the BatchPredictionJob resource. Format: `proj ects/{project}/locations/{location}/batchPredictionJobs/{batch_predictio n_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsBatchPredictionJobsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsBatchPredictionJobsListRequest object. Fields: filter: The standard list filter. Supported fields: * `display_name` supports `=`, `!=` comparisons, and `:` wildcard. * `model_display_name` supports `=`, `!=` comparisons. * `state` supports `=`, `!=` comparisons. * `create_time` supports `=`, `!=`,`<`, `<=`,`>`, `>=` comparisons. `create_time` must be in RFC 3339 format. * `labels` supports general map functions that is: `labels.key=value` - key:value equality `labels.key:* - key existence Some examples of using the filter are: * `state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"` * `state!="JOB_STATE_FAILED" OR display_name="my_job"` * `NOT display_name="my_job"` * `create_time>"2021-05-18T00:00:00Z"` * `labels.keyA=valueA` * `labels.keyB:*` pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via ListBatchPredictionJobsResponse.next_page_token of the previous JobService.ListBatchPredictionJobs call. parent: Required. The resource name of the Location to list the BatchPredictionJobs from. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) readMask = _messages.StringField(5) class AiplatformProjectsLocationsCachedContentsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsCachedContentsCreateRequest object. Fields: googleCloudAiplatformV1CachedContent: A GoogleCloudAiplatformV1CachedContent resource to be passed as the request body. parent: Required. The parent resource where the cached content will be created """ googleCloudAiplatformV1CachedContent = _messages.MessageField('GoogleCloudAiplatformV1CachedContent', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsCachedContentsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsCachedContentsDeleteRequest object. Fields: name: Required. The resource name referring to the cached content """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsCachedContentsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsCachedContentsGetRequest object. Fields: name: Required. The resource name referring to the cached content """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsCachedContentsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsCachedContentsListRequest object. Fields: pageSize: Optional. The maximum number of cached contents to return. The service may return fewer than this value. If unspecified, some default (under maximum) number of items will be returned. The maximum value is 1000; values above 1000 will be coerced to 1000. pageToken: Optional. A page token, received from a previous `ListCachedContents` call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to `ListCachedContents` must match the call that provided the page token. parent: Required. The parent, which owns this collection of cached contents. """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsCachedContentsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsCachedContentsPatchRequest object. Fields: googleCloudAiplatformV1CachedContent: A GoogleCloudAiplatformV1CachedContent resource to be passed as the request body. name: Immutable. Identifier. The server-generated resource name of the cached content Format: projects/{project}/locations/{location}/cachedContents/{cached_content} updateMask: Required. The list of fields to update. """ googleCloudAiplatformV1CachedContent = _messages.MessageField('GoogleCloudAiplatformV1CachedContent', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsCorroborateContentRequest(_messages.Message): r"""A AiplatformProjectsLocationsCorroborateContentRequest object. Fields: googleCloudAiplatformV1CorroborateContentRequest: A GoogleCloudAiplatformV1CorroborateContentRequest resource to be passed as the request body. parent: Required. The resource name of the Location from which to corroborate text. The users must have permission to make a call in the project. Format: `projects/{project}/locations/{location}`. """ googleCloudAiplatformV1CorroborateContentRequest = _messages.MessageField('GoogleCloudAiplatformV1CorroborateContentRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsCustomJobsCancelRequest(_messages.Message): r"""A AiplatformProjectsLocationsCustomJobsCancelRequest object. Fields: googleCloudAiplatformV1CancelCustomJobRequest: A GoogleCloudAiplatformV1CancelCustomJobRequest resource to be passed as the request body. name: Required. The name of the CustomJob to cancel. Format: `projects/{project}/locations/{location}/customJobs/{custom_job}` """ googleCloudAiplatformV1CancelCustomJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelCustomJobRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsCustomJobsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsCustomJobsCreateRequest object. Fields: googleCloudAiplatformV1CustomJob: A GoogleCloudAiplatformV1CustomJob resource to be passed as the request body. parent: Required. The resource name of the Location to create the CustomJob in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1CustomJob = _messages.MessageField('GoogleCloudAiplatformV1CustomJob', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsCustomJobsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsCustomJobsDeleteRequest object. Fields: name: Required. The name of the CustomJob resource to be deleted. Format: `projects/{project}/locations/{location}/customJobs/{custom_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsCustomJobsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsCustomJobsGetRequest object. Fields: name: Required. The name of the CustomJob resource. Format: `projects/{project}/locations/{location}/customJobs/{custom_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsCustomJobsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsCustomJobsListRequest object. Fields: filter: The standard list filter. Supported fields: * `display_name` supports `=`, `!=` comparisons, and `:` wildcard. * `state` supports `=`, `!=` comparisons. * `create_time` supports `=`, `!=`,`<`, `<=`,`>`, `>=` comparisons. `create_time` must be in RFC 3339 format. * `labels` supports general map functions that is: `labels.key=value` - key:value equality `labels.key:* - key existence Some examples of using the filter are: * `state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"` * `state!="JOB_STATE_FAILED" OR display_name="my_job"` * `NOT display_name="my_job"` * `create_time>"2021-05-18T00:00:00Z"` * `labels.keyA=valueA` * `labels.keyB:*` pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via ListCustomJobsResponse.next_page_token of the previous JobService.ListCustomJobs call. parent: Required. The resource name of the Location to list the CustomJobs from. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) readMask = _messages.StringField(5) class AiplatformProjectsLocationsDatasetsAnnotationSpecsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsAnnotationSpecsGetRequest object. Fields: name: Required. The name of the AnnotationSpec resource. Format: `projects /{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{anno tation_spec}` readMask: Mask specifying which fields to read. """ name = _messages.StringField(1, required=True) readMask = _messages.StringField(2) class AiplatformProjectsLocationsDatasetsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsCreateRequest object. Fields: googleCloudAiplatformV1Dataset: A GoogleCloudAiplatformV1Dataset resource to be passed as the request body. parent: Required. The resource name of the Location to create the Dataset in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1Dataset = _messages.MessageField('GoogleCloudAiplatformV1Dataset', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsDatasetsDataItemsAnnotationsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsDataItemsAnnotationsListRequest object. Fields: filter: The standard list filter. orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. pageSize: The standard list page size. pageToken: The standard list page token. parent: Required. The resource name of the DataItem to list Annotations from. Format: `projects/{project}/locations/{location}/datasets/{dataset }/dataItems/{data_item}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsDatasetsDataItemsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsDataItemsListRequest object. Fields: filter: The standard list filter. orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. pageSize: The standard list page size. pageToken: The standard list page token. parent: Required. The resource name of the Dataset to list DataItems from. Format: `projects/{project}/locations/{location}/datasets/{dataset}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsDatasetsDatasetVersionsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsDatasetVersionsCreateRequest object. Fields: googleCloudAiplatformV1DatasetVersion: A GoogleCloudAiplatformV1DatasetVersion resource to be passed as the request body. parent: Required. The name of the Dataset resource. Format: `projects/{project}/locations/{location}/datasets/{dataset}` """ googleCloudAiplatformV1DatasetVersion = _messages.MessageField('GoogleCloudAiplatformV1DatasetVersion', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsDatasetsDatasetVersionsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsDatasetVersionsDeleteRequest object. Fields: name: Required. The resource name of the Dataset version to delete. Format: `projects/{project}/locations/{location}/datasets/{dataset}/data setVersions/{dataset_version}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsDatasetsDatasetVersionsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsDatasetVersionsGetRequest object. Fields: name: Required. The resource name of the Dataset version to delete. Format: `projects/{project}/locations/{location}/datasets/{dataset}/data setVersions/{dataset_version}` readMask: Mask specifying which fields to read. """ name = _messages.StringField(1, required=True) readMask = _messages.StringField(2) class AiplatformProjectsLocationsDatasetsDatasetVersionsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsDatasetVersionsListRequest object. Fields: filter: Optional. The standard list filter. orderBy: Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. parent: Required. The resource name of the Dataset to list DatasetVersions from. Format: `projects/{project}/locations/{location}/datasets/{dataset}` readMask: Optional. Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsDatasetsDatasetVersionsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsDatasetVersionsPatchRequest object. Fields: googleCloudAiplatformV1DatasetVersion: A GoogleCloudAiplatformV1DatasetVersion resource to be passed as the request body. name: Output only. Identifier. The resource name of the DatasetVersion. Format: `projects/{project}/locations/{location}/datasets/{dataset}/data setVersions/{dataset_version}` updateMask: Required. The update mask applies to the resource. For the `FieldMask` definition, see google.protobuf.FieldMask. Updatable fields: * `display_name` """ googleCloudAiplatformV1DatasetVersion = _messages.MessageField('GoogleCloudAiplatformV1DatasetVersion', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsDatasetsDatasetVersionsRestoreRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsDatasetVersionsRestoreRequest object. Fields: name: Required. The name of the DatasetVersion resource. Format: `projects /{project}/locations/{location}/datasets/{dataset}/datasetVersions/{data set_version}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsDatasetsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsDeleteRequest object. Fields: name: Required. The resource name of the Dataset to delete. Format: `projects/{project}/locations/{location}/datasets/{dataset}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsDatasetsExportRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsExportRequest object. Fields: googleCloudAiplatformV1ExportDataRequest: A GoogleCloudAiplatformV1ExportDataRequest resource to be passed as the request body. name: Required. The name of the Dataset resource. Format: `projects/{project}/locations/{location}/datasets/{dataset}` """ googleCloudAiplatformV1ExportDataRequest = _messages.MessageField('GoogleCloudAiplatformV1ExportDataRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsDatasetsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsGetRequest object. Fields: name: Required. The name of the Dataset resource. readMask: Mask specifying which fields to read. """ name = _messages.StringField(1, required=True) readMask = _messages.StringField(2) class AiplatformProjectsLocationsDatasetsImportRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsImportRequest object. Fields: googleCloudAiplatformV1ImportDataRequest: A GoogleCloudAiplatformV1ImportDataRequest resource to be passed as the request body. name: Required. The name of the Dataset resource. Format: `projects/{project}/locations/{location}/datasets/{dataset}` """ googleCloudAiplatformV1ImportDataRequest = _messages.MessageField('GoogleCloudAiplatformV1ImportDataRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsDatasetsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsListRequest object. Fields: filter: An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. * `display_name`: supports = and != * `metadata_schema_uri`: supports = and != * `labels` supports general map functions that is: * `labels.key=value` - key:value equality * `labels.key:* or labels:key - key existence * A key including a space must be quoted. `labels."a key"`. Some examples: * `displayName="myDisplayName"` * `labels.myKey="myValue"` orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `display_name` * `create_time` * `update_time` pageSize: The standard list page size. pageToken: The standard list page token. parent: Required. The name of the Dataset's parent resource. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsDatasetsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsPatchRequest object. Fields: googleCloudAiplatformV1Dataset: A GoogleCloudAiplatformV1Dataset resource to be passed as the request body. name: Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}` updateMask: Required. The update mask applies to the resource. For the `FieldMask` definition, see google.protobuf.FieldMask. Updatable fields: * `display_name` * `description` * `labels` """ googleCloudAiplatformV1Dataset = _messages.MessageField('GoogleCloudAiplatformV1Dataset', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsDatasetsSavedQueriesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsSavedQueriesDeleteRequest object. Fields: name: Required. The resource name of the SavedQuery to delete. Format: `pr ojects/{project}/locations/{location}/datasets/{dataset}/savedQueries/{s aved_query}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsDatasetsSavedQueriesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsSavedQueriesListRequest object. Fields: filter: The standard list filter. orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. pageSize: The standard list page size. pageToken: The standard list page token. parent: Required. The resource name of the Dataset to list SavedQueries from. Format: `projects/{project}/locations/{location}/datasets/{dataset}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsDatasetsSearchDataItemsRequest(_messages.Message): r"""A AiplatformProjectsLocationsDatasetsSearchDataItemsRequest object. Fields: annotationFilters: An expression that specifies what Annotations will be returned per DataItem. Annotations satisfied either of the conditions will be returned. * `annotation_spec_id` - for = or !=. Must specify `saved_query_id=` - saved query id that annotations should belong to. annotationsFilter: An expression for filtering the Annotations that will be returned per DataItem. * `annotation_spec_id` - for = or !=. annotationsLimit: If set, only up to this many of Annotations will be returned per DataItemView. The maximum value is 1000. If not set, the maximum value will be used. dataItemFilter: An expression for filtering the DataItem that will be returned. * `data_item_id` - for = or !=. * `labeled` - for = or !=. * `has_annotation(ANNOTATION_SPEC_ID)` - true only for DataItem that have at least one annotation with annotation_spec_id = `ANNOTATION_SPEC_ID` in the context of SavedQuery or DataLabelingJob. For example: * `data_item=1` * `has_annotation(5)` dataLabelingJob: The resource name of a DataLabelingJob. Format: `projects /{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}` If this field is set, all of the search will be done in the context of this DataLabelingJob. dataset: Required. The resource name of the Dataset from which to search DataItems. Format: `projects/{project}/locations/{location}/datasets/{dataset}` fieldMask: Mask specifying which fields of DataItemView to read. orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. orderByAnnotation_orderBy: A comma-separated list of annotation fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Must also specify saved_query. orderByAnnotation_savedQuery: Required. Saved query of the Annotation. Only Annotations belong to this saved query will be considered for ordering. orderByDataItem: A comma-separated list of data item fields to order by, sorted in ascending order. Use "desc" after a field name for descending. pageSize: Requested page size. Server may return fewer results than requested. Default and maximum page size is 100. pageToken: A token identifying a page of results for the server to return Typically obtained via SearchDataItemsResponse.next_page_token of the previous DatasetService.SearchDataItems call. savedQuery: The resource name of a SavedQuery(annotation set in UI). Format: `projects/{project}/locations/{location}/datasets/{dataset}/save dQueries/{saved_query}` All of the search will be done in the context of this SavedQuery. """ annotationFilters = _messages.StringField(1, repeated=True) annotationsFilter = _messages.StringField(2) annotationsLimit = _messages.IntegerField(3, variant=_messages.Variant.INT32) dataItemFilter = _messages.StringField(4) dataLabelingJob = _messages.StringField(5) dataset = _messages.StringField(6, required=True) fieldMask = _messages.StringField(7) orderBy = _messages.StringField(8) orderByAnnotation_orderBy = _messages.StringField(9) orderByAnnotation_savedQuery = _messages.StringField(10) orderByDataItem = _messages.StringField(11) pageSize = _messages.IntegerField(12, variant=_messages.Variant.INT32) pageToken = _messages.StringField(13) savedQuery = _messages.StringField(14) class AiplatformProjectsLocationsDeployRequest(_messages.Message): r"""A AiplatformProjectsLocationsDeployRequest object. Fields: destination: Required. The resource name of the Location to deploy the model in. Format: `projects/{project}/locations/{location}` googleCloudAiplatformV1DeployRequest: A GoogleCloudAiplatformV1DeployRequest resource to be passed as the request body. """ destination = _messages.StringField(1, required=True) googleCloudAiplatformV1DeployRequest = _messages.MessageField('GoogleCloudAiplatformV1DeployRequest', 2) class AiplatformProjectsLocationsDeploymentResourcePoolsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsDeploymentResourcePoolsCreateRequest object. Fields: googleCloudAiplatformV1CreateDeploymentResourcePoolRequest: A GoogleCloudAiplatformV1CreateDeploymentResourcePoolRequest resource to be passed as the request body. parent: Required. The parent location resource where this DeploymentResourcePool will be created. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1CreateDeploymentResourcePoolRequest = _messages.MessageField('GoogleCloudAiplatformV1CreateDeploymentResourcePoolRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsDeploymentResourcePoolsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsDeploymentResourcePoolsDeleteRequest object. Fields: name: Required. The name of the DeploymentResourcePool to delete. Format: `projects/{project}/locations/{location}/deploymentResourcePools/{deploy ment_resource_pool}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsDeploymentResourcePoolsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsDeploymentResourcePoolsGetRequest object. Fields: name: Required. The name of the DeploymentResourcePool to retrieve. Format: `projects/{project}/locations/{location}/deploymentResourcePools /{deployment_resource_pool}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsDeploymentResourcePoolsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsDeploymentResourcePoolsListRequest object. Fields: pageSize: The maximum number of DeploymentResourcePools to return. The service may return fewer than this value. pageToken: A page token, received from a previous `ListDeploymentResourcePools` call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to `ListDeploymentResourcePools` must match the call that provided the page token. parent: Required. The parent Location which owns this collection of DeploymentResourcePools. Format: `projects/{project}/locations/{location}` """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsDeploymentResourcePoolsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsDeploymentResourcePoolsPatchRequest object. Fields: googleCloudAiplatformV1DeploymentResourcePool: A GoogleCloudAiplatformV1DeploymentResourcePool resource to be passed as the request body. name: Immutable. The resource name of the DeploymentResourcePool. Format: `projects/{project}/locations/{location}/deploymentResourcePools/{deploy ment_resource_pool}` updateMask: Required. The list of fields to update. """ googleCloudAiplatformV1DeploymentResourcePool = _messages.MessageField('GoogleCloudAiplatformV1DeploymentResourcePool', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsDeploymentResourcePoolsQueryDeployedModelsRequest(_messages.Message): r"""A AiplatformProjectsLocationsDeploymentResourcePoolsQueryDeployedModelsRequest object. Fields: deploymentResourcePool: Required. The name of the target DeploymentResourcePool to query. Format: `projects/{project}/locations/{ location}/deploymentResourcePools/{deployment_resource_pool}` pageSize: The maximum number of DeployedModels to return. The service may return fewer than this value. pageToken: A page token, received from a previous `QueryDeployedModels` call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to `QueryDeployedModels` must match the call that provided the page token. """ deploymentResourcePool = _messages.StringField(1, required=True) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) class AiplatformProjectsLocationsEndpointsComputeTokensRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsComputeTokensRequest object. Fields: endpoint: Required. The name of the Endpoint requested to get lists of tokens and token ids. googleCloudAiplatformV1ComputeTokensRequest: A GoogleCloudAiplatformV1ComputeTokensRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1ComputeTokensRequest = _messages.MessageField('GoogleCloudAiplatformV1ComputeTokensRequest', 2) class AiplatformProjectsLocationsEndpointsCountTokensRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsCountTokensRequest object. Fields: endpoint: Required. The name of the Endpoint requested to perform token counting. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1CountTokensRequest: A GoogleCloudAiplatformV1CountTokensRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1CountTokensRequest = _messages.MessageField('GoogleCloudAiplatformV1CountTokensRequest', 2) class AiplatformProjectsLocationsEndpointsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsCreateRequest object. Fields: endpointId: Immutable. The ID to use for endpoint, which will become the final component of the endpoint resource name. If not provided, Vertex AI will generate a value for this ID. If the first character is a letter, this value may be up to 63 characters, and valid characters are `[a-z0-9-]`. The last character must be a letter or number. If the first character is a number, this value may be up to 9 characters, and valid characters are `[0-9]` with no leading zeros. When using HTTP/JSON, this field is populated based on a query string argument, such as `?endpoint_id=12345`. This is the fallback for fields that are not included in either the URI or the body. googleCloudAiplatformV1Endpoint: A GoogleCloudAiplatformV1Endpoint resource to be passed as the request body. parent: Required. The resource name of the Location to create the Endpoint in. Format: `projects/{project}/locations/{location}` """ endpointId = _messages.StringField(1) googleCloudAiplatformV1Endpoint = _messages.MessageField('GoogleCloudAiplatformV1Endpoint', 2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsEndpointsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsDeleteRequest object. Fields: name: Required. The name of the Endpoint resource to be deleted. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsEndpointsDeployModelRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsDeployModelRequest object. Fields: endpoint: Required. The name of the Endpoint resource into which to deploy a Model. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1DeployModelRequest: A GoogleCloudAiplatformV1DeployModelRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1DeployModelRequest = _messages.MessageField('GoogleCloudAiplatformV1DeployModelRequest', 2) class AiplatformProjectsLocationsEndpointsDirectPredictRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsDirectPredictRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1DirectPredictRequest: A GoogleCloudAiplatformV1DirectPredictRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1DirectPredictRequest = _messages.MessageField('GoogleCloudAiplatformV1DirectPredictRequest', 2) class AiplatformProjectsLocationsEndpointsDirectRawPredictRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsDirectRawPredictRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1DirectRawPredictRequest: A GoogleCloudAiplatformV1DirectRawPredictRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1DirectRawPredictRequest = _messages.MessageField('GoogleCloudAiplatformV1DirectRawPredictRequest', 2) class AiplatformProjectsLocationsEndpointsExplainRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsExplainRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the explanation. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1ExplainRequest: A GoogleCloudAiplatformV1ExplainRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1ExplainRequest = _messages.MessageField('GoogleCloudAiplatformV1ExplainRequest', 2) class AiplatformProjectsLocationsEndpointsGenerateContentRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsGenerateContentRequest object. Fields: googleCloudAiplatformV1GenerateContentRequest: A GoogleCloudAiplatformV1GenerateContentRequest resource to be passed as the request body. model: Required. The fully qualified name of the publisher model or tuned model endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}` """ googleCloudAiplatformV1GenerateContentRequest = _messages.MessageField('GoogleCloudAiplatformV1GenerateContentRequest', 1) model = _messages.StringField(2, required=True) class AiplatformProjectsLocationsEndpointsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsGetRequest object. Fields: name: Required. The name of the Endpoint resource. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsEndpointsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsListRequest object. Fields: filter: Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. * `endpoint` supports `=` and `!=`. `endpoint` represents the Endpoint ID, i.e. the last segment of the Endpoint's resource name. * `display_name` supports `=` and `!=`. * `labels` supports general map functions that is: * `labels.key=value` - key:value equality * `labels.key:*` or `labels:key` - key existence * A key including a space must be quoted. `labels."a key"`. * `base_model_name` only supports `=`. Some examples: * `endpoint=1` * `displayName="myDisplayName"` * `labels.myKey="myValue"` * `baseModelName="text-bison"` gdcZone: Optional. Configures the Google Distributed Cloud (GDC) environment for online prediction. Only set this field when the Endpoint is to be deployed in a GDC environment. orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `display_name` * `create_time` * `update_time` Example: `display_name, create_time desc`. pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. Typically obtained via ListEndpointsResponse.next_page_token of the previous EndpointService.ListEndpoints call. parent: Required. The resource name of the Location from which to list the Endpoints. Format: `projects/{project}/locations/{location}` readMask: Optional. Mask specifying which fields to read. """ filter = _messages.StringField(1) gdcZone = _messages.StringField(2) orderBy = _messages.StringField(3) pageSize = _messages.IntegerField(4, variant=_messages.Variant.INT32) pageToken = _messages.StringField(5) parent = _messages.StringField(6, required=True) readMask = _messages.StringField(7) class AiplatformProjectsLocationsEndpointsMutateDeployedModelRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsMutateDeployedModelRequest object. Fields: endpoint: Required. The name of the Endpoint resource into which to mutate a DeployedModel. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1MutateDeployedModelRequest: A GoogleCloudAiplatformV1MutateDeployedModelRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1MutateDeployedModelRequest = _messages.MessageField('GoogleCloudAiplatformV1MutateDeployedModelRequest', 2) class AiplatformProjectsLocationsEndpointsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsPatchRequest object. Fields: googleCloudAiplatformV1Endpoint: A GoogleCloudAiplatformV1Endpoint resource to be passed as the request body. name: Output only. The resource name of the Endpoint. updateMask: Required. The update mask applies to the resource. See google.protobuf.FieldMask. """ googleCloudAiplatformV1Endpoint = _messages.MessageField('GoogleCloudAiplatformV1Endpoint', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsEndpointsPredictLongRunningRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsPredictLongRunningRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` or `proje cts/{project}/locations/{location}/publishers/{publisher}/models/{model} ` googleCloudAiplatformV1PredictLongRunningRequest: A GoogleCloudAiplatformV1PredictLongRunningRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1PredictLongRunningRequest = _messages.MessageField('GoogleCloudAiplatformV1PredictLongRunningRequest', 2) class AiplatformProjectsLocationsEndpointsPredictRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsPredictRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1PredictRequest: A GoogleCloudAiplatformV1PredictRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1PredictRequest = _messages.MessageField('GoogleCloudAiplatformV1PredictRequest', 2) class AiplatformProjectsLocationsEndpointsRawPredictRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsRawPredictRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1RawPredictRequest: A GoogleCloudAiplatformV1RawPredictRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1RawPredictRequest = _messages.MessageField('GoogleCloudAiplatformV1RawPredictRequest', 2) class AiplatformProjectsLocationsEndpointsServerStreamingPredictRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsServerStreamingPredictRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1StreamingPredictRequest: A GoogleCloudAiplatformV1StreamingPredictRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1StreamingPredictRequest = _messages.MessageField('GoogleCloudAiplatformV1StreamingPredictRequest', 2) class AiplatformProjectsLocationsEndpointsStreamGenerateContentRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsStreamGenerateContentRequest object. Fields: googleCloudAiplatformV1GenerateContentRequest: A GoogleCloudAiplatformV1GenerateContentRequest resource to be passed as the request body. model: Required. The fully qualified name of the publisher model or tuned model endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}` """ googleCloudAiplatformV1GenerateContentRequest = _messages.MessageField('GoogleCloudAiplatformV1GenerateContentRequest', 1) model = _messages.StringField(2, required=True) class AiplatformProjectsLocationsEndpointsStreamRawPredictRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsStreamRawPredictRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1StreamRawPredictRequest: A GoogleCloudAiplatformV1StreamRawPredictRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1StreamRawPredictRequest = _messages.MessageField('GoogleCloudAiplatformV1StreamRawPredictRequest', 2) class AiplatformProjectsLocationsEndpointsUndeployModelRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsUndeployModelRequest object. Fields: endpoint: Required. The name of the Endpoint resource from which to undeploy a Model. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1UndeployModelRequest: A GoogleCloudAiplatformV1UndeployModelRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1UndeployModelRequest = _messages.MessageField('GoogleCloudAiplatformV1UndeployModelRequest', 2) class AiplatformProjectsLocationsEndpointsUpdateRequest(_messages.Message): r"""A AiplatformProjectsLocationsEndpointsUpdateRequest object. Fields: googleCloudAiplatformV1UpdateEndpointLongRunningRequest: A GoogleCloudAiplatformV1UpdateEndpointLongRunningRequest resource to be passed as the request body. name: Output only. The resource name of the Endpoint. """ googleCloudAiplatformV1UpdateEndpointLongRunningRequest = _messages.MessageField('GoogleCloudAiplatformV1UpdateEndpointLongRunningRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsEvaluateInstancesRequest(_messages.Message): r"""A AiplatformProjectsLocationsEvaluateInstancesRequest object. Fields: googleCloudAiplatformV1EvaluateInstancesRequest: A GoogleCloudAiplatformV1EvaluateInstancesRequest resource to be passed as the request body. location: Required. The resource name of the Location to evaluate the instances. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1EvaluateInstancesRequest = _messages.MessageField('GoogleCloudAiplatformV1EvaluateInstancesRequest', 1) location = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureGroupsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsCreateRequest object. Fields: featureGroupId: Required. The ID to use for this FeatureGroup, which will become the final component of the FeatureGroup's resource name. This value may be up to 128 characters, and valid characters are `[a-z0-9_]`. The first character cannot be a number. The value must be unique within the project and location. googleCloudAiplatformV1FeatureGroup: A GoogleCloudAiplatformV1FeatureGroup resource to be passed as the request body. parent: Required. The resource name of the Location to create FeatureGroups. Format: `projects/{project}/locations/{location}` """ featureGroupId = _messages.StringField(1) googleCloudAiplatformV1FeatureGroup = _messages.MessageField('GoogleCloudAiplatformV1FeatureGroup', 2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsFeatureGroupsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsDeleteRequest object. Fields: force: If set to true, any Features under this FeatureGroup will also be deleted. (Otherwise, the request will only work if the FeatureGroup has no Features.) name: Required. The name of the FeatureGroup to be deleted. Format: `projects/{project}/locations/{location}/featureGroups/{feature_group}` """ force = _messages.BooleanField(1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureGroupsFeaturesBatchCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsFeaturesBatchCreateRequest object. Fields: googleCloudAiplatformV1BatchCreateFeaturesRequest: A GoogleCloudAiplatformV1BatchCreateFeaturesRequest resource to be passed as the request body. parent: Required. The resource name of the EntityType/FeatureGroup to create the batch of Features under. Format: `projects/{project}/location s/{location}/featurestores/{featurestore}/entityTypes/{entity_type}` `projects/{project}/locations/{location}/featureGroups/{feature_group}` """ googleCloudAiplatformV1BatchCreateFeaturesRequest = _messages.MessageField('GoogleCloudAiplatformV1BatchCreateFeaturesRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureGroupsFeaturesCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsFeaturesCreateRequest object. Fields: featureId: Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 128 characters, and valid characters are `[a-z0-9_]`. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup. googleCloudAiplatformV1Feature: A GoogleCloudAiplatformV1Feature resource to be passed as the request body. parent: Required. The resource name of the EntityType or FeatureGroup to create a Feature. Format for entity_type as parent: `projects/{project}/ locations/{location}/featurestores/{featurestore}/entityTypes/{entity_ty pe}` Format for feature_group as parent: `projects/{project}/locations/{location}/featureGroups/{feature_group}` """ featureId = _messages.StringField(1) googleCloudAiplatformV1Feature = _messages.MessageField('GoogleCloudAiplatformV1Feature', 2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsFeatureGroupsFeaturesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsFeaturesDeleteRequest object. Fields: name: Required. The name of the Features to be deleted. Format: `projects/ {project}/locations/{location}/featurestores/{featurestore}/entityTypes/ {entity_type}/features/{feature}` `projects/{project}/locations/{locatio n}/featureGroups/{feature_group}/features/{feature}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsFeatureGroupsFeaturesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsFeaturesGetRequest object. Fields: name: Required. The name of the Feature resource. Format for entity_type as parent: `projects/{project}/locations/{location}/featurestores/{featu restore}/entityTypes/{entity_type}` Format for feature_group as parent: `projects/{project}/locations/{location}/featureGroups/{feature_group}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsFeatureGroupsFeaturesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsFeaturesListRequest object. Fields: filter: Lists the Features that match the filter expression. The following filters are supported: * `value_type`: Supports = and != comparisons. * `create_time`: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. * `update_time`: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. * `labels`: Supports key-value equality as well as key presence. Examples: * `value_type = DOUBLE` --> Features whose type is DOUBLE. * `create_time > \"2020-01-31T15:30:00.000000Z\" OR update_time > \"2020-01-31T15:30:00.000000Z\"` --> EntityTypes created or updated after 2020-01-31T15:30:00.000000Z. * `labels.active = yes AND labels.env = prod` --> Features having both (active: yes) and (env: prod) labels. * `labels.env: *` --> Any Feature which has a label with 'env' as the key. latestStatsCount: Only applicable for Vertex AI Feature Store (Legacy). If set, return the most recent ListFeaturesRequest.latest_stats_count of stats for each Feature in response. Valid value is [0, 10]. If number of stats exists < ListFeaturesRequest.latest_stats_count, return all existing stats. orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `feature_id` * `value_type` (Not supported for FeatureRegistry Feature) * `create_time` * `update_time` pageSize: The maximum number of Features to return. The service may return fewer than this value. If unspecified, at most 1000 Features will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000. pageToken: A page token, received from a previous FeaturestoreService.ListFeatures call or FeatureRegistryService.ListFeatures call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeaturestoreService.ListFeatures or FeatureRegistryService.ListFeatures must match the call that provided the page token. parent: Required. The resource name of the Location to list Features. Format for entity_type as parent: `projects/{project}/locations/{locatio n}/featurestores/{featurestore}/entityTypes/{entity_type}` Format for feature_group as parent: `projects/{project}/locations/{location}/featureGroups/{feature_group}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) latestStatsCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) orderBy = _messages.StringField(3) pageSize = _messages.IntegerField(4, variant=_messages.Variant.INT32) pageToken = _messages.StringField(5) parent = _messages.StringField(6, required=True) readMask = _messages.StringField(7) class AiplatformProjectsLocationsFeatureGroupsFeaturesPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsFeaturesPatchRequest object. Fields: googleCloudAiplatformV1Feature: A GoogleCloudAiplatformV1Feature resource to be passed as the request body. name: Immutable. Name of the Feature. Format: `projects/{project}/location s/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/feat ures/{feature}` `projects/{project}/locations/{location}/featureGroups/{ feature_group}/features/{feature}` The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type. updateMask: Field mask is used to specify the fields to be overwritten in the Features resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to `*` to override all fields. Updatable fields: * `description` * `labels` * `disable_monitoring` (Not supported for FeatureRegistryService Feature) * `point_of_contact` (Not supported for FeaturestoreService FeatureStore) """ googleCloudAiplatformV1Feature = _messages.MessageField('GoogleCloudAiplatformV1Feature', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsFeatureGroupsGetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsGetIamPolicyRequest object. Fields: options_requestedPolicyVersion: Optional. The maximum policy version that will be used to format the policy. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset. The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). resource: REQUIRED: The resource for which the policy is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ options_requestedPolicyVersion = _messages.IntegerField(1, variant=_messages.Variant.INT32) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureGroupsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsGetRequest object. Fields: name: Required. The name of the FeatureGroup resource. """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsFeatureGroupsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsListRequest object. Fields: filter: Lists the FeatureGroups that match the filter expression. The following fields are supported: * `create_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. * `update_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. * `labels`: Supports key-value equality and key presence. Examples: * `create_time > "2020-01-01" OR update_time > "2020-01-01"` FeatureGroups created or updated after 2020-01-01. * `labels.env = "prod"` FeatureGroups with label "env" set to "prod". orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported Fields: * `create_time` * `update_time` pageSize: The maximum number of FeatureGroups to return. The service may return fewer than this value. If unspecified, at most 100 FeatureGroups will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100. pageToken: A page token, received from a previous FeatureRegistryService.ListFeatureGroups call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeatureRegistryService.ListFeatureGroups must match the call that provided the page token. parent: Required. The resource name of the Location to list FeatureGroups. Format: `projects/{project}/locations/{location}` """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) class AiplatformProjectsLocationsFeatureGroupsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsPatchRequest object. Fields: googleCloudAiplatformV1FeatureGroup: A GoogleCloudAiplatformV1FeatureGroup resource to be passed as the request body. name: Identifier. Name of the FeatureGroup. Format: `projects/{project}/locations/{location}/featureGroups/{featureGroup}` updateMask: Field mask is used to specify the fields to be overwritten in the FeatureGroup resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to `*` to override all fields. Updatable fields: * `labels` * `description` * `big_query` * `big_query.entity_id_columns` * `service_agent_type` """ googleCloudAiplatformV1FeatureGroup = _messages.MessageField('GoogleCloudAiplatformV1FeatureGroup', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsFeatureGroupsSetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsSetIamPolicyRequest object. Fields: googleIamV1SetIamPolicyRequest: A GoogleIamV1SetIamPolicyRequest resource to be passed as the request body. resource: REQUIRED: The resource for which the policy is being specified. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ googleIamV1SetIamPolicyRequest = _messages.MessageField('GoogleIamV1SetIamPolicyRequest', 1) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureGroupsTestIamPermissionsRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureGroupsTestIamPermissionsRequest object. Fields: permissions: The set of permissions to check for the `resource`. Permissions with wildcards (such as `*` or `storage.*`) are not allowed. For more information see [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions). resource: REQUIRED: The resource for which the policy detail is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ permissions = _messages.StringField(1, repeated=True) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresCreateRequest object. Fields: featureOnlineStoreId: Required. The ID to use for this FeatureOnlineStore, which will become the final component of the FeatureOnlineStore's resource name. This value may be up to 60 characters, and valid characters are `[a-z0-9_]`. The first character cannot be a number. The value must be unique within the project and location. googleCloudAiplatformV1FeatureOnlineStore: A GoogleCloudAiplatformV1FeatureOnlineStore resource to be passed as the request body. parent: Required. The resource name of the Location to create FeatureOnlineStores. Format: `projects/{project}/locations/{location}` """ featureOnlineStoreId = _messages.StringField(1) googleCloudAiplatformV1FeatureOnlineStore = _messages.MessageField('GoogleCloudAiplatformV1FeatureOnlineStore', 2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresDeleteRequest object. Fields: force: If set to true, any FeatureViews and Features for this FeatureOnlineStore will also be deleted. (Otherwise, the request will only work if the FeatureOnlineStore has no FeatureViews.) name: Required. The name of the FeatureOnlineStore to be deleted. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_on line_store}` """ force = _messages.BooleanField(1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsCreateRequest object. Fields: featureViewId: Required. The ID to use for the FeatureView, which will become the final component of the FeatureView's resource name. This value may be up to 60 characters, and valid characters are `[a-z0-9_]`. The first character cannot be a number. The value must be unique within a FeatureOnlineStore. googleCloudAiplatformV1FeatureView: A GoogleCloudAiplatformV1FeatureView resource to be passed as the request body. parent: Required. The resource name of the FeatureOnlineStore to create FeatureViews. Format: `projects/{project}/locations/{location}/featureOn lineStores/{feature_online_store}` runSyncImmediately: Immutable. If set to true, one on demand sync will be run immediately, regardless whether the FeatureView.sync_config is configured or not. """ featureViewId = _messages.StringField(1) googleCloudAiplatformV1FeatureView = _messages.MessageField('GoogleCloudAiplatformV1FeatureView', 2) parent = _messages.StringField(3, required=True) runSyncImmediately = _messages.BooleanField(4) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsDeleteRequest object. Fields: name: Required. The name of the FeatureView to be deleted. Format: `projec ts/{project}/locations/{location}/featureOnlineStores/{feature_online_st ore}/featureViews/{feature_view}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsFeatureViewSyncsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsFeatureViewS yncsGetRequest object. Fields: name: Required. The name of the FeatureViewSync resource. Format: `project s/{project}/locations/{location}/featureOnlineStores/{feature_online_sto re}/featureViews/{feature_view}/featureViewSyncs/{feature_view_sync}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsFeatureViewSyncsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsFeatureViewS yncsListRequest object. Fields: filter: Lists the FeatureViewSyncs that match the filter expression. The following filters are supported: * `create_time`: Supports `=`, `!=`, `<`, `>`, `>=`, and `<=` comparisons. Values must be in RFC 3339 format. Examples: * `create_time > \"2020-01-31T15:30:00.000000Z\"` --> FeatureViewSyncs created after 2020-01-31T15:30:00.000000Z. orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `create_time` pageSize: The maximum number of FeatureViewSyncs to return. The service may return fewer than this value. If unspecified, at most 1000 FeatureViewSyncs will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000. pageToken: A page token, received from a previous FeatureOnlineStoreAdminService.ListFeatureViewSyncs call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeatureOnlineStoreAdminService.ListFeatureViewSyncs must match the call that provided the page token. parent: Required. The resource name of the FeatureView to list FeatureViewSyncs. Format: `projects/{project}/locations/{location}/featu reOnlineStores/{feature_online_store}/featureViews/{feature_view}` """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsFetchFeatureValuesRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsFetchFeature ValuesRequest object. Fields: featureView: Required. FeatureView resource format `projects/{project}/loc ations/{location}/featureOnlineStores/{featureOnlineStore}/featureViews/ {featureView}` googleCloudAiplatformV1FetchFeatureValuesRequest: A GoogleCloudAiplatformV1FetchFeatureValuesRequest resource to be passed as the request body. """ featureView = _messages.StringField(1, required=True) googleCloudAiplatformV1FetchFeatureValuesRequest = _messages.MessageField('GoogleCloudAiplatformV1FetchFeatureValuesRequest', 2) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsGetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsGetIamPolicy Request object. Fields: options_requestedPolicyVersion: Optional. The maximum policy version that will be used to format the policy. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset. The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). resource: REQUIRED: The resource for which the policy is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ options_requestedPolicyVersion = _messages.IntegerField(1, variant=_messages.Variant.INT32) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsGetRequest object. Fields: name: Required. The name of the FeatureView resource. Format: `projects/{p roject}/locations/{location}/featureOnlineStores/{feature_online_store}/ featureViews/{feature_view}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsListRequest object. Fields: filter: Lists the FeatureViews that match the filter expression. The following filters are supported: * `create_time`: Supports `=`, `!=`, `<`, `>`, `>=`, and `<=` comparisons. Values must be in RFC 3339 format. * `update_time`: Supports `=`, `!=`, `<`, `>`, `>=`, and `<=` comparisons. Values must be in RFC 3339 format. * `labels`: Supports key-value equality as well as key presence. Examples: * `create_time > \"2020-01-31T15:30:00.000000Z\" OR update_time > \"2020-01-31T15:30:00.000000Z\"` --> FeatureViews created or updated after 2020-01-31T15:30:00.000000Z. * `labels.active = yes AND labels.env = prod` --> FeatureViews having both (active: yes) and (env: prod) labels. * `labels.env: *` --> Any FeatureView which has a label with 'env' as the key. orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `feature_view_id` * `create_time` * `update_time` pageSize: The maximum number of FeatureViews to return. The service may return fewer than this value. If unspecified, at most 1000 FeatureViews will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000. pageToken: A page token, received from a previous FeatureOnlineStoreAdminService.ListFeatureViews call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeatureOnlineStoreAdminService.ListFeatureViews must match the call that provided the page token. parent: Required. The resource name of the FeatureOnlineStore to list FeatureViews. Format: `projects/{project}/locations/{location}/featureOn lineStores/{feature_online_store}` """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsPatchRequest object. Fields: googleCloudAiplatformV1FeatureView: A GoogleCloudAiplatformV1FeatureView resource to be passed as the request body. name: Identifier. Name of the FeatureView. Format: `projects/{project}/loc ations/{location}/featureOnlineStores/{feature_online_store}/featureView s/{feature_view}` updateMask: Field mask is used to specify the fields to be overwritten in the FeatureView resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to `*` to override all fields. Updatable fields: * `labels` * `service_agent_type` * `big_query_source` * `big_query_source.uri` * `big_query_source.entity_id_columns` * `feature_registry_source` * `feature_registry_source.feature_groups` * `sync_config` * `sync_config.cron` * `optimized_config.automatic_resources` """ googleCloudAiplatformV1FeatureView = _messages.MessageField('GoogleCloudAiplatformV1FeatureView', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsSearchNearestEntitiesRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsSearchNeares tEntitiesRequest object. Fields: featureView: Required. FeatureView resource format `projects/{project}/loc ations/{location}/featureOnlineStores/{featureOnlineStore}/featureViews/ {featureView}` googleCloudAiplatformV1SearchNearestEntitiesRequest: A GoogleCloudAiplatformV1SearchNearestEntitiesRequest resource to be passed as the request body. """ featureView = _messages.StringField(1, required=True) googleCloudAiplatformV1SearchNearestEntitiesRequest = _messages.MessageField('GoogleCloudAiplatformV1SearchNearestEntitiesRequest', 2) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsSetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsSetIamPolicy Request object. Fields: googleIamV1SetIamPolicyRequest: A GoogleIamV1SetIamPolicyRequest resource to be passed as the request body. resource: REQUIRED: The resource for which the policy is being specified. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ googleIamV1SetIamPolicyRequest = _messages.MessageField('GoogleIamV1SetIamPolicyRequest', 1) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsSyncRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsSyncRequest object. Fields: featureView: Required. Format: `projects/{project}/locations/{location}/fe atureOnlineStores/{feature_online_store}/featureViews/{feature_view}` googleCloudAiplatformV1SyncFeatureViewRequest: A GoogleCloudAiplatformV1SyncFeatureViewRequest resource to be passed as the request body. """ featureView = _messages.StringField(1, required=True) googleCloudAiplatformV1SyncFeatureViewRequest = _messages.MessageField('GoogleCloudAiplatformV1SyncFeatureViewRequest', 2) class AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsTestIamPermissionsRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsTestIamPermi ssionsRequest object. Fields: permissions: The set of permissions to check for the `resource`. Permissions with wildcards (such as `*` or `storage.*`) are not allowed. For more information see [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions). resource: REQUIRED: The resource for which the policy detail is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ permissions = _messages.StringField(1, repeated=True) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresGetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresGetIamPolicyRequest object. Fields: options_requestedPolicyVersion: Optional. The maximum policy version that will be used to format the policy. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset. The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). resource: REQUIRED: The resource for which the policy is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ options_requestedPolicyVersion = _messages.IntegerField(1, variant=_messages.Variant.INT32) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresGetRequest object. Fields: name: Required. The name of the FeatureOnlineStore resource. """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresListRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresListRequest object. Fields: filter: Lists the FeatureOnlineStores that match the filter expression. The following fields are supported: * `create_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. * `update_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. * `labels`: Supports key-value equality and key presence. Examples: * `create_time > "2020-01-01" OR update_time > "2020-01-01"` FeatureOnlineStores created or updated after 2020-01-01. * `labels.env = "prod"` FeatureOnlineStores with label "env" set to "prod". orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported Fields: * `create_time` * `update_time` pageSize: The maximum number of FeatureOnlineStores to return. The service may return fewer than this value. If unspecified, at most 100 FeatureOnlineStores will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100. pageToken: A page token, received from a previous FeatureOnlineStoreAdminService.ListFeatureOnlineStores call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeatureOnlineStoreAdminService.ListFeatureOnlineStores must match the call that provided the page token. parent: Required. The resource name of the Location to list FeatureOnlineStores. Format: `projects/{project}/locations/{location}` """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresPatchRequest object. Fields: googleCloudAiplatformV1FeatureOnlineStore: A GoogleCloudAiplatformV1FeatureOnlineStore resource to be passed as the request body. name: Identifier. Name of the FeatureOnlineStore. Format: `projects/{proje ct}/locations/{location}/featureOnlineStores/{featureOnlineStore}` updateMask: Field mask is used to specify the fields to be overwritten in the FeatureOnlineStore resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to `*` to override all fields. Updatable fields: * `labels` * `description` * `bigtable` * `bigtable.auto_scaling` * `bigtable.enable_multi_region_replica` """ googleCloudAiplatformV1FeatureOnlineStore = _messages.MessageField('GoogleCloudAiplatformV1FeatureOnlineStore', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsFeatureOnlineStoresSetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresSetIamPolicyRequest object. Fields: googleIamV1SetIamPolicyRequest: A GoogleIamV1SetIamPolicyRequest resource to be passed as the request body. resource: REQUIRED: The resource for which the policy is being specified. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ googleIamV1SetIamPolicyRequest = _messages.MessageField('GoogleIamV1SetIamPolicyRequest', 1) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeatureOnlineStoresTestIamPermissionsRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeatureOnlineStoresTestIamPermissionsRequest object. Fields: permissions: The set of permissions to check for the `resource`. Permissions with wildcards (such as `*` or `storage.*`) are not allowed. For more information see [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions). resource: REQUIRED: The resource for which the policy detail is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ permissions = _messages.StringField(1, repeated=True) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeaturestoresBatchReadFeatureValuesRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresBatchReadFeatureValuesRequest object. Fields: featurestore: Required. The resource name of the Featurestore from which to query Feature values. Format: `projects/{project}/locations/{location}/featurestores/{featurestore}` googleCloudAiplatformV1BatchReadFeatureValuesRequest: A GoogleCloudAiplatformV1BatchReadFeatureValuesRequest resource to be passed as the request body. """ featurestore = _messages.StringField(1, required=True) googleCloudAiplatformV1BatchReadFeatureValuesRequest = _messages.MessageField('GoogleCloudAiplatformV1BatchReadFeatureValuesRequest', 2) class AiplatformProjectsLocationsFeaturestoresCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresCreateRequest object. Fields: featurestoreId: Required. The ID to use for this Featurestore, which will become the final component of the Featurestore's resource name. This value may be up to 60 characters, and valid characters are `[a-z0-9_]`. The first character cannot be a number. The value must be unique within the project and location. googleCloudAiplatformV1Featurestore: A GoogleCloudAiplatformV1Featurestore resource to be passed as the request body. parent: Required. The resource name of the Location to create Featurestores. Format: `projects/{project}/locations/{location}` """ featurestoreId = _messages.StringField(1) googleCloudAiplatformV1Featurestore = _messages.MessageField('GoogleCloudAiplatformV1Featurestore', 2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsFeaturestoresDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresDeleteRequest object. Fields: force: If set to true, any EntityTypes and Features for this Featurestore will also be deleted. (Otherwise, the request will only work if the Featurestore has no EntityTypes.) name: Required. The name of the Featurestore to be deleted. Format: `projects/{project}/locations/{location}/featurestores/{featurestore}` """ force = _messages.BooleanField(1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeaturestoresEntityTypesCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesCreateRequest object. Fields: entityTypeId: Required. The ID to use for the EntityType, which will become the final component of the EntityType's resource name. This value may be up to 60 characters, and valid characters are `[a-z0-9_]`. The first character cannot be a number. The value must be unique within a featurestore. googleCloudAiplatformV1EntityType: A GoogleCloudAiplatformV1EntityType resource to be passed as the request body. parent: Required. The resource name of the Featurestore to create EntityTypes. Format: `projects/{project}/locations/{location}/featurestores/{featurestore}` """ entityTypeId = _messages.StringField(1) googleCloudAiplatformV1EntityType = _messages.MessageField('GoogleCloudAiplatformV1EntityType', 2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsFeaturestoresEntityTypesDeleteFeatureValuesRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesDeleteFeatureValues Request object. Fields: entityType: Required. The resource name of the EntityType grouping the Features for which values are being deleted from. Format: `projects/{pro ject}/locations/{location}/featurestores/{featurestore}/entityTypes/{ent ityType}` googleCloudAiplatformV1DeleteFeatureValuesRequest: A GoogleCloudAiplatformV1DeleteFeatureValuesRequest resource to be passed as the request body. """ entityType = _messages.StringField(1, required=True) googleCloudAiplatformV1DeleteFeatureValuesRequest = _messages.MessageField('GoogleCloudAiplatformV1DeleteFeatureValuesRequest', 2) class AiplatformProjectsLocationsFeaturestoresEntityTypesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesDeleteRequest object. Fields: force: If set to true, any Features for this EntityType will also be deleted. (Otherwise, the request will only work if the EntityType has no Features.) name: Required. The name of the EntityType to be deleted. Format: `project s/{project}/locations/{location}/featurestores/{featurestore}/entityType s/{entity_type}` """ force = _messages.BooleanField(1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeaturestoresEntityTypesExportFeatureValuesRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesExportFeatureValues Request object. Fields: entityType: Required. The resource name of the EntityType from which to export Feature values. Format: `projects/{project}/locations/{location}/ featurestores/{featurestore}/entityTypes/{entity_type}` googleCloudAiplatformV1ExportFeatureValuesRequest: A GoogleCloudAiplatformV1ExportFeatureValuesRequest resource to be passed as the request body. """ entityType = _messages.StringField(1, required=True) googleCloudAiplatformV1ExportFeatureValuesRequest = _messages.MessageField('GoogleCloudAiplatformV1ExportFeatureValuesRequest', 2) class AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesBatchCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesBatchCreate Request object. Fields: googleCloudAiplatformV1BatchCreateFeaturesRequest: A GoogleCloudAiplatformV1BatchCreateFeaturesRequest resource to be passed as the request body. parent: Required. The resource name of the EntityType/FeatureGroup to create the batch of Features under. Format: `projects/{project}/location s/{location}/featurestores/{featurestore}/entityTypes/{entity_type}` `projects/{project}/locations/{location}/featureGroups/{feature_group}` """ googleCloudAiplatformV1BatchCreateFeaturesRequest = _messages.MessageField('GoogleCloudAiplatformV1BatchCreateFeaturesRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesCreateRequest object. Fields: featureId: Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 128 characters, and valid characters are `[a-z0-9_]`. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup. googleCloudAiplatformV1Feature: A GoogleCloudAiplatformV1Feature resource to be passed as the request body. parent: Required. The resource name of the EntityType or FeatureGroup to create a Feature. Format for entity_type as parent: `projects/{project}/ locations/{location}/featurestores/{featurestore}/entityTypes/{entity_ty pe}` Format for feature_group as parent: `projects/{project}/locations/{location}/featureGroups/{feature_group}` """ featureId = _messages.StringField(1) googleCloudAiplatformV1Feature = _messages.MessageField('GoogleCloudAiplatformV1Feature', 2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesDeleteRequest object. Fields: name: Required. The name of the Features to be deleted. Format: `projects/ {project}/locations/{location}/featurestores/{featurestore}/entityTypes/ {entity_type}/features/{feature}` `projects/{project}/locations/{locatio n}/featureGroups/{feature_group}/features/{feature}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesGetRequest object. Fields: name: Required. The name of the Feature resource. Format for entity_type as parent: `projects/{project}/locations/{location}/featurestores/{featu restore}/entityTypes/{entity_type}` Format for feature_group as parent: `projects/{project}/locations/{location}/featureGroups/{feature_group}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesListRequest object. Fields: filter: Lists the Features that match the filter expression. The following filters are supported: * `value_type`: Supports = and != comparisons. * `create_time`: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. * `update_time`: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. * `labels`: Supports key-value equality as well as key presence. Examples: * `value_type = DOUBLE` --> Features whose type is DOUBLE. * `create_time > \"2020-01-31T15:30:00.000000Z\" OR update_time > \"2020-01-31T15:30:00.000000Z\"` --> EntityTypes created or updated after 2020-01-31T15:30:00.000000Z. * `labels.active = yes AND labels.env = prod` --> Features having both (active: yes) and (env: prod) labels. * `labels.env: *` --> Any Feature which has a label with 'env' as the key. latestStatsCount: Only applicable for Vertex AI Feature Store (Legacy). If set, return the most recent ListFeaturesRequest.latest_stats_count of stats for each Feature in response. Valid value is [0, 10]. If number of stats exists < ListFeaturesRequest.latest_stats_count, return all existing stats. orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `feature_id` * `value_type` (Not supported for FeatureRegistry Feature) * `create_time` * `update_time` pageSize: The maximum number of Features to return. The service may return fewer than this value. If unspecified, at most 1000 Features will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000. pageToken: A page token, received from a previous FeaturestoreService.ListFeatures call or FeatureRegistryService.ListFeatures call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeaturestoreService.ListFeatures or FeatureRegistryService.ListFeatures must match the call that provided the page token. parent: Required. The resource name of the Location to list Features. Format for entity_type as parent: `projects/{project}/locations/{locatio n}/featurestores/{featurestore}/entityTypes/{entity_type}` Format for feature_group as parent: `projects/{project}/locations/{location}/featureGroups/{feature_group}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) latestStatsCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) orderBy = _messages.StringField(3) pageSize = _messages.IntegerField(4, variant=_messages.Variant.INT32) pageToken = _messages.StringField(5) parent = _messages.StringField(6, required=True) readMask = _messages.StringField(7) class AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesFeaturesPatchRequest object. Fields: googleCloudAiplatformV1Feature: A GoogleCloudAiplatformV1Feature resource to be passed as the request body. name: Immutable. Name of the Feature. Format: `projects/{project}/location s/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/feat ures/{feature}` `projects/{project}/locations/{location}/featureGroups/{ feature_group}/features/{feature}` The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type. updateMask: Field mask is used to specify the fields to be overwritten in the Features resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to `*` to override all fields. Updatable fields: * `description` * `labels` * `disable_monitoring` (Not supported for FeatureRegistryService Feature) * `point_of_contact` (Not supported for FeaturestoreService FeatureStore) """ googleCloudAiplatformV1Feature = _messages.MessageField('GoogleCloudAiplatformV1Feature', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsFeaturestoresEntityTypesGetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesGetIamPolicyRequest object. Fields: options_requestedPolicyVersion: Optional. The maximum policy version that will be used to format the policy. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset. The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). resource: REQUIRED: The resource for which the policy is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ options_requestedPolicyVersion = _messages.IntegerField(1, variant=_messages.Variant.INT32) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeaturestoresEntityTypesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesGetRequest object. Fields: name: Required. The name of the EntityType resource. Format: `projects/{pr oject}/locations/{location}/featurestores/{featurestore}/entityTypes/{en tity_type}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsFeaturestoresEntityTypesImportFeatureValuesRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesImportFeatureValues Request object. Fields: entityType: Required. The resource name of the EntityType grouping the Features for which values are being imported. Format: `projects/{project }/locations/{location}/featurestores/{featurestore}/entityTypes/{entityT ype}` googleCloudAiplatformV1ImportFeatureValuesRequest: A GoogleCloudAiplatformV1ImportFeatureValuesRequest resource to be passed as the request body. """ entityType = _messages.StringField(1, required=True) googleCloudAiplatformV1ImportFeatureValuesRequest = _messages.MessageField('GoogleCloudAiplatformV1ImportFeatureValuesRequest', 2) class AiplatformProjectsLocationsFeaturestoresEntityTypesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesListRequest object. Fields: filter: Lists the EntityTypes that match the filter expression. The following filters are supported: * `create_time`: Supports `=`, `!=`, `<`, `>`, `>=`, and `<=` comparisons. Values must be in RFC 3339 format. * `update_time`: Supports `=`, `!=`, `<`, `>`, `>=`, and `<=` comparisons. Values must be in RFC 3339 format. * `labels`: Supports key-value equality as well as key presence. Examples: * `create_time > \"2020-01-31T15:30:00.000000Z\" OR update_time > \"2020-01-31T15:30:00.000000Z\"` --> EntityTypes created or updated after 2020-01-31T15:30:00.000000Z. * `labels.active = yes AND labels.env = prod` --> EntityTypes having both (active: yes) and (env: prod) labels. * `labels.env: *` --> Any EntityType which has a label with 'env' as the key. orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `entity_type_id` * `create_time` * `update_time` pageSize: The maximum number of EntityTypes to return. The service may return fewer than this value. If unspecified, at most 1000 EntityTypes will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000. pageToken: A page token, received from a previous FeaturestoreService.ListEntityTypes call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeaturestoreService.ListEntityTypes must match the call that provided the page token. parent: Required. The resource name of the Featurestore to list EntityTypes. Format: `projects/{project}/locations/{location}/featurestores/{featurestore}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsFeaturestoresEntityTypesPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesPatchRequest object. Fields: googleCloudAiplatformV1EntityType: A GoogleCloudAiplatformV1EntityType resource to be passed as the request body. name: Immutable. Name of the EntityType. Format: `projects/{project}/locat ions/{location}/featurestores/{featurestore}/entityTypes/{entity_type}` The last part entity_type is assigned by the client. The entity_type can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z and underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given a featurestore. updateMask: Field mask is used to specify the fields to be overwritten in the EntityType resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to `*` to override all fields. Updatable fields: * `description` * `labels` * `monitoring_config.snapshot_analysis.disabled` * `monitoring_config.snapshot_analysis.monitoring_interval_days` * `monitoring_config.snapshot_analysis.staleness_days` * `monitoring_config.import_features_analysis.state` * `monitoring_config.import_features_analysis.anomaly_detection_baseline` * `monitoring_config.numerical_threshold_config.value` * `monitoring_config.categorical_threshold_config.value` * `offline_storage_ttl_days` """ googleCloudAiplatformV1EntityType = _messages.MessageField('GoogleCloudAiplatformV1EntityType', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsFeaturestoresEntityTypesReadFeatureValuesRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesReadFeatureValuesRequest object. Fields: entityType: Required. The resource name of the EntityType for the entity being read. Value format: `projects/{project}/locations/{location}/featu restores/{featurestore}/entityTypes/{entityType}`. For example, for a machine learning model predicting user clicks on a website, an EntityType ID could be `user`. googleCloudAiplatformV1ReadFeatureValuesRequest: A GoogleCloudAiplatformV1ReadFeatureValuesRequest resource to be passed as the request body. """ entityType = _messages.StringField(1, required=True) googleCloudAiplatformV1ReadFeatureValuesRequest = _messages.MessageField('GoogleCloudAiplatformV1ReadFeatureValuesRequest', 2) class AiplatformProjectsLocationsFeaturestoresEntityTypesSetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesSetIamPolicyRequest object. Fields: googleIamV1SetIamPolicyRequest: A GoogleIamV1SetIamPolicyRequest resource to be passed as the request body. resource: REQUIRED: The resource for which the policy is being specified. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ googleIamV1SetIamPolicyRequest = _messages.MessageField('GoogleIamV1SetIamPolicyRequest', 1) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeaturestoresEntityTypesStreamingReadFeatureValuesRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesStreamingReadFeatur eValuesRequest object. Fields: entityType: Required. The resource name of the entities' type. Value format: `projects/{project}/locations/{location}/featurestores/{features tore}/entityTypes/{entityType}`. For example, for a machine learning model predicting user clicks on a website, an EntityType ID could be `user`. googleCloudAiplatformV1StreamingReadFeatureValuesRequest: A GoogleCloudAiplatformV1StreamingReadFeatureValuesRequest resource to be passed as the request body. """ entityType = _messages.StringField(1, required=True) googleCloudAiplatformV1StreamingReadFeatureValuesRequest = _messages.MessageField('GoogleCloudAiplatformV1StreamingReadFeatureValuesRequest', 2) class AiplatformProjectsLocationsFeaturestoresEntityTypesTestIamPermissionsRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesTestIamPermissionsRequest object. Fields: permissions: The set of permissions to check for the `resource`. Permissions with wildcards (such as `*` or `storage.*`) are not allowed. For more information see [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions). resource: REQUIRED: The resource for which the policy detail is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ permissions = _messages.StringField(1, repeated=True) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeaturestoresEntityTypesWriteFeatureValuesRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresEntityTypesWriteFeatureValuesRequest object. Fields: entityType: Required. The resource name of the EntityType for the entities being written. Value format: `projects/{project}/locations/{location}/featurestores/ {featurestore}/entityTypes/{entityType}`. For example, for a machine learning model predicting user clicks on a website, an EntityType ID could be `user`. googleCloudAiplatformV1WriteFeatureValuesRequest: A GoogleCloudAiplatformV1WriteFeatureValuesRequest resource to be passed as the request body. """ entityType = _messages.StringField(1, required=True) googleCloudAiplatformV1WriteFeatureValuesRequest = _messages.MessageField('GoogleCloudAiplatformV1WriteFeatureValuesRequest', 2) class AiplatformProjectsLocationsFeaturestoresGetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresGetIamPolicyRequest object. Fields: options_requestedPolicyVersion: Optional. The maximum policy version that will be used to format the policy. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset. The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). resource: REQUIRED: The resource for which the policy is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ options_requestedPolicyVersion = _messages.IntegerField(1, variant=_messages.Variant.INT32) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeaturestoresGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresGetRequest object. Fields: name: Required. The name of the Featurestore resource. """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsFeaturestoresListRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresListRequest object. Fields: filter: Lists the featurestores that match the filter expression. The following fields are supported: * `create_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. * `update_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. * `online_serving_config.fixed_node_count`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. * `labels`: Supports key-value equality and key presence. Examples: * `create_time > "2020-01-01" OR update_time > "2020-01-01"` Featurestores created or updated after 2020-01-01. * `labels.env = "prod"` Featurestores with label "env" set to "prod". orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported Fields: * `create_time` * `update_time` * `online_serving_config.fixed_node_count` pageSize: The maximum number of Featurestores to return. The service may return fewer than this value. If unspecified, at most 100 Featurestores will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100. pageToken: A page token, received from a previous FeaturestoreService.ListFeaturestores call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeaturestoreService.ListFeaturestores must match the call that provided the page token. parent: Required. The resource name of the Location to list Featurestores. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsFeaturestoresPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresPatchRequest object. Fields: googleCloudAiplatformV1Featurestore: A GoogleCloudAiplatformV1Featurestore resource to be passed as the request body. name: Output only. Name of the Featurestore. Format: `projects/{project}/locations/{location}/featurestores/{featurestore}` updateMask: Field mask is used to specify the fields to be overwritten in the Featurestore resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to `*` to override all fields. Updatable fields: * `labels` * `online_serving_config.fixed_node_count` * `online_serving_config.scaling` * `online_storage_ttl_days` """ googleCloudAiplatformV1Featurestore = _messages.MessageField('GoogleCloudAiplatformV1Featurestore', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsFeaturestoresSearchFeaturesRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresSearchFeaturesRequest object. Fields: location: Required. The resource name of the Location to search Features. Format: `projects/{project}/locations/{location}` pageSize: The maximum number of Features to return. The service may return fewer than this value. If unspecified, at most 100 Features will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100. pageToken: A page token, received from a previous FeaturestoreService.SearchFeatures call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeaturestoreService.SearchFeatures, except `page_size`, must match the call that provided the page token. query: Query string that is a conjunction of field-restricted queries and/or field-restricted filters. Field-restricted queries and filters can be combined using `AND` to form a conjunction. A field query is in the form FIELD:QUERY. This implicitly checks if QUERY exists as a substring within Feature's FIELD. The QUERY and the FIELD are converted to a sequence of words (i.e. tokens) for comparison. This is done by: * Removing leading/trailing whitespace and tokenizing the search value. Characters that are not one of alphanumeric `[a-zA-Z0-9]`, underscore `_`, or asterisk `*` are treated as delimiters for tokens. `*` is treated as a wildcard that matches characters within a token. * Ignoring case. * Prepending an asterisk to the first and appending an asterisk to the last token in QUERY. A QUERY must be either a singular token or a phrase. A phrase is one or multiple words enclosed in double quotation marks ("). With phrases, the order of the words is important. Words in the phrase must be matching in order and consecutively. Supported FIELDs for field-restricted queries: * `feature_id` * `description` * `entity_type_id` Examples: * `feature_id: foo` --> Matches a Feature with ID containing the substring `foo` (eg. `foo`, `foofeature`, `barfoo`). * `feature_id: foo*feature` --> Matches a Feature with ID containing the substring `foo*feature` (eg. `foobarfeature`). * `feature_id: foo AND description: bar` --> Matches a Feature with ID containing the substring `foo` and description containing the substring `bar`. Besides field queries, the following exact-match filters are supported. The exact-match filters do not support wildcards. Unlike field-restricted queries, exact-match filters are case-sensitive. * `feature_id`: Supports = comparisons. * `description`: Supports = comparisons. Multi-token filters should be enclosed in quotes. * `entity_type_id`: Supports = comparisons. * `value_type`: Supports = and != comparisons. * `labels`: Supports key-value equality as well as key presence. * `featurestore_id`: Supports = comparisons. Examples: * `description = "foo bar"` --> Any Feature with description exactly equal to `foo bar` * `value_type = DOUBLE` --> Features whose type is DOUBLE. * `labels.active = yes AND labels.env = prod` --> Features having both (active: yes) and (env: prod) labels. * `labels.env: *` --> Any Feature which has a label with `env` as the key. """ location = _messages.StringField(1, required=True) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) query = _messages.StringField(4) class AiplatformProjectsLocationsFeaturestoresSetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresSetIamPolicyRequest object. Fields: googleIamV1SetIamPolicyRequest: A GoogleIamV1SetIamPolicyRequest resource to be passed as the request body. resource: REQUIRED: The resource for which the policy is being specified. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ googleIamV1SetIamPolicyRequest = _messages.MessageField('GoogleIamV1SetIamPolicyRequest', 1) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsFeaturestoresTestIamPermissionsRequest(_messages.Message): r"""A AiplatformProjectsLocationsFeaturestoresTestIamPermissionsRequest object. Fields: permissions: The set of permissions to check for the `resource`. Permissions with wildcards (such as `*` or `storage.*`) are not allowed. For more information see [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions). resource: REQUIRED: The resource for which the policy detail is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ permissions = _messages.StringField(1, repeated=True) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsGetRagEngineConfigRequest(_messages.Message): r"""A AiplatformProjectsLocationsGetRagEngineConfigRequest object. Fields: name: Required. The name of the RagEngineConfig resource. Format: `projects/{project}/locations/{location}/ragEngineConfig` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsHyperparameterTuningJobsCancelRequest(_messages.Message): r"""A AiplatformProjectsLocationsHyperparameterTuningJobsCancelRequest object. Fields: googleCloudAiplatformV1CancelHyperparameterTuningJobRequest: A GoogleCloudAiplatformV1CancelHyperparameterTuningJobRequest resource to be passed as the request body. name: Required. The name of the HyperparameterTuningJob to cancel. Format: `projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyper parameter_tuning_job}` """ googleCloudAiplatformV1CancelHyperparameterTuningJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelHyperparameterTuningJobRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsHyperparameterTuningJobsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsHyperparameterTuningJobsCreateRequest object. Fields: googleCloudAiplatformV1HyperparameterTuningJob: A GoogleCloudAiplatformV1HyperparameterTuningJob resource to be passed as the request body. parent: Required. The resource name of the Location to create the HyperparameterTuningJob in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1HyperparameterTuningJob = _messages.MessageField('GoogleCloudAiplatformV1HyperparameterTuningJob', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsHyperparameterTuningJobsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsHyperparameterTuningJobsDeleteRequest object. Fields: name: Required. The name of the HyperparameterTuningJob resource to be deleted. Format: `projects/{project}/locations/{location}/hyperparameter TuningJobs/{hyperparameter_tuning_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsHyperparameterTuningJobsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsHyperparameterTuningJobsGetRequest object. Fields: name: Required. The name of the HyperparameterTuningJob resource. Format: `projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyper parameter_tuning_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsHyperparameterTuningJobsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsHyperparameterTuningJobsListRequest object. Fields: filter: The standard list filter. Supported fields: * `display_name` supports `=`, `!=` comparisons, and `:` wildcard. * `state` supports `=`, `!=` comparisons. * `create_time` supports `=`, `!=`,`<`, `<=`,`>`, `>=` comparisons. `create_time` must be in RFC 3339 format. * `labels` supports general map functions that is: `labels.key=value` - key:value equality `labels.key:* - key existence Some examples of using the filter are: * `state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"` * `state!="JOB_STATE_FAILED" OR display_name="my_job"` * `NOT display_name="my_job"` * `create_time>"2021-05-18T00:00:00Z"` * `labels.keyA=valueA` * `labels.keyB:*` pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via ListHyperparameterTuningJobsResponse.next_page_token of the previous JobService.ListHyperparameterTuningJobs call. parent: Required. The resource name of the Location to list the HyperparameterTuningJobs from. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) readMask = _messages.StringField(5) class AiplatformProjectsLocationsIndexEndpointsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexEndpointsCreateRequest object. Fields: googleCloudAiplatformV1IndexEndpoint: A GoogleCloudAiplatformV1IndexEndpoint resource to be passed as the request body. parent: Required. The resource name of the Location to create the IndexEndpoint in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1IndexEndpoint = _messages.MessageField('GoogleCloudAiplatformV1IndexEndpoint', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsIndexEndpointsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexEndpointsDeleteRequest object. Fields: name: Required. The name of the IndexEndpoint resource to be deleted. Format: `projects/{project}/locations/{location}/indexEndpoints/{index_e ndpoint}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsIndexEndpointsDeployIndexRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexEndpointsDeployIndexRequest object. Fields: googleCloudAiplatformV1DeployIndexRequest: A GoogleCloudAiplatformV1DeployIndexRequest resource to be passed as the request body. indexEndpoint: Required. The name of the IndexEndpoint resource into which to deploy an Index. Format: `projects/{project}/locations/{location}/ind exEndpoints/{index_endpoint}` """ googleCloudAiplatformV1DeployIndexRequest = _messages.MessageField('GoogleCloudAiplatformV1DeployIndexRequest', 1) indexEndpoint = _messages.StringField(2, required=True) class AiplatformProjectsLocationsIndexEndpointsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexEndpointsGetRequest object. Fields: name: Required. The name of the IndexEndpoint resource. Format: `projects/{project}/locations/{location}/indexEndpoints/{index_endpoint} ` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsIndexEndpointsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexEndpointsListRequest object. Fields: filter: Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. * `index_endpoint` supports = and !=. `index_endpoint` represents the IndexEndpoint ID, ie. the last segment of the IndexEndpoint's resourcename. * `display_name` supports =, != and regex() (uses [re2](https://github.com/google/re2/wiki/Syntax) syntax) * `labels` supports general map functions that is: `labels.key=value` - key:value equality `labels.key:* or labels:key - key existence A key including a space must be quoted. `labels."a key"`. Some examples: * `index_endpoint="1"` * `display_name="myDisplayName"` * `regex(display_name, "^A") -> The display name starts with an A. * `labels.myKey="myValue"` pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. Typically obtained via ListIndexEndpointsResponse.next_page_token of the previous IndexEndpointService.ListIndexEndpoints call. parent: Required. The resource name of the Location from which to list the IndexEndpoints. Format: `projects/{project}/locations/{location}` readMask: Optional. Mask specifying which fields to read. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) readMask = _messages.StringField(5) class AiplatformProjectsLocationsIndexEndpointsMutateDeployedIndexRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexEndpointsMutateDeployedIndexRequest object. Fields: googleCloudAiplatformV1DeployedIndex: A GoogleCloudAiplatformV1DeployedIndex resource to be passed as the request body. indexEndpoint: Required. The name of the IndexEndpoint resource into which to deploy an Index. Format: `projects/{project}/locations/{location}/ind exEndpoints/{index_endpoint}` """ googleCloudAiplatformV1DeployedIndex = _messages.MessageField('GoogleCloudAiplatformV1DeployedIndex', 1) indexEndpoint = _messages.StringField(2, required=True) class AiplatformProjectsLocationsIndexEndpointsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexEndpointsPatchRequest object. Fields: googleCloudAiplatformV1IndexEndpoint: A GoogleCloudAiplatformV1IndexEndpoint resource to be passed as the request body. name: Output only. The resource name of the IndexEndpoint. updateMask: Required. The update mask applies to the resource. See google.protobuf.FieldMask. """ googleCloudAiplatformV1IndexEndpoint = _messages.MessageField('GoogleCloudAiplatformV1IndexEndpoint', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsIndexEndpointsUndeployIndexRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexEndpointsUndeployIndexRequest object. Fields: googleCloudAiplatformV1UndeployIndexRequest: A GoogleCloudAiplatformV1UndeployIndexRequest resource to be passed as the request body. indexEndpoint: Required. The name of the IndexEndpoint resource from which to undeploy an Index. Format: `projects/{project}/locations/{location}/i ndexEndpoints/{index_endpoint}` """ googleCloudAiplatformV1UndeployIndexRequest = _messages.MessageField('GoogleCloudAiplatformV1UndeployIndexRequest', 1) indexEndpoint = _messages.StringField(2, required=True) class AiplatformProjectsLocationsIndexesCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexesCreateRequest object. Fields: googleCloudAiplatformV1Index: A GoogleCloudAiplatformV1Index resource to be passed as the request body. parent: Required. The resource name of the Location to create the Index in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1Index = _messages.MessageField('GoogleCloudAiplatformV1Index', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsIndexesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexesDeleteRequest object. Fields: name: Required. The name of the Index resource to be deleted. Format: `projects/{project}/locations/{location}/indexes/{index}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsIndexesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexesGetRequest object. Fields: name: Required. The name of the Index resource. Format: `projects/{project}/locations/{location}/indexes/{index}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsIndexesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexesListRequest object. Fields: filter: The standard list filter. pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via ListIndexesResponse.next_page_token of the previous IndexService.ListIndexes call. parent: Required. The resource name of the Location from which to list the Indexes. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) readMask = _messages.StringField(5) class AiplatformProjectsLocationsIndexesPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexesPatchRequest object. Fields: googleCloudAiplatformV1Index: A GoogleCloudAiplatformV1Index resource to be passed as the request body. name: Output only. The resource name of the Index. updateMask: The update mask applies to the resource. For the `FieldMask` definition, see google.protobuf.FieldMask. """ googleCloudAiplatformV1Index = _messages.MessageField('GoogleCloudAiplatformV1Index', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsIndexesRemoveDatapointsRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexesRemoveDatapointsRequest object. Fields: googleCloudAiplatformV1RemoveDatapointsRequest: A GoogleCloudAiplatformV1RemoveDatapointsRequest resource to be passed as the request body. index: Required. The name of the Index resource to be updated. Format: `projects/{project}/locations/{location}/indexes/{index}` """ googleCloudAiplatformV1RemoveDatapointsRequest = _messages.MessageField('GoogleCloudAiplatformV1RemoveDatapointsRequest', 1) index = _messages.StringField(2, required=True) class AiplatformProjectsLocationsIndexesUpsertDatapointsRequest(_messages.Message): r"""A AiplatformProjectsLocationsIndexesUpsertDatapointsRequest object. Fields: googleCloudAiplatformV1UpsertDatapointsRequest: A GoogleCloudAiplatformV1UpsertDatapointsRequest resource to be passed as the request body. index: Required. The name of the Index resource to be updated. Format: `projects/{project}/locations/{location}/indexes/{index}` """ googleCloudAiplatformV1UpsertDatapointsRequest = _messages.MessageField('GoogleCloudAiplatformV1UpsertDatapointsRequest', 1) index = _messages.StringField(2, required=True) class AiplatformProjectsLocationsMetadataStoresArtifactsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresArtifactsCreateRequest object. Fields: artifactId: The {artifact} portion of the resource name with the format: ` projects/{project}/locations/{location}/metadataStores/{metadatastore}/a rtifacts/{artifact}` If not provided, the Artifact's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are `/a-z-/`. Must be unique across all Artifacts in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting Artifact.) googleCloudAiplatformV1Artifact: A GoogleCloudAiplatformV1Artifact resource to be passed as the request body. parent: Required. The resource name of the MetadataStore where the Artifact should be created. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ artifactId = _messages.StringField(1) googleCloudAiplatformV1Artifact = _messages.MessageField('GoogleCloudAiplatformV1Artifact', 2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsMetadataStoresArtifactsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresArtifactsDeleteRequest object. Fields: etag: Optional. The etag of the Artifact to delete. If this is provided, it must match the server's etag. Otherwise, the request will fail with a FAILED_PRECONDITION. name: Required. The resource name of the Artifact to delete. Format: `proj ects/{project}/locations/{location}/metadataStores/{metadatastore}/artif acts/{artifact}` """ etag = _messages.StringField(1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsMetadataStoresArtifactsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresArtifactsGetRequest object. Fields: name: Required. The resource name of the Artifact to retrieve. Format: `pr ojects/{project}/locations/{location}/metadataStores/{metadatastore}/art ifacts/{artifact}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsMetadataStoresArtifactsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresArtifactsListRequest object. Fields: filter: Filter specifying the boolean condition for the Artifacts to satisfy in order to be part of the result set. The syntax to define filter query is based on https://google.aip.dev/160. The supported set of filters include the following: * **Attribute filtering**: For example: `display_name = "test"`. Supported fields include: `name`, `display_name`, `uri`, `state`, `schema_title`, `create_time`, and `update_time`. Time fields, such as `create_time` and `update_time`, require values specified in RFC-3339 format. For example: `create_time = "2020-11-19T11:30:00-04:00"` * **Metadata field**: To filter on metadata fields use traversal operation as follows: `metadata..`. For example: `metadata.field_1.number_value = 10.0` In case the field name contains special characters (such as colon), one can embed it inside double quote. For example: `metadata."field:1".number_value = 10.0` * **Context based filtering**: To filter Artifacts based on the contexts to which they belong, use the function operator with the full resource name `in_context()`. For example: `in_context("projects//locations//metadataStores//contexts/")` Each of the above supported filter types can be combined together using logical operators (`AND` & `OR`). Maximum nested expression depth allowed is 5. For example: `display_name = "test" AND metadata.field1.bool_value = true`. orderBy: How the list of messages is ordered. Specify the values to order by and an ordering operation. The default sorting order is ascending. To specify descending order for a field, users append a " desc" suffix; for example: "foo desc, bar". Subfields are specified with a `.` character, such as foo.bar. see https://google.aip.dev/132#ordering for more details. pageSize: The maximum number of Artifacts to return. The service may return fewer. Must be in range 1-1000, inclusive. Defaults to 100. pageToken: A page token, received from a previous MetadataService.ListArtifacts call. Provide this to retrieve the subsequent page. When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.) parent: Required. The MetadataStore whose Artifacts should be listed. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) class AiplatformProjectsLocationsMetadataStoresArtifactsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresArtifactsPatchRequest object. Fields: allowMissing: If set to true, and the Artifact is not found, a new Artifact is created. googleCloudAiplatformV1Artifact: A GoogleCloudAiplatformV1Artifact resource to be passed as the request body. name: Output only. The resource name of the Artifact. updateMask: Optional. A FieldMask indicating which fields should be updated. """ allowMissing = _messages.BooleanField(1) googleCloudAiplatformV1Artifact = _messages.MessageField('GoogleCloudAiplatformV1Artifact', 2) name = _messages.StringField(3, required=True) updateMask = _messages.StringField(4) class AiplatformProjectsLocationsMetadataStoresArtifactsPurgeRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresArtifactsPurgeRequest object. Fields: googleCloudAiplatformV1PurgeArtifactsRequest: A GoogleCloudAiplatformV1PurgeArtifactsRequest resource to be passed as the request body. parent: Required. The metadata store to purge Artifacts from. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ googleCloudAiplatformV1PurgeArtifactsRequest = _messages.MessageField('GoogleCloudAiplatformV1PurgeArtifactsRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsMetadataStoresArtifactsQueryArtifactLineageSubgraphRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresArtifactsQueryArtifactLineage SubgraphRequest object. Fields: artifact: Required. The resource name of the Artifact whose Lineage needs to be retrieved as a LineageSubgraph. Format: `projects/{project}/locati ons/{location}/metadataStores/{metadatastore}/artifacts/{artifact}` The request may error with FAILED_PRECONDITION if the number of Artifacts, the number of Executions, or the number of Events that would be returned for the Context exceeds 1000. filter: Filter specifying the boolean condition for the Artifacts to satisfy in order to be part of the Lineage Subgraph. The syntax to define filter query is based on https://google.aip.dev/160. The supported set of filters include the following: * **Attribute filtering**: For example: `display_name = "test"` Supported fields include: `name`, `display_name`, `uri`, `state`, `schema_title`, `create_time`, and `update_time`. Time fields, such as `create_time` and `update_time`, require values specified in RFC-3339 format. For example: `create_time = "2020-11-19T11:30:00-04:00"` * **Metadata field**: To filter on metadata fields use traversal operation as follows: `metadata..`. For example: `metadata.field_1.number_value = 10.0` In case the field name contains special characters (such as colon), one can embed it inside double quote. For example: `metadata."field:1".number_value = 10.0` Each of the above supported filter types can be combined together using logical operators (`AND` & `OR`). Maximum nested expression depth allowed is 5. For example: `display_name = "test" AND metadata.field1.bool_value = true`. maxHops: Specifies the size of the lineage graph in terms of number of hops from the specified artifact. Negative Value: INVALID_ARGUMENT error is returned 0: Only input artifact is returned. No value: Transitive closure is performed to return the complete graph. """ artifact = _messages.StringField(1, required=True) filter = _messages.StringField(2) maxHops = _messages.IntegerField(3, variant=_messages.Variant.INT32) class AiplatformProjectsLocationsMetadataStoresContextsAddContextArtifactsAndExecutionsRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresContextsAddContextArtifactsAn dExecutionsRequest object. Fields: context: Required. The resource name of the Context that the Artifacts and Executions belong to. Format: `projects/{project}/locations/{location}/m etadataStores/{metadatastore}/contexts/{context}` googleCloudAiplatformV1AddContextArtifactsAndExecutionsRequest: A GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsRequest resource to be passed as the request body. """ context = _messages.StringField(1, required=True) googleCloudAiplatformV1AddContextArtifactsAndExecutionsRequest = _messages.MessageField('GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsRequest', 2) class AiplatformProjectsLocationsMetadataStoresContextsAddContextChildrenRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresContextsAddContextChildrenRequest object. Fields: context: Required. The resource name of the parent Context. Format: `proje cts/{project}/locations/{location}/metadataStores/{metadatastore}/contex ts/{context}` googleCloudAiplatformV1AddContextChildrenRequest: A GoogleCloudAiplatformV1AddContextChildrenRequest resource to be passed as the request body. """ context = _messages.StringField(1, required=True) googleCloudAiplatformV1AddContextChildrenRequest = _messages.MessageField('GoogleCloudAiplatformV1AddContextChildrenRequest', 2) class AiplatformProjectsLocationsMetadataStoresContextsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresContextsCreateRequest object. Fields: contextId: The {context} portion of the resource name with the format: `pr ojects/{project}/locations/{location}/metadataStores/{metadatastore}/con texts/{context}`. If not provided, the Context's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are `/a-z-/`. Must be unique across all Contexts in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting Context.) googleCloudAiplatformV1Context: A GoogleCloudAiplatformV1Context resource to be passed as the request body. parent: Required. The resource name of the MetadataStore where the Context should be created. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ contextId = _messages.StringField(1) googleCloudAiplatformV1Context = _messages.MessageField('GoogleCloudAiplatformV1Context', 2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsMetadataStoresContextsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresContextsDeleteRequest object. Fields: etag: Optional. The etag of the Context to delete. If this is provided, it must match the server's etag. Otherwise, the request will fail with a FAILED_PRECONDITION. force: The force deletion semantics is still undefined. Users should not use this field. name: Required. The resource name of the Context to delete. Format: `proje cts/{project}/locations/{location}/metadataStores/{metadatastore}/contex ts/{context}` """ etag = _messages.StringField(1) force = _messages.BooleanField(2) name = _messages.StringField(3, required=True) class AiplatformProjectsLocationsMetadataStoresContextsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresContextsGetRequest object. Fields: name: Required. The resource name of the Context to retrieve. Format: `pro jects/{project}/locations/{location}/metadataStores/{metadatastore}/cont exts/{context}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsMetadataStoresContextsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresContextsListRequest object. Fields: filter: Filter specifying the boolean condition for the Contexts to satisfy in order to be part of the result set. The syntax to define filter query is based on https://google.aip.dev/160. Following are the supported set of filters: * **Attribute filtering**: For example: `display_name = "test"`. Supported fields include: `name`, `display_name`, `schema_title`, `create_time`, and `update_time`. Time fields, such as `create_time` and `update_time`, require values specified in RFC-3339 format. For example: `create_time = "2020-11-19T11:30:00-04:00"`. * **Metadata field**: To filter on metadata fields use traversal operation as follows: `metadata..`. For example: `metadata.field_1.number_value = 10.0`. In case the field name contains special characters (such as colon), one can embed it inside double quote. For example: `metadata."field:1".number_value = 10.0` * **Parent Child filtering**: To filter Contexts based on parent-child relationship use the HAS operator as follows: ``` parent_contexts: "projects//locations//metadataStores//contexts/" child_contexts: "projects//locations//metadataStores//contexts/" ``` Each of the above supported filters can be combined together using logical operators (`AND` & `OR`). Maximum nested expression depth allowed is 5. For example: `display_name = "test" AND metadata.field1.bool_value = true`. orderBy: How the list of messages is ordered. Specify the values to order by and an ordering operation. The default sorting order is ascending. To specify descending order for a field, users append a " desc" suffix; for example: "foo desc, bar". Subfields are specified with a `.` character, such as foo.bar. see https://google.aip.dev/132#ordering for more details. pageSize: The maximum number of Contexts to return. The service may return fewer. Must be in range 1-1000, inclusive. Defaults to 100. pageToken: A page token, received from a previous MetadataService.ListContexts call. Provide this to retrieve the subsequent page. When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.) parent: Required. The MetadataStore whose Contexts should be listed. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) class AiplatformProjectsLocationsMetadataStoresContextsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresContextsPatchRequest object. Fields: allowMissing: If set to true, and the Context is not found, a new Context is created. googleCloudAiplatformV1Context: A GoogleCloudAiplatformV1Context resource to be passed as the request body. name: Immutable. The resource name of the Context. updateMask: Optional. A FieldMask indicating which fields should be updated. """ allowMissing = _messages.BooleanField(1) googleCloudAiplatformV1Context = _messages.MessageField('GoogleCloudAiplatformV1Context', 2) name = _messages.StringField(3, required=True) updateMask = _messages.StringField(4) class AiplatformProjectsLocationsMetadataStoresContextsPurgeRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresContextsPurgeRequest object. Fields: googleCloudAiplatformV1PurgeContextsRequest: A GoogleCloudAiplatformV1PurgeContextsRequest resource to be passed as the request body. parent: Required. The metadata store to purge Contexts from. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ googleCloudAiplatformV1PurgeContextsRequest = _messages.MessageField('GoogleCloudAiplatformV1PurgeContextsRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsMetadataStoresContextsQueryContextLineageSubgraphRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresContextsQueryContextLineageSu bgraphRequest object. Fields: context: Required. The resource name of the Context whose Artifacts and Executions should be retrieved as a LineageSubgraph. Format: `projects/{ project}/locations/{location}/metadataStores/{metadatastore}/contexts/{c ontext}` The request may error with FAILED_PRECONDITION if the number of Artifacts, the number of Executions, or the number of Events that would be returned for the Context exceeds 1000. """ context = _messages.StringField(1, required=True) class AiplatformProjectsLocationsMetadataStoresContextsRemoveContextChildrenRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresContextsRemoveContextChildren Request object. Fields: context: Required. The resource name of the parent Context. Format: `proje cts/{project}/locations/{location}/metadataStores/{metadatastore}/contex ts/{context}` googleCloudAiplatformV1RemoveContextChildrenRequest: A GoogleCloudAiplatformV1RemoveContextChildrenRequest resource to be passed as the request body. """ context = _messages.StringField(1, required=True) googleCloudAiplatformV1RemoveContextChildrenRequest = _messages.MessageField('GoogleCloudAiplatformV1RemoveContextChildrenRequest', 2) class AiplatformProjectsLocationsMetadataStoresCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresCreateRequest object. Fields: googleCloudAiplatformV1MetadataStore: A GoogleCloudAiplatformV1MetadataStore resource to be passed as the request body. metadataStoreId: The {metadatastore} portion of the resource name with the format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` If not provided, the MetadataStore's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are `/a-z-/`. Must be unique across all MetadataStores in the parent Location. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting MetadataStore.) parent: Required. The resource name of the Location where the MetadataStore should be created. Format: `projects/{project}/locations/{location}/` """ googleCloudAiplatformV1MetadataStore = _messages.MessageField('GoogleCloudAiplatformV1MetadataStore', 1) metadataStoreId = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsMetadataStoresDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresDeleteRequest object. Fields: force: Deprecated: Field is no longer supported. name: Required. The resource name of the MetadataStore to delete. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ force = _messages.BooleanField(1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsMetadataStoresExecutionsAddExecutionEventsRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresExecutionsAddExecutionEventsRequest object. Fields: execution: Required. The resource name of the Execution that the Events connect Artifacts with. Format: `projects/{project}/locations/{location} /metadataStores/{metadatastore}/executions/{execution}` googleCloudAiplatformV1AddExecutionEventsRequest: A GoogleCloudAiplatformV1AddExecutionEventsRequest resource to be passed as the request body. """ execution = _messages.StringField(1, required=True) googleCloudAiplatformV1AddExecutionEventsRequest = _messages.MessageField('GoogleCloudAiplatformV1AddExecutionEventsRequest', 2) class AiplatformProjectsLocationsMetadataStoresExecutionsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresExecutionsCreateRequest object. Fields: executionId: The {execution} portion of the resource name with the format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}/ executions/{execution}` If not provided, the Execution's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are `/a-z-/`. Must be unique across all Executions in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting Execution.) googleCloudAiplatformV1Execution: A GoogleCloudAiplatformV1Execution resource to be passed as the request body. parent: Required. The resource name of the MetadataStore where the Execution should be created. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ executionId = _messages.StringField(1) googleCloudAiplatformV1Execution = _messages.MessageField('GoogleCloudAiplatformV1Execution', 2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsMetadataStoresExecutionsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresExecutionsDeleteRequest object. Fields: etag: Optional. The etag of the Execution to delete. If this is provided, it must match the server's etag. Otherwise, the request will fail with a FAILED_PRECONDITION. name: Required. The resource name of the Execution to delete. Format: `pro jects/{project}/locations/{location}/metadataStores/{metadatastore}/exec utions/{execution}` """ etag = _messages.StringField(1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsMetadataStoresExecutionsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresExecutionsGetRequest object. Fields: name: Required. The resource name of the Execution to retrieve. Format: `p rojects/{project}/locations/{location}/metadataStores/{metadatastore}/ex ecutions/{execution}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsMetadataStoresExecutionsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresExecutionsListRequest object. Fields: filter: Filter specifying the boolean condition for the Executions to satisfy in order to be part of the result set. The syntax to define filter query is based on https://google.aip.dev/160. Following are the supported set of filters: * **Attribute filtering**: For example: `display_name = "test"`. Supported fields include: `name`, `display_name`, `state`, `schema_title`, `create_time`, and `update_time`. Time fields, such as `create_time` and `update_time`, require values specified in RFC-3339 format. For example: `create_time = "2020-11-19T11:30:00-04:00"`. * **Metadata field**: To filter on metadata fields use traversal operation as follows: `metadata..` For example: `metadata.field_1.number_value = 10.0` In case the field name contains special characters (such as colon), one can embed it inside double quote. For example: `metadata."field:1".number_value = 10.0` * **Context based filtering**: To filter Executions based on the contexts to which they belong use the function operator with the full resource name: `in_context()`. For example: `in_context("projects//locations//metadataStores//contexts/")` Each of the above supported filters can be combined together using logical operators (`AND` & `OR`). Maximum nested expression depth allowed is 5. For example: `display_name = "test" AND metadata.field1.bool_value = true`. orderBy: How the list of messages is ordered. Specify the values to order by and an ordering operation. The default sorting order is ascending. To specify descending order for a field, users append a " desc" suffix; for example: "foo desc, bar". Subfields are specified with a `.` character, such as foo.bar. see https://google.aip.dev/132#ordering for more details. pageSize: The maximum number of Executions to return. The service may return fewer. Must be in range 1-1000, inclusive. Defaults to 100. pageToken: A page token, received from a previous MetadataService.ListExecutions call. Provide this to retrieve the subsequent page. When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with an INVALID_ARGUMENT error.) parent: Required. The MetadataStore whose Executions should be listed. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) class AiplatformProjectsLocationsMetadataStoresExecutionsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresExecutionsPatchRequest object. Fields: allowMissing: If set to true, and the Execution is not found, a new Execution is created. googleCloudAiplatformV1Execution: A GoogleCloudAiplatformV1Execution resource to be passed as the request body. name: Output only. The resource name of the Execution. updateMask: Optional. A FieldMask indicating which fields should be updated. """ allowMissing = _messages.BooleanField(1) googleCloudAiplatformV1Execution = _messages.MessageField('GoogleCloudAiplatformV1Execution', 2) name = _messages.StringField(3, required=True) updateMask = _messages.StringField(4) class AiplatformProjectsLocationsMetadataStoresExecutionsPurgeRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresExecutionsPurgeRequest object. Fields: googleCloudAiplatformV1PurgeExecutionsRequest: A GoogleCloudAiplatformV1PurgeExecutionsRequest resource to be passed as the request body. parent: Required. The metadata store to purge Executions from. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ googleCloudAiplatformV1PurgeExecutionsRequest = _messages.MessageField('GoogleCloudAiplatformV1PurgeExecutionsRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsMetadataStoresExecutionsQueryExecutionInputsAndOutputsRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresExecutionsQueryExecutionInput sAndOutputsRequest object. Fields: execution: Required. The resource name of the Execution whose input and output Artifacts should be retrieved as a LineageSubgraph. Format: `proj ects/{project}/locations/{location}/metadataStores/{metadatastore}/execu tions/{execution}` """ execution = _messages.StringField(1, required=True) class AiplatformProjectsLocationsMetadataStoresGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresGetRequest object. Fields: name: Required. The resource name of the MetadataStore to retrieve. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsMetadataStoresListRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresListRequest object. Fields: pageSize: The maximum number of Metadata Stores to return. The service may return fewer. Must be in range 1-1000, inclusive. Defaults to 100. pageToken: A page token, received from a previous MetadataService.ListMetadataStores call. Provide this to retrieve the subsequent page. When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.) parent: Required. The Location whose MetadataStores should be listed. Format: `projects/{project}/locations/{location}` """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsMetadataStoresMetadataSchemasCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresMetadataSchemasCreateRequest object. Fields: googleCloudAiplatformV1MetadataSchema: A GoogleCloudAiplatformV1MetadataSchema resource to be passed as the request body. metadataSchemaId: The {metadata_schema} portion of the resource name with the format: `projects/{project}/locations/{location}/metadataStores/{met adatastore}/metadataSchemas/{metadataschema}` If not provided, the MetadataStore's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are `/a-z-/`. Must be unique across all MetadataSchemas in the parent Location. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting MetadataSchema.) parent: Required. The resource name of the MetadataStore where the MetadataSchema should be created. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ googleCloudAiplatformV1MetadataSchema = _messages.MessageField('GoogleCloudAiplatformV1MetadataSchema', 1) metadataSchemaId = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsMetadataStoresMetadataSchemasGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresMetadataSchemasGetRequest object. Fields: name: Required. The resource name of the MetadataSchema to retrieve. Format: `projects/{project}/locations/{location}/metadataStores/{metadat astore}/metadataSchemas/{metadataschema}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsMetadataStoresMetadataSchemasListRequest(_messages.Message): r"""A AiplatformProjectsLocationsMetadataStoresMetadataSchemasListRequest object. Fields: filter: A query to filter available MetadataSchemas for matching results. pageSize: The maximum number of MetadataSchemas to return. The service may return fewer. Must be in range 1-1000, inclusive. Defaults to 100. pageToken: A page token, received from a previous MetadataService.ListMetadataSchemas call. Provide this to retrieve the next page. When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.) parent: Required. The MetadataStore whose MetadataSchemas should be listed. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}` """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) class AiplatformProjectsLocationsMigratableResourcesBatchMigrateRequest(_messages.Message): r"""A AiplatformProjectsLocationsMigratableResourcesBatchMigrateRequest object. Fields: googleCloudAiplatformV1BatchMigrateResourcesRequest: A GoogleCloudAiplatformV1BatchMigrateResourcesRequest resource to be passed as the request body. parent: Required. The location of the migrated resource will live in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1BatchMigrateResourcesRequest = _messages.MessageField('GoogleCloudAiplatformV1BatchMigrateResourcesRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsMigratableResourcesSearchRequest(_messages.Message): r"""A AiplatformProjectsLocationsMigratableResourcesSearchRequest object. Fields: googleCloudAiplatformV1SearchMigratableResourcesRequest: A GoogleCloudAiplatformV1SearchMigratableResourcesRequest resource to be passed as the request body. parent: Required. The location that the migratable resources should be searched from. It's the Vertex AI location that the resources can be migrated to, not the resources' original location. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1SearchMigratableResourcesRequest = _messages.MessageField('GoogleCloudAiplatformV1SearchMigratableResourcesRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelDeploymentMonitoringJobsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelDeploymentMonitoringJobsCreateRequest object. Fields: googleCloudAiplatformV1ModelDeploymentMonitoringJob: A GoogleCloudAiplatformV1ModelDeploymentMonitoringJob resource to be passed as the request body. parent: Required. The parent of the ModelDeploymentMonitoringJob. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1ModelDeploymentMonitoringJob = _messages.MessageField('GoogleCloudAiplatformV1ModelDeploymentMonitoringJob', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelDeploymentMonitoringJobsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelDeploymentMonitoringJobsDeleteRequest object. Fields: name: Required. The resource name of the model monitoring job to delete. Format: `projects/{project}/locations/{location}/modelDeploymentMonitori ngJobs/{model_deployment_monitoring_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsModelDeploymentMonitoringJobsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelDeploymentMonitoringJobsGetRequest object. Fields: name: Required. The resource name of the ModelDeploymentMonitoringJob. Format: `projects/{project}/locations/{location}/modelDeploymentMonitori ngJobs/{model_deployment_monitoring_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsModelDeploymentMonitoringJobsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelDeploymentMonitoringJobsListRequest object. Fields: filter: The standard list filter. Supported fields: * `display_name` supports `=`, `!=` comparisons, and `:` wildcard. * `state` supports `=`, `!=` comparisons. * `create_time` supports `=`, `!=`,`<`, `<=`,`>`, `>=` comparisons. `create_time` must be in RFC 3339 format. * `labels` supports general map functions that is: `labels.key=value` - key:value equality `labels.key:* - key existence Some examples of using the filter are: * `state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"` * `state!="JOB_STATE_FAILED" OR display_name="my_job"` * `NOT display_name="my_job"` * `create_time>"2021-05-18T00:00:00Z"` * `labels.keyA=valueA` * `labels.keyB:*` pageSize: The standard list page size. pageToken: The standard list page token. parent: Required. The parent of the ModelDeploymentMonitoringJob. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) readMask = _messages.StringField(5) class AiplatformProjectsLocationsModelDeploymentMonitoringJobsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelDeploymentMonitoringJobsPatchRequest object. Fields: googleCloudAiplatformV1ModelDeploymentMonitoringJob: A GoogleCloudAiplatformV1ModelDeploymentMonitoringJob resource to be passed as the request body. name: Output only. Resource name of a ModelDeploymentMonitoringJob. updateMask: Required. The update mask is used to specify the fields to be overwritten in the ModelDeploymentMonitoringJob resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to `*` to override all fields. For the objective config, the user can either provide the update mask for model_deployment_monitoring_objective_configs or any combination of its nested fields, such as: model_deployment_monitoring_objective_configs.ob jective_config.training_dataset. Updatable fields: * `display_name` * `model_deployment_monitoring_schedule_config` * `model_monitoring_alert_config` * `logging_sampling_strategy` * `labels` * `log_ttl` * `enable_monitoring_pipeline_logs` . and * `model_deployment_monitoring_objective_configs` . or * `model_deployment _monitoring_objective_configs.objective_config.training_dataset` * `mode l_deployment_monitoring_objective_configs.objective_config.training_pred iction_skew_detection_config` * `model_deployment_monitoring_objective_c onfigs.objective_config.prediction_drift_detection_config` """ googleCloudAiplatformV1ModelDeploymentMonitoringJob = _messages.MessageField('GoogleCloudAiplatformV1ModelDeploymentMonitoringJob', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsModelDeploymentMonitoringJobsPauseRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelDeploymentMonitoringJobsPauseRequest object. Fields: googleCloudAiplatformV1PauseModelDeploymentMonitoringJobRequest: A GoogleCloudAiplatformV1PauseModelDeploymentMonitoringJobRequest resource to be passed as the request body. name: Required. The resource name of the ModelDeploymentMonitoringJob to pause. Format: `projects/{project}/locations/{location}/modelDeploymentM onitoringJobs/{model_deployment_monitoring_job}` """ googleCloudAiplatformV1PauseModelDeploymentMonitoringJobRequest = _messages.MessageField('GoogleCloudAiplatformV1PauseModelDeploymentMonitoringJobRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelDeploymentMonitoringJobsResumeRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelDeploymentMonitoringJobsResumeRequest object. Fields: googleCloudAiplatformV1ResumeModelDeploymentMonitoringJobRequest: A GoogleCloudAiplatformV1ResumeModelDeploymentMonitoringJobRequest resource to be passed as the request body. name: Required. The resource name of the ModelDeploymentMonitoringJob to resume. Format: `projects/{project}/locations/{location}/modelDeployment MonitoringJobs/{model_deployment_monitoring_job}` """ googleCloudAiplatformV1ResumeModelDeploymentMonitoringJobRequest = _messages.MessageField('GoogleCloudAiplatformV1ResumeModelDeploymentMonitoringJobRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelDeploymentMonitoringJobsSearchModelDeploymentMonitoringStatsAnomaliesRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelDeploymentMonitoringJobsSearchModelDep loymentMonitoringStatsAnomaliesRequest object. Fields: googleCloudAiplatformV1SearchModelDeploymentMonitoringStatsAnomaliesReques t: A GoogleCloudAiplatformV1SearchModelDeploymentMonitoringStatsAnomalie sRequest resource to be passed as the request body. modelDeploymentMonitoringJob: Required. ModelDeploymentMonitoring Job resource name. Format: `projects/{project}/locations/{location}/modelDep loymentMonitoringJobs/{model_deployment_monitoring_job}` """ googleCloudAiplatformV1SearchModelDeploymentMonitoringStatsAnomaliesRequest = _messages.MessageField('GoogleCloudAiplatformV1SearchModelDeploymentMonitoringStatsAnomaliesRequest', 1) modelDeploymentMonitoringJob = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelsCopyRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsCopyRequest object. Fields: googleCloudAiplatformV1CopyModelRequest: A GoogleCloudAiplatformV1CopyModelRequest resource to be passed as the request body. parent: Required. The resource name of the Location into which to copy the Model. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1CopyModelRequest = _messages.MessageField('GoogleCloudAiplatformV1CopyModelRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsDeleteRequest object. Fields: name: Required. The name of the Model resource to be deleted. Format: `projects/{project}/locations/{location}/models/{model}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsModelsDeleteVersionRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsDeleteVersionRequest object. Fields: name: Required. The name of the model version to be deleted, with a version ID explicitly included. Example: `projects/{project}/locations/{location}/models/{model}@1234` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsModelsEvaluationsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsEvaluationsGetRequest object. Fields: name: Required. The name of the ModelEvaluation resource. Format: `project s/{project}/locations/{location}/models/{model}/evaluations/{evaluation} ` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsModelsEvaluationsImportRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsEvaluationsImportRequest object. Fields: googleCloudAiplatformV1ImportModelEvaluationRequest: A GoogleCloudAiplatformV1ImportModelEvaluationRequest resource to be passed as the request body. parent: Required. The name of the parent model resource. Format: `projects/{project}/locations/{location}/models/{model}` """ googleCloudAiplatformV1ImportModelEvaluationRequest = _messages.MessageField('GoogleCloudAiplatformV1ImportModelEvaluationRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelsEvaluationsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsEvaluationsListRequest object. Fields: filter: The standard list filter. pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via ListModelEvaluationsResponse.next_page_token of the previous ModelService.ListModelEvaluations call. parent: Required. The resource name of the Model to list the ModelEvaluations from. Format: `projects/{project}/locations/{location}/models/{model}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) readMask = _messages.StringField(5) class AiplatformProjectsLocationsModelsEvaluationsSlicesBatchImportRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsEvaluationsSlicesBatchImportRequest object. Fields: googleCloudAiplatformV1BatchImportEvaluatedAnnotationsRequest: A GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsRequest resource to be passed as the request body. parent: Required. The name of the parent ModelEvaluationSlice resource. Format: `projects/{project}/locations/{location}/models/{model}/evaluati ons/{evaluation}/slices/{slice}` """ googleCloudAiplatformV1BatchImportEvaluatedAnnotationsRequest = _messages.MessageField('GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelsEvaluationsSlicesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsEvaluationsSlicesGetRequest object. Fields: name: Required. The name of the ModelEvaluationSlice resource. Format: `pr ojects/{project}/locations/{location}/models/{model}/evaluations/{evalua tion}/slices/{slice}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsModelsEvaluationsSlicesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsEvaluationsSlicesListRequest object. Fields: filter: The standard list filter. * `slice.dimension` - for =. pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via ListModelEvaluationSlicesResponse.next_page_token of the previous ModelService.ListModelEvaluationSlices call. parent: Required. The resource name of the ModelEvaluation to list the ModelEvaluationSlices from. Format: `projects/{project}/locations/{locat ion}/models/{model}/evaluations/{evaluation}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) readMask = _messages.StringField(5) class AiplatformProjectsLocationsModelsExportRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsExportRequest object. Fields: googleCloudAiplatformV1ExportModelRequest: A GoogleCloudAiplatformV1ExportModelRequest resource to be passed as the request body. name: Required. The resource name of the Model to export. The resource name may contain version id or version alias to specify the version, if no version is specified, the default version will be exported. """ googleCloudAiplatformV1ExportModelRequest = _messages.MessageField('GoogleCloudAiplatformV1ExportModelRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelsGetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsGetIamPolicyRequest object. Fields: options_requestedPolicyVersion: Optional. The maximum policy version that will be used to format the policy. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset. The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). resource: REQUIRED: The resource for which the policy is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ options_requestedPolicyVersion = _messages.IntegerField(1, variant=_messages.Variant.INT32) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsGetRequest object. Fields: name: Required. The name of the Model resource. Format: `projects/{project}/locations/{location}/models/{model}` In order to retrieve a specific version of the model, also provide the version ID or version alias. Example: `projects/{project}/locations/{location}/models/{model}@2` or `projects/{project}/locations/{location}/models/{model}@golden` If no version ID or alias is specified, the "default" version will be returned. The "default" version alias is created for the first version of the model, and can be moved to other versions later on. There will be exactly one default version. """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsModelsListCheckpointsRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsListCheckpointsRequest object. Fields: name: Required. The name of the model version to list checkpoints for. `projects/{project}/locations/{location}/models/{model}@{version}` Example: `projects/{project}/locations/{location}/models/{model}@2` or `projects/{project}/locations/{location}/models/{model}@golden` If no version ID or alias is specified, the latest version will be used. pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. Typically obtained via next_page_token of the previous ListModelVersionCheckpoints call. """ name = _messages.StringField(1, required=True) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) class AiplatformProjectsLocationsModelsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsListRequest object. Fields: filter: An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. * `model` supports = and !=. `model` represents the Model ID, i.e. the last segment of the Model's resource name. * `display_name` supports = and != * `labels` supports general map functions that is: * `labels.key=value` - key:value equality * `labels.key:* or labels:key - key existence * A key including a space must be quoted. `labels."a key"`. * `base_model_name` only supports = Some examples: * `model=1234` * `displayName="myDisplayName"` * `labels.myKey="myValue"` * `baseModelName="text-bison"` orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `display_name` * `create_time` * `update_time` Example: `display_name, create_time desc`. pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via ListModelsResponse.next_page_token of the previous ModelService.ListModels call. parent: Required. The resource name of the Location to list the Models from. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsModelsListVersionsRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsListVersionsRequest object. Fields: filter: An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. * `labels` supports general map functions that is: * `labels.key=value` - key:value equality * `labels.key:* or labels:key - key existence * A key including a space must be quoted. `labels."a key"`. Some examples: * `labels.myKey="myValue"` name: Required. The name of the model to list versions for. orderBy: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `create_time` * `update_time` Example: `update_time asc, create_time desc`. pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via next_page_token of the previous ListModelVersions call. readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) name = _messages.StringField(2, required=True) orderBy = _messages.StringField(3) pageSize = _messages.IntegerField(4, variant=_messages.Variant.INT32) pageToken = _messages.StringField(5) readMask = _messages.StringField(6) class AiplatformProjectsLocationsModelsMergeVersionAliasesRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsMergeVersionAliasesRequest object. Fields: googleCloudAiplatformV1MergeVersionAliasesRequest: A GoogleCloudAiplatformV1MergeVersionAliasesRequest resource to be passed as the request body. name: Required. The name of the model version to merge aliases, with a version ID explicitly included. Example: `projects/{project}/locations/{location}/models/{model}@1234` """ googleCloudAiplatformV1MergeVersionAliasesRequest = _messages.MessageField('GoogleCloudAiplatformV1MergeVersionAliasesRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsPatchRequest object. Fields: googleCloudAiplatformV1Model: A GoogleCloudAiplatformV1Model resource to be passed as the request body. name: The resource name of the Model. updateMask: Required. The update mask applies to the resource. For the `FieldMask` definition, see google.protobuf.FieldMask. """ googleCloudAiplatformV1Model = _messages.MessageField('GoogleCloudAiplatformV1Model', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsModelsSetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsSetIamPolicyRequest object. Fields: googleIamV1SetIamPolicyRequest: A GoogleIamV1SetIamPolicyRequest resource to be passed as the request body. resource: REQUIRED: The resource for which the policy is being specified. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ googleIamV1SetIamPolicyRequest = _messages.MessageField('GoogleIamV1SetIamPolicyRequest', 1) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelsTestIamPermissionsRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsTestIamPermissionsRequest object. Fields: permissions: The set of permissions to check for the `resource`. Permissions with wildcards (such as `*` or `storage.*`) are not allowed. For more information see [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions). resource: REQUIRED: The resource for which the policy detail is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ permissions = _messages.StringField(1, repeated=True) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelsUpdateExplanationDatasetRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsUpdateExplanationDatasetRequest object. Fields: googleCloudAiplatformV1UpdateExplanationDatasetRequest: A GoogleCloudAiplatformV1UpdateExplanationDatasetRequest resource to be passed as the request body. model: Required. The resource name of the Model to update. Format: `projects/{project}/locations/{location}/models/{model}` """ googleCloudAiplatformV1UpdateExplanationDatasetRequest = _messages.MessageField('GoogleCloudAiplatformV1UpdateExplanationDatasetRequest', 1) model = _messages.StringField(2, required=True) class AiplatformProjectsLocationsModelsUploadRequest(_messages.Message): r"""A AiplatformProjectsLocationsModelsUploadRequest object. Fields: googleCloudAiplatformV1UploadModelRequest: A GoogleCloudAiplatformV1UploadModelRequest resource to be passed as the request body. parent: Required. The resource name of the Location into which to upload the Model. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1UploadModelRequest = _messages.MessageField('GoogleCloudAiplatformV1UploadModelRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsNasJobsCancelRequest(_messages.Message): r"""A AiplatformProjectsLocationsNasJobsCancelRequest object. Fields: googleCloudAiplatformV1CancelNasJobRequest: A GoogleCloudAiplatformV1CancelNasJobRequest resource to be passed as the request body. name: Required. The name of the NasJob to cancel. Format: `projects/{project}/locations/{location}/nasJobs/{nas_job}` """ googleCloudAiplatformV1CancelNasJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelNasJobRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsNasJobsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsNasJobsCreateRequest object. Fields: googleCloudAiplatformV1NasJob: A GoogleCloudAiplatformV1NasJob resource to be passed as the request body. parent: Required. The resource name of the Location to create the NasJob in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1NasJob = _messages.MessageField('GoogleCloudAiplatformV1NasJob', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsNasJobsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsNasJobsDeleteRequest object. Fields: name: Required. The name of the NasJob resource to be deleted. Format: `projects/{project}/locations/{location}/nasJobs/{nas_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsNasJobsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsNasJobsGetRequest object. Fields: name: Required. The name of the NasJob resource. Format: `projects/{project}/locations/{location}/nasJobs/{nas_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsNasJobsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsNasJobsListRequest object. Fields: filter: The standard list filter. Supported fields: * `display_name` supports `=`, `!=` comparisons, and `:` wildcard. * `state` supports `=`, `!=` comparisons. * `create_time` supports `=`, `!=`,`<`, `<=`,`>`, `>=` comparisons. `create_time` must be in RFC 3339 format. * `labels` supports general map functions that is: `labels.key=value` - key:value equality `labels.key:* - key existence Some examples of using the filter are: * `state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"` * `state!="JOB_STATE_FAILED" OR display_name="my_job"` * `NOT display_name="my_job"` * `create_time>"2021-05-18T00:00:00Z"` * `labels.keyA=valueA` * `labels.keyB:*` pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via ListNasJobsResponse.next_page_token of the previous JobService.ListNasJobs call. parent: Required. The resource name of the Location to list the NasJobs from. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) readMask = _messages.StringField(5) class AiplatformProjectsLocationsNasJobsNasTrialDetailsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsNasJobsNasTrialDetailsGetRequest object. Fields: name: Required. The name of the NasTrialDetail resource. Format: `projects /{project}/locations/{location}/nasJobs/{nas_job}/nasTrialDetails/{nas_t rial_detail}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsNasJobsNasTrialDetailsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsNasJobsNasTrialDetailsListRequest object. Fields: pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via ListNasTrialDetailsResponse.next_page_token of the previous JobService.ListNasTrialDetails call. parent: Required. The name of the NasJob resource. Format: `projects/{project}/locations/{location}/nasJobs/{nas_job}` """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsNotebookExecutionJobsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookExecutionJobsCreateRequest object. Fields: googleCloudAiplatformV1NotebookExecutionJob: A GoogleCloudAiplatformV1NotebookExecutionJob resource to be passed as the request body. notebookExecutionJobId: Optional. User specified ID for the NotebookExecutionJob. parent: Required. The resource name of the Location to create the NotebookExecutionJob. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1NotebookExecutionJob = _messages.MessageField('GoogleCloudAiplatformV1NotebookExecutionJob', 1) notebookExecutionJobId = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsNotebookExecutionJobsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookExecutionJobsDeleteRequest object. Fields: name: Required. The name of the NotebookExecutionJob resource to be deleted. """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsNotebookExecutionJobsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookExecutionJobsGetRequest object. Enums: ViewValueValuesEnum: Optional. The NotebookExecutionJob view. Defaults to BASIC. Fields: name: Required. The name of the NotebookExecutionJob resource. view: Optional. The NotebookExecutionJob view. Defaults to BASIC. """ class ViewValueValuesEnum(_messages.Enum): r"""Optional. The NotebookExecutionJob view. Defaults to BASIC. Values: NOTEBOOK_EXECUTION_JOB_VIEW_UNSPECIFIED: When unspecified, the API defaults to the BASIC view. NOTEBOOK_EXECUTION_JOB_VIEW_BASIC: Includes all fields except for direct notebook inputs. NOTEBOOK_EXECUTION_JOB_VIEW_FULL: Includes all fields. """ NOTEBOOK_EXECUTION_JOB_VIEW_UNSPECIFIED = 0 NOTEBOOK_EXECUTION_JOB_VIEW_BASIC = 1 NOTEBOOK_EXECUTION_JOB_VIEW_FULL = 2 name = _messages.StringField(1, required=True) view = _messages.EnumField('ViewValueValuesEnum', 2) class AiplatformProjectsLocationsNotebookExecutionJobsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookExecutionJobsListRequest object. Enums: ViewValueValuesEnum: Optional. The NotebookExecutionJob view. Defaults to BASIC. Fields: filter: Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. * `notebookExecutionJob` supports = and !=. `notebookExecutionJob` represents the NotebookExecutionJob ID. * `displayName` supports = and != and regex. * `schedule` supports = and != and regex. Some examples: * `notebookExecutionJob="123"` * `notebookExecutionJob="my-execution-job"` * `displayName="myDisplayName"` and `displayName=~"myDisplayNameRegex"` orderBy: Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `display_name` * `create_time` * `update_time` Example: `display_name, create_time desc`. pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. Typically obtained via ListNotebookExecutionJobsResponse.next_page_token of the previous NotebookService.ListNotebookExecutionJobs call. parent: Required. The resource name of the Location from which to list the NotebookExecutionJobs. Format: `projects/{project}/locations/{location}` view: Optional. The NotebookExecutionJob view. Defaults to BASIC. """ class ViewValueValuesEnum(_messages.Enum): r"""Optional. The NotebookExecutionJob view. Defaults to BASIC. Values: NOTEBOOK_EXECUTION_JOB_VIEW_UNSPECIFIED: When unspecified, the API defaults to the BASIC view. NOTEBOOK_EXECUTION_JOB_VIEW_BASIC: Includes all fields except for direct notebook inputs. NOTEBOOK_EXECUTION_JOB_VIEW_FULL: Includes all fields. """ NOTEBOOK_EXECUTION_JOB_VIEW_UNSPECIFIED = 0 NOTEBOOK_EXECUTION_JOB_VIEW_BASIC = 1 NOTEBOOK_EXECUTION_JOB_VIEW_FULL = 2 filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) view = _messages.EnumField('ViewValueValuesEnum', 6) class AiplatformProjectsLocationsNotebookRuntimeTemplatesCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimeTemplatesCreateRequest object. Fields: googleCloudAiplatformV1NotebookRuntimeTemplate: A GoogleCloudAiplatformV1NotebookRuntimeTemplate resource to be passed as the request body. notebookRuntimeTemplateId: Optional. User specified ID for the notebook runtime template. parent: Required. The resource name of the Location to create the NotebookRuntimeTemplate. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1NotebookRuntimeTemplate = _messages.MessageField('GoogleCloudAiplatformV1NotebookRuntimeTemplate', 1) notebookRuntimeTemplateId = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsNotebookRuntimeTemplatesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimeTemplatesDeleteRequest object. Fields: name: Required. The name of the NotebookRuntimeTemplate resource to be deleted. Format: `projects/{project}/locations/{location}/notebookRuntim eTemplates/{notebook_runtime_template}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsNotebookRuntimeTemplatesGetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimeTemplatesGetIamPolicyRequest object. Fields: options_requestedPolicyVersion: Optional. The maximum policy version that will be used to format the policy. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset. The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). resource: REQUIRED: The resource for which the policy is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ options_requestedPolicyVersion = _messages.IntegerField(1, variant=_messages.Variant.INT32) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsNotebookRuntimeTemplatesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimeTemplatesGetRequest object. Fields: name: Required. The name of the NotebookRuntimeTemplate resource. Format: `projects/{project}/locations/{location}/notebookRuntimeTemplates/{noteb ook_runtime_template}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsNotebookRuntimeTemplatesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimeTemplatesListRequest object. Fields: filter: Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. * `notebookRuntimeTemplate` supports = and !=. `notebookRuntimeTemplate` represents the NotebookRuntimeTemplate ID, i.e. the last segment of the NotebookRuntimeTemplate's resource name. * `display_name` supports = and != * `labels` supports general map functions that is: * `labels.key=value` - key:value equality * `labels.key:* or labels:key - key existence * A key including a space must be quoted. `labels."a key"`. * `notebookRuntimeType` supports = and !=. notebookRuntimeType enum: [USER_DEFINED, ONE_CLICK]. * `machineType` supports = and !=. * `acceleratorType` supports = and !=. Some examples: * `notebookRuntimeTemplate=notebookRuntimeTemplate123` * `displayName="myDisplayName"` * `labels.myKey="myValue"` * `notebookRuntimeType=USER_DEFINED` * `machineType=e2-standard-4` * `acceleratorType=NVIDIA_TESLA_T4` orderBy: Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `display_name` * `create_time` * `update_time` Example: `display_name, create_time desc`. pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. Typically obtained via ListNotebookRuntimeTemplatesResponse.next_page_token of the previous NotebookService.ListNotebookRuntimeTemplates call. parent: Required. The resource name of the Location from which to list the NotebookRuntimeTemplates. Format: `projects/{project}/locations/{location}` readMask: Optional. Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsNotebookRuntimeTemplatesPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimeTemplatesPatchRequest object. Fields: googleCloudAiplatformV1NotebookRuntimeTemplate: A GoogleCloudAiplatformV1NotebookRuntimeTemplate resource to be passed as the request body. name: The resource name of the NotebookRuntimeTemplate. updateMask: Required. The update mask applies to the resource. For the `FieldMask` definition, see google.protobuf.FieldMask. Input format: `{paths: "${updated_filed}"}` Updatable fields: * `encryption_spec.kms_key_name` """ googleCloudAiplatformV1NotebookRuntimeTemplate = _messages.MessageField('GoogleCloudAiplatformV1NotebookRuntimeTemplate', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsNotebookRuntimeTemplatesSetIamPolicyRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimeTemplatesSetIamPolicyRequest object. Fields: googleIamV1SetIamPolicyRequest: A GoogleIamV1SetIamPolicyRequest resource to be passed as the request body. resource: REQUIRED: The resource for which the policy is being specified. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ googleIamV1SetIamPolicyRequest = _messages.MessageField('GoogleIamV1SetIamPolicyRequest', 1) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsNotebookRuntimeTemplatesTestIamPermissionsRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimeTemplatesTestIamPermissionsRequest object. Fields: permissions: The set of permissions to check for the `resource`. Permissions with wildcards (such as `*` or `storage.*`) are not allowed. For more information see [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions). resource: REQUIRED: The resource for which the policy detail is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. """ permissions = _messages.StringField(1, repeated=True) resource = _messages.StringField(2, required=True) class AiplatformProjectsLocationsNotebookRuntimesAssignRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimesAssignRequest object. Fields: googleCloudAiplatformV1AssignNotebookRuntimeRequest: A GoogleCloudAiplatformV1AssignNotebookRuntimeRequest resource to be passed as the request body. parent: Required. The resource name of the Location to get the NotebookRuntime assignment. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1AssignNotebookRuntimeRequest = _messages.MessageField('GoogleCloudAiplatformV1AssignNotebookRuntimeRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsNotebookRuntimesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimesDeleteRequest object. Fields: name: Required. The name of the NotebookRuntime resource to be deleted. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner. """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsNotebookRuntimesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimesGetRequest object. Fields: name: Required. The name of the NotebookRuntime resource. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner. """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsNotebookRuntimesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimesListRequest object. Fields: filter: Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. * `notebookRuntime` supports = and !=. `notebookRuntime` represents the NotebookRuntime ID, i.e. the last segment of the NotebookRuntime's resource name. * `displayName` supports = and != and regex. * `notebookRuntimeTemplate` supports = and !=. `notebookRuntimeTemplate` represents the NotebookRuntimeTemplate ID, i.e. the last segment of the NotebookRuntimeTemplate's resource name. * `healthState` supports = and !=. healthState enum: [HEALTHY, UNHEALTHY, HEALTH_STATE_UNSPECIFIED]. * `runtimeState` supports = and !=. runtimeState enum: [RUNTIME_STATE_UNSPECIFIED, RUNNING, BEING_STARTED, BEING_STOPPED, STOPPED, BEING_UPGRADED, ERROR, INVALID]. * `runtimeUser` supports = and !=. * API version is UI only: `uiState` supports = and !=. uiState enum: [UI_RESOURCE_STATE_UNSPECIFIED, UI_RESOURCE_STATE_BEING_CREATED, UI_RESOURCE_STATE_ACTIVE, UI_RESOURCE_STATE_BEING_DELETED, UI_RESOURCE_STATE_CREATION_FAILED]. * `notebookRuntimeType` supports = and !=. notebookRuntimeType enum: [USER_DEFINED, ONE_CLICK]. * `machineType` supports = and !=. * `acceleratorType` supports = and !=. Some examples: * `notebookRuntime="notebookRuntime123"` * `displayName="myDisplayName"` and `displayName=~"myDisplayNameRegex"` * `notebookRuntimeTemplate="notebookRuntimeTemplate321"` * `healthState=HEALTHY` * `runtimeState=RUNNING` * `runtimeUser="test@google.com"` * `uiState=UI_RESOURCE_STATE_BEING_DELETED` * `notebookRuntimeType=USER_DEFINED` * `machineType=e2-standard-4` * `acceleratorType=NVIDIA_TESLA_T4` orderBy: Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `display_name` * `create_time` * `update_time` Example: `display_name, create_time desc`. pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. Typically obtained via ListNotebookRuntimesResponse.next_page_token of the previous NotebookService.ListNotebookRuntimes call. parent: Required. The resource name of the Location from which to list the NotebookRuntimes. Format: `projects/{project}/locations/{location}` readMask: Optional. Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsNotebookRuntimesStartRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimesStartRequest object. Fields: googleCloudAiplatformV1StartNotebookRuntimeRequest: A GoogleCloudAiplatformV1StartNotebookRuntimeRequest resource to be passed as the request body. name: Required. The name of the NotebookRuntime resource to be started. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner. """ googleCloudAiplatformV1StartNotebookRuntimeRequest = _messages.MessageField('GoogleCloudAiplatformV1StartNotebookRuntimeRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsNotebookRuntimesStopRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimesStopRequest object. Fields: googleCloudAiplatformV1StopNotebookRuntimeRequest: A GoogleCloudAiplatformV1StopNotebookRuntimeRequest resource to be passed as the request body. name: Required. The name of the NotebookRuntime resource to be stopped. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner. """ googleCloudAiplatformV1StopNotebookRuntimeRequest = _messages.MessageField('GoogleCloudAiplatformV1StopNotebookRuntimeRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsNotebookRuntimesUpgradeRequest(_messages.Message): r"""A AiplatformProjectsLocationsNotebookRuntimesUpgradeRequest object. Fields: googleCloudAiplatformV1UpgradeNotebookRuntimeRequest: A GoogleCloudAiplatformV1UpgradeNotebookRuntimeRequest resource to be passed as the request body. name: Required. The name of the NotebookRuntime resource to be upgrade. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner. """ googleCloudAiplatformV1UpgradeNotebookRuntimeRequest = _messages.MessageField('GoogleCloudAiplatformV1UpgradeNotebookRuntimeRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsOperationsCancelRequest(_messages.Message): r"""A AiplatformProjectsLocationsOperationsCancelRequest object. Fields: name: The name of the operation resource to be cancelled. """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsOperationsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsOperationsDeleteRequest object. Fields: name: The name of the operation resource to be deleted. """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsOperationsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsOperationsGetRequest object. Fields: name: The name of the operation resource. """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsOperationsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsOperationsListRequest object. Fields: filter: The standard list filter. name: The name of the operation's parent resource. pageSize: The standard list page size. pageToken: The standard list page token. """ filter = _messages.StringField(1) name = _messages.StringField(2, required=True) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) class AiplatformProjectsLocationsOperationsWaitRequest(_messages.Message): r"""A AiplatformProjectsLocationsOperationsWaitRequest object. Fields: name: The name of the operation resource to wait on. timeout: The maximum duration to wait before timing out. If left blank, the wait will be at most the time permitted by the underlying HTTP/RPC protocol. If RPC context deadline is also specified, the shorter one will be used. """ name = _messages.StringField(1, required=True) timeout = _messages.StringField(2) class AiplatformProjectsLocationsPersistentResourcesCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsPersistentResourcesCreateRequest object. Fields: googleCloudAiplatformV1PersistentResource: A GoogleCloudAiplatformV1PersistentResource resource to be passed as the request body. parent: Required. The resource name of the Location to create the PersistentResource in. Format: `projects/{project}/locations/{location}` persistentResourceId: Required. The ID to use for the PersistentResource, which become the final component of the PersistentResource's resource name. The maximum length is 63 characters, and valid characters are `/^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/`. """ googleCloudAiplatformV1PersistentResource = _messages.MessageField('GoogleCloudAiplatformV1PersistentResource', 1) parent = _messages.StringField(2, required=True) persistentResourceId = _messages.StringField(3) class AiplatformProjectsLocationsPersistentResourcesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsPersistentResourcesDeleteRequest object. Fields: name: Required. The name of the PersistentResource to be deleted. Format: `projects/{project}/locations/{location}/persistentResources/{persistent _resource}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsPersistentResourcesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsPersistentResourcesGetRequest object. Fields: name: Required. The name of the PersistentResource resource. Format: `proj ects/{project_id_or_number}/locations/{location_id}/persistentResources/ {persistent_resource_id}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsPersistentResourcesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsPersistentResourcesListRequest object. Fields: pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. Typically obtained via ListPersistentResourcesResponse.next_page_token of the previous PersistentResourceService.ListPersistentResource call. parent: Required. The resource name of the Location to list the PersistentResources from. Format: `projects/{project}/locations/{location}` """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsPersistentResourcesPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsPersistentResourcesPatchRequest object. Fields: googleCloudAiplatformV1PersistentResource: A GoogleCloudAiplatformV1PersistentResource resource to be passed as the request body. name: Immutable. Resource name of a PersistentResource. updateMask: Required. Specify the fields to be overwritten in the PersistentResource by the update method. """ googleCloudAiplatformV1PersistentResource = _messages.MessageField('GoogleCloudAiplatformV1PersistentResource', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsPersistentResourcesRebootRequest(_messages.Message): r"""A AiplatformProjectsLocationsPersistentResourcesRebootRequest object. Fields: googleCloudAiplatformV1RebootPersistentResourceRequest: A GoogleCloudAiplatformV1RebootPersistentResourceRequest resource to be passed as the request body. name: Required. The name of the PersistentResource resource. Format: `proj ects/{project_id_or_number}/locations/{location_id}/persistentResources/ {persistent_resource_id}` """ googleCloudAiplatformV1RebootPersistentResourceRequest = _messages.MessageField('GoogleCloudAiplatformV1RebootPersistentResourceRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsPipelineJobsBatchCancelRequest(_messages.Message): r"""A AiplatformProjectsLocationsPipelineJobsBatchCancelRequest object. Fields: googleCloudAiplatformV1BatchCancelPipelineJobsRequest: A GoogleCloudAiplatformV1BatchCancelPipelineJobsRequest resource to be passed as the request body. parent: Required. The name of the PipelineJobs' parent resource. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1BatchCancelPipelineJobsRequest = _messages.MessageField('GoogleCloudAiplatformV1BatchCancelPipelineJobsRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsPipelineJobsBatchDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsPipelineJobsBatchDeleteRequest object. Fields: googleCloudAiplatformV1BatchDeletePipelineJobsRequest: A GoogleCloudAiplatformV1BatchDeletePipelineJobsRequest resource to be passed as the request body. parent: Required. The name of the PipelineJobs' parent resource. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1BatchDeletePipelineJobsRequest = _messages.MessageField('GoogleCloudAiplatformV1BatchDeletePipelineJobsRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsPipelineJobsCancelRequest(_messages.Message): r"""A AiplatformProjectsLocationsPipelineJobsCancelRequest object. Fields: googleCloudAiplatformV1CancelPipelineJobRequest: A GoogleCloudAiplatformV1CancelPipelineJobRequest resource to be passed as the request body. name: Required. The name of the PipelineJob to cancel. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}` """ googleCloudAiplatformV1CancelPipelineJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelPipelineJobRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsPipelineJobsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsPipelineJobsCreateRequest object. Fields: googleCloudAiplatformV1PipelineJob: A GoogleCloudAiplatformV1PipelineJob resource to be passed as the request body. parent: Required. The resource name of the Location to create the PipelineJob in. Format: `projects/{project}/locations/{location}` pipelineJobId: The ID to use for the PipelineJob, which will become the final component of the PipelineJob name. If not provided, an ID will be automatically generated. This value should be less than 128 characters, and valid characters are `/a-z-/`. """ googleCloudAiplatformV1PipelineJob = _messages.MessageField('GoogleCloudAiplatformV1PipelineJob', 1) parent = _messages.StringField(2, required=True) pipelineJobId = _messages.StringField(3) class AiplatformProjectsLocationsPipelineJobsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsPipelineJobsDeleteRequest object. Fields: name: Required. The name of the PipelineJob resource to be deleted. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsPipelineJobsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsPipelineJobsGetRequest object. Fields: name: Required. The name of the PipelineJob resource. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsPipelineJobsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsPipelineJobsListRequest object. Fields: filter: Lists the PipelineJobs that match the filter expression. The following fields are supported: * `pipeline_name`: Supports `=` and `!=` comparisons. * `display_name`: Supports `=`, `!=` comparisons, and `:` wildcard. * `pipeline_job_user_id`: Supports `=`, `!=` comparisons, and `:` wildcard. for example, can check if pipeline's display_name contains *step* by doing display_name:\"*step*\" * `state`: Supports `=` and `!=` comparisons. * `create_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. * `update_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. * `end_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. * `labels`: Supports key-value equality and key presence. * `template_uri`: Supports `=`, `!=` comparisons, and `:` wildcard. * `template_metadata.version`: Supports `=`, `!=` comparisons, and `:` wildcard. Filter expressions can be combined together using logical operators (`AND` & `OR`). For example: `pipeline_name="test" AND create_time>"2020-05-18T13:30:00Z"`. The syntax to define filter expression is based on https://google.aip.dev/160. Examples: * `create_time>"2021-05-18T00:00:00Z" OR update_time>"2020-05-18T00:00:00Z"` PipelineJobs created or updated after 2020-05-18 00:00:00 UTC. * `labels.env = "prod"` PipelineJobs with label "env" set to "prod". orderBy: A comma-separated list of fields to order by. The default sort order is in ascending order. Use "desc" after a field name for descending. You can have multiple order_by fields provided e.g. "create_time desc, end_time", "end_time, start_time, update_time" For example, using "create_time desc, end_time" will order results by create time in descending order, and if there are multiple jobs having the same create time, order them by the end time in ascending order. if order_by is not specified, it will order by default order is create time in descending order. Supported fields: * `create_time` * `update_time` * `end_time` * `start_time` pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via ListPipelineJobsResponse.next_page_token of the previous PipelineService.ListPipelineJobs call. parent: Required. The resource name of the Location to list the PipelineJobs from. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsPublishersModelsComputeTokensRequest(_messages.Message): r"""A AiplatformProjectsLocationsPublishersModelsComputeTokensRequest object. Fields: endpoint: Required. The name of the Endpoint requested to get lists of tokens and token ids. googleCloudAiplatformV1ComputeTokensRequest: A GoogleCloudAiplatformV1ComputeTokensRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1ComputeTokensRequest = _messages.MessageField('GoogleCloudAiplatformV1ComputeTokensRequest', 2) class AiplatformProjectsLocationsPublishersModelsCountTokensRequest(_messages.Message): r"""A AiplatformProjectsLocationsPublishersModelsCountTokensRequest object. Fields: endpoint: Required. The name of the Endpoint requested to perform token counting. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1CountTokensRequest: A GoogleCloudAiplatformV1CountTokensRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1CountTokensRequest = _messages.MessageField('GoogleCloudAiplatformV1CountTokensRequest', 2) class AiplatformProjectsLocationsPublishersModelsGenerateContentRequest(_messages.Message): r"""A AiplatformProjectsLocationsPublishersModelsGenerateContentRequest object. Fields: googleCloudAiplatformV1GenerateContentRequest: A GoogleCloudAiplatformV1GenerateContentRequest resource to be passed as the request body. model: Required. The fully qualified name of the publisher model or tuned model endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}` """ googleCloudAiplatformV1GenerateContentRequest = _messages.MessageField('GoogleCloudAiplatformV1GenerateContentRequest', 1) model = _messages.StringField(2, required=True) class AiplatformProjectsLocationsPublishersModelsPredictLongRunningRequest(_messages.Message): r"""A AiplatformProjectsLocationsPublishersModelsPredictLongRunningRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` or `proje cts/{project}/locations/{location}/publishers/{publisher}/models/{model} ` googleCloudAiplatformV1PredictLongRunningRequest: A GoogleCloudAiplatformV1PredictLongRunningRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1PredictLongRunningRequest = _messages.MessageField('GoogleCloudAiplatformV1PredictLongRunningRequest', 2) class AiplatformProjectsLocationsPublishersModelsPredictRequest(_messages.Message): r"""A AiplatformProjectsLocationsPublishersModelsPredictRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1PredictRequest: A GoogleCloudAiplatformV1PredictRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1PredictRequest = _messages.MessageField('GoogleCloudAiplatformV1PredictRequest', 2) class AiplatformProjectsLocationsPublishersModelsRawPredictRequest(_messages.Message): r"""A AiplatformProjectsLocationsPublishersModelsRawPredictRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1RawPredictRequest: A GoogleCloudAiplatformV1RawPredictRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1RawPredictRequest = _messages.MessageField('GoogleCloudAiplatformV1RawPredictRequest', 2) class AiplatformProjectsLocationsPublishersModelsServerStreamingPredictRequest(_messages.Message): r"""A AiplatformProjectsLocationsPublishersModelsServerStreamingPredictRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1StreamingPredictRequest: A GoogleCloudAiplatformV1StreamingPredictRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1StreamingPredictRequest = _messages.MessageField('GoogleCloudAiplatformV1StreamingPredictRequest', 2) class AiplatformProjectsLocationsPublishersModelsStreamGenerateContentRequest(_messages.Message): r"""A AiplatformProjectsLocationsPublishersModelsStreamGenerateContentRequest object. Fields: googleCloudAiplatformV1GenerateContentRequest: A GoogleCloudAiplatformV1GenerateContentRequest resource to be passed as the request body. model: Required. The fully qualified name of the publisher model or tuned model endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}` """ googleCloudAiplatformV1GenerateContentRequest = _messages.MessageField('GoogleCloudAiplatformV1GenerateContentRequest', 1) model = _messages.StringField(2, required=True) class AiplatformProjectsLocationsPublishersModelsStreamRawPredictRequest(_messages.Message): r"""A AiplatformProjectsLocationsPublishersModelsStreamRawPredictRequest object. Fields: endpoint: Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` googleCloudAiplatformV1StreamRawPredictRequest: A GoogleCloudAiplatformV1StreamRawPredictRequest resource to be passed as the request body. """ endpoint = _messages.StringField(1, required=True) googleCloudAiplatformV1StreamRawPredictRequest = _messages.MessageField('GoogleCloudAiplatformV1StreamRawPredictRequest', 2) class AiplatformProjectsLocationsRagCorporaCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsRagCorporaCreateRequest object. Fields: googleCloudAiplatformV1RagCorpus: A GoogleCloudAiplatformV1RagCorpus resource to be passed as the request body. parent: Required. The resource name of the Location to create the RagCorpus in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1RagCorpus = _messages.MessageField('GoogleCloudAiplatformV1RagCorpus', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsRagCorporaDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsRagCorporaDeleteRequest object. Fields: force: Optional. If set to true, any RagFiles in this RagCorpus will also be deleted. Otherwise, the request will only work if the RagCorpus has no RagFiles. name: Required. The name of the RagCorpus resource to be deleted. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}` """ force = _messages.BooleanField(1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsRagCorporaGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsRagCorporaGetRequest object. Fields: name: Required. The name of the RagCorpus resource. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsRagCorporaListRequest(_messages.Message): r"""A AiplatformProjectsLocationsRagCorporaListRequest object. Fields: pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. Typically obtained via ListRagCorporaResponse.next_page_token of the previous VertexRagDataService.ListRagCorpora call. parent: Required. The resource name of the Location from which to list the RagCorpora. Format: `projects/{project}/locations/{location}` """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsRagCorporaRagFilesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsRagCorporaRagFilesDeleteRequest object. Fields: name: Required. The name of the RagFile resource to be deleted. Format: `p rojects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/ {rag_file}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsRagCorporaRagFilesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsRagCorporaRagFilesGetRequest object. Fields: name: Required. The name of the RagFile resource. Format: `projects/{proje ct}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsRagCorporaRagFilesImportRequest(_messages.Message): r"""A AiplatformProjectsLocationsRagCorporaRagFilesImportRequest object. Fields: googleCloudAiplatformV1ImportRagFilesRequest: A GoogleCloudAiplatformV1ImportRagFilesRequest resource to be passed as the request body. parent: Required. The name of the RagCorpus resource into which to import files. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}` """ googleCloudAiplatformV1ImportRagFilesRequest = _messages.MessageField('GoogleCloudAiplatformV1ImportRagFilesRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsRagCorporaRagFilesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsRagCorporaRagFilesListRequest object. Fields: pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. Typically obtained via ListRagFilesResponse.next_page_token of the previous VertexRagDataService.ListRagFiles call. parent: Required. The resource name of the RagCorpus from which to list the RagFiles. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}` """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsReasoningEnginesCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsReasoningEnginesCreateRequest object. Fields: googleCloudAiplatformV1ReasoningEngine: A GoogleCloudAiplatformV1ReasoningEngine resource to be passed as the request body. parent: Required. The resource name of the Location to create the ReasoningEngine in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1ReasoningEngine = _messages.MessageField('GoogleCloudAiplatformV1ReasoningEngine', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsReasoningEnginesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsReasoningEnginesDeleteRequest object. Fields: force: Optional. If set to true, child resources of this reasoning engine will also be deleted. Otherwise, the request will fail with FAILED_PRECONDITION error when the reasoning engine has undeleted child resources. name: Required. The name of the ReasoningEngine resource to be deleted. Format: `projects/{project}/locations/{location}/reasoningEngines/{reaso ning_engine}` """ force = _messages.BooleanField(1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsReasoningEnginesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsReasoningEnginesGetRequest object. Fields: name: Required. The name of the ReasoningEngine resource. Format: `project s/{project}/locations/{location}/reasoningEngines/{reasoning_engine}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsReasoningEnginesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsReasoningEnginesListRequest object. Fields: filter: Optional. The standard list filter. More detail in [AIP-160](https://google.aip.dev/160). pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. parent: Required. The resource name of the Location to list the ReasoningEngines from. Format: `projects/{project}/locations/{location}` """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) class AiplatformProjectsLocationsReasoningEnginesPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsReasoningEnginesPatchRequest object. Fields: googleCloudAiplatformV1ReasoningEngine: A GoogleCloudAiplatformV1ReasoningEngine resource to be passed as the request body. name: Identifier. The resource name of the ReasoningEngine. Format: `proje cts/{project}/locations/{location}/reasoningEngines/{reasoning_engine}` updateMask: Optional. Mask specifying which fields to update. """ googleCloudAiplatformV1ReasoningEngine = _messages.MessageField('GoogleCloudAiplatformV1ReasoningEngine', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsReasoningEnginesQueryRequest(_messages.Message): r"""A AiplatformProjectsLocationsReasoningEnginesQueryRequest object. Fields: googleCloudAiplatformV1QueryReasoningEngineRequest: A GoogleCloudAiplatformV1QueryReasoningEngineRequest resource to be passed as the request body. name: Required. The name of the ReasoningEngine resource to use. Format: ` projects/{project}/locations/{location}/reasoningEngines/{reasoning_engi ne}` """ googleCloudAiplatformV1QueryReasoningEngineRequest = _messages.MessageField('GoogleCloudAiplatformV1QueryReasoningEngineRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsReasoningEnginesStreamQueryRequest(_messages.Message): r"""A AiplatformProjectsLocationsReasoningEnginesStreamQueryRequest object. Fields: googleCloudAiplatformV1StreamQueryReasoningEngineRequest: A GoogleCloudAiplatformV1StreamQueryReasoningEngineRequest resource to be passed as the request body. name: Required. The name of the ReasoningEngine resource to use. Format: ` projects/{project}/locations/{location}/reasoningEngines/{reasoning_engi ne}` """ googleCloudAiplatformV1StreamQueryReasoningEngineRequest = _messages.MessageField('GoogleCloudAiplatformV1StreamQueryReasoningEngineRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsRetrieveContextsRequest(_messages.Message): r"""A AiplatformProjectsLocationsRetrieveContextsRequest object. Fields: googleCloudAiplatformV1RetrieveContextsRequest: A GoogleCloudAiplatformV1RetrieveContextsRequest resource to be passed as the request body. parent: Required. The resource name of the Location from which to retrieve RagContexts. The users must have permission to make a call in the project. Format: `projects/{project}/locations/{location}`. """ googleCloudAiplatformV1RetrieveContextsRequest = _messages.MessageField('GoogleCloudAiplatformV1RetrieveContextsRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsSchedulesCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsSchedulesCreateRequest object. Fields: googleCloudAiplatformV1Schedule: A GoogleCloudAiplatformV1Schedule resource to be passed as the request body. parent: Required. The resource name of the Location to create the Schedule in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1Schedule = _messages.MessageField('GoogleCloudAiplatformV1Schedule', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsSchedulesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsSchedulesDeleteRequest object. Fields: name: Required. The name of the Schedule resource to be deleted. Format: `projects/{project}/locations/{location}/schedules/{schedule}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsSchedulesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsSchedulesGetRequest object. Fields: name: Required. The name of the Schedule resource. Format: `projects/{project}/locations/{location}/schedules/{schedule}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsSchedulesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsSchedulesListRequest object. Fields: filter: Lists the Schedules that match the filter expression. The following fields are supported: * `display_name`: Supports `=`, `!=` comparisons, and `:` wildcard. * `state`: Supports `=` and `!=` comparisons. * `request`: Supports existence of the check. (e.g. `create_pipeline_job_request:*` --> Schedule has create_pipeline_job_request). * `create_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. * `start_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. * `end_time`: Supports `=`, `!=`, `<`, `>`, `<=`, `>=` comparisons and `:*` existence check. Values must be in RFC 3339 format. * `next_run_time`: Supports `=`, `!=`, `<`, `>`, `<=`, and `>=` comparisons. Values must be in RFC 3339 format. Filter expressions can be combined together using logical operators (`NOT`, `AND` & `OR`). The syntax to define filter expression is based on https://google.aip.dev/160. Examples: * `state="ACTIVE" AND display_name:"my_schedule_*"` * `NOT display_name="my_schedule"` * `create_time>"2021-05-18T00:00:00Z"` * `end_time>"2021-05-18T00:00:00Z" OR NOT end_time:*` * `create_pipeline_job_request:*` orderBy: A comma-separated list of fields to order by. The default sort order is in ascending order. Use "desc" after a field name for descending. You can have multiple order_by fields provided. For example, using "create_time desc, end_time" will order results by create time in descending order, and if there are multiple schedules having the same create time, order them by the end time in ascending order. If order_by is not specified, it will order by default with create_time in descending order. Supported fields: * `create_time` * `start_time` * `end_time` * `next_run_time` pageSize: The standard list page size. Default to 100 if not specified. pageToken: The standard list page token. Typically obtained via ListSchedulesResponse.next_page_token of the previous ScheduleService.ListSchedules call. parent: Required. The resource name of the Location to list the Schedules from. Format: `projects/{project}/locations/{location}` """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) class AiplatformProjectsLocationsSchedulesPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsSchedulesPatchRequest object. Fields: googleCloudAiplatformV1Schedule: A GoogleCloudAiplatformV1Schedule resource to be passed as the request body. name: Immutable. The resource name of the Schedule. updateMask: Required. The update mask applies to the resource. See google.protobuf.FieldMask. """ googleCloudAiplatformV1Schedule = _messages.MessageField('GoogleCloudAiplatformV1Schedule', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsSchedulesPauseRequest(_messages.Message): r"""A AiplatformProjectsLocationsSchedulesPauseRequest object. Fields: googleCloudAiplatformV1PauseScheduleRequest: A GoogleCloudAiplatformV1PauseScheduleRequest resource to be passed as the request body. name: Required. The name of the Schedule resource to be paused. Format: `projects/{project}/locations/{location}/schedules/{schedule}` """ googleCloudAiplatformV1PauseScheduleRequest = _messages.MessageField('GoogleCloudAiplatformV1PauseScheduleRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsSchedulesResumeRequest(_messages.Message): r"""A AiplatformProjectsLocationsSchedulesResumeRequest object. Fields: googleCloudAiplatformV1ResumeScheduleRequest: A GoogleCloudAiplatformV1ResumeScheduleRequest resource to be passed as the request body. name: Required. The name of the Schedule resource to be resumed. Format: `projects/{project}/locations/{location}/schedules/{schedule}` """ googleCloudAiplatformV1ResumeScheduleRequest = _messages.MessageField('GoogleCloudAiplatformV1ResumeScheduleRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsSpecialistPoolsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsSpecialistPoolsCreateRequest object. Fields: googleCloudAiplatformV1SpecialistPool: A GoogleCloudAiplatformV1SpecialistPool resource to be passed as the request body. parent: Required. The parent Project name for the new SpecialistPool. The form is `projects/{project}/locations/{location}`. """ googleCloudAiplatformV1SpecialistPool = _messages.MessageField('GoogleCloudAiplatformV1SpecialistPool', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsSpecialistPoolsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsSpecialistPoolsDeleteRequest object. Fields: force: If set to true, any specialist managers in this SpecialistPool will also be deleted. (Otherwise, the request will only work if the SpecialistPool has no specialist managers.) name: Required. The resource name of the SpecialistPool to delete. Format: `projects/{project}/locations/{location}/specialistPools/{specialist_poo l}` """ force = _messages.BooleanField(1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsSpecialistPoolsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsSpecialistPoolsGetRequest object. Fields: name: Required. The name of the SpecialistPool resource. The form is `proj ects/{project}/locations/{location}/specialistPools/{specialist_pool}`. """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsSpecialistPoolsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsSpecialistPoolsListRequest object. Fields: pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained by ListSpecialistPoolsResponse.next_page_token of the previous SpecialistPoolService.ListSpecialistPools call. Return first page if empty. parent: Required. The name of the SpecialistPool's parent resource. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. FieldMask represents a set of """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) readMask = _messages.StringField(4) class AiplatformProjectsLocationsSpecialistPoolsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsSpecialistPoolsPatchRequest object. Fields: googleCloudAiplatformV1SpecialistPool: A GoogleCloudAiplatformV1SpecialistPool resource to be passed as the request body. name: Required. The resource name of the SpecialistPool. updateMask: Required. The update mask applies to the resource. """ googleCloudAiplatformV1SpecialistPool = _messages.MessageField('GoogleCloudAiplatformV1SpecialistPool', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsStudiesCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesCreateRequest object. Fields: googleCloudAiplatformV1Study: A GoogleCloudAiplatformV1Study resource to be passed as the request body. parent: Required. The resource name of the Location to create the CustomJob in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1Study = _messages.MessageField('GoogleCloudAiplatformV1Study', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsStudiesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesDeleteRequest object. Fields: name: Required. The name of the Study resource to be deleted. Format: `projects/{project}/locations/{location}/studies/{study}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsStudiesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesGetRequest object. Fields: name: Required. The name of the Study resource. Format: `projects/{project}/locations/{location}/studies/{study}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsStudiesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesListRequest object. Fields: pageSize: Optional. The maximum number of studies to return per "page" of results. If unspecified, service will pick an appropriate default. pageToken: Optional. A page token to request the next page of results. If unspecified, there are no subsequent pages. parent: Required. The resource name of the Location to list the Study from. Format: `projects/{project}/locations/{location}` """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsStudiesLookupRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesLookupRequest object. Fields: googleCloudAiplatformV1LookupStudyRequest: A GoogleCloudAiplatformV1LookupStudyRequest resource to be passed as the request body. parent: Required. The resource name of the Location to get the Study from. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1LookupStudyRequest = _messages.MessageField('GoogleCloudAiplatformV1LookupStudyRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsStudiesTrialsAddTrialMeasurementRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesTrialsAddTrialMeasurementRequest object. Fields: googleCloudAiplatformV1AddTrialMeasurementRequest: A GoogleCloudAiplatformV1AddTrialMeasurementRequest resource to be passed as the request body. trialName: Required. The name of the trial to add measurement. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` """ googleCloudAiplatformV1AddTrialMeasurementRequest = _messages.MessageField('GoogleCloudAiplatformV1AddTrialMeasurementRequest', 1) trialName = _messages.StringField(2, required=True) class AiplatformProjectsLocationsStudiesTrialsCheckTrialEarlyStoppingStateRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesTrialsCheckTrialEarlyStoppingStateRequest object. Fields: googleCloudAiplatformV1CheckTrialEarlyStoppingStateRequest: A GoogleCloudAiplatformV1CheckTrialEarlyStoppingStateRequest resource to be passed as the request body. trialName: Required. The Trial's name. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` """ googleCloudAiplatformV1CheckTrialEarlyStoppingStateRequest = _messages.MessageField('GoogleCloudAiplatformV1CheckTrialEarlyStoppingStateRequest', 1) trialName = _messages.StringField(2, required=True) class AiplatformProjectsLocationsStudiesTrialsCompleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesTrialsCompleteRequest object. Fields: googleCloudAiplatformV1CompleteTrialRequest: A GoogleCloudAiplatformV1CompleteTrialRequest resource to be passed as the request body. name: Required. The Trial's name. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` """ googleCloudAiplatformV1CompleteTrialRequest = _messages.MessageField('GoogleCloudAiplatformV1CompleteTrialRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsStudiesTrialsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesTrialsCreateRequest object. Fields: googleCloudAiplatformV1Trial: A GoogleCloudAiplatformV1Trial resource to be passed as the request body. parent: Required. The resource name of the Study to create the Trial in. Format: `projects/{project}/locations/{location}/studies/{study}` """ googleCloudAiplatformV1Trial = _messages.MessageField('GoogleCloudAiplatformV1Trial', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsStudiesTrialsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesTrialsDeleteRequest object. Fields: name: Required. The Trial's name. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsStudiesTrialsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesTrialsGetRequest object. Fields: name: Required. The name of the Trial resource. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsStudiesTrialsListOptimalTrialsRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesTrialsListOptimalTrialsRequest object. Fields: googleCloudAiplatformV1ListOptimalTrialsRequest: A GoogleCloudAiplatformV1ListOptimalTrialsRequest resource to be passed as the request body. parent: Required. The name of the Study that the optimal Trial belongs to. """ googleCloudAiplatformV1ListOptimalTrialsRequest = _messages.MessageField('GoogleCloudAiplatformV1ListOptimalTrialsRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsStudiesTrialsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesTrialsListRequest object. Fields: pageSize: Optional. The number of Trials to retrieve per "page" of results. If unspecified, the service will pick an appropriate default. pageToken: Optional. A page token to request the next page of results. If unspecified, there are no subsequent pages. parent: Required. The resource name of the Study to list the Trial from. Format: `projects/{project}/locations/{location}/studies/{study}` """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class AiplatformProjectsLocationsStudiesTrialsStopRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesTrialsStopRequest object. Fields: googleCloudAiplatformV1StopTrialRequest: A GoogleCloudAiplatformV1StopTrialRequest resource to be passed as the request body. name: Required. The Trial's name. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` """ googleCloudAiplatformV1StopTrialRequest = _messages.MessageField('GoogleCloudAiplatformV1StopTrialRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsStudiesTrialsSuggestRequest(_messages.Message): r"""A AiplatformProjectsLocationsStudiesTrialsSuggestRequest object. Fields: googleCloudAiplatformV1SuggestTrialsRequest: A GoogleCloudAiplatformV1SuggestTrialsRequest resource to be passed as the request body. parent: Required. The project and location that the Study belongs to. Format: `projects/{project}/locations/{location}/studies/{study}` """ googleCloudAiplatformV1SuggestTrialsRequest = _messages.MessageField('GoogleCloudAiplatformV1SuggestTrialsRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsTensorboardsBatchReadRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsBatchReadRequest object. Fields: tensorboard: Required. The resource name of the Tensorboard containing TensorboardTimeSeries to read data from. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}`. The TensorboardTimeSeries referenced by time_series must be sub resources of this Tensorboard. timeSeries: Required. The resource names of the TensorboardTimeSeries to read data from. Format: `projects/{project}/locations/{location}/tensorb oards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time _series}` """ tensorboard = _messages.StringField(1, required=True) timeSeries = _messages.StringField(2, repeated=True) class AiplatformProjectsLocationsTensorboardsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsCreateRequest object. Fields: googleCloudAiplatformV1Tensorboard: A GoogleCloudAiplatformV1Tensorboard resource to be passed as the request body. parent: Required. The resource name of the Location to create the Tensorboard in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1Tensorboard = _messages.MessageField('GoogleCloudAiplatformV1Tensorboard', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsTensorboardsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsDeleteRequest object. Fields: name: Required. The name of the Tensorboard to be deleted. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsBatchCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsBatchCreateRequest object. Fields: googleCloudAiplatformV1BatchCreateTensorboardTimeSeriesRequest: A GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesRequest resource to be passed as the request body. parent: Required. The resource name of the TensorboardExperiment to create the TensorboardTimeSeries in. Format: `projects/{project}/locations/{loc ation}/tensorboards/{tensorboard}/experiments/{experiment}` The TensorboardRuns referenced by the parent fields in the CreateTensorboardTimeSeriesRequest messages must be sub resources of this TensorboardExperiment. """ googleCloudAiplatformV1BatchCreateTensorboardTimeSeriesRequest = _messages.MessageField('GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsCreateRequest object. Fields: googleCloudAiplatformV1TensorboardExperiment: A GoogleCloudAiplatformV1TensorboardExperiment resource to be passed as the request body. parent: Required. The resource name of the Tensorboard to create the TensorboardExperiment in. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}` tensorboardExperimentId: Required. The ID to use for the Tensorboard experiment, which becomes the final component of the Tensorboard experiment's resource name. This value should be 1-128 characters, and valid characters are `/a-z-/`. """ googleCloudAiplatformV1TensorboardExperiment = _messages.MessageField('GoogleCloudAiplatformV1TensorboardExperiment', 1) parent = _messages.StringField(2, required=True) tensorboardExperimentId = _messages.StringField(3) class AiplatformProjectsLocationsTensorboardsExperimentsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsDeleteRequest object. Fields: name: Required. The name of the TensorboardExperiment to be deleted. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboa rd}/experiments/{experiment}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsGetRequest object. Fields: name: Required. The name of the TensorboardExperiment resource. Format: `p rojects/{project}/locations/{location}/tensorboards/{tensorboard}/experi ments/{experiment}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsListRequest object. Fields: filter: Lists the TensorboardExperiments that match the filter expression. orderBy: Field to use to sort the list. pageSize: The maximum number of TensorboardExperiments to return. The service may return fewer than this value. If unspecified, at most 50 TensorboardExperiments are returned. The maximum value is 1000; values above 1000 are coerced to 1000. pageToken: A page token, received from a previous TensorboardService.ListTensorboardExperiments call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to TensorboardService.ListTensorboardExperiments must match the call that provided the page token. parent: Required. The resource name of the Tensorboard to list TensorboardExperiments. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsTensorboardsExperimentsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsPatchRequest object. Fields: googleCloudAiplatformV1TensorboardExperiment: A GoogleCloudAiplatformV1TensorboardExperiment resource to be passed as the request body. name: Output only. Name of the TensorboardExperiment. Format: `projects/{p roject}/locations/{location}/tensorboards/{tensorboard}/experiments/{exp eriment}` updateMask: Required. Field mask is used to specify the fields to be overwritten in the TensorboardExperiment resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field is overwritten if it's in the mask. If the user does not provide a mask then all fields are overwritten if new values are specified. """ googleCloudAiplatformV1TensorboardExperiment = _messages.MessageField('GoogleCloudAiplatformV1TensorboardExperiment', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsTensorboardsExperimentsRunsBatchCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsBatchCreateRequest object. Fields: googleCloudAiplatformV1BatchCreateTensorboardRunsRequest: A GoogleCloudAiplatformV1BatchCreateTensorboardRunsRequest resource to be passed as the request body. parent: Required. The resource name of the TensorboardExperiment to create the TensorboardRuns in. Format: `projects/{project}/locations/{location} /tensorboards/{tensorboard}/experiments/{experiment}` The parent field in the CreateTensorboardRunRequest messages must match this field. """ googleCloudAiplatformV1BatchCreateTensorboardRunsRequest = _messages.MessageField('GoogleCloudAiplatformV1BatchCreateTensorboardRunsRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsRunsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsCreateRequest object. Fields: googleCloudAiplatformV1TensorboardRun: A GoogleCloudAiplatformV1TensorboardRun resource to be passed as the request body. parent: Required. The resource name of the TensorboardExperiment to create the TensorboardRun in. Format: `projects/{project}/locations/{location}/ tensorboards/{tensorboard}/experiments/{experiment}` tensorboardRunId: Required. The ID to use for the Tensorboard run, which becomes the final component of the Tensorboard run's resource name. This value should be 1-128 characters, and valid characters are `/a-z-/`. """ googleCloudAiplatformV1TensorboardRun = _messages.MessageField('GoogleCloudAiplatformV1TensorboardRun', 1) parent = _messages.StringField(2, required=True) tensorboardRunId = _messages.StringField(3) class AiplatformProjectsLocationsTensorboardsExperimentsRunsDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsDeleteRequest object. Fields: name: Required. The name of the TensorboardRun to be deleted. Format: `pro jects/{project}/locations/{location}/tensorboards/{tensorboard}/experime nts/{experiment}/runs/{run}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsRunsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsGetRequest object. Fields: name: Required. The name of the TensorboardRun resource. Format: `projects /{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{ experiment}/runs/{run}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsRunsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsListRequest object. Fields: filter: Lists the TensorboardRuns that match the filter expression. orderBy: Field to use to sort the list. pageSize: The maximum number of TensorboardRuns to return. The service may return fewer than this value. If unspecified, at most 50 TensorboardRuns are returned. The maximum value is 1000; values above 1000 are coerced to 1000. pageToken: A page token, received from a previous TensorboardService.ListTensorboardRuns call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to TensorboardService.ListTensorboardRuns must match the call that provided the page token. parent: Required. The resource name of the TensorboardExperiment to list TensorboardRuns. Format: `projects/{project}/locations/{location}/tensor boards/{tensorboard}/experiments/{experiment}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsTensorboardsExperimentsRunsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsPatchRequest object. Fields: googleCloudAiplatformV1TensorboardRun: A GoogleCloudAiplatformV1TensorboardRun resource to be passed as the request body. name: Output only. Name of the TensorboardRun. Format: `projects/{project} /locations/{location}/tensorboards/{tensorboard}/experiments/{experiment }/runs/{run}` updateMask: Required. Field mask is used to specify the fields to be overwritten in the TensorboardRun resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field is overwritten if it's in the mask. If the user does not provide a mask then all fields are overwritten if new values are specified. """ googleCloudAiplatformV1TensorboardRun = _messages.MessageField('GoogleCloudAiplatformV1TensorboardRun', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesCreate Request object. Fields: googleCloudAiplatformV1TensorboardTimeSeries: A GoogleCloudAiplatformV1TensorboardTimeSeries resource to be passed as the request body. parent: Required. The resource name of the TensorboardRun to create the TensorboardTimeSeries in. Format: `projects/{project}/locations/{locatio n}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}` tensorboardTimeSeriesId: Optional. The user specified unique ID to use for the TensorboardTimeSeries, which becomes the final component of the TensorboardTimeSeries's resource name. This value should match "a-z0-9{0, 127}" """ googleCloudAiplatformV1TensorboardTimeSeries = _messages.MessageField('GoogleCloudAiplatformV1TensorboardTimeSeries', 1) parent = _messages.StringField(2, required=True) tensorboardTimeSeriesId = _messages.StringField(3) class AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesDelete Request object. Fields: name: Required. The name of the TensorboardTimeSeries to be deleted. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboa rd}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesExportTensorboardTimeSeriesRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesExport TensorboardTimeSeriesRequest object. Fields: googleCloudAiplatformV1ExportTensorboardTimeSeriesDataRequest: A GoogleCloudAiplatformV1ExportTensorboardTimeSeriesDataRequest resource to be passed as the request body. tensorboardTimeSeries: Required. The resource name of the TensorboardTimeSeries to export data from. Format: `projects/{project}/l ocations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/ runs/{run}/timeSeries/{time_series}` """ googleCloudAiplatformV1ExportTensorboardTimeSeriesDataRequest = _messages.MessageField('GoogleCloudAiplatformV1ExportTensorboardTimeSeriesDataRequest', 1) tensorboardTimeSeries = _messages.StringField(2, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesGetRequest object. Fields: name: Required. The name of the TensorboardTimeSeries resource. Format: `p rojects/{project}/locations/{location}/tensorboards/{tensorboard}/experi ments/{experiment}/runs/{run}/timeSeries/{time_series}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesListRequest object. Fields: filter: Lists the TensorboardTimeSeries that match the filter expression. orderBy: Field to use to sort the list. pageSize: The maximum number of TensorboardTimeSeries to return. The service may return fewer than this value. If unspecified, at most 50 TensorboardTimeSeries are returned. The maximum value is 1000; values above 1000 are coerced to 1000. pageToken: A page token, received from a previous TensorboardService.ListTensorboardTimeSeries call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to TensorboardService.ListTensorboardTimeSeries must match the call that provided the page token. parent: Required. The resource name of the TensorboardRun to list TensorboardTimeSeries. Format: `projects/{project}/locations/{location}/ tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesPatchRequest object. Fields: googleCloudAiplatformV1TensorboardTimeSeries: A GoogleCloudAiplatformV1TensorboardTimeSeries resource to be passed as the request body. name: Output only. Name of the TensorboardTimeSeries. updateMask: Required. Field mask is used to specify the fields to be overwritten in the TensorboardTimeSeries resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field is overwritten if it's in the mask. If the user does not provide a mask then all fields are overwritten if new values are specified. """ googleCloudAiplatformV1TensorboardTimeSeries = _messages.MessageField('GoogleCloudAiplatformV1TensorboardTimeSeries', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesReadBlobDataRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesReadBl obDataRequest object. Fields: blobIds: IDs of the blobs to read. timeSeries: Required. The resource name of the TensorboardTimeSeries to list Blobs. Format: `projects/{project}/locations/{location}/tensorboard s/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_ser ies}` """ blobIds = _messages.StringField(1, repeated=True) timeSeries = _messages.StringField(2, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesReadRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsRunsTimeSeriesReadRequest object. Fields: filter: Reads the TensorboardTimeSeries' data that match the filter expression. maxDataPoints: The maximum number of TensorboardTimeSeries' data to return. This value should be a positive integer. This value can be set to -1 to return all data. tensorboardTimeSeries: Required. The resource name of the TensorboardTimeSeries to read data from. Format: `projects/{project}/loc ations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/ru ns/{run}/timeSeries/{time_series}` """ filter = _messages.StringField(1) maxDataPoints = _messages.IntegerField(2, variant=_messages.Variant.INT32) tensorboardTimeSeries = _messages.StringField(3, required=True) class AiplatformProjectsLocationsTensorboardsExperimentsWriteRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsExperimentsWriteRequest object. Fields: googleCloudAiplatformV1WriteTensorboardExperimentDataRequest: A GoogleCloudAiplatformV1WriteTensorboardExperimentDataRequest resource to be passed as the request body. tensorboardExperiment: Required. The resource name of the TensorboardExperiment to write data to. Format: `projects/{project}/loca tions/{location}/tensorboards/{tensorboard}/experiments/{experiment}` """ googleCloudAiplatformV1WriteTensorboardExperimentDataRequest = _messages.MessageField('GoogleCloudAiplatformV1WriteTensorboardExperimentDataRequest', 1) tensorboardExperiment = _messages.StringField(2, required=True) class AiplatformProjectsLocationsTensorboardsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsGetRequest object. Fields: name: Required. The name of the Tensorboard resource. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTensorboardsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsListRequest object. Fields: filter: Lists the Tensorboards that match the filter expression. orderBy: Field to use to sort the list. pageSize: The maximum number of Tensorboards to return. The service may return fewer than this value. If unspecified, at most 100 Tensorboards are returned. The maximum value is 100; values above 100 are coerced to 100. pageToken: A page token, received from a previous TensorboardService.ListTensorboards call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to TensorboardService.ListTensorboards must match the call that provided the page token. parent: Required. The resource name of the Location to list Tensorboards. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) parent = _messages.StringField(5, required=True) readMask = _messages.StringField(6) class AiplatformProjectsLocationsTensorboardsPatchRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsPatchRequest object. Fields: googleCloudAiplatformV1Tensorboard: A GoogleCloudAiplatformV1Tensorboard resource to be passed as the request body. name: Output only. Name of the Tensorboard. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}` updateMask: Required. Field mask is used to specify the fields to be overwritten in the Tensorboard resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field is overwritten if it's in the mask. If the user does not provide a mask then all fields are overwritten if new values are specified. """ googleCloudAiplatformV1Tensorboard = _messages.MessageField('GoogleCloudAiplatformV1Tensorboard', 1) name = _messages.StringField(2, required=True) updateMask = _messages.StringField(3) class AiplatformProjectsLocationsTensorboardsReadSizeRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsReadSizeRequest object. Fields: tensorboard: Required. The name of the Tensorboard resource. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}` """ tensorboard = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTensorboardsReadUsageRequest(_messages.Message): r"""A AiplatformProjectsLocationsTensorboardsReadUsageRequest object. Fields: tensorboard: Required. The name of the Tensorboard resource. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}` """ tensorboard = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTrainingPipelinesCancelRequest(_messages.Message): r"""A AiplatformProjectsLocationsTrainingPipelinesCancelRequest object. Fields: googleCloudAiplatformV1CancelTrainingPipelineRequest: A GoogleCloudAiplatformV1CancelTrainingPipelineRequest resource to be passed as the request body. name: Required. The name of the TrainingPipeline to cancel. Format: `proje cts/{project}/locations/{location}/trainingPipelines/{training_pipeline} ` """ googleCloudAiplatformV1CancelTrainingPipelineRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelTrainingPipelineRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsTrainingPipelinesCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsTrainingPipelinesCreateRequest object. Fields: googleCloudAiplatformV1TrainingPipeline: A GoogleCloudAiplatformV1TrainingPipeline resource to be passed as the request body. parent: Required. The resource name of the Location to create the TrainingPipeline in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1TrainingPipeline = _messages.MessageField('GoogleCloudAiplatformV1TrainingPipeline', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsTrainingPipelinesDeleteRequest(_messages.Message): r"""A AiplatformProjectsLocationsTrainingPipelinesDeleteRequest object. Fields: name: Required. The name of the TrainingPipeline resource to be deleted. Format: `projects/{project}/locations/{location}/trainingPipelines/{trai ning_pipeline}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTrainingPipelinesGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsTrainingPipelinesGetRequest object. Fields: name: Required. The name of the TrainingPipeline resource. Format: `projec ts/{project}/locations/{location}/trainingPipelines/{training_pipeline}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTrainingPipelinesListRequest(_messages.Message): r"""A AiplatformProjectsLocationsTrainingPipelinesListRequest object. Fields: filter: The standard list filter. Supported fields: * `display_name` supports `=`, `!=` comparisons, and `:` wildcard. * `state` supports `=`, `!=` comparisons. * `training_task_definition` `=`, `!=` comparisons, and `:` wildcard. * `create_time` supports `=`, `!=`,`<`, `<=`,`>`, `>=` comparisons. `create_time` must be in RFC 3339 format. * `labels` supports general map functions that is: `labels.key=value` - key:value equality `labels.key:* - key existence Some examples of using the filter are: * `state="PIPELINE_STATE_SUCCEEDED" AND display_name:"my_pipeline_*"` * `state!="PIPELINE_STATE_FAILED" OR display_name="my_pipeline"` * `NOT display_name="my_pipeline"` * `create_time>"2021-05-18T00:00:00Z"` * `training_task_definition:"*automl_text_classification*"` pageSize: The standard list page size. pageToken: The standard list page token. Typically obtained via ListTrainingPipelinesResponse.next_page_token of the previous PipelineService.ListTrainingPipelines call. parent: Required. The resource name of the Location to list the TrainingPipelines from. Format: `projects/{project}/locations/{location}` readMask: Mask specifying which fields to read. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) readMask = _messages.StringField(5) class AiplatformProjectsLocationsTuningJobsCancelRequest(_messages.Message): r"""A AiplatformProjectsLocationsTuningJobsCancelRequest object. Fields: googleCloudAiplatformV1CancelTuningJobRequest: A GoogleCloudAiplatformV1CancelTuningJobRequest resource to be passed as the request body. name: Required. The name of the TuningJob to cancel. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}` """ googleCloudAiplatformV1CancelTuningJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelTuningJobRequest', 1) name = _messages.StringField(2, required=True) class AiplatformProjectsLocationsTuningJobsCreateRequest(_messages.Message): r"""A AiplatformProjectsLocationsTuningJobsCreateRequest object. Fields: googleCloudAiplatformV1TuningJob: A GoogleCloudAiplatformV1TuningJob resource to be passed as the request body. parent: Required. The resource name of the Location to create the TuningJob in. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1TuningJob = _messages.MessageField('GoogleCloudAiplatformV1TuningJob', 1) parent = _messages.StringField(2, required=True) class AiplatformProjectsLocationsTuningJobsGetRequest(_messages.Message): r"""A AiplatformProjectsLocationsTuningJobsGetRequest object. Fields: name: Required. The name of the TuningJob resource. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}` """ name = _messages.StringField(1, required=True) class AiplatformProjectsLocationsTuningJobsListRequest(_messages.Message): r"""A AiplatformProjectsLocationsTuningJobsListRequest object. Fields: filter: Optional. The standard list filter. pageSize: Optional. The standard list page size. pageToken: Optional. The standard list page token. Typically obtained via ListTuningJobsResponse.next_page_token of the previous GenAiTuningService.ListTuningJob][] call. parent: Required. The resource name of the Location to list the TuningJobs from. Format: `projects/{project}/locations/{location}` """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) parent = _messages.StringField(4, required=True) class AiplatformProjectsLocationsTuningJobsRebaseTunedModelRequest(_messages.Message): r"""A AiplatformProjectsLocationsTuningJobsRebaseTunedModelRequest object. Fields: googleCloudAiplatformV1RebaseTunedModelRequest: A GoogleCloudAiplatformV1RebaseTunedModelRequest resource to be passed as the request body. parent: Required. The resource name of the Location into which to rebase the Model. Format: `projects/{project}/locations/{location}` """ googleCloudAiplatformV1RebaseTunedModelRequest = _messages.MessageField('GoogleCloudAiplatformV1RebaseTunedModelRequest', 1) parent = _messages.StringField(2, required=True) class AiplatformPublishersModelsGetRequest(_messages.Message): r"""A AiplatformPublishersModelsGetRequest object. Enums: ViewValueValuesEnum: Optional. PublisherModel view specifying which fields to read. Fields: huggingFaceToken: Optional. Token used to access Hugging Face gated models. isHuggingFaceModel: Optional. Boolean indicates whether the requested model is a Hugging Face model. languageCode: Optional. The IETF BCP-47 language code representing the language in which the publisher model's text information should be written in. name: Required. The name of the PublisherModel resource. Format: `publishers/{publisher}/models/{publisher_model}` view: Optional. PublisherModel view specifying which fields to read. """ class ViewValueValuesEnum(_messages.Enum): r"""Optional. PublisherModel view specifying which fields to read. Values: PUBLISHER_MODEL_VIEW_UNSPECIFIED: The default / unset value. The API will default to the BASIC view. PUBLISHER_MODEL_VIEW_BASIC: Include basic metadata about the publisher model, but not the full contents. PUBLISHER_MODEL_VIEW_FULL: Include everything. PUBLISHER_MODEL_VERSION_VIEW_BASIC: Include: VersionId, ModelVersionExternalName, and SupportedActions. """ PUBLISHER_MODEL_VIEW_UNSPECIFIED = 0 PUBLISHER_MODEL_VIEW_BASIC = 1 PUBLISHER_MODEL_VIEW_FULL = 2 PUBLISHER_MODEL_VERSION_VIEW_BASIC = 3 huggingFaceToken = _messages.StringField(1) isHuggingFaceModel = _messages.BooleanField(2) languageCode = _messages.StringField(3) name = _messages.StringField(4, required=True) view = _messages.EnumField('ViewValueValuesEnum', 5) class CloudAiLargeModelsVisionGenerateVideoResponse(_messages.Message): r"""Generate video response. Fields: generatedSamples: The generates samples. raiMediaFilteredCount: Returns if any videos were filtered due to RAI policies. raiMediaFilteredReasons: Returns rai failure reasons if any. """ generatedSamples = _messages.MessageField('CloudAiLargeModelsVisionMedia', 1, repeated=True) raiMediaFilteredCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) raiMediaFilteredReasons = _messages.StringField(3, repeated=True) class CloudAiLargeModelsVisionImage(_messages.Message): r"""Image. Fields: encoding: Image encoding, encoded as "image/png" or "image/jpg". image: Raw bytes. imageRaiScores: RAI scores for generated image. imageSize: Image size. The size of the image. Can be self reported, or computed from the image bytes. raiInfo: RAI info for image. semanticFilterResponse: Semantic filter info for image. text: Text/Expanded text input for imagen. uri: Path to another storage (typically Google Cloud Storage). """ encoding = _messages.StringField(1) image = _messages.BytesField(2) imageRaiScores = _messages.MessageField('CloudAiLargeModelsVisionImageRAIScores', 3) imageSize = _messages.MessageField('CloudAiLargeModelsVisionImageImageSize', 4) raiInfo = _messages.MessageField('CloudAiLargeModelsVisionRaiInfo', 5) semanticFilterResponse = _messages.MessageField('CloudAiLargeModelsVisionSemanticFilterResponse', 6) text = _messages.StringField(7) uri = _messages.StringField(8) class CloudAiLargeModelsVisionImageImageSize(_messages.Message): r"""Image size. Fields: channels: A integer attribute. height: A integer attribute. width: A integer attribute. """ channels = _messages.IntegerField(1, variant=_messages.Variant.INT32) height = _messages.IntegerField(2, variant=_messages.Variant.INT32) width = _messages.IntegerField(3, variant=_messages.Variant.INT32) class CloudAiLargeModelsVisionImageRAIScores(_messages.Message): r"""RAI scores for generated image returned. Fields: agileWatermarkDetectionScore: Agile watermark score for image. """ agileWatermarkDetectionScore = _messages.FloatField(1) class CloudAiLargeModelsVisionMedia(_messages.Message): r"""Media. Fields: image: Image. video: Video """ image = _messages.MessageField('CloudAiLargeModelsVisionImage', 1) video = _messages.MessageField('CloudAiLargeModelsVisionVideo', 2) class CloudAiLargeModelsVisionNamedBoundingBox(_messages.Message): r"""A CloudAiLargeModelsVisionNamedBoundingBox object. Fields: classes: A string attribute. entities: A string attribute. scores: A number attribute. x1: A number attribute. x2: A number attribute. y1: A number attribute. y2: A number attribute. """ classes = _messages.StringField(1, repeated=True) entities = _messages.StringField(2, repeated=True) scores = _messages.FloatField(3, repeated=True, variant=_messages.Variant.FLOAT) x1 = _messages.FloatField(4, variant=_messages.Variant.FLOAT) x2 = _messages.FloatField(5, variant=_messages.Variant.FLOAT) y1 = _messages.FloatField(6, variant=_messages.Variant.FLOAT) y2 = _messages.FloatField(7, variant=_messages.Variant.FLOAT) class CloudAiLargeModelsVisionRaiInfo(_messages.Message): r"""Next ID: 6 Fields: blockedEntities: List of blocked entities from the blocklist if it is detected. modelName: The model name used to indexing into the RaiFilterConfig map. Would either be one of imagegeneration@002-006, imagen-3.0-... api endpoint names, or internal names used for mapping to different filter configs (genselfie, ai_watermark) than its api endpoint. raiCategories: List of rai categories' information to return scores: List of rai scores mapping to the rai categories. Rounded to 1 decimal place. """ blockedEntities = _messages.StringField(1, repeated=True) modelName = _messages.StringField(2) raiCategories = _messages.StringField(3, repeated=True) scores = _messages.FloatField(4, repeated=True, variant=_messages.Variant.FLOAT) class CloudAiLargeModelsVisionSemanticFilterResponse(_messages.Message): r"""A CloudAiLargeModelsVisionSemanticFilterResponse object. Fields: namedBoundingBoxes: Class labels of the bounding boxes that failed the semantic filtering. Bounding box coordinates. passedSemanticFilter: This response is added when semantic filter config is turned on in EditConfig. It reports if this image is passed semantic filter response. If passed_semantic_filter is false, the bounding box information will be populated for user to check what caused the semantic filter to fail. """ namedBoundingBoxes = _messages.MessageField('CloudAiLargeModelsVisionNamedBoundingBox', 1, repeated=True) passedSemanticFilter = _messages.BooleanField(2) class CloudAiLargeModelsVisionVideo(_messages.Message): r"""Video Fields: encodedVideo: Base 64 encoded video bytes. encoding: Video encoding, for example "video/mp4". text: Text/Expanded text input for Help Me Write. uri: Path to another storage (typically Google Cloud Storage). video: Raw bytes. """ encodedVideo = _messages.StringField(1) encoding = _messages.StringField(2) text = _messages.StringField(3) uri = _messages.StringField(4) video = _messages.BytesField(5) class GoogleApiHttpBody(_messages.Message): r"""Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top- level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged. Messages: ExtensionsValueListEntry: A ExtensionsValueListEntry object. Fields: contentType: The HTTP Content-Type header value specifying the content type of the body. data: The HTTP request/response body as raw binary. extensions: Application specific response metadata. Must be set in the first response for streaming APIs. """ @encoding.MapUnrecognizedFields('additionalProperties') class ExtensionsValueListEntry(_messages.Message): r"""A ExtensionsValueListEntry object. Messages: AdditionalProperty: An additional property for a ExtensionsValueListEntry object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a ExtensionsValueListEntry object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) contentType = _messages.StringField(1) data = _messages.BytesField(2) extensions = _messages.MessageField('ExtensionsValueListEntry', 3, repeated=True) class GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsRequest(_messages.Message): r"""Request message for MetadataService.AddContextArtifactsAndExecutions. Fields: artifacts: The resource names of the Artifacts to attribute to the Context. Format: `projects/{project}/locations/{location}/metadataStores /{metadatastore}/artifacts/{artifact}` executions: The resource names of the Executions to associate with the Context. Format: `projects/{project}/locations/{location}/metadataStores /{metadatastore}/executions/{execution}` """ artifacts = _messages.StringField(1, repeated=True) executions = _messages.StringField(2, repeated=True) class GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsResponse(_messages.Message): r"""Response message for MetadataService.AddContextArtifactsAndExecutions. """ class GoogleCloudAiplatformV1AddContextChildrenRequest(_messages.Message): r"""Request message for MetadataService.AddContextChildren. Fields: childContexts: The resource names of the child Contexts. """ childContexts = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1AddContextChildrenResponse(_messages.Message): r"""Response message for MetadataService.AddContextChildren.""" class GoogleCloudAiplatformV1AddExecutionEventsRequest(_messages.Message): r"""Request message for MetadataService.AddExecutionEvents. Fields: events: The Events to create and add. """ events = _messages.MessageField('GoogleCloudAiplatformV1Event', 1, repeated=True) class GoogleCloudAiplatformV1AddExecutionEventsResponse(_messages.Message): r"""Response message for MetadataService.AddExecutionEvents.""" class GoogleCloudAiplatformV1AddTrialMeasurementRequest(_messages.Message): r"""Request message for VizierService.AddTrialMeasurement. Fields: measurement: Required. The measurement to be added to a Trial. """ measurement = _messages.MessageField('GoogleCloudAiplatformV1Measurement', 1) class GoogleCloudAiplatformV1Annotation(_messages.Message): r"""Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem. Messages: LabelsValue: Optional. The labels with user-defined metadata to organize your Annotations. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Annotation(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Annotation: * "aiplatform.googleapis.com/annotation_set_name": optional, name of the UI's annotation set this Annotation belongs to. If not set, the Annotation is not visible in the UI. * "aiplatform.googleapis.com/payload_schema": output only, its value is the payload_schema's title. Fields: annotationSource: Output only. The source of the Annotation. createTime: Output only. Timestamp when this Annotation was created. etag: Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: Optional. The labels with user-defined metadata to organize your Annotations. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Annotation(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Annotation: * "aiplatform.googleapis.com/annotation_set_name": optional, name of the UI's annotation set this Annotation belongs to. If not set, the Annotation is not visible in the UI. * "aiplatform.googleapis.com/payload_schema": output only, its value is the payload_schema's title. name: Output only. Resource name of the Annotation. payload: Required. The schema of the payload can be found in payload_schema. payloadSchemaUri: Required. Google Cloud Storage URI points to a YAML file describing payload. The schema is defined as an [OpenAPI 3.0.2 Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). The schema files that can be used here are found in gs://google-cloud- aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with the parent Dataset's metadata. updateTime: Output only. Timestamp when this Annotation was last updated. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels with user-defined metadata to organize your Annotations. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Annotation(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Annotation: * "aiplatform.googleapis.com/annotation_set_name": optional, name of the UI's annotation set this Annotation belongs to. If not set, the Annotation is not visible in the UI. * "aiplatform.googleapis.com/payload_schema": output only, its value is the payload_schema's title. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) annotationSource = _messages.MessageField('GoogleCloudAiplatformV1UserActionReference', 1) createTime = _messages.StringField(2) etag = _messages.StringField(3) labels = _messages.MessageField('LabelsValue', 4) name = _messages.StringField(5) payload = _messages.MessageField('extra_types.JsonValue', 6) payloadSchemaUri = _messages.StringField(7) updateTime = _messages.StringField(8) class GoogleCloudAiplatformV1AnnotationSpec(_messages.Message): r"""Identifies a concept with which DataItems may be annotated with. Fields: createTime: Output only. Timestamp when this AnnotationSpec was created. displayName: Required. The user-defined name of the AnnotationSpec. The name can be up to 128 characters long and can consist of any UTF-8 characters. etag: Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. name: Output only. Resource name of the AnnotationSpec. updateTime: Output only. Timestamp when AnnotationSpec was last updated. """ createTime = _messages.StringField(1) displayName = _messages.StringField(2) etag = _messages.StringField(3) name = _messages.StringField(4) updateTime = _messages.StringField(5) class GoogleCloudAiplatformV1ApiAuth(_messages.Message): r"""The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. Fields: apiKeyConfig: The API secret. """ apiKeyConfig = _messages.MessageField('GoogleCloudAiplatformV1ApiAuthApiKeyConfig', 1) class GoogleCloudAiplatformV1ApiAuthApiKeyConfig(_messages.Message): r"""The API secret. Fields: apiKeySecretVersion: Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version} apiKeyString: The API key string. Either this or `api_key_secret_version` must be set. """ apiKeySecretVersion = _messages.StringField(1) apiKeyString = _messages.StringField(2) class GoogleCloudAiplatformV1Artifact(_messages.Message): r"""Instance of a general artifact. Enums: StateValueValuesEnum: The state of this Artifact. This is a property of the Artifact, and does not imply or capture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines), and the system does not prescribe or check the validity of state transitions. Messages: LabelsValue: The labels with user-defined metadata to organize your Artifacts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Artifact (System labels are excluded). MetadataValue: Properties of the Artifact. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB. Fields: createTime: Output only. Timestamp when this Artifact was created. description: Description of the Artifact displayName: User provided display name of the Artifact. May be up to 128 Unicode characters. etag: An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: The labels with user-defined metadata to organize your Artifacts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Artifact (System labels are excluded). metadata: Properties of the Artifact. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB. name: Output only. The resource name of the Artifact. schemaTitle: The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store. schemaVersion: The version of the schema in schema_name to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store. state: The state of this Artifact. This is a property of the Artifact, and does not imply or capture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines), and the system does not prescribe or check the validity of state transitions. updateTime: Output only. Timestamp when this Artifact was last updated. uri: The uniform resource identifier of the artifact file. May be empty if there is no actual artifact file. """ class StateValueValuesEnum(_messages.Enum): r"""The state of this Artifact. This is a property of the Artifact, and does not imply or capture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines), and the system does not prescribe or check the validity of state transitions. Values: STATE_UNSPECIFIED: Unspecified state for the Artifact. PENDING: A state used by systems like Vertex AI Pipelines to indicate that the underlying data item represented by this Artifact is being created. LIVE: A state indicating that the Artifact should exist, unless something external to the system deletes it. """ STATE_UNSPECIFIED = 0 PENDING = 1 LIVE = 2 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your Artifacts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Artifact (System labels are excluded). Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class MetadataValue(_messages.Message): r"""Properties of the Artifact. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB. Messages: AdditionalProperty: An additional property for a MetadataValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a MetadataValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) description = _messages.StringField(2) displayName = _messages.StringField(3) etag = _messages.StringField(4) labels = _messages.MessageField('LabelsValue', 5) metadata = _messages.MessageField('MetadataValue', 6) name = _messages.StringField(7) schemaTitle = _messages.StringField(8) schemaVersion = _messages.StringField(9) state = _messages.EnumField('StateValueValuesEnum', 10) updateTime = _messages.StringField(11) uri = _messages.StringField(12) class GoogleCloudAiplatformV1AssignNotebookRuntimeOperationMetadata(_messages.Message): r"""Metadata information for NotebookService.AssignNotebookRuntime. Fields: genericMetadata: The operation generic information. progressMessage: A human-readable message that shows the intermediate progress details of NotebookRuntime. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) progressMessage = _messages.StringField(2) class GoogleCloudAiplatformV1AssignNotebookRuntimeRequest(_messages.Message): r"""Request message for NotebookService.AssignNotebookRuntime. Fields: notebookRuntime: Required. Provide runtime specific information (e.g. runtime owner, notebook id) used for NotebookRuntime assignment. notebookRuntimeId: Optional. User specified ID for the notebook runtime. notebookRuntimeTemplate: Required. The resource name of the NotebookRuntimeTemplate based on which a NotebookRuntime will be assigned (reuse or create a new one). """ notebookRuntime = _messages.MessageField('GoogleCloudAiplatformV1NotebookRuntime', 1) notebookRuntimeId = _messages.StringField(2) notebookRuntimeTemplate = _messages.StringField(3) class GoogleCloudAiplatformV1Attribution(_messages.Message): r"""Attribution that explains a particular prediction output. Fields: approximationError: Output only. Error of feature_attributions caused by approximation used in the explanation method. Lower value means more precise attributions. * For Sampled Shapley attribution, increasing path_count might reduce the error. * For Integrated Gradients attribution, increasing step_count might reduce the error. * For XRAI attribution, increasing step_count might reduce the error. See [this introduction](/vertex-ai/docs/explainable-ai/overview) for more information. baselineOutputValue: Output only. Model predicted output if the input instance is constructed from the baselines of all the features defined in ExplanationMetadata.inputs. The field name of the output is determined by the key in ExplanationMetadata.outputs. If the Model's predicted output has multiple dimensions (rank > 1), this is the value in the output located by output_index. If there are multiple baselines, their output values are averaged. featureAttributions: Output only. Attributions of each explained feature. Features are extracted from the prediction instances according to explanation metadata for inputs. The value is a struct, whose keys are the name of the feature. The values are how much the feature in the instance contributed to the predicted result. The format of the value is determined by the feature's input format: * If the feature is a scalar value, the attribution value is a floating number. * If the feature is an array of scalar values, the attribution value is an array. * If the feature is a struct, the attribution value is a struct. The keys in the attribution value struct are the same as the keys in the feature struct. The formats of the values in the attribution struct are determined by the formats of the values in the feature struct. The ExplanationMetadata.feature_attributions_schema_uri field, pointed to by the ExplanationSpec field of the Endpoint.deployed_models object, points to the schema file that describes the features and their attribution values (if it is populated). instanceOutputValue: Output only. Model predicted output on the corresponding explanation instance. The field name of the output is determined by the key in ExplanationMetadata.outputs. If the Model predicted output has multiple dimensions, this is the value in the output located by output_index. outputDisplayName: Output only. The display name of the output identified by output_index. For example, the predicted class name by a multi- classification Model. This field is only populated iff the Model predicts display names as a separate field along with the explained output. The predicted display name must has the same shape of the explained output, and can be located using output_index. outputIndex: Output only. The index that locates the explained prediction output. If the prediction output is a scalar value, output_index is not populated. If the prediction output has multiple dimensions, the length of the output_index list is the same as the number of dimensions of the output. The i-th element in output_index is the element index of the i-th dimension of the output vector. Indices start from 0. outputName: Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs. """ approximationError = _messages.FloatField(1) baselineOutputValue = _messages.FloatField(2) featureAttributions = _messages.MessageField('extra_types.JsonValue', 3) instanceOutputValue = _messages.FloatField(4) outputDisplayName = _messages.StringField(5) outputIndex = _messages.IntegerField(6, repeated=True, variant=_messages.Variant.INT32) outputName = _messages.StringField(7) class GoogleCloudAiplatformV1AugmentPromptRequest(_messages.Message): r"""Request message for AugmentPrompt. Fields: contents: Optional. Input content to augment, only text format is supported for now. model: Optional. Metadata of the backend deployed model. vertexRagStore: Optional. Retrieves contexts from the Vertex RagStore. """ contents = _messages.MessageField('GoogleCloudAiplatformV1Content', 1, repeated=True) model = _messages.MessageField('GoogleCloudAiplatformV1AugmentPromptRequestModel', 2) vertexRagStore = _messages.MessageField('GoogleCloudAiplatformV1VertexRagStore', 3) class GoogleCloudAiplatformV1AugmentPromptRequestModel(_messages.Message): r"""Metadata of the backend deployed model. Fields: model: Optional. The model that the user will send the augmented prompt for content generation. modelVersion: Optional. The model version of the backend deployed model. """ model = _messages.StringField(1) modelVersion = _messages.StringField(2) class GoogleCloudAiplatformV1AugmentPromptResponse(_messages.Message): r"""Response message for AugmentPrompt. Fields: augmentedPrompt: Augmented prompt, only text format is supported for now. facts: Retrieved facts from RAG data sources. """ augmentedPrompt = _messages.MessageField('GoogleCloudAiplatformV1Content', 1, repeated=True) facts = _messages.MessageField('GoogleCloudAiplatformV1Fact', 2, repeated=True) class GoogleCloudAiplatformV1AutomaticResources(_messages.Message): r"""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. Fields: maxReplicaCount: Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number. minReplicaCount: Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error. """ maxReplicaCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) minReplicaCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1AutoscalingMetricSpec(_messages.Message): r"""The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count. Fields: metricName: Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count` target: The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided. """ metricName = _messages.StringField(1) target = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1AvroSource(_messages.Message): r"""The storage details for Avro input content. Fields: gcsSource: Required. Google Cloud Storage location. """ gcsSource = _messages.MessageField('GoogleCloudAiplatformV1GcsSource', 1) class GoogleCloudAiplatformV1BatchCancelPipelineJobsRequest(_messages.Message): r"""Request message for PipelineService.BatchCancelPipelineJobs. Fields: names: Required. The names of the PipelineJobs to cancel. A maximum of 32 PipelineJobs can be cancelled in a batch. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipelineJob}` """ names = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1BatchCreateFeaturesOperationMetadata(_messages.Message): r"""Details of operations that perform batch create Features. Fields: genericMetadata: Operation metadata for Feature. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1BatchCreateFeaturesRequest(_messages.Message): r"""Request message for FeaturestoreService.BatchCreateFeatures. Request message for FeatureRegistryService.BatchCreateFeatures. Fields: requests: Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType / FeatureGroup. The `parent` field in each child request message can be omitted. If `parent` is set in a child request, then the value must match the `parent` value in this request message. """ requests = _messages.MessageField('GoogleCloudAiplatformV1CreateFeatureRequest', 1, repeated=True) class GoogleCloudAiplatformV1BatchCreateFeaturesResponse(_messages.Message): r"""Response message for FeaturestoreService.BatchCreateFeatures. Fields: features: The Features created. """ features = _messages.MessageField('GoogleCloudAiplatformV1Feature', 1, repeated=True) class GoogleCloudAiplatformV1BatchCreateTensorboardRunsRequest(_messages.Message): r"""Request message for TensorboardService.BatchCreateTensorboardRuns. Fields: requests: Required. The request message specifying the TensorboardRuns to create. A maximum of 1000 TensorboardRuns can be created in a batch. """ requests = _messages.MessageField('GoogleCloudAiplatformV1CreateTensorboardRunRequest', 1, repeated=True) class GoogleCloudAiplatformV1BatchCreateTensorboardRunsResponse(_messages.Message): r"""Response message for TensorboardService.BatchCreateTensorboardRuns. Fields: tensorboardRuns: The created TensorboardRuns. """ tensorboardRuns = _messages.MessageField('GoogleCloudAiplatformV1TensorboardRun', 1, repeated=True) class GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesRequest(_messages.Message): r"""Request message for TensorboardService.BatchCreateTensorboardTimeSeries. Fields: requests: Required. The request message specifying the TensorboardTimeSeries to create. A maximum of 1000 TensorboardTimeSeries can be created in a batch. """ requests = _messages.MessageField('GoogleCloudAiplatformV1CreateTensorboardTimeSeriesRequest', 1, repeated=True) class GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesResponse(_messages.Message): r"""Response message for TensorboardService.BatchCreateTensorboardTimeSeries. Fields: tensorboardTimeSeries: The created TensorboardTimeSeries. """ tensorboardTimeSeries = _messages.MessageField('GoogleCloudAiplatformV1TensorboardTimeSeries', 1, repeated=True) class GoogleCloudAiplatformV1BatchDedicatedResources(_messages.Message): r"""A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration. Fields: machineSpec: Required. Immutable. The specification of a single machine. maxReplicaCount: Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10. startingReplicaCount: Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count """ machineSpec = _messages.MessageField('GoogleCloudAiplatformV1MachineSpec', 1) maxReplicaCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) startingReplicaCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1BatchDeletePipelineJobsRequest(_messages.Message): r"""Request message for PipelineService.BatchDeletePipelineJobs. Fields: names: Required. The names of the PipelineJobs to delete. A maximum of 32 PipelineJobs can be deleted in a batch. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipelineJob}` """ names = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsRequest(_messages.Message): r"""Request message for ModelService.BatchImportEvaluatedAnnotations Fields: evaluatedAnnotations: Required. Evaluated annotations resource to be imported. """ evaluatedAnnotations = _messages.MessageField('GoogleCloudAiplatformV1EvaluatedAnnotation', 1, repeated=True) class GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsResponse(_messages.Message): r"""Response message for ModelService.BatchImportEvaluatedAnnotations Fields: importedEvaluatedAnnotationsCount: Output only. Number of EvaluatedAnnotations imported. """ importedEvaluatedAnnotationsCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1BatchImportModelEvaluationSlicesRequest(_messages.Message): r"""Request message for ModelService.BatchImportModelEvaluationSlices Fields: modelEvaluationSlices: Required. Model evaluation slice resource to be imported. """ modelEvaluationSlices = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluationSlice', 1, repeated=True) class GoogleCloudAiplatformV1BatchImportModelEvaluationSlicesResponse(_messages.Message): r"""Response message for ModelService.BatchImportModelEvaluationSlices Fields: importedModelEvaluationSlices: Output only. List of imported ModelEvaluationSlice.name. """ importedModelEvaluationSlices = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadata(_messages.Message): r"""Runtime operation information for MigrationService.BatchMigrateResources. Fields: genericMetadata: The common part of the operation metadata. partialResults: Partial results that reflect the latest migration operation progress. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) partialResults = _messages.MessageField('GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadataPartialResult', 2, repeated=True) class GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadataPartialResult(_messages.Message): r"""Represents a partial result in batch migration operation for one MigrateResourceRequest. Fields: dataset: Migrated dataset resource name. error: The error result of the migration request in case of failure. model: Migrated model resource name. request: It's the same as the value in BatchMigrateResourcesRequest.migrate_resource_requests. """ dataset = _messages.StringField(1) error = _messages.MessageField('GoogleRpcStatus', 2) model = _messages.StringField(3) request = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequest', 4) class GoogleCloudAiplatformV1BatchMigrateResourcesRequest(_messages.Message): r"""Request message for MigrationService.BatchMigrateResources. Fields: migrateResourceRequests: Required. The request messages specifying the resources to migrate. They must be in the same location as the destination. Up to 50 resources can be migrated in one batch. """ migrateResourceRequests = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequest', 1, repeated=True) class GoogleCloudAiplatformV1BatchMigrateResourcesResponse(_messages.Message): r"""Response message for MigrationService.BatchMigrateResources. Fields: migrateResourceResponses: Successfully migrated resources. """ migrateResourceResponses = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceResponse', 1, repeated=True) class GoogleCloudAiplatformV1BatchPredictionJob(_messages.Message): r"""A job that uses a Model to produce predictions on multiple input instances. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances. Enums: StateValueValuesEnum: Output only. The detailed state of the job. Messages: LabelsValue: The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Fields: completionStats: Output only. Statistics on completed and failed prediction instances. createTime: Output only. Time when the BatchPredictionJob was created. dedicatedResources: The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided. disableContainerLogging: For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true. displayName: Required. The user-defined name of this BatchPredictionJob. encryptionSpec: Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key. endTime: Output only. Time when the BatchPredictionJob entered any of the following states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`. error: Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED. explanationSpec: Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to `true`. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited. generateExplanation: Generate explanation with the batch prediction results. When set to `true`, the batch prediction output changes based on the `predictions_format` field of the BatchPredictionJob.output_config object: * `bigquery`: output includes a column named `explanation`. The value is a struct that conforms to the Explanation object. * `jsonl`: The JSON objects on each line include an additional entry keyed `explanation`. The value of the entry is a JSON object that conforms to the Explanation object. * `csv`: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated. inputConfig: Required. Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri. instanceConfig: Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model. labels: The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. manualBatchTuningParameters: Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself). model: The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: `projects/{project}/locations/{location}/models/{model}@2` or `projects/{project}/locations/{location}/models/{model}@golden` if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: `publishers/{publisher}/models/{model}` or `projects/{project}/locations /{location}/publishers/{publisher}/models/{model}` modelParameters: The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri. modelVersionId: Output only. The version ID of the Model that produces the predictions via this job. name: Output only. Resource name of the BatchPredictionJob. outputConfig: Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri. outputInfo: Output only. Information further describing the output of this job. partialFailures: Output only. Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details. resourcesConsumed: Output only. Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. serviceAccount: The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the `iam.serviceAccounts.actAs` permission on this service account. startTime: Output only. Time when the BatchPredictionJob for the first time entered the `JOB_STATE_RUNNING` state. state: Output only. The detailed state of the job. unmanagedContainerModel: Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set. updateTime: Output only. Time when the BatchPredictionJob was most recently updated. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of the job. Values: JOB_STATE_UNSPECIFIED: The job state is unspecified. JOB_STATE_QUEUED: The job has been just created or resumed and processing has not yet begun. JOB_STATE_PENDING: The service is preparing to run the job. JOB_STATE_RUNNING: The job is in progress. JOB_STATE_SUCCEEDED: The job completed successfully. JOB_STATE_FAILED: The job failed. JOB_STATE_CANCELLING: The job is being cancelled. From this state the job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. JOB_STATE_CANCELLED: The job has been cancelled. JOB_STATE_PAUSED: The job has been stopped, and can be resumed. JOB_STATE_EXPIRED: The job has expired. JOB_STATE_UPDATING: The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state. JOB_STATE_PARTIALLY_SUCCEEDED: The job is partially succeeded, some results may be missing due to errors. """ JOB_STATE_UNSPECIFIED = 0 JOB_STATE_QUEUED = 1 JOB_STATE_PENDING = 2 JOB_STATE_RUNNING = 3 JOB_STATE_SUCCEEDED = 4 JOB_STATE_FAILED = 5 JOB_STATE_CANCELLING = 6 JOB_STATE_CANCELLED = 7 JOB_STATE_PAUSED = 8 JOB_STATE_EXPIRED = 9 JOB_STATE_UPDATING = 10 JOB_STATE_PARTIALLY_SUCCEEDED = 11 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) completionStats = _messages.MessageField('GoogleCloudAiplatformV1CompletionStats', 1) createTime = _messages.StringField(2) dedicatedResources = _messages.MessageField('GoogleCloudAiplatformV1BatchDedicatedResources', 3) disableContainerLogging = _messages.BooleanField(4) displayName = _messages.StringField(5) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 6) endTime = _messages.StringField(7) error = _messages.MessageField('GoogleRpcStatus', 8) explanationSpec = _messages.MessageField('GoogleCloudAiplatformV1ExplanationSpec', 9) generateExplanation = _messages.BooleanField(10) inputConfig = _messages.MessageField('GoogleCloudAiplatformV1BatchPredictionJobInputConfig', 11) instanceConfig = _messages.MessageField('GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig', 12) labels = _messages.MessageField('LabelsValue', 13) manualBatchTuningParameters = _messages.MessageField('GoogleCloudAiplatformV1ManualBatchTuningParameters', 14) model = _messages.StringField(15) modelParameters = _messages.MessageField('extra_types.JsonValue', 16) modelVersionId = _messages.StringField(17) name = _messages.StringField(18) outputConfig = _messages.MessageField('GoogleCloudAiplatformV1BatchPredictionJobOutputConfig', 19) outputInfo = _messages.MessageField('GoogleCloudAiplatformV1BatchPredictionJobOutputInfo', 20) partialFailures = _messages.MessageField('GoogleRpcStatus', 21, repeated=True) resourcesConsumed = _messages.MessageField('GoogleCloudAiplatformV1ResourcesConsumed', 22) satisfiesPzi = _messages.BooleanField(23) satisfiesPzs = _messages.BooleanField(24) serviceAccount = _messages.StringField(25) startTime = _messages.StringField(26) state = _messages.EnumField('StateValueValuesEnum', 27) unmanagedContainerModel = _messages.MessageField('GoogleCloudAiplatformV1UnmanagedContainerModel', 28) updateTime = _messages.StringField(29) class GoogleCloudAiplatformV1BatchPredictionJobInputConfig(_messages.Message): r"""Configures the input to BatchPredictionJob. See Model.supported_input_storage_formats for Model's supported input formats, and how instances should be expressed via any of them. Fields: bigquerySource: The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored. gcsSource: The Cloud Storage location for the input instances. instancesFormat: Required. The format in which instances are given, must be one of the Model's supported_input_storage_formats. """ bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1BigQuerySource', 1) gcsSource = _messages.MessageField('GoogleCloudAiplatformV1GcsSource', 2) instancesFormat = _messages.StringField(3) class GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig(_messages.Message): r"""Configuration defining how to transform batch prediction input instances to the instances that the Model accepts. Fields: excludedFields: Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, BigQuery or TfRecord. includedFields: Fields that will be included in the prediction instance that is sent to the Model. If instance_type is `array`, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, BigQuery or TfRecord. instanceType: The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * `object`: Each input is converted to JSON object format. * For `bigquery`, each row is converted to an object. * For `jsonl`, each line of the JSONL input must be an object. * Does not apply to `csv`, `file-list`, `tf-record`, or `tf- record-gzip`. * `array`: Each input is converted to JSON array format. * For `bigquery`, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For `jsonl`, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to `csv`, `file-list`, `tf-record`, or `tf-record-gzip`. If not specified, Vertex AI converts the batch prediction input as follows: * For `bigquery` and `csv`, the behavior is the same as `array`. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For `jsonl`, the prediction instance format is determined by each line of the input. * For `tf-record`/`tf- record-gzip`, each record will be converted to an object in the format of `{"b64": }`, where `` is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where `` is the Base64-encoded string of the content of the file. keyField: The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named `key` in the output: * For `jsonl` output format, the output will have a `key` field instead of the `instance` field. * For `csv`/`bigquery` output format, the output will have have a `key` column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord. """ excludedFields = _messages.StringField(1, repeated=True) includedFields = _messages.StringField(2, repeated=True) instanceType = _messages.StringField(3) keyField = _messages.StringField(4) class GoogleCloudAiplatformV1BatchPredictionJobOutputConfig(_messages.Message): r"""Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them. Fields: bigqueryDestination: The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name `prediction__` where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, `predictions`, and `errors`. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The `predictions` table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The `errors` table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only `code` and `message`. gcsDestination: The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is `prediction--`, where timestamp is in YYYY-MM- DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files `predictions_0001.`, `predictions_0002.`, ..., `predictions_N.` are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional `errors_0001.`, `errors_0002.`,..., `errors_N.` files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional `error` field which as value has google.rpc.Status containing only `code` and `message` fields. predictionsFormat: Required. The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats. """ bigqueryDestination = _messages.MessageField('GoogleCloudAiplatformV1BigQueryDestination', 1) gcsDestination = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 2) predictionsFormat = _messages.StringField(3) class GoogleCloudAiplatformV1BatchPredictionJobOutputInfo(_messages.Message): r"""Further describes this job's output. Supplements output_config. Fields: bigqueryOutputDataset: Output only. The path of the BigQuery dataset created, in `bq://projectId.bqDatasetId` format, into which the prediction output is written. bigqueryOutputTable: Output only. The name of the BigQuery table created, in `predictions_` format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example. gcsOutputDirectory: Output only. The full path of the Cloud Storage directory created, into which the prediction output is written. """ bigqueryOutputDataset = _messages.StringField(1) bigqueryOutputTable = _messages.StringField(2) gcsOutputDirectory = _messages.StringField(3) class GoogleCloudAiplatformV1BatchReadFeatureValuesOperationMetadata(_messages.Message): r"""Details of operations that batch reads Feature values. Fields: genericMetadata: Operation metadata for Featurestore batch read Features values. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1BatchReadFeatureValuesRequest(_messages.Message): r"""Request message for FeaturestoreService.BatchReadFeatureValues. Fields: bigqueryReadInstances: Similar to csv_read_instances, but from BigQuery source. csvReadInstances: Each read instance consists of exactly one read timestamp and one or more entity IDs identifying entities of the corresponding EntityTypes whose Features are requested. Each output instance contains Feature values of requested entities concatenated together as of the read time. An example read instance may be `foo_entity_id, bar_entity_id, 2020-01-01T10:00:00.123Z`. An example output instance may be `foo_entity_id, bar_entity_id, 2020-01-01T10:00:00.123Z, foo_entity_feature1_value, bar_entity_feature2_value`. Timestamp in each read instance must be millisecond-aligned. `csv_read_instances` are read instances stored in a plain-text CSV file. The header should be: [ENTITY_TYPE_ID1], [ENTITY_TYPE_ID2], ..., timestamp The columns can be in any order. Values in the timestamp column must use the RFC 3339 format, e.g. `2012-07-30T10:43:17.123Z`. destination: Required. Specifies output location and format. entityTypeSpecs: Required. Specifies EntityType grouping Features to read values of and settings. passThroughFields: When not empty, the specified fields in the *_read_instances source will be joined as-is in the output, in addition to those fields from the Featurestore Entity. For BigQuery source, the type of the pass-through values will be automatically inferred. For CSV source, the pass-through values will be passed as opaque bytes. startTime: Optional. Excludes Feature values with feature generation timestamp before this timestamp. If not set, retrieve oldest values kept in Feature Store. Timestamp, if present, must not have higher than millisecond precision. """ bigqueryReadInstances = _messages.MessageField('GoogleCloudAiplatformV1BigQuerySource', 1) csvReadInstances = _messages.MessageField('GoogleCloudAiplatformV1CsvSource', 2) destination = _messages.MessageField('GoogleCloudAiplatformV1FeatureValueDestination', 3) entityTypeSpecs = _messages.MessageField('GoogleCloudAiplatformV1BatchReadFeatureValuesRequestEntityTypeSpec', 4, repeated=True) passThroughFields = _messages.MessageField('GoogleCloudAiplatformV1BatchReadFeatureValuesRequestPassThroughField', 5, repeated=True) startTime = _messages.StringField(6) class GoogleCloudAiplatformV1BatchReadFeatureValuesRequestEntityTypeSpec(_messages.Message): r"""Selects Features of an EntityType to read values of and specifies read settings. Fields: entityTypeId: Required. ID of the EntityType to select Features. The EntityType id is the entity_type_id specified during EntityType creation. featureSelector: Required. Selectors choosing which Feature values to read from the EntityType. settings: Per-Feature settings for the batch read. """ entityTypeId = _messages.StringField(1) featureSelector = _messages.MessageField('GoogleCloudAiplatformV1FeatureSelector', 2) settings = _messages.MessageField('GoogleCloudAiplatformV1DestinationFeatureSetting', 3, repeated=True) class GoogleCloudAiplatformV1BatchReadFeatureValuesRequestPassThroughField(_messages.Message): r"""Describe pass-through fields in read_instance source. Fields: fieldName: Required. The name of the field in the CSV header or the name of the column in BigQuery table. The naming restriction is the same as Feature.name. """ fieldName = _messages.StringField(1) class GoogleCloudAiplatformV1BatchReadFeatureValuesResponse(_messages.Message): r"""Response message for FeaturestoreService.BatchReadFeatureValues.""" class GoogleCloudAiplatformV1BatchReadTensorboardTimeSeriesDataResponse(_messages.Message): r"""Response message for TensorboardService.BatchReadTensorboardTimeSeriesData. Fields: timeSeriesData: The returned time series data. """ timeSeriesData = _messages.MessageField('GoogleCloudAiplatformV1TimeSeriesData', 1, repeated=True) class GoogleCloudAiplatformV1BigQueryDestination(_messages.Message): r"""The BigQuery location for the output content. Fields: outputUri: Required. BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: `bq://projectId` or `bq://projectId.bqDatasetId` or `bq://projectId.bqDatasetId.bqTableId`. """ outputUri = _messages.StringField(1) class GoogleCloudAiplatformV1BigQuerySource(_messages.Message): r"""The BigQuery location for the input content. Fields: inputUri: Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`. """ inputUri = _messages.StringField(1) class GoogleCloudAiplatformV1BleuInput(_messages.Message): r"""Input for bleu metric. Fields: instances: Required. Repeated bleu instances. metricSpec: Required. Spec for bleu score metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1BleuInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1BleuSpec', 2) class GoogleCloudAiplatformV1BleuInstance(_messages.Message): r"""Spec for bleu instance. Fields: prediction: Required. Output of the evaluated model. reference: Required. Ground truth used to compare against the prediction. """ prediction = _messages.StringField(1) reference = _messages.StringField(2) class GoogleCloudAiplatformV1BleuMetricValue(_messages.Message): r"""Bleu metric value for an instance. Fields: score: Output only. Bleu score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1BleuResults(_messages.Message): r"""Results for bleu metric. Fields: bleuMetricValues: Output only. Bleu metric values. """ bleuMetricValues = _messages.MessageField('GoogleCloudAiplatformV1BleuMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1BleuSpec(_messages.Message): r"""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. Fields: useEffectiveOrder: Optional. Whether to use_effective_order to compute bleu score. """ useEffectiveOrder = _messages.BooleanField(1) class GoogleCloudAiplatformV1Blob(_messages.Message): r"""Content blob. Fields: data: Required. Raw bytes. mimeType: Required. The IANA standard MIME type of the source data. """ data = _messages.BytesField(1) mimeType = _messages.StringField(2) class GoogleCloudAiplatformV1BlurBaselineConfig(_messages.Message): r"""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 Fields: maxBlurSigma: The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline. """ maxBlurSigma = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1BoolArray(_messages.Message): r"""A list of boolean values. Fields: values: A list of bool values. """ values = _messages.BooleanField(1, repeated=True) class GoogleCloudAiplatformV1CachedContent(_messages.Message): r"""A resource used in LLM queries for users to explicitly specify what to cache and how to cache. Fields: contents: Optional. Input only. Immutable. The content to cache createTime: Output only. Creation time of the cache entry. displayName: Optional. Immutable. The user-generated meaningful display name of the cached content. encryptionSpec: Input only. Immutable. Customer-managed encryption key spec for a `CachedContent`. If set, this `CachedContent` and all its sub-resources will be secured by this key. expireTime: Timestamp of when this resource is considered expired. This is *always* provided on output, regardless of what was sent on input. model: Immutable. The name of the `Model` to use for cached content. Currently, only the published Gemini base models are supported, in form of projects/{PROJECT}/locations/{LOCATION}/publishers/google/models/{MODEL} name: Immutable. Identifier. The server-generated resource name of the cached content Format: projects/{project}/locations/{location}/cachedContents/{cached_content} systemInstruction: Optional. Input only. Immutable. Developer set system instruction. Currently, text only toolConfig: Optional. Input only. Immutable. Tool config. This config is shared for all tools tools: Optional. Input only. Immutable. A list of `Tools` the model may use to generate the next response ttl: Input only. The TTL for this resource. The expiration time is computed: now + TTL. updateTime: Output only. When the cache entry was last updated in UTC time. usageMetadata: Output only. Metadata on the usage of the cached content. """ contents = _messages.MessageField('GoogleCloudAiplatformV1Content', 1, repeated=True) createTime = _messages.StringField(2) displayName = _messages.StringField(3) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 4) expireTime = _messages.StringField(5) model = _messages.StringField(6) name = _messages.StringField(7) systemInstruction = _messages.MessageField('GoogleCloudAiplatformV1Content', 8) toolConfig = _messages.MessageField('GoogleCloudAiplatformV1ToolConfig', 9) tools = _messages.MessageField('GoogleCloudAiplatformV1Tool', 10, repeated=True) ttl = _messages.StringField(11) updateTime = _messages.StringField(12) usageMetadata = _messages.MessageField('GoogleCloudAiplatformV1CachedContentUsageMetadata', 13) class GoogleCloudAiplatformV1CachedContentUsageMetadata(_messages.Message): r"""Metadata on the usage of the cached content. Fields: audioDurationSeconds: Duration of audio in seconds. imageCount: Number of images. textCount: Number of text characters. totalTokenCount: Total number of tokens that the cached content consumes. videoDurationSeconds: Duration of video in seconds. """ audioDurationSeconds = _messages.IntegerField(1, variant=_messages.Variant.INT32) imageCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) textCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) totalTokenCount = _messages.IntegerField(4, variant=_messages.Variant.INT32) videoDurationSeconds = _messages.IntegerField(5, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1CancelBatchPredictionJobRequest(_messages.Message): r"""Request message for JobService.CancelBatchPredictionJob.""" class GoogleCloudAiplatformV1CancelCustomJobRequest(_messages.Message): r"""Request message for JobService.CancelCustomJob.""" class GoogleCloudAiplatformV1CancelHyperparameterTuningJobRequest(_messages.Message): r"""Request message for JobService.CancelHyperparameterTuningJob.""" class GoogleCloudAiplatformV1CancelNasJobRequest(_messages.Message): r"""Request message for JobService.CancelNasJob.""" class GoogleCloudAiplatformV1CancelPipelineJobRequest(_messages.Message): r"""Request message for PipelineService.CancelPipelineJob.""" class GoogleCloudAiplatformV1CancelTrainingPipelineRequest(_messages.Message): r"""Request message for PipelineService.CancelTrainingPipeline.""" class GoogleCloudAiplatformV1CancelTuningJobRequest(_messages.Message): r"""Request message for GenAiTuningService.CancelTuningJob.""" class GoogleCloudAiplatformV1Candidate(_messages.Message): r"""A response candidate generated from the model. Enums: FinishReasonValueValuesEnum: Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens. Fields: avgLogprobs: Output only. Average log probability score of the candidate. citationMetadata: Output only. Source attribution of the generated content. content: Output only. Content parts of the candidate. finishMessage: Output only. Describes the reason the mode stopped generating tokens in more detail. This is only filled when `finish_reason` is set. finishReason: Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens. groundingMetadata: Output only. Metadata specifies sources used to ground generated content. index: Output only. Index of the candidate. logprobsResult: Output only. Log-likelihood scores for the response tokens and top tokens safetyRatings: Output only. List of ratings for the safety of a response candidate. There is at most one rating per category. urlContextMetadata: Output only. Metadata related to url context retrieval tool. """ class FinishReasonValueValuesEnum(_messages.Enum): r"""Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens. Values: FINISH_REASON_UNSPECIFIED: The finish reason is unspecified. STOP: Token generation reached a natural stopping point or a configured stop sequence. MAX_TOKENS: Token generation reached the configured maximum output tokens. SAFETY: Token generation stopped because the content potentially contains safety violations. NOTE: When streaming, content is empty if content filters blocks the output. RECITATION: The token generation stopped because of potential recitation. OTHER: All other reasons that stopped the token generation. BLOCKLIST: Token generation stopped because the content contains forbidden terms. PROHIBITED_CONTENT: Token generation stopped for potentially containing prohibited content. SPII: Token generation stopped because the content potentially contains Sensitive Personally Identifiable Information (SPII). MALFORMED_FUNCTION_CALL: The function call generated by the model is invalid. MODEL_ARMOR: The model response was blocked by Model Armor. IMAGE_SAFETY: Token generation stopped because generated images has safety violations. IMAGE_PROHIBITED_CONTENT: Image generation stopped because generated images has other prohibited content. IMAGE_RECITATION: Image generation stopped due to recitation. IMAGE_OTHER: Image generation stopped because of other miscellaneous issue. UNEXPECTED_TOOL_CALL: The tool call generated by the model is invalid. """ FINISH_REASON_UNSPECIFIED = 0 STOP = 1 MAX_TOKENS = 2 SAFETY = 3 RECITATION = 4 OTHER = 5 BLOCKLIST = 6 PROHIBITED_CONTENT = 7 SPII = 8 MALFORMED_FUNCTION_CALL = 9 MODEL_ARMOR = 10 IMAGE_SAFETY = 11 IMAGE_PROHIBITED_CONTENT = 12 IMAGE_RECITATION = 13 IMAGE_OTHER = 14 UNEXPECTED_TOOL_CALL = 15 avgLogprobs = _messages.FloatField(1) citationMetadata = _messages.MessageField('GoogleCloudAiplatformV1CitationMetadata', 2) content = _messages.MessageField('GoogleCloudAiplatformV1Content', 3) finishMessage = _messages.StringField(4) finishReason = _messages.EnumField('FinishReasonValueValuesEnum', 5) groundingMetadata = _messages.MessageField('GoogleCloudAiplatformV1GroundingMetadata', 6) index = _messages.IntegerField(7, variant=_messages.Variant.INT32) logprobsResult = _messages.MessageField('GoogleCloudAiplatformV1LogprobsResult', 8) safetyRatings = _messages.MessageField('GoogleCloudAiplatformV1SafetyRating', 9, repeated=True) urlContextMetadata = _messages.MessageField('GoogleCloudAiplatformV1UrlContextMetadata', 10) class GoogleCloudAiplatformV1CheckTrialEarlyStoppingStateMetatdata(_messages.Message): r"""This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request. Fields: genericMetadata: Operation metadata for suggesting Trials. study: The name of the Study that the Trial belongs to. trial: The Trial name. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) study = _messages.StringField(2) trial = _messages.StringField(3) class GoogleCloudAiplatformV1CheckTrialEarlyStoppingStateRequest(_messages.Message): r"""Request message for VizierService.CheckTrialEarlyStoppingState.""" class GoogleCloudAiplatformV1CheckTrialEarlyStoppingStateResponse(_messages.Message): r"""Response message for VizierService.CheckTrialEarlyStoppingState. Fields: shouldStop: True if the Trial should stop. """ shouldStop = _messages.BooleanField(1) class GoogleCloudAiplatformV1Checkpoint(_messages.Message): r"""Describes the machine learning model version checkpoint. Fields: checkpointId: The ID of the checkpoint. epoch: The epoch of the checkpoint. step: The step of the checkpoint. """ checkpointId = _messages.StringField(1) epoch = _messages.IntegerField(2) step = _messages.IntegerField(3) class GoogleCloudAiplatformV1Citation(_messages.Message): r"""Source attributions for content. Fields: endIndex: Output only. End index into the content. license: Output only. License of the attribution. publicationDate: Output only. Publication date of the attribution. startIndex: Output only. Start index into the content. title: Output only. Title of the attribution. uri: Output only. Url reference of the attribution. """ endIndex = _messages.IntegerField(1, variant=_messages.Variant.INT32) license = _messages.StringField(2) publicationDate = _messages.MessageField('GoogleTypeDate', 3) startIndex = _messages.IntegerField(4, variant=_messages.Variant.INT32) title = _messages.StringField(5) uri = _messages.StringField(6) class GoogleCloudAiplatformV1CitationMetadata(_messages.Message): r"""A collection of source attributions for a piece of content. Fields: citations: Output only. List of citations. """ citations = _messages.MessageField('GoogleCloudAiplatformV1Citation', 1, repeated=True) class GoogleCloudAiplatformV1Claim(_messages.Message): r"""Claim that is extracted from the input text and facts that support it. Fields: endIndex: Index in the input text where the claim ends (exclusive). factIndexes: Indexes of the facts supporting this claim. score: Confidence score of this corroboration. startIndex: Index in the input text where the claim starts (inclusive). """ endIndex = _messages.IntegerField(1, variant=_messages.Variant.INT32) factIndexes = _messages.IntegerField(2, repeated=True, variant=_messages.Variant.INT32) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) startIndex = _messages.IntegerField(4, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1ClientConnectionConfig(_messages.Message): r"""Configurations (e.g. inference timeout) that are applied on your endpoints. Fields: inferenceTimeout: Customizable online prediction request timeout. """ inferenceTimeout = _messages.StringField(1) class GoogleCloudAiplatformV1CodeExecutionResult(_messages.Message): r"""Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. Enums: OutcomeValueValuesEnum: Required. Outcome of the code execution. Fields: outcome: Required. Outcome of the code execution. output: Optional. Contains stdout when code execution is successful, stderr or other description otherwise. """ class OutcomeValueValuesEnum(_messages.Enum): r"""Required. Outcome of the code execution. Values: OUTCOME_UNSPECIFIED: Unspecified status. This value should not be used. OUTCOME_OK: Code execution completed successfully. OUTCOME_FAILED: Code execution finished but with a failure. `stderr` should contain the reason. OUTCOME_DEADLINE_EXCEEDED: Code execution ran for too long, and was cancelled. There may or may not be a partial output present. """ OUTCOME_UNSPECIFIED = 0 OUTCOME_OK = 1 OUTCOME_FAILED = 2 OUTCOME_DEADLINE_EXCEEDED = 3 outcome = _messages.EnumField('OutcomeValueValuesEnum', 1) output = _messages.StringField(2) class GoogleCloudAiplatformV1CoherenceInput(_messages.Message): r"""Input for coherence metric. Fields: instance: Required. Coherence instance. metricSpec: Required. Spec for coherence score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1CoherenceInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1CoherenceSpec', 2) class GoogleCloudAiplatformV1CoherenceInstance(_messages.Message): r"""Spec for coherence instance. Fields: prediction: Required. Output of the evaluated model. """ prediction = _messages.StringField(1) class GoogleCloudAiplatformV1CoherenceResult(_messages.Message): r"""Spec for coherence result. Fields: confidence: Output only. Confidence for coherence score. explanation: Output only. Explanation for coherence score. score: Output only. Coherence score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1CoherenceSpec(_messages.Message): r"""Spec for coherence score metric. Fields: version: Optional. Which version to use for evaluation. """ version = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1CometInput(_messages.Message): r"""Input for Comet metric. Fields: instance: Required. Comet instance. metricSpec: Required. Spec for comet metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1CometInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1CometSpec', 2) class GoogleCloudAiplatformV1CometInstance(_messages.Message): r"""Spec for Comet instance - The fields used for evaluation are dependent on the comet version. Fields: prediction: Required. Output of the evaluated model. reference: Optional. Ground truth used to compare against the prediction. source: Optional. Source text in original language. """ prediction = _messages.StringField(1) reference = _messages.StringField(2) source = _messages.StringField(3) class GoogleCloudAiplatformV1CometResult(_messages.Message): r"""Spec for Comet result - calculates the comet score for the given instance using the version specified in the spec. Fields: score: Output only. Comet score. Range depends on version. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1CometSpec(_messages.Message): r"""Spec for Comet metric. Enums: VersionValueValuesEnum: Required. Which version to use for evaluation. Fields: sourceLanguage: Optional. Source language in BCP-47 format. targetLanguage: Optional. Target language in BCP-47 format. Covers both prediction and reference. version: Required. Which version to use for evaluation. """ class VersionValueValuesEnum(_messages.Enum): r"""Required. Which version to use for evaluation. Values: COMET_VERSION_UNSPECIFIED: Comet version unspecified. COMET_22_SRC_REF: Comet 22 for translation + source + reference (source- reference-combined). """ COMET_VERSION_UNSPECIFIED = 0 COMET_22_SRC_REF = 1 sourceLanguage = _messages.StringField(1) targetLanguage = _messages.StringField(2) version = _messages.EnumField('VersionValueValuesEnum', 3) class GoogleCloudAiplatformV1CompleteTrialRequest(_messages.Message): r"""Request message for VizierService.CompleteTrial. Fields: finalMeasurement: Optional. If provided, it will be used as the completed Trial's final_measurement; Otherwise, the service will auto-select a previously reported measurement as the final-measurement infeasibleReason: Optional. A human readable reason why the trial was infeasible. This should only be provided if `trial_infeasible` is true. trialInfeasible: Optional. True if the Trial cannot be run with the given Parameter, and final_measurement will be ignored. """ finalMeasurement = _messages.MessageField('GoogleCloudAiplatformV1Measurement', 1) infeasibleReason = _messages.StringField(2) trialInfeasible = _messages.BooleanField(3) class GoogleCloudAiplatformV1CompletionStats(_messages.Message): r"""Success and error statistics of processing multiple entities (for example, DataItems or structured data rows) in batch. Fields: failedCount: Output only. The number of entities for which any error was encountered. incompleteCount: Output only. In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected). successfulCount: Output only. The number of entities that had been processed successfully. successfulForecastPointCount: Output only. The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction. """ failedCount = _messages.IntegerField(1) incompleteCount = _messages.IntegerField(2) successfulCount = _messages.IntegerField(3) successfulForecastPointCount = _messages.IntegerField(4) class GoogleCloudAiplatformV1ComputeTokensRequest(_messages.Message): r"""Request message for ComputeTokens RPC call. Fields: contents: Optional. Input content. instances: Optional. The instances that are the input to token computing API call. Schema is identical to the prediction schema of the text model, even for the non-text models, like chat models, or Codey models. model: Optional. The name of the publisher model requested to serve the prediction. Format: projects/{project}/locations/{location}/publishers/*/models/* """ contents = _messages.MessageField('GoogleCloudAiplatformV1Content', 1, repeated=True) instances = _messages.MessageField('extra_types.JsonValue', 2, repeated=True) model = _messages.StringField(3) class GoogleCloudAiplatformV1ComputeTokensResponse(_messages.Message): r"""Response message for ComputeTokens RPC call. Fields: tokensInfo: Lists of tokens info from the input. A ComputeTokensRequest could have multiple instances with a prompt in each instance. We also need to return lists of tokens info for the request with multiple instances. """ tokensInfo = _messages.MessageField('GoogleCloudAiplatformV1TokensInfo', 1, repeated=True) class GoogleCloudAiplatformV1ContainerRegistryDestination(_messages.Message): r"""The Container Registry location for the container image. Fields: outputUri: Required. Container Registry URI of a container image. Only Google Container Registry and Artifact Registry are supported now. Accepted forms: * Google Container Registry path. For example: `gcr.io/projectId/imageName:tag`. * Artifact Registry path. For example: `us-central1-docker.pkg.dev/projectId/repoName/imageName:tag`. If a tag is not specified, "latest" will be used as the default tag. """ outputUri = _messages.StringField(1) class GoogleCloudAiplatformV1ContainerSpec(_messages.Message): r"""The spec of a Container. Fields: args: The arguments to be passed when starting the container. command: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided. env: Environment variables to be passed to the container. Maximum limit is 100. imageUri: Required. The URI of a container image in the Container Registry that is to be run on each worker replica. """ args = _messages.StringField(1, repeated=True) command = _messages.StringField(2, repeated=True) env = _messages.MessageField('GoogleCloudAiplatformV1EnvVar', 3, repeated=True) imageUri = _messages.StringField(4) class GoogleCloudAiplatformV1Content(_messages.Message): r"""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. Fields: parts: Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. role: Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset. """ parts = _messages.MessageField('GoogleCloudAiplatformV1Part', 1, repeated=True) role = _messages.StringField(2) class GoogleCloudAiplatformV1Context(_messages.Message): r"""Instance of a general context. Messages: LabelsValue: The labels with user-defined metadata to organize your Contexts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Context (System labels are excluded). MetadataValue: Properties of the Context. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB. Fields: createTime: Output only. Timestamp when this Context was created. description: Description of the Context displayName: User provided display name of the Context. May be up to 128 Unicode characters. etag: An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: The labels with user-defined metadata to organize your Contexts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Context (System labels are excluded). metadata: Properties of the Context. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB. name: Immutable. The resource name of the Context. parentContexts: Output only. A list of resource names of Contexts that are parents of this Context. A Context may have at most 10 parent_contexts. schemaTitle: The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store. schemaVersion: The version of the schema in schema_name to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store. updateTime: Output only. Timestamp when this Context was last updated. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your Contexts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Context (System labels are excluded). Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class MetadataValue(_messages.Message): r"""Properties of the Context. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB. Messages: AdditionalProperty: An additional property for a MetadataValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a MetadataValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) description = _messages.StringField(2) displayName = _messages.StringField(3) etag = _messages.StringField(4) labels = _messages.MessageField('LabelsValue', 5) metadata = _messages.MessageField('MetadataValue', 6) name = _messages.StringField(7) parentContexts = _messages.StringField(8, repeated=True) schemaTitle = _messages.StringField(9) schemaVersion = _messages.StringField(10) updateTime = _messages.StringField(11) class GoogleCloudAiplatformV1CopyModelOperationMetadata(_messages.Message): r"""Details of ModelService.CopyModel operation. Fields: genericMetadata: The common part of the operation metadata. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CopyModelRequest(_messages.Message): r"""Request message for ModelService.CopyModel. Fields: encryptionSpec: Customer-managed encryption key options. If this is set, then the Model copy will be encrypted with the provided encryption key. modelId: Optional. Copy source_model into a new Model with this ID. The ID will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are `[a-z0-9_-]`. The first character cannot be a number or hyphen. parentModel: Optional. Specify this field to copy source_model into this existing Model as a new version. Format: `projects/{project}/locations/{location}/models/{model}` sourceModel: Required. The resource name of the Model to copy. That Model must be in the same Project. Format: `projects/{project}/locations/{location}/models/{model}` """ encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 1) modelId = _messages.StringField(2) parentModel = _messages.StringField(3) sourceModel = _messages.StringField(4) class GoogleCloudAiplatformV1CopyModelResponse(_messages.Message): r"""Response message of ModelService.CopyModel operation. Fields: model: The name of the copied Model resource. Format: `projects/{project}/locations/{location}/models/{model}` modelVersionId: Output only. The version ID of the model that is copied. """ model = _messages.StringField(1) modelVersionId = _messages.StringField(2) class GoogleCloudAiplatformV1CorpusStatus(_messages.Message): r"""RagCorpus status. Enums: StateValueValuesEnum: Output only. RagCorpus life state. Fields: errorStatus: Output only. Only when the `state` field is ERROR. state: Output only. RagCorpus life state. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. RagCorpus life state. Values: UNKNOWN: This state is not supposed to happen. INITIALIZED: RagCorpus resource entry is initialized, but hasn't done validation. ACTIVE: RagCorpus is provisioned successfully and is ready to serve. ERROR: RagCorpus is in a problematic situation. See `error_message` field for details. """ UNKNOWN = 0 INITIALIZED = 1 ACTIVE = 2 ERROR = 3 errorStatus = _messages.StringField(1) state = _messages.EnumField('StateValueValuesEnum', 2) class GoogleCloudAiplatformV1CorroborateContentRequest(_messages.Message): r"""Request message for CorroborateContent. Fields: content: Optional. Input content to corroborate, only text format is supported for now. facts: Optional. Facts used to generate the text can also be used to corroborate the text. parameters: Optional. Parameters that can be set to override default settings per request. """ content = _messages.MessageField('GoogleCloudAiplatformV1Content', 1) facts = _messages.MessageField('GoogleCloudAiplatformV1Fact', 2, repeated=True) parameters = _messages.MessageField('GoogleCloudAiplatformV1CorroborateContentRequestParameters', 3) class GoogleCloudAiplatformV1CorroborateContentRequestParameters(_messages.Message): r"""Parameters that can be overrided per request. Fields: citationThreshold: Optional. Only return claims with citation score larger than the threshold. """ citationThreshold = _messages.FloatField(1) class GoogleCloudAiplatformV1CorroborateContentResponse(_messages.Message): r"""Response message for CorroborateContent. Fields: claims: Claims that are extracted from the input content and facts that support the claims. corroborationScore: Confidence score of corroborating content. Value is [0,1] with 1 is the most confidence. """ claims = _messages.MessageField('GoogleCloudAiplatformV1Claim', 1, repeated=True) corroborationScore = _messages.FloatField(2, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1CountTokensRequest(_messages.Message): r"""Request message for PredictionService.CountTokens. Fields: contents: Optional. Input content. generationConfig: Optional. Generation config that the model will use to generate the response. instances: Optional. The instances that are the input to token counting call. Schema is identical to the prediction schema of the underlying model. model: Optional. The name of the publisher model requested to serve the prediction. Format: `projects/{project}/locations/{location}/publishers/*/models/*` systemInstruction: Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph. tools: Optional. A list of `Tools` the model may use to generate the next 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. """ contents = _messages.MessageField('GoogleCloudAiplatformV1Content', 1, repeated=True) generationConfig = _messages.MessageField('GoogleCloudAiplatformV1GenerationConfig', 2) instances = _messages.MessageField('extra_types.JsonValue', 3, repeated=True) model = _messages.StringField(4) systemInstruction = _messages.MessageField('GoogleCloudAiplatformV1Content', 5) tools = _messages.MessageField('GoogleCloudAiplatformV1Tool', 6, repeated=True) class GoogleCloudAiplatformV1CountTokensResponse(_messages.Message): r"""Response message for PredictionService.CountTokens. Fields: promptTokensDetails: Output only. List of modalities that were processed in the request input. totalBillableCharacters: The total number of billable characters counted across all instances from the request. totalTokens: The total number of tokens counted across all instances from the request. """ promptTokensDetails = _messages.MessageField('GoogleCloudAiplatformV1ModalityTokenCount', 1, repeated=True) totalBillableCharacters = _messages.IntegerField(2, variant=_messages.Variant.INT32) totalTokens = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1CreateDatasetOperationMetadata(_messages.Message): r"""Runtime operation information for DatasetService.CreateDataset. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateDatasetVersionOperationMetadata(_messages.Message): r"""Runtime operation information for DatasetService.CreateDatasetVersion. Fields: genericMetadata: The common part of the operation metadata. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateDeploymentResourcePoolOperationMetadata(_messages.Message): r"""Runtime operation information for CreateDeploymentResourcePool method. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateDeploymentResourcePoolRequest(_messages.Message): r"""Request message for CreateDeploymentResourcePool method. Fields: deploymentResourcePool: Required. The DeploymentResourcePool to create. deploymentResourcePoolId: Required. The ID to use for the DeploymentResourcePool, which will become the final component of the DeploymentResourcePool's resource name. The maximum length is 63 characters, and valid characters are `/^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/`. """ deploymentResourcePool = _messages.MessageField('GoogleCloudAiplatformV1DeploymentResourcePool', 1) deploymentResourcePoolId = _messages.StringField(2) class GoogleCloudAiplatformV1CreateEndpointOperationMetadata(_messages.Message): r"""Runtime operation information for EndpointService.CreateEndpoint. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateEntityTypeOperationMetadata(_messages.Message): r"""Details of operations that perform create EntityType. Fields: genericMetadata: Operation metadata for EntityType. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateFeatureGroupOperationMetadata(_messages.Message): r"""Details of operations that perform create FeatureGroup. Fields: genericMetadata: Operation metadata for FeatureGroup. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateFeatureOnlineStoreOperationMetadata(_messages.Message): r"""Details of operations that perform create FeatureOnlineStore. Fields: genericMetadata: Operation metadata for FeatureOnlineStore. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateFeatureOperationMetadata(_messages.Message): r"""Details of operations that perform create Feature. Fields: genericMetadata: Operation metadata for Feature. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateFeatureRequest(_messages.Message): r"""Request message for FeaturestoreService.CreateFeature. Request message for FeatureRegistryService.CreateFeature. Fields: feature: Required. The Feature to create. featureId: Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 128 characters, and valid characters are `[a-z0-9_]`. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup. parent: Required. The resource name of the EntityType or FeatureGroup to create a Feature. Format for entity_type as parent: `projects/{project}/ locations/{location}/featurestores/{featurestore}/entityTypes/{entity_ty pe}` Format for feature_group as parent: `projects/{project}/locations/{location}/featureGroups/{feature_group}` """ feature = _messages.MessageField('GoogleCloudAiplatformV1Feature', 1) featureId = _messages.StringField(2) parent = _messages.StringField(3) class GoogleCloudAiplatformV1CreateFeatureViewOperationMetadata(_messages.Message): r"""Details of operations that perform create FeatureView. Fields: genericMetadata: Operation metadata for FeatureView Create. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateFeaturestoreOperationMetadata(_messages.Message): r"""Details of operations that perform create Featurestore. Fields: genericMetadata: Operation metadata for Featurestore. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateIndexEndpointOperationMetadata(_messages.Message): r"""Runtime operation information for IndexEndpointService.CreateIndexEndpoint. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateIndexOperationMetadata(_messages.Message): r"""Runtime operation information for IndexService.CreateIndex. Fields: genericMetadata: The operation generic information. nearestNeighborSearchOperationMetadata: The operation metadata with regard to Matching Engine Index operation. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) nearestNeighborSearchOperationMetadata = _messages.MessageField('GoogleCloudAiplatformV1NearestNeighborSearchOperationMetadata', 2) class GoogleCloudAiplatformV1CreateMetadataStoreOperationMetadata(_messages.Message): r"""Details of operations that perform MetadataService.CreateMetadataStore. Fields: genericMetadata: Operation metadata for creating a MetadataStore. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateNotebookExecutionJobOperationMetadata(_messages.Message): r"""Metadata information for NotebookService.CreateNotebookExecutionJob. Fields: genericMetadata: The operation generic information. progressMessage: A human-readable message that shows the intermediate progress details of NotebookRuntime. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) progressMessage = _messages.StringField(2) class GoogleCloudAiplatformV1CreateNotebookExecutionJobRequest(_messages.Message): r"""Request message for [NotebookService.CreateNotebookExecutionJob] Fields: notebookExecutionJob: Required. The NotebookExecutionJob to create. notebookExecutionJobId: Optional. User specified ID for the NotebookExecutionJob. parent: Required. The resource name of the Location to create the NotebookExecutionJob. Format: `projects/{project}/locations/{location}` """ notebookExecutionJob = _messages.MessageField('GoogleCloudAiplatformV1NotebookExecutionJob', 1) notebookExecutionJobId = _messages.StringField(2) parent = _messages.StringField(3) class GoogleCloudAiplatformV1CreateNotebookRuntimeTemplateOperationMetadata(_messages.Message): r"""Metadata information for NotebookService.CreateNotebookRuntimeTemplate. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreatePersistentResourceOperationMetadata(_messages.Message): r"""Details of operations that perform create PersistentResource. Fields: genericMetadata: Operation metadata for PersistentResource. progressMessage: Progress Message for Create LRO """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) progressMessage = _messages.StringField(2) class GoogleCloudAiplatformV1CreatePipelineJobRequest(_messages.Message): r"""Request message for PipelineService.CreatePipelineJob. Fields: parent: Required. The resource name of the Location to create the PipelineJob in. Format: `projects/{project}/locations/{location}` pipelineJob: Required. The PipelineJob to create. pipelineJobId: The ID to use for the PipelineJob, which will become the final component of the PipelineJob name. If not provided, an ID will be automatically generated. This value should be less than 128 characters, and valid characters are `/a-z-/`. """ parent = _messages.StringField(1) pipelineJob = _messages.MessageField('GoogleCloudAiplatformV1PipelineJob', 2) pipelineJobId = _messages.StringField(3) class GoogleCloudAiplatformV1CreateRegistryFeatureOperationMetadata(_messages.Message): r"""Details of operations that perform create FeatureGroup. Fields: genericMetadata: Operation metadata for Feature. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateSpecialistPoolOperationMetadata(_messages.Message): r"""Runtime operation information for SpecialistPoolService.CreateSpecialistPool. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateTensorboardOperationMetadata(_messages.Message): r"""Details of operations that perform create Tensorboard. Fields: genericMetadata: Operation metadata for Tensorboard. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1CreateTensorboardRunRequest(_messages.Message): r"""Request message for TensorboardService.CreateTensorboardRun. Fields: parent: Required. The resource name of the TensorboardExperiment to create the TensorboardRun in. Format: `projects/{project}/locations/{location}/ tensorboards/{tensorboard}/experiments/{experiment}` tensorboardRun: Required. The TensorboardRun to create. tensorboardRunId: Required. The ID to use for the Tensorboard run, which becomes the final component of the Tensorboard run's resource name. This value should be 1-128 characters, and valid characters are `/a-z-/`. """ parent = _messages.StringField(1) tensorboardRun = _messages.MessageField('GoogleCloudAiplatformV1TensorboardRun', 2) tensorboardRunId = _messages.StringField(3) class GoogleCloudAiplatformV1CreateTensorboardTimeSeriesRequest(_messages.Message): r"""Request message for TensorboardService.CreateTensorboardTimeSeries. Fields: parent: Required. The resource name of the TensorboardRun to create the TensorboardTimeSeries in. Format: `projects/{project}/locations/{locatio n}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}` tensorboardTimeSeries: Required. The TensorboardTimeSeries to create. tensorboardTimeSeriesId: Optional. The user specified unique ID to use for the TensorboardTimeSeries, which becomes the final component of the TensorboardTimeSeries's resource name. This value should match "a-z0-9{0, 127}" """ parent = _messages.StringField(1) tensorboardTimeSeries = _messages.MessageField('GoogleCloudAiplatformV1TensorboardTimeSeries', 2) tensorboardTimeSeriesId = _messages.StringField(3) class GoogleCloudAiplatformV1CsvDestination(_messages.Message): r"""The storage details for CSV output content. Fields: gcsDestination: Required. Google Cloud Storage location. """ gcsDestination = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 1) class GoogleCloudAiplatformV1CsvSource(_messages.Message): r"""The storage details for CSV input content. Fields: gcsSource: Required. Google Cloud Storage location. """ gcsSource = _messages.MessageField('GoogleCloudAiplatformV1GcsSource', 1) class GoogleCloudAiplatformV1CustomJob(_messages.Message): r"""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). Enums: StateValueValuesEnum: Output only. The detailed state of the job. Messages: LabelsValue: The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. WebAccessUrisValue: Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug- interactive-shell) (one URI for each training node). Only available if job_spec.enable_web_access is `true`. The keys are names of each node in the training job; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell. Fields: createTime: Output only. Time when the CustomJob was created. displayName: Required. The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. encryptionSpec: Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key. endTime: Output only. Time when the CustomJob entered any of the following states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`. error: Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. jobSpec: Required. Job spec. labels: The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. name: Output only. Resource name of a CustomJob. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. startTime: Output only. Time when the CustomJob for the first time entered the `JOB_STATE_RUNNING` state. state: Output only. The detailed state of the job. updateTime: Output only. Time when the CustomJob was most recently updated. webAccessUris: Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug- interactive-shell) (one URI for each training node). Only available if job_spec.enable_web_access is `true`. The keys are names of each node in the training job; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of the job. Values: JOB_STATE_UNSPECIFIED: The job state is unspecified. JOB_STATE_QUEUED: The job has been just created or resumed and processing has not yet begun. JOB_STATE_PENDING: The service is preparing to run the job. JOB_STATE_RUNNING: The job is in progress. JOB_STATE_SUCCEEDED: The job completed successfully. JOB_STATE_FAILED: The job failed. JOB_STATE_CANCELLING: The job is being cancelled. From this state the job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. JOB_STATE_CANCELLED: The job has been cancelled. JOB_STATE_PAUSED: The job has been stopped, and can be resumed. JOB_STATE_EXPIRED: The job has expired. JOB_STATE_UPDATING: The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state. JOB_STATE_PARTIALLY_SUCCEEDED: The job is partially succeeded, some results may be missing due to errors. """ JOB_STATE_UNSPECIFIED = 0 JOB_STATE_QUEUED = 1 JOB_STATE_PENDING = 2 JOB_STATE_RUNNING = 3 JOB_STATE_SUCCEEDED = 4 JOB_STATE_FAILED = 5 JOB_STATE_CANCELLING = 6 JOB_STATE_CANCELLED = 7 JOB_STATE_PAUSED = 8 JOB_STATE_EXPIRED = 9 JOB_STATE_UPDATING = 10 JOB_STATE_PARTIALLY_SUCCEEDED = 11 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class WebAccessUrisValue(_messages.Message): r"""Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug- interactive-shell) (one URI for each training node). Only available if job_spec.enable_web_access is `true`. The keys are names of each node in the training job; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell. Messages: AdditionalProperty: An additional property for a WebAccessUrisValue object. Fields: additionalProperties: Additional properties of type WebAccessUrisValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a WebAccessUrisValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) displayName = _messages.StringField(2) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 3) endTime = _messages.StringField(4) error = _messages.MessageField('GoogleRpcStatus', 5) jobSpec = _messages.MessageField('GoogleCloudAiplatformV1CustomJobSpec', 6) labels = _messages.MessageField('LabelsValue', 7) name = _messages.StringField(8) satisfiesPzi = _messages.BooleanField(9) satisfiesPzs = _messages.BooleanField(10) startTime = _messages.StringField(11) state = _messages.EnumField('StateValueValuesEnum', 12) updateTime = _messages.StringField(13) webAccessUris = _messages.MessageField('WebAccessUrisValue', 14) class GoogleCloudAiplatformV1CustomJobSpec(_messages.Message): r"""Represents the spec of a CustomJob. Fields: baseOutputDirectory: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = `/model/` * AIP_CHECKPOINT_DIR = `/checkpoints/` * AIP_TENSORBOARD_LOG_DIR = `/logs/` For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = `//model/` * AIP_CHECKPOINT_DIR = `//checkpoints/` * AIP_TENSORBOARD_LOG_DIR = `//logs/` enableDashboardAccess: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to `true`, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). enableWebAccess: Optional. Whether you want Vertex AI to enable [interactive shell access](https://cloud.google.com/vertex- ai/docs/training/monitor-debug-interactive-shell) to training containers. If set to `true`, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). experiment: Optional. The Experiment associated with this job. Format: `pr ojects/{project}/locations/{location}/metadataStores/{metadataStores}/co ntexts/{experiment-name}` experimentRun: Optional. The Experiment Run associated with this job. Format: `projects/{project}/locations/{location}/metadataStores/{metadat aStores}/contexts/{experiment-name}-{experiment-run-name}` models: Optional. The name of the Model resources for which to generate a mapping to artifact URIs. Applicable only to some of the Google-provided custom jobs. Format: `projects/{project}/locations/{location}/models/{model}` In order to retrieve a specific version of the model, also provide the version ID or version alias. Example: `projects/{project}/locations/{location}/models/{model}@2` or `projects/{project}/locations/{location}/models/{model}@golden` If no version ID or alias is specified, the "default" version will be returned. The "default" version alias is created for the first version of the model, and can be moved to other versions later on. There will be exactly one default version. network: Optional. The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. To specify this field, you must have already [configured VPC Network Peering for Vertex AI](https://cloud.google.com/vertex- ai/docs/general/vpc-peering). If this field is left unspecified, the job is not peered with any network. persistentResourceId: Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected. protectedArtifactLocationId: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations pscInterfaceConfig: Optional. Configuration for PSC-I for CustomJob. reservedIpRanges: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range']. scheduling: Scheduling options for a CustomJob. serviceAccount: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the [Vertex AI Custom Code Service Agent](https://cloud.google.com/vertex-ai/docs/general/access- control#service-agents) for the CustomJob's project is used. tensorboard: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}` workerPoolSpecs: Required. The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value. """ baseOutputDirectory = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 1) enableDashboardAccess = _messages.BooleanField(2) enableWebAccess = _messages.BooleanField(3) experiment = _messages.StringField(4) experimentRun = _messages.StringField(5) models = _messages.StringField(6, repeated=True) network = _messages.StringField(7) persistentResourceId = _messages.StringField(8) protectedArtifactLocationId = _messages.StringField(9) pscInterfaceConfig = _messages.MessageField('GoogleCloudAiplatformV1PscInterfaceConfig', 10) reservedIpRanges = _messages.StringField(11, repeated=True) scheduling = _messages.MessageField('GoogleCloudAiplatformV1Scheduling', 12) serviceAccount = _messages.StringField(13) tensorboard = _messages.StringField(14) workerPoolSpecs = _messages.MessageField('GoogleCloudAiplatformV1WorkerPoolSpec', 15, repeated=True) class GoogleCloudAiplatformV1DataItem(_messages.Message): r"""A piece of data in a Dataset. Could be an image, a video, a document or plain text. Messages: LabelsValue: Optional. The labels with user-defined metadata to organize your DataItems. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one DataItem(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Fields: createTime: Output only. Timestamp when this DataItem was created. etag: Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: Optional. The labels with user-defined metadata to organize your DataItems. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one DataItem(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. name: Output only. The resource name of the DataItem. payload: Required. The data that the DataItem represents (for example, an image or a text snippet). The schema of the payload is stored in the parent Dataset's metadata schema's dataItemSchemaUri field. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. updateTime: Output only. Timestamp when this DataItem was last updated. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels with user-defined metadata to organize your DataItems. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one DataItem(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) etag = _messages.StringField(2) labels = _messages.MessageField('LabelsValue', 3) name = _messages.StringField(4) payload = _messages.MessageField('extra_types.JsonValue', 5) satisfiesPzi = _messages.BooleanField(6) satisfiesPzs = _messages.BooleanField(7) updateTime = _messages.StringField(8) class GoogleCloudAiplatformV1DataItemView(_messages.Message): r"""A container for a single DataItem and Annotations on it. Fields: annotations: The Annotations on the DataItem. If too many Annotations should be returned for the DataItem, this field will be truncated per annotations_limit in request. If it was, then the has_truncated_annotations will be set to true. dataItem: The DataItem. hasTruncatedAnnotations: True if and only if the Annotations field has been truncated. It happens if more Annotations for this DataItem met the request's annotation_filter than are allowed to be returned by annotations_limit. Note that if Annotations field is not being returned due to field mask, then this field will not be set to true no matter how many Annotations are there. """ annotations = _messages.MessageField('GoogleCloudAiplatformV1Annotation', 1, repeated=True) dataItem = _messages.MessageField('GoogleCloudAiplatformV1DataItem', 2) hasTruncatedAnnotations = _messages.BooleanField(3) class GoogleCloudAiplatformV1Dataset(_messages.Message): r"""A collection of DataItems and Annotations on them. Messages: LabelsValue: The labels with user-defined metadata to organize your Datasets. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title. Fields: createTime: Output only. Timestamp when this Dataset was created. dataItemCount: Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset. description: The description of the Dataset. displayName: Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters. encryptionSpec: Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key. etag: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: The labels with user-defined metadata to organize your Datasets. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title. metadata: Required. Additional information about the Dataset. metadataArtifact: Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is `projects/{project}/locations/{location}/metada taStores/{metadata_store}/artifacts/{artifact}`. metadataSchemaUri: Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud- aiplatform/schema/dataset/metadata/. modelReference: Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets. name: Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}` satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. savedQueries: All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec. updateTime: Output only. Timestamp when this Dataset was last updated. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your Datasets. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) dataItemCount = _messages.IntegerField(2) description = _messages.StringField(3) displayName = _messages.StringField(4) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 5) etag = _messages.StringField(6) labels = _messages.MessageField('LabelsValue', 7) metadata = _messages.MessageField('extra_types.JsonValue', 8) metadataArtifact = _messages.StringField(9) metadataSchemaUri = _messages.StringField(10) modelReference = _messages.StringField(11) name = _messages.StringField(12) satisfiesPzi = _messages.BooleanField(13) satisfiesPzs = _messages.BooleanField(14) savedQueries = _messages.MessageField('GoogleCloudAiplatformV1SavedQuery', 15, repeated=True) updateTime = _messages.StringField(16) class GoogleCloudAiplatformV1DatasetVersion(_messages.Message): r"""Describes the dataset version. Fields: bigQueryDatasetName: Output only. Name of the associated BigQuery dataset. createTime: Output only. Timestamp when this DatasetVersion was created. displayName: The user-defined name of the DatasetVersion. The name can be up to 128 characters long and can consist of any UTF-8 characters. etag: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. metadata: Required. Output only. Additional information about the DatasetVersion. modelReference: Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions. name: Output only. Identifier. The resource name of the DatasetVersion. Format: `projects/{project}/locations/{location}/datasets/{dataset}/data setVersions/{dataset_version}` satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. updateTime: Output only. Timestamp when this DatasetVersion was last updated. """ bigQueryDatasetName = _messages.StringField(1) createTime = _messages.StringField(2) displayName = _messages.StringField(3) etag = _messages.StringField(4) metadata = _messages.MessageField('extra_types.JsonValue', 5) modelReference = _messages.StringField(6) name = _messages.StringField(7) satisfiesPzi = _messages.BooleanField(8) satisfiesPzs = _messages.BooleanField(9) updateTime = _messages.StringField(10) class GoogleCloudAiplatformV1DedicatedResources(_messages.Message): r"""A description of resources that are dedicated to a DeployedModel or DeployedIndex, and that need a higher degree of manual configuration. Fields: autoscalingMetricSpecs: Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`. machineSpec: Required. Immutable. The specification of a single machine being used. maxReplicaCount: Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). minReplicaCount: Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. requiredReplicaCount: Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. spot: Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms). """ autoscalingMetricSpecs = _messages.MessageField('GoogleCloudAiplatformV1AutoscalingMetricSpec', 1, repeated=True) machineSpec = _messages.MessageField('GoogleCloudAiplatformV1MachineSpec', 2) maxReplicaCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) minReplicaCount = _messages.IntegerField(4, variant=_messages.Variant.INT32) requiredReplicaCount = _messages.IntegerField(5, variant=_messages.Variant.INT32) spot = _messages.BooleanField(6) class GoogleCloudAiplatformV1DeleteFeatureValuesOperationMetadata(_messages.Message): r"""Details of operations that delete Feature values. Fields: genericMetadata: Operation metadata for Featurestore delete Features values. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1DeleteFeatureValuesRequest(_messages.Message): r"""Request message for FeaturestoreService.DeleteFeatureValues. Fields: selectEntity: Select feature values to be deleted by specifying entities. selectTimeRangeAndFeature: Select feature values to be deleted by specifying time range and features. """ selectEntity = _messages.MessageField('GoogleCloudAiplatformV1DeleteFeatureValuesRequestSelectEntity', 1) selectTimeRangeAndFeature = _messages.MessageField('GoogleCloudAiplatformV1DeleteFeatureValuesRequestSelectTimeRangeAndFeature', 2) class GoogleCloudAiplatformV1DeleteFeatureValuesRequestSelectEntity(_messages.Message): r"""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. Fields: entityIdSelector: Required. Selectors choosing feature values of which entity id to be deleted from the EntityType. """ entityIdSelector = _messages.MessageField('GoogleCloudAiplatformV1EntityIdSelector', 1) class GoogleCloudAiplatformV1DeleteFeatureValuesRequestSelectTimeRangeAndFeature(_messages.Message): r"""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. Fields: featureSelector: Required. Selectors choosing which feature values to be deleted from the EntityType. skipOnlineStorageDelete: If set, data will not be deleted from online storage. When time range is older than the data in online storage, setting this to be true will make the deletion have no impact on online serving. timeRange: Required. Select feature generated within a half-inclusive time range. The time range is lower inclusive and upper exclusive. """ featureSelector = _messages.MessageField('GoogleCloudAiplatformV1FeatureSelector', 1) skipOnlineStorageDelete = _messages.BooleanField(2) timeRange = _messages.MessageField('GoogleTypeInterval', 3) class GoogleCloudAiplatformV1DeleteFeatureValuesResponse(_messages.Message): r"""Response message for FeaturestoreService.DeleteFeatureValues. Fields: selectEntity: Response for request specifying the entities to delete selectTimeRangeAndFeature: Response for request specifying time range and feature """ selectEntity = _messages.MessageField('GoogleCloudAiplatformV1DeleteFeatureValuesResponseSelectEntity', 1) selectTimeRangeAndFeature = _messages.MessageField('GoogleCloudAiplatformV1DeleteFeatureValuesResponseSelectTimeRangeAndFeature', 2) class GoogleCloudAiplatformV1DeleteFeatureValuesResponseSelectEntity(_messages.Message): r"""Response message if the request uses the SelectEntity option. Fields: offlineStorageDeletedEntityRowCount: The count of deleted entity rows in the offline storage. Each row corresponds to the combination of an entity ID and a timestamp. One entity ID can have multiple rows in the offline storage. onlineStorageDeletedEntityCount: The count of deleted entities in the online storage. Each entity ID corresponds to one entity. """ offlineStorageDeletedEntityRowCount = _messages.IntegerField(1) onlineStorageDeletedEntityCount = _messages.IntegerField(2) class GoogleCloudAiplatformV1DeleteFeatureValuesResponseSelectTimeRangeAndFeature(_messages.Message): r"""Response message if the request uses the SelectTimeRangeAndFeature option. Fields: impactedFeatureCount: The count of the features or columns impacted. This is the same as the feature count in the request. offlineStorageModifiedEntityRowCount: The count of modified entity rows in the offline storage. Each row corresponds to the combination of an entity ID and a timestamp. One entity ID can have multiple rows in the offline storage. Within each row, only the features specified in the request are deleted. onlineStorageModifiedEntityCount: The count of modified entities in the online storage. Each entity ID corresponds to one entity. Within each entity, only the features specified in the request are deleted. """ impactedFeatureCount = _messages.IntegerField(1) offlineStorageModifiedEntityRowCount = _messages.IntegerField(2) onlineStorageModifiedEntityCount = _messages.IntegerField(3) class GoogleCloudAiplatformV1DeleteMetadataStoreOperationMetadata(_messages.Message): r"""Details of operations that perform MetadataService.DeleteMetadataStore. Fields: genericMetadata: Operation metadata for deleting a MetadataStore. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1DeleteOperationMetadata(_messages.Message): r"""Details of operations that perform deletes of any entities. Fields: genericMetadata: The common part of the operation metadata. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1DeployIndexOperationMetadata(_messages.Message): r"""Runtime operation information for IndexEndpointService.DeployIndex. Fields: deployedIndexId: The unique index id specified by user genericMetadata: The operation generic information. """ deployedIndexId = _messages.StringField(1) genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 2) class GoogleCloudAiplatformV1DeployIndexRequest(_messages.Message): r"""Request message for IndexEndpointService.DeployIndex. Fields: deployedIndex: Required. The DeployedIndex to be created within the IndexEndpoint. """ deployedIndex = _messages.MessageField('GoogleCloudAiplatformV1DeployedIndex', 1) class GoogleCloudAiplatformV1DeployIndexResponse(_messages.Message): r"""Response message for IndexEndpointService.DeployIndex. Fields: deployedIndex: The DeployedIndex that had been deployed in the IndexEndpoint. """ deployedIndex = _messages.MessageField('GoogleCloudAiplatformV1DeployedIndex', 1) class GoogleCloudAiplatformV1DeployModelOperationMetadata(_messages.Message): r"""Runtime operation information for EndpointService.DeployModel. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1DeployModelRequest(_messages.Message): r"""Request message for EndpointService.DeployModel. Messages: TrafficSplitValue: A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If this field is non-empty, then the Endpoint's traffic_split will be overwritten with it. To refer to the ID of the just being deployed Model, a "0" should be used, and the actual ID of the new DeployedModel will be filled in its place by this method. The traffic percentage values must add up to 100. If this field is empty, then the Endpoint's traffic_split is not updated. Fields: deployedModel: Required. The DeployedModel to be created within the Endpoint. Note that Endpoint.traffic_split must be updated for the DeployedModel to start receiving traffic, either as part of this call, or via EndpointService.UpdateEndpoint. trafficSplit: A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If this field is non-empty, then the Endpoint's traffic_split will be overwritten with it. To refer to the ID of the just being deployed Model, a "0" should be used, and the actual ID of the new DeployedModel will be filled in its place by this method. The traffic percentage values must add up to 100. If this field is empty, then the Endpoint's traffic_split is not updated. """ @encoding.MapUnrecognizedFields('additionalProperties') class TrafficSplitValue(_messages.Message): r"""A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If this field is non-empty, then the Endpoint's traffic_split will be overwritten with it. To refer to the ID of the just being deployed Model, a "0" should be used, and the actual ID of the new DeployedModel will be filled in its place by this method. The traffic percentage values must add up to 100. If this field is empty, then the Endpoint's traffic_split is not updated. Messages: AdditionalProperty: An additional property for a TrafficSplitValue object. Fields: additionalProperties: Additional properties of type TrafficSplitValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a TrafficSplitValue object. Fields: key: Name of the additional property. value: A integer attribute. """ key = _messages.StringField(1) value = _messages.IntegerField(2, variant=_messages.Variant.INT32) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) deployedModel = _messages.MessageField('GoogleCloudAiplatformV1DeployedModel', 1) trafficSplit = _messages.MessageField('TrafficSplitValue', 2) class GoogleCloudAiplatformV1DeployModelResponse(_messages.Message): r"""Response message for EndpointService.DeployModel. Fields: deployedModel: The DeployedModel that had been deployed in the Endpoint. """ deployedModel = _messages.MessageField('GoogleCloudAiplatformV1DeployedModel', 1) class GoogleCloudAiplatformV1DeployOperationMetadata(_messages.Message): r"""Runtime operation information for ModelGardenService.Deploy. Fields: destination: Output only. The resource name of the Location to deploy the model in. Format: `projects/{project}/locations/{location}` genericMetadata: The operation generic information. modelId: Output only. The model id to be used at query time. projectNumber: Output only. The project number where the deploy model request is sent. publisherModel: Output only. The name of the model resource. """ destination = _messages.StringField(1) genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 2) modelId = _messages.StringField(3) projectNumber = _messages.IntegerField(4) publisherModel = _messages.StringField(5) class GoogleCloudAiplatformV1DeployRequest(_messages.Message): r"""Request message for ModelGardenService.Deploy. Fields: deployConfig: Optional. The deploy config to use for the deployment. If not specified, the default deploy config will be used. endpointConfig: Optional. The endpoint config to use for the deployment. If not specified, the default endpoint config will be used. huggingFaceModelId: The Hugging Face model to deploy. Format: Hugging Face model ID like `google/gemma-2-2b-it`. modelConfig: Optional. The model config to use for the deployment. If not specified, the default model config will be used. publisherModelName: The Model Garden model to deploy. Format: `publishers/{publisher}/models/{publisher_model}@{version_id}`, or `publishers/hf-{hugging-face-author}/models/{hugging-face-model- name}@001`. """ deployConfig = _messages.MessageField('GoogleCloudAiplatformV1DeployRequestDeployConfig', 1) endpointConfig = _messages.MessageField('GoogleCloudAiplatformV1DeployRequestEndpointConfig', 2) huggingFaceModelId = _messages.StringField(3) modelConfig = _messages.MessageField('GoogleCloudAiplatformV1DeployRequestModelConfig', 4) publisherModelName = _messages.StringField(5) class GoogleCloudAiplatformV1DeployRequestDeployConfig(_messages.Message): r"""The deploy config to use for the deployment. Fields: dedicatedResources: Optional. The dedicated resources to use for the endpoint. If not set, the default resources will be used. fastTryoutEnabled: Optional. If true, enable the QMT fast tryout feature for this model if possible. """ dedicatedResources = _messages.MessageField('GoogleCloudAiplatformV1DedicatedResources', 1) fastTryoutEnabled = _messages.BooleanField(2) class GoogleCloudAiplatformV1DeployRequestEndpointConfig(_messages.Message): r"""The endpoint config to use for the deployment. Fields: dedicatedEndpointDisabled: Optional. By default, if dedicated endpoint is enabled, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitations will be removed soon. If this field is set to true, the dedicated endpoint will be disabled and the deployed model will be exposed through the shared DNS {region}-aiplatform.googleapis.com. dedicatedEndpointEnabled: Optional. Deprecated. Use dedicated_endpoint_disabled instead. If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitations will be removed soon. endpointDisplayName: Optional. The user-specified display name of the endpoint. If not set, a default name will be used. """ dedicatedEndpointDisabled = _messages.BooleanField(1) dedicatedEndpointEnabled = _messages.BooleanField(2) endpointDisplayName = _messages.StringField(3) class GoogleCloudAiplatformV1DeployRequestModelConfig(_messages.Message): r"""The model config to use for the deployment. Fields: acceptEula: Optional. Whether the user accepts the End User License Agreement (EULA) for the model. containerSpec: Optional. The specification of the container that is to be used when deploying. If not set, the default container spec will be used. huggingFaceAccessToken: Optional. The Hugging Face read access token used to access the model artifacts of gated models. huggingFaceCacheEnabled: Optional. If true, the model will deploy with a cached version instead of directly downloading the model artifacts from Hugging Face. This is suitable for VPC-SC users with limited internet access. modelDisplayName: Optional. The user-specified display name of the uploaded model. If not set, a default name will be used. modelUserId: Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. When not provided, Vertex AI will generate a value for this ID. When Model Registry model is provided, this field will be ignored. This value may be up to 63 characters, and valid characters are `[a-z0-9_-]`. The first character cannot be a number or hyphen. """ acceptEula = _messages.BooleanField(1) containerSpec = _messages.MessageField('GoogleCloudAiplatformV1ModelContainerSpec', 2) huggingFaceAccessToken = _messages.StringField(3) huggingFaceCacheEnabled = _messages.BooleanField(4) modelDisplayName = _messages.StringField(5) modelUserId = _messages.StringField(6) class GoogleCloudAiplatformV1DeployResponse(_messages.Message): r"""Response message for ModelGardenService.Deploy. Fields: endpoint: Output only. The name of the Endpoint created. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` model: Output only. The name of the Model created. Format: `projects/{project}/locations/{location}/models/{model}` publisherModel: Output only. The name of the PublisherModel resource. Format: `publishers/{publisher}/models/{publisher_model}@{version_id}`, or `publishers/hf-{hugging-face-author}/models/{hugging-face-model- name}@001` """ endpoint = _messages.StringField(1) model = _messages.StringField(2) publisherModel = _messages.StringField(3) class GoogleCloudAiplatformV1DeployedIndex(_messages.Message): r"""A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes. Fields: automaticResources: Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000. createTime: Output only. Timestamp when the DeployedIndex was created. dedicatedResources: Optional. A description of resources that are dedicated to the DeployedIndex, and that need a higher degree of manual configuration. The field min_replica_count must be set to a value strictly greater than 0, or else validation will fail. We don't provide SLA when min_replica_count=1. If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000. Available machine types for SMALL shard: e2-standard-2 and all machine types available for MEDIUM and LARGE shard. Available machine types for MEDIUM shard: e2-standard-16 and all machine types available for LARGE shard. Available machine types for LARGE shard: e2-highmem-16, n2d-standard-32. n1-standard-16 and n1-standard-32 are still available, but we recommend e2-standard-16 and e2-highmem-16 for cost efficiency. deployedIndexAuthConfig: Optional. If set, the authentication is enabled for the private endpoint. deploymentGroup: Optional. The deployment group can be no longer than 64 characters (eg: 'test', 'prod'). If not set, we will use the 'default' deployment group. Creating `deployment_groups` with `reserved_ip_ranges` is a recommended practice when the peered network has multiple peering ranges. This creates your deployments from predictable IP spaces for easier traffic administration. Also, one deployment_group (except 'default') can only be used with the same reserved_ip_ranges which means if the deployment_group has been used with reserved_ip_ranges: [a, b, c], using it with [a, b] or [d, e] is disallowed. Note: we only support up to 5 deployment groups(not including 'default'). displayName: The display name of the DeployedIndex. If not provided upon creation, the Index's display_name is used. enableAccessLogging: Optional. If true, private endpoint's access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each MatchRequest. Note that logs may incur a cost, especially if the deployed index receives a high queries per second rate (QPS). Estimate your costs before enabling this option. enableDatapointUpsertLogging: Optional. If true, logs to Cloud Logging errors relating to datapoint upserts. Under normal operation conditions, these log entries should be very rare. However, if incompatible datapoint updates are being uploaded to an index, a high volume of log entries may be generated in a short period of time. Note that logs may incur a cost, especially if the deployed index receives a high volume of datapoint upserts. Estimate your costs before enabling this option. id: Required. The user specified ID of the DeployedIndex. The ID can be up to 128 characters long and must start with a letter and only contain letters, numbers, and underscores. The ID must be unique within the project it is created in. index: Required. The name of the Index this is the deployment of. We may refer to this Index as the DeployedIndex's "original" Index. indexSyncTime: Output only. The DeployedIndex may depend on various data on its original Index. Additionally when certain changes to the original Index are being done (e.g. when what the Index contains is being changed) the DeployedIndex may be asynchronously updated in the background to reflect these changes. If this timestamp's value is at least the Index.update_time of the original Index, it means that this DeployedIndex and the original Index are in sync. If this timestamp is older, then to see which updates this DeployedIndex already contains (and which it does not), one must list the operations that are running on the original Index. Only the successfully completed Operations with update_time equal or before this sync time are contained in this DeployedIndex. privateEndpoints: Output only. Provides paths for users to send requests directly to the deployed index services running on Cloud via private services access. This field is populated if network is configured. pscAutomationConfigs: Optional. If set for PSC deployed index, PSC connection will be automatically created after deployment is done and the endpoint information is populated in private_endpoints.psc_automated_endpoints. reservedIpRanges: Optional. A list of reserved ip ranges under the VPC network that can be used for this DeployedIndex. If set, we will deploy the index within the provided ip ranges. Otherwise, the index might be deployed to any ip ranges under the provided VPC network. The value should be the name of the address (https://cloud.google.com/compute/docs/reference/rest/v1/addresses) Example: ['vertex-ai-ip-range']. For more information about subnets and network IP ranges, please see https://cloud.google.com/vpc/docs/subnets# manually_created_subnet_ip_ranges. """ automaticResources = _messages.MessageField('GoogleCloudAiplatformV1AutomaticResources', 1) createTime = _messages.StringField(2) dedicatedResources = _messages.MessageField('GoogleCloudAiplatformV1DedicatedResources', 3) deployedIndexAuthConfig = _messages.MessageField('GoogleCloudAiplatformV1DeployedIndexAuthConfig', 4) deploymentGroup = _messages.StringField(5) displayName = _messages.StringField(6) enableAccessLogging = _messages.BooleanField(7) enableDatapointUpsertLogging = _messages.BooleanField(8) id = _messages.StringField(9) index = _messages.StringField(10) indexSyncTime = _messages.StringField(11) privateEndpoints = _messages.MessageField('GoogleCloudAiplatformV1IndexPrivateEndpoints', 12) pscAutomationConfigs = _messages.MessageField('GoogleCloudAiplatformV1PSCAutomationConfig', 13, repeated=True) reservedIpRanges = _messages.StringField(14, repeated=True) class GoogleCloudAiplatformV1DeployedIndexAuthConfig(_messages.Message): r"""Used to set up the auth on the DeployedIndex's private endpoint. Fields: authProvider: Defines the authentication provider that the DeployedIndex uses. """ authProvider = _messages.MessageField('GoogleCloudAiplatformV1DeployedIndexAuthConfigAuthProvider', 1) class GoogleCloudAiplatformV1DeployedIndexAuthConfigAuthProvider(_messages.Message): r"""Configuration for an authentication provider, including support for [JSON Web Token (JWT)](https://tools.ietf.org/html/draft-ietf-oauth-json- web-token-32). Fields: allowedIssuers: A list of allowed JWT issuers. Each entry must be a valid Google service account, in the following format: `service-account- name@project-id.iam.gserviceaccount.com` audiences: The list of JWT [audiences](https://tools.ietf.org/html/draft- ietf-oauth-json-web-token-32#section-4.1.3). that are allowed to access. A JWT containing any of these audiences will be accepted. """ allowedIssuers = _messages.StringField(1, repeated=True) audiences = _messages.StringField(2, repeated=True) class GoogleCloudAiplatformV1DeployedIndexRef(_messages.Message): r"""Points to a DeployedIndex. Fields: deployedIndexId: Immutable. The ID of the DeployedIndex in the above IndexEndpoint. displayName: Output only. The display name of the DeployedIndex. indexEndpoint: Immutable. A resource name of the IndexEndpoint. """ deployedIndexId = _messages.StringField(1) displayName = _messages.StringField(2) indexEndpoint = _messages.StringField(3) class GoogleCloudAiplatformV1DeployedModel(_messages.Message): r"""A deployment of a Model. Endpoints contain one or more DeployedModels. Messages: SystemLabelsValue: System labels to apply to Model Garden deployments. System labels are managed by Google for internal use only. Fields: automaticResources: A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. checkpointId: The checkpoint id of the model. createTime: Output only. Timestamp when the DeployedModel was created. dedicatedResources: A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration. disableContainerLogging: For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true. disableExplanations: If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec. displayName: The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used. enableAccessLogging: If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option. explanationSpec: Explanation configuration for this DeployedModel. When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration. fasterDeploymentConfig: Configuration for faster model deployment. gdcConnectedModel: GDC pretrained / Gemini model name. The model name is a plain model name, e.g. gemini-1.5-flash-002. id: Immutable. The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are `/[0-9]/`. model: The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint. The resource name may contain version id or version alias to specify the version. Example: `projects/{project}/locations/{location}/models/{model}@2` or `projects/{project}/locations/{location}/models/{model}@golden` if no version is specified, the default version will be deployed. modelVersionId: Output only. The version ID of the model that is deployed. privateEndpoints: Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured. serviceAccount: The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the `iam.serviceAccounts.actAs` permission on this service account. sharedResources: The resource name of the shared DeploymentResourcePool to deploy on. Format: `projects/{project}/locations/{location}/deploymentRe sourcePools/{deployment_resource_pool}` speculativeDecodingSpec: Optional. Spec for configuring speculative decoding. status: Output only. Runtime status of the deployed model. systemLabels: System labels to apply to Model Garden deployments. System labels are managed by Google for internal use only. """ @encoding.MapUnrecognizedFields('additionalProperties') class SystemLabelsValue(_messages.Message): r"""System labels to apply to Model Garden deployments. System labels are managed by Google for internal use only. Messages: AdditionalProperty: An additional property for a SystemLabelsValue object. Fields: additionalProperties: Additional properties of type SystemLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a SystemLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) automaticResources = _messages.MessageField('GoogleCloudAiplatformV1AutomaticResources', 1) checkpointId = _messages.StringField(2) createTime = _messages.StringField(3) dedicatedResources = _messages.MessageField('GoogleCloudAiplatformV1DedicatedResources', 4) disableContainerLogging = _messages.BooleanField(5) disableExplanations = _messages.BooleanField(6) displayName = _messages.StringField(7) enableAccessLogging = _messages.BooleanField(8) explanationSpec = _messages.MessageField('GoogleCloudAiplatformV1ExplanationSpec', 9) fasterDeploymentConfig = _messages.MessageField('GoogleCloudAiplatformV1FasterDeploymentConfig', 10) gdcConnectedModel = _messages.StringField(11) id = _messages.StringField(12) model = _messages.StringField(13) modelVersionId = _messages.StringField(14) privateEndpoints = _messages.MessageField('GoogleCloudAiplatformV1PrivateEndpoints', 15) serviceAccount = _messages.StringField(16) sharedResources = _messages.StringField(17) speculativeDecodingSpec = _messages.MessageField('GoogleCloudAiplatformV1SpeculativeDecodingSpec', 18) status = _messages.MessageField('GoogleCloudAiplatformV1DeployedModelStatus', 19) systemLabels = _messages.MessageField('SystemLabelsValue', 20) class GoogleCloudAiplatformV1DeployedModelRef(_messages.Message): r"""Points to a DeployedModel. Fields: checkpointId: Immutable. The ID of the Checkpoint deployed in the DeployedModel. deployedModelId: Immutable. An ID of a DeployedModel in the above Endpoint. endpoint: Immutable. A resource name of an Endpoint. """ checkpointId = _messages.StringField(1) deployedModelId = _messages.StringField(2) endpoint = _messages.StringField(3) class GoogleCloudAiplatformV1DeployedModelStatus(_messages.Message): r"""Runtime status of the deployed model. Fields: availableReplicaCount: Output only. The number of available replicas of the deployed model. lastUpdateTime: Output only. The time at which the status was last updated. message: Output only. The latest deployed model's status message (if any). """ availableReplicaCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) lastUpdateTime = _messages.StringField(2) message = _messages.StringField(3) class GoogleCloudAiplatformV1DeploymentResourcePool(_messages.Message): r"""A description of resources that can be shared by multiple DeployedModels, whose underlying specification consists of a DedicatedResources. Fields: createTime: Output only. Timestamp when this DeploymentResourcePool was created. dedicatedResources: Required. The underlying DedicatedResources that the DeploymentResourcePool uses. disableContainerLogging: If the DeploymentResourcePool is deployed with custom-trained Models or AutoML Tabular Models, the container(s) of the DeploymentResourcePool will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true. encryptionSpec: Customer-managed encryption key spec for a DeploymentResourcePool. If set, this DeploymentResourcePool will be secured by this key. Endpoints and the DeploymentResourcePool they deploy in need to have the same EncryptionSpec. name: Immutable. The resource name of the DeploymentResourcePool. Format: `projects/{project}/locations/{location}/deploymentResourcePools/{deploy ment_resource_pool}` satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. serviceAccount: The service account that the DeploymentResourcePool's container(s) run as. Specify the email address of the service account. If this service account is not specified, the container(s) run as a service account that doesn't have access to the resource project. Users deploying the Models to this DeploymentResourcePool must have the `iam.serviceAccounts.actAs` permission on this service account. """ createTime = _messages.StringField(1) dedicatedResources = _messages.MessageField('GoogleCloudAiplatformV1DedicatedResources', 2) disableContainerLogging = _messages.BooleanField(3) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 4) name = _messages.StringField(5) satisfiesPzi = _messages.BooleanField(6) satisfiesPzs = _messages.BooleanField(7) serviceAccount = _messages.StringField(8) class GoogleCloudAiplatformV1DestinationFeatureSetting(_messages.Message): r"""A GoogleCloudAiplatformV1DestinationFeatureSetting object. Fields: destinationField: Specify the field name in the export destination. If not specified, Feature ID is used. featureId: Required. The ID of the Feature to apply the setting to. """ destinationField = _messages.StringField(1) featureId = _messages.StringField(2) class GoogleCloudAiplatformV1DirectPredictRequest(_messages.Message): r"""Request message for PredictionService.DirectPredict. Fields: inputs: The prediction input. parameters: The parameters that govern the prediction. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1Tensor', 1, repeated=True) parameters = _messages.MessageField('GoogleCloudAiplatformV1Tensor', 2) class GoogleCloudAiplatformV1DirectPredictResponse(_messages.Message): r"""Response message for PredictionService.DirectPredict. Fields: outputs: The prediction output. parameters: The parameters that govern the prediction. """ outputs = _messages.MessageField('GoogleCloudAiplatformV1Tensor', 1, repeated=True) parameters = _messages.MessageField('GoogleCloudAiplatformV1Tensor', 2) class GoogleCloudAiplatformV1DirectRawPredictRequest(_messages.Message): r"""Request message for PredictionService.DirectRawPredict. Fields: input: The prediction input. methodName: Fully qualified name of the API method being invoked to perform predictions. Format: `/namespace.Service/Method/` Example: `/tensorflow.serving.PredictionService/Predict` """ input = _messages.BytesField(1) methodName = _messages.StringField(2) class GoogleCloudAiplatformV1DirectRawPredictResponse(_messages.Message): r"""Response message for PredictionService.DirectRawPredict. Fields: output: The prediction output. """ output = _messages.BytesField(1) class GoogleCloudAiplatformV1DirectUploadSource(_messages.Message): r"""The input content is encapsulated and uploaded in the request.""" class GoogleCloudAiplatformV1DiskSpec(_messages.Message): r"""Represents the spec of disk options. Fields: bootDiskSizeGb: Size in GB of the boot disk (default is 100GB). bootDiskType: Type of the boot disk. For non-A3U machines, the default value is "pd-ssd", for A3U machines, the default value is "hyperdisk- balanced". Valid values: "pd-ssd" (Persistent Disk Solid State Drive), "pd-standard" (Persistent Disk Hard Disk Drive) or "hyperdisk-balanced". """ bootDiskSizeGb = _messages.IntegerField(1, variant=_messages.Variant.INT32) bootDiskType = _messages.StringField(2) class GoogleCloudAiplatformV1DnsPeeringConfig(_messages.Message): r"""DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS. Fields: domain: Required. The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot. targetNetwork: Required. The VPC network name in the target_project where the DNS zone specified by 'domain' is visible. targetProject: Required. The project ID hosting the Cloud DNS managed zone that contains the 'domain'. The Vertex AI Service Agent requires the dns.peer role on this project. """ domain = _messages.StringField(1) targetNetwork = _messages.StringField(2) targetProject = _messages.StringField(3) class GoogleCloudAiplatformV1DoubleArray(_messages.Message): r"""A list of double values. Fields: values: A list of double values. """ values = _messages.FloatField(1, repeated=True) class GoogleCloudAiplatformV1DynamicRetrievalConfig(_messages.Message): r"""Describes the options to customize dynamic retrieval. Enums: ModeValueValuesEnum: The mode of the predictor to be used in dynamic retrieval. Fields: dynamicThreshold: Optional. The threshold to be used in dynamic retrieval. If not set, a system default value is used. mode: The mode of the predictor to be used in dynamic retrieval. """ class ModeValueValuesEnum(_messages.Enum): r"""The mode of the predictor to be used in dynamic retrieval. Values: MODE_UNSPECIFIED: Always trigger retrieval. MODE_DYNAMIC: Run retrieval only when system decides it is necessary. """ MODE_UNSPECIFIED = 0 MODE_DYNAMIC = 1 dynamicThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) mode = _messages.EnumField('ModeValueValuesEnum', 2) class GoogleCloudAiplatformV1EncryptionSpec(_messages.Message): r"""Represents a customer-managed encryption key spec that can be applied to a top-level resource. Fields: kmsKeyName: Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my- key`. The key needs to be in the same region as where the compute resource is created. """ kmsKeyName = _messages.StringField(1) class GoogleCloudAiplatformV1Endpoint(_messages.Message): r"""Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations. Messages: LabelsValue: The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. TrafficSplitValue: A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment. Fields: clientConnectionConfig: Configurations that are applied to the endpoint for online prediction. createTime: Output only. Timestamp when this Endpoint was created. dedicatedEndpointDns: Output only. DNS of the dedicated endpoint. Will only be populated if dedicated_endpoint_enabled is true. Depending on the features enabled, uid might be a random number or a string. For example, if fast_tryout is enabled, uid will be fasttryout. Format: `https://{endpoint_id}.{region}-{uid}.prediction.vertexai.goog`. dedicatedEndpointEnabled: If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon. deployedModels: Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively. description: The description of the Endpoint. displayName: Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters. enablePrivateServiceConnect: Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set. encryptionSpec: Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. etag: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. modelDeploymentMonitoringJob: Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob. Format: `projects/{projec t}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_ monitoring_job}` name: Output only. The resource name of the Endpoint. network: Optional. The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and- firewalls#networks) to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. [Form at](https://cloud.google.com/compute/docs/reference/rest/v1/networks/ins ert): `projects/{project}/global/networks/{network}`. Where `{project}` is a project number, as in `12345`, and `{network}` is network name. predictRequestResponseLoggingConfig: Configures the request-response logging for online prediction. privateServiceConnectConfig: Optional. Configuration for private service connect. network and private_service_connect_config are mutually exclusive. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. trafficSplit: A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment. updateTime: Output only. Timestamp when this Endpoint was last updated. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class TrafficSplitValue(_messages.Message): r"""A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment. Messages: AdditionalProperty: An additional property for a TrafficSplitValue object. Fields: additionalProperties: Additional properties of type TrafficSplitValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a TrafficSplitValue object. Fields: key: Name of the additional property. value: A integer attribute. """ key = _messages.StringField(1) value = _messages.IntegerField(2, variant=_messages.Variant.INT32) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clientConnectionConfig = _messages.MessageField('GoogleCloudAiplatformV1ClientConnectionConfig', 1) createTime = _messages.StringField(2) dedicatedEndpointDns = _messages.StringField(3) dedicatedEndpointEnabled = _messages.BooleanField(4) deployedModels = _messages.MessageField('GoogleCloudAiplatformV1DeployedModel', 5, repeated=True) description = _messages.StringField(6) displayName = _messages.StringField(7) enablePrivateServiceConnect = _messages.BooleanField(8) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 9) etag = _messages.StringField(10) labels = _messages.MessageField('LabelsValue', 11) modelDeploymentMonitoringJob = _messages.StringField(12) name = _messages.StringField(13) network = _messages.StringField(14) predictRequestResponseLoggingConfig = _messages.MessageField('GoogleCloudAiplatformV1PredictRequestResponseLoggingConfig', 15) privateServiceConnectConfig = _messages.MessageField('GoogleCloudAiplatformV1PrivateServiceConnectConfig', 16) satisfiesPzi = _messages.BooleanField(17) satisfiesPzs = _messages.BooleanField(18) trafficSplit = _messages.MessageField('TrafficSplitValue', 19) updateTime = _messages.StringField(20) class GoogleCloudAiplatformV1EnterpriseWebSearch(_messages.Message): r"""Tool to search public web data, powered by Vertex AI Search and Sec4 compliance. """ class GoogleCloudAiplatformV1EntityIdSelector(_messages.Message): r"""Selector for entityId. Getting ids from the given source. Fields: csvSource: Source of Csv entityIdField: Source column that holds entity IDs. If not provided, entity IDs are extracted from the column named entity_id. """ csvSource = _messages.MessageField('GoogleCloudAiplatformV1CsvSource', 1) entityIdField = _messages.StringField(2) class GoogleCloudAiplatformV1EntityType(_messages.Message): r"""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. Messages: LabelsValue: Optional. The labels with user-defined metadata to organize your EntityTypes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one EntityType (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Fields: createTime: Output only. Timestamp when this EntityType was created. description: Optional. Description of the EntityType. etag: Optional. Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: Optional. The labels with user-defined metadata to organize your EntityTypes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one EntityType (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. monitoringConfig: Optional. The default monitoring configuration for all Features with value type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 under this EntityType. If this is populated with [FeaturestoreMonitoringConfig.monitoring_interval] specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring is disabled. name: Immutable. Name of the EntityType. Format: `projects/{project}/locat ions/{location}/featurestores/{featurestore}/entityTypes/{entity_type}` The last part entity_type is assigned by the client. The entity_type can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z and underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given a featurestore. offlineStorageTtlDays: Optional. Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than `offline_storage_ttl_days` since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. updateTime: Output only. Timestamp when this EntityType was most recently updated. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels with user-defined metadata to organize your EntityTypes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one EntityType (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) description = _messages.StringField(2) etag = _messages.StringField(3) labels = _messages.MessageField('LabelsValue', 4) monitoringConfig = _messages.MessageField('GoogleCloudAiplatformV1FeaturestoreMonitoringConfig', 5) name = _messages.StringField(6) offlineStorageTtlDays = _messages.IntegerField(7, variant=_messages.Variant.INT32) satisfiesPzi = _messages.BooleanField(8) satisfiesPzs = _messages.BooleanField(9) updateTime = _messages.StringField(10) class GoogleCloudAiplatformV1EnvVar(_messages.Message): r"""Represents an environment variable present in a Container or Python Module. Fields: name: Required. Name of the environment variable. Must be a valid C identifier. value: Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. """ name = _messages.StringField(1) value = _messages.StringField(2) class GoogleCloudAiplatformV1ErrorAnalysisAnnotation(_messages.Message): r"""Model error analysis for each annotation. Enums: QueryTypeValueValuesEnum: The query type used for finding the attributed items. Fields: attributedItems: Attributed items for a given annotation, typically representing neighbors from the training sets constrained by the query type. outlierScore: The outlier score of this annotated item. Usually defined as the min of all distances from attributed items. outlierThreshold: The threshold used to determine if this annotation is an outlier or not. queryType: The query type used for finding the attributed items. """ class QueryTypeValueValuesEnum(_messages.Enum): r"""The query type used for finding the attributed items. Values: QUERY_TYPE_UNSPECIFIED: Unspecified query type for model error analysis. ALL_SIMILAR: Query similar samples across all classes in the dataset. SAME_CLASS_SIMILAR: Query similar samples from the same class of the input sample. SAME_CLASS_DISSIMILAR: Query dissimilar samples from the same class of the input sample. """ QUERY_TYPE_UNSPECIFIED = 0 ALL_SIMILAR = 1 SAME_CLASS_SIMILAR = 2 SAME_CLASS_DISSIMILAR = 3 attributedItems = _messages.MessageField('GoogleCloudAiplatformV1ErrorAnalysisAnnotationAttributedItem', 1, repeated=True) outlierScore = _messages.FloatField(2) outlierThreshold = _messages.FloatField(3) queryType = _messages.EnumField('QueryTypeValueValuesEnum', 4) class GoogleCloudAiplatformV1ErrorAnalysisAnnotationAttributedItem(_messages.Message): r"""Attributed items for a given annotation, typically representing neighbors from the training sets constrained by the query type. Fields: annotationResourceName: The unique ID for each annotation. Used by FE to allocate the annotation in DB. distance: The distance of this item to the annotation. """ annotationResourceName = _messages.StringField(1) distance = _messages.FloatField(2) class GoogleCloudAiplatformV1EvaluateInstancesRequest(_messages.Message): r"""Request message for EvaluationService.EvaluateInstances. Fields: bleuInput: Instances and metric spec for bleu metric. coherenceInput: Input for coherence metric. cometInput: Translation metrics. Input for Comet metric. exactMatchInput: Auto metric instances. Instances and metric spec for exact match metric. fluencyInput: LLM-based metric instance. General text generation metrics, applicable to other categories. Input for fluency metric. fulfillmentInput: Input for fulfillment metric. groundednessInput: Input for groundedness metric. metricxInput: Input for Metricx metric. pairwiseMetricInput: Input for pairwise metric. pairwiseQuestionAnsweringQualityInput: Input for pairwise question answering quality metric. pairwiseSummarizationQualityInput: Input for pairwise summarization quality metric. pointwiseMetricInput: Input for pointwise metric. questionAnsweringCorrectnessInput: Input for question answering correctness metric. questionAnsweringHelpfulnessInput: Input for question answering helpfulness metric. questionAnsweringQualityInput: Input for question answering quality metric. questionAnsweringRelevanceInput: Input for question answering relevance metric. rougeInput: Instances and metric spec for rouge metric. safetyInput: Input for safety metric. summarizationHelpfulnessInput: Input for summarization helpfulness metric. summarizationQualityInput: Input for summarization quality metric. summarizationVerbosityInput: Input for summarization verbosity metric. toolCallValidInput: Tool call metric instances. Input for tool call valid metric. toolNameMatchInput: Input for tool name match metric. toolParameterKeyMatchInput: Input for tool parameter key match metric. toolParameterKvMatchInput: Input for tool parameter key value match metric. trajectoryAnyOrderMatchInput: Input for trajectory match any order metric. trajectoryExactMatchInput: Input for trajectory exact match metric. trajectoryInOrderMatchInput: Input for trajectory in order match metric. trajectoryPrecisionInput: Input for trajectory precision metric. trajectoryRecallInput: Input for trajectory recall metric. trajectorySingleToolUseInput: Input for trajectory single tool use metric. """ bleuInput = _messages.MessageField('GoogleCloudAiplatformV1BleuInput', 1) coherenceInput = _messages.MessageField('GoogleCloudAiplatformV1CoherenceInput', 2) cometInput = _messages.MessageField('GoogleCloudAiplatformV1CometInput', 3) exactMatchInput = _messages.MessageField('GoogleCloudAiplatformV1ExactMatchInput', 4) fluencyInput = _messages.MessageField('GoogleCloudAiplatformV1FluencyInput', 5) fulfillmentInput = _messages.MessageField('GoogleCloudAiplatformV1FulfillmentInput', 6) groundednessInput = _messages.MessageField('GoogleCloudAiplatformV1GroundednessInput', 7) metricxInput = _messages.MessageField('GoogleCloudAiplatformV1MetricxInput', 8) pairwiseMetricInput = _messages.MessageField('GoogleCloudAiplatformV1PairwiseMetricInput', 9) pairwiseQuestionAnsweringQualityInput = _messages.MessageField('GoogleCloudAiplatformV1PairwiseQuestionAnsweringQualityInput', 10) pairwiseSummarizationQualityInput = _messages.MessageField('GoogleCloudAiplatformV1PairwiseSummarizationQualityInput', 11) pointwiseMetricInput = _messages.MessageField('GoogleCloudAiplatformV1PointwiseMetricInput', 12) questionAnsweringCorrectnessInput = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringCorrectnessInput', 13) questionAnsweringHelpfulnessInput = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringHelpfulnessInput', 14) questionAnsweringQualityInput = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringQualityInput', 15) questionAnsweringRelevanceInput = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringRelevanceInput', 16) rougeInput = _messages.MessageField('GoogleCloudAiplatformV1RougeInput', 17) safetyInput = _messages.MessageField('GoogleCloudAiplatformV1SafetyInput', 18) summarizationHelpfulnessInput = _messages.MessageField('GoogleCloudAiplatformV1SummarizationHelpfulnessInput', 19) summarizationQualityInput = _messages.MessageField('GoogleCloudAiplatformV1SummarizationQualityInput', 20) summarizationVerbosityInput = _messages.MessageField('GoogleCloudAiplatformV1SummarizationVerbosityInput', 21) toolCallValidInput = _messages.MessageField('GoogleCloudAiplatformV1ToolCallValidInput', 22) toolNameMatchInput = _messages.MessageField('GoogleCloudAiplatformV1ToolNameMatchInput', 23) toolParameterKeyMatchInput = _messages.MessageField('GoogleCloudAiplatformV1ToolParameterKeyMatchInput', 24) toolParameterKvMatchInput = _messages.MessageField('GoogleCloudAiplatformV1ToolParameterKVMatchInput', 25) trajectoryAnyOrderMatchInput = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryAnyOrderMatchInput', 26) trajectoryExactMatchInput = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryExactMatchInput', 27) trajectoryInOrderMatchInput = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryInOrderMatchInput', 28) trajectoryPrecisionInput = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryPrecisionInput', 29) trajectoryRecallInput = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryRecallInput', 30) trajectorySingleToolUseInput = _messages.MessageField('GoogleCloudAiplatformV1TrajectorySingleToolUseInput', 31) class GoogleCloudAiplatformV1EvaluateInstancesResponse(_messages.Message): r"""Response message for EvaluationService.EvaluateInstances. Fields: bleuResults: Results for bleu metric. coherenceResult: Result for coherence metric. cometResult: Translation metrics. Result for Comet metric. exactMatchResults: Auto metric evaluation results. Results for exact match metric. fluencyResult: LLM-based metric evaluation result. General text generation metrics, applicable to other categories. Result for fluency metric. fulfillmentResult: Result for fulfillment metric. groundednessResult: Result for groundedness metric. metricxResult: Result for Metricx metric. pairwiseMetricResult: Result for pairwise metric. pairwiseQuestionAnsweringQualityResult: Result for pairwise question answering quality metric. pairwiseSummarizationQualityResult: Result for pairwise summarization quality metric. pointwiseMetricResult: Generic metrics. Result for pointwise metric. questionAnsweringCorrectnessResult: Result for question answering correctness metric. questionAnsweringHelpfulnessResult: Result for question answering helpfulness metric. questionAnsweringQualityResult: Question answering only metrics. Result for question answering quality metric. questionAnsweringRelevanceResult: Result for question answering relevance metric. rougeResults: Results for rouge metric. safetyResult: Result for safety metric. summarizationHelpfulnessResult: Result for summarization helpfulness metric. summarizationQualityResult: Summarization only metrics. Result for summarization quality metric. summarizationVerbosityResult: Result for summarization verbosity metric. toolCallValidResults: Tool call metrics. Results for tool call valid metric. toolNameMatchResults: Results for tool name match metric. toolParameterKeyMatchResults: Results for tool parameter key match metric. toolParameterKvMatchResults: Results for tool parameter key value match metric. trajectoryAnyOrderMatchResults: Result for trajectory any order match metric. trajectoryExactMatchResults: Result for trajectory exact match metric. trajectoryInOrderMatchResults: Result for trajectory in order match metric. trajectoryPrecisionResults: Result for trajectory precision metric. trajectoryRecallResults: Results for trajectory recall metric. trajectorySingleToolUseResults: Results for trajectory single tool use metric. """ bleuResults = _messages.MessageField('GoogleCloudAiplatformV1BleuResults', 1) coherenceResult = _messages.MessageField('GoogleCloudAiplatformV1CoherenceResult', 2) cometResult = _messages.MessageField('GoogleCloudAiplatformV1CometResult', 3) exactMatchResults = _messages.MessageField('GoogleCloudAiplatformV1ExactMatchResults', 4) fluencyResult = _messages.MessageField('GoogleCloudAiplatformV1FluencyResult', 5) fulfillmentResult = _messages.MessageField('GoogleCloudAiplatformV1FulfillmentResult', 6) groundednessResult = _messages.MessageField('GoogleCloudAiplatformV1GroundednessResult', 7) metricxResult = _messages.MessageField('GoogleCloudAiplatformV1MetricxResult', 8) pairwiseMetricResult = _messages.MessageField('GoogleCloudAiplatformV1PairwiseMetricResult', 9) pairwiseQuestionAnsweringQualityResult = _messages.MessageField('GoogleCloudAiplatformV1PairwiseQuestionAnsweringQualityResult', 10) pairwiseSummarizationQualityResult = _messages.MessageField('GoogleCloudAiplatformV1PairwiseSummarizationQualityResult', 11) pointwiseMetricResult = _messages.MessageField('GoogleCloudAiplatformV1PointwiseMetricResult', 12) questionAnsweringCorrectnessResult = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringCorrectnessResult', 13) questionAnsweringHelpfulnessResult = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringHelpfulnessResult', 14) questionAnsweringQualityResult = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringQualityResult', 15) questionAnsweringRelevanceResult = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringRelevanceResult', 16) rougeResults = _messages.MessageField('GoogleCloudAiplatformV1RougeResults', 17) safetyResult = _messages.MessageField('GoogleCloudAiplatformV1SafetyResult', 18) summarizationHelpfulnessResult = _messages.MessageField('GoogleCloudAiplatformV1SummarizationHelpfulnessResult', 19) summarizationQualityResult = _messages.MessageField('GoogleCloudAiplatformV1SummarizationQualityResult', 20) summarizationVerbosityResult = _messages.MessageField('GoogleCloudAiplatformV1SummarizationVerbosityResult', 21) toolCallValidResults = _messages.MessageField('GoogleCloudAiplatformV1ToolCallValidResults', 22) toolNameMatchResults = _messages.MessageField('GoogleCloudAiplatformV1ToolNameMatchResults', 23) toolParameterKeyMatchResults = _messages.MessageField('GoogleCloudAiplatformV1ToolParameterKeyMatchResults', 24) toolParameterKvMatchResults = _messages.MessageField('GoogleCloudAiplatformV1ToolParameterKVMatchResults', 25) trajectoryAnyOrderMatchResults = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryAnyOrderMatchResults', 26) trajectoryExactMatchResults = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryExactMatchResults', 27) trajectoryInOrderMatchResults = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryInOrderMatchResults', 28) trajectoryPrecisionResults = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryPrecisionResults', 29) trajectoryRecallResults = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryRecallResults', 30) trajectorySingleToolUseResults = _messages.MessageField('GoogleCloudAiplatformV1TrajectorySingleToolUseResults', 31) class GoogleCloudAiplatformV1EvaluatedAnnotation(_messages.Message): r"""True positive, false positive, or false negative. EvaluatedAnnotation is only available under ModelEvaluationSlice with slice of `annotationSpec` dimension. Enums: TypeValueValuesEnum: Output only. Type of the EvaluatedAnnotation. Fields: dataItemPayload: Output only. The data item payload that the Model predicted this EvaluatedAnnotation on. errorAnalysisAnnotations: Annotations of model error analysis results. evaluatedDataItemViewId: Output only. ID of the EvaluatedDataItemView under the same ancestor ModelEvaluation. The EvaluatedDataItemView consists of all ground truths and predictions on data_item_payload. explanations: Explanations of predictions. Each element of the explanations indicates the explanation for one explanation Method. The attributions list in the EvaluatedAnnotationExplanation.explanation object corresponds to the predictions list. For example, the second element in the attributions list explains the second element in the predictions list. groundTruths: Output only. The ground truth Annotations, i.e. the Annotations that exist in the test data the Model is evaluated on. For true positive, there is one and only one ground truth annotation, which matches the only prediction in predictions. For false positive, there are zero or more ground truth annotations that are similar to the only prediction in predictions, but not enough for a match. For false negative, there is one and only one ground truth annotation, which doesn't match any predictions created by the model. The schema of the ground truth is stored in ModelEvaluation.annotation_schema_uri predictions: Output only. The model predicted annotations. For true positive, there is one and only one prediction, which matches the only one ground truth annotation in ground_truths. For false positive, there is one and only one prediction, which doesn't match any ground truth annotation of the corresponding data_item_view_id. For false negative, there are zero or more predictions which are similar to the only ground truth annotation in ground_truths but not enough for a match. The schema of the prediction is stored in ModelEvaluation.annotation_schema_uri type: Output only. Type of the EvaluatedAnnotation. """ class TypeValueValuesEnum(_messages.Enum): r"""Output only. Type of the EvaluatedAnnotation. Values: EVALUATED_ANNOTATION_TYPE_UNSPECIFIED: Invalid value. TRUE_POSITIVE: The EvaluatedAnnotation is a true positive. It has a prediction created by the Model and a ground truth Annotation which the prediction matches. FALSE_POSITIVE: The EvaluatedAnnotation is false positive. It has a prediction created by the Model which does not match any ground truth annotation. FALSE_NEGATIVE: The EvaluatedAnnotation is false negative. It has a ground truth annotation which is not matched by any of the model created predictions. """ EVALUATED_ANNOTATION_TYPE_UNSPECIFIED = 0 TRUE_POSITIVE = 1 FALSE_POSITIVE = 2 FALSE_NEGATIVE = 3 dataItemPayload = _messages.MessageField('extra_types.JsonValue', 1) errorAnalysisAnnotations = _messages.MessageField('GoogleCloudAiplatformV1ErrorAnalysisAnnotation', 2, repeated=True) evaluatedDataItemViewId = _messages.StringField(3) explanations = _messages.MessageField('GoogleCloudAiplatformV1EvaluatedAnnotationExplanation', 4, repeated=True) groundTruths = _messages.MessageField('extra_types.JsonValue', 5, repeated=True) predictions = _messages.MessageField('extra_types.JsonValue', 6, repeated=True) type = _messages.EnumField('TypeValueValuesEnum', 7) class GoogleCloudAiplatformV1EvaluatedAnnotationExplanation(_messages.Message): r"""Explanation result of the prediction produced by the Model. Fields: explanation: Explanation attribution response details. explanationType: Explanation type. For AutoML Image Classification models, possible values are: * `image-integrated-gradients` * `image-xrai` """ explanation = _messages.MessageField('GoogleCloudAiplatformV1Explanation', 1) explanationType = _messages.StringField(2) class GoogleCloudAiplatformV1Event(_messages.Message): r"""An edge describing the relationship between an Artifact and an Execution in a lineage graph. Enums: TypeValueValuesEnum: Required. The type of the Event. Messages: LabelsValue: The labels with user-defined metadata to annotate Events. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Event (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Fields: artifact: Required. The relative resource name of the Artifact in the Event. eventTime: Output only. Time the Event occurred. execution: Output only. The relative resource name of the Execution in the Event. labels: The labels with user-defined metadata to annotate Events. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Event (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. type: Required. The type of the Event. """ class TypeValueValuesEnum(_messages.Enum): r"""Required. The type of the Event. Values: TYPE_UNSPECIFIED: Unspecified whether input or output of the Execution. INPUT: An input of the Execution. OUTPUT: An output of the Execution. """ TYPE_UNSPECIFIED = 0 INPUT = 1 OUTPUT = 2 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to annotate Events. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Event (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) artifact = _messages.StringField(1) eventTime = _messages.StringField(2) execution = _messages.StringField(3) labels = _messages.MessageField('LabelsValue', 4) type = _messages.EnumField('TypeValueValuesEnum', 5) class GoogleCloudAiplatformV1ExactMatchInput(_messages.Message): r"""Input for exact match metric. Fields: instances: Required. Repeated exact match instances. metricSpec: Required. Spec for exact match metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1ExactMatchInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1ExactMatchSpec', 2) class GoogleCloudAiplatformV1ExactMatchInstance(_messages.Message): r"""Spec for exact match instance. Fields: prediction: Required. Output of the evaluated model. reference: Required. Ground truth used to compare against the prediction. """ prediction = _messages.StringField(1) reference = _messages.StringField(2) class GoogleCloudAiplatformV1ExactMatchMetricValue(_messages.Message): r"""Exact match metric value for an instance. Fields: score: Output only. Exact match score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1ExactMatchResults(_messages.Message): r"""Results for exact match metric. Fields: exactMatchMetricValues: Output only. Exact match metric values. """ exactMatchMetricValues = _messages.MessageField('GoogleCloudAiplatformV1ExactMatchMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1ExactMatchSpec(_messages.Message): r"""Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0. """ class GoogleCloudAiplatformV1Examples(_messages.Message): r"""Example-based explainability that returns the nearest neighbors from the provided dataset. Fields: exampleGcsSource: The Cloud Storage input instances. nearestNeighborSearchConfig: The full configuration for the generated index, the semantics are the same as metadata and should match [NearestNeighborSearchConfig](https://cloud.google.com/vertex- ai/docs/explainable-ai/configuring-explanations-example-based#nearest- neighbor-search-config). neighborCount: The number of neighbors to return when querying for examples. presets: Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade- off and modality. """ exampleGcsSource = _messages.MessageField('GoogleCloudAiplatformV1ExamplesExampleGcsSource', 1) nearestNeighborSearchConfig = _messages.MessageField('extra_types.JsonValue', 2) neighborCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) presets = _messages.MessageField('GoogleCloudAiplatformV1Presets', 4) class GoogleCloudAiplatformV1ExamplesExampleGcsSource(_messages.Message): r"""The Cloud Storage input instances. Enums: DataFormatValueValuesEnum: The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported. Fields: dataFormat: The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported. gcsSource: The Cloud Storage location for the input instances. """ class DataFormatValueValuesEnum(_messages.Enum): r"""The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported. Values: DATA_FORMAT_UNSPECIFIED: Format unspecified, used when unset. JSONL: Examples are stored in JSONL files. """ DATA_FORMAT_UNSPECIFIED = 0 JSONL = 1 dataFormat = _messages.EnumField('DataFormatValueValuesEnum', 1) gcsSource = _messages.MessageField('GoogleCloudAiplatformV1GcsSource', 2) class GoogleCloudAiplatformV1ExamplesOverride(_messages.Message): r"""Overrides for example-based explanations. Enums: DataFormatValueValuesEnum: The format of the data being provided with each call. Fields: crowdingCount: The number of neighbors to return that have the same crowding tag. dataFormat: The format of the data being provided with each call. neighborCount: The number of neighbors to return. restrictions: Restrict the resulting nearest neighbors to respect these constraints. returnEmbeddings: If true, return the embeddings instead of neighbors. """ class DataFormatValueValuesEnum(_messages.Enum): r"""The format of the data being provided with each call. Values: DATA_FORMAT_UNSPECIFIED: Unspecified format. Must not be used. INSTANCES: Provided data is a set of model inputs. EMBEDDINGS: Provided data is a set of embeddings. """ DATA_FORMAT_UNSPECIFIED = 0 INSTANCES = 1 EMBEDDINGS = 2 crowdingCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) dataFormat = _messages.EnumField('DataFormatValueValuesEnum', 2) neighborCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) restrictions = _messages.MessageField('GoogleCloudAiplatformV1ExamplesRestrictionsNamespace', 4, repeated=True) returnEmbeddings = _messages.BooleanField(5) class GoogleCloudAiplatformV1ExamplesRestrictionsNamespace(_messages.Message): r"""Restrictions namespace for example-based explanations overrides. Fields: allow: The list of allowed tags. deny: The list of deny tags. namespaceName: The namespace name. """ allow = _messages.StringField(1, repeated=True) deny = _messages.StringField(2, repeated=True) namespaceName = _messages.StringField(3) class GoogleCloudAiplatformV1ExecutableCode(_messages.Message): r"""Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. Enums: LanguageValueValuesEnum: Required. Programming language of the `code`. Fields: code: Required. The code to be executed. language: Required. Programming language of the `code`. """ class LanguageValueValuesEnum(_messages.Enum): r"""Required. Programming language of the `code`. Values: LANGUAGE_UNSPECIFIED: Unspecified language. This value should not be used. PYTHON: Python >= 3.10, with numpy and simpy available. """ LANGUAGE_UNSPECIFIED = 0 PYTHON = 1 code = _messages.StringField(1) language = _messages.EnumField('LanguageValueValuesEnum', 2) class GoogleCloudAiplatformV1Execution(_messages.Message): r"""Instance of a general execution. Enums: StateValueValuesEnum: The state of this Execution. This is a property of the Execution, and does not imply or capture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines) and the system does not prescribe or check the validity of state transitions. Messages: LabelsValue: The labels with user-defined metadata to organize your Executions. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Execution (System labels are excluded). MetadataValue: Properties of the Execution. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB. Fields: createTime: Output only. Timestamp when this Execution was created. description: Description of the Execution displayName: User provided display name of the Execution. May be up to 128 Unicode characters. etag: An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: The labels with user-defined metadata to organize your Executions. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Execution (System labels are excluded). metadata: Properties of the Execution. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB. name: Output only. The resource name of the Execution. schemaTitle: The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store. schemaVersion: The version of the schema in `schema_title` to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store. state: The state of this Execution. This is a property of the Execution, and does not imply or capture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines) and the system does not prescribe or check the validity of state transitions. updateTime: Output only. Timestamp when this Execution was last updated. """ class StateValueValuesEnum(_messages.Enum): r"""The state of this Execution. This is a property of the Execution, and does not imply or capture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines) and the system does not prescribe or check the validity of state transitions. Values: STATE_UNSPECIFIED: Unspecified Execution state NEW: The Execution is new RUNNING: The Execution is running COMPLETE: The Execution has finished running FAILED: The Execution has failed CACHED: The Execution completed through Cache hit. CANCELLED: The Execution was cancelled. """ STATE_UNSPECIFIED = 0 NEW = 1 RUNNING = 2 COMPLETE = 3 FAILED = 4 CACHED = 5 CANCELLED = 6 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your Executions. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Execution (System labels are excluded). Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class MetadataValue(_messages.Message): r"""Properties of the Execution. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB. Messages: AdditionalProperty: An additional property for a MetadataValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a MetadataValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) description = _messages.StringField(2) displayName = _messages.StringField(3) etag = _messages.StringField(4) labels = _messages.MessageField('LabelsValue', 5) metadata = _messages.MessageField('MetadataValue', 6) name = _messages.StringField(7) schemaTitle = _messages.StringField(8) schemaVersion = _messages.StringField(9) state = _messages.EnumField('StateValueValuesEnum', 10) updateTime = _messages.StringField(11) class GoogleCloudAiplatformV1ExplainRequest(_messages.Message): r"""Request message for PredictionService.Explain. Fields: deployedModelId: If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split. explanationSpecOverride: If specified, overrides the explanation_spec of the DeployedModel. Can be used for explaining prediction results with different configurations, such as: - Explaining top-5 predictions results as opposed to top-1; - Increasing path count or step count of the attribution methods to reduce approximate errors; - Using different baselines for explaining the prediction results. instances: Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri. parameters: The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri. """ deployedModelId = _messages.StringField(1) explanationSpecOverride = _messages.MessageField('GoogleCloudAiplatformV1ExplanationSpecOverride', 2) instances = _messages.MessageField('extra_types.JsonValue', 3, repeated=True) parameters = _messages.MessageField('extra_types.JsonValue', 4) class GoogleCloudAiplatformV1ExplainResponse(_messages.Message): r"""Response message for PredictionService.Explain. Fields: deployedModelId: ID of the Endpoint's DeployedModel that served this explanation. explanations: The explanations of the Model's PredictResponse.predictions. It has the same number of elements as instances to be explained. predictions: The predictions that are the output of the predictions call. Same as PredictResponse.predictions. """ deployedModelId = _messages.StringField(1) explanations = _messages.MessageField('GoogleCloudAiplatformV1Explanation', 2, repeated=True) predictions = _messages.MessageField('extra_types.JsonValue', 3, repeated=True) class GoogleCloudAiplatformV1Explanation(_messages.Message): r"""Explanation of a prediction (provided in PredictResponse.predictions) produced by the Model on a given instance. Fields: attributions: Output only. Feature attributions grouped by predicted outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. By default, we provide Shapley values for the predicted class. However, you can configure the explanation request to generate Shapley values for any other classes too. For example, if a model predicts a probability of `0.4` for approving a loan application, the model's decision is to reject the application since `p(reject) = 0.6 > p(approve) = 0.4`, and the default Shapley values would be computed for rejection decision and not approval, even though the latter might be the positive class. If users set ExplanationParameters.top_k, the attributions are sorted by instance_output_value in descending order. If ExplanationParameters.output_indices is specified, the attributions are stored by Attribution.output_index in the same order as they appear in the output_indices. neighbors: Output only. List of the nearest neighbors for example-based explanations. For models deployed with the examples explanations feature enabled, the attributions field is empty and instead the neighbors field is populated. """ attributions = _messages.MessageField('GoogleCloudAiplatformV1Attribution', 1, repeated=True) neighbors = _messages.MessageField('GoogleCloudAiplatformV1Neighbor', 2, repeated=True) class GoogleCloudAiplatformV1ExplanationMetadata(_messages.Message): r"""Metadata describing the Model's input and output for explanation. Messages: InputsValue: Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI- provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance. OutputsValue: Required. Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed. Fields: featureAttributionsSchemaUri: Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. inputs: Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI- provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance. latentSpaceSource: Name of the source to generate embeddings for example based explanations. outputs: Required. Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed. """ @encoding.MapUnrecognizedFields('additionalProperties') class InputsValue(_messages.Message): r"""Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance. Messages: AdditionalProperty: An additional property for a InputsValue object. Fields: additionalProperties: Additional properties of type InputsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a InputsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1ExplanationMetadataInputMetadata attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1ExplanationMetadataInputMetadata', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class OutputsValue(_messages.Message): r"""Required. Map from output names to output metadata. For Vertex AI- provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed. Messages: AdditionalProperty: An additional property for a OutputsValue object. Fields: additionalProperties: Additional properties of type OutputsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a OutputsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1ExplanationMetadataOutputMetadata attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1ExplanationMetadataOutputMetadata', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) featureAttributionsSchemaUri = _messages.StringField(1) inputs = _messages.MessageField('InputsValue', 2) latentSpaceSource = _messages.StringField(3) outputs = _messages.MessageField('OutputsValue', 4) class GoogleCloudAiplatformV1ExplanationMetadataInputMetadata(_messages.Message): r"""Metadata of the input of a feature. Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow. Enums: EncodingValueValuesEnum: Defines how the feature is encoded into the input tensor. Defaults to IDENTITY. Fields: denseShapeTensorName: Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor. encodedBaselines: A list of baselines for the encoded tensor. The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor. encodedTensorName: Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table. encoding: Defines how the feature is encoded into the input tensor. Defaults to IDENTITY. featureValueDomain: The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized. groupName: Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions, keyed by the group name. indexFeatureMapping: A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR. indicesTensorName: Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor. inputBaselines: Baseline inputs for this feature. If no baseline is specified, Vertex AI chooses the baseline for this feature. If multiple baselines are specified, Vertex AI returns the average attributions across them in Attribution.feature_attributions. For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor. For custom images, the element of the baselines must be in the same format as the feature's input in the instance[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri. inputTensorName: Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow. modality: Modality of the feature. Valid values are: numeric, image. Defaults to numeric. visualization: Visualization configurations for image explanation. """ class EncodingValueValuesEnum(_messages.Enum): r"""Defines how the feature is encoded into the input tensor. Defaults to IDENTITY. Values: ENCODING_UNSPECIFIED: Default value. This is the same as IDENTITY. IDENTITY: The tensor represents one feature. BAG_OF_FEATURES: The tensor represents a bag of features where each index maps to a feature. InputMetadata.index_feature_mapping must be provided for this encoding. For example: ``` input = [27, 6.0, 150] index_feature_mapping = ["age", "height", "weight"] ``` BAG_OF_FEATURES_SPARSE: The tensor represents a bag of features where each index maps to a feature. Zero values in the tensor indicates feature being non-existent. InputMetadata.index_feature_mapping must be provided for this encoding. For example: ``` input = [2, 0, 5, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"] ``` INDICATOR: The tensor is a list of binaries representing whether a feature exists or not (1 indicates existence). InputMetadata.index_feature_mapping must be provided for this encoding. For example: ``` input = [1, 0, 1, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"] ``` COMBINED_EMBEDDING: The tensor is encoded into a 1-dimensional array represented by an encoded tensor. InputMetadata.encoded_tensor_name must be provided for this encoding. For example: ``` input = ["This", "is", "a", "test", "."] encoded = [0.1, 0.2, 0.3, 0.4, 0.5] ``` CONCAT_EMBEDDING: Select this encoding when the input tensor is encoded into a 2-dimensional array represented by an encoded tensor. InputMetadata.encoded_tensor_name must be provided for this encoding. The first dimension of the encoded tensor's shape is the same as the input tensor's shape. For example: ``` input = ["This", "is", "a", "test", "."] encoded = [[0.1, 0.2, 0.3, 0.4, 0.5], [0.2, 0.1, 0.4, 0.3, 0.5], [0.5, 0.1, 0.3, 0.5, 0.4], [0.5, 0.3, 0.1, 0.2, 0.4], [0.4, 0.3, 0.2, 0.5, 0.1]] ``` """ ENCODING_UNSPECIFIED = 0 IDENTITY = 1 BAG_OF_FEATURES = 2 BAG_OF_FEATURES_SPARSE = 3 INDICATOR = 4 COMBINED_EMBEDDING = 5 CONCAT_EMBEDDING = 6 denseShapeTensorName = _messages.StringField(1) encodedBaselines = _messages.MessageField('extra_types.JsonValue', 2, repeated=True) encodedTensorName = _messages.StringField(3) encoding = _messages.EnumField('EncodingValueValuesEnum', 4) featureValueDomain = _messages.MessageField('GoogleCloudAiplatformV1ExplanationMetadataInputMetadataFeatureValueDomain', 5) groupName = _messages.StringField(6) indexFeatureMapping = _messages.StringField(7, repeated=True) indicesTensorName = _messages.StringField(8) inputBaselines = _messages.MessageField('extra_types.JsonValue', 9, repeated=True) inputTensorName = _messages.StringField(10) modality = _messages.StringField(11) visualization = _messages.MessageField('GoogleCloudAiplatformV1ExplanationMetadataInputMetadataVisualization', 12) class GoogleCloudAiplatformV1ExplanationMetadataInputMetadataFeatureValueDomain(_messages.Message): r"""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. Fields: maxValue: The maximum permissible value for this feature. minValue: The minimum permissible value for this feature. originalMean: If this input feature has been normalized to a mean value of 0, the original_mean specifies the mean value of the domain prior to normalization. originalStddev: If this input feature has been normalized to a standard deviation of 1.0, the original_stddev specifies the standard deviation of the domain prior to normalization. """ maxValue = _messages.FloatField(1, variant=_messages.Variant.FLOAT) minValue = _messages.FloatField(2, variant=_messages.Variant.FLOAT) originalMean = _messages.FloatField(3, variant=_messages.Variant.FLOAT) originalStddev = _messages.FloatField(4, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1ExplanationMetadataInputMetadataVisualization(_messages.Message): r"""Visualization configurations for image explanation. Enums: ColorMapValueValuesEnum: The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue. OverlayTypeValueValuesEnum: How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE. PolarityValueValuesEnum: Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE. TypeValueValuesEnum: Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES. Fields: clipPercentLowerbound: Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62. clipPercentUpperbound: Excludes attributions above the specified percentile from the highlighted areas. Using the clip_percent_upperbound and clip_percent_lowerbound together can be useful for filtering out noise and making it easier to see areas of strong attribution. Defaults to 99.9. colorMap: The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue. overlayType: How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE. polarity: Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE. type: Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES. """ class ColorMapValueValuesEnum(_messages.Enum): r"""The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue. Values: COLOR_MAP_UNSPECIFIED: Should not be used. PINK_GREEN: Positive: green. Negative: pink. VIRIDIS: Viridis color map: A perceptually uniform color mapping which is easier to see by those with colorblindness and progresses from yellow to green to blue. Positive: yellow. Negative: blue. RED: Positive: red. Negative: red. GREEN: Positive: green. Negative: green. RED_GREEN: Positive: green. Negative: red. PINK_WHITE_GREEN: PiYG palette. """ COLOR_MAP_UNSPECIFIED = 0 PINK_GREEN = 1 VIRIDIS = 2 RED = 3 GREEN = 4 RED_GREEN = 5 PINK_WHITE_GREEN = 6 class OverlayTypeValueValuesEnum(_messages.Enum): r"""How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE. Values: OVERLAY_TYPE_UNSPECIFIED: Default value. This is the same as NONE. NONE: No overlay. ORIGINAL: The attributions are shown on top of the original image. GRAYSCALE: The attributions are shown on top of grayscaled version of the original image. MASK_BLACK: The attributions are used as a mask to reveal predictive parts of the image and hide the un-predictive parts. """ OVERLAY_TYPE_UNSPECIFIED = 0 NONE = 1 ORIGINAL = 2 GRAYSCALE = 3 MASK_BLACK = 4 class PolarityValueValuesEnum(_messages.Enum): r"""Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE. Values: POLARITY_UNSPECIFIED: Default value. This is the same as POSITIVE. POSITIVE: Highlights the pixels/outlines that were most influential to the model's prediction. NEGATIVE: Setting polarity to negative highlights areas that does not lead to the models's current prediction. BOTH: Shows both positive and negative attributions. """ POLARITY_UNSPECIFIED = 0 POSITIVE = 1 NEGATIVE = 2 BOTH = 3 class TypeValueValuesEnum(_messages.Enum): r"""Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES. Values: TYPE_UNSPECIFIED: Should not be used. PIXELS: Shows which pixel contributed to the image prediction. OUTLINES: Shows which region contributed to the image prediction by outlining the region. """ TYPE_UNSPECIFIED = 0 PIXELS = 1 OUTLINES = 2 clipPercentLowerbound = _messages.FloatField(1, variant=_messages.Variant.FLOAT) clipPercentUpperbound = _messages.FloatField(2, variant=_messages.Variant.FLOAT) colorMap = _messages.EnumField('ColorMapValueValuesEnum', 3) overlayType = _messages.EnumField('OverlayTypeValueValuesEnum', 4) polarity = _messages.EnumField('PolarityValueValuesEnum', 5) type = _messages.EnumField('TypeValueValuesEnum', 6) class GoogleCloudAiplatformV1ExplanationMetadataOutputMetadata(_messages.Message): r"""Metadata of the prediction output to be explained. Fields: displayNameMappingKey: Specify a field name in the prediction to look for the display name. Use this if the prediction contains the display names for the outputs. The display names in the prediction must have the same shape of the outputs, so that it can be located by Attribution.output_index for a specific output. indexDisplayNameMapping: Static mapping between the index and display name. Use this if the outputs are a deterministic n-dimensional array, e.g. a list of scores of all the classes in a pre-defined order for a multi-classification Model. It's not feasible if the outputs are non- deterministic, e.g. the Model produces top-k classes or sort the outputs by their values. The shape of the value must be an n-dimensional array of strings. The number of dimensions must match that of the outputs to be explained. The Attribution.output_display_name is populated by locating in the mapping with Attribution.output_index. outputTensorName: Name of the output tensor. Required and is only applicable to Vertex AI provided images for Tensorflow. """ displayNameMappingKey = _messages.StringField(1) indexDisplayNameMapping = _messages.MessageField('extra_types.JsonValue', 2) outputTensorName = _messages.StringField(3) class GoogleCloudAiplatformV1ExplanationMetadataOverride(_messages.Message): r"""The ExplanationMetadata entries that can be overridden at online explanation time. Messages: InputsValue: Required. Overrides the input metadata of the features. The key is the name of the feature to be overridden. The keys specified here must exist in the input metadata to be overridden. If a feature is not specified here, the corresponding feature's input metadata is not overridden. Fields: inputs: Required. Overrides the input metadata of the features. The key is the name of the feature to be overridden. The keys specified here must exist in the input metadata to be overridden. If a feature is not specified here, the corresponding feature's input metadata is not overridden. """ @encoding.MapUnrecognizedFields('additionalProperties') class InputsValue(_messages.Message): r"""Required. Overrides the input metadata of the features. The key is the name of the feature to be overridden. The keys specified here must exist in the input metadata to be overridden. If a feature is not specified here, the corresponding feature's input metadata is not overridden. Messages: AdditionalProperty: An additional property for a InputsValue object. Fields: additionalProperties: Additional properties of type InputsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a InputsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1ExplanationMetadataOverrideInputMetada taOverride attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1ExplanationMetadataOverrideInputMetadataOverride', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) inputs = _messages.MessageField('InputsValue', 1) class GoogleCloudAiplatformV1ExplanationMetadataOverrideInputMetadataOverride(_messages.Message): r"""The input metadata entries to be overridden. Fields: inputBaselines: Baseline inputs for this feature. This overrides the `input_baseline` field of the ExplanationMetadata.InputMetadata object of the corresponding feature's input metadata. If it's not specified, the original baselines are not overridden. """ inputBaselines = _messages.MessageField('extra_types.JsonValue', 1, repeated=True) class GoogleCloudAiplatformV1ExplanationParameters(_messages.Message): r"""Parameters to configure explaining for Model's predictions. Fields: examples: Example-based explanations that returns the nearest neighbors from the provided dataset. integratedGradientsAttribution: An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365 outputIndices: If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes). 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. Refer to this paper for model details: https://arxiv.org/abs/1306.4265. topK: If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs. xraiAttribution: An attribution method that redistributes Integrated Gradients attribution 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 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead. """ examples = _messages.MessageField('GoogleCloudAiplatformV1Examples', 1) integratedGradientsAttribution = _messages.MessageField('GoogleCloudAiplatformV1IntegratedGradientsAttribution', 2) outputIndices = _messages.MessageField('extra_types.JsonValue', 3, repeated=True) sampledShapleyAttribution = _messages.MessageField('GoogleCloudAiplatformV1SampledShapleyAttribution', 4) topK = _messages.IntegerField(5, variant=_messages.Variant.INT32) xraiAttribution = _messages.MessageField('GoogleCloudAiplatformV1XraiAttribution', 6) class GoogleCloudAiplatformV1ExplanationSpec(_messages.Message): r"""Specification of Model explanation. Fields: metadata: Optional. Metadata describing the Model's input and output for explanation. parameters: Required. Parameters that configure explaining of the Model's predictions. """ metadata = _messages.MessageField('GoogleCloudAiplatformV1ExplanationMetadata', 1) parameters = _messages.MessageField('GoogleCloudAiplatformV1ExplanationParameters', 2) class GoogleCloudAiplatformV1ExplanationSpecOverride(_messages.Message): r"""The ExplanationSpec entries that can be overridden at online explanation time. Fields: examplesOverride: The example-based explanations parameter overrides. metadata: The metadata to be overridden. If not specified, no metadata is overridden. parameters: The parameters to be overridden. Note that the attribution method cannot be changed. If not specified, no parameter is overridden. """ examplesOverride = _messages.MessageField('GoogleCloudAiplatformV1ExamplesOverride', 1) metadata = _messages.MessageField('GoogleCloudAiplatformV1ExplanationMetadataOverride', 2) parameters = _messages.MessageField('GoogleCloudAiplatformV1ExplanationParameters', 3) class GoogleCloudAiplatformV1ExportDataConfig(_messages.Message): r"""Describes what part of the Dataset is to be exported, the destination of the export and how to export. Enums: ExportUseValueValuesEnum: Indicates the usage of the exported files. Fields: annotationSchemaUri: The Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). The schema files that can be used here are found in gs://google-cloud- aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with metadata of the Dataset specified by ExportDataRequest.name. Only used for custom training data export use cases. Only applicable to Datasets that have DataItems and Annotations. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri. annotationsFilter: An expression for filtering what part of the Dataset is to be exported. Only Annotations that match this filter will be exported. The filter syntax is the same as in ListAnnotations. exportUse: Indicates the usage of the exported files. filterSplit: Split based on the provided filters for each set. fractionSplit: Split based on fractions defining the size of each set. gcsDestination: The Google Cloud Storage location where the output is to be written to. In the given directory a new directory will be created with name: `export-data--` where timestamp is in YYYY-MM- DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory. Inside that directory, annotations with the same schema will be grouped into sub directories which are named with the corresponding annotations' schema title. Inside these sub directories, a schema.yaml will be created to describe the output format. savedQueryId: The ID of a SavedQuery (annotation set) under the Dataset specified by ExportDataRequest.name used for filtering Annotations for training. Only used for custom training data export use cases. Only applicable to Datasets that have SavedQueries. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type. """ class ExportUseValueValuesEnum(_messages.Enum): r"""Indicates the usage of the exported files. Values: EXPORT_USE_UNSPECIFIED: Regular user export. CUSTOM_CODE_TRAINING: Export for custom code training. """ EXPORT_USE_UNSPECIFIED = 0 CUSTOM_CODE_TRAINING = 1 annotationSchemaUri = _messages.StringField(1) annotationsFilter = _messages.StringField(2) exportUse = _messages.EnumField('ExportUseValueValuesEnum', 3) filterSplit = _messages.MessageField('GoogleCloudAiplatformV1ExportFilterSplit', 4) fractionSplit = _messages.MessageField('GoogleCloudAiplatformV1ExportFractionSplit', 5) gcsDestination = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 6) savedQueryId = _messages.StringField(7) class GoogleCloudAiplatformV1ExportDataOperationMetadata(_messages.Message): r"""Runtime operation information for DatasetService.ExportData. Fields: gcsOutputDirectory: A Google Cloud Storage directory which path ends with '/'. The exported data is stored in the directory. genericMetadata: The common part of the operation metadata. """ gcsOutputDirectory = _messages.StringField(1) genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 2) class GoogleCloudAiplatformV1ExportDataRequest(_messages.Message): r"""Request message for DatasetService.ExportData. Fields: exportConfig: Required. The desired output location. """ exportConfig = _messages.MessageField('GoogleCloudAiplatformV1ExportDataConfig', 1) class GoogleCloudAiplatformV1ExportDataResponse(_messages.Message): r"""Response message for DatasetService.ExportData. Fields: dataStats: Only present for custom code training export use case. Records data stats, i.e., train/validation/test item/annotation counts calculated during the export operation. exportedFiles: All of the files that are exported in this export operation. For custom code training export, only three (training, validation and test) Cloud Storage paths in wildcard format are populated (for example, gs://.../training-*). """ dataStats = _messages.MessageField('GoogleCloudAiplatformV1ModelDataStats', 1) exportedFiles = _messages.StringField(2, repeated=True) class GoogleCloudAiplatformV1ExportFeatureValuesOperationMetadata(_messages.Message): r"""Details of operations that exports Features values. Fields: genericMetadata: Operation metadata for Featurestore export Feature values. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1ExportFeatureValuesRequest(_messages.Message): r"""Request message for FeaturestoreService.ExportFeatureValues. Fields: destination: Required. Specifies destination location and format. featureSelector: Required. Selects Features to export values of. fullExport: Exports all historical values of all entities of the EntityType within a time range settings: Per-Feature export settings. snapshotExport: Exports the latest Feature values of all entities of the EntityType within a time range. """ destination = _messages.MessageField('GoogleCloudAiplatformV1FeatureValueDestination', 1) featureSelector = _messages.MessageField('GoogleCloudAiplatformV1FeatureSelector', 2) fullExport = _messages.MessageField('GoogleCloudAiplatformV1ExportFeatureValuesRequestFullExport', 3) settings = _messages.MessageField('GoogleCloudAiplatformV1DestinationFeatureSetting', 4, repeated=True) snapshotExport = _messages.MessageField('GoogleCloudAiplatformV1ExportFeatureValuesRequestSnapshotExport', 5) class GoogleCloudAiplatformV1ExportFeatureValuesRequestFullExport(_messages.Message): r"""Describes exporting all historical Feature values of all entities of the EntityType between [start_time, end_time]. Fields: endTime: Exports Feature values as of this timestamp. If not set, retrieve values as of now. Timestamp, if present, must not have higher than millisecond precision. startTime: Excludes Feature values with feature generation timestamp before this timestamp. If not set, retrieve oldest values kept in Feature Store. Timestamp, if present, must not have higher than millisecond precision. """ endTime = _messages.StringField(1) startTime = _messages.StringField(2) class GoogleCloudAiplatformV1ExportFeatureValuesRequestSnapshotExport(_messages.Message): r"""Describes exporting the latest Feature values of all entities of the EntityType between [start_time, snapshot_time]. Fields: snapshotTime: Exports Feature values as of this timestamp. If not set, retrieve values as of now. Timestamp, if present, must not have higher than millisecond precision. startTime: Excludes Feature values with feature generation timestamp before this timestamp. If not set, retrieve oldest values kept in Feature Store. Timestamp, if present, must not have higher than millisecond precision. """ snapshotTime = _messages.StringField(1) startTime = _messages.StringField(2) class GoogleCloudAiplatformV1ExportFeatureValuesResponse(_messages.Message): r"""Response message for FeaturestoreService.ExportFeatureValues.""" class GoogleCloudAiplatformV1ExportFilterSplit(_messages.Message): r"""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. Fields: testFilter: Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. trainingFilter: Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. validationFilter: Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. """ testFilter = _messages.StringField(1) trainingFilter = _messages.StringField(2) validationFilter = _messages.StringField(3) class GoogleCloudAiplatformV1ExportFractionSplit(_messages.Message): r"""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. Fields: testFraction: The fraction of the input data that is to be used to evaluate the Model. trainingFraction: The fraction of the input data that is to be used to train the Model. validationFraction: The fraction of the input data that is to be used to validate the Model. """ testFraction = _messages.FloatField(1) trainingFraction = _messages.FloatField(2) validationFraction = _messages.FloatField(3) class GoogleCloudAiplatformV1ExportModelOperationMetadata(_messages.Message): r"""Details of ModelService.ExportModel operation. Fields: genericMetadata: The common part of the operation metadata. outputInfo: Output only. Information further describing the output of this Model export. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) outputInfo = _messages.MessageField('GoogleCloudAiplatformV1ExportModelOperationMetadataOutputInfo', 2) class GoogleCloudAiplatformV1ExportModelOperationMetadataOutputInfo(_messages.Message): r"""Further describes the output of the ExportModel. Supplements ExportModelRequest.OutputConfig. Fields: artifactOutputUri: Output only. If the Model artifact is being exported to Google Cloud Storage this is the full path of the directory created, into which the Model files are being written to. imageOutputUri: Output only. If the Model image is being exported to Google Container Registry or Artifact Registry this is the full path of the image created. """ artifactOutputUri = _messages.StringField(1) imageOutputUri = _messages.StringField(2) class GoogleCloudAiplatformV1ExportModelRequest(_messages.Message): r"""Request message for ModelService.ExportModel. Fields: outputConfig: Required. The desired output location and configuration. """ outputConfig = _messages.MessageField('GoogleCloudAiplatformV1ExportModelRequestOutputConfig', 1) class GoogleCloudAiplatformV1ExportModelRequestOutputConfig(_messages.Message): r"""Output configuration for the Model export. Fields: artifactDestination: The Cloud Storage location where the Model artifact is to be written to. Under the directory given as the destination a new one with name "`model-export--`", where timestamp is in YYYY-MM- DDThh:mm:ss.sssZ ISO-8601 format, will be created. Inside, the Model and any of its supporting files will be written. This field should only be set when the `exportableContent` field of the [Model.supported_export_formats] object contains `ARTIFACT`. exportFormatId: The ID of the format in which the Model must be exported. Each Model lists the export formats it supports. If no value is provided here, then the first from the list of the Model's supported formats is used by default. imageDestination: The Google Container Registry or Artifact Registry uri where the Model container image will be copied to. This field should only be set when the `exportableContent` field of the [Model.supported_export_formats] object contains `IMAGE`. """ artifactDestination = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 1) exportFormatId = _messages.StringField(2) imageDestination = _messages.MessageField('GoogleCloudAiplatformV1ContainerRegistryDestination', 3) class GoogleCloudAiplatformV1ExportModelResponse(_messages.Message): r"""Response message of ModelService.ExportModel operation.""" class GoogleCloudAiplatformV1ExportTensorboardTimeSeriesDataRequest(_messages.Message): r"""Request message for TensorboardService.ExportTensorboardTimeSeriesData. Fields: filter: Exports the TensorboardTimeSeries' data that match the filter expression. orderBy: Field to use to sort the TensorboardTimeSeries' data. By default, TensorboardTimeSeries' data is returned in a pseudo random order. pageSize: The maximum number of data points to return per page. The default page_size is 1000. Values must be between 1 and 10000. Values above 10000 are coerced to 10000. pageToken: A page token, received from a previous ExportTensorboardTimeSeriesData call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to ExportTensorboardTimeSeriesData must match the call that provided the page token. """ filter = _messages.StringField(1) orderBy = _messages.StringField(2) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) class GoogleCloudAiplatformV1ExportTensorboardTimeSeriesDataResponse(_messages.Message): r"""Response message for TensorboardService.ExportTensorboardTimeSeriesData. Fields: nextPageToken: A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. timeSeriesDataPoints: The returned time series data points. """ nextPageToken = _messages.StringField(1) timeSeriesDataPoints = _messages.MessageField('GoogleCloudAiplatformV1TimeSeriesDataPoint', 2, repeated=True) class GoogleCloudAiplatformV1Fact(_messages.Message): r"""The fact used in grounding. Fields: chunk: If present, chunk properties. query: Query that is used to retrieve this fact. score: If present, according to the underlying Vector DB and the selected metric type, the score can be either the distance or the similarity between the query and the fact and its range depends on the metric type. For example, if the metric type is COSINE_DISTANCE, it represents the distance between the query and the fact. The larger the distance, the less relevant the fact is to the query. The range is [0, 2], while 0 means the most relevant and 2 means the least relevant. summary: If present, the summary/snippet of the fact. title: If present, it refers to the title of this fact. uri: If present, this uri links to the source of the fact. vectorDistance: If present, the distance between the query vector and this fact vector. """ chunk = _messages.MessageField('GoogleCloudAiplatformV1RagChunk', 1) query = _messages.StringField(2) score = _messages.FloatField(3) summary = _messages.StringField(4) title = _messages.StringField(5) uri = _messages.StringField(6) vectorDistance = _messages.FloatField(7) class GoogleCloudAiplatformV1FasterDeploymentConfig(_messages.Message): r"""Configuration for faster model deployment. Fields: fastTryoutEnabled: If true, enable fast tryout feature for this deployed model. """ fastTryoutEnabled = _messages.BooleanField(1) class GoogleCloudAiplatformV1Feature(_messages.Message): r"""Feature Metadata information. For example, color is a feature that describes an apple. Enums: ValueTypeValueValuesEnum: Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value. Messages: LabelsValue: Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Fields: createTime: Output only. Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created. description: Description of the Feature. disableMonitoring: Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType. etag: Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. monitoringStatsAnomalies: Output only. Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives. name: Immutable. Name of the Feature. Format: `projects/{project}/location s/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/feat ures/{feature}` `projects/{project}/locations/{location}/featureGroups/{ feature_group}/features/{feature}` The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type. pointOfContact: Entity responsible for maintaining this feature. Can be comma separated list of email addresses or URIs. updateTime: Output only. Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated. valueType: Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value. versionColumnName: Only applicable for Vertex AI Feature Store. The name of the BigQuery Table/View column hosting data for this version. If no value is provided, will use feature_id. """ class ValueTypeValueValuesEnum(_messages.Enum): r"""Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value. Values: VALUE_TYPE_UNSPECIFIED: The value type is unspecified. BOOL: Used for Feature that is a boolean. BOOL_ARRAY: Used for Feature that is a list of boolean. DOUBLE: Used for Feature that is double. DOUBLE_ARRAY: Used for Feature that is a list of double. INT64: Used for Feature that is INT64. INT64_ARRAY: Used for Feature that is a list of INT64. STRING: Used for Feature that is string. STRING_ARRAY: Used for Feature that is a list of String. BYTES: Used for Feature that is bytes. STRUCT: Used for Feature that is struct. """ VALUE_TYPE_UNSPECIFIED = 0 BOOL = 1 BOOL_ARRAY = 2 DOUBLE = 3 DOUBLE_ARRAY = 4 INT64 = 5 INT64_ARRAY = 6 STRING = 7 STRING_ARRAY = 8 BYTES = 9 STRUCT = 10 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) description = _messages.StringField(2) disableMonitoring = _messages.BooleanField(3) etag = _messages.StringField(4) labels = _messages.MessageField('LabelsValue', 5) monitoringStatsAnomalies = _messages.MessageField('GoogleCloudAiplatformV1FeatureMonitoringStatsAnomaly', 6, repeated=True) name = _messages.StringField(7) pointOfContact = _messages.StringField(8) updateTime = _messages.StringField(9) valueType = _messages.EnumField('ValueTypeValueValuesEnum', 10) versionColumnName = _messages.StringField(11) class GoogleCloudAiplatformV1FeatureGroup(_messages.Message): r"""Vertex AI Feature Group. Enums: ServiceAgentTypeValueValuesEnum: Optional. Service agent type used during jobs under a FeatureGroup. By default, the Vertex AI Service Agent is used. When using an IAM Policy to isolate this FeatureGroup within a project, a separate service account should be provisioned by setting this field to `SERVICE_AGENT_TYPE_FEATURE_GROUP`. This will generate a separate service account to access the BigQuery source table. Messages: LabelsValue: Optional. The labels with user-defined metadata to organize your FeatureGroup. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureGroup(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Fields: bigQuery: Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source. The BigQuery source table or view must have at least one entity ID column and a column named `feature_timestamp`. createTime: Output only. Timestamp when this FeatureGroup was created. description: Optional. Description of the FeatureGroup. etag: Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: Optional. The labels with user-defined metadata to organize your FeatureGroup. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureGroup(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. name: Identifier. Name of the FeatureGroup. Format: `projects/{project}/locations/{location}/featureGroups/{featureGroup}` serviceAccountEmail: Output only. A Service Account unique to this FeatureGroup. The role bigquery.dataViewer should be granted to this service account to allow Vertex AI Feature Store to access source data while running jobs under this FeatureGroup. serviceAgentType: Optional. Service agent type used during jobs under a FeatureGroup. By default, the Vertex AI Service Agent is used. When using an IAM Policy to isolate this FeatureGroup within a project, a separate service account should be provisioned by setting this field to `SERVICE_AGENT_TYPE_FEATURE_GROUP`. This will generate a separate service account to access the BigQuery source table. updateTime: Output only. Timestamp when this FeatureGroup was last updated. """ class ServiceAgentTypeValueValuesEnum(_messages.Enum): r"""Optional. Service agent type used during jobs under a FeatureGroup. By default, the Vertex AI Service Agent is used. When using an IAM Policy to isolate this FeatureGroup within a project, a separate service account should be provisioned by setting this field to `SERVICE_AGENT_TYPE_FEATURE_GROUP`. This will generate a separate service account to access the BigQuery source table. Values: SERVICE_AGENT_TYPE_UNSPECIFIED: By default, the project-level Vertex AI Service Agent is enabled. SERVICE_AGENT_TYPE_PROJECT: Specifies the project-level Vertex AI Service Agent (https://cloud.google.com/vertex-ai/docs/general/access- control#service-agents). SERVICE_AGENT_TYPE_FEATURE_GROUP: Enable a FeatureGroup service account to be created by Vertex AI and output in the field `service_account_email`. This service account will be used to read from the source BigQuery table during jobs under a FeatureGroup. """ SERVICE_AGENT_TYPE_UNSPECIFIED = 0 SERVICE_AGENT_TYPE_PROJECT = 1 SERVICE_AGENT_TYPE_FEATURE_GROUP = 2 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels with user-defined metadata to organize your FeatureGroup. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureGroup(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) bigQuery = _messages.MessageField('GoogleCloudAiplatformV1FeatureGroupBigQuery', 1) createTime = _messages.StringField(2) description = _messages.StringField(3) etag = _messages.StringField(4) labels = _messages.MessageField('LabelsValue', 5) name = _messages.StringField(6) serviceAccountEmail = _messages.StringField(7) serviceAgentType = _messages.EnumField('ServiceAgentTypeValueValuesEnum', 8) updateTime = _messages.StringField(9) class GoogleCloudAiplatformV1FeatureGroupBigQuery(_messages.Message): r"""Input source type for BigQuery Tables and Views. Fields: bigQuerySource: Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View. dense: Optional. If set, all feature values will be fetched from a single row per unique entityId including nulls. If not set, will collapse all rows for each unique entityId into a singe row with any non-null values if present, if no non-null values are present will sync null. ex: If source has schema `(entity_id, feature_timestamp, f0, f1)` and the following rows: `(e1, 2020-01-01T10:00:00.123Z, 10, 15)` `(e1, 2020-02-01T10:00:00.123Z, 20, null)` If dense is set, `(e1, 20, null)` is synced to online stores. If dense is not set, `(e1, 20, 15)` is synced to online stores. entityIdColumns: Optional. Columns to construct entity_id / row keys. If not provided defaults to `entity_id`. staticDataSource: Optional. Set if the data source is not a time-series. timeSeries: Optional. If the source is a time-series source, this can be set to control how downstream sources (ex: FeatureView ) will treat time-series sources. If not set, will treat the source as a time-series source with `feature_timestamp` as timestamp column and no scan boundary. """ bigQuerySource = _messages.MessageField('GoogleCloudAiplatformV1BigQuerySource', 1) dense = _messages.BooleanField(2) entityIdColumns = _messages.StringField(3, repeated=True) staticDataSource = _messages.BooleanField(4) timeSeries = _messages.MessageField('GoogleCloudAiplatformV1FeatureGroupBigQueryTimeSeries', 5) class GoogleCloudAiplatformV1FeatureGroupBigQueryTimeSeries(_messages.Message): r"""A GoogleCloudAiplatformV1FeatureGroupBigQueryTimeSeries object. Fields: timestampColumn: Optional. Column hosting timestamp values for a time- series source. Will be used to determine the latest `feature_values` for each entity. Optional. If not provided, column named `feature_timestamp` of type `TIMESTAMP` will be used. """ timestampColumn = _messages.StringField(1) class GoogleCloudAiplatformV1FeatureMonitoringStatsAnomaly(_messages.Message): r"""A list of historical SnapshotAnalysis or ImportFeaturesAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending. Enums: ObjectiveValueValuesEnum: Output only. The objective for each stats. Fields: featureStatsAnomaly: Output only. The stats and anomalies generated at specific timestamp. objective: Output only. The objective for each stats. """ class ObjectiveValueValuesEnum(_messages.Enum): r"""Output only. The objective for each stats. Values: OBJECTIVE_UNSPECIFIED: If it's OBJECTIVE_UNSPECIFIED, monitoring_stats will be empty. IMPORT_FEATURE_ANALYSIS: Stats are generated by Import Feature Analysis. SNAPSHOT_ANALYSIS: Stats are generated by Snapshot Analysis. """ OBJECTIVE_UNSPECIFIED = 0 IMPORT_FEATURE_ANALYSIS = 1 SNAPSHOT_ANALYSIS = 2 featureStatsAnomaly = _messages.MessageField('GoogleCloudAiplatformV1FeatureStatsAnomaly', 1) objective = _messages.EnumField('ObjectiveValueValuesEnum', 2) class GoogleCloudAiplatformV1FeatureNoiseSigma(_messages.Message): r"""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. Fields: noiseSigma: Noise sigma per feature. No noise is added to features that are not set. """ noiseSigma = _messages.MessageField('GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature', 1, repeated=True) class GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature(_messages.Message): r"""Noise sigma for a single feature. Fields: name: The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs. sigma: This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1. """ name = _messages.StringField(1) sigma = _messages.FloatField(2, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1FeatureOnlineStore(_messages.Message): r"""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. Enums: StateValueValuesEnum: Output only. State of the featureOnlineStore. Messages: LabelsValue: Optional. The labels with user-defined metadata to organize your FeatureOnlineStore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Fields: bigtable: Contains settings for the Cloud Bigtable instance that will be created to serve featureValues for all FeatureViews under this FeatureOnlineStore. createTime: Output only. Timestamp when this FeatureOnlineStore was created. dedicatedServingEndpoint: Optional. The dedicated serving endpoint for this FeatureOnlineStore, which is different from common Vertex service endpoint. encryptionSpec: Optional. Customer-managed encryption key spec for data storage. If set, online store will be secured by this key. etag: Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: Optional. The labels with user-defined metadata to organize your FeatureOnlineStore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. name: Identifier. Name of the FeatureOnlineStore. Format: `projects/{proje ct}/locations/{location}/featureOnlineStores/{featureOnlineStore}` optimized: Contains settings for the Optimized store that will be created to serve featureValues for all FeatureViews under this FeatureOnlineStore. When choose Optimized storage type, need to set PrivateServiceConnectConfig.enable_private_service_connect to use private endpoint. Otherwise will use public endpoint by default. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. state: Output only. State of the featureOnlineStore. updateTime: Output only. Timestamp when this FeatureOnlineStore was last updated. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. State of the featureOnlineStore. Values: STATE_UNSPECIFIED: Default value. This value is unused. STABLE: State when the featureOnlineStore configuration is not being updated and the fields reflect the current configuration of the featureOnlineStore. The featureOnlineStore is usable in this state. UPDATING: The state of the featureOnlineStore configuration when it is being updated. During an update, the fields reflect either the original configuration or the updated configuration of the featureOnlineStore. The featureOnlineStore is still usable in this state. """ STATE_UNSPECIFIED = 0 STABLE = 1 UPDATING = 2 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels with user-defined metadata to organize your FeatureOnlineStore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) bigtable = _messages.MessageField('GoogleCloudAiplatformV1FeatureOnlineStoreBigtable', 1) createTime = _messages.StringField(2) dedicatedServingEndpoint = _messages.MessageField('GoogleCloudAiplatformV1FeatureOnlineStoreDedicatedServingEndpoint', 3) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 4) etag = _messages.StringField(5) labels = _messages.MessageField('LabelsValue', 6) name = _messages.StringField(7) optimized = _messages.MessageField('GoogleCloudAiplatformV1FeatureOnlineStoreOptimized', 8) satisfiesPzi = _messages.BooleanField(9) satisfiesPzs = _messages.BooleanField(10) state = _messages.EnumField('StateValueValuesEnum', 11) updateTime = _messages.StringField(12) class GoogleCloudAiplatformV1FeatureOnlineStoreBigtable(_messages.Message): r"""A GoogleCloudAiplatformV1FeatureOnlineStoreBigtable object. Fields: autoScaling: Required. Autoscaling config applied to Bigtable Instance. """ autoScaling = _messages.MessageField('GoogleCloudAiplatformV1FeatureOnlineStoreBigtableAutoScaling', 1) class GoogleCloudAiplatformV1FeatureOnlineStoreBigtableAutoScaling(_messages.Message): r"""A GoogleCloudAiplatformV1FeatureOnlineStoreBigtableAutoScaling object. Fields: cpuUtilizationTarget: Optional. A percentage of the cluster's CPU capacity. Can be from 10% to 80%. When a cluster's CPU utilization exceeds the target that you have set, Bigtable immediately adds nodes to the cluster. When CPU utilization is substantially lower than the target, Bigtable removes nodes. If not set will default to 50%. maxNodeCount: Required. The maximum number of nodes to scale up to. Must be greater than or equal to min_node_count, and less than or equal to 10 times of 'min_node_count'. minNodeCount: Required. The minimum number of nodes to scale down to. Must be greater than or equal to 1. """ cpuUtilizationTarget = _messages.IntegerField(1, variant=_messages.Variant.INT32) maxNodeCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) minNodeCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1FeatureOnlineStoreDedicatedServingEndpoint(_messages.Message): r"""The dedicated serving endpoint for this FeatureOnlineStore. Only need to set when you choose Optimized storage type. Public endpoint is provisioned by default. Fields: privateServiceConnectConfig: Optional. Private service connect config. The private service connection is available only for Optimized storage type, not for embedding management now. If PrivateServiceConnectConfig.enable_private_service_connect set to true, customers will use private service connection to send request. Otherwise, the connection will set to public endpoint. publicEndpointDomainName: Output only. This field will be populated with the domain name to use for this FeatureOnlineStore serviceAttachment: Output only. The name of the service attachment resource. Populated if private service connect is enabled and after FeatureViewSync is created. """ privateServiceConnectConfig = _messages.MessageField('GoogleCloudAiplatformV1PrivateServiceConnectConfig', 1) publicEndpointDomainName = _messages.StringField(2) serviceAttachment = _messages.StringField(3) class GoogleCloudAiplatformV1FeatureOnlineStoreOptimized(_messages.Message): r"""Optimized storage type""" class GoogleCloudAiplatformV1FeatureSelector(_messages.Message): r"""Selector for Features of an EntityType. Fields: idMatcher: Required. Matches Features based on ID. """ idMatcher = _messages.MessageField('GoogleCloudAiplatformV1IdMatcher', 1) class GoogleCloudAiplatformV1FeatureStatsAnomaly(_messages.Message): r"""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. Fields: anomalyDetectionThreshold: This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value. anomalyUri: Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https ://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/ v0/anomalies.proto). distributionDeviation: Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen\u2013Shannon divergence. endTime: The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values). score: Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT. startTime: The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval). statsUri: Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message [tensorflow.metadata.v0.FeatureNameStatisti cs](https://github.com/tensorflow/metadata/blob/master/tensorflow_metada ta/proto/v0/statistics.proto). """ anomalyDetectionThreshold = _messages.FloatField(1) anomalyUri = _messages.StringField(2) distributionDeviation = _messages.FloatField(3) endTime = _messages.StringField(4) score = _messages.FloatField(5) startTime = _messages.StringField(6) statsUri = _messages.StringField(7) class GoogleCloudAiplatformV1FeatureValue(_messages.Message): r"""Value for a feature. Fields: boolArrayValue: A list of bool type feature value. boolValue: Bool type feature value. bytesValue: Bytes feature value. doubleArrayValue: A list of double type feature value. doubleValue: Double type feature value. int64ArrayValue: A list of int64 type feature value. int64Value: Int64 feature value. metadata: Metadata of feature value. stringArrayValue: A list of string type feature value. stringValue: String feature value. structValue: A struct type feature value. """ boolArrayValue = _messages.MessageField('GoogleCloudAiplatformV1BoolArray', 1) boolValue = _messages.BooleanField(2) bytesValue = _messages.BytesField(3) doubleArrayValue = _messages.MessageField('GoogleCloudAiplatformV1DoubleArray', 4) doubleValue = _messages.FloatField(5) int64ArrayValue = _messages.MessageField('GoogleCloudAiplatformV1Int64Array', 6) int64Value = _messages.IntegerField(7) metadata = _messages.MessageField('GoogleCloudAiplatformV1FeatureValueMetadata', 8) stringArrayValue = _messages.MessageField('GoogleCloudAiplatformV1StringArray', 9) stringValue = _messages.StringField(10) structValue = _messages.MessageField('GoogleCloudAiplatformV1StructValue', 11) class GoogleCloudAiplatformV1FeatureValueDestination(_messages.Message): r"""A destination location for Feature values and format. Fields: bigqueryDestination: Output in BigQuery format. BigQueryDestination.output_uri in FeatureValueDestination.bigquery_destination must refer to a table. csvDestination: Output in CSV format. Array Feature value types are not allowed in CSV format. tfrecordDestination: Output in TFRecord format. Below are the mapping from Feature value type in Featurestore to Feature value type in TFRecord: Value type in Featurestore | Value type in TFRecord DOUBLE, DOUBLE_ARRAY | FLOAT_LIST INT64, INT64_ARRAY | INT64_LIST STRING, STRING_ARRAY, BYTES | BYTES_LIST true -> byte_string("true"), false -> byte_string("false") BOOL, BOOL_ARRAY (true, false) | BYTES_LIST """ bigqueryDestination = _messages.MessageField('GoogleCloudAiplatformV1BigQueryDestination', 1) csvDestination = _messages.MessageField('GoogleCloudAiplatformV1CsvDestination', 2) tfrecordDestination = _messages.MessageField('GoogleCloudAiplatformV1TFRecordDestination', 3) class GoogleCloudAiplatformV1FeatureValueList(_messages.Message): r"""Container for list of values. Fields: values: A list of feature values. All of them should be the same data type. """ values = _messages.MessageField('GoogleCloudAiplatformV1FeatureValue', 1, repeated=True) class GoogleCloudAiplatformV1FeatureValueMetadata(_messages.Message): r"""Metadata of feature value. Fields: generateTime: Feature generation timestamp. Typically, it is provided by user at feature ingestion time. If not, feature store will use the system timestamp when the data is ingested into feature store. Legacy Feature Store: For streaming ingestion, the time, aligned by days, must be no older than five years (1825 days) and no later than one year (366 days) in the future. """ generateTime = _messages.StringField(1) class GoogleCloudAiplatformV1FeatureView(_messages.Message): r"""FeatureView is representation of values that the FeatureOnlineStore will serve based on its syncConfig. Enums: ServiceAgentTypeValueValuesEnum: Optional. Service agent type used during data sync. By default, the Vertex AI Service Agent is used. When using an IAM Policy to isolate this FeatureView within a project, a separate service account should be provisioned by setting this field to `SERVICE_AGENT_TYPE_FEATURE_VIEW`. This will generate a separate service account to access the BigQuery source table. Messages: LabelsValue: Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Fields: bigQuerySource: Optional. Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. createTime: Output only. Timestamp when this FeatureView was created. etag: Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. featureRegistrySource: Optional. Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. indexConfig: Optional. Configuration for index preparation for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving. labels: Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. name: Identifier. Name of the FeatureView. Format: `projects/{project}/loc ations/{location}/featureOnlineStores/{feature_online_store}/featureView s/{feature_view}` optimizedConfig: Optional. Configuration for FeatureView created under Optimized FeatureOnlineStore. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. serviceAccountEmail: Output only. A Service Account unique to this FeatureView. The role bigquery.dataViewer should be granted to this service account to allow Vertex AI Feature Store to sync data to the online store. serviceAgentType: Optional. Service agent type used during data sync. By default, the Vertex AI Service Agent is used. When using an IAM Policy to isolate this FeatureView within a project, a separate service account should be provisioned by setting this field to `SERVICE_AGENT_TYPE_FEATURE_VIEW`. This will generate a separate service account to access the BigQuery source table. syncConfig: Configures when data is to be synced/updated for this FeatureView. At the end of the sync the latest featureValues for each entityId of this FeatureView are made ready for online serving. updateTime: Output only. Timestamp when this FeatureView was last updated. vertexRagSource: Optional. The Vertex RAG Source that the FeatureView is linked to. """ class ServiceAgentTypeValueValuesEnum(_messages.Enum): r"""Optional. Service agent type used during data sync. By default, the Vertex AI Service Agent is used. When using an IAM Policy to isolate this FeatureView within a project, a separate service account should be provisioned by setting this field to `SERVICE_AGENT_TYPE_FEATURE_VIEW`. This will generate a separate service account to access the BigQuery source table. Values: SERVICE_AGENT_TYPE_UNSPECIFIED: By default, the project-level Vertex AI Service Agent is enabled. SERVICE_AGENT_TYPE_PROJECT: Indicates the project-level Vertex AI Service Agent (https://cloud.google.com/vertex-ai/docs/general/access- control#service-agents) will be used during sync jobs. SERVICE_AGENT_TYPE_FEATURE_VIEW: Enable a FeatureView service account to be created by Vertex AI and output in the field `service_account_email`. This service account will be used to read from the source BigQuery table during sync. """ SERVICE_AGENT_TYPE_UNSPECIFIED = 0 SERVICE_AGENT_TYPE_PROJECT = 1 SERVICE_AGENT_TYPE_FEATURE_VIEW = 2 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) bigQuerySource = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewBigQuerySource', 1) createTime = _messages.StringField(2) etag = _messages.StringField(3) featureRegistrySource = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewFeatureRegistrySource', 4) indexConfig = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewIndexConfig', 5) labels = _messages.MessageField('LabelsValue', 6) name = _messages.StringField(7) optimizedConfig = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewOptimizedConfig', 8) satisfiesPzi = _messages.BooleanField(9) satisfiesPzs = _messages.BooleanField(10) serviceAccountEmail = _messages.StringField(11) serviceAgentType = _messages.EnumField('ServiceAgentTypeValueValuesEnum', 12) syncConfig = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewSyncConfig', 13) updateTime = _messages.StringField(14) vertexRagSource = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewVertexRagSource', 15) class GoogleCloudAiplatformV1FeatureViewBigQuerySource(_messages.Message): r"""A GoogleCloudAiplatformV1FeatureViewBigQuerySource object. Fields: entityIdColumns: Required. Columns to construct entity_id / row keys. uri: Required. The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig. """ entityIdColumns = _messages.StringField(1, repeated=True) uri = _messages.StringField(2) class GoogleCloudAiplatformV1FeatureViewDataKey(_messages.Message): r"""Lookup key for a feature view. Fields: compositeKey: The actual Entity ID will be composed from this struct. This should match with the way ID is defined in the FeatureView spec. key: String key to use for lookup. """ compositeKey = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewDataKeyCompositeKey', 1) key = _messages.StringField(2) class GoogleCloudAiplatformV1FeatureViewDataKeyCompositeKey(_messages.Message): r"""ID that is comprised from several parts (columns). Fields: parts: Parts to construct Entity ID. Should match with the same ID columns as defined in FeatureView in the same order. """ parts = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1FeatureViewFeatureRegistrySource(_messages.Message): r"""A Feature Registry source for features that need to be synced to Online Store. Fields: featureGroups: Required. List of features that need to be synced to Online Store. projectNumber: Optional. The project number of the parent project of the Feature Groups. """ featureGroups = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewFeatureRegistrySourceFeatureGroup', 1, repeated=True) projectNumber = _messages.IntegerField(2) class GoogleCloudAiplatformV1FeatureViewFeatureRegistrySourceFeatureGroup(_messages.Message): r"""Features belonging to a single feature group that will be synced to Online Store. Fields: featureGroupId: Required. Identifier of the feature group. featureIds: Required. Identifiers of features under the feature group. """ featureGroupId = _messages.StringField(1) featureIds = _messages.StringField(2, repeated=True) class GoogleCloudAiplatformV1FeatureViewIndexConfig(_messages.Message): r"""Configuration for vector indexing. Enums: DistanceMeasureTypeValueValuesEnum: Optional. The distance measure used in nearest neighbor search. Fields: bruteForceConfig: Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search. crowdingColumn: Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it's guaranteed that no more than K entities of the same crowding attribute are returned in the response. distanceMeasureType: Optional. The distance measure used in nearest neighbor search. embeddingColumn: Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search. embeddingDimension: Optional. The number of dimensions of the input embedding. filterColumns: Optional. Columns of features that're used to filter vector search results. treeAhConfig: Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396 """ class DistanceMeasureTypeValueValuesEnum(_messages.Enum): r"""Optional. The distance measure used in nearest neighbor search. Values: DISTANCE_MEASURE_TYPE_UNSPECIFIED: Should not be set. SQUARED_L2_DISTANCE: Euclidean (L_2) Distance. COSINE_DISTANCE: Cosine Distance. Defined as 1 - cosine similarity. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of COSINE distance. Our algorithms have been more optimized for DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is mathematically equivalent to COSINE distance and results in the same ranking. DOT_PRODUCT_DISTANCE: Dot Product Distance. Defined as a negative of the dot product. """ DISTANCE_MEASURE_TYPE_UNSPECIFIED = 0 SQUARED_L2_DISTANCE = 1 COSINE_DISTANCE = 2 DOT_PRODUCT_DISTANCE = 3 bruteForceConfig = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewIndexConfigBruteForceConfig', 1) crowdingColumn = _messages.StringField(2) distanceMeasureType = _messages.EnumField('DistanceMeasureTypeValueValuesEnum', 3) embeddingColumn = _messages.StringField(4) embeddingDimension = _messages.IntegerField(5, variant=_messages.Variant.INT32) filterColumns = _messages.StringField(6, repeated=True) treeAhConfig = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewIndexConfigTreeAHConfig', 7) class GoogleCloudAiplatformV1FeatureViewIndexConfigBruteForceConfig(_messages.Message): r"""Configuration options for using brute force search.""" class GoogleCloudAiplatformV1FeatureViewIndexConfigTreeAHConfig(_messages.Message): r"""Configuration options for the tree-AH algorithm. Fields: leafNodeEmbeddingCount: Optional. Number of embeddings on each leaf node. The default value is 1000 if not set. """ leafNodeEmbeddingCount = _messages.IntegerField(1) class GoogleCloudAiplatformV1FeatureViewOptimizedConfig(_messages.Message): r"""Configuration for FeatureViews created in Optimized FeatureOnlineStore. Fields: automaticResources: Optional. A description of resources that the FeatureView uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000. """ automaticResources = _messages.MessageField('GoogleCloudAiplatformV1AutomaticResources', 1) class GoogleCloudAiplatformV1FeatureViewSync(_messages.Message): r"""FeatureViewSync is a representation of sync operation which copies data from data source to Feature View in Online Store. Fields: createTime: Output only. Time when this FeatureViewSync is created. Creation of a FeatureViewSync means that the job is pending / waiting for sufficient resources but may not have started the actual data transfer yet. finalStatus: Output only. Final status of the FeatureViewSync. name: Identifier. Name of the FeatureViewSync. Format: `projects/{project} /locations/{location}/featureOnlineStores/{feature_online_store}/feature Views/{feature_view}/featureViewSyncs/{feature_view_sync}` runTime: Output only. Time when this FeatureViewSync is finished. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. syncSummary: Output only. Summary of the sync job. """ createTime = _messages.StringField(1) finalStatus = _messages.MessageField('GoogleRpcStatus', 2) name = _messages.StringField(3) runTime = _messages.MessageField('GoogleTypeInterval', 4) satisfiesPzi = _messages.BooleanField(5) satisfiesPzs = _messages.BooleanField(6) syncSummary = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewSyncSyncSummary', 7) class GoogleCloudAiplatformV1FeatureViewSyncConfig(_messages.Message): r"""Configuration for Sync. Only one option is set. Fields: continuous: Optional. If true, syncs the FeatureView in a continuous manner to Online Store. cron: Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *". """ continuous = _messages.BooleanField(1) cron = _messages.StringField(2) class GoogleCloudAiplatformV1FeatureViewSyncSyncSummary(_messages.Message): r"""Summary from the Sync job. For continuous syncs, the summary is updated periodically. For batch syncs, it gets updated on completion of the sync. Fields: rowSynced: Output only. Total number of rows synced. systemWatermarkTime: Lower bound of the system time watermark for the sync job. This is only set for continuously syncing feature views. totalSlot: Output only. BigQuery slot milliseconds consumed for the sync job. """ rowSynced = _messages.IntegerField(1) systemWatermarkTime = _messages.StringField(2) totalSlot = _messages.IntegerField(3) class GoogleCloudAiplatformV1FeatureViewVertexRagSource(_messages.Message): r"""A Vertex Rag source for features that need to be synced to Online Store. Fields: ragCorpusId: Optional. The RAG corpus id corresponding to this FeatureView. uri: Required. The BigQuery view/table URI that will be materialized on each manual sync trigger. The table/view is expected to have the following columns and types at least: - `corpus_id` (STRING, NULLABLE/REQUIRED) - `file_id` (STRING, NULLABLE/REQUIRED) - `chunk_id` (STRING, NULLABLE/REQUIRED) - `chunk_data_type` (STRING, NULLABLE/REQUIRED) - `chunk_data` (STRING, NULLABLE/REQUIRED) - `embeddings` (FLOAT, REPEATED) - `file_original_uri` (STRING, NULLABLE/REQUIRED) """ ragCorpusId = _messages.IntegerField(1) uri = _messages.StringField(2) class GoogleCloudAiplatformV1Featurestore(_messages.Message): r"""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. Enums: StateValueValuesEnum: Output only. State of the featurestore. Messages: LabelsValue: Optional. The labels with user-defined metadata to organize your Featurestore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Featurestore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Fields: createTime: Output only. Timestamp when this Featurestore was created. encryptionSpec: Optional. Customer-managed encryption key spec for data storage. If set, both of the online and offline data storage will be secured by this key. etag: Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: Optional. The labels with user-defined metadata to organize your Featurestore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Featurestore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. name: Output only. Name of the Featurestore. Format: `projects/{project}/locations/{location}/featurestores/{featurestore}` onlineServingConfig: Optional. Config for online storage resources. The field should not co-exist with the field of `OnlineStoreReplicationConfig`. If both of it and OnlineStoreReplicationConfig are unset, the feature store will not have an online store and cannot be used for online serving. onlineStorageTtlDays: Optional. TTL in days for feature values that will be stored in online serving storage. The Feature Store online storage periodically removes obsolete feature values older than `online_storage_ttl_days` since the feature generation time. Note that `online_storage_ttl_days` should be less than or equal to `offline_storage_ttl_days` for each EntityType under a featurestore. If not set, default to 4000 days satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. state: Output only. State of the featurestore. updateTime: Output only. Timestamp when this Featurestore was last updated. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. State of the featurestore. Values: STATE_UNSPECIFIED: Default value. This value is unused. STABLE: State when the featurestore configuration is not being updated and the fields reflect the current configuration of the featurestore. The featurestore is usable in this state. UPDATING: The state of the featurestore configuration when it is being updated. During an update, the fields reflect either the original configuration or the updated configuration of the featurestore. For example, `online_serving_config.fixed_node_count` can take minutes to update. While the update is in progress, the featurestore is in the UPDATING state, and the value of `fixed_node_count` can be the original value or the updated value, depending on the progress of the operation. Until the update completes, the actual number of nodes can still be the original value of `fixed_node_count`. The featurestore is still usable in this state. """ STATE_UNSPECIFIED = 0 STABLE = 1 UPDATING = 2 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels with user-defined metadata to organize your Featurestore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Featurestore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 2) etag = _messages.StringField(3) labels = _messages.MessageField('LabelsValue', 4) name = _messages.StringField(5) onlineServingConfig = _messages.MessageField('GoogleCloudAiplatformV1FeaturestoreOnlineServingConfig', 6) onlineStorageTtlDays = _messages.IntegerField(7, variant=_messages.Variant.INT32) satisfiesPzi = _messages.BooleanField(8) satisfiesPzs = _messages.BooleanField(9) state = _messages.EnumField('StateValueValuesEnum', 10) updateTime = _messages.StringField(11) class GoogleCloudAiplatformV1FeaturestoreMonitoringConfig(_messages.Message): r"""Configuration of how features in Featurestore are monitored. Fields: categoricalThresholdConfig: Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING). importFeaturesAnalysis: The config for ImportFeatures Analysis Based Feature Monitoring. numericalThresholdConfig: Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64). snapshotAnalysis: The config for Snapshot Analysis Based Feature Monitoring. """ categoricalThresholdConfig = _messages.MessageField('GoogleCloudAiplatformV1FeaturestoreMonitoringConfigThresholdConfig', 1) importFeaturesAnalysis = _messages.MessageField('GoogleCloudAiplatformV1FeaturestoreMonitoringConfigImportFeaturesAnalysis', 2) numericalThresholdConfig = _messages.MessageField('GoogleCloudAiplatformV1FeaturestoreMonitoringConfigThresholdConfig', 3) snapshotAnalysis = _messages.MessageField('GoogleCloudAiplatformV1FeaturestoreMonitoringConfigSnapshotAnalysis', 4) class GoogleCloudAiplatformV1FeaturestoreMonitoringConfigImportFeaturesAnalysis(_messages.Message): r"""Configuration of the Featurestore's ImportFeature Analysis Based Monitoring. This type of analysis generates statistics for values of each Feature imported by every ImportFeatureValues operation. Enums: AnomalyDetectionBaselineValueValuesEnum: The baseline used to do anomaly detection for the statistics generated by import features analysis. StateValueValuesEnum: Whether to enable / disable / inherite default hebavior for import features analysis. Fields: anomalyDetectionBaseline: The baseline used to do anomaly detection for the statistics generated by import features analysis. state: Whether to enable / disable / inherite default hebavior for import features analysis. """ class AnomalyDetectionBaselineValueValuesEnum(_messages.Enum): r"""The baseline used to do anomaly detection for the statistics generated by import features analysis. Values: BASELINE_UNSPECIFIED: Should not be used. LATEST_STATS: Choose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics. MOST_RECENT_SNAPSHOT_STATS: Use the statistics generated by the most recent snapshot analysis if exists. PREVIOUS_IMPORT_FEATURES_STATS: Use the statistics generated by the previous import features analysis if exists. """ BASELINE_UNSPECIFIED = 0 LATEST_STATS = 1 MOST_RECENT_SNAPSHOT_STATS = 2 PREVIOUS_IMPORT_FEATURES_STATS = 3 class StateValueValuesEnum(_messages.Enum): r"""Whether to enable / disable / inherite default hebavior for import features analysis. Values: STATE_UNSPECIFIED: Should not be used. DEFAULT: The default behavior of whether to enable the monitoring. EntityType-level config: disabled. Feature-level config: inherited from the configuration of EntityType this Feature belongs to. ENABLED: Explicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it. Feature-level config: enables import features analysis regardless of the EntityType-level config. DISABLED: Explicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it. Feature-level config: disables import features analysis regardless of the EntityType-level config. """ STATE_UNSPECIFIED = 0 DEFAULT = 1 ENABLED = 2 DISABLED = 3 anomalyDetectionBaseline = _messages.EnumField('AnomalyDetectionBaselineValueValuesEnum', 1) state = _messages.EnumField('StateValueValuesEnum', 2) class GoogleCloudAiplatformV1FeaturestoreMonitoringConfigSnapshotAnalysis(_messages.Message): r"""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. Fields: disabled: The monitoring schedule for snapshot analysis. For EntityType- level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType- level config; unset monitoring_interval indicates going with EntityType- level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring. monitoringIntervalDays: Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days. stalenessDays: Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days. """ disabled = _messages.BooleanField(1) monitoringIntervalDays = _messages.IntegerField(2, variant=_messages.Variant.INT32) stalenessDays = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1FeaturestoreMonitoringConfigThresholdConfig(_messages.Message): r"""The config for Featurestore Monitoring threshold. Fields: value: Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen\u2013Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature. """ value = _messages.FloatField(1) class GoogleCloudAiplatformV1FeaturestoreOnlineServingConfig(_messages.Message): r"""OnlineServingConfig specifies the details for provisioning online serving resources. Fields: fixedNodeCount: The number of nodes for the online store. The number of nodes doesn't scale automatically, but you can manually update the number of nodes. If set to 0, the featurestore will not have an online store and cannot be used for online serving. scaling: Online serving scaling configuration. Only one of `fixed_node_count` and `scaling` can be set. Setting one will reset the other. """ fixedNodeCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) scaling = _messages.MessageField('GoogleCloudAiplatformV1FeaturestoreOnlineServingConfigScaling', 2) class GoogleCloudAiplatformV1FeaturestoreOnlineServingConfigScaling(_messages.Message): r"""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). Fields: cpuUtilizationTarget: Optional. The cpu utilization that the Autoscaler should be trying to achieve. This number is on a scale from 0 (no utilization) to 100 (total utilization), and is limited between 10 and 80. When a cluster's CPU utilization exceeds the target that you have set, Bigtable immediately adds nodes to the cluster. When CPU utilization is substantially lower than the target, Bigtable removes nodes. If not set or set to 0, default to 50. maxNodeCount: The maximum number of nodes to scale up to. Must be greater than min_node_count, and less than or equal to 10 times of 'min_node_count'. minNodeCount: Required. The minimum number of nodes to scale down to. Must be greater than or equal to 1. """ cpuUtilizationTarget = _messages.IntegerField(1, variant=_messages.Variant.INT32) maxNodeCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) minNodeCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1FetchFeatureValuesRequest(_messages.Message): r"""Request message for FeatureOnlineStoreService.FetchFeatureValues. All the features under the requested feature view will be returned. Enums: DataFormatValueValuesEnum: Optional. Response data format. If not set, FeatureViewDataFormat.KEY_VALUE will be used. Fields: dataFormat: Optional. Response data format. If not set, FeatureViewDataFormat.KEY_VALUE will be used. dataKey: Optional. The request key to fetch feature values for. """ class DataFormatValueValuesEnum(_messages.Enum): r"""Optional. Response data format. If not set, FeatureViewDataFormat.KEY_VALUE will be used. Values: FEATURE_VIEW_DATA_FORMAT_UNSPECIFIED: Not set. Will be treated as the KeyValue format. KEY_VALUE: Return response data in key-value format. PROTO_STRUCT: Return response data in proto Struct format. """ FEATURE_VIEW_DATA_FORMAT_UNSPECIFIED = 0 KEY_VALUE = 1 PROTO_STRUCT = 2 dataFormat = _messages.EnumField('DataFormatValueValuesEnum', 1) dataKey = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewDataKey', 2) class GoogleCloudAiplatformV1FetchFeatureValuesResponse(_messages.Message): r"""Response message for FeatureOnlineStoreService.FetchFeatureValues Messages: ProtoStructValue: Feature values in proto Struct format. Fields: dataKey: The data key associated with this response. Will only be populated for FeatureOnlineStoreService.StreamingFetchFeatureValues RPCs. keyValues: Feature values in KeyValue format. protoStruct: Feature values in proto Struct format. """ @encoding.MapUnrecognizedFields('additionalProperties') class ProtoStructValue(_messages.Message): r"""Feature values in proto Struct format. Messages: AdditionalProperty: An additional property for a ProtoStructValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a ProtoStructValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) dataKey = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewDataKey', 1) keyValues = _messages.MessageField('GoogleCloudAiplatformV1FetchFeatureValuesResponseFeatureNameValuePairList', 2) protoStruct = _messages.MessageField('ProtoStructValue', 3) class GoogleCloudAiplatformV1FetchFeatureValuesResponseFeatureNameValuePairList(_messages.Message): r"""Response structure in the format of key (feature name) and (feature) value pair. Fields: features: List of feature names and values. """ features = _messages.MessageField('GoogleCloudAiplatformV1FetchFeatureValuesResponseFeatureNameValuePairListFeatureNameValuePair', 1, repeated=True) class GoogleCloudAiplatformV1FetchFeatureValuesResponseFeatureNameValuePairListFeatureNameValuePair(_messages.Message): r"""Feature name & value pair. Fields: name: Feature short name. value: Feature value. """ name = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1FeatureValue', 2) class GoogleCloudAiplatformV1FileData(_messages.Message): r"""URI based data. Fields: fileUri: Required. URI. mimeType: Required. The IANA standard MIME type of the source data. """ fileUri = _messages.StringField(1) mimeType = _messages.StringField(2) class GoogleCloudAiplatformV1FileStatus(_messages.Message): r"""RagFile status. Enums: StateValueValuesEnum: Output only. RagFile state. Fields: errorStatus: Output only. Only when the `state` field is ERROR. state: Output only. RagFile state. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. RagFile state. Values: STATE_UNSPECIFIED: RagFile state is unspecified. ACTIVE: RagFile resource has been created and indexed successfully. ERROR: RagFile resource is in a problematic state. See `error_message` field for details. """ STATE_UNSPECIFIED = 0 ACTIVE = 1 ERROR = 2 errorStatus = _messages.StringField(1) state = _messages.EnumField('StateValueValuesEnum', 2) class GoogleCloudAiplatformV1FilterSplit(_messages.Message): r"""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. Fields: testFilter: Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. trainingFilter: Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. validationFilter: Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. """ testFilter = _messages.StringField(1) trainingFilter = _messages.StringField(2) validationFilter = _messages.StringField(3) class GoogleCloudAiplatformV1FluencyInput(_messages.Message): r"""Input for fluency metric. Fields: instance: Required. Fluency instance. metricSpec: Required. Spec for fluency score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1FluencyInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1FluencySpec', 2) class GoogleCloudAiplatformV1FluencyInstance(_messages.Message): r"""Spec for fluency instance. Fields: prediction: Required. Output of the evaluated model. """ prediction = _messages.StringField(1) class GoogleCloudAiplatformV1FluencyResult(_messages.Message): r"""Spec for fluency result. Fields: confidence: Output only. Confidence for fluency score. explanation: Output only. Explanation for fluency score. score: Output only. Fluency score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1FluencySpec(_messages.Message): r"""Spec for fluency score metric. Fields: version: Optional. Which version to use for evaluation. """ version = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1FractionSplit(_messages.Message): r"""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. Fields: testFraction: The fraction of the input data that is to be used to evaluate the Model. trainingFraction: The fraction of the input data that is to be used to train the Model. validationFraction: The fraction of the input data that is to be used to validate the Model. """ testFraction = _messages.FloatField(1) trainingFraction = _messages.FloatField(2) validationFraction = _messages.FloatField(3) class GoogleCloudAiplatformV1FulfillmentInput(_messages.Message): r"""Input for fulfillment metric. Fields: instance: Required. Fulfillment instance. metricSpec: Required. Spec for fulfillment score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1FulfillmentInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1FulfillmentSpec', 2) class GoogleCloudAiplatformV1FulfillmentInstance(_messages.Message): r"""Spec for fulfillment instance. Fields: instruction: Required. Inference instruction prompt to compare prediction with. prediction: Required. Output of the evaluated model. """ instruction = _messages.StringField(1) prediction = _messages.StringField(2) class GoogleCloudAiplatformV1FulfillmentResult(_messages.Message): r"""Spec for fulfillment result. Fields: confidence: Output only. Confidence for fulfillment score. explanation: Output only. Explanation for fulfillment score. score: Output only. Fulfillment score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1FulfillmentSpec(_messages.Message): r"""Spec for fulfillment metric. Fields: version: Optional. Which version to use for evaluation. """ version = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1FunctionCall(_messages.Message): r"""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. Messages: ArgsValue: Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. Fields: args: Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. name: Required. The name of the function to call. Matches [FunctionDeclaration.name]. """ @encoding.MapUnrecognizedFields('additionalProperties') class ArgsValue(_messages.Message): r"""Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. Messages: AdditionalProperty: An additional property for a ArgsValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a ArgsValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) args = _messages.MessageField('ArgsValue', 1) name = _messages.StringField(2) class GoogleCloudAiplatformV1FunctionCallingConfig(_messages.Message): r"""Function calling config. Enums: ModeValueValuesEnum: Optional. Function calling mode. Fields: allowedFunctionNames: Optional. Function names to call. Only set when the Mode is ANY. Function names should match [FunctionDeclaration.name]. With mode set to ANY, model will predict a function call from the set of function names provided. mode: Optional. Function calling mode. """ class ModeValueValuesEnum(_messages.Enum): r"""Optional. Function calling mode. Values: MODE_UNSPECIFIED: Unspecified function calling mode. This value should not be used. AUTO: Default model behavior, model decides to predict either function calls or natural language response. ANY: Model is constrained to always predicting function calls only. If "allowed_function_names" are set, the predicted function calls will be limited to any one of "allowed_function_names", else the predicted function calls will be any one of the provided "function_declarations". NONE: Model will not predict any function calls. Model behavior is same as when not passing any function declarations. """ MODE_UNSPECIFIED = 0 AUTO = 1 ANY = 2 NONE = 3 allowedFunctionNames = _messages.StringField(1, repeated=True) mode = _messages.EnumField('ModeValueValuesEnum', 2) class GoogleCloudAiplatformV1FunctionDeclaration(_messages.Message): r"""Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. 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. Fields: description: Optional. Description and purpose of the function. Model uses it to decide how and whether to call the function. name: Required. The name of the function to call. Must start with a letter or an underscore. Must be a-z, A-Z, 0-9, or contain underscores, dots and dashes, with a maximum length of 64. parameters: Optional. Describes the parameters to this function in JSON Schema Object format. Reflects the Open API 3.03 Parameter Object. string Key: the name of the parameter. Parameter names are case sensitive. Schema Value: the Schema defining the type used for the parameter. For function with no parameters, this can be left unset. Parameter names must start with a letter or an underscore and must only contain chars a-z, A-Z, 0-9, or underscores with a maximum length of 64. Example with 1 required and 1 optional parameter: type: OBJECT properties: param1: type: STRING param2: type: INTEGER required: - param1 parametersJsonSchema: Optional. Describes the parameters to the function in JSON Schema format. The schema must describe an object where the properties are the parameters to the function. For example: ``` { "type": "object", "properties": { "name": { "type": "string" }, "age": { "type": "integer" } }, "additionalProperties": false, "required": ["name", "age"], "propertyOrdering": ["name", "age"] } ``` This field is mutually exclusive with `parameters`. response: Optional. Describes the output from this function in JSON Schema format. Reflects the Open API 3.03 Response Object. The Schema defines the type used for the response value of the function. responseJsonSchema: Optional. Describes the output from this function in JSON Schema format. The value specified by the schema is the response value of the function. This field is mutually exclusive with `response`. """ description = _messages.StringField(1) name = _messages.StringField(2) parameters = _messages.MessageField('GoogleCloudAiplatformV1Schema', 3) parametersJsonSchema = _messages.MessageField('extra_types.JsonValue', 4) response = _messages.MessageField('GoogleCloudAiplatformV1Schema', 5) responseJsonSchema = _messages.MessageField('extra_types.JsonValue', 6) class GoogleCloudAiplatformV1FunctionResponse(_messages.Message): r"""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. Messages: ResponseValue: Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output. Fields: name: Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. response: Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output. """ @encoding.MapUnrecognizedFields('additionalProperties') class ResponseValue(_messages.Message): r"""Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output. Messages: AdditionalProperty: An additional property for a ResponseValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a ResponseValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) name = _messages.StringField(1) response = _messages.MessageField('ResponseValue', 2) class GoogleCloudAiplatformV1GcsDestination(_messages.Message): r"""The Google Cloud Storage location where the output is to be written to. Fields: outputUriPrefix: Required. Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist. """ outputUriPrefix = _messages.StringField(1) class GoogleCloudAiplatformV1GcsSource(_messages.Message): r"""The Google Cloud Storage location for the input content. Fields: uris: Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards. """ uris = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1GenerateContentRequest(_messages.Message): r"""Request message for [PredictionService.GenerateContent]. Messages: LabelsValue: Optional. The labels with user-defined metadata for the request. It is used for billing and reporting only. Label keys and values can be no longer than 63 characters (Unicode codepoints) and can only contain lowercase letters, numeric characters, underscores, and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter. Fields: cachedContent: Optional. The name of the cached content used as context to serve the prediction. Note: only used in explicit caching, where users can have control over caching (e.g. what content to cache) and enjoy guaranteed cost savings. Format: `projects/{project}/locations/{location}/cachedContents/{cachedContent}` contents: Required. The content of the current conversation with the model. For single-turn queries, this is a single instance. For multi- turn queries, this is a repeated field that contains conversation history + latest request. generationConfig: Optional. Generation config. labels: Optional. The labels with user-defined metadata for the request. It is used for billing and reporting only. Label keys and values can be no longer than 63 characters (Unicode codepoints) and can only contain lowercase letters, numeric characters, underscores, and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter. modelArmorConfig: Optional. Settings for prompt and response sanitization using the Model Armor service. If supplied, safety_settings must not be supplied. safetySettings: Optional. Per request settings for blocking unsafe content. Enforced on GenerateContentResponse.candidates. systemInstruction: Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph. toolConfig: Optional. Tool config. This config is shared for all tools provided in the request. tools: Optional. A list of `Tools` the model may use to generate the next 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. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels with user-defined metadata for the request. It is used for billing and reporting only. Label keys and values can be no longer than 63 characters (Unicode codepoints) and can only contain lowercase letters, numeric characters, underscores, and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) cachedContent = _messages.StringField(1) contents = _messages.MessageField('GoogleCloudAiplatformV1Content', 2, repeated=True) generationConfig = _messages.MessageField('GoogleCloudAiplatformV1GenerationConfig', 3) labels = _messages.MessageField('LabelsValue', 4) modelArmorConfig = _messages.MessageField('GoogleCloudAiplatformV1ModelArmorConfig', 5) safetySettings = _messages.MessageField('GoogleCloudAiplatformV1SafetySetting', 6, repeated=True) systemInstruction = _messages.MessageField('GoogleCloudAiplatformV1Content', 7) toolConfig = _messages.MessageField('GoogleCloudAiplatformV1ToolConfig', 8) tools = _messages.MessageField('GoogleCloudAiplatformV1Tool', 9, repeated=True) class GoogleCloudAiplatformV1GenerateContentResponse(_messages.Message): r"""Response message for [PredictionService.GenerateContent]. Fields: candidates: Output only. Generated candidates. createTime: Output only. Timestamp when the request is made to the server. modelVersion: Output only. The model version used to generate the response. promptFeedback: Output only. Content filter results for a prompt sent in the request. Note: Sent only in the first stream chunk. Only happens when no candidates were generated due to content violations. responseId: Output only. response_id is used to identify each response. It is the encoding of the event_id. usageMetadata: Usage metadata about the response(s). """ candidates = _messages.MessageField('GoogleCloudAiplatformV1Candidate', 1, repeated=True) createTime = _messages.StringField(2) modelVersion = _messages.StringField(3) promptFeedback = _messages.MessageField('GoogleCloudAiplatformV1GenerateContentResponsePromptFeedback', 4) responseId = _messages.StringField(5) usageMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenerateContentResponseUsageMetadata', 6) class GoogleCloudAiplatformV1GenerateContentResponsePromptFeedback(_messages.Message): r"""Content filter results for a prompt sent in the request. Enums: BlockReasonValueValuesEnum: Output only. Blocked reason. Fields: blockReason: Output only. Blocked reason. blockReasonMessage: Output only. A readable block reason message. safetyRatings: Output only. Safety ratings. """ class BlockReasonValueValuesEnum(_messages.Enum): r"""Output only. Blocked reason. Values: BLOCKED_REASON_UNSPECIFIED: Unspecified blocked reason. SAFETY: Candidates blocked due to safety. OTHER: Candidates blocked due to other reason. BLOCKLIST: Candidates blocked due to the terms which are included from the terminology blocklist. PROHIBITED_CONTENT: Candidates blocked due to prohibited content. MODEL_ARMOR: The user prompt was blocked by Model Armor. IMAGE_SAFETY: Candidates blocked due to unsafe image generation content. """ BLOCKED_REASON_UNSPECIFIED = 0 SAFETY = 1 OTHER = 2 BLOCKLIST = 3 PROHIBITED_CONTENT = 4 MODEL_ARMOR = 5 IMAGE_SAFETY = 6 blockReason = _messages.EnumField('BlockReasonValueValuesEnum', 1) blockReasonMessage = _messages.StringField(2) safetyRatings = _messages.MessageField('GoogleCloudAiplatformV1SafetyRating', 3, repeated=True) class GoogleCloudAiplatformV1GenerateContentResponseUsageMetadata(_messages.Message): r"""Usage metadata about response(s). Enums: TrafficTypeValueValuesEnum: Output only. Traffic type. This shows whether a request consumes Pay-As-You-Go or Provisioned Throughput quota. Fields: cacheTokensDetails: Output only. List of modalities of the cached content in the request input. cachedContentTokenCount: Output only. Number of tokens in the cached part in the input (the cached content). candidatesTokenCount: Number of tokens in the response(s). candidatesTokensDetails: Output only. List of modalities that were returned in the response. promptTokenCount: Number of tokens in the request. When `cached_content` is set, this is still the total effective prompt size meaning this includes the number of tokens in the cached content. promptTokensDetails: Output only. List of modalities that were processed in the request input. thoughtsTokenCount: Output only. Number of tokens present in thoughts output. toolUsePromptTokenCount: Output only. Number of tokens present in tool-use prompt(s). toolUsePromptTokensDetails: Output only. List of modalities that were processed for tool-use request inputs. totalTokenCount: Total token count for prompt, response candidates, and tool-use prompts (if present). trafficType: Output only. Traffic type. This shows whether a request consumes Pay-As-You-Go or Provisioned Throughput quota. """ class TrafficTypeValueValuesEnum(_messages.Enum): r"""Output only. Traffic type. This shows whether a request consumes Pay- As-You-Go or Provisioned Throughput quota. Values: TRAFFIC_TYPE_UNSPECIFIED: Unspecified request traffic type. ON_DEMAND: Type for Pay-As-You-Go traffic. PROVISIONED_THROUGHPUT: Type for Provisioned Throughput traffic. """ TRAFFIC_TYPE_UNSPECIFIED = 0 ON_DEMAND = 1 PROVISIONED_THROUGHPUT = 2 cacheTokensDetails = _messages.MessageField('GoogleCloudAiplatformV1ModalityTokenCount', 1, repeated=True) cachedContentTokenCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) candidatesTokenCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) candidatesTokensDetails = _messages.MessageField('GoogleCloudAiplatformV1ModalityTokenCount', 4, repeated=True) promptTokenCount = _messages.IntegerField(5, variant=_messages.Variant.INT32) promptTokensDetails = _messages.MessageField('GoogleCloudAiplatformV1ModalityTokenCount', 6, repeated=True) thoughtsTokenCount = _messages.IntegerField(7, variant=_messages.Variant.INT32) toolUsePromptTokenCount = _messages.IntegerField(8, variant=_messages.Variant.INT32) toolUsePromptTokensDetails = _messages.MessageField('GoogleCloudAiplatformV1ModalityTokenCount', 9, repeated=True) totalTokenCount = _messages.IntegerField(10, variant=_messages.Variant.INT32) trafficType = _messages.EnumField('TrafficTypeValueValuesEnum', 11) class GoogleCloudAiplatformV1GenerateVideoResponse(_messages.Message): r"""Generate video response. Fields: generatedSamples: The cloud storage uris of the generated videos. raiMediaFilteredCount: Returns if any videos were filtered due to RAI policies. raiMediaFilteredReasons: Returns rai failure reasons if any. """ generatedSamples = _messages.StringField(1, repeated=True) raiMediaFilteredCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) raiMediaFilteredReasons = _messages.StringField(3, repeated=True) class GoogleCloudAiplatformV1GenerationConfig(_messages.Message): r"""Generation config. Fields: candidateCount: Optional. Number of candidates to generate. enableAffectiveDialog: Optional. If enabled, the model will detect emotions and adapt its responses accordingly. frequencyPenalty: Optional. Frequency penalties. logprobs: Optional. Logit probabilities. maxOutputTokens: Optional. The maximum number of output tokens to generate per message. presencePenalty: Optional. Positive penalties. responseJsonSchema: Optional. Output schema of the generated response. This is an alternative to `response_schema` that accepts [JSON Schema](https://json-schema.org/). If set, `response_schema` must be omitted, but `response_mime_type` is required. While the full JSON Schema may be sent, not all features are supported. Specifically, only the following properties are supported: - `$id` - `$defs` - `$ref` - `$anchor` - `type` - `format` - `title` - `description` - `enum` (for strings and numbers) - `items` - `prefixItems` - `minItems` - `maxItems` - `minimum` - `maximum` - `anyOf` - `oneOf` (interpreted the same as `anyOf`) - `properties` - `additionalProperties` - `required` The non- standard `propertyOrdering` property may also be set. Cyclic references are unrolled to a limited degree and, as such, may only be used within non-required properties. (Nullable properties are not sufficient.) If `$ref` is set on a sub-schema, no other properties, except for than those starting as a `$`, may be set. responseLogprobs: Optional. If true, export the logprobs results in response. responseMimeType: Optional. Output response mimetype of the generated candidate text. Supported mimetype: - `text/plain`: (default) Text output. - `application/json`: JSON response in the candidates. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature. responseSchema: Optional. The `Schema` object allows the definition of input and output data types. These types can be objects, but also primitives and arrays. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema). If set, a compatible response_mime_type must also be set. Compatible mimetypes: `application/json`: Schema for JSON response. routingConfig: Optional. Routing configuration. seed: Optional. Seed. stopSequences: Optional. Stop sequences. temperature: Optional. Controls the randomness of predictions. thinkingConfig: Optional. Config for thinking features. An error will be returned if this field is set for models that don't support thinking. topK: Optional. If specified, top-k sampling will be used. topP: Optional. If specified, nucleus sampling will be used. """ candidateCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) enableAffectiveDialog = _messages.BooleanField(2) frequencyPenalty = _messages.FloatField(3, variant=_messages.Variant.FLOAT) logprobs = _messages.IntegerField(4, variant=_messages.Variant.INT32) maxOutputTokens = _messages.IntegerField(5, variant=_messages.Variant.INT32) presencePenalty = _messages.FloatField(6, variant=_messages.Variant.FLOAT) responseJsonSchema = _messages.MessageField('extra_types.JsonValue', 7) responseLogprobs = _messages.BooleanField(8) responseMimeType = _messages.StringField(9) responseSchema = _messages.MessageField('GoogleCloudAiplatformV1Schema', 10) routingConfig = _messages.MessageField('GoogleCloudAiplatformV1GenerationConfigRoutingConfig', 11) seed = _messages.IntegerField(12, variant=_messages.Variant.INT32) stopSequences = _messages.StringField(13, repeated=True) temperature = _messages.FloatField(14, variant=_messages.Variant.FLOAT) thinkingConfig = _messages.MessageField('GoogleCloudAiplatformV1GenerationConfigThinkingConfig', 15) topK = _messages.FloatField(16, variant=_messages.Variant.FLOAT) topP = _messages.FloatField(17, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1GenerationConfigRoutingConfig(_messages.Message): r"""The configuration for routing the request to a specific model. Fields: autoMode: Automated routing. manualMode: Manual routing. """ autoMode = _messages.MessageField('GoogleCloudAiplatformV1GenerationConfigRoutingConfigAutoRoutingMode', 1) manualMode = _messages.MessageField('GoogleCloudAiplatformV1GenerationConfigRoutingConfigManualRoutingMode', 2) class GoogleCloudAiplatformV1GenerationConfigRoutingConfigAutoRoutingMode(_messages.Message): r"""When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. Enums: ModelRoutingPreferenceValueValuesEnum: The model routing preference. Fields: modelRoutingPreference: The model routing preference. """ class ModelRoutingPreferenceValueValuesEnum(_messages.Enum): r"""The model routing preference. Values: UNKNOWN: Unspecified model routing preference. PRIORITIZE_QUALITY: Prefer higher quality over low cost. BALANCED: Balanced model routing preference. PRIORITIZE_COST: Prefer lower cost over higher quality. """ UNKNOWN = 0 PRIORITIZE_QUALITY = 1 BALANCED = 2 PRIORITIZE_COST = 3 modelRoutingPreference = _messages.EnumField('ModelRoutingPreferenceValueValuesEnum', 1) class GoogleCloudAiplatformV1GenerationConfigRoutingConfigManualRoutingMode(_messages.Message): r"""When manual routing is set, the specified model will be used directly. Fields: modelName: The model name to use. Only the public LLM models are accepted. See [Supported models](https://cloud.google.com/vertex-ai/generative- ai/docs/model-reference/inference#supported-models). """ modelName = _messages.StringField(1) class GoogleCloudAiplatformV1GenerationConfigThinkingConfig(_messages.Message): r"""Config for thinking features. Fields: includeThoughts: Optional. Indicates whether to include thoughts in the response. If true, thoughts are returned only when available. thinkingBudget: Optional. Indicates the thinking budget in tokens. """ includeThoughts = _messages.BooleanField(1) thinkingBudget = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1GenericOperationMetadata(_messages.Message): r"""Generic Metadata shared by all operations. Fields: createTime: Output only. Time when the operation was created. partialFailures: Output only. Partial failures encountered. E.g. single files that couldn't be read. This field should never exceed 20 entries. Status details field will contain standard Google Cloud error details. updateTime: Output only. Time when the operation was updated for the last time. If the operation has finished (successfully or not), this is the finish time. """ createTime = _messages.StringField(1) partialFailures = _messages.MessageField('GoogleRpcStatus', 2, repeated=True) updateTime = _messages.StringField(3) class GoogleCloudAiplatformV1GenieSource(_messages.Message): r"""Contains information about the source of the models generated from Generative AI Studio. Fields: baseModelUri: Required. The public base model URI. """ baseModelUri = _messages.StringField(1) class GoogleCloudAiplatformV1GoogleDriveSource(_messages.Message): r"""The Google Drive location for the input content. Fields: resourceIds: Required. Google Drive resource IDs. """ resourceIds = _messages.MessageField('GoogleCloudAiplatformV1GoogleDriveSourceResourceId', 1, repeated=True) class GoogleCloudAiplatformV1GoogleDriveSourceResourceId(_messages.Message): r"""The type and ID of the Google Drive resource. Enums: ResourceTypeValueValuesEnum: Required. The type of the Google Drive resource. Fields: resourceId: Required. The ID of the Google Drive resource. resourceType: Required. The type of the Google Drive resource. """ class ResourceTypeValueValuesEnum(_messages.Enum): r"""Required. The type of the Google Drive resource. Values: RESOURCE_TYPE_UNSPECIFIED: Unspecified resource type. RESOURCE_TYPE_FILE: File resource type. RESOURCE_TYPE_FOLDER: Folder resource type. """ RESOURCE_TYPE_UNSPECIFIED = 0 RESOURCE_TYPE_FILE = 1 RESOURCE_TYPE_FOLDER = 2 resourceId = _messages.StringField(1) resourceType = _messages.EnumField('ResourceTypeValueValuesEnum', 2) class GoogleCloudAiplatformV1GoogleSearchRetrieval(_messages.Message): r"""Tool to retrieve public web data for grounding, powered by Google. Fields: dynamicRetrievalConfig: Specifies the dynamic retrieval configuration for the given source. """ dynamicRetrievalConfig = _messages.MessageField('GoogleCloudAiplatformV1DynamicRetrievalConfig', 1) class GoogleCloudAiplatformV1GroundednessInput(_messages.Message): r"""Input for groundedness metric. Fields: instance: Required. Groundedness instance. metricSpec: Required. Spec for groundedness metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1GroundednessInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1GroundednessSpec', 2) class GoogleCloudAiplatformV1GroundednessInstance(_messages.Message): r"""Spec for groundedness instance. Fields: context: Required. Background information provided in context used to compare against the prediction. prediction: Required. Output of the evaluated model. """ context = _messages.StringField(1) prediction = _messages.StringField(2) class GoogleCloudAiplatformV1GroundednessResult(_messages.Message): r"""Spec for groundedness result. Fields: confidence: Output only. Confidence for groundedness score. explanation: Output only. Explanation for groundedness score. score: Output only. Groundedness score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1GroundednessSpec(_messages.Message): r"""Spec for groundedness metric. Fields: version: Optional. Which version to use for evaluation. """ version = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1GroundingChunk(_messages.Message): r"""Grounding chunk. Fields: retrievedContext: Grounding chunk from context retrieved by the retrieval tools. web: Grounding chunk from the web. """ retrievedContext = _messages.MessageField('GoogleCloudAiplatformV1GroundingChunkRetrievedContext', 1) web = _messages.MessageField('GoogleCloudAiplatformV1GroundingChunkWeb', 2) class GoogleCloudAiplatformV1GroundingChunkRetrievedContext(_messages.Message): r"""Chunk from context retrieved by the retrieval tools. Fields: ragChunk: Additional context for the RAG retrieval result. This is only populated when using the RAG retrieval tool. text: Text of the attribution. title: Title of the attribution. uri: URI reference of the attribution. """ ragChunk = _messages.MessageField('GoogleCloudAiplatformV1RagChunk', 1) text = _messages.StringField(2) title = _messages.StringField(3) uri = _messages.StringField(4) class GoogleCloudAiplatformV1GroundingChunkWeb(_messages.Message): r"""Chunk from the web. Fields: domain: Domain of the (original) URI. title: Title of the chunk. uri: URI reference of the chunk. """ domain = _messages.StringField(1) title = _messages.StringField(2) uri = _messages.StringField(3) class GoogleCloudAiplatformV1GroundingMetadata(_messages.Message): r"""Metadata returned to client when grounding is enabled. Fields: groundingChunks: List of supporting references retrieved from specified grounding source. groundingSupports: Optional. List of grounding support. retrievalMetadata: Optional. Output only. Retrieval metadata. searchEntryPoint: Optional. Google search entry for the following-up web searches. webSearchQueries: Optional. Web search queries for the following-up web search. """ groundingChunks = _messages.MessageField('GoogleCloudAiplatformV1GroundingChunk', 1, repeated=True) groundingSupports = _messages.MessageField('GoogleCloudAiplatformV1GroundingSupport', 2, repeated=True) retrievalMetadata = _messages.MessageField('GoogleCloudAiplatformV1RetrievalMetadata', 3) searchEntryPoint = _messages.MessageField('GoogleCloudAiplatformV1SearchEntryPoint', 4) webSearchQueries = _messages.StringField(5, repeated=True) class GoogleCloudAiplatformV1GroundingSupport(_messages.Message): r"""Grounding support. Fields: confidenceScores: Confidence score of the support references. Ranges from 0 to 1. 1 is the most confident. For Gemini 2.0 and before, this list must have the same size as the grounding_chunk_indices. For Gemini 2.5 and after, this list will be empty and should be ignored. groundingChunkIndices: A list of indices (into 'grounding_chunk') specifying the citations associated with the claim. For instance [1,3,4] means that grounding_chunk[1], grounding_chunk[3], grounding_chunk[4] are the retrieved content attributed to the claim. segment: Segment of the content this support belongs to. """ confidenceScores = _messages.FloatField(1, repeated=True, variant=_messages.Variant.FLOAT) groundingChunkIndices = _messages.IntegerField(2, repeated=True, variant=_messages.Variant.INT32) segment = _messages.MessageField('GoogleCloudAiplatformV1Segment', 3) class GoogleCloudAiplatformV1HyperparameterTuningJob(_messages.Message): r"""Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification. Enums: StateValueValuesEnum: Output only. The detailed state of the job. Messages: LabelsValue: The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Fields: createTime: Output only. Time when the HyperparameterTuningJob was created. displayName: Required. The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. encryptionSpec: Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key. endTime: Output only. Time when the HyperparameterTuningJob entered any of the following states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`. error: Output only. Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED. labels: The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. maxFailedTrialCount: The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails. maxTrialCount: Required. The desired total number of Trials. name: Output only. Resource name of the HyperparameterTuningJob. parallelTrialCount: Required. The desired number of Trials to run in parallel. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. startTime: Output only. Time when the HyperparameterTuningJob for the first time entered the `JOB_STATE_RUNNING` state. state: Output only. The detailed state of the job. studySpec: Required. Study configuration of the HyperparameterTuningJob. trialJobSpec: Required. The spec of a trial job. The same spec applies to the CustomJobs created in all the trials. trials: Output only. Trials of the HyperparameterTuningJob. updateTime: Output only. Time when the HyperparameterTuningJob was most recently updated. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of the job. Values: JOB_STATE_UNSPECIFIED: The job state is unspecified. JOB_STATE_QUEUED: The job has been just created or resumed and processing has not yet begun. JOB_STATE_PENDING: The service is preparing to run the job. JOB_STATE_RUNNING: The job is in progress. JOB_STATE_SUCCEEDED: The job completed successfully. JOB_STATE_FAILED: The job failed. JOB_STATE_CANCELLING: The job is being cancelled. From this state the job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. JOB_STATE_CANCELLED: The job has been cancelled. JOB_STATE_PAUSED: The job has been stopped, and can be resumed. JOB_STATE_EXPIRED: The job has expired. JOB_STATE_UPDATING: The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state. JOB_STATE_PARTIALLY_SUCCEEDED: The job is partially succeeded, some results may be missing due to errors. """ JOB_STATE_UNSPECIFIED = 0 JOB_STATE_QUEUED = 1 JOB_STATE_PENDING = 2 JOB_STATE_RUNNING = 3 JOB_STATE_SUCCEEDED = 4 JOB_STATE_FAILED = 5 JOB_STATE_CANCELLING = 6 JOB_STATE_CANCELLED = 7 JOB_STATE_PAUSED = 8 JOB_STATE_EXPIRED = 9 JOB_STATE_UPDATING = 10 JOB_STATE_PARTIALLY_SUCCEEDED = 11 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) displayName = _messages.StringField(2) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 3) endTime = _messages.StringField(4) error = _messages.MessageField('GoogleRpcStatus', 5) labels = _messages.MessageField('LabelsValue', 6) maxFailedTrialCount = _messages.IntegerField(7, variant=_messages.Variant.INT32) maxTrialCount = _messages.IntegerField(8, variant=_messages.Variant.INT32) name = _messages.StringField(9) parallelTrialCount = _messages.IntegerField(10, variant=_messages.Variant.INT32) satisfiesPzi = _messages.BooleanField(11) satisfiesPzs = _messages.BooleanField(12) startTime = _messages.StringField(13) state = _messages.EnumField('StateValueValuesEnum', 14) studySpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpec', 15) trialJobSpec = _messages.MessageField('GoogleCloudAiplatformV1CustomJobSpec', 16) trials = _messages.MessageField('GoogleCloudAiplatformV1Trial', 17, repeated=True) updateTime = _messages.StringField(18) class GoogleCloudAiplatformV1IdMatcher(_messages.Message): r"""Matcher for Features of an EntityType by Feature ID. Fields: ids: Required. The following are accepted as `ids`: * A single-element list containing only `*`, which selects all Features in the target EntityType, or * A list containing only Feature IDs, which selects only Features with those IDs in the target EntityType. """ ids = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1ImportDataConfig(_messages.Message): r"""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. Messages: AnnotationLabelsValue: Labels that will be applied to newly imported Annotations. If two Annotations are identical, one of them will be deduped. Two Annotations are considered identical if their payload, payload_schema_uri and all of their labels are the same. These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file. DataItemLabelsValue: Labels that will be applied to newly imported DataItems. If an identical DataItem as one being imported already exists in the Dataset, then these labels will be appended to these of the already existing one, and if labels with identical key is imported before, the old label value will be overwritten. If two DataItems are identical in the same import data operation, the labels will be combined and if key collision happens in this case, one of the values will be picked randomly. Two DataItems are considered identical if their content bytes are identical (e.g. image bytes or pdf bytes). These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file. Fields: annotationLabels: Labels that will be applied to newly imported Annotations. If two Annotations are identical, one of them will be deduped. Two Annotations are considered identical if their payload, payload_schema_uri and all of their labels are the same. These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file. dataItemLabels: Labels that will be applied to newly imported DataItems. If an identical DataItem as one being imported already exists in the Dataset, then these labels will be appended to these of the already existing one, and if labels with identical key is imported before, the old label value will be overwritten. If two DataItems are identical in the same import data operation, the labels will be combined and if key collision happens in this case, one of the values will be picked randomly. Two DataItems are considered identical if their content bytes are identical (e.g. image bytes or pdf bytes). These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file. gcsSource: The Google Cloud Storage location for the input content. importSchemaUri: Required. Points to a YAML file stored on Google Cloud Storage describing the import format. Validation will be done against the schema. The schema is defined as an [OpenAPI 3.0.2 Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). """ @encoding.MapUnrecognizedFields('additionalProperties') class AnnotationLabelsValue(_messages.Message): r"""Labels that will be applied to newly imported Annotations. If two Annotations are identical, one of them will be deduped. Two Annotations are considered identical if their payload, payload_schema_uri and all of their labels are the same. These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file. Messages: AdditionalProperty: An additional property for a AnnotationLabelsValue object. Fields: additionalProperties: Additional properties of type AnnotationLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a AnnotationLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class DataItemLabelsValue(_messages.Message): r"""Labels that will be applied to newly imported DataItems. If an identical DataItem as one being imported already exists in the Dataset, then these labels will be appended to these of the already existing one, and if labels with identical key is imported before, the old label value will be overwritten. If two DataItems are identical in the same import data operation, the labels will be combined and if key collision happens in this case, one of the values will be picked randomly. Two DataItems are considered identical if their content bytes are identical (e.g. image bytes or pdf bytes). These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file. Messages: AdditionalProperty: An additional property for a DataItemLabelsValue object. Fields: additionalProperties: Additional properties of type DataItemLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a DataItemLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) annotationLabels = _messages.MessageField('AnnotationLabelsValue', 1) dataItemLabels = _messages.MessageField('DataItemLabelsValue', 2) gcsSource = _messages.MessageField('GoogleCloudAiplatformV1GcsSource', 3) importSchemaUri = _messages.StringField(4) class GoogleCloudAiplatformV1ImportDataOperationMetadata(_messages.Message): r"""Runtime operation information for DatasetService.ImportData. Fields: genericMetadata: The common part of the operation metadata. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1ImportDataRequest(_messages.Message): r"""Request message for DatasetService.ImportData. Fields: importConfigs: Required. The desired input locations. The contents of all input locations will be imported in one batch. """ importConfigs = _messages.MessageField('GoogleCloudAiplatformV1ImportDataConfig', 1, repeated=True) class GoogleCloudAiplatformV1ImportDataResponse(_messages.Message): r"""Response message for DatasetService.ImportData.""" class GoogleCloudAiplatformV1ImportFeatureValuesOperationMetadata(_messages.Message): r"""Details of operations that perform import Feature values. Fields: blockingOperationIds: List of ImportFeatureValues operations running under a single EntityType that are blocking this operation. genericMetadata: Operation metadata for Featurestore import Feature values. importedEntityCount: Number of entities that have been imported by the operation. importedFeatureValueCount: Number of Feature values that have been imported by the operation. invalidRowCount: The number of rows in input source that weren't imported due to either * Not having any featureValues. * Having a null entityId. * Having a null timestamp. * Not being parsable (applicable for CSV sources). sourceUris: The source URI from where Feature values are imported. timestampOutsideRetentionRowsCount: The number rows that weren't ingested due to having timestamps outside the retention boundary. """ blockingOperationIds = _messages.IntegerField(1, repeated=True) genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 2) importedEntityCount = _messages.IntegerField(3) importedFeatureValueCount = _messages.IntegerField(4) invalidRowCount = _messages.IntegerField(5) sourceUris = _messages.StringField(6, repeated=True) timestampOutsideRetentionRowsCount = _messages.IntegerField(7) class GoogleCloudAiplatformV1ImportFeatureValuesRequest(_messages.Message): r"""Request message for FeaturestoreService.ImportFeatureValues. Fields: avroSource: A GoogleCloudAiplatformV1AvroSource attribute. bigquerySource: A GoogleCloudAiplatformV1BigQuerySource attribute. csvSource: A GoogleCloudAiplatformV1CsvSource attribute. disableIngestionAnalysis: If true, API doesn't start ingestion analysis pipeline. disableOnlineServing: If set, data will not be imported for online serving. This is typically used for backfilling, where Feature generation timestamps are not in the timestamp range needed for online serving. entityIdField: Source column that holds entity IDs. If not provided, entity IDs are extracted from the column named entity_id. featureSpecs: Required. Specifications defining which Feature values to import from the entity. The request fails if no feature_specs are provided, and having multiple feature_specs for one Feature is not allowed. featureTime: Single Feature timestamp for all entities being imported. The timestamp must not have higher than millisecond precision. featureTimeField: Source column that holds the Feature timestamp for all Feature values in each entity. workerCount: Specifies the number of workers that are used to write data to the Featurestore. Consider the online serving capacity that you require to achieve the desired import throughput without interfering with online serving. The value must be positive, and less than or equal to 100. If not set, defaults to using 1 worker. The low count ensures minimal impact on online serving performance. """ avroSource = _messages.MessageField('GoogleCloudAiplatformV1AvroSource', 1) bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1BigQuerySource', 2) csvSource = _messages.MessageField('GoogleCloudAiplatformV1CsvSource', 3) disableIngestionAnalysis = _messages.BooleanField(4) disableOnlineServing = _messages.BooleanField(5) entityIdField = _messages.StringField(6) featureSpecs = _messages.MessageField('GoogleCloudAiplatformV1ImportFeatureValuesRequestFeatureSpec', 7, repeated=True) featureTime = _messages.StringField(8) featureTimeField = _messages.StringField(9) workerCount = _messages.IntegerField(10, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1ImportFeatureValuesRequestFeatureSpec(_messages.Message): r"""Defines the Feature value(s) to import. Fields: id: Required. ID of the Feature to import values of. This Feature must exist in the target EntityType, or the request will fail. sourceField: Source column to get the Feature values from. If not set, uses the column with the same name as the Feature ID. """ id = _messages.StringField(1) sourceField = _messages.StringField(2) class GoogleCloudAiplatformV1ImportFeatureValuesResponse(_messages.Message): r"""Response message for FeaturestoreService.ImportFeatureValues. Fields: importedEntityCount: Number of entities that have been imported by the operation. importedFeatureValueCount: Number of Feature values that have been imported by the operation. invalidRowCount: The number of rows in input source that weren't imported due to either * Not having any featureValues. * Having a null entityId. * Having a null timestamp. * Not being parsable (applicable for CSV sources). timestampOutsideRetentionRowsCount: The number rows that weren't ingested due to having feature timestamps outside the retention boundary. """ importedEntityCount = _messages.IntegerField(1) importedFeatureValueCount = _messages.IntegerField(2) invalidRowCount = _messages.IntegerField(3) timestampOutsideRetentionRowsCount = _messages.IntegerField(4) class GoogleCloudAiplatformV1ImportModelEvaluationRequest(_messages.Message): r"""Request message for ModelService.ImportModelEvaluation Fields: modelEvaluation: Required. Model evaluation resource to be imported. """ modelEvaluation = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluation', 1) class GoogleCloudAiplatformV1ImportRagFilesConfig(_messages.Message): r"""Config for importing RagFiles. Fields: gcsSource: Google Cloud Storage location. Supports importing individual files as well as entire Google Cloud Storage directories. Sample formats: - `gs://bucket_name/my_directory/object_name/my_file.txt` - `gs://bucket_name/my_directory` googleDriveSource: Google Drive location. Supports importing individual files as well as Google Drive folders. importResultBigquerySink: The BigQuery destination to write import result to. It should be a bigquery table resource name (e.g. "bq://projectId.bqDatasetId.bqTableId"). The dataset must exist. If the table does not exist, it will be created with the expected schema. If the table exists, the schema will be validated and data will be added to this existing table. importResultGcsSink: The Cloud Storage path to write import result to. jiraSource: Jira queries with their corresponding authentication. maxEmbeddingRequestsPerMin: Optional. The max number of queries per minute that this job is allowed to make to the embedding model specified on the corpus. This value is specific to this job and not shared across other import jobs. Consult the Quotas page on the project to set an appropriate value here. If unspecified, a default value of 1,000 QPM would be used. partialFailureBigquerySink: The BigQuery destination to write partial failures to. It should be a bigquery table resource name (e.g. "bq://projectId.bqDatasetId.bqTableId"). The dataset must exist. If the table does not exist, it will be created with the expected schema. If the table exists, the schema will be validated and data will be added to this existing table. Deprecated. Prefer to use `import_result_bq_sink`. partialFailureGcsSink: The Cloud Storage path to write partial failures to. Deprecated. Prefer to use `import_result_gcs_sink`. ragFileParsingConfig: Optional. Specifies the parsing config for RagFiles. RAG will use the default parser if this field is not set. ragFileTransformationConfig: Specifies the transformation config for RagFiles. rebuildAnnIndex: Rebuilds the ANN index to optimize for recall on the imported data. Only applicable for RagCorpora running on RagManagedDb with `retrieval_strategy` set to `ANN`. The rebuild will be performed using the existing ANN config set on the RagCorpus. To change the ANN config, please use the UpdateRagCorpus API. Default is false, i.e., index is not rebuilt. sharePointSources: SharePoint sources. slackSource: Slack channels with their corresponding access tokens. """ gcsSource = _messages.MessageField('GoogleCloudAiplatformV1GcsSource', 1) googleDriveSource = _messages.MessageField('GoogleCloudAiplatformV1GoogleDriveSource', 2) importResultBigquerySink = _messages.MessageField('GoogleCloudAiplatformV1BigQueryDestination', 3) importResultGcsSink = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 4) jiraSource = _messages.MessageField('GoogleCloudAiplatformV1JiraSource', 5) maxEmbeddingRequestsPerMin = _messages.IntegerField(6, variant=_messages.Variant.INT32) partialFailureBigquerySink = _messages.MessageField('GoogleCloudAiplatformV1BigQueryDestination', 7) partialFailureGcsSink = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 8) ragFileParsingConfig = _messages.MessageField('GoogleCloudAiplatformV1RagFileParsingConfig', 9) ragFileTransformationConfig = _messages.MessageField('GoogleCloudAiplatformV1RagFileTransformationConfig', 10) rebuildAnnIndex = _messages.BooleanField(11) sharePointSources = _messages.MessageField('GoogleCloudAiplatformV1SharePointSources', 12) slackSource = _messages.MessageField('GoogleCloudAiplatformV1SlackSource', 13) class GoogleCloudAiplatformV1ImportRagFilesRequest(_messages.Message): r"""Request message for VertexRagDataService.ImportRagFiles. Fields: importRagFilesConfig: Required. The config for the RagFiles to be synced and imported into the RagCorpus. VertexRagDataService.ImportRagFiles. """ importRagFilesConfig = _messages.MessageField('GoogleCloudAiplatformV1ImportRagFilesConfig', 1) class GoogleCloudAiplatformV1Index(_messages.Message): r"""A representation of a collection of database items organized in a way that allows for approximate nearest neighbor (a.k.a ANN) algorithms search. Enums: IndexUpdateMethodValueValuesEnum: Immutable. The update method to use with this Index. If not set, BATCH_UPDATE will be used by default. Messages: LabelsValue: The labels with user-defined metadata to organize your Indexes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Fields: createTime: Output only. Timestamp when this Index was created. deployedIndexes: Output only. The pointers to DeployedIndexes created from this Index. An Index can be only deleted if all its DeployedIndexes had been undeployed first. description: The description of the Index. displayName: Required. The display name of the Index. The name can be up to 128 characters long and can consist of any UTF-8 characters. encryptionSpec: Immutable. Customer-managed encryption key spec for an Index. If set, this Index and all sub-resources of this Index will be secured by this key. etag: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. indexStats: Output only. Stats of the index resource. indexUpdateMethod: Immutable. The update method to use with this Index. If not set, BATCH_UPDATE will be used by default. labels: The labels with user-defined metadata to organize your Indexes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. metadata: An additional information about the Index; the schema of the metadata can be found in metadata_schema. metadataSchemaUri: Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Index, that is specific to it. Unset if the Index does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. name: Output only. The resource name of the Index. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. updateTime: Output only. Timestamp when this Index was most recently updated. This also includes any update to the contents of the Index. Note that Operations working on this Index may have their Operations.metadata.generic_metadata.update_time a little after the value of this timestamp, yet that does not mean their results are not already reflected in the Index. Result of any successfully completed Operation on the Index is reflected in it. """ class IndexUpdateMethodValueValuesEnum(_messages.Enum): r"""Immutable. The update method to use with this Index. If not set, BATCH_UPDATE will be used by default. Values: INDEX_UPDATE_METHOD_UNSPECIFIED: Should not be used. BATCH_UPDATE: BatchUpdate: user can call UpdateIndex with files on Cloud Storage of Datapoints to update. STREAM_UPDATE: StreamUpdate: user can call UpsertDatapoints/DeleteDatapoints to update the Index and the updates will be applied in corresponding DeployedIndexes in nearly real-time. """ INDEX_UPDATE_METHOD_UNSPECIFIED = 0 BATCH_UPDATE = 1 STREAM_UPDATE = 2 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your Indexes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) deployedIndexes = _messages.MessageField('GoogleCloudAiplatformV1DeployedIndexRef', 2, repeated=True) description = _messages.StringField(3) displayName = _messages.StringField(4) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 5) etag = _messages.StringField(6) indexStats = _messages.MessageField('GoogleCloudAiplatformV1IndexStats', 7) indexUpdateMethod = _messages.EnumField('IndexUpdateMethodValueValuesEnum', 8) labels = _messages.MessageField('LabelsValue', 9) metadata = _messages.MessageField('extra_types.JsonValue', 10) metadataSchemaUri = _messages.StringField(11) name = _messages.StringField(12) satisfiesPzi = _messages.BooleanField(13) satisfiesPzs = _messages.BooleanField(14) updateTime = _messages.StringField(15) class GoogleCloudAiplatformV1IndexDatapoint(_messages.Message): r"""A datapoint of Index. Messages: EmbeddingMetadataValue: Optional. The key-value map of additional metadata for the datapoint. Fields: crowdingTag: Optional. CrowdingTag of the datapoint, the number of neighbors to return in each crowding can be configured during query. datapointId: Required. Unique identifier of the datapoint. embeddingMetadata: Optional. The key-value map of additional metadata for the datapoint. featureVector: Required. Feature embedding vector for dense index. An array of numbers with the length of [NearestNeighborSearchConfig.dimensions]. numericRestricts: Optional. List of Restrict of the datapoint, used to perform "restricted searches" where boolean rule are used to filter the subset of the database eligible for matching. This uses numeric comparisons. restricts: Optional. List of Restrict of the datapoint, used to perform "restricted searches" where boolean rule are used to filter the subset of the database eligible for matching. This uses categorical tokens. See: https://cloud.google.com/vertex-ai/docs/matching-engine/filtering sparseEmbedding: Optional. Feature embedding vector for sparse index. """ @encoding.MapUnrecognizedFields('additionalProperties') class EmbeddingMetadataValue(_messages.Message): r"""Optional. The key-value map of additional metadata for the datapoint. Messages: AdditionalProperty: An additional property for a EmbeddingMetadataValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a EmbeddingMetadataValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) crowdingTag = _messages.MessageField('GoogleCloudAiplatformV1IndexDatapointCrowdingTag', 1) datapointId = _messages.StringField(2) embeddingMetadata = _messages.MessageField('EmbeddingMetadataValue', 3) featureVector = _messages.FloatField(4, repeated=True, variant=_messages.Variant.FLOAT) numericRestricts = _messages.MessageField('GoogleCloudAiplatformV1IndexDatapointNumericRestriction', 5, repeated=True) restricts = _messages.MessageField('GoogleCloudAiplatformV1IndexDatapointRestriction', 6, repeated=True) sparseEmbedding = _messages.MessageField('GoogleCloudAiplatformV1IndexDatapointSparseEmbedding', 7) class GoogleCloudAiplatformV1IndexDatapointCrowdingTag(_messages.Message): r"""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. Fields: crowdingAttribute: The attribute value used for crowding. The maximum number of neighbors to return per crowding attribute value (per_crowding_attribute_num_neighbors) is configured per-query. This field is ignored if per_crowding_attribute_num_neighbors is larger than the total number of neighbors to return for a given query. """ crowdingAttribute = _messages.StringField(1) class GoogleCloudAiplatformV1IndexDatapointNumericRestriction(_messages.Message): r"""This field allows restricts to be based on numeric comparisons rather than categorical tokens. Enums: OpValueValuesEnum: This MUST be specified for queries and must NOT be specified for datapoints. Fields: namespace: The namespace of this restriction. e.g.: cost. op: This MUST be specified for queries and must NOT be specified for datapoints. valueDouble: Represents 64 bit float. valueFloat: Represents 32 bit float. valueInt: Represents 64 bit integer. """ class OpValueValuesEnum(_messages.Enum): r"""This MUST be specified for queries and must NOT be specified for datapoints. Values: OPERATOR_UNSPECIFIED: Default value of the enum. LESS: Datapoints are eligible iff their value is < the query's. LESS_EQUAL: Datapoints are eligible iff their value is <= the query's. EQUAL: Datapoints are eligible iff their value is == the query's. GREATER_EQUAL: Datapoints are eligible iff their value is >= the query's. GREATER: Datapoints are eligible iff their value is > the query's. NOT_EQUAL: Datapoints are eligible iff their value is != the query's. """ OPERATOR_UNSPECIFIED = 0 LESS = 1 LESS_EQUAL = 2 EQUAL = 3 GREATER_EQUAL = 4 GREATER = 5 NOT_EQUAL = 6 namespace = _messages.StringField(1) op = _messages.EnumField('OpValueValuesEnum', 2) valueDouble = _messages.FloatField(3) valueFloat = _messages.FloatField(4, variant=_messages.Variant.FLOAT) valueInt = _messages.IntegerField(5) class GoogleCloudAiplatformV1IndexDatapointRestriction(_messages.Message): r"""Restriction of a datapoint which describe its attributes(tokens) from each of several attribute categories(namespaces). Fields: allowList: The attributes to allow in this namespace. e.g.: 'red' denyList: The attributes to deny in this namespace. e.g.: 'blue' namespace: The namespace of this restriction. e.g.: color. """ allowList = _messages.StringField(1, repeated=True) denyList = _messages.StringField(2, repeated=True) namespace = _messages.StringField(3) class GoogleCloudAiplatformV1IndexDatapointSparseEmbedding(_messages.Message): r"""Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions. Fields: dimensions: Required. The list of indexes for the embedding values of the sparse vector. values: Required. The list of embedding values of the sparse vector. """ dimensions = _messages.IntegerField(1, repeated=True) values = _messages.FloatField(2, repeated=True, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1IndexEndpoint(_messages.Message): r"""Indexes are deployed into it. An IndexEndpoint can have multiple DeployedIndexes. Messages: LabelsValue: The labels with user-defined metadata to organize your IndexEndpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Fields: createTime: Output only. Timestamp when this IndexEndpoint was created. deployedIndexes: Output only. The indexes deployed in this endpoint. description: The description of the IndexEndpoint. displayName: Required. The display name of the IndexEndpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters. enablePrivateServiceConnect: Optional. Deprecated: If true, expose the IndexEndpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set. encryptionSpec: Immutable. Customer-managed encryption key spec for an IndexEndpoint. If set, this IndexEndpoint and all sub-resources of this IndexEndpoint will be secured by this key. etag: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: The labels with user-defined metadata to organize your IndexEndpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. name: Output only. The resource name of the IndexEndpoint. network: Optional. The full name of the Google Compute Engine [network](https://cloud.google.com/compute/docs/networks-and- firewalls#networks) to which the IndexEndpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. network and private_service_connect_config are mutually exclusive. [Format](https:// cloud.google.com/compute/docs/reference/rest/v1/networks/insert): `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in '12345', and {network} is network name. privateServiceConnectConfig: Optional. Configuration for private service connect. network and private_service_connect_config are mutually exclusive. publicEndpointDomainName: Output only. If public_endpoint_enabled is true, this field will be populated with the domain name to use for this index endpoint. publicEndpointEnabled: Optional. If true, the deployed index will be accessible through public endpoint. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. updateTime: Output only. Timestamp when this IndexEndpoint was last updated. This timestamp is not updated when the endpoint's DeployedIndexes are updated, e.g. due to updates of the original Indexes they are the deployments of. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your IndexEndpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) deployedIndexes = _messages.MessageField('GoogleCloudAiplatformV1DeployedIndex', 2, repeated=True) description = _messages.StringField(3) displayName = _messages.StringField(4) enablePrivateServiceConnect = _messages.BooleanField(5) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 6) etag = _messages.StringField(7) labels = _messages.MessageField('LabelsValue', 8) name = _messages.StringField(9) network = _messages.StringField(10) privateServiceConnectConfig = _messages.MessageField('GoogleCloudAiplatformV1PrivateServiceConnectConfig', 11) publicEndpointDomainName = _messages.StringField(12) publicEndpointEnabled = _messages.BooleanField(13) satisfiesPzi = _messages.BooleanField(14) satisfiesPzs = _messages.BooleanField(15) updateTime = _messages.StringField(16) class GoogleCloudAiplatformV1IndexPrivateEndpoints(_messages.Message): r"""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. Fields: matchGrpcAddress: Output only. The ip address used to send match gRPC requests. pscAutomatedEndpoints: Output only. PscAutomatedEndpoints is populated if private service connect is enabled if PscAutomatedConfig is set. serviceAttachment: Output only. The name of the service attachment resource. Populated if private service connect is enabled. """ matchGrpcAddress = _messages.StringField(1) pscAutomatedEndpoints = _messages.MessageField('GoogleCloudAiplatformV1PscAutomatedEndpoints', 2, repeated=True) serviceAttachment = _messages.StringField(3) class GoogleCloudAiplatformV1IndexStats(_messages.Message): r"""Stats of the Index. Fields: shardsCount: Output only. The number of shards in the Index. sparseVectorsCount: Output only. The number of sparse vectors in the Index. vectorsCount: Output only. The number of dense vectors in the Index. """ shardsCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) sparseVectorsCount = _messages.IntegerField(2) vectorsCount = _messages.IntegerField(3) class GoogleCloudAiplatformV1InputDataConfig(_messages.Message): r"""Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model. Fields: annotationSchemaUri: Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). The schema files that can be used here are found in gs://google-cloud- aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri. annotationsFilter: Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem. bigqueryDestination: Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name `dataset___` where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, `training`, `validation` and `test`. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test" datasetId: Required. The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from. filterSplit: Split based on the provided filters for each set. fractionSplit: Split based on fractions defining the size of each set. gcsDestination: The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: `dataset---` where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-*.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-*.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-*.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-*.${AIP_DATA_FORMAT}" persistMlUseAssignment: Whether to persist the ML use assignment to data item system labels. predefinedSplit: Supported only for tabular Datasets. Split based on a predefined key. savedQueryId: Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type. stratifiedSplit: Supported only for tabular Datasets. Split based on the distribution of the specified column. timestampSplit: Supported only for tabular Datasets. Split based on the timestamp of the input data pieces. """ annotationSchemaUri = _messages.StringField(1) annotationsFilter = _messages.StringField(2) bigqueryDestination = _messages.MessageField('GoogleCloudAiplatformV1BigQueryDestination', 3) datasetId = _messages.StringField(4) filterSplit = _messages.MessageField('GoogleCloudAiplatformV1FilterSplit', 5) fractionSplit = _messages.MessageField('GoogleCloudAiplatformV1FractionSplit', 6) gcsDestination = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 7) persistMlUseAssignment = _messages.BooleanField(8) predefinedSplit = _messages.MessageField('GoogleCloudAiplatformV1PredefinedSplit', 9) savedQueryId = _messages.StringField(10) stratifiedSplit = _messages.MessageField('GoogleCloudAiplatformV1StratifiedSplit', 11) timestampSplit = _messages.MessageField('GoogleCloudAiplatformV1TimestampSplit', 12) class GoogleCloudAiplatformV1Int64Array(_messages.Message): r"""A list of int64 values. Fields: values: A list of int64 values. """ values = _messages.IntegerField(1, repeated=True) class GoogleCloudAiplatformV1IntegratedGradientsAttribution(_messages.Message): r"""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 Fields: blurBaselineConfig: Config for IG with 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 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 stepCount: Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively. """ blurBaselineConfig = _messages.MessageField('GoogleCloudAiplatformV1BlurBaselineConfig', 1) smoothGradConfig = _messages.MessageField('GoogleCloudAiplatformV1SmoothGradConfig', 2) stepCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1JiraSource(_messages.Message): r"""The Jira source for the ImportRagFilesRequest. Fields: jiraQueries: Required. The Jira queries. """ jiraQueries = _messages.MessageField('GoogleCloudAiplatformV1JiraSourceJiraQueries', 1, repeated=True) class GoogleCloudAiplatformV1JiraSourceJiraQueries(_messages.Message): r"""JiraQueries contains the Jira queries and corresponding authentication. Fields: apiKeyConfig: Required. The SecretManager secret version resource name (e.g. projects/{project}/secrets/{secret}/versions/{version}) storing the Jira API key. See [Manage API tokens for your Atlassian account](https://support.atlassian.com/atlassian-account/docs/manage- api-tokens-for-your-atlassian-account/). customQueries: A list of custom Jira queries to import. For information about JQL (Jira Query Language), see https://support.atlassian.com/jira- service-management-cloud/docs/use-advanced-search-with-jira-query- language-jql/ email: Required. The Jira email address. projects: A list of Jira projects to import in their entirety. serverUri: Required. The Jira server URI. """ apiKeyConfig = _messages.MessageField('GoogleCloudAiplatformV1ApiAuthApiKeyConfig', 1) customQueries = _messages.StringField(2, repeated=True) email = _messages.StringField(3) projects = _messages.StringField(4, repeated=True) serverUri = _messages.StringField(5) class GoogleCloudAiplatformV1LargeModelReference(_messages.Message): r"""Contains information about the Large Model. Fields: name: Required. The unique name of the large Foundation or pre-built model. Like "chat-bison", "text-bison". Or model name with version ID, like "chat-bison@001", "text-bison@005", etc. """ name = _messages.StringField(1) class GoogleCloudAiplatformV1LineageSubgraph(_messages.Message): r"""A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes. Fields: artifacts: The Artifact nodes in the subgraph. events: The Event edges between Artifacts and Executions in the subgraph. executions: The Execution nodes in the subgraph. """ artifacts = _messages.MessageField('GoogleCloudAiplatformV1Artifact', 1, repeated=True) events = _messages.MessageField('GoogleCloudAiplatformV1Event', 2, repeated=True) executions = _messages.MessageField('GoogleCloudAiplatformV1Execution', 3, repeated=True) class GoogleCloudAiplatformV1ListAnnotationsResponse(_messages.Message): r"""Response message for DatasetService.ListAnnotations. Fields: annotations: A list of Annotations that matches the specified filter in the request. nextPageToken: The standard List next-page token. """ annotations = _messages.MessageField('GoogleCloudAiplatformV1Annotation', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListArtifactsResponse(_messages.Message): r"""Response message for MetadataService.ListArtifacts. Fields: artifacts: The Artifacts retrieved from the MetadataStore. nextPageToken: A token, which can be sent as ListArtifactsRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages. """ artifacts = _messages.MessageField('GoogleCloudAiplatformV1Artifact', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListBatchPredictionJobsResponse(_messages.Message): r"""Response message for JobService.ListBatchPredictionJobs Fields: batchPredictionJobs: List of BatchPredictionJobs in the requested page. nextPageToken: A token to retrieve the next page of results. Pass to ListBatchPredictionJobsRequest.page_token to obtain that page. """ batchPredictionJobs = _messages.MessageField('GoogleCloudAiplatformV1BatchPredictionJob', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListCachedContentsResponse(_messages.Message): r"""Response with a list of CachedContents. Fields: cachedContents: List of cached contents. nextPageToken: A token, which can be sent as `page_token` to retrieve the next page. If this field is omitted, there are no subsequent pages. """ cachedContents = _messages.MessageField('GoogleCloudAiplatformV1CachedContent', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListContextsResponse(_messages.Message): r"""Response message for MetadataService.ListContexts. Fields: contexts: The Contexts retrieved from the MetadataStore. nextPageToken: A token, which can be sent as ListContextsRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages. """ contexts = _messages.MessageField('GoogleCloudAiplatformV1Context', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListCustomJobsResponse(_messages.Message): r"""Response message for JobService.ListCustomJobs Fields: customJobs: List of CustomJobs in the requested page. nextPageToken: A token to retrieve the next page of results. Pass to ListCustomJobsRequest.page_token to obtain that page. """ customJobs = _messages.MessageField('GoogleCloudAiplatformV1CustomJob', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListDataItemsResponse(_messages.Message): r"""Response message for DatasetService.ListDataItems. Fields: dataItems: A list of DataItems that matches the specified filter in the request. nextPageToken: The standard List next-page token. """ dataItems = _messages.MessageField('GoogleCloudAiplatformV1DataItem', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListDatasetVersionsResponse(_messages.Message): r"""Response message for DatasetService.ListDatasetVersions. Fields: datasetVersions: A list of DatasetVersions that matches the specified filter in the request. nextPageToken: The standard List next-page token. """ datasetVersions = _messages.MessageField('GoogleCloudAiplatformV1DatasetVersion', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListDatasetsResponse(_messages.Message): r"""Response message for DatasetService.ListDatasets. Fields: datasets: A list of Datasets that matches the specified filter in the request. nextPageToken: The standard List next-page token. """ datasets = _messages.MessageField('GoogleCloudAiplatformV1Dataset', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListDeploymentResourcePoolsResponse(_messages.Message): r"""Response message for ListDeploymentResourcePools method. Fields: deploymentResourcePools: The DeploymentResourcePools from the specified location. nextPageToken: A token, which can be sent as `page_token` to retrieve the next page. If this field is omitted, there are no subsequent pages. """ deploymentResourcePools = _messages.MessageField('GoogleCloudAiplatformV1DeploymentResourcePool', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListEndpointsResponse(_messages.Message): r"""Response message for EndpointService.ListEndpoints. Fields: endpoints: List of Endpoints in the requested page. nextPageToken: A token to retrieve the next page of results. Pass to ListEndpointsRequest.page_token to obtain that page. """ endpoints = _messages.MessageField('GoogleCloudAiplatformV1Endpoint', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListEntityTypesResponse(_messages.Message): r"""Response message for FeaturestoreService.ListEntityTypes. Fields: entityTypes: The EntityTypes matching the request. nextPageToken: A token, which can be sent as ListEntityTypesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. """ entityTypes = _messages.MessageField('GoogleCloudAiplatformV1EntityType', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListExecutionsResponse(_messages.Message): r"""Response message for MetadataService.ListExecutions. Fields: executions: The Executions retrieved from the MetadataStore. nextPageToken: A token, which can be sent as ListExecutionsRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages. """ executions = _messages.MessageField('GoogleCloudAiplatformV1Execution', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListFeatureGroupsResponse(_messages.Message): r"""Response message for FeatureRegistryService.ListFeatureGroups. Fields: featureGroups: The FeatureGroups matching the request. nextPageToken: A token, which can be sent as ListFeatureGroupsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. """ featureGroups = _messages.MessageField('GoogleCloudAiplatformV1FeatureGroup', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListFeatureOnlineStoresResponse(_messages.Message): r"""Response message for FeatureOnlineStoreAdminService.ListFeatureOnlineStores. Fields: featureOnlineStores: The FeatureOnlineStores matching the request. nextPageToken: A token, which can be sent as ListFeatureOnlineStoresRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. """ featureOnlineStores = _messages.MessageField('GoogleCloudAiplatformV1FeatureOnlineStore', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListFeatureViewSyncsResponse(_messages.Message): r"""Response message for FeatureOnlineStoreAdminService.ListFeatureViewSyncs. Fields: featureViewSyncs: The FeatureViewSyncs matching the request. nextPageToken: A token, which can be sent as ListFeatureViewSyncsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. """ featureViewSyncs = _messages.MessageField('GoogleCloudAiplatformV1FeatureViewSync', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListFeatureViewsResponse(_messages.Message): r"""Response message for FeatureOnlineStoreAdminService.ListFeatureViews. Fields: featureViews: The FeatureViews matching the request. nextPageToken: A token, which can be sent as ListFeatureViewsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. """ featureViews = _messages.MessageField('GoogleCloudAiplatformV1FeatureView', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListFeaturesResponse(_messages.Message): r"""Response message for FeaturestoreService.ListFeatures. Response message for FeatureRegistryService.ListFeatures. Fields: features: The Features matching the request. nextPageToken: A token, which can be sent as ListFeaturesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. """ features = _messages.MessageField('GoogleCloudAiplatformV1Feature', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListFeaturestoresResponse(_messages.Message): r"""Response message for FeaturestoreService.ListFeaturestores. Fields: featurestores: The Featurestores matching the request. nextPageToken: A token, which can be sent as ListFeaturestoresRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. """ featurestores = _messages.MessageField('GoogleCloudAiplatformV1Featurestore', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListHyperparameterTuningJobsResponse(_messages.Message): r"""Response message for JobService.ListHyperparameterTuningJobs Fields: hyperparameterTuningJobs: List of HyperparameterTuningJobs in the requested page. HyperparameterTuningJob.trials of the jobs will be not be returned. nextPageToken: A token to retrieve the next page of results. Pass to ListHyperparameterTuningJobsRequest.page_token to obtain that page. """ hyperparameterTuningJobs = _messages.MessageField('GoogleCloudAiplatformV1HyperparameterTuningJob', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListIndexEndpointsResponse(_messages.Message): r"""Response message for IndexEndpointService.ListIndexEndpoints. Fields: indexEndpoints: List of IndexEndpoints in the requested page. nextPageToken: A token to retrieve next page of results. Pass to ListIndexEndpointsRequest.page_token to obtain that page. """ indexEndpoints = _messages.MessageField('GoogleCloudAiplatformV1IndexEndpoint', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListIndexesResponse(_messages.Message): r"""Response message for IndexService.ListIndexes. Fields: indexes: List of indexes in the requested page. nextPageToken: A token to retrieve next page of results. Pass to ListIndexesRequest.page_token to obtain that page. """ indexes = _messages.MessageField('GoogleCloudAiplatformV1Index', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListMetadataSchemasResponse(_messages.Message): r"""Response message for MetadataService.ListMetadataSchemas. Fields: metadataSchemas: The MetadataSchemas found for the MetadataStore. nextPageToken: A token, which can be sent as ListMetadataSchemasRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages. """ metadataSchemas = _messages.MessageField('GoogleCloudAiplatformV1MetadataSchema', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListMetadataStoresResponse(_messages.Message): r"""Response message for MetadataService.ListMetadataStores. Fields: metadataStores: The MetadataStores found for the Location. nextPageToken: A token, which can be sent as ListMetadataStoresRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages. """ metadataStores = _messages.MessageField('GoogleCloudAiplatformV1MetadataStore', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListModelDeploymentMonitoringJobsResponse(_messages.Message): r"""Response message for JobService.ListModelDeploymentMonitoringJobs. Fields: modelDeploymentMonitoringJobs: A list of ModelDeploymentMonitoringJobs that matches the specified filter in the request. nextPageToken: The standard List next-page token. """ modelDeploymentMonitoringJobs = _messages.MessageField('GoogleCloudAiplatformV1ModelDeploymentMonitoringJob', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListModelEvaluationSlicesResponse(_messages.Message): r"""Response message for ModelService.ListModelEvaluationSlices. Fields: modelEvaluationSlices: List of ModelEvaluations in the requested page. nextPageToken: A token to retrieve next page of results. Pass to ListModelEvaluationSlicesRequest.page_token to obtain that page. """ modelEvaluationSlices = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluationSlice', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListModelEvaluationsResponse(_messages.Message): r"""Response message for ModelService.ListModelEvaluations. Fields: modelEvaluations: List of ModelEvaluations in the requested page. nextPageToken: A token to retrieve next page of results. Pass to ListModelEvaluationsRequest.page_token to obtain that page. """ modelEvaluations = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluation', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListModelVersionCheckpointsResponse(_messages.Message): r"""Response message for ModelService.ListModelVersionCheckpoints Fields: checkpoints: List of Model Version checkpoints. nextPageToken: A token to retrieve the next page of results. Pass to ListModelVersionCheckpointsRequest.page_token to obtain that page. """ checkpoints = _messages.MessageField('GoogleCloudAiplatformV1ModelVersionCheckpoint', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListModelVersionsResponse(_messages.Message): r"""Response message for ModelService.ListModelVersions Fields: models: List of Model versions in the requested page. In the returned Model name field, version ID instead of regvision tag will be included. nextPageToken: A token to retrieve the next page of results. Pass to ListModelVersionsRequest.page_token to obtain that page. """ models = _messages.MessageField('GoogleCloudAiplatformV1Model', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListModelsResponse(_messages.Message): r"""Response message for ModelService.ListModels Fields: models: List of Models in the requested page. nextPageToken: A token to retrieve next page of results. Pass to ListModelsRequest.page_token to obtain that page. """ models = _messages.MessageField('GoogleCloudAiplatformV1Model', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListNasJobsResponse(_messages.Message): r"""Response message for JobService.ListNasJobs Fields: nasJobs: List of NasJobs in the requested page. NasJob.nas_job_output of the jobs will not be returned. nextPageToken: A token to retrieve the next page of results. Pass to ListNasJobsRequest.page_token to obtain that page. """ nasJobs = _messages.MessageField('GoogleCloudAiplatformV1NasJob', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListNasTrialDetailsResponse(_messages.Message): r"""Response message for JobService.ListNasTrialDetails Fields: nasTrialDetails: List of top NasTrials in the requested page. nextPageToken: A token to retrieve the next page of results. Pass to ListNasTrialDetailsRequest.page_token to obtain that page. """ nasTrialDetails = _messages.MessageField('GoogleCloudAiplatformV1NasTrialDetail', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1ListNotebookExecutionJobsResponse(_messages.Message): r"""Response message for [NotebookService.CreateNotebookExecutionJob] Fields: nextPageToken: A token to retrieve next page of results. Pass to ListNotebookExecutionJobsRequest.page_token to obtain that page. notebookExecutionJobs: List of NotebookExecutionJobs in the requested page. """ nextPageToken = _messages.StringField(1) notebookExecutionJobs = _messages.MessageField('GoogleCloudAiplatformV1NotebookExecutionJob', 2, repeated=True) class GoogleCloudAiplatformV1ListNotebookRuntimeTemplatesResponse(_messages.Message): r"""Response message for NotebookService.ListNotebookRuntimeTemplates. Fields: nextPageToken: A token to retrieve next page of results. Pass to ListNotebookRuntimeTemplatesRequest.page_token to obtain that page. notebookRuntimeTemplates: List of NotebookRuntimeTemplates in the requested page. """ nextPageToken = _messages.StringField(1) notebookRuntimeTemplates = _messages.MessageField('GoogleCloudAiplatformV1NotebookRuntimeTemplate', 2, repeated=True) class GoogleCloudAiplatformV1ListNotebookRuntimesResponse(_messages.Message): r"""Response message for NotebookService.ListNotebookRuntimes. Fields: nextPageToken: A token to retrieve next page of results. Pass to ListNotebookRuntimesRequest.page_token to obtain that page. notebookRuntimes: List of NotebookRuntimes in the requested page. """ nextPageToken = _messages.StringField(1) notebookRuntimes = _messages.MessageField('GoogleCloudAiplatformV1NotebookRuntime', 2, repeated=True) class GoogleCloudAiplatformV1ListOptimalTrialsRequest(_messages.Message): r"""Request message for VizierService.ListOptimalTrials.""" class GoogleCloudAiplatformV1ListOptimalTrialsResponse(_messages.Message): r"""Response message for VizierService.ListOptimalTrials. Fields: optimalTrials: The pareto-optimal Trials for multiple objective Study or the optimal trial for single objective Study. The definition of pareto- optimal can be checked in wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency """ optimalTrials = _messages.MessageField('GoogleCloudAiplatformV1Trial', 1, repeated=True) class GoogleCloudAiplatformV1ListPersistentResourcesResponse(_messages.Message): r"""Response message for PersistentResourceService.ListPersistentResources Fields: nextPageToken: A token to retrieve next page of results. Pass to ListPersistentResourcesRequest.page_token to obtain that page. persistentResources: A GoogleCloudAiplatformV1PersistentResource attribute. """ nextPageToken = _messages.StringField(1) persistentResources = _messages.MessageField('GoogleCloudAiplatformV1PersistentResource', 2, repeated=True) class GoogleCloudAiplatformV1ListPipelineJobsResponse(_messages.Message): r"""Response message for PipelineService.ListPipelineJobs Fields: nextPageToken: A token to retrieve the next page of results. Pass to ListPipelineJobsRequest.page_token to obtain that page. pipelineJobs: List of PipelineJobs in the requested page. """ nextPageToken = _messages.StringField(1) pipelineJobs = _messages.MessageField('GoogleCloudAiplatformV1PipelineJob', 2, repeated=True) class GoogleCloudAiplatformV1ListRagCorporaResponse(_messages.Message): r"""Response message for VertexRagDataService.ListRagCorpora. Fields: nextPageToken: A token to retrieve the next page of results. Pass to ListRagCorporaRequest.page_token to obtain that page. ragCorpora: List of RagCorpora in the requested page. """ nextPageToken = _messages.StringField(1) ragCorpora = _messages.MessageField('GoogleCloudAiplatformV1RagCorpus', 2, repeated=True) class GoogleCloudAiplatformV1ListRagFilesResponse(_messages.Message): r"""Response message for VertexRagDataService.ListRagFiles. Fields: nextPageToken: A token to retrieve the next page of results. Pass to ListRagFilesRequest.page_token to obtain that page. ragFiles: List of RagFiles in the requested page. """ nextPageToken = _messages.StringField(1) ragFiles = _messages.MessageField('GoogleCloudAiplatformV1RagFile', 2, repeated=True) class GoogleCloudAiplatformV1ListReasoningEnginesResponse(_messages.Message): r"""Response message for ReasoningEngineService.ListReasoningEngines Fields: nextPageToken: A token to retrieve the next page of results. Pass to ListReasoningEnginesRequest.page_token to obtain that page. reasoningEngines: List of ReasoningEngines in the requested page. """ nextPageToken = _messages.StringField(1) reasoningEngines = _messages.MessageField('GoogleCloudAiplatformV1ReasoningEngine', 2, repeated=True) class GoogleCloudAiplatformV1ListSavedQueriesResponse(_messages.Message): r"""Response message for DatasetService.ListSavedQueries. Fields: nextPageToken: The standard List next-page token. savedQueries: A list of SavedQueries that match the specified filter in the request. """ nextPageToken = _messages.StringField(1) savedQueries = _messages.MessageField('GoogleCloudAiplatformV1SavedQuery', 2, repeated=True) class GoogleCloudAiplatformV1ListSchedulesResponse(_messages.Message): r"""Response message for ScheduleService.ListSchedules Fields: nextPageToken: A token to retrieve the next page of results. Pass to ListSchedulesRequest.page_token to obtain that page. schedules: List of Schedules in the requested page. """ nextPageToken = _messages.StringField(1) schedules = _messages.MessageField('GoogleCloudAiplatformV1Schedule', 2, repeated=True) class GoogleCloudAiplatformV1ListSpecialistPoolsResponse(_messages.Message): r"""Response message for SpecialistPoolService.ListSpecialistPools. Fields: nextPageToken: The standard List next-page token. specialistPools: A list of SpecialistPools that matches the specified filter in the request. """ nextPageToken = _messages.StringField(1) specialistPools = _messages.MessageField('GoogleCloudAiplatformV1SpecialistPool', 2, repeated=True) class GoogleCloudAiplatformV1ListStudiesResponse(_messages.Message): r"""Response message for VizierService.ListStudies. Fields: nextPageToken: Passes this token as the `page_token` field of the request for a subsequent call. If this field is omitted, there are no subsequent pages. studies: The studies associated with the project. """ nextPageToken = _messages.StringField(1) studies = _messages.MessageField('GoogleCloudAiplatformV1Study', 2, repeated=True) class GoogleCloudAiplatformV1ListTensorboardExperimentsResponse(_messages.Message): r"""Response message for TensorboardService.ListTensorboardExperiments. Fields: nextPageToken: A token, which can be sent as ListTensorboardExperimentsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. tensorboardExperiments: The TensorboardExperiments mathching the request. """ nextPageToken = _messages.StringField(1) tensorboardExperiments = _messages.MessageField('GoogleCloudAiplatformV1TensorboardExperiment', 2, repeated=True) class GoogleCloudAiplatformV1ListTensorboardRunsResponse(_messages.Message): r"""Response message for TensorboardService.ListTensorboardRuns. Fields: nextPageToken: A token, which can be sent as ListTensorboardRunsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. tensorboardRuns: The TensorboardRuns mathching the request. """ nextPageToken = _messages.StringField(1) tensorboardRuns = _messages.MessageField('GoogleCloudAiplatformV1TensorboardRun', 2, repeated=True) class GoogleCloudAiplatformV1ListTensorboardTimeSeriesResponse(_messages.Message): r"""Response message for TensorboardService.ListTensorboardTimeSeries. Fields: nextPageToken: A token, which can be sent as ListTensorboardTimeSeriesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. tensorboardTimeSeries: The TensorboardTimeSeries mathching the request. """ nextPageToken = _messages.StringField(1) tensorboardTimeSeries = _messages.MessageField('GoogleCloudAiplatformV1TensorboardTimeSeries', 2, repeated=True) class GoogleCloudAiplatformV1ListTensorboardsResponse(_messages.Message): r"""Response message for TensorboardService.ListTensorboards. Fields: nextPageToken: A token, which can be sent as ListTensorboardsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. tensorboards: The Tensorboards mathching the request. """ nextPageToken = _messages.StringField(1) tensorboards = _messages.MessageField('GoogleCloudAiplatformV1Tensorboard', 2, repeated=True) class GoogleCloudAiplatformV1ListTrainingPipelinesResponse(_messages.Message): r"""Response message for PipelineService.ListTrainingPipelines Fields: nextPageToken: A token to retrieve the next page of results. Pass to ListTrainingPipelinesRequest.page_token to obtain that page. trainingPipelines: List of TrainingPipelines in the requested page. """ nextPageToken = _messages.StringField(1) trainingPipelines = _messages.MessageField('GoogleCloudAiplatformV1TrainingPipeline', 2, repeated=True) class GoogleCloudAiplatformV1ListTrialsResponse(_messages.Message): r"""Response message for VizierService.ListTrials. Fields: nextPageToken: Pass this token as the `page_token` field of the request for a subsequent call. If this field is omitted, there are no subsequent pages. trials: The Trials associated with the Study. """ nextPageToken = _messages.StringField(1) trials = _messages.MessageField('GoogleCloudAiplatformV1Trial', 2, repeated=True) class GoogleCloudAiplatformV1ListTuningJobsResponse(_messages.Message): r"""Response message for GenAiTuningService.ListTuningJobs Fields: nextPageToken: A token to retrieve the next page of results. Pass to ListTuningJobsRequest.page_token to obtain that page. tuningJobs: List of TuningJobs in the requested page. """ nextPageToken = _messages.StringField(1) tuningJobs = _messages.MessageField('GoogleCloudAiplatformV1TuningJob', 2, repeated=True) class GoogleCloudAiplatformV1LogprobsResult(_messages.Message): r"""Logprobs Result Fields: chosenCandidates: Length = total number of decoding steps. The chosen candidates may or may not be in top_candidates. topCandidates: Length = total number of decoding steps. """ chosenCandidates = _messages.MessageField('GoogleCloudAiplatformV1LogprobsResultCandidate', 1, repeated=True) topCandidates = _messages.MessageField('GoogleCloudAiplatformV1LogprobsResultTopCandidates', 2, repeated=True) class GoogleCloudAiplatformV1LogprobsResultCandidate(_messages.Message): r"""Candidate for the logprobs token and score. Fields: logProbability: The candidate's log probability. token: The candidate's token string value. tokenId: The candidate's token id value. """ logProbability = _messages.FloatField(1, variant=_messages.Variant.FLOAT) token = _messages.StringField(2) tokenId = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1LogprobsResultTopCandidates(_messages.Message): r"""Candidates with top log probabilities at each decoding step. Fields: candidates: Sorted by log probability in descending order. """ candidates = _messages.MessageField('GoogleCloudAiplatformV1LogprobsResultCandidate', 1, repeated=True) class GoogleCloudAiplatformV1LookupStudyRequest(_messages.Message): r"""Request message for VizierService.LookupStudy. Fields: displayName: Required. The user-defined display name of the Study """ displayName = _messages.StringField(1) class GoogleCloudAiplatformV1MachineSpec(_messages.Message): r"""Specification of a single machine. Enums: AcceleratorTypeValueValuesEnum: Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. Fields: acceleratorCount: The number of accelerators to attach to the machine. acceleratorType: Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. machineType: Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex- ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure- compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. reservationAffinity: Optional. Immutable. Configuration controlling how this resource pool consumes reservation. tpuTopology: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). """ class AcceleratorTypeValueValuesEnum(_messages.Enum): r"""Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. Values: ACCELERATOR_TYPE_UNSPECIFIED: Unspecified accelerator type, which means no accelerator. NVIDIA_TESLA_K80: Deprecated: Nvidia Tesla K80 GPU has reached end of support, see https://cloud.google.com/compute/docs/eol/k80-eol. NVIDIA_TESLA_P100: Nvidia Tesla P100 GPU. NVIDIA_TESLA_V100: Nvidia Tesla V100 GPU. NVIDIA_TESLA_P4: Nvidia Tesla P4 GPU. NVIDIA_TESLA_T4: Nvidia Tesla T4 GPU. NVIDIA_TESLA_A100: Nvidia Tesla A100 GPU. NVIDIA_A100_80GB: Nvidia A100 80GB GPU. NVIDIA_L4: Nvidia L4 GPU. NVIDIA_H100_80GB: Nvidia H100 80Gb GPU. NVIDIA_H100_MEGA_80GB: Nvidia H100 Mega 80Gb GPU. NVIDIA_H200_141GB: Nvidia H200 141Gb GPU. NVIDIA_B200: Nvidia B200 GPU. NVIDIA_GB200: Nvidia GB200 GPU. TPU_V2: TPU v2. TPU_V3: TPU v3. TPU_V4_POD: TPU v4. TPU_V5_LITEPOD: TPU v5. """ ACCELERATOR_TYPE_UNSPECIFIED = 0 NVIDIA_TESLA_K80 = 1 NVIDIA_TESLA_P100 = 2 NVIDIA_TESLA_V100 = 3 NVIDIA_TESLA_P4 = 4 NVIDIA_TESLA_T4 = 5 NVIDIA_TESLA_A100 = 6 NVIDIA_A100_80GB = 7 NVIDIA_L4 = 8 NVIDIA_H100_80GB = 9 NVIDIA_H100_MEGA_80GB = 10 NVIDIA_H200_141GB = 11 NVIDIA_B200 = 12 NVIDIA_GB200 = 13 TPU_V2 = 14 TPU_V3 = 15 TPU_V4_POD = 16 TPU_V5_LITEPOD = 17 acceleratorCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) acceleratorType = _messages.EnumField('AcceleratorTypeValueValuesEnum', 2) machineType = _messages.StringField(3) reservationAffinity = _messages.MessageField('GoogleCloudAiplatformV1ReservationAffinity', 4) tpuTopology = _messages.StringField(5) class GoogleCloudAiplatformV1ManualBatchTuningParameters(_messages.Message): r"""Manual batch tuning parameters. Fields: batchSize: Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64. """ batchSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1Measurement(_messages.Message): r"""A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values. Fields: elapsedDuration: Output only. Time that the Trial has been running at the point of this Measurement. metrics: Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values. stepCount: Output only. The number of steps the machine learning model has been trained for. Must be non-negative. """ elapsedDuration = _messages.StringField(1) metrics = _messages.MessageField('GoogleCloudAiplatformV1MeasurementMetric', 2, repeated=True) stepCount = _messages.IntegerField(3) class GoogleCloudAiplatformV1MeasurementMetric(_messages.Message): r"""A message representing a metric in the measurement. Fields: metricId: Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics. value: Output only. The value for this metric. """ metricId = _messages.StringField(1) value = _messages.FloatField(2) class GoogleCloudAiplatformV1MergeVersionAliasesRequest(_messages.Message): r"""Request message for ModelService.MergeVersionAliases. Fields: versionAliases: Required. The set of version aliases to merge. The alias should be at most 128 characters, and match `a-z{0,126}[a-z-0-9]`. Add the `-` prefix to an alias means removing that alias from the version. `-` is NOT counted in the 128 characters. Example: `-golden` means removing the `golden` alias from the version. There is NO ordering in aliases, which means 1) The aliases returned from GetModel API might not have the exactly same order from this MergeVersionAliases API. 2) Adding and deleting the same alias in the request is not recommended, and the 2 operations will be cancelled out. """ versionAliases = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1MetadataSchema(_messages.Message): r"""Instance of a general MetadataSchema. Enums: SchemaTypeValueValuesEnum: The type of the MetadataSchema. This is a property that identifies which metadata types will use the MetadataSchema. Fields: createTime: Output only. Timestamp when this MetadataSchema was created. description: Description of the Metadata Schema name: Output only. The resource name of the MetadataSchema. schema: Required. The raw YAML string representation of the MetadataSchema. The combination of [MetadataSchema.version] and the schema name given by `title` in [MetadataSchema.schema] must be unique within a MetadataStore. The schema is defined as an OpenAPI 3.0.2 [MetadataSchema Object](https://github.com/OAI/OpenAPI- Specification/blob/master/versions/3.0.2.md#schemaObject) schemaType: The type of the MetadataSchema. This is a property that identifies which metadata types will use the MetadataSchema. schemaVersion: The version of the MetadataSchema. The version's format must match the following regular expression: `^[0-9]+.+.+$`, which would allow to order/compare different versions. Example: 1.0.0, 1.0.1, etc. """ class SchemaTypeValueValuesEnum(_messages.Enum): r"""The type of the MetadataSchema. This is a property that identifies which metadata types will use the MetadataSchema. Values: METADATA_SCHEMA_TYPE_UNSPECIFIED: Unspecified type for the MetadataSchema. ARTIFACT_TYPE: A type indicating that the MetadataSchema will be used by Artifacts. EXECUTION_TYPE: A typee indicating that the MetadataSchema will be used by Executions. CONTEXT_TYPE: A state indicating that the MetadataSchema will be used by Contexts. """ METADATA_SCHEMA_TYPE_UNSPECIFIED = 0 ARTIFACT_TYPE = 1 EXECUTION_TYPE = 2 CONTEXT_TYPE = 3 createTime = _messages.StringField(1) description = _messages.StringField(2) name = _messages.StringField(3) schema = _messages.StringField(4) schemaType = _messages.EnumField('SchemaTypeValueValuesEnum', 5) schemaVersion = _messages.StringField(6) class GoogleCloudAiplatformV1MetadataStore(_messages.Message): r"""Instance of a metadata store. Contains a set of metadata that can be queried. Fields: createTime: Output only. Timestamp when this MetadataStore was created. dataplexConfig: Optional. Dataplex integration settings. description: Description of the MetadataStore. encryptionSpec: Customer-managed encryption key spec for a Metadata Store. If set, this Metadata Store and all sub-resources of this Metadata Store are secured using this key. name: Output only. The resource name of the MetadataStore instance. state: Output only. State information of the MetadataStore. updateTime: Output only. Timestamp when this MetadataStore was last updated. """ createTime = _messages.StringField(1) dataplexConfig = _messages.MessageField('GoogleCloudAiplatformV1MetadataStoreDataplexConfig', 2) description = _messages.StringField(3) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 4) name = _messages.StringField(5) state = _messages.MessageField('GoogleCloudAiplatformV1MetadataStoreMetadataStoreState', 6) updateTime = _messages.StringField(7) class GoogleCloudAiplatformV1MetadataStoreDataplexConfig(_messages.Message): r"""Represents Dataplex integration settings. Fields: enabledPipelinesLineage: Optional. Whether or not Data Lineage synchronization is enabled for Vertex Pipelines. """ enabledPipelinesLineage = _messages.BooleanField(1) class GoogleCloudAiplatformV1MetadataStoreMetadataStoreState(_messages.Message): r"""Represents state information for a MetadataStore. Fields: diskUtilizationBytes: The disk utilization of the MetadataStore in bytes. """ diskUtilizationBytes = _messages.IntegerField(1) class GoogleCloudAiplatformV1MetricxInput(_messages.Message): r"""Input for MetricX metric. Fields: instance: Required. Metricx instance. metricSpec: Required. Spec for Metricx metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1MetricxInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1MetricxSpec', 2) class GoogleCloudAiplatformV1MetricxInstance(_messages.Message): r"""Spec for MetricX instance - The fields used for evaluation are dependent on the MetricX version. Fields: prediction: Required. Output of the evaluated model. reference: Optional. Ground truth used to compare against the prediction. source: Optional. Source text in original language. """ prediction = _messages.StringField(1) reference = _messages.StringField(2) source = _messages.StringField(3) class GoogleCloudAiplatformV1MetricxResult(_messages.Message): r"""Spec for MetricX result - calculates the MetricX score for the given instance using the version specified in the spec. Fields: score: Output only. MetricX score. Range depends on version. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1MetricxSpec(_messages.Message): r"""Spec for MetricX metric. Enums: VersionValueValuesEnum: Required. Which version to use for evaluation. Fields: sourceLanguage: Optional. Source language in BCP-47 format. targetLanguage: Optional. Target language in BCP-47 format. Covers both prediction and reference. version: Required. Which version to use for evaluation. """ class VersionValueValuesEnum(_messages.Enum): r"""Required. Which version to use for evaluation. Values: METRICX_VERSION_UNSPECIFIED: MetricX version unspecified. METRICX_24_REF: MetricX 2024 (2.6) for translation + reference (reference-based). METRICX_24_SRC: MetricX 2024 (2.6) for translation + source (QE). METRICX_24_SRC_REF: MetricX 2024 (2.6) for translation + source + reference (source-reference-combined). """ METRICX_VERSION_UNSPECIFIED = 0 METRICX_24_REF = 1 METRICX_24_SRC = 2 METRICX_24_SRC_REF = 3 sourceLanguage = _messages.StringField(1) targetLanguage = _messages.StringField(2) version = _messages.EnumField('VersionValueValuesEnum', 3) class GoogleCloudAiplatformV1MigratableResource(_messages.Message): r"""Represents one resource that exists in automl.googleapis.com, datalabeling.googleapis.com or ml.googleapis.com. Fields: automlDataset: Output only. Represents one Dataset in automl.googleapis.com. automlModel: Output only. Represents one Model in automl.googleapis.com. dataLabelingDataset: Output only. Represents one Dataset in datalabeling.googleapis.com. lastMigrateTime: Output only. Timestamp when the last migration attempt on this MigratableResource started. Will not be set if there's no migration attempt on this MigratableResource. lastUpdateTime: Output only. Timestamp when this MigratableResource was last updated. mlEngineModelVersion: Output only. Represents one Version in ml.googleapis.com. """ automlDataset = _messages.MessageField('GoogleCloudAiplatformV1MigratableResourceAutomlDataset', 1) automlModel = _messages.MessageField('GoogleCloudAiplatformV1MigratableResourceAutomlModel', 2) dataLabelingDataset = _messages.MessageField('GoogleCloudAiplatformV1MigratableResourceDataLabelingDataset', 3) lastMigrateTime = _messages.StringField(4) lastUpdateTime = _messages.StringField(5) mlEngineModelVersion = _messages.MessageField('GoogleCloudAiplatformV1MigratableResourceMlEngineModelVersion', 6) class GoogleCloudAiplatformV1MigratableResourceAutomlDataset(_messages.Message): r"""Represents one Dataset in automl.googleapis.com. Fields: dataset: Full resource name of automl Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`. datasetDisplayName: The Dataset's display name in automl.googleapis.com. """ dataset = _messages.StringField(1) datasetDisplayName = _messages.StringField(2) class GoogleCloudAiplatformV1MigratableResourceAutomlModel(_messages.Message): r"""Represents one Model in automl.googleapis.com. Fields: model: Full resource name of automl Model. Format: `projects/{project}/locations/{location}/models/{model}`. modelDisplayName: The Model's display name in automl.googleapis.com. """ model = _messages.StringField(1) modelDisplayName = _messages.StringField(2) class GoogleCloudAiplatformV1MigratableResourceDataLabelingDataset(_messages.Message): r"""Represents one Dataset in datalabeling.googleapis.com. Fields: dataLabelingAnnotatedDatasets: The migratable AnnotatedDataset in datalabeling.googleapis.com belongs to the data labeling Dataset. dataset: Full resource name of data labeling Dataset. Format: `projects/{project}/datasets/{dataset}`. datasetDisplayName: The Dataset's display name in datalabeling.googleapis.com. """ dataLabelingAnnotatedDatasets = _messages.MessageField('GoogleCloudAiplatformV1MigratableResourceDataLabelingDatasetDataLabelingAnnotatedDataset', 1, repeated=True) dataset = _messages.StringField(2) datasetDisplayName = _messages.StringField(3) class GoogleCloudAiplatformV1MigratableResourceDataLabelingDatasetDataLabelingAnnotatedDataset(_messages.Message): r"""Represents one AnnotatedDataset in datalabeling.googleapis.com. Fields: annotatedDataset: Full resource name of data labeling AnnotatedDataset. Format: `projects/{project}/datasets/{dataset}/annotatedDatasets/{annota ted_dataset}`. annotatedDatasetDisplayName: The AnnotatedDataset's display name in datalabeling.googleapis.com. """ annotatedDataset = _messages.StringField(1) annotatedDatasetDisplayName = _messages.StringField(2) class GoogleCloudAiplatformV1MigratableResourceMlEngineModelVersion(_messages.Message): r"""Represents one model Version in ml.googleapis.com. Fields: endpoint: The ml.googleapis.com endpoint that this model Version currently lives in. Example values: * ml.googleapis.com * us-centrall- ml.googleapis.com * europe-west4-ml.googleapis.com * asia- east1-ml.googleapis.com version: Full resource name of ml engine model Version. Format: `projects/{project}/models/{model}/versions/{version}`. """ endpoint = _messages.StringField(1) version = _messages.StringField(2) class GoogleCloudAiplatformV1MigrateResourceRequest(_messages.Message): r"""Config of migrating one resource from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI. Fields: migrateAutomlDatasetConfig: Config for migrating Dataset in automl.googleapis.com to Vertex AI's Dataset. migrateAutomlModelConfig: Config for migrating Model in automl.googleapis.com to Vertex AI's Model. migrateDataLabelingDatasetConfig: Config for migrating Dataset in datalabeling.googleapis.com to Vertex AI's Dataset. migrateMlEngineModelVersionConfig: Config for migrating Version in ml.googleapis.com to Vertex AI's Model. """ migrateAutomlDatasetConfig = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequestMigrateAutomlDatasetConfig', 1) migrateAutomlModelConfig = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequestMigrateAutomlModelConfig', 2) migrateDataLabelingDatasetConfig = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequestMigrateDataLabelingDatasetConfig', 3) migrateMlEngineModelVersionConfig = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequestMigrateMlEngineModelVersionConfig', 4) class GoogleCloudAiplatformV1MigrateResourceRequestMigrateAutomlDatasetConfig(_messages.Message): r"""Config for migrating Dataset in automl.googleapis.com to Vertex AI's Dataset. Fields: dataset: Required. Full resource name of automl Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`. datasetDisplayName: Required. Display name of the Dataset in Vertex AI. System will pick a display name if unspecified. """ dataset = _messages.StringField(1) datasetDisplayName = _messages.StringField(2) class GoogleCloudAiplatformV1MigrateResourceRequestMigrateAutomlModelConfig(_messages.Message): r"""Config for migrating Model in automl.googleapis.com to Vertex AI's Model. Fields: model: Required. Full resource name of automl Model. Format: `projects/{project}/locations/{location}/models/{model}`. modelDisplayName: Optional. Display name of the model in Vertex AI. System will pick a display name if unspecified. """ model = _messages.StringField(1) modelDisplayName = _messages.StringField(2) class GoogleCloudAiplatformV1MigrateResourceRequestMigrateDataLabelingDatasetConfig(_messages.Message): r"""Config for migrating Dataset in datalabeling.googleapis.com to Vertex AI's Dataset. Fields: dataset: Required. Full resource name of data labeling Dataset. Format: `projects/{project}/datasets/{dataset}`. datasetDisplayName: Optional. Display name of the Dataset in Vertex AI. System will pick a display name if unspecified. migrateDataLabelingAnnotatedDatasetConfigs: Optional. Configs for migrating AnnotatedDataset in datalabeling.googleapis.com to Vertex AI's SavedQuery. The specified AnnotatedDatasets have to belong to the datalabeling Dataset. """ dataset = _messages.StringField(1) datasetDisplayName = _messages.StringField(2) migrateDataLabelingAnnotatedDatasetConfigs = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequestMigrateDataLabelingDatasetConfigMigrateDataLabelingAnnotatedDatasetConfig', 3, repeated=True) class GoogleCloudAiplatformV1MigrateResourceRequestMigrateDataLabelingDatasetConfigMigrateDataLabelingAnnotatedDatasetConfig(_messages.Message): r"""Config for migrating AnnotatedDataset in datalabeling.googleapis.com to Vertex AI's SavedQuery. Fields: annotatedDataset: Required. Full resource name of data labeling AnnotatedDataset. Format: `projects/{project}/datasets/{dataset}/annotat edDatasets/{annotated_dataset}`. """ annotatedDataset = _messages.StringField(1) class GoogleCloudAiplatformV1MigrateResourceRequestMigrateMlEngineModelVersionConfig(_messages.Message): r"""Config for migrating version in ml.googleapis.com to Vertex AI's Model. Fields: endpoint: Required. The ml.googleapis.com endpoint that this model version should be migrated from. Example values: * ml.googleapis.com * us- centrall-ml.googleapis.com * europe-west4-ml.googleapis.com * asia- east1-ml.googleapis.com modelDisplayName: Required. Display name of the model in Vertex AI. System will pick a display name if unspecified. modelVersion: Required. Full resource name of ml engine model version. Format: `projects/{project}/models/{model}/versions/{version}`. """ endpoint = _messages.StringField(1) modelDisplayName = _messages.StringField(2) modelVersion = _messages.StringField(3) class GoogleCloudAiplatformV1MigrateResourceResponse(_messages.Message): r"""Describes a successfully migrated resource. Fields: dataset: Migrated Dataset's resource name. migratableResource: Before migration, the identifier in ml.googleapis.com, automl.googleapis.com or datalabeling.googleapis.com. model: Migrated Model's resource name. """ dataset = _messages.StringField(1) migratableResource = _messages.MessageField('GoogleCloudAiplatformV1MigratableResource', 2) model = _messages.StringField(3) class GoogleCloudAiplatformV1ModalityTokenCount(_messages.Message): r"""Represents token counting info for a single modality. Enums: ModalityValueValuesEnum: The modality associated with this token count. Fields: modality: The modality associated with this token count. tokenCount: Number of tokens. """ class ModalityValueValuesEnum(_messages.Enum): r"""The modality associated with this token count. Values: MODALITY_UNSPECIFIED: Unspecified modality. TEXT: Plain text. IMAGE: Image. VIDEO: Video. AUDIO: Audio. DOCUMENT: Document, e.g. PDF. """ MODALITY_UNSPECIFIED = 0 TEXT = 1 IMAGE = 2 VIDEO = 3 AUDIO = 4 DOCUMENT = 5 modality = _messages.EnumField('ModalityValueValuesEnum', 1) tokenCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1Model(_messages.Message): r"""A trained machine learning Model. Enums: SupportedDeploymentResourcesTypesValueListEntryValuesEnum: Messages: LabelsValue: The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Fields: artifactUri: Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models. baseModelSource: Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models. checkpoints: Optional. Output only. The checkpoints of the model. containerSpec: Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models. createTime: Output only. Timestamp when this Model was uploaded into Vertex AI. dataStats: Stats of data used for training or evaluating the Model. Only populated when the Model is trained by a TrainingPipeline with data_input_config. defaultCheckpointId: The default checkpoint id of a model version. deployedModels: Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations. description: The description of the Model. displayName: Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters. encryptionSpec: Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key. etag: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. explanationSpec: The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob. labels: The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. metadata: Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information. metadataArtifact: Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/ {metadata_store}/artifacts/{artifact}`. metadataSchemaUri: Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. modelSourceInfo: Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden. name: The resource name of the Model. originalModelInfo: Output only. If this Model is a copy of another Model, this contains info about the original. pipelineJob: Optional. This field is populated if the model is produced by a pipeline job. predictSchemata: The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. supportedDeploymentResourcesTypes: Output only. When this Model is deployed, its prediction resources are described by the `prediction_resources` field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats. supportedExportFormats: Output only. The formats in which this Model may be exported. If empty, this Model is not available for export. supportedInputStorageFormats: Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: * `jsonl` The JSON Lines format, where each instance is a single line. Uses GcsSource. * `csv` The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma- separated field names. Uses GcsSource. * `tf-record` The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. * `tf-record-gzip` Similar to `tf-record`, but the file is gzipped. Uses GcsSource. * `bigquery` Each instance is a single row in BigQuery. Uses BigQuerySource. * `file-list` Each line of the file is the location of an instance to process, uses `gcs_source` field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain. supportedOutputStorageFormats: Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: * `jsonl` The JSON Lines format, where each prediction is a single line. Uses GcsDestination. * `csv` The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. * `bigquery` Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain. trainingPipeline: Output only. The resource name of the TrainingPipeline that uploaded this Model, if any. updateTime: Output only. Timestamp when this Model was most recently updated. versionAliases: User provided version aliases so that a model version can be referenced via alias (i.e. `projects/{project}/locations/{location}/m odels/{model_id}@{version_alias}` instead of auto-generated version id (i.e. `projects/{project}/locations/{location}/models/{model_id}@{versio n_id})`. The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model. versionCreateTime: Output only. Timestamp when this version was created. versionDescription: The description of this version. versionId: Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation. versionUpdateTime: Output only. Timestamp when this version was most recently updated. """ class SupportedDeploymentResourcesTypesValueListEntryValuesEnum(_messages.Enum): r"""SupportedDeploymentResourcesTypesValueListEntryValuesEnum enum type. Values: DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED: Should not be used. DEDICATED_RESOURCES: Resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration. AUTOMATIC_RESOURCES: Resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. SHARED_RESOURCES: Resources that can be shared by multiple DeployedModels. A pre-configured DeploymentResourcePool is required. """ DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED = 0 DEDICATED_RESOURCES = 1 AUTOMATIC_RESOURCES = 2 SHARED_RESOURCES = 3 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) artifactUri = _messages.StringField(1) baseModelSource = _messages.MessageField('GoogleCloudAiplatformV1ModelBaseModelSource', 2) checkpoints = _messages.MessageField('GoogleCloudAiplatformV1Checkpoint', 3, repeated=True) containerSpec = _messages.MessageField('GoogleCloudAiplatformV1ModelContainerSpec', 4) createTime = _messages.StringField(5) dataStats = _messages.MessageField('GoogleCloudAiplatformV1ModelDataStats', 6) defaultCheckpointId = _messages.StringField(7) deployedModels = _messages.MessageField('GoogleCloudAiplatformV1DeployedModelRef', 8, repeated=True) description = _messages.StringField(9) displayName = _messages.StringField(10) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 11) etag = _messages.StringField(12) explanationSpec = _messages.MessageField('GoogleCloudAiplatformV1ExplanationSpec', 13) labels = _messages.MessageField('LabelsValue', 14) metadata = _messages.MessageField('extra_types.JsonValue', 15) metadataArtifact = _messages.StringField(16) metadataSchemaUri = _messages.StringField(17) modelSourceInfo = _messages.MessageField('GoogleCloudAiplatformV1ModelSourceInfo', 18) name = _messages.StringField(19) originalModelInfo = _messages.MessageField('GoogleCloudAiplatformV1ModelOriginalModelInfo', 20) pipelineJob = _messages.StringField(21) predictSchemata = _messages.MessageField('GoogleCloudAiplatformV1PredictSchemata', 22) satisfiesPzi = _messages.BooleanField(23) satisfiesPzs = _messages.BooleanField(24) supportedDeploymentResourcesTypes = _messages.EnumField('SupportedDeploymentResourcesTypesValueListEntryValuesEnum', 25, repeated=True) supportedExportFormats = _messages.MessageField('GoogleCloudAiplatformV1ModelExportFormat', 26, repeated=True) supportedInputStorageFormats = _messages.StringField(27, repeated=True) supportedOutputStorageFormats = _messages.StringField(28, repeated=True) trainingPipeline = _messages.StringField(29) updateTime = _messages.StringField(30) versionAliases = _messages.StringField(31, repeated=True) versionCreateTime = _messages.StringField(32) versionDescription = _messages.StringField(33) versionId = _messages.StringField(34) versionUpdateTime = _messages.StringField(35) class GoogleCloudAiplatformV1ModelArmorConfig(_messages.Message): r"""Configuration for Model Armor integrations of prompt and responses. Fields: promptTemplateName: Optional. The name of the Model Armor template to use for prompt sanitization. responseTemplateName: Optional. The name of the Model Armor template to use for response sanitization. """ promptTemplateName = _messages.StringField(1) responseTemplateName = _messages.StringField(2) class GoogleCloudAiplatformV1ModelBaseModelSource(_messages.Message): r"""User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models. Fields: genieSource: Information about the base model of Genie models. modelGardenSource: Source information of Model Garden models. """ genieSource = _messages.MessageField('GoogleCloudAiplatformV1GenieSource', 1) modelGardenSource = _messages.MessageField('GoogleCloudAiplatformV1ModelGardenSource', 2) class GoogleCloudAiplatformV1ModelContainerSpec(_messages.Message): r"""Specification of a container for serving predictions. Some fields in this message correspond to fields in the [Kubernetes Container v1 core specification](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.23/#container-v1-core). Fields: args: Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd). Specify this field as an array of executable and arguments, similar to a Docker `CMD`'s "default parameters" form. If you don't specify this field but do specify the command field, then the command from the `command` field runs without any additional arguments. See the [Kubernetes documentation about how the `command` and `args` fields interact with a container's `ENTRYPOINT` and `CMD`](https://kubernetes.io/docs/tasks/inject-data- application/define-command-argument-container/#notes). If you don't specify this field and don't specify the `command` field, then the container's [`ENTRYPOINT`](https://docs.docker.com/engine/reference/builder/#cmd) and `CMD` determine what runs based on their default behavior. See the Docker documentation about [how `CMD` and `ENTRYPOINT` interact](https://docs.docker.com/engine/reference/builder/#understand- how-cmd-and-entrypoint-interact). In this field, you can reference [environment variables set by Vertex AI](https://cloud.google.com/vertex-ai/docs/predictions/custom- container-requirements#aip-variables) and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with `$$`; for example: $$(VARIABLE_NAME) This field corresponds to the `args` field of the Kubernetes Containers [v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.23/#container-v1-core). command: Immutable. Specifies the command that runs when the container starts. This overrides the container's [ENTRYPOINT](https://docs.docker. com/engine/reference/builder/#entrypoint). Specify this field as an array of executable and arguments, similar to a Docker `ENTRYPOINT`'s "exec" form, not its "shell" form. If you do not specify this field, then the container's `ENTRYPOINT` runs, in conjunction with the args field or the container's [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd), if either exists. If this field is not specified and the container does not have an `ENTRYPOINT`, then refer to the Docker documentation about [how `CMD` and `ENTRYPOINT` interact](https://docs.docker.com/engine/reference/builder/#understand- how-cmd-and-entrypoint-interact). If you specify this field, then you can also specify the `args` field to provide additional arguments for this command. However, if you specify this field, then the container's `CMD` is ignored. See the [Kubernetes documentation about how the `command` and `args` fields interact with a container's `ENTRYPOINT` and `CMD`](https://kubernetes.io/docs/tasks/inject-data-application/define- command-argument-container/#notes). In this field, you can reference [environment variables set by Vertex AI](https://cloud.google.com/vertex-ai/docs/predictions/custom- container-requirements#aip-variables) and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with `$$`; for example: $$(VARIABLE_NAME) This field corresponds to the `command` field of the Kubernetes Containers [v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.23/#container-v1-core). deploymentTimeout: Immutable. Deployment timeout. Limit for deployment timeout is 2 hours. env: Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable `VAR_2` to have the value `foo bar`: ```json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] ``` If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the `env` field of the Kubernetes Containers [v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.23/#container-v1-core). grpcPorts: Immutable. List of ports to expose from the container. Vertex AI sends gRPC prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, gRPC requests to the container will be disabled. Vertex AI does not use ports other than the first one listed. This field corresponds to the `ports` field of the Kubernetes Containers v1 core API. healthProbe: Immutable. Specification for Kubernetes readiness probe. healthRoute: Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about [health checks](https://cloud.google.com/vertex- ai/docs/predictions/custom-container-requirements#health). For example, if you set this field to `/bar`, then Vertex AI intermittently sends a GET request to the `/bar` path on the port of your container specified by the first value of this `ModelContainerSpec`'s ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following `endpoints/`)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the [`AIP_ENDPOINT_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom- container-requirements#aip-variables).) * DEPLOYED_MODEL: DeployedModel.id of the `DeployedModel`. (Vertex AI makes this value available to your container code as the [`AIP_DEPLOYED_MODEL_ID` environment variable](https://cloud.google.com/vertex- ai/docs/predictions/custom-container-requirements#aip-variables).) imageUri: Required. Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the [container publishing requirements](https://cloud.google.com/vertex- ai/docs/predictions/custom-container-requirements#publishing), including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see [Custom container requirements](https://cloud.google.com/vertex- ai/docs/predictions/custom-container-requirements#). You can use the URI to one of Vertex AI's [pre-built container images for prediction](https://cloud.google.com/vertex-ai/docs/predictions/pre- built-containers) in this field. invokeRoutePrefix: Immutable. Invoke route prefix for the custom container. "/*" is the only supported value right now. By setting this field, any non-root route on this model will be accessible with invoke http call eg: "/invoke/foo/bar", however the [PredictionService.Invoke] RPC is not supported yet. Only one of `predict_route` or `invoke_route_prefix` can be set, and we default to using `predict_route` if this field is not set. If this field is set, the Model can only be deployed to dedicated endpoint. livenessProbe: Immutable. Specification for Kubernetes liveness probe. ports: Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends [liveness and health checks](https://cloud.google.com/vertex-ai/docs/predictions/custom- container-requirements#liveness) to this port. If you do not specify this field, it defaults to following value: ```json [ { "containerPort": 8080 } ] ``` Vertex AI does not use ports other than the first one listed. This field corresponds to the `ports` field of the Kubernetes Containers [v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes- api/v1.23/#container-v1-core). predictRoute: Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to `/foo`, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the `/foo` path on the port of your container specified by the first value of this `ModelContainerSpec`'s ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following `endpoints/`)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the [`AIP_ENDPOINT_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom- container-requirements#aip-variables).) * DEPLOYED_MODEL: DeployedModel.id of the `DeployedModel`. (Vertex AI makes this value available to your container code as the [`AIP_DEPLOYED_MODEL_ID` environment variable](https://cloud.google.com/vertex- ai/docs/predictions/custom-container-requirements#aip-variables).) sharedMemorySizeMb: Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. startupProbe: Immutable. Specification for Kubernetes startup probe. """ args = _messages.StringField(1, repeated=True) command = _messages.StringField(2, repeated=True) deploymentTimeout = _messages.StringField(3) env = _messages.MessageField('GoogleCloudAiplatformV1EnvVar', 4, repeated=True) grpcPorts = _messages.MessageField('GoogleCloudAiplatformV1Port', 5, repeated=True) healthProbe = _messages.MessageField('GoogleCloudAiplatformV1Probe', 6) healthRoute = _messages.StringField(7) imageUri = _messages.StringField(8) invokeRoutePrefix = _messages.StringField(9) livenessProbe = _messages.MessageField('GoogleCloudAiplatformV1Probe', 10) ports = _messages.MessageField('GoogleCloudAiplatformV1Port', 11, repeated=True) predictRoute = _messages.StringField(12) sharedMemorySizeMb = _messages.IntegerField(13) startupProbe = _messages.MessageField('GoogleCloudAiplatformV1Probe', 14) class GoogleCloudAiplatformV1ModelDataStats(_messages.Message): r"""Stats of data used for train or evaluate the Model. Fields: testAnnotationsCount: Number of Annotations that are used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test Annotations used by the first evaluation. If the Model is not evaluated, the number is 0. testDataItemsCount: Number of DataItems that were used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test DataItems used by the first evaluation. If the Model is not evaluated, the number is 0. trainingAnnotationsCount: Number of Annotations that are used for training this Model. trainingDataItemsCount: Number of DataItems that were used for training this Model. validationAnnotationsCount: Number of Annotations that are used for validating this Model during training. validationDataItemsCount: Number of DataItems that were used for validating this Model during training. """ testAnnotationsCount = _messages.IntegerField(1) testDataItemsCount = _messages.IntegerField(2) trainingAnnotationsCount = _messages.IntegerField(3) trainingDataItemsCount = _messages.IntegerField(4) validationAnnotationsCount = _messages.IntegerField(5) validationDataItemsCount = _messages.IntegerField(6) class GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTable(_messages.Message): r"""ModelDeploymentMonitoringBigQueryTable specifies the BigQuery table name as well as some information of the logs stored in this table. Enums: LogSourceValueValuesEnum: The source of log. LogTypeValueValuesEnum: The type of log. Fields: bigqueryTablePath: The created BigQuery table to store logs. Customer could do their own query & analysis. Format: `bq://.model_deployment_monitoring_._` logSource: The source of log. logType: The type of log. requestResponseLoggingSchemaVersion: Output only. The schema version of the request/response logging BigQuery table. Default to v1 if unset. """ class LogSourceValueValuesEnum(_messages.Enum): r"""The source of log. Values: LOG_SOURCE_UNSPECIFIED: Unspecified source. TRAINING: Logs coming from Training dataset. SERVING: Logs coming from Serving traffic. """ LOG_SOURCE_UNSPECIFIED = 0 TRAINING = 1 SERVING = 2 class LogTypeValueValuesEnum(_messages.Enum): r"""The type of log. Values: LOG_TYPE_UNSPECIFIED: Unspecified type. PREDICT: Predict logs. EXPLAIN: Explain logs. """ LOG_TYPE_UNSPECIFIED = 0 PREDICT = 1 EXPLAIN = 2 bigqueryTablePath = _messages.StringField(1) logSource = _messages.EnumField('LogSourceValueValuesEnum', 2) logType = _messages.EnumField('LogTypeValueValuesEnum', 3) requestResponseLoggingSchemaVersion = _messages.StringField(4) class GoogleCloudAiplatformV1ModelDeploymentMonitoringJob(_messages.Message): r"""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. Enums: ScheduleStateValueValuesEnum: Output only. Schedule state when the monitoring job is in Running state. StateValueValuesEnum: Output only. The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'. Messages: LabelsValue: The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Fields: analysisInstanceSchemaUri: YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string. bigqueryTables: Output only. The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response createTime: Output only. Timestamp when this ModelDeploymentMonitoringJob was created. displayName: Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob. enableMonitoringPipelineLogs: If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging#pricing). encryptionSpec: Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key. endpoint: Required. Endpoint resource name. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}` error: Output only. Only populated when the job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. labels: The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. latestMonitoringPipelineMetadata: Output only. Latest triggered monitoring pipeline metadata. logTtl: The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day. loggingSamplingStrategy: Required. Sample Strategy for logging. modelDeploymentMonitoringObjectiveConfigs: Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately. modelDeploymentMonitoringScheduleConfig: Required. Schedule config for running the monitoring job. modelMonitoringAlertConfig: Alert config for model monitoring. name: Output only. Resource name of a ModelDeploymentMonitoringJob. nextScheduleTime: Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round. predictInstanceSchemaUri: YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests. samplePredictInstance: Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. scheduleState: Output only. Schedule state when the monitoring job is in Running state. state: Output only. The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'. statsAnomaliesBaseDirectory: Stats anomalies base folder path. updateTime: Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently. """ class ScheduleStateValueValuesEnum(_messages.Enum): r"""Output only. Schedule state when the monitoring job is in Running state. Values: MONITORING_SCHEDULE_STATE_UNSPECIFIED: Unspecified state. PENDING: The pipeline is picked up and wait to run. OFFLINE: The pipeline is offline and will be scheduled for next run. RUNNING: The pipeline is running. """ MONITORING_SCHEDULE_STATE_UNSPECIFIED = 0 PENDING = 1 OFFLINE = 2 RUNNING = 3 class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'. Values: JOB_STATE_UNSPECIFIED: The job state is unspecified. JOB_STATE_QUEUED: The job has been just created or resumed and processing has not yet begun. JOB_STATE_PENDING: The service is preparing to run the job. JOB_STATE_RUNNING: The job is in progress. JOB_STATE_SUCCEEDED: The job completed successfully. JOB_STATE_FAILED: The job failed. JOB_STATE_CANCELLING: The job is being cancelled. From this state the job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. JOB_STATE_CANCELLED: The job has been cancelled. JOB_STATE_PAUSED: The job has been stopped, and can be resumed. JOB_STATE_EXPIRED: The job has expired. JOB_STATE_UPDATING: The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state. JOB_STATE_PARTIALLY_SUCCEEDED: The job is partially succeeded, some results may be missing due to errors. """ JOB_STATE_UNSPECIFIED = 0 JOB_STATE_QUEUED = 1 JOB_STATE_PENDING = 2 JOB_STATE_RUNNING = 3 JOB_STATE_SUCCEEDED = 4 JOB_STATE_FAILED = 5 JOB_STATE_CANCELLING = 6 JOB_STATE_CANCELLED = 7 JOB_STATE_PAUSED = 8 JOB_STATE_EXPIRED = 9 JOB_STATE_UPDATING = 10 JOB_STATE_PARTIALLY_SUCCEEDED = 11 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) analysisInstanceSchemaUri = _messages.StringField(1) bigqueryTables = _messages.MessageField('GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTable', 2, repeated=True) createTime = _messages.StringField(3) displayName = _messages.StringField(4) enableMonitoringPipelineLogs = _messages.BooleanField(5) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 6) endpoint = _messages.StringField(7) error = _messages.MessageField('GoogleRpcStatus', 8) labels = _messages.MessageField('LabelsValue', 9) latestMonitoringPipelineMetadata = _messages.MessageField('GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadata', 10) logTtl = _messages.StringField(11) loggingSamplingStrategy = _messages.MessageField('GoogleCloudAiplatformV1SamplingStrategy', 12) modelDeploymentMonitoringObjectiveConfigs = _messages.MessageField('GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfig', 13, repeated=True) modelDeploymentMonitoringScheduleConfig = _messages.MessageField('GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfig', 14) modelMonitoringAlertConfig = _messages.MessageField('GoogleCloudAiplatformV1ModelMonitoringAlertConfig', 15) name = _messages.StringField(16) nextScheduleTime = _messages.StringField(17) predictInstanceSchemaUri = _messages.StringField(18) samplePredictInstance = _messages.MessageField('extra_types.JsonValue', 19) satisfiesPzi = _messages.BooleanField(20) satisfiesPzs = _messages.BooleanField(21) scheduleState = _messages.EnumField('ScheduleStateValueValuesEnum', 22) state = _messages.EnumField('StateValueValuesEnum', 23) statsAnomaliesBaseDirectory = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 24) updateTime = _messages.StringField(25) class GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadata(_messages.Message): r"""All metadata of most recent monitoring pipelines. Fields: runTime: The time that most recent monitoring pipelines that is related to this run. status: The status of the most recent monitoring pipeline. """ runTime = _messages.StringField(1) status = _messages.MessageField('GoogleRpcStatus', 2) class GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfig(_messages.Message): r"""ModelDeploymentMonitoringObjectiveConfig contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig. Fields: deployedModelId: The DeployedModel ID of the objective config. objectiveConfig: The objective config of for the modelmonitoring job of this deployed model. """ deployedModelId = _messages.StringField(1) objectiveConfig = _messages.MessageField('GoogleCloudAiplatformV1ModelMonitoringObjectiveConfig', 2) class GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfig(_messages.Message): r"""The config for scheduling monitoring job. Fields: monitorInterval: Required. The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered. monitorWindow: The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics. """ monitorInterval = _messages.StringField(1) monitorWindow = _messages.StringField(2) class GoogleCloudAiplatformV1ModelEvaluation(_messages.Message): r"""A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data. Fields: annotationSchemaUri: Points to a YAML file stored on Google Cloud Storage describing EvaluatedDataItemView.predictions, EvaluatedDataItemView.ground_truths, EvaluatedAnnotation.predictions, and EvaluatedAnnotation.ground_truths. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). This field is not populated if there are neither EvaluatedDataItemViews nor EvaluatedAnnotations under this ModelEvaluation. createTime: Output only. Timestamp when this ModelEvaluation was created. dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage describing EvaluatedDataItemView.data_item_payload and EvaluatedAnnotation.data_item_payload. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). This field is not populated if there are neither EvaluatedDataItemViews nor EvaluatedAnnotations under this ModelEvaluation. displayName: The display name of the ModelEvaluation. explanationSpecs: Describes the values of ExplanationSpec that are used for explaining the predicted values on the evaluated data. metadata: The metadata of the ModelEvaluation. For the ModelEvaluation uploaded from Managed Pipeline, metadata contains a structured value with keys of "pipeline_job_id", "evaluation_dataset_type", "evaluation_dataset_path", "row_based_metrics_path". metrics: Evaluation metrics of the Model. The schema of the metrics is stored in metrics_schema_uri metricsSchemaUri: Points to a YAML file stored on Google Cloud Storage describing the metrics of this ModelEvaluation. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). modelExplanation: Aggregated explanation metrics for the Model's prediction output over the data this ModelEvaluation uses. This field is populated only if the Model is evaluated with explanations, and only for AutoML tabular Models. name: Output only. The resource name of the ModelEvaluation. sliceDimensions: All possible dimensions of ModelEvaluationSlices. The dimensions can be used as the filter of the ModelService.ListModelEvaluationSlices request, in the form of `slice.dimension = `. """ annotationSchemaUri = _messages.StringField(1) createTime = _messages.StringField(2) dataItemSchemaUri = _messages.StringField(3) displayName = _messages.StringField(4) explanationSpecs = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluationModelEvaluationExplanationSpec', 5, repeated=True) metadata = _messages.MessageField('extra_types.JsonValue', 6) metrics = _messages.MessageField('extra_types.JsonValue', 7) metricsSchemaUri = _messages.StringField(8) modelExplanation = _messages.MessageField('GoogleCloudAiplatformV1ModelExplanation', 9) name = _messages.StringField(10) sliceDimensions = _messages.StringField(11, repeated=True) class GoogleCloudAiplatformV1ModelEvaluationModelEvaluationExplanationSpec(_messages.Message): r"""A GoogleCloudAiplatformV1ModelEvaluationModelEvaluationExplanationSpec object. Fields: explanationSpec: Explanation spec details. explanationType: Explanation type. For AutoML Image Classification models, possible values are: * `image-integrated-gradients` * `image-xrai` """ explanationSpec = _messages.MessageField('GoogleCloudAiplatformV1ExplanationSpec', 1) explanationType = _messages.StringField(2) class GoogleCloudAiplatformV1ModelEvaluationSlice(_messages.Message): r"""A collection of metrics calculated by comparing Model's predictions on a slice of the test data against ground truth annotations. Fields: createTime: Output only. Timestamp when this ModelEvaluationSlice was created. metrics: Output only. Sliced evaluation metrics of the Model. The schema of the metrics is stored in metrics_schema_uri metricsSchemaUri: Output only. Points to a YAML file stored on Google Cloud Storage describing the metrics of this ModelEvaluationSlice. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). modelExplanation: Output only. Aggregated explanation metrics for the Model's prediction output over the data this ModelEvaluation uses. This field is populated only if the Model is evaluated with explanations, and only for tabular Models. name: Output only. The resource name of the ModelEvaluationSlice. slice: Output only. The slice of the test data that is used to evaluate the Model. """ createTime = _messages.StringField(1) metrics = _messages.MessageField('extra_types.JsonValue', 2) metricsSchemaUri = _messages.StringField(3) modelExplanation = _messages.MessageField('GoogleCloudAiplatformV1ModelExplanation', 4) name = _messages.StringField(5) slice = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluationSliceSlice', 6) class GoogleCloudAiplatformV1ModelEvaluationSliceSlice(_messages.Message): r"""Definition of a slice. Fields: dimension: Output only. The dimension of the slice. Well-known dimensions are: * `annotationSpec`: This slice is on the test data that has either ground truth or prediction with AnnotationSpec.display_name equals to value. * `slice`: This slice is a user customized slice defined by its SliceSpec. sliceSpec: Output only. Specification for how the data was sliced. value: Output only. The value of the dimension in this slice. """ dimension = _messages.StringField(1) sliceSpec = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluationSliceSliceSliceSpec', 2) value = _messages.StringField(3) class GoogleCloudAiplatformV1ModelEvaluationSliceSliceSliceSpec(_messages.Message): r"""Specification for how the data should be sliced. Messages: ConfigsValue: Mapping configuration for this SliceSpec. The key is the name of the feature. By default, the key will be prefixed by "instance" as a dictionary prefix for Vertex Batch Predictions output format. Fields: configs: Mapping configuration for this SliceSpec. The key is the name of the feature. By default, the key will be prefixed by "instance" as a dictionary prefix for Vertex Batch Predictions output format. """ @encoding.MapUnrecognizedFields('additionalProperties') class ConfigsValue(_messages.Message): r"""Mapping configuration for this SliceSpec. The key is the name of the feature. By default, the key will be prefixed by "instance" as a dictionary prefix for Vertex Batch Predictions output format. Messages: AdditionalProperty: An additional property for a ConfigsValue object. Fields: additionalProperties: Additional properties of type ConfigsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ConfigsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1ModelEvaluationSliceSliceSliceSpecSliceConfig attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluationSliceSliceSliceSpecSliceConfig', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) configs = _messages.MessageField('ConfigsValue', 1) class GoogleCloudAiplatformV1ModelEvaluationSliceSliceSliceSpecRange(_messages.Message): r"""A range of values for slice(s). `low` is inclusive, `high` is exclusive. Fields: high: Exclusive high value for the range. low: Inclusive low value for the range. """ high = _messages.FloatField(1, variant=_messages.Variant.FLOAT) low = _messages.FloatField(2, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1ModelEvaluationSliceSliceSliceSpecSliceConfig(_messages.Message): r"""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. Fields: allValues: If all_values is set to true, then all possible labels of the keyed feature will have another slice computed. Example: `{"all_values":{"value":true}}` range: A range of values for a numerical feature. Example: `{"range":{"low":10000.0,"high":50000.0}}` will capture 12345 and 23334 in the slice. value: A unique specific value for a given feature. Example: `{ "value": { "string_value": "12345" } }` """ allValues = _messages.BooleanField(1) range = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluationSliceSliceSliceSpecRange', 2) value = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluationSliceSliceSliceSpecValue', 3) class GoogleCloudAiplatformV1ModelEvaluationSliceSliceSliceSpecValue(_messages.Message): r"""Single value that supports strings and floats. Fields: floatValue: Float type. stringValue: String type. """ floatValue = _messages.FloatField(1, variant=_messages.Variant.FLOAT) stringValue = _messages.StringField(2) class GoogleCloudAiplatformV1ModelExplanation(_messages.Message): r"""Aggregated explanation metrics for a Model over a set of instances. Fields: meanAttributions: Output only. Aggregated attributions explaining the Model's prediction outputs over the set of instances. The attributions are grouped by outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. The baselineOutputValue, instanceOutputValue and featureAttributions fields are averaged over the test data. NOTE: Currently AutoML tabular classification Models produce only one attribution, which averages attributions over all the classes it predicts. Attribution.approximation_error is not populated. """ meanAttributions = _messages.MessageField('GoogleCloudAiplatformV1Attribution', 1, repeated=True) class GoogleCloudAiplatformV1ModelExportFormat(_messages.Message): r"""Represents export format supported by the Model. All formats export to Google Cloud Storage. Enums: ExportableContentsValueListEntryValuesEnum: Fields: exportableContents: Output only. The content of this Model that may be exported. id: Output only. The ID of the export format. The possible format IDs are: * `tflite` Used for Android mobile devices. * `edgetpu-tflite` Used for [Edge TPU](https://cloud.google.com/edge-tpu/) devices. * `tf-saved- model` A tensorflow model in SavedModel format. * `tf-js` A [TensorFlow.js](https://www.tensorflow.org/js) model that can be used in the browser and in Node.js using JavaScript. * `core-ml` Used for iOS mobile devices. * `custom-trained` A Model that was uploaded or trained by custom code. * `genie` A tuned Model Garden model. """ class ExportableContentsValueListEntryValuesEnum(_messages.Enum): r"""ExportableContentsValueListEntryValuesEnum enum type. Values: EXPORTABLE_CONTENT_UNSPECIFIED: Should not be used. ARTIFACT: Model artifact and any of its supported files. Will be exported to the location specified by the `artifactDestination` field of the ExportModelRequest.output_config object. IMAGE: The container image that is to be used when deploying this Model. Will be exported to the location specified by the `imageDestination` field of the ExportModelRequest.output_config object. """ EXPORTABLE_CONTENT_UNSPECIFIED = 0 ARTIFACT = 1 IMAGE = 2 exportableContents = _messages.EnumField('ExportableContentsValueListEntryValuesEnum', 1, repeated=True) id = _messages.StringField(2) class GoogleCloudAiplatformV1ModelGardenSource(_messages.Message): r"""Contains information about the source of the models generated from Model Garden. Fields: publicModelName: Required. The model garden source model resource name. """ publicModelName = _messages.StringField(1) class GoogleCloudAiplatformV1ModelMonitoringAlertConfig(_messages.Message): r"""The alert config for model monitoring. Fields: emailAlertConfig: Email alert config. enableLogging: Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto ModelMonitoringStatsAnomalies. This can be further synced to Pub/Sub or any other services supported by Cloud Logging. notificationChannels: Resource names of the NotificationChannels to send alert. Must be of the format `projects//notificationChannels/` """ emailAlertConfig = _messages.MessageField('GoogleCloudAiplatformV1ModelMonitoringAlertConfigEmailAlertConfig', 1) enableLogging = _messages.BooleanField(2) notificationChannels = _messages.StringField(3, repeated=True) class GoogleCloudAiplatformV1ModelMonitoringAlertConfigEmailAlertConfig(_messages.Message): r"""The config for email alert. Fields: userEmails: The email addresses to send the alert. """ userEmails = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1ModelMonitoringObjectiveConfig(_messages.Message): r"""The objective configuration for model monitoring, including the information needed to detect anomalies for one particular model. Fields: explanationConfig: The config for integrating with Vertex Explainable AI. predictionDriftDetectionConfig: The config for drift of prediction data. trainingDataset: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified. trainingPredictionSkewDetectionConfig: The config for skew between training data and prediction data. """ explanationConfig = _messages.MessageField('GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfig', 1) predictionDriftDetectionConfig = _messages.MessageField('GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfig', 2) trainingDataset = _messages.MessageField('GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingDataset', 3) trainingPredictionSkewDetectionConfig = _messages.MessageField('GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfig', 4) class GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfig(_messages.Message): r"""The config for integrating with Vertex Explainable AI. Only applicable if the Model has explanation_spec populated. Fields: enableFeatureAttributes: If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them. explanationBaseline: Predictions generated by the BatchPredictionJob using baseline dataset. """ enableFeatureAttributes = _messages.BooleanField(1) explanationBaseline = _messages.MessageField('GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline', 2) class GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline(_messages.Message): r"""Output from BatchPredictionJob for Model Monitoring baseline dataset, which can be used to generate baseline attribution scores. Enums: PredictionFormatValueValuesEnum: The storage format of the predictions generated BatchPrediction job. Fields: bigquery: BigQuery location for BatchExplain output. gcs: Cloud Storage location for BatchExplain output. predictionFormat: The storage format of the predictions generated BatchPrediction job. """ class PredictionFormatValueValuesEnum(_messages.Enum): r"""The storage format of the predictions generated BatchPrediction job. Values: PREDICTION_FORMAT_UNSPECIFIED: Should not be set. JSONL: Predictions are in JSONL files. BIGQUERY: Predictions are in BigQuery. """ PREDICTION_FORMAT_UNSPECIFIED = 0 JSONL = 1 BIGQUERY = 2 bigquery = _messages.MessageField('GoogleCloudAiplatformV1BigQueryDestination', 1) gcs = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 2) predictionFormat = _messages.EnumField('PredictionFormatValueValuesEnum', 3) class GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfig(_messages.Message): r"""The config for Prediction data drift detection. Messages: AttributionScoreDriftThresholdsValue: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows. DriftThresholdsValue: Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws. Fields: attributionScoreDriftThresholds: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows. defaultDriftThreshold: Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features. driftThresholds: Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws. """ @encoding.MapUnrecognizedFields('additionalProperties') class AttributionScoreDriftThresholdsValue(_messages.Message): r"""Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows. Messages: AdditionalProperty: An additional property for a AttributionScoreDriftThresholdsValue object. Fields: additionalProperties: Additional properties of type AttributionScoreDriftThresholdsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a AttributionScoreDriftThresholdsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1ThresholdConfig attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1ThresholdConfig', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class DriftThresholdsValue(_messages.Message): r"""Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws. Messages: AdditionalProperty: An additional property for a DriftThresholdsValue object. Fields: additionalProperties: Additional properties of type DriftThresholdsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a DriftThresholdsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1ThresholdConfig attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1ThresholdConfig', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) attributionScoreDriftThresholds = _messages.MessageField('AttributionScoreDriftThresholdsValue', 1) defaultDriftThreshold = _messages.MessageField('GoogleCloudAiplatformV1ThresholdConfig', 2) driftThresholds = _messages.MessageField('DriftThresholdsValue', 3) class GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingDataset(_messages.Message): r"""Training Dataset information. Fields: bigquerySource: The BigQuery table of the unmanaged Dataset used to train this Model. dataFormat: Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file. dataset: The resource name of the Dataset used to train this Model. gcsSource: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model. loggingSamplingStrategy: Strategy to sample data from Training Dataset. If not set, we process the whole dataset. targetField: The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data. """ bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1BigQuerySource', 1) dataFormat = _messages.StringField(2) dataset = _messages.StringField(3) gcsSource = _messages.MessageField('GoogleCloudAiplatformV1GcsSource', 4) loggingSamplingStrategy = _messages.MessageField('GoogleCloudAiplatformV1SamplingStrategy', 5) targetField = _messages.StringField(6) class GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfig(_messages.Message): r"""The config for Training & Prediction data skew detection. It specifies the training dataset sources and the skew detection parameters. Messages: AttributionScoreSkewThresholdsValue: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature. SkewThresholdsValue: Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature. Fields: attributionScoreSkewThresholds: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature. defaultSkewThreshold: Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features. skewThresholds: Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature. """ @encoding.MapUnrecognizedFields('additionalProperties') class AttributionScoreSkewThresholdsValue(_messages.Message): r"""Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature. Messages: AdditionalProperty: An additional property for a AttributionScoreSkewThresholdsValue object. Fields: additionalProperties: Additional properties of type AttributionScoreSkewThresholdsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a AttributionScoreSkewThresholdsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1ThresholdConfig attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1ThresholdConfig', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class SkewThresholdsValue(_messages.Message): r"""Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature. Messages: AdditionalProperty: An additional property for a SkewThresholdsValue object. Fields: additionalProperties: Additional properties of type SkewThresholdsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a SkewThresholdsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1ThresholdConfig attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1ThresholdConfig', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) attributionScoreSkewThresholds = _messages.MessageField('AttributionScoreSkewThresholdsValue', 1) defaultSkewThreshold = _messages.MessageField('GoogleCloudAiplatformV1ThresholdConfig', 2) skewThresholds = _messages.MessageField('SkewThresholdsValue', 3) class GoogleCloudAiplatformV1ModelMonitoringStatsAnomalies(_messages.Message): r"""Statistics and anomalies generated by Model Monitoring. Enums: ObjectiveValueValuesEnum: Model Monitoring Objective those stats and anomalies belonging to. Fields: anomalyCount: Number of anomalies within all stats. deployedModelId: Deployed Model ID. featureStats: A list of historical Stats and Anomalies generated for all Features. objective: Model Monitoring Objective those stats and anomalies belonging to. """ class ObjectiveValueValuesEnum(_messages.Enum): r"""Model Monitoring Objective those stats and anomalies belonging to. Values: MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED: Default value, should not be set. RAW_FEATURE_SKEW: Raw feature values' stats to detect skew between Training-Prediction datasets. RAW_FEATURE_DRIFT: Raw feature values' stats to detect drift between Serving-Prediction datasets. FEATURE_ATTRIBUTION_SKEW: Feature attribution scores to detect skew between Training-Prediction datasets. FEATURE_ATTRIBUTION_DRIFT: Feature attribution scores to detect skew between Prediction datasets collected within different time windows. """ MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED = 0 RAW_FEATURE_SKEW = 1 RAW_FEATURE_DRIFT = 2 FEATURE_ATTRIBUTION_SKEW = 3 FEATURE_ATTRIBUTION_DRIFT = 4 anomalyCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) deployedModelId = _messages.StringField(2) featureStats = _messages.MessageField('GoogleCloudAiplatformV1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomalies', 3, repeated=True) objective = _messages.EnumField('ObjectiveValueValuesEnum', 4) class GoogleCloudAiplatformV1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomalies(_messages.Message): r"""Historical Stats (and Anomalies) for a specific Feature. Fields: featureDisplayName: Display Name of the Feature. predictionStats: A list of historical stats generated by different time window's Prediction Dataset. threshold: Threshold for anomaly detection. trainingStats: Stats calculated for the Training Dataset. """ featureDisplayName = _messages.StringField(1) predictionStats = _messages.MessageField('GoogleCloudAiplatformV1FeatureStatsAnomaly', 2, repeated=True) threshold = _messages.MessageField('GoogleCloudAiplatformV1ThresholdConfig', 3) trainingStats = _messages.MessageField('GoogleCloudAiplatformV1FeatureStatsAnomaly', 4) class GoogleCloudAiplatformV1ModelOriginalModelInfo(_messages.Message): r"""Contains information about the original Model if this Model is a copy. Fields: model: Output only. The resource name of the Model this Model is a copy of, including the revision. Format: `projects/{project}/locations/{location}/models/{model_id}@{version_id}` """ model = _messages.StringField(1) class GoogleCloudAiplatformV1ModelSourceInfo(_messages.Message): r"""Detail description of the source information of the model. Enums: SourceTypeValueValuesEnum: Type of the model source. Fields: copy: If this Model is copy of another Model. If true then source_type pertains to the original. sourceType: Type of the model source. """ class SourceTypeValueValuesEnum(_messages.Enum): r"""Type of the model source. Values: MODEL_SOURCE_TYPE_UNSPECIFIED: Should not be used. AUTOML: The Model is uploaded by automl training pipeline. CUSTOM: The Model is uploaded by user or custom training pipeline. BQML: The Model is registered and sync'ed from BigQuery ML. MODEL_GARDEN: The Model is saved or tuned from Model Garden. GENIE: The Model is saved or tuned from Genie. CUSTOM_TEXT_EMBEDDING: The Model is uploaded by text embedding finetuning pipeline. MARKETPLACE: The Model is saved or tuned from Marketplace. """ MODEL_SOURCE_TYPE_UNSPECIFIED = 0 AUTOML = 1 CUSTOM = 2 BQML = 3 MODEL_GARDEN = 4 GENIE = 5 CUSTOM_TEXT_EMBEDDING = 6 MARKETPLACE = 7 copy = _messages.BooleanField(1) sourceType = _messages.EnumField('SourceTypeValueValuesEnum', 2) class GoogleCloudAiplatformV1ModelVersionCheckpoint(_messages.Message): r"""Describes the machine learning model version checkpoint. Fields: checkpointId: The ID of the checkpoint. epoch: The epoch of the checkpoint. step: The step of the checkpoint. """ checkpointId = _messages.StringField(1) epoch = _messages.IntegerField(2) step = _messages.IntegerField(3) class GoogleCloudAiplatformV1MutateDeployedIndexOperationMetadata(_messages.Message): r"""Runtime operation information for IndexEndpointService.MutateDeployedIndex. Fields: deployedIndexId: The unique index id specified by user genericMetadata: The operation generic information. """ deployedIndexId = _messages.StringField(1) genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 2) class GoogleCloudAiplatformV1MutateDeployedIndexResponse(_messages.Message): r"""Response message for IndexEndpointService.MutateDeployedIndex. Fields: deployedIndex: The DeployedIndex that had been updated in the IndexEndpoint. """ deployedIndex = _messages.MessageField('GoogleCloudAiplatformV1DeployedIndex', 1) class GoogleCloudAiplatformV1MutateDeployedModelOperationMetadata(_messages.Message): r"""Runtime operation information for EndpointService.MutateDeployedModel. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1MutateDeployedModelRequest(_messages.Message): r"""Request message for EndpointService.MutateDeployedModel. Fields: deployedModel: Required. The DeployedModel to be mutated within the Endpoint. Only the following fields can be mutated: * `min_replica_count` in either DedicatedResources or AutomaticResources * `max_replica_count` in either DedicatedResources or AutomaticResources * `required_replica_count` in DedicatedResources * autoscaling_metric_specs * `disable_container_logging` (v1 only) * `enable_container_logging` (v1beta1 only) updateMask: Required. The update mask applies to the resource. See google.protobuf.FieldMask. """ deployedModel = _messages.MessageField('GoogleCloudAiplatformV1DeployedModel', 1) updateMask = _messages.StringField(2) class GoogleCloudAiplatformV1MutateDeployedModelResponse(_messages.Message): r"""Response message for EndpointService.MutateDeployedModel. Fields: deployedModel: The DeployedModel that's being mutated. """ deployedModel = _messages.MessageField('GoogleCloudAiplatformV1DeployedModel', 1) class GoogleCloudAiplatformV1NasJob(_messages.Message): r"""Represents a Neural Architecture Search (NAS) job. Enums: StateValueValuesEnum: Output only. The detailed state of the job. Messages: LabelsValue: The labels with user-defined metadata to organize NasJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Fields: createTime: Output only. Time when the NasJob was created. displayName: Required. The display name of the NasJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. enableRestrictedImageTraining: Optional. Enable a separation of Custom model training and restricted image training for tenant project. encryptionSpec: Customer-managed encryption key options for a NasJob. If this is set, then all resources created by the NasJob will be encrypted with the provided encryption key. endTime: Output only. Time when the NasJob entered any of the following states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`. error: Output only. Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED. labels: The labels with user-defined metadata to organize NasJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. name: Output only. Resource name of the NasJob. nasJobOutput: Output only. Output of the NasJob. nasJobSpec: Required. The specification of a NasJob. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. startTime: Output only. Time when the NasJob for the first time entered the `JOB_STATE_RUNNING` state. state: Output only. The detailed state of the job. updateTime: Output only. Time when the NasJob was most recently updated. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of the job. Values: JOB_STATE_UNSPECIFIED: The job state is unspecified. JOB_STATE_QUEUED: The job has been just created or resumed and processing has not yet begun. JOB_STATE_PENDING: The service is preparing to run the job. JOB_STATE_RUNNING: The job is in progress. JOB_STATE_SUCCEEDED: The job completed successfully. JOB_STATE_FAILED: The job failed. JOB_STATE_CANCELLING: The job is being cancelled. From this state the job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. JOB_STATE_CANCELLED: The job has been cancelled. JOB_STATE_PAUSED: The job has been stopped, and can be resumed. JOB_STATE_EXPIRED: The job has expired. JOB_STATE_UPDATING: The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state. JOB_STATE_PARTIALLY_SUCCEEDED: The job is partially succeeded, some results may be missing due to errors. """ JOB_STATE_UNSPECIFIED = 0 JOB_STATE_QUEUED = 1 JOB_STATE_PENDING = 2 JOB_STATE_RUNNING = 3 JOB_STATE_SUCCEEDED = 4 JOB_STATE_FAILED = 5 JOB_STATE_CANCELLING = 6 JOB_STATE_CANCELLED = 7 JOB_STATE_PAUSED = 8 JOB_STATE_EXPIRED = 9 JOB_STATE_UPDATING = 10 JOB_STATE_PARTIALLY_SUCCEEDED = 11 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize NasJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) displayName = _messages.StringField(2) enableRestrictedImageTraining = _messages.BooleanField(3) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 4) endTime = _messages.StringField(5) error = _messages.MessageField('GoogleRpcStatus', 6) labels = _messages.MessageField('LabelsValue', 7) name = _messages.StringField(8) nasJobOutput = _messages.MessageField('GoogleCloudAiplatformV1NasJobOutput', 9) nasJobSpec = _messages.MessageField('GoogleCloudAiplatformV1NasJobSpec', 10) satisfiesPzi = _messages.BooleanField(11) satisfiesPzs = _messages.BooleanField(12) startTime = _messages.StringField(13) state = _messages.EnumField('StateValueValuesEnum', 14) updateTime = _messages.StringField(15) class GoogleCloudAiplatformV1NasJobOutput(_messages.Message): r"""Represents a uCAIP NasJob output. Fields: multiTrialJobOutput: Output only. The output of this multi-trial Neural Architecture Search (NAS) job. """ multiTrialJobOutput = _messages.MessageField('GoogleCloudAiplatformV1NasJobOutputMultiTrialJobOutput', 1) class GoogleCloudAiplatformV1NasJobOutputMultiTrialJobOutput(_messages.Message): r"""The output of a multi-trial Neural Architecture Search (NAS) jobs. Fields: searchTrials: Output only. List of NasTrials that were started as part of search stage. trainTrials: Output only. List of NasTrials that were started as part of train stage. """ searchTrials = _messages.MessageField('GoogleCloudAiplatformV1NasTrial', 1, repeated=True) trainTrials = _messages.MessageField('GoogleCloudAiplatformV1NasTrial', 2, repeated=True) class GoogleCloudAiplatformV1NasJobSpec(_messages.Message): r"""Represents the spec of a NasJob. Fields: multiTrialAlgorithmSpec: The spec of multi-trial algorithms. resumeNasJobId: The ID of the existing NasJob in the same Project and Location which will be used to resume search. search_space_spec and nas_algorithm_spec are obtained from previous NasJob hence should not provide them again for this NasJob. searchSpaceSpec: It defines the search space for Neural Architecture Search (NAS). """ multiTrialAlgorithmSpec = _messages.MessageField('GoogleCloudAiplatformV1NasJobSpecMultiTrialAlgorithmSpec', 1) resumeNasJobId = _messages.StringField(2) searchSpaceSpec = _messages.StringField(3) class GoogleCloudAiplatformV1NasJobSpecMultiTrialAlgorithmSpec(_messages.Message): r"""The spec of multi-trial Neural Architecture Search (NAS). Enums: MultiTrialAlgorithmValueValuesEnum: The multi-trial Neural Architecture Search (NAS) algorithm type. Defaults to `REINFORCEMENT_LEARNING`. Fields: metric: Metric specs for the NAS job. Validation for this field is done at `multi_trial_algorithm_spec` field. multiTrialAlgorithm: The multi-trial Neural Architecture Search (NAS) algorithm type. Defaults to `REINFORCEMENT_LEARNING`. searchTrialSpec: Required. Spec for search trials. trainTrialSpec: Spec for train trials. Top N [TrainTrialSpec.max_parallel_trial_count] search trials will be trained for every M [TrainTrialSpec.frequency] trials searched. """ class MultiTrialAlgorithmValueValuesEnum(_messages.Enum): r"""The multi-trial Neural Architecture Search (NAS) algorithm type. Defaults to `REINFORCEMENT_LEARNING`. Values: MULTI_TRIAL_ALGORITHM_UNSPECIFIED: Defaults to `REINFORCEMENT_LEARNING`. REINFORCEMENT_LEARNING: The Reinforcement Learning Algorithm for Multi- trial Neural Architecture Search (NAS). GRID_SEARCH: The Grid Search Algorithm for Multi-trial Neural Architecture Search (NAS). """ MULTI_TRIAL_ALGORITHM_UNSPECIFIED = 0 REINFORCEMENT_LEARNING = 1 GRID_SEARCH = 2 metric = _messages.MessageField('GoogleCloudAiplatformV1NasJobSpecMultiTrialAlgorithmSpecMetricSpec', 1) multiTrialAlgorithm = _messages.EnumField('MultiTrialAlgorithmValueValuesEnum', 2) searchTrialSpec = _messages.MessageField('GoogleCloudAiplatformV1NasJobSpecMultiTrialAlgorithmSpecSearchTrialSpec', 3) trainTrialSpec = _messages.MessageField('GoogleCloudAiplatformV1NasJobSpecMultiTrialAlgorithmSpecTrainTrialSpec', 4) class GoogleCloudAiplatformV1NasJobSpecMultiTrialAlgorithmSpecMetricSpec(_messages.Message): r"""Represents a metric to optimize. Enums: GoalValueValuesEnum: Required. The optimization goal of the metric. Fields: goal: Required. The optimization goal of the metric. metricId: Required. The ID of the metric. Must not contain whitespaces. """ class GoalValueValuesEnum(_messages.Enum): r"""Required. The optimization goal of the metric. Values: GOAL_TYPE_UNSPECIFIED: Goal Type will default to maximize. MAXIMIZE: Maximize the goal metric. MINIMIZE: Minimize the goal metric. """ GOAL_TYPE_UNSPECIFIED = 0 MAXIMIZE = 1 MINIMIZE = 2 goal = _messages.EnumField('GoalValueValuesEnum', 1) metricId = _messages.StringField(2) class GoogleCloudAiplatformV1NasJobSpecMultiTrialAlgorithmSpecSearchTrialSpec(_messages.Message): r"""Represent spec for search trials. Fields: maxFailedTrialCount: The number of failed trials that need to be seen before failing the NasJob. If set to 0, Vertex AI decides how many trials must fail before the whole job fails. maxParallelTrialCount: Required. The maximum number of trials to run in parallel. maxTrialCount: Required. The maximum number of Neural Architecture Search (NAS) trials to run. searchTrialJobSpec: Required. The spec of a search trial job. The same spec applies to all search trials. """ maxFailedTrialCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) maxParallelTrialCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) maxTrialCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) searchTrialJobSpec = _messages.MessageField('GoogleCloudAiplatformV1CustomJobSpec', 4) class GoogleCloudAiplatformV1NasJobSpecMultiTrialAlgorithmSpecTrainTrialSpec(_messages.Message): r"""Represent spec for train trials. Fields: frequency: Required. Frequency of search trials to start train stage. Top N [TrainTrialSpec.max_parallel_trial_count] search trials will be trained for every M [TrainTrialSpec.frequency] trials searched. maxParallelTrialCount: Required. The maximum number of trials to run in parallel. trainTrialJobSpec: Required. The spec of a train trial job. The same spec applies to all train trials. """ frequency = _messages.IntegerField(1, variant=_messages.Variant.INT32) maxParallelTrialCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) trainTrialJobSpec = _messages.MessageField('GoogleCloudAiplatformV1CustomJobSpec', 3) class GoogleCloudAiplatformV1NasTrial(_messages.Message): r"""Represents a uCAIP NasJob trial. Enums: StateValueValuesEnum: Output only. The detailed state of the NasTrial. Fields: endTime: Output only. Time when the NasTrial's status changed to `SUCCEEDED` or `INFEASIBLE`. finalMeasurement: Output only. The final measurement containing the objective value. id: Output only. The identifier of the NasTrial assigned by the service. startTime: Output only. Time when the NasTrial was started. state: Output only. The detailed state of the NasTrial. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of the NasTrial. Values: STATE_UNSPECIFIED: The NasTrial state is unspecified. REQUESTED: Indicates that a specific NasTrial has been requested, but it has not yet been suggested by the service. ACTIVE: Indicates that the NasTrial has been suggested. STOPPING: Indicates that the NasTrial should stop according to the service. SUCCEEDED: Indicates that the NasTrial is completed successfully. INFEASIBLE: Indicates that the NasTrial should not be attempted again. The service will set a NasTrial to INFEASIBLE when it's done but missing the final_measurement. """ STATE_UNSPECIFIED = 0 REQUESTED = 1 ACTIVE = 2 STOPPING = 3 SUCCEEDED = 4 INFEASIBLE = 5 endTime = _messages.StringField(1) finalMeasurement = _messages.MessageField('GoogleCloudAiplatformV1Measurement', 2) id = _messages.StringField(3) startTime = _messages.StringField(4) state = _messages.EnumField('StateValueValuesEnum', 5) class GoogleCloudAiplatformV1NasTrialDetail(_messages.Message): r"""Represents a NasTrial details along with its parameters. If there is a corresponding train NasTrial, the train NasTrial is also returned. Fields: name: Output only. Resource name of the NasTrialDetail. parameters: The parameters for the NasJob NasTrial. searchTrial: The requested search NasTrial. trainTrial: The train NasTrial corresponding to search_trial. Only populated if search_trial is used for training. """ name = _messages.StringField(1) parameters = _messages.StringField(2) searchTrial = _messages.MessageField('GoogleCloudAiplatformV1NasTrial', 3) trainTrial = _messages.MessageField('GoogleCloudAiplatformV1NasTrial', 4) class GoogleCloudAiplatformV1NearestNeighborQuery(_messages.Message): r"""A query to find a number of similar entities. Fields: embedding: Optional. The embedding vector that be used for similar search. entityId: Optional. The entity id whose similar entities should be searched for. If embedding is set, search will use embedding instead of entity_id. neighborCount: Optional. The number of similar entities to be retrieved from feature view for each query. numericFilters: Optional. The list of numeric filters. parameters: Optional. Parameters that can be set to tune query on the fly. perCrowdingAttributeNeighborCount: Optional. Crowding is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than sper_crowding_attribute_neighbor_count of the k neighbors returned have the same value of crowding_attribute. It's used for improving result diversity. stringFilters: Optional. The list of string filters. """ embedding = _messages.MessageField('GoogleCloudAiplatformV1NearestNeighborQueryEmbedding', 1) entityId = _messages.StringField(2) neighborCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) numericFilters = _messages.MessageField('GoogleCloudAiplatformV1NearestNeighborQueryNumericFilter', 4, repeated=True) parameters = _messages.MessageField('GoogleCloudAiplatformV1NearestNeighborQueryParameters', 5) perCrowdingAttributeNeighborCount = _messages.IntegerField(6, variant=_messages.Variant.INT32) stringFilters = _messages.MessageField('GoogleCloudAiplatformV1NearestNeighborQueryStringFilter', 7, repeated=True) class GoogleCloudAiplatformV1NearestNeighborQueryEmbedding(_messages.Message): r"""The embedding vector. Fields: value: Optional. Individual value in the embedding. """ value = _messages.FloatField(1, repeated=True, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1NearestNeighborQueryNumericFilter(_messages.Message): r"""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 Enums: OpValueValuesEnum: Optional. This MUST be specified for queries and must NOT be specified for database points. Fields: name: Required. Column name in BigQuery that used as filters. op: Optional. This MUST be specified for queries and must NOT be specified for database points. valueDouble: double value type. valueFloat: float value type. valueInt: int value type. """ class OpValueValuesEnum(_messages.Enum): r"""Optional. This MUST be specified for queries and must NOT be specified for database points. Values: OPERATOR_UNSPECIFIED: Unspecified operator. LESS: Entities are eligible if their value is < the query's. LESS_EQUAL: Entities are eligible if their value is <= the query's. EQUAL: Entities are eligible if their value is == the query's. GREATER_EQUAL: Entities are eligible if their value is >= the query's. GREATER: Entities are eligible if their value is > the query's. NOT_EQUAL: Entities are eligible if their value is != the query's. """ OPERATOR_UNSPECIFIED = 0 LESS = 1 LESS_EQUAL = 2 EQUAL = 3 GREATER_EQUAL = 4 GREATER = 5 NOT_EQUAL = 6 name = _messages.StringField(1) op = _messages.EnumField('OpValueValuesEnum', 2) valueDouble = _messages.FloatField(3) valueFloat = _messages.FloatField(4, variant=_messages.Variant.FLOAT) valueInt = _messages.IntegerField(5) class GoogleCloudAiplatformV1NearestNeighborQueryParameters(_messages.Message): r"""Parameters that can be overrided in each query to tune query latency and recall. Fields: approximateNeighborCandidates: Optional. The number of neighbors to find via approximate search before exact reordering is performed; if set, this value must be > neighbor_count. leafNodesSearchFraction: Optional. The fraction of the number of leaves to search, set at query time allows user to tune search performance. This value increase result in both search accuracy and latency increase. The value should be between 0.0 and 1.0. """ approximateNeighborCandidates = _messages.IntegerField(1, variant=_messages.Variant.INT32) leafNodesSearchFraction = _messages.FloatField(2) class GoogleCloudAiplatformV1NearestNeighborQueryStringFilter(_messages.Message): r"""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. Fields: allowTokens: Optional. The allowed tokens. denyTokens: Optional. The denied tokens. name: Required. Column names in BigQuery that used as filters. """ allowTokens = _messages.StringField(1, repeated=True) denyTokens = _messages.StringField(2, repeated=True) name = _messages.StringField(3) class GoogleCloudAiplatformV1NearestNeighborSearchOperationMetadata(_messages.Message): r"""Runtime operation metadata with regard to Matching Engine Index. Fields: contentValidationStats: The validation stats of the content (per file) to be inserted or updated on the Matching Engine Index resource. Populated if contentsDeltaUri is provided as part of Index.metadata. Please note that, currently for those files that are broken or has unsupported file format, we will not have the stats for those files. dataBytesCount: The ingested data size in bytes. """ contentValidationStats = _messages.MessageField('GoogleCloudAiplatformV1NearestNeighborSearchOperationMetadataContentValidationStats', 1, repeated=True) dataBytesCount = _messages.IntegerField(2) class GoogleCloudAiplatformV1NearestNeighborSearchOperationMetadataContentValidationStats(_messages.Message): r"""A GoogleCloudAiplatformV1NearestNeighborSearchOperationMetadataContentVa lidationStats object. Fields: invalidRecordCount: Number of records in this file we skipped due to validate errors. invalidSparseRecordCount: Number of sparse records in this file we skipped due to validate errors. partialErrors: The detail information of the partial failures encountered for those invalid records that couldn't be parsed. Up to 50 partial errors will be reported. sourceGcsUri: Cloud Storage URI pointing to the original file in user's bucket. validRecordCount: Number of records in this file that were successfully processed. validSparseRecordCount: Number of sparse records in this file that were successfully processed. """ invalidRecordCount = _messages.IntegerField(1) invalidSparseRecordCount = _messages.IntegerField(2) partialErrors = _messages.MessageField('GoogleCloudAiplatformV1NearestNeighborSearchOperationMetadataRecordError', 3, repeated=True) sourceGcsUri = _messages.StringField(4) validRecordCount = _messages.IntegerField(5) validSparseRecordCount = _messages.IntegerField(6) class GoogleCloudAiplatformV1NearestNeighborSearchOperationMetadataRecordError(_messages.Message): r"""A GoogleCloudAiplatformV1NearestNeighborSearchOperationMetadataRecordError object. Enums: ErrorTypeValueValuesEnum: The error type of this record. Fields: embeddingId: Empty if the embedding id is failed to parse. errorMessage: A human-readable message that is shown to the user to help them fix the error. Note that this message may change from time to time, your code should check against error_type as the source of truth. errorType: The error type of this record. rawRecord: The original content of this record. sourceGcsUri: Cloud Storage URI pointing to the original file in user's bucket. """ class ErrorTypeValueValuesEnum(_messages.Enum): r"""The error type of this record. Values: ERROR_TYPE_UNSPECIFIED: Default, shall not be used. EMPTY_LINE: The record is empty. INVALID_JSON_SYNTAX: Invalid json format. INVALID_CSV_SYNTAX: Invalid csv format. INVALID_AVRO_SYNTAX: Invalid avro format. INVALID_EMBEDDING_ID: The embedding id is not valid. EMBEDDING_SIZE_MISMATCH: The size of the dense embedding vectors does not match with the specified dimension. NAMESPACE_MISSING: The `namespace` field is missing. PARSING_ERROR: Generic catch-all error. Only used for validation failure where the root cause cannot be easily retrieved programmatically. DUPLICATE_NAMESPACE: There are multiple restricts with the same `namespace` value. OP_IN_DATAPOINT: Numeric restrict has operator specified in datapoint. MULTIPLE_VALUES: Numeric restrict has multiple values specified. INVALID_NUMERIC_VALUE: Numeric restrict has invalid numeric value specified. INVALID_ENCODING: File is not in UTF_8 format. INVALID_SPARSE_DIMENSIONS: Error parsing sparse dimensions field. INVALID_TOKEN_VALUE: Token restrict value is invalid. INVALID_SPARSE_EMBEDDING: Invalid sparse embedding. INVALID_EMBEDDING: Invalid dense embedding. INVALID_EMBEDDING_METADATA: Invalid embedding metadata. EMBEDDING_METADATA_EXCEEDS_SIZE_LIMIT: Embedding metadata exceeds size limit. """ ERROR_TYPE_UNSPECIFIED = 0 EMPTY_LINE = 1 INVALID_JSON_SYNTAX = 2 INVALID_CSV_SYNTAX = 3 INVALID_AVRO_SYNTAX = 4 INVALID_EMBEDDING_ID = 5 EMBEDDING_SIZE_MISMATCH = 6 NAMESPACE_MISSING = 7 PARSING_ERROR = 8 DUPLICATE_NAMESPACE = 9 OP_IN_DATAPOINT = 10 MULTIPLE_VALUES = 11 INVALID_NUMERIC_VALUE = 12 INVALID_ENCODING = 13 INVALID_SPARSE_DIMENSIONS = 14 INVALID_TOKEN_VALUE = 15 INVALID_SPARSE_EMBEDDING = 16 INVALID_EMBEDDING = 17 INVALID_EMBEDDING_METADATA = 18 EMBEDDING_METADATA_EXCEEDS_SIZE_LIMIT = 19 embeddingId = _messages.StringField(1) errorMessage = _messages.StringField(2) errorType = _messages.EnumField('ErrorTypeValueValuesEnum', 3) rawRecord = _messages.StringField(4) sourceGcsUri = _messages.StringField(5) class GoogleCloudAiplatformV1NearestNeighbors(_messages.Message): r"""Nearest neighbors for one query. Fields: neighbors: All its neighbors. """ neighbors = _messages.MessageField('GoogleCloudAiplatformV1NearestNeighborsNeighbor', 1, repeated=True) class GoogleCloudAiplatformV1NearestNeighborsNeighbor(_messages.Message): r"""A neighbor of the query vector. Fields: distance: The distance between the neighbor and the query vector. entityId: The id of the similar entity. entityKeyValues: The attributes of the neighbor, e.g. filters, crowding and metadata Note that full entities are returned only when "return_full_entity" is set to true. Otherwise, only the "entity_id" and "distance" fields are populated. """ distance = _messages.FloatField(1) entityId = _messages.StringField(2) entityKeyValues = _messages.MessageField('GoogleCloudAiplatformV1FetchFeatureValuesResponse', 3) class GoogleCloudAiplatformV1Neighbor(_messages.Message): r"""Neighbors for example-based explanations. Fields: neighborDistance: Output only. The neighbor distance. neighborId: Output only. The neighbor id. """ neighborDistance = _messages.FloatField(1) neighborId = _messages.StringField(2) class GoogleCloudAiplatformV1NetworkSpec(_messages.Message): r"""Network spec. Fields: enableInternetAccess: Whether to enable public internet access. Default false. network: The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and- firewalls#networks) subnetwork: The name of the subnet that this instance is in. Format: `proj ects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id} ` """ enableInternetAccess = _messages.BooleanField(1) network = _messages.StringField(2) subnetwork = _messages.StringField(3) class GoogleCloudAiplatformV1NfsMount(_messages.Message): r"""Represents a mount configuration for Network File System (NFS) to mount. Fields: mountPoint: Required. Destination mount path. The NFS will be mounted for the user under /mnt/nfs/ path: Required. Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of `server:path` server: Required. IP address of the NFS server. """ mountPoint = _messages.StringField(1) path = _messages.StringField(2) server = _messages.StringField(3) class GoogleCloudAiplatformV1NotebookEucConfig(_messages.Message): r"""The euc configuration of NotebookRuntimeTemplate. Fields: bypassActasCheck: Output only. Whether ActAs check is bypassed for service account attached to the VM. If false, we need ActAs check for the default Compute Engine Service account. When a Runtime is created, a VM is allocated using Default Compute Engine Service Account. Any user requesting to use this Runtime requires Service Account User (ActAs) permission over this SA. If true, Runtime owner is using EUC and does not require the above permission as VM no longer use default Compute Engine SA, but a P4SA. eucDisabled: Input only. Whether EUC is disabled in this NotebookRuntimeTemplate. In proto3, the default value of a boolean is false. In this way, by default EUC will be enabled for NotebookRuntimeTemplate. """ bypassActasCheck = _messages.BooleanField(1) eucDisabled = _messages.BooleanField(2) class GoogleCloudAiplatformV1NotebookExecutionJob(_messages.Message): r"""NotebookExecutionJob represents an instance of a notebook execution. Enums: JobStateValueValuesEnum: Output only. The state of the NotebookExecutionJob. Messages: LabelsValue: The labels with user-defined metadata to organize NotebookExecutionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Fields: createTime: Output only. Timestamp when this NotebookExecutionJob was created. customEnvironmentSpec: The custom compute configuration for an execution job. dataformRepositorySource: The Dataform Repository pointing to a single file notebook repository. directNotebookSource: The contents of an input notebook file. displayName: The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. encryptionSpec: Customer-managed encryption key spec for the notebook execution job. This field is auto-populated if the NotebookRuntimeTemplate has an encryption spec. executionTimeout: Max running time of the execution job in seconds (default 86400s / 24 hrs). executionUser: The user email to run the execution as. Only supported by Colab runtimes. gcsNotebookSource: The Cloud Storage url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` gcsOutputUri: The Cloud Storage location to upload the result to. Format: `gs://bucket-name` jobState: Output only. The state of the NotebookExecutionJob. kernelName: The name of the kernel to use during notebook execution. If unset, the default kernel is used. labels: The labels with user-defined metadata to organize NotebookExecutionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. name: Output only. The resource name of this NotebookExecutionJob. Format: `projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_i d}` notebookRuntimeTemplateResourceName: The NotebookRuntimeTemplate to source compute configuration from. scheduleResourceName: The Schedule resource name if this job is triggered by one. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` serviceAccount: The service account to run the execution as. status: Output only. Populated when the NotebookExecutionJob is completed. When there is an error during notebook execution, the error details are populated. updateTime: Output only. Timestamp when this NotebookExecutionJob was most recently updated. workbenchRuntime: The Workbench runtime configuration to use for the notebook execution. """ class JobStateValueValuesEnum(_messages.Enum): r"""Output only. The state of the NotebookExecutionJob. Values: JOB_STATE_UNSPECIFIED: The job state is unspecified. JOB_STATE_QUEUED: The job has been just created or resumed and processing has not yet begun. JOB_STATE_PENDING: The service is preparing to run the job. JOB_STATE_RUNNING: The job is in progress. JOB_STATE_SUCCEEDED: The job completed successfully. JOB_STATE_FAILED: The job failed. JOB_STATE_CANCELLING: The job is being cancelled. From this state the job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. JOB_STATE_CANCELLED: The job has been cancelled. JOB_STATE_PAUSED: The job has been stopped, and can be resumed. JOB_STATE_EXPIRED: The job has expired. JOB_STATE_UPDATING: The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state. JOB_STATE_PARTIALLY_SUCCEEDED: The job is partially succeeded, some results may be missing due to errors. """ JOB_STATE_UNSPECIFIED = 0 JOB_STATE_QUEUED = 1 JOB_STATE_PENDING = 2 JOB_STATE_RUNNING = 3 JOB_STATE_SUCCEEDED = 4 JOB_STATE_FAILED = 5 JOB_STATE_CANCELLING = 6 JOB_STATE_CANCELLED = 7 JOB_STATE_PAUSED = 8 JOB_STATE_EXPIRED = 9 JOB_STATE_UPDATING = 10 JOB_STATE_PARTIALLY_SUCCEEDED = 11 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize NotebookExecutionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) customEnvironmentSpec = _messages.MessageField('GoogleCloudAiplatformV1NotebookExecutionJobCustomEnvironmentSpec', 2) dataformRepositorySource = _messages.MessageField('GoogleCloudAiplatformV1NotebookExecutionJobDataformRepositorySource', 3) directNotebookSource = _messages.MessageField('GoogleCloudAiplatformV1NotebookExecutionJobDirectNotebookSource', 4) displayName = _messages.StringField(5) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 6) executionTimeout = _messages.StringField(7) executionUser = _messages.StringField(8) gcsNotebookSource = _messages.MessageField('GoogleCloudAiplatformV1NotebookExecutionJobGcsNotebookSource', 9) gcsOutputUri = _messages.StringField(10) jobState = _messages.EnumField('JobStateValueValuesEnum', 11) kernelName = _messages.StringField(12) labels = _messages.MessageField('LabelsValue', 13) name = _messages.StringField(14) notebookRuntimeTemplateResourceName = _messages.StringField(15) scheduleResourceName = _messages.StringField(16) serviceAccount = _messages.StringField(17) status = _messages.MessageField('GoogleRpcStatus', 18) updateTime = _messages.StringField(19) workbenchRuntime = _messages.MessageField('GoogleCloudAiplatformV1NotebookExecutionJobWorkbenchRuntime', 20) class GoogleCloudAiplatformV1NotebookExecutionJobCustomEnvironmentSpec(_messages.Message): r"""Compute configuration to use for an execution job. Fields: machineSpec: The specification of a single machine for the execution job. networkSpec: The network configuration to use for the execution job. persistentDiskSpec: The specification of a persistent disk to attach for the execution job. """ machineSpec = _messages.MessageField('GoogleCloudAiplatformV1MachineSpec', 1) networkSpec = _messages.MessageField('GoogleCloudAiplatformV1NetworkSpec', 2) persistentDiskSpec = _messages.MessageField('GoogleCloudAiplatformV1PersistentDiskSpec', 3) class GoogleCloudAiplatformV1NotebookExecutionJobDataformRepositorySource(_messages.Message): r"""The Dataform Repository containing the input notebook. Fields: commitSha: The commit SHA to read repository with. If unset, the file will be read at HEAD. dataformRepositoryResourceName: The resource name of the Dataform Repository. Format: `projects/{project_id}/locations/{location}/reposito ries/{repository_id}` """ commitSha = _messages.StringField(1) dataformRepositoryResourceName = _messages.StringField(2) class GoogleCloudAiplatformV1NotebookExecutionJobDirectNotebookSource(_messages.Message): r"""The content of the input notebook in ipynb format. Fields: content: The base64-encoded contents of the input notebook file. """ content = _messages.BytesField(1) class GoogleCloudAiplatformV1NotebookExecutionJobGcsNotebookSource(_messages.Message): r"""The Cloud Storage uri for the input notebook. Fields: generation: The version of the Cloud Storage object to read. If unset, the current version of the object is read. See https://cloud.google.com/storage/docs/metadata#generation-number. uri: The Cloud Storage uri pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` """ generation = _messages.StringField(1) uri = _messages.StringField(2) class GoogleCloudAiplatformV1NotebookExecutionJobWorkbenchRuntime(_messages.Message): r"""Configuration for a Workbench Instances-based environment.""" class GoogleCloudAiplatformV1NotebookIdleShutdownConfig(_messages.Message): r"""The idle shutdown configuration of NotebookRuntimeTemplate, which contains the idle_timeout as required field. Fields: idleShutdownDisabled: Whether Idle Shutdown is disabled in this NotebookRuntimeTemplate. idleTimeout: Required. Duration is accurate to the second. In Notebook, Idle Timeout is accurate to minute so the range of idle_timeout (second) is: 10 * 60 ~ 1440 * 60. """ idleShutdownDisabled = _messages.BooleanField(1) idleTimeout = _messages.StringField(2) class GoogleCloudAiplatformV1NotebookRuntime(_messages.Message): r"""A runtime is a virtual machine allocated to a particular user for a particular Notebook file on temporary basis with lifetime. Default runtimes have a lifetime of 18 hours, while custom runtimes last for 6 months from their creation or last upgrade. Enums: HealthStateValueValuesEnum: Output only. The health state of the NotebookRuntime. NotebookRuntimeTypeValueValuesEnum: Output only. The type of the notebook runtime. RuntimeStateValueValuesEnum: Output only. The runtime (instance) state of the NotebookRuntime. Messages: LabelsValue: The labels with user-defined metadata to organize your NotebookRuntime. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one NotebookRuntime (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for NotebookRuntime: * "aiplatform.googleapis.com/notebook_runtime_gce_instance_id": output only, its value is the Compute Engine instance id. * "aiplatform.googleapis.com/colab_enterprise_entry_service": its value is either "bigquery" or "vertex"; if absent, it should be "vertex". This is to describe the entry service, either BigQuery or Vertex. Fields: createTime: Output only. Timestamp when this NotebookRuntime was created. dataPersistentDiskSpec: Output only. The specification of persistent disk attached to the notebook runtime as data disk storage. description: The description of the NotebookRuntime. displayName: Required. The display name of the NotebookRuntime. The name can be up to 128 characters long and can consist of any UTF-8 characters. encryptionSpec: Output only. Customer-managed encryption key spec for the notebook runtime. eucConfig: Output only. EUC configuration of the notebook runtime. expirationTime: Output only. Timestamp when this NotebookRuntime will be expired: 1. System Predefined NotebookRuntime: 24 hours after creation. After expiration, system predifined runtime will be deleted. 2. User created NotebookRuntime: 6 months after last upgrade. After expiration, user created runtime will be stopped and allowed for upgrade. healthState: Output only. The health state of the NotebookRuntime. idleShutdownConfig: Output only. The idle shutdown configuration of the notebook runtime. isUpgradable: Output only. Whether NotebookRuntime is upgradable. labels: The labels with user-defined metadata to organize your NotebookRuntime. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one NotebookRuntime (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for NotebookRuntime: * "aiplatform.googleapis.com/notebook_runtime_gce_instance_id": output only, its value is the Compute Engine instance id. * "aiplatform.googleapis.com/colab_enterprise_entry_service": its value is either "bigquery" or "vertex"; if absent, it should be "vertex". This is to describe the entry service, either BigQuery or Vertex. machineSpec: Output only. The specification of a single machine used by the notebook runtime. name: Output only. The resource name of the NotebookRuntime. networkSpec: Output only. Network spec of the notebook runtime. networkTags: Optional. The Compute Engine tags to add to runtime (see [Tagging instances](https://cloud.google.com/vpc/docs/add-remove- network-tags)). notebookRuntimeTemplateRef: Output only. The pointer to NotebookRuntimeTemplate this NotebookRuntime is created from. notebookRuntimeType: Output only. The type of the notebook runtime. proxyUri: Output only. The proxy endpoint used to access the NotebookRuntime. runtimeState: Output only. The runtime (instance) state of the NotebookRuntime. runtimeUser: Required. The user email of the NotebookRuntime. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. serviceAccount: Output only. Deprecated: This field is no longer used and the "Vertex AI Notebook Service Account" (service-PROJECT_NUMBER@gcp-sa- aiplatform-vm.iam.gserviceaccount.com) is used for the runtime workload identity. See https://cloud.google.com/iam/docs/service-agents#vertex- ai-notebook-service-account for more details. The service account that the NotebookRuntime workload runs as. shieldedVmConfig: Output only. Runtime Shielded VM spec. softwareConfig: Output only. Software config of the notebook runtime. updateTime: Output only. Timestamp when this NotebookRuntime was most recently updated. version: Output only. The VM os image version of NotebookRuntime. """ class HealthStateValueValuesEnum(_messages.Enum): r"""Output only. The health state of the NotebookRuntime. Values: HEALTH_STATE_UNSPECIFIED: Unspecified health state. HEALTHY: NotebookRuntime is in healthy state. Applies to ACTIVE state. UNHEALTHY: NotebookRuntime is in unhealthy state. Applies to ACTIVE state. """ HEALTH_STATE_UNSPECIFIED = 0 HEALTHY = 1 UNHEALTHY = 2 class NotebookRuntimeTypeValueValuesEnum(_messages.Enum): r"""Output only. The type of the notebook runtime. Values: NOTEBOOK_RUNTIME_TYPE_UNSPECIFIED: Unspecified notebook runtime type, NotebookRuntimeType will default to USER_DEFINED. USER_DEFINED: runtime or template with coustomized configurations from user. ONE_CLICK: runtime or template with system defined configurations. """ NOTEBOOK_RUNTIME_TYPE_UNSPECIFIED = 0 USER_DEFINED = 1 ONE_CLICK = 2 class RuntimeStateValueValuesEnum(_messages.Enum): r"""Output only. The runtime (instance) state of the NotebookRuntime. Values: RUNTIME_STATE_UNSPECIFIED: Unspecified runtime state. RUNNING: NotebookRuntime is in running state. BEING_STARTED: NotebookRuntime is in starting state. This is when the runtime is being started from a stopped state. BEING_STOPPED: NotebookRuntime is in stopping state. STOPPED: NotebookRuntime is in stopped state. BEING_UPGRADED: NotebookRuntime is in upgrading state. It is in the middle of upgrading process. ERROR: NotebookRuntime was unable to start/stop properly. INVALID: NotebookRuntime is in invalid state. Cannot be recovered. """ RUNTIME_STATE_UNSPECIFIED = 0 RUNNING = 1 BEING_STARTED = 2 BEING_STOPPED = 3 STOPPED = 4 BEING_UPGRADED = 5 ERROR = 6 INVALID = 7 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your NotebookRuntime. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one NotebookRuntime (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for NotebookRuntime: * "aiplatform.googleapis.com/notebook_runtime_gce_instance_id": output only, its value is the Compute Engine instance id. * "aiplatform.googleapis.com/colab_enterprise_entry_service": its value is either "bigquery" or "vertex"; if absent, it should be "vertex". This is to describe the entry service, either BigQuery or Vertex. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) dataPersistentDiskSpec = _messages.MessageField('GoogleCloudAiplatformV1PersistentDiskSpec', 2) description = _messages.StringField(3) displayName = _messages.StringField(4) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 5) eucConfig = _messages.MessageField('GoogleCloudAiplatformV1NotebookEucConfig', 6) expirationTime = _messages.StringField(7) healthState = _messages.EnumField('HealthStateValueValuesEnum', 8) idleShutdownConfig = _messages.MessageField('GoogleCloudAiplatformV1NotebookIdleShutdownConfig', 9) isUpgradable = _messages.BooleanField(10) labels = _messages.MessageField('LabelsValue', 11) machineSpec = _messages.MessageField('GoogleCloudAiplatformV1MachineSpec', 12) name = _messages.StringField(13) networkSpec = _messages.MessageField('GoogleCloudAiplatformV1NetworkSpec', 14) networkTags = _messages.StringField(15, repeated=True) notebookRuntimeTemplateRef = _messages.MessageField('GoogleCloudAiplatformV1NotebookRuntimeTemplateRef', 16) notebookRuntimeType = _messages.EnumField('NotebookRuntimeTypeValueValuesEnum', 17) proxyUri = _messages.StringField(18) runtimeState = _messages.EnumField('RuntimeStateValueValuesEnum', 19) runtimeUser = _messages.StringField(20) satisfiesPzi = _messages.BooleanField(21) satisfiesPzs = _messages.BooleanField(22) serviceAccount = _messages.StringField(23) shieldedVmConfig = _messages.MessageField('GoogleCloudAiplatformV1ShieldedVmConfig', 24) softwareConfig = _messages.MessageField('GoogleCloudAiplatformV1NotebookSoftwareConfig', 25) updateTime = _messages.StringField(26) version = _messages.StringField(27) class GoogleCloudAiplatformV1NotebookRuntimeTemplate(_messages.Message): r"""A template that specifies runtime configurations such as machine type, runtime version, network configurations, etc. Multiple runtimes can be created from a runtime template. Enums: NotebookRuntimeTypeValueValuesEnum: Optional. Immutable. The type of the notebook runtime template. Messages: LabelsValue: The labels with user-defined metadata to organize the NotebookRuntimeTemplates. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Fields: createTime: Output only. Timestamp when this NotebookRuntimeTemplate was created. dataPersistentDiskSpec: Optional. The specification of persistent disk attached to the runtime as data disk storage. description: The description of the NotebookRuntimeTemplate. displayName: Required. The display name of the NotebookRuntimeTemplate. The name can be up to 128 characters long and can consist of any UTF-8 characters. encryptionSpec: Customer-managed encryption key spec for the notebook runtime. etag: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. eucConfig: EUC configuration of the NotebookRuntimeTemplate. idleShutdownConfig: The idle shutdown configuration of NotebookRuntimeTemplate. This config will only be set when idle shutdown is enabled. isDefault: Output only. Deprecated: This field has no behavior. Use notebook_runtime_type = 'ONE_CLICK' instead. The default template to use if not specified. labels: The labels with user-defined metadata to organize the NotebookRuntimeTemplates. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. machineSpec: Optional. Immutable. The specification of a single machine for the template. name: The resource name of the NotebookRuntimeTemplate. networkSpec: Optional. Network spec. networkTags: Optional. The Compute Engine tags to add to runtime (see [Tagging instances](https://cloud.google.com/vpc/docs/add-remove- network-tags)). notebookRuntimeType: Optional. Immutable. The type of the notebook runtime template. serviceAccount: Deprecated: This field is ignored and the "Vertex AI Notebook Service Account" (service-PROJECT_NUMBER@gcp-sa-aiplatform- vm.iam.gserviceaccount.com) is used for the runtime workload identity. See https://cloud.google.com/iam/docs/service-agents#vertex-ai-notebook- service-account for more details. For NotebookExecutionJob, use NotebookExecutionJob.service_account instead. The service account that the runtime workload runs as. You can use any service account within the same project, but you must have the service account user permission to use the instance. If not specified, the [Compute Engine default service account](https://cloud.google.com/compute/docs/access/service- accounts#default_service_account) is used. shieldedVmConfig: Optional. Immutable. Runtime Shielded VM spec. softwareConfig: Optional. The notebook software configuration of the notebook runtime. updateTime: Output only. Timestamp when this NotebookRuntimeTemplate was most recently updated. """ class NotebookRuntimeTypeValueValuesEnum(_messages.Enum): r"""Optional. Immutable. The type of the notebook runtime template. Values: NOTEBOOK_RUNTIME_TYPE_UNSPECIFIED: Unspecified notebook runtime type, NotebookRuntimeType will default to USER_DEFINED. USER_DEFINED: runtime or template with coustomized configurations from user. ONE_CLICK: runtime or template with system defined configurations. """ NOTEBOOK_RUNTIME_TYPE_UNSPECIFIED = 0 USER_DEFINED = 1 ONE_CLICK = 2 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize the NotebookRuntimeTemplates. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) dataPersistentDiskSpec = _messages.MessageField('GoogleCloudAiplatformV1PersistentDiskSpec', 2) description = _messages.StringField(3) displayName = _messages.StringField(4) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 5) etag = _messages.StringField(6) eucConfig = _messages.MessageField('GoogleCloudAiplatformV1NotebookEucConfig', 7) idleShutdownConfig = _messages.MessageField('GoogleCloudAiplatformV1NotebookIdleShutdownConfig', 8) isDefault = _messages.BooleanField(9) labels = _messages.MessageField('LabelsValue', 10) machineSpec = _messages.MessageField('GoogleCloudAiplatformV1MachineSpec', 11) name = _messages.StringField(12) networkSpec = _messages.MessageField('GoogleCloudAiplatformV1NetworkSpec', 13) networkTags = _messages.StringField(14, repeated=True) notebookRuntimeType = _messages.EnumField('NotebookRuntimeTypeValueValuesEnum', 15) serviceAccount = _messages.StringField(16) shieldedVmConfig = _messages.MessageField('GoogleCloudAiplatformV1ShieldedVmConfig', 17) softwareConfig = _messages.MessageField('GoogleCloudAiplatformV1NotebookSoftwareConfig', 18) updateTime = _messages.StringField(19) class GoogleCloudAiplatformV1NotebookRuntimeTemplateRef(_messages.Message): r"""Points to a NotebookRuntimeTemplateRef. Fields: notebookRuntimeTemplate: Immutable. A resource name of the NotebookRuntimeTemplate. """ notebookRuntimeTemplate = _messages.StringField(1) class GoogleCloudAiplatformV1NotebookSoftwareConfig(_messages.Message): r"""Notebook Software Config. This is passed to the backend when user makes software configurations in UI. Fields: env: Optional. Environment variables to be passed to the container. Maximum limit is 100. """ env = _messages.MessageField('GoogleCloudAiplatformV1EnvVar', 1, repeated=True) class GoogleCloudAiplatformV1PSCAutomationConfig(_messages.Message): r"""PSC config that is used to automatically create PSC endpoints in the user projects. Enums: StateValueValuesEnum: Output only. The state of the PSC service automation. Fields: errorMessage: Output only. Error message if the PSC service automation failed. forwardingRule: Output only. Forwarding rule created by the PSC service automation. ipAddress: Output only. IP address rule created by the PSC service automation. network: Required. The full name of the Google Compute Engine [network](https://cloud.google.com/compute/docs/networks-and- firewalls#networks). [Format](https://cloud.google.com/compute/docs/refe rence/rest/v1/networks/get): `projects/{project}/global/networks/{network}`. projectId: Required. Project id used to create forwarding rule. state: Output only. The state of the PSC service automation. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The state of the PSC service automation. Values: PSC_AUTOMATION_STATE_UNSPECIFIED: Should not be used. PSC_AUTOMATION_STATE_SUCCESSFUL: The PSC service automation is successful. PSC_AUTOMATION_STATE_FAILED: The PSC service automation has failed. """ PSC_AUTOMATION_STATE_UNSPECIFIED = 0 PSC_AUTOMATION_STATE_SUCCESSFUL = 1 PSC_AUTOMATION_STATE_FAILED = 2 errorMessage = _messages.StringField(1) forwardingRule = _messages.StringField(2) ipAddress = _messages.StringField(3) network = _messages.StringField(4) projectId = _messages.StringField(5) state = _messages.EnumField('StateValueValuesEnum', 6) class GoogleCloudAiplatformV1PairwiseMetricInput(_messages.Message): r"""Input for pairwise metric. Fields: instance: Required. Pairwise metric instance. metricSpec: Required. Spec for pairwise metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1PairwiseMetricInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1PairwiseMetricSpec', 2) class GoogleCloudAiplatformV1PairwiseMetricInstance(_messages.Message): r"""Pairwise metric instance. Usually one instance corresponds to one row in an evaluation dataset. Fields: jsonInstance: Instance specified as a json string. String key-value pairs are expected in the json_instance to render PairwiseMetricSpec.instance_prompt_template. """ jsonInstance = _messages.StringField(1) class GoogleCloudAiplatformV1PairwiseMetricResult(_messages.Message): r"""Spec for pairwise metric result. Enums: PairwiseChoiceValueValuesEnum: Output only. Pairwise metric choice. Fields: explanation: Output only. Explanation for pairwise metric score. pairwiseChoice: Output only. Pairwise metric choice. """ class PairwiseChoiceValueValuesEnum(_messages.Enum): r"""Output only. Pairwise metric choice. Values: PAIRWISE_CHOICE_UNSPECIFIED: Unspecified prediction choice. BASELINE: Baseline prediction wins CANDIDATE: Candidate prediction wins TIE: Winner cannot be determined """ PAIRWISE_CHOICE_UNSPECIFIED = 0 BASELINE = 1 CANDIDATE = 2 TIE = 3 explanation = _messages.StringField(1) pairwiseChoice = _messages.EnumField('PairwiseChoiceValueValuesEnum', 2) class GoogleCloudAiplatformV1PairwiseMetricSpec(_messages.Message): r"""Spec for pairwise metric. Fields: metricPromptTemplate: Required. Metric prompt template for pairwise metric. """ metricPromptTemplate = _messages.StringField(1) class GoogleCloudAiplatformV1PairwiseQuestionAnsweringQualityInput(_messages.Message): r"""Input for pairwise question answering quality metric. Fields: instance: Required. Pairwise question answering quality instance. metricSpec: Required. Spec for pairwise question answering quality score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1PairwiseQuestionAnsweringQualityInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1PairwiseQuestionAnsweringQualitySpec', 2) class GoogleCloudAiplatformV1PairwiseQuestionAnsweringQualityInstance(_messages.Message): r"""Spec for pairwise question answering quality instance. Fields: baselinePrediction: Required. Output of the baseline model. context: Required. Text to answer the question. instruction: Required. Question Answering prompt for LLM. prediction: Required. Output of the candidate model. reference: Optional. Ground truth used to compare against the prediction. """ baselinePrediction = _messages.StringField(1) context = _messages.StringField(2) instruction = _messages.StringField(3) prediction = _messages.StringField(4) reference = _messages.StringField(5) class GoogleCloudAiplatformV1PairwiseQuestionAnsweringQualityResult(_messages.Message): r"""Spec for pairwise question answering quality result. Enums: PairwiseChoiceValueValuesEnum: Output only. Pairwise question answering prediction choice. Fields: confidence: Output only. Confidence for question answering quality score. explanation: Output only. Explanation for question answering quality score. pairwiseChoice: Output only. Pairwise question answering prediction choice. """ class PairwiseChoiceValueValuesEnum(_messages.Enum): r"""Output only. Pairwise question answering prediction choice. Values: PAIRWISE_CHOICE_UNSPECIFIED: Unspecified prediction choice. BASELINE: Baseline prediction wins CANDIDATE: Candidate prediction wins TIE: Winner cannot be determined """ PAIRWISE_CHOICE_UNSPECIFIED = 0 BASELINE = 1 CANDIDATE = 2 TIE = 3 confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) pairwiseChoice = _messages.EnumField('PairwiseChoiceValueValuesEnum', 3) class GoogleCloudAiplatformV1PairwiseQuestionAnsweringQualitySpec(_messages.Message): r"""Spec for pairwise question answering quality score metric. Fields: useReference: Optional. Whether to use instance.reference to compute question answering quality. version: Optional. Which version to use for evaluation. """ useReference = _messages.BooleanField(1) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1PairwiseSummarizationQualityInput(_messages.Message): r"""Input for pairwise summarization quality metric. Fields: instance: Required. Pairwise summarization quality instance. metricSpec: Required. Spec for pairwise summarization quality score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1PairwiseSummarizationQualityInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1PairwiseSummarizationQualitySpec', 2) class GoogleCloudAiplatformV1PairwiseSummarizationQualityInstance(_messages.Message): r"""Spec for pairwise summarization quality instance. Fields: baselinePrediction: Required. Output of the baseline model. context: Required. Text to be summarized. instruction: Required. Summarization prompt for LLM. prediction: Required. Output of the candidate model. reference: Optional. Ground truth used to compare against the prediction. """ baselinePrediction = _messages.StringField(1) context = _messages.StringField(2) instruction = _messages.StringField(3) prediction = _messages.StringField(4) reference = _messages.StringField(5) class GoogleCloudAiplatformV1PairwiseSummarizationQualityResult(_messages.Message): r"""Spec for pairwise summarization quality result. Enums: PairwiseChoiceValueValuesEnum: Output only. Pairwise summarization prediction choice. Fields: confidence: Output only. Confidence for summarization quality score. explanation: Output only. Explanation for summarization quality score. pairwiseChoice: Output only. Pairwise summarization prediction choice. """ class PairwiseChoiceValueValuesEnum(_messages.Enum): r"""Output only. Pairwise summarization prediction choice. Values: PAIRWISE_CHOICE_UNSPECIFIED: Unspecified prediction choice. BASELINE: Baseline prediction wins CANDIDATE: Candidate prediction wins TIE: Winner cannot be determined """ PAIRWISE_CHOICE_UNSPECIFIED = 0 BASELINE = 1 CANDIDATE = 2 TIE = 3 confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) pairwiseChoice = _messages.EnumField('PairwiseChoiceValueValuesEnum', 3) class GoogleCloudAiplatformV1PairwiseSummarizationQualitySpec(_messages.Message): r"""Spec for pairwise summarization quality score metric. Fields: useReference: Optional. Whether to use instance.reference to compute pairwise summarization quality. version: Optional. Which version to use for evaluation. """ useReference = _messages.BooleanField(1) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1Part(_messages.Message): r"""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. Fields: codeExecutionResult: Optional. Result of executing the [ExecutableCode]. executableCode: Optional. Code generated by the model that is meant to be executed. fileData: Optional. URI based data. functionCall: Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. functionResponse: Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model. inlineData: Optional. Inlined bytes data. text: Optional. Text part (can be code). thought: Optional. Indicates if the part is thought from the model. thoughtSignature: Optional. An opaque signature for the thought so it can be reused in subsequent requests. videoMetadata: Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. """ codeExecutionResult = _messages.MessageField('GoogleCloudAiplatformV1CodeExecutionResult', 1) executableCode = _messages.MessageField('GoogleCloudAiplatformV1ExecutableCode', 2) fileData = _messages.MessageField('GoogleCloudAiplatformV1FileData', 3) functionCall = _messages.MessageField('GoogleCloudAiplatformV1FunctionCall', 4) functionResponse = _messages.MessageField('GoogleCloudAiplatformV1FunctionResponse', 5) inlineData = _messages.MessageField('GoogleCloudAiplatformV1Blob', 6) text = _messages.StringField(7) thought = _messages.BooleanField(8) thoughtSignature = _messages.BytesField(9) videoMetadata = _messages.MessageField('GoogleCloudAiplatformV1VideoMetadata', 10) class GoogleCloudAiplatformV1PauseModelDeploymentMonitoringJobRequest(_messages.Message): r"""Request message for JobService.PauseModelDeploymentMonitoringJob.""" class GoogleCloudAiplatformV1PauseScheduleRequest(_messages.Message): r"""Request message for ScheduleService.PauseSchedule.""" class GoogleCloudAiplatformV1PersistentDiskSpec(_messages.Message): r"""Represents the spec of persistent disk options. Fields: diskSizeGb: Size in GB of the disk (default is 100GB). diskType: Type of the disk (default is "pd-standard"). Valid values: "pd- ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk) """ diskSizeGb = _messages.IntegerField(1) diskType = _messages.StringField(2) class GoogleCloudAiplatformV1PersistentResource(_messages.Message): r"""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. Enums: StateValueValuesEnum: Output only. The detailed state of a Study. Messages: LabelsValue: Optional. The labels with user-defined metadata to organize PersistentResource. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Fields: createTime: Output only. Time when the PersistentResource was created. displayName: Optional. The display name of the PersistentResource. The name can be up to 128 characters long and can consist of any UTF-8 characters. encryptionSpec: Optional. Customer-managed encryption key spec for a PersistentResource. If set, this PersistentResource and all sub- resources of this PersistentResource will be secured by this key. error: Output only. Only populated when persistent resource's state is `STOPPING` or `ERROR`. labels: Optional. The labels with user-defined metadata to organize PersistentResource. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. name: Immutable. Resource name of a PersistentResource. network: Optional. The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to peered with Vertex AI to host the persistent resources. For example, `projects/12345/global/networks/myVPC`. [Format](/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. To specify this field, you must have already [configured VPC Network Peering for Vertex AI](https://cloud.google.com/vertex- ai/docs/general/vpc-peering). If this field is left unspecified, the resources aren't peered with any network. pscInterfaceConfig: Optional. Configuration for PSC-I for PersistentResource. reservedIpRanges: Optional. A list of names for the reserved IP ranges under the VPC network that can be used for this persistent resource. If set, we will deploy the persistent resource within the provided IP ranges. Otherwise, the persistent resource is deployed to any IP ranges under the provided VPC network. Example: ['vertex-ai-ip-range']. resourcePools: Required. The spec of the pools of different resources. resourceRuntime: Output only. Runtime information of the Persistent Resource. resourceRuntimeSpec: Optional. Persistent Resource runtime spec. For example, used for Ray cluster configuration. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. startTime: Output only. Time when the PersistentResource for the first time entered the `RUNNING` state. state: Output only. The detailed state of a Study. updateTime: Output only. Time when the PersistentResource was most recently updated. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of a Study. Values: STATE_UNSPECIFIED: Not set. PROVISIONING: The PROVISIONING state indicates the persistent resources is being created. RUNNING: The RUNNING state indicates the persistent resource is healthy and fully usable. STOPPING: The STOPPING state indicates the persistent resource is being deleted. ERROR: The ERROR state indicates the persistent resource may be unusable. Details can be found in the `error` field. REBOOTING: The REBOOTING state indicates the persistent resource is being rebooted (PR is not available right now but is expected to be ready again later). UPDATING: The UPDATING state indicates the persistent resource is being updated. """ STATE_UNSPECIFIED = 0 PROVISIONING = 1 RUNNING = 2 STOPPING = 3 ERROR = 4 REBOOTING = 5 UPDATING = 6 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels with user-defined metadata to organize PersistentResource. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) displayName = _messages.StringField(2) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 3) error = _messages.MessageField('GoogleRpcStatus', 4) labels = _messages.MessageField('LabelsValue', 5) name = _messages.StringField(6) network = _messages.StringField(7) pscInterfaceConfig = _messages.MessageField('GoogleCloudAiplatformV1PscInterfaceConfig', 8) reservedIpRanges = _messages.StringField(9, repeated=True) resourcePools = _messages.MessageField('GoogleCloudAiplatformV1ResourcePool', 10, repeated=True) resourceRuntime = _messages.MessageField('GoogleCloudAiplatformV1ResourceRuntime', 11) resourceRuntimeSpec = _messages.MessageField('GoogleCloudAiplatformV1ResourceRuntimeSpec', 12) satisfiesPzi = _messages.BooleanField(13) satisfiesPzs = _messages.BooleanField(14) startTime = _messages.StringField(15) state = _messages.EnumField('StateValueValuesEnum', 16) updateTime = _messages.StringField(17) class GoogleCloudAiplatformV1PipelineJob(_messages.Message): r"""An instance of a machine learning PipelineJob. Enums: StateValueValuesEnum: Output only. The detailed state of the job. Messages: LabelsValue: The labels with user-defined metadata to organize PipelineJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Note there is some reserved label key for Vertex AI Pipelines. - `vertex-ai-pipelines-run-billing-id`, user set value will get overrided. PipelineSpecValue: The spec of the pipeline. Fields: createTime: Output only. Pipeline creation time. displayName: The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters. encryptionSpec: Customer-managed encryption key spec for a pipelineJob. If set, this PipelineJob and all of its sub-resources will be secured by this key. endTime: Output only. Pipeline end time. error: Output only. The error that occurred during pipeline execution. Only populated when the pipeline's state is FAILED or CANCELLED. jobDetail: Output only. The details of pipeline run. Not available in the list view. labels: The labels with user-defined metadata to organize PipelineJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Note there is some reserved label key for Vertex AI Pipelines. - `vertex-ai- pipelines-run-billing-id`, user set value will get overrided. name: Output only. The resource name of the PipelineJob. network: The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Pipeline Job's workload should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. Pipeline job will apply the network configuration to the Google Cloud resources being launched, if applied, such as Vertex AI Training or Dataflow job. If left unspecified, the workload is not peered with any network. pipelineSpec: The spec of the pipeline. preflightValidations: Optional. Whether to do component level validations before job creation. pscInterfaceConfig: Optional. Configuration for PSC-I for PipelineJob. reservedIpRanges: A list of names for the reserved ip ranges under the VPC network that can be used for this Pipeline Job's workload. If set, we will deploy the Pipeline Job's workload within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range']. runtimeConfig: Runtime config of the pipeline. scheduleName: Output only. The schedule resource name. Only returned if the Pipeline is created by Schedule API. serviceAccount: The service account that the pipeline workload runs as. If not specified, the Compute Engine default service account in the project will be used. See https://cloud.google.com/compute/docs/access/service- accounts#default_service_account Users starting the pipeline must have the `iam.serviceAccounts.actAs` permission on this service account. startTime: Output only. Pipeline start time. state: Output only. The detailed state of the job. templateMetadata: Output only. Pipeline template metadata. Will fill up fields if PipelineJob.template_uri is from supported template registry. templateUri: A template uri from where the PipelineJob.pipeline_spec, if empty, will be downloaded. Currently, only uri from Vertex Template Registry & Gallery is supported. Reference to https://cloud.google.com/vertex-ai/docs/pipelines/create-pipeline- template. updateTime: Output only. Timestamp when this PipelineJob was most recently updated. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of the job. Values: PIPELINE_STATE_UNSPECIFIED: The pipeline state is unspecified. PIPELINE_STATE_QUEUED: The pipeline has been created or resumed, and processing has not yet begun. PIPELINE_STATE_PENDING: The service is preparing to run the pipeline. PIPELINE_STATE_RUNNING: The pipeline is in progress. PIPELINE_STATE_SUCCEEDED: The pipeline completed successfully. PIPELINE_STATE_FAILED: The pipeline failed. PIPELINE_STATE_CANCELLING: The pipeline is being cancelled. From this state, the pipeline may only go to either PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED. PIPELINE_STATE_CANCELLED: The pipeline has been cancelled. PIPELINE_STATE_PAUSED: The pipeline has been stopped, and can be resumed. """ PIPELINE_STATE_UNSPECIFIED = 0 PIPELINE_STATE_QUEUED = 1 PIPELINE_STATE_PENDING = 2 PIPELINE_STATE_RUNNING = 3 PIPELINE_STATE_SUCCEEDED = 4 PIPELINE_STATE_FAILED = 5 PIPELINE_STATE_CANCELLING = 6 PIPELINE_STATE_CANCELLED = 7 PIPELINE_STATE_PAUSED = 8 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize PipelineJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Note there is some reserved label key for Vertex AI Pipelines. - `vertex-ai-pipelines-run-billing-id`, user set value will get overrided. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class PipelineSpecValue(_messages.Message): r"""The spec of the pipeline. Messages: AdditionalProperty: An additional property for a PipelineSpecValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a PipelineSpecValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) displayName = _messages.StringField(2) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 3) endTime = _messages.StringField(4) error = _messages.MessageField('GoogleRpcStatus', 5) jobDetail = _messages.MessageField('GoogleCloudAiplatformV1PipelineJobDetail', 6) labels = _messages.MessageField('LabelsValue', 7) name = _messages.StringField(8) network = _messages.StringField(9) pipelineSpec = _messages.MessageField('PipelineSpecValue', 10) preflightValidations = _messages.BooleanField(11) pscInterfaceConfig = _messages.MessageField('GoogleCloudAiplatformV1PscInterfaceConfig', 12) reservedIpRanges = _messages.StringField(13, repeated=True) runtimeConfig = _messages.MessageField('GoogleCloudAiplatformV1PipelineJobRuntimeConfig', 14) scheduleName = _messages.StringField(15) serviceAccount = _messages.StringField(16) startTime = _messages.StringField(17) state = _messages.EnumField('StateValueValuesEnum', 18) templateMetadata = _messages.MessageField('GoogleCloudAiplatformV1PipelineTemplateMetadata', 19) templateUri = _messages.StringField(20) updateTime = _messages.StringField(21) class GoogleCloudAiplatformV1PipelineJobDetail(_messages.Message): r"""The runtime detail of PipelineJob. Fields: pipelineContext: Output only. The context of the pipeline. pipelineRunContext: Output only. The context of the current pipeline run. taskDetails: Output only. The runtime details of the tasks under the pipeline. """ pipelineContext = _messages.MessageField('GoogleCloudAiplatformV1Context', 1) pipelineRunContext = _messages.MessageField('GoogleCloudAiplatformV1Context', 2) taskDetails = _messages.MessageField('GoogleCloudAiplatformV1PipelineTaskDetail', 3, repeated=True) class GoogleCloudAiplatformV1PipelineJobRuntimeConfig(_messages.Message): r"""The runtime config of a PipelineJob. Enums: FailurePolicyValueValuesEnum: 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. Messages: InputArtifactsValue: The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact. ParameterValuesValue: The runtime parameters of the PipelineJob. The parameters will be passed into PipelineJob.pipeline_spec to replace the placeholders at runtime. This field is used by pipelines built using `PipelineJob.pipeline_spec.schema_version` 2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL. ParametersValue: Deprecated. Use RuntimeConfig.parameter_values instead. The runtime parameters of the PipelineJob. The parameters will be passed into PipelineJob.pipeline_spec to replace the placeholders at runtime. This field is used by pipelines built using `PipelineJob.pipeline_spec.schema_version` 2.0.0 or lower, such as pipelines built using Kubeflow Pipelines SDK 1.8 or lower. Fields: failurePolicy: 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. gcsOutputDirectory: Required. A path in a Cloud Storage bucket, which will be treated as the root output directory of the pipeline. It is used by the system to generate the paths of output artifacts. The artifact paths are generated with a sub-path pattern `{job_id}/{task_id}/{output_key}` under the specified output directory. The service account specified in this pipeline must have the `storage.objects.get` and `storage.objects.create` permissions for this bucket. inputArtifacts: The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact. parameterValues: The runtime parameters of the PipelineJob. The parameters will be passed into PipelineJob.pipeline_spec to replace the placeholders at runtime. This field is used by pipelines built using `PipelineJob.pipeline_spec.schema_version` 2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL. parameters: Deprecated. Use RuntimeConfig.parameter_values instead. The runtime parameters of the PipelineJob. The parameters will be passed into PipelineJob.pipeline_spec to replace the placeholders at runtime. This field is used by pipelines built using `PipelineJob.pipeline_spec.schema_version` 2.0.0 or lower, such as pipelines built using Kubeflow Pipelines SDK 1.8 or lower. """ class FailurePolicyValueValuesEnum(_messages.Enum): r"""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. Values: PIPELINE_FAILURE_POLICY_UNSPECIFIED: Default value, and follows fail slow behavior. PIPELINE_FAILURE_POLICY_FAIL_SLOW: Indicates that the pipeline should continue to run until all possible tasks have been scheduled and completed. PIPELINE_FAILURE_POLICY_FAIL_FAST: Indicates that the pipeline should stop scheduling new tasks after a task has failed. """ PIPELINE_FAILURE_POLICY_UNSPECIFIED = 0 PIPELINE_FAILURE_POLICY_FAIL_SLOW = 1 PIPELINE_FAILURE_POLICY_FAIL_FAST = 2 @encoding.MapUnrecognizedFields('additionalProperties') class InputArtifactsValue(_messages.Message): r"""The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact. Messages: AdditionalProperty: An additional property for a InputArtifactsValue object. Fields: additionalProperties: Additional properties of type InputArtifactsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a InputArtifactsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1PipelineJobRuntimeConfigInputArtifact attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1PipelineJobRuntimeConfigInputArtifact', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class ParameterValuesValue(_messages.Message): r"""The runtime parameters of the PipelineJob. The parameters will be passed into PipelineJob.pipeline_spec to replace the placeholders at runtime. This field is used by pipelines built using `PipelineJob.pipeline_spec.schema_version` 2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL. Messages: AdditionalProperty: An additional property for a ParameterValuesValue object. Fields: additionalProperties: Additional properties of type ParameterValuesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ParameterValuesValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class ParametersValue(_messages.Message): r"""Deprecated. Use RuntimeConfig.parameter_values instead. The runtime parameters of the PipelineJob. The parameters will be passed into PipelineJob.pipeline_spec to replace the placeholders at runtime. This field is used by pipelines built using `PipelineJob.pipeline_spec.schema_version` 2.0.0 or lower, such as pipelines built using Kubeflow Pipelines SDK 1.8 or lower. Messages: AdditionalProperty: An additional property for a ParametersValue object. Fields: additionalProperties: Additional properties of type ParametersValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ParametersValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1Value attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1Value', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) failurePolicy = _messages.EnumField('FailurePolicyValueValuesEnum', 1) gcsOutputDirectory = _messages.StringField(2) inputArtifacts = _messages.MessageField('InputArtifactsValue', 3) parameterValues = _messages.MessageField('ParameterValuesValue', 4) parameters = _messages.MessageField('ParametersValue', 5) class GoogleCloudAiplatformV1PipelineJobRuntimeConfigInputArtifact(_messages.Message): r"""The type of an input artifact. Fields: artifactId: Artifact resource id from MLMD. Which is the last portion of an artifact resource name: `projects/{project}/locations/{location}/meta dataStores/default/artifacts/{artifact_id}`. The artifact must stay within the same project, location and default metadatastore as the pipeline. """ artifactId = _messages.StringField(1) class GoogleCloudAiplatformV1PipelineTaskDetail(_messages.Message): r"""The runtime detail of a task execution. Enums: StateValueValuesEnum: Output only. State of the task. Messages: InputsValue: Output only. The runtime input artifacts of the task. OutputsValue: Output only. The runtime output artifacts of the task. Fields: createTime: Output only. Task create time. endTime: Output only. Task end time. error: Output only. The error that occurred during task execution. Only populated when the task's state is FAILED or CANCELLED. execution: Output only. The execution metadata of the task. executorDetail: Output only. The detailed execution info. inputs: Output only. The runtime input artifacts of the task. outputs: Output only. The runtime output artifacts of the task. parentTaskId: Output only. The id of the parent task if the task is within a component scope. Empty if the task is at the root level. pipelineTaskStatus: Output only. A list of task status. This field keeps a record of task status evolving over time. startTime: Output only. Task start time. state: Output only. State of the task. taskId: Output only. The system generated ID of the task. taskName: Output only. The user specified name of the task that is defined in pipeline_spec. taskUniqueName: Output only. The unique name of a task. This field is used by rerun pipeline job. Console UI and Vertex AI SDK will support triggering pipeline job reruns. The name is constructed by concatenating all the parent tasks name with the task name. For example, if a task named "child_task" has a parent task named "parent_task_1" and parent task 1 has a parent task named "parent_task_2", the task unique name will be "parent_task_2.parent_task_1.child_task". """ class StateValueValuesEnum(_messages.Enum): r"""Output only. State of the task. Values: STATE_UNSPECIFIED: Unspecified. PENDING: Specifies pending state for the task. RUNNING: Specifies task is being executed. SUCCEEDED: Specifies task completed successfully. CANCEL_PENDING: Specifies Task cancel is in pending state. CANCELLING: Specifies task is being cancelled. CANCELLED: Specifies task was cancelled. FAILED: Specifies task failed. SKIPPED: Specifies task was skipped due to cache hit. NOT_TRIGGERED: Specifies that the task was not triggered because the task's trigger policy is not satisfied. The trigger policy is specified in the `condition` field of PipelineJob.pipeline_spec. """ STATE_UNSPECIFIED = 0 PENDING = 1 RUNNING = 2 SUCCEEDED = 3 CANCEL_PENDING = 4 CANCELLING = 5 CANCELLED = 6 FAILED = 7 SKIPPED = 8 NOT_TRIGGERED = 9 @encoding.MapUnrecognizedFields('additionalProperties') class InputsValue(_messages.Message): r"""Output only. The runtime input artifacts of the task. Messages: AdditionalProperty: An additional property for a InputsValue object. Fields: additionalProperties: Additional properties of type InputsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a InputsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1PipelineTaskDetailArtifactList attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1PipelineTaskDetailArtifactList', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class OutputsValue(_messages.Message): r"""Output only. The runtime output artifacts of the task. Messages: AdditionalProperty: An additional property for a OutputsValue object. Fields: additionalProperties: Additional properties of type OutputsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a OutputsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1PipelineTaskDetailArtifactList attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1PipelineTaskDetailArtifactList', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) endTime = _messages.StringField(2) error = _messages.MessageField('GoogleRpcStatus', 3) execution = _messages.MessageField('GoogleCloudAiplatformV1Execution', 4) executorDetail = _messages.MessageField('GoogleCloudAiplatformV1PipelineTaskExecutorDetail', 5) inputs = _messages.MessageField('InputsValue', 6) outputs = _messages.MessageField('OutputsValue', 7) parentTaskId = _messages.IntegerField(8) pipelineTaskStatus = _messages.MessageField('GoogleCloudAiplatformV1PipelineTaskDetailPipelineTaskStatus', 9, repeated=True) startTime = _messages.StringField(10) state = _messages.EnumField('StateValueValuesEnum', 11) taskId = _messages.IntegerField(12) taskName = _messages.StringField(13) taskUniqueName = _messages.StringField(14) class GoogleCloudAiplatformV1PipelineTaskDetailArtifactList(_messages.Message): r"""A list of artifact metadata. Fields: artifacts: Output only. A list of artifact metadata. """ artifacts = _messages.MessageField('GoogleCloudAiplatformV1Artifact', 1, repeated=True) class GoogleCloudAiplatformV1PipelineTaskDetailPipelineTaskStatus(_messages.Message): r"""A single record of the task status. Enums: StateValueValuesEnum: Output only. The state of the task. Fields: error: Output only. The error that occurred during the state. May be set when the state is any of the non-final state (PENDING/RUNNING/CANCELLING) or FAILED state. If the state is FAILED, the error here is final and not going to be retried. If the state is a non-final state, the error indicates a system-error being retried. state: Output only. The state of the task. updateTime: Output only. Update time of this status. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The state of the task. Values: STATE_UNSPECIFIED: Unspecified. PENDING: Specifies pending state for the task. RUNNING: Specifies task is being executed. SUCCEEDED: Specifies task completed successfully. CANCEL_PENDING: Specifies Task cancel is in pending state. CANCELLING: Specifies task is being cancelled. CANCELLED: Specifies task was cancelled. FAILED: Specifies task failed. SKIPPED: Specifies task was skipped due to cache hit. NOT_TRIGGERED: Specifies that the task was not triggered because the task's trigger policy is not satisfied. The trigger policy is specified in the `condition` field of PipelineJob.pipeline_spec. """ STATE_UNSPECIFIED = 0 PENDING = 1 RUNNING = 2 SUCCEEDED = 3 CANCEL_PENDING = 4 CANCELLING = 5 CANCELLED = 6 FAILED = 7 SKIPPED = 8 NOT_TRIGGERED = 9 error = _messages.MessageField('GoogleRpcStatus', 1) state = _messages.EnumField('StateValueValuesEnum', 2) updateTime = _messages.StringField(3) class GoogleCloudAiplatformV1PipelineTaskExecutorDetail(_messages.Message): r"""The runtime detail of a pipeline executor. Fields: containerDetail: Output only. The detailed info for a container executor. customJobDetail: Output only. The detailed info for a custom job executor. """ containerDetail = _messages.MessageField('GoogleCloudAiplatformV1PipelineTaskExecutorDetailContainerDetail', 1) customJobDetail = _messages.MessageField('GoogleCloudAiplatformV1PipelineTaskExecutorDetailCustomJobDetail', 2) class GoogleCloudAiplatformV1PipelineTaskExecutorDetailContainerDetail(_messages.Message): r"""The detail of a container execution. It contains the job names of the lifecycle of a container execution. Fields: failedMainJobs: Output only. The names of the previously failed CustomJob for the main container executions. The list includes the all attempts in chronological order. failedPreCachingCheckJobs: Output only. The names of the previously failed CustomJob for the pre-caching-check container executions. This job will be available if the PipelineJob.pipeline_spec specifies the `pre_caching_check` hook in the lifecycle events. The list includes the all attempts in chronological order. mainJob: Output only. The name of the CustomJob for the main container execution. preCachingCheckJob: Output only. The name of the CustomJob for the pre- caching-check container execution. This job will be available if the PipelineJob.pipeline_spec specifies the `pre_caching_check` hook in the lifecycle events. """ failedMainJobs = _messages.StringField(1, repeated=True) failedPreCachingCheckJobs = _messages.StringField(2, repeated=True) mainJob = _messages.StringField(3) preCachingCheckJob = _messages.StringField(4) class GoogleCloudAiplatformV1PipelineTaskExecutorDetailCustomJobDetail(_messages.Message): r"""The detailed info for a custom job executor. Fields: failedJobs: Output only. The names of the previously failed CustomJob. The list includes the all attempts in chronological order. job: Output only. The name of the CustomJob. """ failedJobs = _messages.StringField(1, repeated=True) job = _messages.StringField(2) class GoogleCloudAiplatformV1PipelineTemplateMetadata(_messages.Message): r"""Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry. Fields: version: The version_name in artifact registry. Will always be presented in output if the PipelineJob.template_uri is from supported template registry. Format is "sha256:abcdef123456...". """ version = _messages.StringField(1) class GoogleCloudAiplatformV1PointwiseMetricInput(_messages.Message): r"""Input for pointwise metric. Fields: instance: Required. Pointwise metric instance. metricSpec: Required. Spec for pointwise metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1PointwiseMetricInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1PointwiseMetricSpec', 2) class GoogleCloudAiplatformV1PointwiseMetricInstance(_messages.Message): r"""Pointwise metric instance. Usually one instance corresponds to one row in an evaluation dataset. Fields: jsonInstance: Instance specified as a json string. String key-value pairs are expected in the json_instance to render PointwiseMetricSpec.instance_prompt_template. """ jsonInstance = _messages.StringField(1) class GoogleCloudAiplatformV1PointwiseMetricResult(_messages.Message): r"""Spec for pointwise metric result. Fields: explanation: Output only. Explanation for pointwise metric score. score: Output only. Pointwise metric score. """ explanation = _messages.StringField(1) score = _messages.FloatField(2, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1PointwiseMetricSpec(_messages.Message): r"""Spec for pointwise metric. Fields: metricPromptTemplate: Required. Metric prompt template for pointwise metric. """ metricPromptTemplate = _messages.StringField(1) class GoogleCloudAiplatformV1Port(_messages.Message): r"""Represents a network port in a container. Fields: containerPort: The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive. """ containerPort = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1PredefinedSplit(_messages.Message): r"""Assigns input data to training, validation, and test sets based on the value of a provided key. Supported only for tabular Datasets. Fields: key: Required. The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {`training`, `validation`, `test`}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. """ key = _messages.StringField(1) class GoogleCloudAiplatformV1PredictLongRunningRequest(_messages.Message): r"""Request message for PredictionService.PredictLongRunning. Fields: instances: Required. The instances that are the input to the prediction call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the prediction call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri. parameters: Optional. The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri. """ instances = _messages.MessageField('extra_types.JsonValue', 1, repeated=True) parameters = _messages.MessageField('extra_types.JsonValue', 2) class GoogleCloudAiplatformV1PredictRequest(_messages.Message): r"""Request message for PredictionService.Predict. Fields: instances: Required. The instances that are the input to the prediction call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the prediction call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri. parameters: The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri. """ instances = _messages.MessageField('extra_types.JsonValue', 1, repeated=True) parameters = _messages.MessageField('extra_types.JsonValue', 2) class GoogleCloudAiplatformV1PredictRequestResponseLoggingConfig(_messages.Message): r"""Configuration for logging request-response to a BigQuery table. Fields: bigqueryDestination: BigQuery table for logging. If only given a project, a new dataset will be created with name `logging__` where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores). If no table name is given, a new table will be created with name `request_response_logging` enabled: If logging is enabled or not. samplingRate: Percentage of requests to be logged, expressed as a fraction in range(0,1]. """ bigqueryDestination = _messages.MessageField('GoogleCloudAiplatformV1BigQueryDestination', 1) enabled = _messages.BooleanField(2) samplingRate = _messages.FloatField(3) class GoogleCloudAiplatformV1PredictResponse(_messages.Message): r"""Response message for PredictionService.Predict. Fields: deployedModelId: ID of the Endpoint's DeployedModel that served this prediction. metadata: Output only. Request-level metadata returned by the model. The metadata type will be dependent upon the model implementation. model: Output only. The resource name of the Model which is deployed as the DeployedModel that this prediction hits. modelDisplayName: Output only. The display name of the Model which is deployed as the DeployedModel that this prediction hits. modelVersionId: Output only. The version ID of the Model which is deployed as the DeployedModel that this prediction hits. predictions: The predictions that are the output of the predictions call. The schema of any single prediction may be specified via Endpoint's DeployedModels' Model's PredictSchemata's prediction_schema_uri. """ deployedModelId = _messages.StringField(1) metadata = _messages.MessageField('extra_types.JsonValue', 2) model = _messages.StringField(3) modelDisplayName = _messages.StringField(4) modelVersionId = _messages.StringField(5) predictions = _messages.MessageField('extra_types.JsonValue', 6, repeated=True) class GoogleCloudAiplatformV1PredictSchemata(_messages.Message): r"""Contains the schemata used in Model's predictions and explanations via PredictionService.Predict, PredictionService.Explain and BatchPredictionJob. Fields: instanceSchemaUri: Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. parametersSchemaUri: Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. predictionSchemaUri: Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI- Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. """ instanceSchemaUri = _messages.StringField(1) parametersSchemaUri = _messages.StringField(2) predictionSchemaUri = _messages.StringField(3) class GoogleCloudAiplatformV1Presets(_messages.Message): r"""Preset configuration for example-based explanations Enums: ModalityValueValuesEnum: The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type. QueryValueValuesEnum: Preset option controlling parameters for speed- precision trade-off when querying for examples. If omitted, defaults to `PRECISE`. Fields: modality: The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type. query: Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to `PRECISE`. """ class ModalityValueValuesEnum(_messages.Enum): r"""The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type. Values: MODALITY_UNSPECIFIED: Should not be set. Added as a recommended best practice for enums IMAGE: IMAGE modality TEXT: TEXT modality TABULAR: TABULAR modality """ MODALITY_UNSPECIFIED = 0 IMAGE = 1 TEXT = 2 TABULAR = 3 class QueryValueValuesEnum(_messages.Enum): r"""Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to `PRECISE`. Values: PRECISE: More precise neighbors as a trade-off against slower response. FAST: Faster response as a trade-off against less precise neighbors. """ PRECISE = 0 FAST = 1 modality = _messages.EnumField('ModalityValueValuesEnum', 1) query = _messages.EnumField('QueryValueValuesEnum', 2) class GoogleCloudAiplatformV1PrivateEndpoints(_messages.Message): r"""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. Fields: explainHttpUri: Output only. Http(s) path to send explain requests. healthHttpUri: Output only. Http(s) path to send health check requests. predictHttpUri: Output only. Http(s) path to send prediction requests. serviceAttachment: Output only. The name of the service attachment resource. Populated if private service connect is enabled. """ explainHttpUri = _messages.StringField(1) healthHttpUri = _messages.StringField(2) predictHttpUri = _messages.StringField(3) serviceAttachment = _messages.StringField(4) class GoogleCloudAiplatformV1PrivateServiceConnectConfig(_messages.Message): r"""Represents configuration for private service connect. Fields: enablePrivateServiceConnect: Required. If true, expose the IndexEndpoint via private service connect. projectAllowlist: A list of Projects from which the forwarding rule will target the service attachment. pscAutomationConfigs: Optional. List of projects and networks where the PSC endpoints will be created. This field is used by Online Inference(Prediction) only. serviceAttachment: Output only. The name of the generated service attachment resource. This is only populated if the endpoint is deployed with PrivateServiceConnect. """ enablePrivateServiceConnect = _messages.BooleanField(1) projectAllowlist = _messages.StringField(2, repeated=True) pscAutomationConfigs = _messages.MessageField('GoogleCloudAiplatformV1PSCAutomationConfig', 3, repeated=True) serviceAttachment = _messages.StringField(4) class GoogleCloudAiplatformV1Probe(_messages.Message): r"""Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic. Fields: exec_: ExecAction probes the health of a container by executing a command. failureThreshold: Number of consecutive failures before the probe is considered failed. Defaults to 3. Minimum value is 1. Maps to Kubernetes probe argument 'failureThreshold'. grpc: GrpcAction probes the health of a container by sending a gRPC request. httpGet: HttpGetAction probes the health of a container by sending an HTTP GET request. initialDelaySeconds: Number of seconds to wait before starting the probe. Defaults to 0. Minimum value is 0. Maps to Kubernetes probe argument 'initialDelaySeconds'. periodSeconds: How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'. successThreshold: Number of consecutive successes before the probe is considered successful. Defaults to 1. Minimum value is 1. Maps to Kubernetes probe argument 'successThreshold'. tcpSocket: TcpSocketAction probes the health of a container by opening a TCP socket connection. timeoutSeconds: Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'. """ exec_ = _messages.MessageField('GoogleCloudAiplatformV1ProbeExecAction', 1) failureThreshold = _messages.IntegerField(2, variant=_messages.Variant.INT32) grpc = _messages.MessageField('GoogleCloudAiplatformV1ProbeGrpcAction', 3) httpGet = _messages.MessageField('GoogleCloudAiplatformV1ProbeHttpGetAction', 4) initialDelaySeconds = _messages.IntegerField(5, variant=_messages.Variant.INT32) periodSeconds = _messages.IntegerField(6, variant=_messages.Variant.INT32) successThreshold = _messages.IntegerField(7, variant=_messages.Variant.INT32) tcpSocket = _messages.MessageField('GoogleCloudAiplatformV1ProbeTcpSocketAction', 8) timeoutSeconds = _messages.IntegerField(9, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1ProbeExecAction(_messages.Message): r"""ExecAction specifies a command to execute. Fields: command: Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy. """ command = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1ProbeGrpcAction(_messages.Message): r"""GrpcAction checks the health of a container using a gRPC service. Fields: port: Port number of the gRPC service. Number must be in the range 1 to 65535. service: Service is the name of the service to place in the gRPC HealthCheckRequest. See https://github.com/grpc/grpc/blob/master/doc/health-checking.md. If this is not specified, the default behavior is defined by gRPC. """ port = _messages.IntegerField(1, variant=_messages.Variant.INT32) service = _messages.StringField(2) class GoogleCloudAiplatformV1ProbeHttpGetAction(_messages.Message): r"""HttpGetAction describes an action based on HTTP Get requests. Fields: host: Host name to connect to, defaults to the model serving container's IP. You probably want to set "Host" in httpHeaders instead. httpHeaders: Custom headers to set in the request. HTTP allows repeated headers. path: Path to access on the HTTP server. port: Number of the port to access on the container. Number must be in the range 1 to 65535. scheme: Scheme to use for connecting to the host. Defaults to HTTP. Acceptable values are "HTTP" or "HTTPS". """ host = _messages.StringField(1) httpHeaders = _messages.MessageField('GoogleCloudAiplatformV1ProbeHttpHeader', 2, repeated=True) path = _messages.StringField(3) port = _messages.IntegerField(4, variant=_messages.Variant.INT32) scheme = _messages.StringField(5) class GoogleCloudAiplatformV1ProbeHttpHeader(_messages.Message): r"""HttpHeader describes a custom header to be used in HTTP probes Fields: name: The header field name. This will be canonicalized upon output, so case-variant names will be understood as the same header. value: The header field value """ name = _messages.StringField(1) value = _messages.StringField(2) class GoogleCloudAiplatformV1ProbeTcpSocketAction(_messages.Message): r"""TcpSocketAction probes the health of a container by opening a TCP socket connection. Fields: host: Optional: Host name to connect to, defaults to the model serving container's IP. port: Number of the port to access on the container. Number must be in the range 1 to 65535. """ host = _messages.StringField(1) port = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1PscAutomatedEndpoints(_messages.Message): r"""PscAutomatedEndpoints defines the output of the forwarding rule automatically created by each PscAutomationConfig. Fields: matchAddress: Ip Address created by the automated forwarding rule. network: Corresponding network in pscAutomationConfigs. projectId: Corresponding project_id in pscAutomationConfigs """ matchAddress = _messages.StringField(1) network = _messages.StringField(2) projectId = _messages.StringField(3) class GoogleCloudAiplatformV1PscInterfaceConfig(_messages.Message): r"""Configuration for PSC-I. Fields: dnsPeeringConfigs: Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project. networkAttachment: Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network- attachments#create-network-attachments). This field is only used for resources using PSC-I. """ dnsPeeringConfigs = _messages.MessageField('GoogleCloudAiplatformV1DnsPeeringConfig', 1, repeated=True) networkAttachment = _messages.StringField(2) class GoogleCloudAiplatformV1PublisherModel(_messages.Message): r"""A Model Garden Publisher Model. Enums: LaunchStageValueValuesEnum: Optional. Indicates the launch stage of the model. OpenSourceCategoryValueValuesEnum: Required. Indicates the open source category of the publisher model. VersionStateValueValuesEnum: Optional. Indicates the state of the model version. Fields: frameworks: Optional. Additional information about the model's Frameworks. launchStage: Optional. Indicates the launch stage of the model. name: Output only. The resource name of the PublisherModel. openSourceCategory: Required. Indicates the open source category of the publisher model. predictSchemata: Optional. The schemata that describes formats of the PublisherModel's predictions and explanations as given and returned via PredictionService.Predict. publisherModelTemplate: Optional. Output only. Immutable. Used to indicate this model has a publisher model and provide the template of the publisher model resource name. supportedActions: Optional. Supported call-to-action options. versionId: Output only. Immutable. The version ID of the PublisherModel. A new version is committed when a new model version is uploaded under an existing model id. It is an auto-incrementing decimal number in string representation. versionState: Optional. Indicates the state of the model version. """ class LaunchStageValueValuesEnum(_messages.Enum): r"""Optional. Indicates the launch stage of the model. Values: LAUNCH_STAGE_UNSPECIFIED: The model launch stage is unspecified. EXPERIMENTAL: Used to indicate the PublisherModel is at Experimental launch stage, available to a small set of customers. PRIVATE_PREVIEW: Used to indicate the PublisherModel is at Private Preview launch stage, only available to a small set of customers, although a larger set of customers than an Experimental launch. Previews are the first launch stage used to get feedback from customers. PUBLIC_PREVIEW: Used to indicate the PublisherModel is at Public Preview launch stage, available to all customers, although not supported for production workloads. GA: Used to indicate the PublisherModel is at GA launch stage, available to all customers and ready for production workload. """ LAUNCH_STAGE_UNSPECIFIED = 0 EXPERIMENTAL = 1 PRIVATE_PREVIEW = 2 PUBLIC_PREVIEW = 3 GA = 4 class OpenSourceCategoryValueValuesEnum(_messages.Enum): r"""Required. Indicates the open source category of the publisher model. Values: OPEN_SOURCE_CATEGORY_UNSPECIFIED: The open source category is unspecified, which should not be used. PROPRIETARY: Used to indicate the PublisherModel is not open sourced. GOOGLE_OWNED_OSS_WITH_GOOGLE_CHECKPOINT: Used to indicate the PublisherModel is a Google-owned open source model w/ Google checkpoint. THIRD_PARTY_OWNED_OSS_WITH_GOOGLE_CHECKPOINT: Used to indicate the PublisherModel is a 3p-owned open source model w/ Google checkpoint. GOOGLE_OWNED_OSS: Used to indicate the PublisherModel is a Google-owned pure open source model. THIRD_PARTY_OWNED_OSS: Used to indicate the PublisherModel is a 3p-owned pure open source model. """ OPEN_SOURCE_CATEGORY_UNSPECIFIED = 0 PROPRIETARY = 1 GOOGLE_OWNED_OSS_WITH_GOOGLE_CHECKPOINT = 2 THIRD_PARTY_OWNED_OSS_WITH_GOOGLE_CHECKPOINT = 3 GOOGLE_OWNED_OSS = 4 THIRD_PARTY_OWNED_OSS = 5 class VersionStateValueValuesEnum(_messages.Enum): r"""Optional. Indicates the state of the model version. Values: VERSION_STATE_UNSPECIFIED: The version state is unspecified. VERSION_STATE_STABLE: Used to indicate the version is stable. VERSION_STATE_UNSTABLE: Used to indicate the version is unstable. """ VERSION_STATE_UNSPECIFIED = 0 VERSION_STATE_STABLE = 1 VERSION_STATE_UNSTABLE = 2 frameworks = _messages.StringField(1, repeated=True) launchStage = _messages.EnumField('LaunchStageValueValuesEnum', 2) name = _messages.StringField(3) openSourceCategory = _messages.EnumField('OpenSourceCategoryValueValuesEnum', 4) predictSchemata = _messages.MessageField('GoogleCloudAiplatformV1PredictSchemata', 5) publisherModelTemplate = _messages.StringField(6) supportedActions = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToAction', 7) versionId = _messages.StringField(8) versionState = _messages.EnumField('VersionStateValueValuesEnum', 9) class GoogleCloudAiplatformV1PublisherModelCallToAction(_messages.Message): r"""Actions could take on this Publisher Model. Fields: createApplication: Optional. Create application using the PublisherModel. deploy: Optional. Deploy the PublisherModel to Vertex Endpoint. deployGke: Optional. Deploy PublisherModel to Google Kubernetes Engine. openEvaluationPipeline: Optional. Open evaluation pipeline of the PublisherModel. openFineTuningPipeline: Optional. Open fine-tuning pipeline of the PublisherModel. openFineTuningPipelines: Optional. Open fine-tuning pipelines of the PublisherModel. openGenerationAiStudio: Optional. Open in Generation AI Studio. openGenie: Optional. Open Genie / Playground. openNotebook: Optional. Open notebook of the PublisherModel. openNotebooks: Optional. Open notebooks of the PublisherModel. openPromptTuningPipeline: Optional. Open prompt-tuning pipeline of the PublisherModel. requestAccess: Optional. Request for access. viewRestApi: Optional. To view Rest API docs. """ createApplication = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionRegionalResourceReferences', 1) deploy = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionDeploy', 2) deployGke = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionDeployGke', 3) openEvaluationPipeline = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionRegionalResourceReferences', 4) openFineTuningPipeline = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionRegionalResourceReferences', 5) openFineTuningPipelines = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionOpenFineTuningPipelines', 6) openGenerationAiStudio = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionRegionalResourceReferences', 7) openGenie = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionRegionalResourceReferences', 8) openNotebook = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionRegionalResourceReferences', 9) openNotebooks = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionOpenNotebooks', 10) openPromptTuningPipeline = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionRegionalResourceReferences', 11) requestAccess = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionRegionalResourceReferences', 12) viewRestApi = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionViewRestApi', 13) class GoogleCloudAiplatformV1PublisherModelCallToActionDeploy(_messages.Message): r"""Model metadata that is needed for UploadModel or DeployModel/CreateEndpoint requests. Fields: artifactUri: Optional. The path to the directory containing the Model artifact and any of its supporting files. automaticResources: A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. containerSpec: Optional. The specification of the container that is to be used when deploying this Model in Vertex AI. Not present for Large Models. dedicatedResources: A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration. deployMetadata: Optional. Metadata information about this deployment config. deployTaskName: Optional. The name of the deploy task (e.g., "text to image generation"). largeModelReference: Optional. Large model reference. When this is set, model_artifact_spec is not needed. modelDisplayName: Optional. Default model display name. publicArtifactUri: Optional. The signed URI for ephemeral Cloud Storage access to model artifact. sharedResources: The resource name of the shared DeploymentResourcePool to deploy on. Format: `projects/{project}/locations/{location}/deploymentRe sourcePools/{deployment_resource_pool}` title: Required. The title of the regional resource reference. """ artifactUri = _messages.StringField(1) automaticResources = _messages.MessageField('GoogleCloudAiplatformV1AutomaticResources', 2) containerSpec = _messages.MessageField('GoogleCloudAiplatformV1ModelContainerSpec', 3) dedicatedResources = _messages.MessageField('GoogleCloudAiplatformV1DedicatedResources', 4) deployMetadata = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionDeployDeployMetadata', 5) deployTaskName = _messages.StringField(6) largeModelReference = _messages.MessageField('GoogleCloudAiplatformV1LargeModelReference', 7) modelDisplayName = _messages.StringField(8) publicArtifactUri = _messages.StringField(9) sharedResources = _messages.StringField(10) title = _messages.StringField(11) class GoogleCloudAiplatformV1PublisherModelCallToActionDeployDeployMetadata(_messages.Message): r"""Metadata information about the deployment for managing deployment config. Messages: LabelsValue: Optional. Labels for the deployment config. For managing deployment config like verifying, source of deployment config, etc. Fields: labels: Optional. Labels for the deployment config. For managing deployment config like verifying, source of deployment config, etc. sampleRequest: Optional. Sample request for deployed endpoint. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. Labels for the deployment config. For managing deployment config like verifying, source of deployment config, etc. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) labels = _messages.MessageField('LabelsValue', 1) sampleRequest = _messages.StringField(2) class GoogleCloudAiplatformV1PublisherModelCallToActionDeployGke(_messages.Message): r"""Configurations for PublisherModel GKE deployment Fields: gkeYamlConfigs: Optional. GKE deployment configuration in yaml format. """ gkeYamlConfigs = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1PublisherModelCallToActionOpenFineTuningPipelines(_messages.Message): r"""Open fine tuning pipelines. Fields: fineTuningPipelines: Required. Regional resource references to fine tuning pipelines. """ fineTuningPipelines = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionRegionalResourceReferences', 1, repeated=True) class GoogleCloudAiplatformV1PublisherModelCallToActionOpenNotebooks(_messages.Message): r"""Open notebooks. Fields: notebooks: Required. Regional resource references to notebooks. """ notebooks = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelCallToActionRegionalResourceReferences', 1, repeated=True) class GoogleCloudAiplatformV1PublisherModelCallToActionRegionalResourceReferences(_messages.Message): r"""The regional resource name or the URI. Key is region, e.g., us-central1, europe-west2, global, etc.. Messages: ReferencesValue: Required. Fields: references: Required. resourceDescription: Optional. Description of the resource. resourceTitle: Optional. Title of the resource. resourceUseCase: Optional. Use case (CUJ) of the resource. title: Required. """ @encoding.MapUnrecognizedFields('additionalProperties') class ReferencesValue(_messages.Message): r"""Required. Messages: AdditionalProperty: An additional property for a ReferencesValue object. Fields: additionalProperties: Additional properties of type ReferencesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ReferencesValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1PublisherModelResourceReference attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelResourceReference', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) references = _messages.MessageField('ReferencesValue', 1) resourceDescription = _messages.StringField(2) resourceTitle = _messages.StringField(3) resourceUseCase = _messages.StringField(4) title = _messages.StringField(5) class GoogleCloudAiplatformV1PublisherModelCallToActionViewRestApi(_messages.Message): r"""Rest API docs. Fields: documentations: Required. title: Required. The title of the view rest API. """ documentations = _messages.MessageField('GoogleCloudAiplatformV1PublisherModelDocumentation', 1, repeated=True) title = _messages.StringField(2) class GoogleCloudAiplatformV1PublisherModelDocumentation(_messages.Message): r"""A named piece of documentation. Fields: content: Required. Content of this piece of document (in Markdown format). title: Required. E.g., OVERVIEW, USE CASES, DOCUMENTATION, SDK & SAMPLES, JAVA, NODE.JS, etc.. """ content = _messages.StringField(1) title = _messages.StringField(2) class GoogleCloudAiplatformV1PublisherModelResourceReference(_messages.Message): r"""Reference to a resource. Fields: description: Description of the resource. resourceName: The resource name of the Google Cloud resource. uri: The URI of the resource. useCase: Use case (CUJ) of the resource. """ description = _messages.StringField(1) resourceName = _messages.StringField(2) uri = _messages.StringField(3) useCase = _messages.StringField(4) class GoogleCloudAiplatformV1PurgeArtifactsMetadata(_messages.Message): r"""Details of operations that perform MetadataService.PurgeArtifacts. Fields: genericMetadata: Operation metadata for purging Artifacts. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1PurgeArtifactsRequest(_messages.Message): r"""Request message for MetadataService.PurgeArtifacts. Fields: filter: Required. A required filter matching the Artifacts to be purged. E.g., `update_time <= 2020-11-19T11:30:00-04:00`. force: Optional. Flag to indicate to actually perform the purge. If `force` is set to false, the method will return a sample of Artifact names that would be deleted. """ filter = _messages.StringField(1) force = _messages.BooleanField(2) class GoogleCloudAiplatformV1PurgeArtifactsResponse(_messages.Message): r"""Response message for MetadataService.PurgeArtifacts. Fields: purgeCount: The number of Artifacts that this request deleted (or, if `force` is false, the number of Artifacts that will be deleted). This can be an estimate. purgeSample: A sample of the Artifact names that will be deleted. Only populated if `force` is set to false. The maximum number of samples is 100 (it is possible to return fewer). """ purgeCount = _messages.IntegerField(1) purgeSample = _messages.StringField(2, repeated=True) class GoogleCloudAiplatformV1PurgeContextsMetadata(_messages.Message): r"""Details of operations that perform MetadataService.PurgeContexts. Fields: genericMetadata: Operation metadata for purging Contexts. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1PurgeContextsRequest(_messages.Message): r"""Request message for MetadataService.PurgeContexts. Fields: filter: Required. A required filter matching the Contexts to be purged. E.g., `update_time <= 2020-11-19T11:30:00-04:00`. force: Optional. Flag to indicate to actually perform the purge. If `force` is set to false, the method will return a sample of Context names that would be deleted. """ filter = _messages.StringField(1) force = _messages.BooleanField(2) class GoogleCloudAiplatformV1PurgeContextsResponse(_messages.Message): r"""Response message for MetadataService.PurgeContexts. Fields: purgeCount: The number of Contexts that this request deleted (or, if `force` is false, the number of Contexts that will be deleted). This can be an estimate. purgeSample: A sample of the Context names that will be deleted. Only populated if `force` is set to false. The maximum number of samples is 100 (it is possible to return fewer). """ purgeCount = _messages.IntegerField(1) purgeSample = _messages.StringField(2, repeated=True) class GoogleCloudAiplatformV1PurgeExecutionsMetadata(_messages.Message): r"""Details of operations that perform MetadataService.PurgeExecutions. Fields: genericMetadata: Operation metadata for purging Executions. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1PurgeExecutionsRequest(_messages.Message): r"""Request message for MetadataService.PurgeExecutions. Fields: filter: Required. A required filter matching the Executions to be purged. E.g., `update_time <= 2020-11-19T11:30:00-04:00`. force: Optional. Flag to indicate to actually perform the purge. If `force` is set to false, the method will return a sample of Execution names that would be deleted. """ filter = _messages.StringField(1) force = _messages.BooleanField(2) class GoogleCloudAiplatformV1PurgeExecutionsResponse(_messages.Message): r"""Response message for MetadataService.PurgeExecutions. Fields: purgeCount: The number of Executions that this request deleted (or, if `force` is false, the number of Executions that will be deleted). This can be an estimate. purgeSample: A sample of the Execution names that will be deleted. Only populated if `force` is set to false. The maximum number of samples is 100 (it is possible to return fewer). """ purgeCount = _messages.IntegerField(1) purgeSample = _messages.StringField(2, repeated=True) class GoogleCloudAiplatformV1PythonPackageSpec(_messages.Message): r"""The spec of a Python packaged code. Fields: args: Command line arguments to be passed to the Python task. env: Environment variables to be passed to the python module. Maximum limit is 100. executorImageUri: Required. The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of [pre-built containers for training](https://cloud.google.com/vertex-ai/docs/training/pre-built- containers). You must use an image from this list. packageUris: Required. The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100. pythonModule: Required. The Python module name to run after installing the packages. """ args = _messages.StringField(1, repeated=True) env = _messages.MessageField('GoogleCloudAiplatformV1EnvVar', 2, repeated=True) executorImageUri = _messages.StringField(3) packageUris = _messages.StringField(4, repeated=True) pythonModule = _messages.StringField(5) class GoogleCloudAiplatformV1QueryDeployedModelsResponse(_messages.Message): r"""Response message for QueryDeployedModels method. Fields: deployedModelRefs: References to the DeployedModels that share the specified deploymentResourcePool. deployedModels: DEPRECATED Use deployed_model_refs instead. nextPageToken: A token, which can be sent as `page_token` to retrieve the next page. If this field is omitted, there are no subsequent pages. totalDeployedModelCount: The total number of DeployedModels on this DeploymentResourcePool. totalEndpointCount: The total number of Endpoints that have DeployedModels on this DeploymentResourcePool. """ deployedModelRefs = _messages.MessageField('GoogleCloudAiplatformV1DeployedModelRef', 1, repeated=True) deployedModels = _messages.MessageField('GoogleCloudAiplatformV1DeployedModel', 2, repeated=True) nextPageToken = _messages.StringField(3) totalDeployedModelCount = _messages.IntegerField(4, variant=_messages.Variant.INT32) totalEndpointCount = _messages.IntegerField(5, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1QueryReasoningEngineRequest(_messages.Message): r"""Request message for ReasoningEngineExecutionService.Query. Messages: InputValue: Optional. Input content provided by users in JSON object format. Examples include text query, function calling parameters, media bytes, etc. Fields: classMethod: Optional. Class method to be used for the query. It is optional and defaults to "query" if unspecified. input: Optional. Input content provided by users in JSON object format. Examples include text query, function calling parameters, media bytes, etc. """ @encoding.MapUnrecognizedFields('additionalProperties') class InputValue(_messages.Message): r"""Optional. Input content provided by users in JSON object format. Examples include text query, function calling parameters, media bytes, etc. Messages: AdditionalProperty: An additional property for a InputValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a InputValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) classMethod = _messages.StringField(1) input = _messages.MessageField('InputValue', 2) class GoogleCloudAiplatformV1QueryReasoningEngineResponse(_messages.Message): r"""Response message for ReasoningEngineExecutionService.Query Fields: output: Response provided by users in JSON object format. """ output = _messages.MessageField('extra_types.JsonValue', 1) class GoogleCloudAiplatformV1QuestionAnsweringCorrectnessInput(_messages.Message): r"""Input for question answering correctness metric. Fields: instance: Required. Question answering correctness instance. metricSpec: Required. Spec for question answering correctness score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringCorrectnessInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringCorrectnessSpec', 2) class GoogleCloudAiplatformV1QuestionAnsweringCorrectnessInstance(_messages.Message): r"""Spec for question answering correctness instance. Fields: context: Optional. Text provided as context to answer the question. instruction: Required. The question asked and other instruction in the inference prompt. prediction: Required. Output of the evaluated model. reference: Optional. Ground truth used to compare against the prediction. """ context = _messages.StringField(1) instruction = _messages.StringField(2) prediction = _messages.StringField(3) reference = _messages.StringField(4) class GoogleCloudAiplatformV1QuestionAnsweringCorrectnessResult(_messages.Message): r"""Spec for question answering correctness result. Fields: confidence: Output only. Confidence for question answering correctness score. explanation: Output only. Explanation for question answering correctness score. score: Output only. Question Answering Correctness score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1QuestionAnsweringCorrectnessSpec(_messages.Message): r"""Spec for question answering correctness metric. Fields: useReference: Optional. Whether to use instance.reference to compute question answering correctness. version: Optional. Which version to use for evaluation. """ useReference = _messages.BooleanField(1) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1QuestionAnsweringHelpfulnessInput(_messages.Message): r"""Input for question answering helpfulness metric. Fields: instance: Required. Question answering helpfulness instance. metricSpec: Required. Spec for question answering helpfulness score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringHelpfulnessInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringHelpfulnessSpec', 2) class GoogleCloudAiplatformV1QuestionAnsweringHelpfulnessInstance(_messages.Message): r"""Spec for question answering helpfulness instance. Fields: context: Optional. Text provided as context to answer the question. instruction: Required. The question asked and other instruction in the inference prompt. prediction: Required. Output of the evaluated model. reference: Optional. Ground truth used to compare against the prediction. """ context = _messages.StringField(1) instruction = _messages.StringField(2) prediction = _messages.StringField(3) reference = _messages.StringField(4) class GoogleCloudAiplatformV1QuestionAnsweringHelpfulnessResult(_messages.Message): r"""Spec for question answering helpfulness result. Fields: confidence: Output only. Confidence for question answering helpfulness score. explanation: Output only. Explanation for question answering helpfulness score. score: Output only. Question Answering Helpfulness score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1QuestionAnsweringHelpfulnessSpec(_messages.Message): r"""Spec for question answering helpfulness metric. Fields: useReference: Optional. Whether to use instance.reference to compute question answering helpfulness. version: Optional. Which version to use for evaluation. """ useReference = _messages.BooleanField(1) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1QuestionAnsweringQualityInput(_messages.Message): r"""Input for question answering quality metric. Fields: instance: Required. Question answering quality instance. metricSpec: Required. Spec for question answering quality score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringQualityInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringQualitySpec', 2) class GoogleCloudAiplatformV1QuestionAnsweringQualityInstance(_messages.Message): r"""Spec for question answering quality instance. Fields: context: Required. Text to answer the question. instruction: Required. Question Answering prompt for LLM. prediction: Required. Output of the evaluated model. reference: Optional. Ground truth used to compare against the prediction. """ context = _messages.StringField(1) instruction = _messages.StringField(2) prediction = _messages.StringField(3) reference = _messages.StringField(4) class GoogleCloudAiplatformV1QuestionAnsweringQualityResult(_messages.Message): r"""Spec for question answering quality result. Fields: confidence: Output only. Confidence for question answering quality score. explanation: Output only. Explanation for question answering quality score. score: Output only. Question Answering Quality score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1QuestionAnsweringQualitySpec(_messages.Message): r"""Spec for question answering quality score metric. Fields: useReference: Optional. Whether to use instance.reference to compute question answering quality. version: Optional. Which version to use for evaluation. """ useReference = _messages.BooleanField(1) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1QuestionAnsweringRelevanceInput(_messages.Message): r"""Input for question answering relevance metric. Fields: instance: Required. Question answering relevance instance. metricSpec: Required. Spec for question answering relevance score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringRelevanceInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1QuestionAnsweringRelevanceSpec', 2) class GoogleCloudAiplatformV1QuestionAnsweringRelevanceInstance(_messages.Message): r"""Spec for question answering relevance instance. Fields: context: Optional. Text provided as context to answer the question. instruction: Required. The question asked and other instruction in the inference prompt. prediction: Required. Output of the evaluated model. reference: Optional. Ground truth used to compare against the prediction. """ context = _messages.StringField(1) instruction = _messages.StringField(2) prediction = _messages.StringField(3) reference = _messages.StringField(4) class GoogleCloudAiplatformV1QuestionAnsweringRelevanceResult(_messages.Message): r"""Spec for question answering relevance result. Fields: confidence: Output only. Confidence for question answering relevance score. explanation: Output only. Explanation for question answering relevance score. score: Output only. Question Answering Relevance score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1QuestionAnsweringRelevanceSpec(_messages.Message): r"""Spec for question answering relevance metric. Fields: useReference: Optional. Whether to use instance.reference to compute question answering relevance. version: Optional. Which version to use for evaluation. """ useReference = _messages.BooleanField(1) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1RagChunk(_messages.Message): r"""A RagChunk includes the content of a chunk of a RagFile, and associated metadata. Fields: pageSpan: If populated, represents where the chunk starts and ends in the document. text: The content of the chunk. """ pageSpan = _messages.MessageField('GoogleCloudAiplatformV1RagChunkPageSpan', 1) text = _messages.StringField(2) class GoogleCloudAiplatformV1RagChunkPageSpan(_messages.Message): r"""Represents where the chunk starts and ends in the document. Fields: firstPage: Page where chunk starts in the document. Inclusive. 1-indexed. lastPage: Page where chunk ends in the document. Inclusive. 1-indexed. """ firstPage = _messages.IntegerField(1, variant=_messages.Variant.INT32) lastPage = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1RagContexts(_messages.Message): r"""Relevant contexts for one query. Fields: contexts: All its contexts. """ contexts = _messages.MessageField('GoogleCloudAiplatformV1RagContextsContext', 1, repeated=True) class GoogleCloudAiplatformV1RagContextsContext(_messages.Message): r"""A context of the query. Fields: chunk: Context of the retrieved chunk. score: According to the underlying Vector DB and the selected metric type, the score can be either the distance or the similarity between the query and the context and its range depends on the metric type. For example, if the metric type is COSINE_DISTANCE, it represents the distance between the query and the context. The larger the distance, the less relevant the context is to the query. The range is [0, 2], while 0 means the most relevant and 2 means the least relevant. sourceDisplayName: The file display name. sourceUri: If the file is imported from Cloud Storage or Google Drive, source_uri will be original file URI in Cloud Storage or Google Drive; if file is uploaded, source_uri will be file display name. text: The text chunk. """ chunk = _messages.MessageField('GoogleCloudAiplatformV1RagChunk', 1) score = _messages.FloatField(2) sourceDisplayName = _messages.StringField(3) sourceUri = _messages.StringField(4) text = _messages.StringField(5) class GoogleCloudAiplatformV1RagCorpus(_messages.Message): r"""A RagCorpus is a RagFile container and a project can have multiple RagCorpora. Fields: corpusStatus: Output only. RagCorpus state. createTime: Output only. Timestamp when this RagCorpus was created. description: Optional. The description of the RagCorpus. displayName: Required. The display name of the RagCorpus. The name can be up to 128 characters long and can consist of any UTF-8 characters. encryptionSpec: Optional. Immutable. The CMEK key name used to encrypt at- rest data related to this Corpus. Only applicable to RagManagedDb option for Vector DB. This field can only be set at corpus creation time, and cannot be updated or deleted. name: Output only. The resource name of the RagCorpus. updateTime: Output only. Timestamp when this RagCorpus was last updated. vectorDbConfig: Optional. Immutable. The config for the Vector DBs. vertexAiSearchConfig: Optional. Immutable. The config for the Vertex AI Search. """ corpusStatus = _messages.MessageField('GoogleCloudAiplatformV1CorpusStatus', 1) createTime = _messages.StringField(2) description = _messages.StringField(3) displayName = _messages.StringField(4) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 5) name = _messages.StringField(6) updateTime = _messages.StringField(7) vectorDbConfig = _messages.MessageField('GoogleCloudAiplatformV1RagVectorDbConfig', 8) vertexAiSearchConfig = _messages.MessageField('GoogleCloudAiplatformV1VertexAiSearchConfig', 9) class GoogleCloudAiplatformV1RagEmbeddingModelConfig(_messages.Message): r"""Config for the embedding model to use for RAG. Fields: vertexPredictionEndpoint: The Vertex AI Prediction Endpoint that either refers to a publisher model or an endpoint that is hosting a 1P fine- tuned text embedding model. Endpoints hosting non-1P fine-tuned text embedding models are currently not supported. This is used for dense vector search. """ vertexPredictionEndpoint = _messages.MessageField('GoogleCloudAiplatformV1RagEmbeddingModelConfigVertexPredictionEndpoint', 1) class GoogleCloudAiplatformV1RagEmbeddingModelConfigVertexPredictionEndpoint(_messages.Message): r"""Config representing a model hosted on Vertex Prediction Endpoint. Fields: endpoint: Required. The endpoint resource name. Format: `projects/{project }/locations/{location}/publishers/{publisher}/models/{model}` or `projects/{project}/locations/{location}/endpoints/{endpoint}` model: Output only. The resource name of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. Pattern: `projects/{project}/locations/{location}/models/{model}` modelVersionId: Output only. Version ID of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. """ endpoint = _messages.StringField(1) model = _messages.StringField(2) modelVersionId = _messages.StringField(3) class GoogleCloudAiplatformV1RagEngineConfig(_messages.Message): r"""Config for RagEngine. Fields: name: Identifier. The name of the RagEngineConfig. Format: `projects/{project}/locations/{location}/ragEngineConfig` ragManagedDbConfig: The config of the RagManagedDb used by RagEngine. """ name = _messages.StringField(1) ragManagedDbConfig = _messages.MessageField('GoogleCloudAiplatformV1RagManagedDbConfig', 2) class GoogleCloudAiplatformV1RagFile(_messages.Message): r"""A RagFile contains user data for chunking, embedding and indexing. Fields: createTime: Output only. Timestamp when this RagFile was created. description: Optional. The description of the RagFile. directUploadSource: Output only. The RagFile is encapsulated and uploaded in the UploadRagFile request. displayName: Required. The display name of the RagFile. The name can be up to 128 characters long and can consist of any UTF-8 characters. fileStatus: Output only. State of the RagFile. gcsSource: Output only. Google Cloud Storage location of the RagFile. It does not support wildcards in the Cloud Storage uri for now. googleDriveSource: Output only. Google Drive location. Supports importing individual files as well as Google Drive folders. jiraSource: The RagFile is imported from a Jira query. name: Output only. The resource name of the RagFile. sharePointSources: The RagFile is imported from a SharePoint source. slackSource: The RagFile is imported from a Slack channel. updateTime: Output only. Timestamp when this RagFile was last updated. userMetadata: Output only. The metadata for metadata search. The user_metadata Needs to be in JSON format. """ createTime = _messages.StringField(1) description = _messages.StringField(2) directUploadSource = _messages.MessageField('GoogleCloudAiplatformV1DirectUploadSource', 3) displayName = _messages.StringField(4) fileStatus = _messages.MessageField('GoogleCloudAiplatformV1FileStatus', 5) gcsSource = _messages.MessageField('GoogleCloudAiplatformV1GcsSource', 6) googleDriveSource = _messages.MessageField('GoogleCloudAiplatformV1GoogleDriveSource', 7) jiraSource = _messages.MessageField('GoogleCloudAiplatformV1JiraSource', 8) name = _messages.StringField(9) sharePointSources = _messages.MessageField('GoogleCloudAiplatformV1SharePointSources', 10) slackSource = _messages.MessageField('GoogleCloudAiplatformV1SlackSource', 11) updateTime = _messages.StringField(12) userMetadata = _messages.StringField(13) class GoogleCloudAiplatformV1RagFileChunkingConfig(_messages.Message): r"""Specifies the size and overlap of chunks for RagFiles. Fields: fixedLengthChunking: Specifies the fixed length chunking config. """ fixedLengthChunking = _messages.MessageField('GoogleCloudAiplatformV1RagFileChunkingConfigFixedLengthChunking', 1) class GoogleCloudAiplatformV1RagFileChunkingConfigFixedLengthChunking(_messages.Message): r"""Specifies the fixed length chunking config. Fields: chunkOverlap: The overlap between chunks. chunkSize: The size of the chunks. """ chunkOverlap = _messages.IntegerField(1, variant=_messages.Variant.INT32) chunkSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1RagFileParsingConfig(_messages.Message): r"""Specifies the parsing config for RagFiles. Fields: layoutParser: The Layout Parser to use for RagFiles. llmParser: The LLM Parser to use for RagFiles. """ layoutParser = _messages.MessageField('GoogleCloudAiplatformV1RagFileParsingConfigLayoutParser', 1) llmParser = _messages.MessageField('GoogleCloudAiplatformV1RagFileParsingConfigLlmParser', 2) class GoogleCloudAiplatformV1RagFileParsingConfigLayoutParser(_messages.Message): r"""Document AI Layout Parser config. Fields: maxParsingRequestsPerMin: The maximum number of requests the job is allowed to make to the Document AI processor per minute. Consult https://cloud.google.com/document-ai/quotas and the Quota page for your project to set an appropriate value here. If unspecified, a default value of 120 QPM would be used. processorName: The full resource name of a Document AI processor or processor version. The processor must have type `LAYOUT_PARSER_PROCESSOR`. If specified, the `additional_config.parse_as_scanned_pdf` field must be false. Format: * `projects/{project_id}/locations/{location}/processors/{processor_id}` * `projects/{project_id}/locations/{location}/processors/{processor_id}/pr ocessorVersions/{processor_version_id}` """ maxParsingRequestsPerMin = _messages.IntegerField(1, variant=_messages.Variant.INT32) processorName = _messages.StringField(2) class GoogleCloudAiplatformV1RagFileParsingConfigLlmParser(_messages.Message): r"""Specifies the LLM parsing for RagFiles. Fields: customParsingPrompt: The prompt to use for parsing. If not specified, a default prompt will be used. maxParsingRequestsPerMin: The maximum number of requests the job is allowed to make to the LLM model per minute. Consult https://cloud.google.com/vertex-ai/generative-ai/docs/quotas and your document size to set an appropriate value here. If unspecified, a default value of 5000 QPM would be used. modelName: The name of a LLM model used for parsing. Format: * `projects/{ project_id}/locations/{location}/publishers/{publisher}/models/{model}` """ customParsingPrompt = _messages.StringField(1) maxParsingRequestsPerMin = _messages.IntegerField(2, variant=_messages.Variant.INT32) modelName = _messages.StringField(3) class GoogleCloudAiplatformV1RagFileTransformationConfig(_messages.Message): r"""Specifies the transformation config for RagFiles. Fields: ragFileChunkingConfig: Specifies the chunking config for RagFiles. """ ragFileChunkingConfig = _messages.MessageField('GoogleCloudAiplatformV1RagFileChunkingConfig', 1) class GoogleCloudAiplatformV1RagManagedDbConfig(_messages.Message): r"""Configuration message for RagManagedDb used by RagEngine. Fields: basic: Sets the RagManagedDb to the Basic tier. scaled: Sets the RagManagedDb to the Scaled tier. unprovisioned: Sets the RagManagedDb to the Unprovisioned tier. """ basic = _messages.MessageField('GoogleCloudAiplatformV1RagManagedDbConfigBasic', 1) scaled = _messages.MessageField('GoogleCloudAiplatformV1RagManagedDbConfigScaled', 2) unprovisioned = _messages.MessageField('GoogleCloudAiplatformV1RagManagedDbConfigUnprovisioned', 3) class GoogleCloudAiplatformV1RagManagedDbConfigBasic(_messages.Message): r"""Basic tier is a cost-effective and low compute tier suitable for the following cases: * Experimenting with RagManagedDb. * Small data size. * Latency insensitive workload. * Only using RAG Engine with external vector DBs. NOTE: This is the default tier if not explicitly chosen. """ class GoogleCloudAiplatformV1RagManagedDbConfigScaled(_messages.Message): r"""Scaled tier offers production grade performance along with autoscaling functionality. It is suitable for customers with large amounts of data or performance sensitive workloads. """ class GoogleCloudAiplatformV1RagManagedDbConfigUnprovisioned(_messages.Message): r"""Disables the RAG Engine service and deletes all your data held within this service. This will halt the billing of the service. NOTE: Once deleted the data cannot be recovered. To start using RAG Engine again, you will need to update the tier by calling the UpdateRagEngineConfig API. """ class GoogleCloudAiplatformV1RagQuery(_messages.Message): r"""A query to retrieve relevant contexts. Fields: ragRetrievalConfig: Optional. The retrieval config for the query. text: Optional. The query in text format to get relevant contexts. """ ragRetrievalConfig = _messages.MessageField('GoogleCloudAiplatformV1RagRetrievalConfig', 1) text = _messages.StringField(2) class GoogleCloudAiplatformV1RagRetrievalConfig(_messages.Message): r"""Specifies the context retrieval config. Fields: filter: Optional. Config for filters. ranking: Optional. Config for ranking and reranking. topK: Optional. The number of contexts to retrieve. """ filter = _messages.MessageField('GoogleCloudAiplatformV1RagRetrievalConfigFilter', 1) ranking = _messages.MessageField('GoogleCloudAiplatformV1RagRetrievalConfigRanking', 2) topK = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1RagRetrievalConfigFilter(_messages.Message): r"""Config for filters. Fields: metadataFilter: Optional. String for metadata filtering. vectorDistanceThreshold: Optional. Only returns contexts with vector distance smaller than the threshold. vectorSimilarityThreshold: Optional. Only returns contexts with vector similarity larger than the threshold. """ metadataFilter = _messages.StringField(1) vectorDistanceThreshold = _messages.FloatField(2) vectorSimilarityThreshold = _messages.FloatField(3) class GoogleCloudAiplatformV1RagRetrievalConfigRanking(_messages.Message): r"""Config for ranking and reranking. Fields: llmRanker: Optional. Config for LlmRanker. rankService: Optional. Config for Rank Service. """ llmRanker = _messages.MessageField('GoogleCloudAiplatformV1RagRetrievalConfigRankingLlmRanker', 1) rankService = _messages.MessageField('GoogleCloudAiplatformV1RagRetrievalConfigRankingRankService', 2) class GoogleCloudAiplatformV1RagRetrievalConfigRankingLlmRanker(_messages.Message): r"""Config for LlmRanker. Fields: modelName: Optional. The model name used for ranking. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model- reference/inference#supported-models). """ modelName = _messages.StringField(1) class GoogleCloudAiplatformV1RagRetrievalConfigRankingRankService(_messages.Message): r"""Config for Rank Service. Fields: modelName: Optional. The model name of the rank service. Format: `semantic-ranker-512@latest` """ modelName = _messages.StringField(1) class GoogleCloudAiplatformV1RagVectorDbConfig(_messages.Message): r"""Config for the Vector DB to use for RAG. Fields: apiAuth: Authentication config for the chosen Vector DB. pinecone: The config for the Pinecone. ragEmbeddingModelConfig: Optional. Immutable. The embedding model config of the Vector DB. ragManagedDb: The config for the RAG-managed Vector DB. vertexVectorSearch: The config for the Vertex Vector Search. """ apiAuth = _messages.MessageField('GoogleCloudAiplatformV1ApiAuth', 1) pinecone = _messages.MessageField('GoogleCloudAiplatformV1RagVectorDbConfigPinecone', 2) ragEmbeddingModelConfig = _messages.MessageField('GoogleCloudAiplatformV1RagEmbeddingModelConfig', 3) ragManagedDb = _messages.MessageField('GoogleCloudAiplatformV1RagVectorDbConfigRagManagedDb', 4) vertexVectorSearch = _messages.MessageField('GoogleCloudAiplatformV1RagVectorDbConfigVertexVectorSearch', 5) class GoogleCloudAiplatformV1RagVectorDbConfigPinecone(_messages.Message): r"""The config for the Pinecone. Fields: indexName: Pinecone index name. This value cannot be changed after it's set. """ indexName = _messages.StringField(1) class GoogleCloudAiplatformV1RagVectorDbConfigRagManagedDb(_messages.Message): r"""The config for the default RAG-managed Vector DB. Fields: ann: Performs an ANN search on RagCorpus. Use this if you have a lot of files (> 10K) in your RagCorpus and want to reduce the search latency. knn: Performs a KNN search on RagCorpus. Default choice if not specified. """ ann = _messages.MessageField('GoogleCloudAiplatformV1RagVectorDbConfigRagManagedDbANN', 1) knn = _messages.MessageField('GoogleCloudAiplatformV1RagVectorDbConfigRagManagedDbKNN', 2) class GoogleCloudAiplatformV1RagVectorDbConfigRagManagedDbANN(_messages.Message): r"""Config for ANN search. RagManagedDb uses a tree-based structure to partition data and facilitate faster searches. As a tradeoff, it requires longer indexing time and manual triggering of index rebuild via the ImportRagFiles and UpdateRagCorpus API. Fields: leafCount: Number of leaf nodes in the tree-based structure. Each leaf node contains groups of closely related vectors along with their corresponding centroid. Recommended value is 10 * sqrt(num of RagFiles in your RagCorpus). Default value is 500. treeDepth: The depth of the tree-based structure. Only depth values of 2 and 3 are supported. Recommended value is 2 if you have if you have O(10K) files in the RagCorpus and set this to 3 if more than that. Default value is 2. """ leafCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) treeDepth = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1RagVectorDbConfigRagManagedDbKNN(_messages.Message): r"""Config for KNN search.""" class GoogleCloudAiplatformV1RagVectorDbConfigVertexVectorSearch(_messages.Message): r"""The config for the Vertex Vector Search. Fields: index: The resource name of the Index. Format: `projects/{project}/locations/{location}/indexes/{index}` indexEndpoint: The resource name of the Index Endpoint. Format: `projects/{project}/locations/{location}/indexEndpoints/{index_endpoint} ` """ index = _messages.StringField(1) indexEndpoint = _messages.StringField(2) class GoogleCloudAiplatformV1RawPredictRequest(_messages.Message): r"""Request message for PredictionService.RawPredict. Fields: httpBody: The prediction input. Supports HTTP headers and arbitrary data payload. A DeployedModel may have an upper limit on the number of instances it supports per request. When this limit it is exceeded for an AutoML model, the RawPredict method returns an error. When this limit is exceeded for a custom-trained model, the behavior varies depending on the model. You can specify the schema for each instance in the predict_schemata.instance_schema_uri field when you create a Model. This schema applies when you deploy the `Model` as a `DeployedModel` to an Endpoint and use the `RawPredict` method. """ httpBody = _messages.MessageField('GoogleApiHttpBody', 1) class GoogleCloudAiplatformV1RayLogsSpec(_messages.Message): r"""Configuration for the Ray OSS Logs. Fields: disabled: Optional. Flag to disable the export of Ray OSS logs to Cloud Logging. """ disabled = _messages.BooleanField(1) class GoogleCloudAiplatformV1RayMetricSpec(_messages.Message): r"""Configuration for the Ray metrics. Fields: disabled: Optional. Flag to disable the Ray metrics collection. """ disabled = _messages.BooleanField(1) class GoogleCloudAiplatformV1RaySpec(_messages.Message): r"""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. Messages: ResourcePoolImagesValue: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" } Fields: headNodeResourcePoolId: Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set. imageUri: Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from [Vertex prebuilt images](https://cloud.google.com/vertex-ai/docs/training/pre- built-containers). Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field. rayLogsSpec: Optional. OSS Ray logging configurations. rayMetricSpec: Optional. Ray metrics configurations. resourcePoolImages: Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" } """ @encoding.MapUnrecognizedFields('additionalProperties') class ResourcePoolImagesValue(_messages.Message): r"""Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" } Messages: AdditionalProperty: An additional property for a ResourcePoolImagesValue object. Fields: additionalProperties: Additional properties of type ResourcePoolImagesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ResourcePoolImagesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) headNodeResourcePoolId = _messages.StringField(1) imageUri = _messages.StringField(2) rayLogsSpec = _messages.MessageField('GoogleCloudAiplatformV1RayLogsSpec', 3) rayMetricSpec = _messages.MessageField('GoogleCloudAiplatformV1RayMetricSpec', 4) resourcePoolImages = _messages.MessageField('ResourcePoolImagesValue', 5) class GoogleCloudAiplatformV1ReadFeatureValuesRequest(_messages.Message): r"""Request message for FeaturestoreOnlineServingService.ReadFeatureValues. Fields: entityId: Required. ID for a specific entity. For example, for a machine learning model predicting user clicks on a website, an entity ID could be `user_123`. featureSelector: Required. Selector choosing Features of the target EntityType. """ entityId = _messages.StringField(1) featureSelector = _messages.MessageField('GoogleCloudAiplatformV1FeatureSelector', 2) class GoogleCloudAiplatformV1ReadFeatureValuesResponse(_messages.Message): r"""Response message for FeaturestoreOnlineServingService.ReadFeatureValues. Fields: entityView: Entity view with Feature values. This may be the entity in the Featurestore if values for all Features were requested, or a projection of the entity in the Featurestore if values for only some Features were requested. header: Response header. """ entityView = _messages.MessageField('GoogleCloudAiplatformV1ReadFeatureValuesResponseEntityView', 1) header = _messages.MessageField('GoogleCloudAiplatformV1ReadFeatureValuesResponseHeader', 2) class GoogleCloudAiplatformV1ReadFeatureValuesResponseEntityView(_messages.Message): r"""Entity view with Feature values. Fields: data: Each piece of data holds the k requested values for one requested Feature. If no values for the requested Feature exist, the corresponding cell will be empty. This has the same size and is in the same order as the features from the header ReadFeatureValuesResponse.header. entityId: ID of the requested entity. """ data = _messages.MessageField('GoogleCloudAiplatformV1ReadFeatureValuesResponseEntityViewData', 1, repeated=True) entityId = _messages.StringField(2) class GoogleCloudAiplatformV1ReadFeatureValuesResponseEntityViewData(_messages.Message): r"""Container to hold value(s), successive in time, for one Feature from the request. Fields: value: Feature value if a single value is requested. values: Feature values list if values, successive in time, are requested. If the requested number of values is greater than the number of existing Feature values, nonexistent values are omitted instead of being returned as empty. """ value = _messages.MessageField('GoogleCloudAiplatformV1FeatureValue', 1) values = _messages.MessageField('GoogleCloudAiplatformV1FeatureValueList', 2) class GoogleCloudAiplatformV1ReadFeatureValuesResponseFeatureDescriptor(_messages.Message): r"""Metadata for requested Features. Fields: id: Feature ID. """ id = _messages.StringField(1) class GoogleCloudAiplatformV1ReadFeatureValuesResponseHeader(_messages.Message): r"""Response header with metadata for the requested ReadFeatureValuesRequest.entity_type and Features. Fields: entityType: The resource name of the EntityType from the ReadFeatureValuesRequest. Value format: `projects/{project}/locations/{l ocation}/featurestores/{featurestore}/entityTypes/{entityType}`. featureDescriptors: List of Feature metadata corresponding to each piece of ReadFeatureValuesResponse.EntityView.data. """ entityType = _messages.StringField(1) featureDescriptors = _messages.MessageField('GoogleCloudAiplatformV1ReadFeatureValuesResponseFeatureDescriptor', 2, repeated=True) class GoogleCloudAiplatformV1ReadTensorboardBlobDataResponse(_messages.Message): r"""Response message for TensorboardService.ReadTensorboardBlobData. Fields: blobs: Blob messages containing blob bytes. """ blobs = _messages.MessageField('GoogleCloudAiplatformV1TensorboardBlob', 1, repeated=True) class GoogleCloudAiplatformV1ReadTensorboardSizeResponse(_messages.Message): r"""Response message for TensorboardService.ReadTensorboardSize. Fields: storageSizeByte: Payload storage size for the TensorBoard """ storageSizeByte = _messages.IntegerField(1) class GoogleCloudAiplatformV1ReadTensorboardTimeSeriesDataResponse(_messages.Message): r"""Response message for TensorboardService.ReadTensorboardTimeSeriesData. Fields: timeSeriesData: The returned time series data. """ timeSeriesData = _messages.MessageField('GoogleCloudAiplatformV1TimeSeriesData', 1) class GoogleCloudAiplatformV1ReadTensorboardUsageResponse(_messages.Message): r"""Response message for TensorboardService.ReadTensorboardUsage. Messages: MonthlyUsageDataValue: Maps year-month (YYYYMM) string to per month usage data. Fields: monthlyUsageData: Maps year-month (YYYYMM) string to per month usage data. """ @encoding.MapUnrecognizedFields('additionalProperties') class MonthlyUsageDataValue(_messages.Message): r"""Maps year-month (YYYYMM) string to per month usage data. Messages: AdditionalProperty: An additional property for a MonthlyUsageDataValue object. Fields: additionalProperties: Additional properties of type MonthlyUsageDataValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a MonthlyUsageDataValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1ReadTensorboardUsageResponsePerMonthUsageData attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1ReadTensorboardUsageResponsePerMonthUsageData', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) monthlyUsageData = _messages.MessageField('MonthlyUsageDataValue', 1) class GoogleCloudAiplatformV1ReadTensorboardUsageResponsePerMonthUsageData(_messages.Message): r"""Per month usage data Fields: userUsageData: Usage data for each user in the given month. """ userUsageData = _messages.MessageField('GoogleCloudAiplatformV1ReadTensorboardUsageResponsePerUserUsageData', 1, repeated=True) class GoogleCloudAiplatformV1ReadTensorboardUsageResponsePerUserUsageData(_messages.Message): r"""Per user usage data. Fields: username: User's username viewCount: Number of times the user has read data within the Tensorboard. """ username = _messages.StringField(1) viewCount = _messages.IntegerField(2) class GoogleCloudAiplatformV1ReasoningEngine(_messages.Message): r"""ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order. Fields: createTime: Output only. Timestamp when this ReasoningEngine was created. description: Optional. The description of the ReasoningEngine. displayName: Required. The display name of the ReasoningEngine. encryptionSpec: Customer-managed encryption key spec for a ReasoningEngine. If set, this ReasoningEngine and all sub-resources of this ReasoningEngine will be secured by this key. etag: Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. name: Identifier. The resource name of the ReasoningEngine. Format: `proje cts/{project}/locations/{location}/reasoningEngines/{reasoning_engine}` spec: Optional. Configurations of the ReasoningEngine updateTime: Output only. Timestamp when this ReasoningEngine was most recently updated. """ createTime = _messages.StringField(1) description = _messages.StringField(2) displayName = _messages.StringField(3) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 4) etag = _messages.StringField(5) name = _messages.StringField(6) spec = _messages.MessageField('GoogleCloudAiplatformV1ReasoningEngineSpec', 7) updateTime = _messages.StringField(8) class GoogleCloudAiplatformV1ReasoningEngineSpec(_messages.Message): r"""ReasoningEngine configurations Messages: ClassMethodsValueListEntry: A ClassMethodsValueListEntry object. Fields: agentFramework: Optional. The OSS agent framework used to develop the agent. Currently supported values: "google-adk", "langchain", "langgraph", "ag2", "llama-index", "custom". classMethods: Optional. Declarations for object class methods in OpenAPI specification format. deploymentSpec: Optional. The specification of a Reasoning Engine deployment. packageSpec: Optional. User provided package spec of the ReasoningEngine. Ignored when users directly specify a deployment image through `deployment_spec.first_party_image_override`, but keeping the field_behavior to avoid introducing breaking changes. serviceAccount: Optional. The service account that the Reasoning Engine artifact runs as. It should have "roles/storage.objectViewer" for reading the user project's Cloud Storage and "roles/aiplatform.user" for using Vertex extensions. If not specified, the Vertex AI Reasoning Engine Service Agent in the project will be used. """ @encoding.MapUnrecognizedFields('additionalProperties') class ClassMethodsValueListEntry(_messages.Message): r"""A ClassMethodsValueListEntry object. Messages: AdditionalProperty: An additional property for a ClassMethodsValueListEntry object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a ClassMethodsValueListEntry object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) agentFramework = _messages.StringField(1) classMethods = _messages.MessageField('ClassMethodsValueListEntry', 2, repeated=True) deploymentSpec = _messages.MessageField('GoogleCloudAiplatformV1ReasoningEngineSpecDeploymentSpec', 3) packageSpec = _messages.MessageField('GoogleCloudAiplatformV1ReasoningEngineSpecPackageSpec', 4) serviceAccount = _messages.StringField(5) class GoogleCloudAiplatformV1ReasoningEngineSpecDeploymentSpec(_messages.Message): r"""The specification of a Reasoning Engine deployment. Messages: ResourceLimitsValue: Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits Fields: containerConcurrency: Optional. Concurrency for each container and agent server. Recommended value: 2 * cpu + 1. Defaults to 9. env: Optional. Environment variables to be set with the Reasoning Engine deployment. The environment variables can be updated through the UpdateReasoningEngine API. maxInstances: Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. minInstances: Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. pscInterfaceConfig: Optional. Configuration for PSC-I. resourceLimits: Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits secretEnv: Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent. """ @encoding.MapUnrecognizedFields('additionalProperties') class ResourceLimitsValue(_messages.Message): r"""Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits Messages: AdditionalProperty: An additional property for a ResourceLimitsValue object. Fields: additionalProperties: Additional properties of type ResourceLimitsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ResourceLimitsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) containerConcurrency = _messages.IntegerField(1, variant=_messages.Variant.INT32) env = _messages.MessageField('GoogleCloudAiplatformV1EnvVar', 2, repeated=True) maxInstances = _messages.IntegerField(3, variant=_messages.Variant.INT32) minInstances = _messages.IntegerField(4, variant=_messages.Variant.INT32) pscInterfaceConfig = _messages.MessageField('GoogleCloudAiplatformV1PscInterfaceConfig', 5) resourceLimits = _messages.MessageField('ResourceLimitsValue', 6) secretEnv = _messages.MessageField('GoogleCloudAiplatformV1SecretEnvVar', 7, repeated=True) class GoogleCloudAiplatformV1ReasoningEngineSpecPackageSpec(_messages.Message): r"""User provided package spec like pickled object and package requirements. Fields: dependencyFilesGcsUri: Optional. The Cloud Storage URI of the dependency files in tar.gz format. pickleObjectGcsUri: Optional. The Cloud Storage URI of the pickled python object. pythonVersion: Optional. The Python version. Currently support 3.8, 3.9, 3.10, 3.11. If not specified, default value is 3.10. requirementsGcsUri: Optional. The Cloud Storage URI of the `requirements.txt` file """ dependencyFilesGcsUri = _messages.StringField(1) pickleObjectGcsUri = _messages.StringField(2) pythonVersion = _messages.StringField(3) requirementsGcsUri = _messages.StringField(4) class GoogleCloudAiplatformV1RebaseTunedModelRequest(_messages.Message): r"""Request message for GenAiTuningService.RebaseTunedModel. Fields: artifactDestination: Optional. The Google Cloud Storage location to write the artifacts. deployToSameEndpoint: Optional. By default, bison to gemini migration will always create new model/endpoint, but for gemini-1.0 to gemini-1.5 migration, we default deploy to the same endpoint. See details in this Section. tunedModelRef: Required. TunedModel reference to retrieve the legacy model information. tuningJob: Optional. The TuningJob to be updated. Users can use this TuningJob field to overwrite tuning configs. """ artifactDestination = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 1) deployToSameEndpoint = _messages.BooleanField(2) tunedModelRef = _messages.MessageField('GoogleCloudAiplatformV1TunedModelRef', 3) tuningJob = _messages.MessageField('GoogleCloudAiplatformV1TuningJob', 4) class GoogleCloudAiplatformV1RebootPersistentResourceOperationMetadata(_messages.Message): r"""Details of operations that perform reboot PersistentResource. Fields: genericMetadata: Operation metadata for PersistentResource. progressMessage: Progress Message for Reboot LRO """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) progressMessage = _messages.StringField(2) class GoogleCloudAiplatformV1RebootPersistentResourceRequest(_messages.Message): r"""Request message for PersistentResourceService.RebootPersistentResource. """ class GoogleCloudAiplatformV1RemoveContextChildrenRequest(_messages.Message): r"""Request message for MetadataService.DeleteContextChildrenRequest. Fields: childContexts: The resource names of the child Contexts. """ childContexts = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1RemoveContextChildrenResponse(_messages.Message): r"""Response message for MetadataService.RemoveContextChildren.""" class GoogleCloudAiplatformV1RemoveDatapointsRequest(_messages.Message): r"""Request message for IndexService.RemoveDatapoints Fields: datapointIds: A list of datapoint ids to be deleted. """ datapointIds = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1RemoveDatapointsResponse(_messages.Message): r"""Response message for IndexService.RemoveDatapoints""" class GoogleCloudAiplatformV1ReservationAffinity(_messages.Message): r"""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. Enums: ReservationAffinityTypeValueValuesEnum: Required. Specifies the reservation affinity type. Fields: key: Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value. reservationAffinityType: Required. Specifies the reservation affinity type. values: Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block. """ class ReservationAffinityTypeValueValuesEnum(_messages.Enum): r"""Required. Specifies the reservation affinity type. Values: TYPE_UNSPECIFIED: Default value. This should not be used. NO_RESERVATION: Do not consume from any reserved capacity, only use on- demand. ANY_RESERVATION: Consume any reservation available, falling back to on- demand. SPECIFIC_RESERVATION: Consume from a specific reservation. When chosen, the reservation must be identified via the `key` and `values` fields. """ TYPE_UNSPECIFIED = 0 NO_RESERVATION = 1 ANY_RESERVATION = 2 SPECIFIC_RESERVATION = 3 key = _messages.StringField(1) reservationAffinityType = _messages.EnumField('ReservationAffinityTypeValueValuesEnum', 2) values = _messages.StringField(3, repeated=True) class GoogleCloudAiplatformV1ResourcePool(_messages.Message): r"""Represents the spec of a group of resources of the same type, for example machine type, disk, and accelerators, in a PersistentResource. Fields: autoscalingSpec: Optional. Optional spec to configure GKE or Ray-on-Vertex autoscaling diskSpec: Optional. Disk spec for the machine in this node pool. id: Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically. machineSpec: Required. Immutable. The specification of a single machine. replicaCount: Optional. The total number of machines to use for this resource pool. usedReplicaCount: Output only. The number of machines currently in use by training jobs for this resource pool. Will replace idle_replica_count. """ autoscalingSpec = _messages.MessageField('GoogleCloudAiplatformV1ResourcePoolAutoscalingSpec', 1) diskSpec = _messages.MessageField('GoogleCloudAiplatformV1DiskSpec', 2) id = _messages.StringField(3) machineSpec = _messages.MessageField('GoogleCloudAiplatformV1MachineSpec', 4) replicaCount = _messages.IntegerField(5) usedReplicaCount = _messages.IntegerField(6) class GoogleCloudAiplatformV1ResourcePoolAutoscalingSpec(_messages.Message): r"""The min/max number of replicas allowed if enabling autoscaling Fields: maxReplicaCount: Optional. max replicas in the node pool, must be \u2265 replica_count and > min_replica_count or will throw error minReplicaCount: Optional. min replicas in the node pool, must be \u2264 replica_count and < max_replica_count or will throw error. For autoscaling enabled Ray-on-Vertex, we allow min_replica_count of a resource_pool to be 0 to match the OSS Ray behavior(https://docs.ray.io/en/latest/cluster/vms/user- guides/configuring-autoscaling.html#cluster-config-parameters). As for Persistent Resource, the min_replica_count must be > 0, we added a corresponding validation inside CreatePersistentResourceRequestValidator.java. """ maxReplicaCount = _messages.IntegerField(1) minReplicaCount = _messages.IntegerField(2) class GoogleCloudAiplatformV1ResourceRuntime(_messages.Message): r"""Persistent Cluster runtime information as output Messages: AccessUrisValue: Output only. URIs for user to connect to the Cluster. Example: { "RAY_HEAD_NODE_INTERNAL_IP": "head-node-IP:10001" "RAY_DASHBOARD_URI": "ray-dashboard-address:8888" } Fields: accessUris: Output only. URIs for user to connect to the Cluster. Example: { "RAY_HEAD_NODE_INTERNAL_IP": "head-node-IP:10001" "RAY_DASHBOARD_URI": "ray-dashboard-address:8888" } """ @encoding.MapUnrecognizedFields('additionalProperties') class AccessUrisValue(_messages.Message): r"""Output only. URIs for user to connect to the Cluster. Example: { "RAY_HEAD_NODE_INTERNAL_IP": "head-node-IP:10001" "RAY_DASHBOARD_URI": "ray-dashboard-address:8888" } Messages: AdditionalProperty: An additional property for a AccessUrisValue object. Fields: additionalProperties: Additional properties of type AccessUrisValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a AccessUrisValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) accessUris = _messages.MessageField('AccessUrisValue', 1) class GoogleCloudAiplatformV1ResourceRuntimeSpec(_messages.Message): r"""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. Fields: raySpec: Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource. serviceAccountSpec: Optional. Configure the use of workload identity on the PersistentResource """ raySpec = _messages.MessageField('GoogleCloudAiplatformV1RaySpec', 1) serviceAccountSpec = _messages.MessageField('GoogleCloudAiplatformV1ServiceAccountSpec', 2) class GoogleCloudAiplatformV1ResourcesConsumed(_messages.Message): r"""Statistics information about resource consumption. Fields: replicaHours: Output only. The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time. """ replicaHours = _messages.FloatField(1) class GoogleCloudAiplatformV1RestoreDatasetVersionOperationMetadata(_messages.Message): r"""Runtime operation information for DatasetService.RestoreDatasetVersion. Fields: genericMetadata: The common part of the operation metadata. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1ResumeModelDeploymentMonitoringJobRequest(_messages.Message): r"""Request message for JobService.ResumeModelDeploymentMonitoringJob.""" class GoogleCloudAiplatformV1ResumeScheduleRequest(_messages.Message): r"""Request message for ScheduleService.ResumeSchedule. Fields: catchUp: Optional. Whether to backfill missed runs when the schedule is resumed from PAUSED state. If set to true, all missed runs will be scheduled. New runs will be scheduled after the backfill is complete. This will also update Schedule.catch_up field. Default to false. """ catchUp = _messages.BooleanField(1) class GoogleCloudAiplatformV1Retrieval(_messages.Message): r"""Defines a retrieval tool that model can call to access external knowledge. Fields: disableAttribution: Optional. Deprecated. This option is no longer supported. vertexAiSearch: Set to use data source powered by Vertex AI Search. vertexRagStore: Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService. """ disableAttribution = _messages.BooleanField(1) vertexAiSearch = _messages.MessageField('GoogleCloudAiplatformV1VertexAISearch', 2) vertexRagStore = _messages.MessageField('GoogleCloudAiplatformV1VertexRagStore', 3) class GoogleCloudAiplatformV1RetrievalConfig(_messages.Message): r"""Retrieval config. Fields: languageCode: The language code of the user. latLng: The location of the user. """ languageCode = _messages.StringField(1) latLng = _messages.MessageField('GoogleTypeLatLng', 2) class GoogleCloudAiplatformV1RetrievalMetadata(_messages.Message): r"""Metadata related to retrieval in the grounding flow. Fields: googleSearchDynamicRetrievalScore: Optional. Score indicating how likely information from Google Search could help answer the prompt. The score is in the range `[0, 1]`, where 0 is the least likely and 1 is the most likely. This score is only populated when Google Search grounding and dynamic retrieval is enabled. It will be compared to the threshold to determine whether to trigger Google Search. """ googleSearchDynamicRetrievalScore = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1RetrieveContextsRequest(_messages.Message): r"""Request message for VertexRagService.RetrieveContexts. Fields: query: Required. Single RAG retrieve query. vertexRagStore: The data source for Vertex RagStore. """ query = _messages.MessageField('GoogleCloudAiplatformV1RagQuery', 1) vertexRagStore = _messages.MessageField('GoogleCloudAiplatformV1RetrieveContextsRequestVertexRagStore', 2) class GoogleCloudAiplatformV1RetrieveContextsRequestVertexRagStore(_messages.Message): r"""The data source for Vertex RagStore. Fields: ragResources: Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support. vectorDistanceThreshold: Optional. Only return contexts with vector distance smaller than the threshold. """ ragResources = _messages.MessageField('GoogleCloudAiplatformV1RetrieveContextsRequestVertexRagStoreRagResource', 1, repeated=True) vectorDistanceThreshold = _messages.FloatField(2) class GoogleCloudAiplatformV1RetrieveContextsRequestVertexRagStoreRagResource(_messages.Message): r"""The definition of the Rag resource. Fields: ragCorpus: Optional. RagCorpora resource name. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}` ragFileIds: Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field. """ ragCorpus = _messages.StringField(1) ragFileIds = _messages.StringField(2, repeated=True) class GoogleCloudAiplatformV1RetrieveContextsResponse(_messages.Message): r"""Response message for VertexRagService.RetrieveContexts. Fields: contexts: The contexts of the query. """ contexts = _messages.MessageField('GoogleCloudAiplatformV1RagContexts', 1) class GoogleCloudAiplatformV1RougeInput(_messages.Message): r"""Input for rouge metric. Fields: instances: Required. Repeated rouge instances. metricSpec: Required. Spec for rouge score metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1RougeInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1RougeSpec', 2) class GoogleCloudAiplatformV1RougeInstance(_messages.Message): r"""Spec for rouge instance. Fields: prediction: Required. Output of the evaluated model. reference: Required. Ground truth used to compare against the prediction. """ prediction = _messages.StringField(1) reference = _messages.StringField(2) class GoogleCloudAiplatformV1RougeMetricValue(_messages.Message): r"""Rouge metric value for an instance. Fields: score: Output only. Rouge score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1RougeResults(_messages.Message): r"""Results for rouge metric. Fields: rougeMetricValues: Output only. Rouge metric values. """ rougeMetricValues = _messages.MessageField('GoogleCloudAiplatformV1RougeMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1RougeSpec(_messages.Message): r"""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. Fields: rougeType: Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum. splitSummaries: Optional. Whether to split summaries while using rougeLsum. useStemmer: Optional. Whether to use stemmer to compute rouge score. """ rougeType = _messages.StringField(1) splitSummaries = _messages.BooleanField(2) useStemmer = _messages.BooleanField(3) class GoogleCloudAiplatformV1SafetyInput(_messages.Message): r"""Input for safety metric. Fields: instance: Required. Safety instance. metricSpec: Required. Spec for safety metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1SafetyInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1SafetySpec', 2) class GoogleCloudAiplatformV1SafetyInstance(_messages.Message): r"""Spec for safety instance. Fields: prediction: Required. Output of the evaluated model. """ prediction = _messages.StringField(1) class GoogleCloudAiplatformV1SafetyRating(_messages.Message): r"""Safety rating corresponding to the generated content. Enums: CategoryValueValuesEnum: Output only. Harm category. OverwrittenThresholdValueValuesEnum: Output only. The overwritten threshold for the safety category of Gemini 2.0 image out. If minors are detected in the output image, the threshold of each safety category will be overwritten if user sets a lower threshold. ProbabilityValueValuesEnum: Output only. Harm probability levels in the content. SeverityValueValuesEnum: Output only. Harm severity levels in the content. Fields: blocked: Output only. Indicates whether the content was filtered out because of this rating. category: Output only. Harm category. overwrittenThreshold: Output only. The overwritten threshold for the safety category of Gemini 2.0 image out. If minors are detected in the output image, the threshold of each safety category will be overwritten if user sets a lower threshold. probability: Output only. Harm probability levels in the content. probabilityScore: Output only. Harm probability score. severity: Output only. Harm severity levels in the content. severityScore: Output only. Harm severity score. """ class CategoryValueValuesEnum(_messages.Enum): r"""Output only. Harm category. Values: HARM_CATEGORY_UNSPECIFIED: The harm category is unspecified. HARM_CATEGORY_HATE_SPEECH: The harm category is hate speech. HARM_CATEGORY_DANGEROUS_CONTENT: The harm category is dangerous content. HARM_CATEGORY_HARASSMENT: The harm category is harassment. HARM_CATEGORY_SEXUALLY_EXPLICIT: The harm category is sexually explicit content. HARM_CATEGORY_CIVIC_INTEGRITY: Deprecated: Election filter is not longer supported. The harm category is civic integrity. HARM_CATEGORY_IMAGE_HATE: The harm category is image hate. HARM_CATEGORY_IMAGE_DANGEROUS_CONTENT: The harm category is image dangerous content. HARM_CATEGORY_IMAGE_HARASSMENT: The harm category is image harassment. HARM_CATEGORY_IMAGE_SEXUALLY_EXPLICIT: The harm category is image sexually explicit content. """ HARM_CATEGORY_UNSPECIFIED = 0 HARM_CATEGORY_HATE_SPEECH = 1 HARM_CATEGORY_DANGEROUS_CONTENT = 2 HARM_CATEGORY_HARASSMENT = 3 HARM_CATEGORY_SEXUALLY_EXPLICIT = 4 HARM_CATEGORY_CIVIC_INTEGRITY = 5 HARM_CATEGORY_IMAGE_HATE = 6 HARM_CATEGORY_IMAGE_DANGEROUS_CONTENT = 7 HARM_CATEGORY_IMAGE_HARASSMENT = 8 HARM_CATEGORY_IMAGE_SEXUALLY_EXPLICIT = 9 class OverwrittenThresholdValueValuesEnum(_messages.Enum): r"""Output only. The overwritten threshold for the safety category of Gemini 2.0 image out. If minors are detected in the output image, the threshold of each safety category will be overwritten if user sets a lower threshold. Values: HARM_BLOCK_THRESHOLD_UNSPECIFIED: Unspecified harm block threshold. BLOCK_LOW_AND_ABOVE: Block low threshold and above (i.e. block more). BLOCK_MEDIUM_AND_ABOVE: Block medium threshold and above. BLOCK_ONLY_HIGH: Block only high threshold (i.e. block less). BLOCK_NONE: Block none. OFF: Turn off the safety filter. """ HARM_BLOCK_THRESHOLD_UNSPECIFIED = 0 BLOCK_LOW_AND_ABOVE = 1 BLOCK_MEDIUM_AND_ABOVE = 2 BLOCK_ONLY_HIGH = 3 BLOCK_NONE = 4 OFF = 5 class ProbabilityValueValuesEnum(_messages.Enum): r"""Output only. Harm probability levels in the content. Values: HARM_PROBABILITY_UNSPECIFIED: Harm probability unspecified. NEGLIGIBLE: Negligible level of harm. LOW: Low level of harm. MEDIUM: Medium level of harm. HIGH: High level of harm. """ HARM_PROBABILITY_UNSPECIFIED = 0 NEGLIGIBLE = 1 LOW = 2 MEDIUM = 3 HIGH = 4 class SeverityValueValuesEnum(_messages.Enum): r"""Output only. Harm severity levels in the content. Values: HARM_SEVERITY_UNSPECIFIED: Harm severity unspecified. HARM_SEVERITY_NEGLIGIBLE: Negligible level of harm severity. HARM_SEVERITY_LOW: Low level of harm severity. HARM_SEVERITY_MEDIUM: Medium level of harm severity. HARM_SEVERITY_HIGH: High level of harm severity. """ HARM_SEVERITY_UNSPECIFIED = 0 HARM_SEVERITY_NEGLIGIBLE = 1 HARM_SEVERITY_LOW = 2 HARM_SEVERITY_MEDIUM = 3 HARM_SEVERITY_HIGH = 4 blocked = _messages.BooleanField(1) category = _messages.EnumField('CategoryValueValuesEnum', 2) overwrittenThreshold = _messages.EnumField('OverwrittenThresholdValueValuesEnum', 3) probability = _messages.EnumField('ProbabilityValueValuesEnum', 4) probabilityScore = _messages.FloatField(5, variant=_messages.Variant.FLOAT) severity = _messages.EnumField('SeverityValueValuesEnum', 6) severityScore = _messages.FloatField(7, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SafetyResult(_messages.Message): r"""Spec for safety result. Fields: confidence: Output only. Confidence for safety score. explanation: Output only. Explanation for safety score. score: Output only. Safety score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SafetySetting(_messages.Message): r"""Safety settings. Enums: CategoryValueValuesEnum: Required. Harm category. MethodValueValuesEnum: Optional. Specify if the threshold is used for probability or severity score. If not specified, the threshold is used for probability score. ThresholdValueValuesEnum: Required. The harm block threshold. Fields: category: Required. Harm category. method: Optional. Specify if the threshold is used for probability or severity score. If not specified, the threshold is used for probability score. threshold: Required. The harm block threshold. """ class CategoryValueValuesEnum(_messages.Enum): r"""Required. Harm category. Values: HARM_CATEGORY_UNSPECIFIED: The harm category is unspecified. HARM_CATEGORY_HATE_SPEECH: The harm category is hate speech. HARM_CATEGORY_DANGEROUS_CONTENT: The harm category is dangerous content. HARM_CATEGORY_HARASSMENT: The harm category is harassment. HARM_CATEGORY_SEXUALLY_EXPLICIT: The harm category is sexually explicit content. HARM_CATEGORY_CIVIC_INTEGRITY: Deprecated: Election filter is not longer supported. The harm category is civic integrity. HARM_CATEGORY_IMAGE_HATE: The harm category is image hate. HARM_CATEGORY_IMAGE_DANGEROUS_CONTENT: The harm category is image dangerous content. HARM_CATEGORY_IMAGE_HARASSMENT: The harm category is image harassment. HARM_CATEGORY_IMAGE_SEXUALLY_EXPLICIT: The harm category is image sexually explicit content. """ HARM_CATEGORY_UNSPECIFIED = 0 HARM_CATEGORY_HATE_SPEECH = 1 HARM_CATEGORY_DANGEROUS_CONTENT = 2 HARM_CATEGORY_HARASSMENT = 3 HARM_CATEGORY_SEXUALLY_EXPLICIT = 4 HARM_CATEGORY_CIVIC_INTEGRITY = 5 HARM_CATEGORY_IMAGE_HATE = 6 HARM_CATEGORY_IMAGE_DANGEROUS_CONTENT = 7 HARM_CATEGORY_IMAGE_HARASSMENT = 8 HARM_CATEGORY_IMAGE_SEXUALLY_EXPLICIT = 9 class MethodValueValuesEnum(_messages.Enum): r"""Optional. Specify if the threshold is used for probability or severity score. If not specified, the threshold is used for probability score. Values: HARM_BLOCK_METHOD_UNSPECIFIED: The harm block method is unspecified. SEVERITY: The harm block method uses both probability and severity scores. PROBABILITY: The harm block method uses the probability score. """ HARM_BLOCK_METHOD_UNSPECIFIED = 0 SEVERITY = 1 PROBABILITY = 2 class ThresholdValueValuesEnum(_messages.Enum): r"""Required. The harm block threshold. Values: HARM_BLOCK_THRESHOLD_UNSPECIFIED: Unspecified harm block threshold. BLOCK_LOW_AND_ABOVE: Block low threshold and above (i.e. block more). BLOCK_MEDIUM_AND_ABOVE: Block medium threshold and above. BLOCK_ONLY_HIGH: Block only high threshold (i.e. block less). BLOCK_NONE: Block none. OFF: Turn off the safety filter. """ HARM_BLOCK_THRESHOLD_UNSPECIFIED = 0 BLOCK_LOW_AND_ABOVE = 1 BLOCK_MEDIUM_AND_ABOVE = 2 BLOCK_ONLY_HIGH = 3 BLOCK_NONE = 4 OFF = 5 category = _messages.EnumField('CategoryValueValuesEnum', 1) method = _messages.EnumField('MethodValueValuesEnum', 2) threshold = _messages.EnumField('ThresholdValueValuesEnum', 3) class GoogleCloudAiplatformV1SafetySpec(_messages.Message): r"""Spec for safety metric. Fields: version: Optional. Which version to use for evaluation. """ version = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SampledShapleyAttribution(_messages.Message): r"""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. Fields: pathCount: Required. The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively. """ pathCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SamplingStrategy(_messages.Message): r"""Sampling Strategy for logging, can be for both training and prediction dataset. Fields: randomSampleConfig: Random sample config. Will support more sampling strategies later. """ randomSampleConfig = _messages.MessageField('GoogleCloudAiplatformV1SamplingStrategyRandomSampleConfig', 1) class GoogleCloudAiplatformV1SamplingStrategyRandomSampleConfig(_messages.Message): r"""Requests are randomly selected. Fields: sampleRate: Sample rate (0, 1] """ sampleRate = _messages.FloatField(1) class GoogleCloudAiplatformV1SavedQuery(_messages.Message): r"""A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters. Fields: annotationFilter: Output only. Filters on the Annotations in the dataset. annotationSpecCount: Output only. Number of AnnotationSpecs in the context of the SavedQuery. createTime: Output only. Timestamp when this SavedQuery was created. displayName: Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters. etag: Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens. metadata: Some additional information about the SavedQuery. name: Output only. Resource name of the SavedQuery. problemType: Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING supportAutomlTraining: Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training. updateTime: Output only. Timestamp when SavedQuery was last updated. """ annotationFilter = _messages.StringField(1) annotationSpecCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) createTime = _messages.StringField(3) displayName = _messages.StringField(4) etag = _messages.StringField(5) metadata = _messages.MessageField('extra_types.JsonValue', 6) name = _messages.StringField(7) problemType = _messages.StringField(8) supportAutomlTraining = _messages.BooleanField(9) updateTime = _messages.StringField(10) class GoogleCloudAiplatformV1Scalar(_messages.Message): r"""One point viewable on a scalar metric plot. Fields: value: Value of the point at this step / timestamp. """ value = _messages.FloatField(1) class GoogleCloudAiplatformV1Schedule(_messages.Message): r"""An instance of a Schedule periodically schedules runs to make API calls based on user specified time specification and API request type. Enums: StateValueValuesEnum: Output only. The state of this Schedule. Fields: allowQueueing: Optional. Whether new scheduled runs can be queued when max_concurrent_runs limit is reached. If set to true, new runs will be queued instead of skipped. Default to false. catchUp: Output only. Whether to backfill missed runs when the schedule is resumed from PAUSED state. If set to true, all missed runs will be scheduled. New runs will be scheduled after the backfill is complete. Default to false. createNotebookExecutionJobRequest: Request for NotebookService.CreateNotebookExecutionJob. createPipelineJobRequest: Request for PipelineService.CreatePipelineJob. CreatePipelineJobRequest.parent field is required (format: projects/{project}/locations/{location}). createTime: Output only. Timestamp when this Schedule was created. cron: Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *". displayName: Required. User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters. endTime: Optional. Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either end_time is reached or when scheduled_run_count >= max_run_count. If not specified, new runs will keep getting scheduled until this Schedule is paused or deleted. Already scheduled runs will be allowed to complete. Unset if not specified. lastPauseTime: Output only. Timestamp when this Schedule was last paused. Unset if never paused. lastResumeTime: Output only. Timestamp when this Schedule was last resumed. Unset if never resumed from pause. lastScheduledRunResponse: Output only. Response of the last scheduled run. This is the response for starting the scheduled requests and not the execution of the operations/jobs created by the requests (if applicable). Unset if no run has been scheduled yet. maxConcurrentRunCount: Required. Maximum number of runs that can be started concurrently for this Schedule. This is the limit for starting the scheduled requests and not the execution of the operations/jobs created by the requests (if applicable). maxRunCount: Optional. Maximum run count of the schedule. If specified, The schedule will be completed when either started_run_count >= max_run_count or when end_time is reached. If not specified, new runs will keep getting scheduled until this Schedule is paused or deleted. Already scheduled runs will be allowed to complete. Unset if not specified. name: Immutable. The resource name of the Schedule. nextRunTime: Output only. Timestamp when this Schedule should schedule the next run. Having a next_run_time in the past means the runs are being started behind schedule. startTime: Optional. Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified. startedRunCount: Output only. The number of runs started by this schedule. state: Output only. The state of this Schedule. updateTime: Output only. Timestamp when this Schedule was updated. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The state of this Schedule. Values: STATE_UNSPECIFIED: Unspecified. ACTIVE: The Schedule is active. Runs are being scheduled on the user- specified timespec. PAUSED: The schedule is paused. No new runs will be created until the schedule is resumed. Already started runs will be allowed to complete. COMPLETED: The Schedule is completed. No new runs will be scheduled. Already started runs will be allowed to complete. Schedules in completed state cannot be paused or resumed. """ STATE_UNSPECIFIED = 0 ACTIVE = 1 PAUSED = 2 COMPLETED = 3 allowQueueing = _messages.BooleanField(1) catchUp = _messages.BooleanField(2) createNotebookExecutionJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CreateNotebookExecutionJobRequest', 3) createPipelineJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CreatePipelineJobRequest', 4) createTime = _messages.StringField(5) cron = _messages.StringField(6) displayName = _messages.StringField(7) endTime = _messages.StringField(8) lastPauseTime = _messages.StringField(9) lastResumeTime = _messages.StringField(10) lastScheduledRunResponse = _messages.MessageField('GoogleCloudAiplatformV1ScheduleRunResponse', 11) maxConcurrentRunCount = _messages.IntegerField(12) maxRunCount = _messages.IntegerField(13) name = _messages.StringField(14) nextRunTime = _messages.StringField(15) startTime = _messages.StringField(16) startedRunCount = _messages.IntegerField(17) state = _messages.EnumField('StateValueValuesEnum', 18) updateTime = _messages.StringField(19) class GoogleCloudAiplatformV1ScheduleRunResponse(_messages.Message): r"""Status of a scheduled run. Fields: runResponse: The response of the scheduled run. scheduledRunTime: The scheduled run time based on the user-specified schedule. """ runResponse = _messages.StringField(1) scheduledRunTime = _messages.StringField(2) class GoogleCloudAiplatformV1Scheduling(_messages.Message): r"""All parameters related to queuing and scheduling of custom jobs. Enums: StrategyValueValuesEnum: Optional. This determines which type of scheduling strategy to use. Fields: disableRetries: Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides `Scheduling.restart_job_on_worker_restart` to false. maxWaitDuration: Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours. restartJobOnWorkerRestart: Optional. Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job. strategy: Optional. This determines which type of scheduling strategy to use. timeout: Optional. The maximum job running time. The default is 7 days. """ class StrategyValueValuesEnum(_messages.Enum): r"""Optional. This determines which type of scheduling strategy to use. Values: STRATEGY_UNSPECIFIED: Strategy will default to STANDARD. ON_DEMAND: Deprecated. Regular on-demand provisioning strategy. LOW_COST: Deprecated. Low cost by making potential use of spot resources. STANDARD: Standard provisioning strategy uses regular on-demand resources. SPOT: Spot provisioning strategy uses spot resources. FLEX_START: Flex Start strategy uses DWS to queue for resources. """ STRATEGY_UNSPECIFIED = 0 ON_DEMAND = 1 LOW_COST = 2 STANDARD = 3 SPOT = 4 FLEX_START = 5 disableRetries = _messages.BooleanField(1) maxWaitDuration = _messages.StringField(2) restartJobOnWorkerRestart = _messages.BooleanField(3) strategy = _messages.EnumField('StrategyValueValuesEnum', 4) timeout = _messages.StringField(5) class GoogleCloudAiplatformV1Schema(_messages.Message): r"""Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. Enums: TypeValueValuesEnum: Optional. The type of the data. Messages: DefsValue: Optional. A map of definitions for use by `ref` Only allowed at the root of the schema. PropertiesValue: Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT. Fields: additionalProperties: Optional. Can either be a boolean or an object; controls the presence of additional properties. anyOf: Optional. The value should be validated against any (one or more) of the subschemas in the list. default: Optional. Default value of the data. defs: Optional. A map of definitions for use by `ref` Only allowed at the root of the schema. description: Optional. The description of the data. enum: Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:["EAST", NORTH", "SOUTH", "WEST"]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:["101", "201", "301"]} example: Optional. Example of the object. Will only populated when the object is the root. format: Optional. The format of the data. Supported formats: for NUMBER type: "float", "double" for INTEGER type: "int32", "int64" for STRING type: "email", "byte", etc items: Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY. maxItems: Optional. Maximum number of the elements for Type.ARRAY. maxLength: Optional. Maximum length of the Type.STRING maxProperties: Optional. Maximum number of the properties for Type.OBJECT. maximum: Optional. Maximum value of the Type.INTEGER and Type.NUMBER minItems: Optional. Minimum number of the elements for Type.ARRAY. minLength: Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING minProperties: Optional. Minimum number of the properties for Type.OBJECT. minimum: Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER nullable: Optional. Indicates if the value may be null. pattern: Optional. Pattern of the Type.STRING to restrict a string to a regular expression. properties: Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT. propertyOrdering: Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties. ref: Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named "Pet": type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the "pet" property is a reference to the schema node named "Pet". See details in https://json- schema.org/understanding-json-schema/structuring required: Optional. Required properties of Type.OBJECT. title: Optional. The title of the Schema. type: Optional. The type of the data. """ class TypeValueValuesEnum(_messages.Enum): r"""Optional. The type of the data. Values: TYPE_UNSPECIFIED: Not specified, should not be used. STRING: OpenAPI string type NUMBER: OpenAPI number type INTEGER: OpenAPI integer type BOOLEAN: OpenAPI boolean type ARRAY: OpenAPI array type OBJECT: OpenAPI object type NULL: Null type """ TYPE_UNSPECIFIED = 0 STRING = 1 NUMBER = 2 INTEGER = 3 BOOLEAN = 4 ARRAY = 5 OBJECT = 6 NULL = 7 @encoding.MapUnrecognizedFields('additionalProperties') class DefsValue(_messages.Message): r"""Optional. A map of definitions for use by `ref` Only allowed at the root of the schema. Messages: AdditionalProperty: An additional property for a DefsValue object. Fields: additionalProperties: Additional properties of type DefsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a DefsValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1Schema attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1Schema', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1Schema attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1Schema', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) additionalProperties = _messages.MessageField('extra_types.JsonValue', 1) anyOf = _messages.MessageField('GoogleCloudAiplatformV1Schema', 2, repeated=True) default = _messages.MessageField('extra_types.JsonValue', 3) defs = _messages.MessageField('DefsValue', 4) description = _messages.StringField(5) enum = _messages.StringField(6, repeated=True) example = _messages.MessageField('extra_types.JsonValue', 7) format = _messages.StringField(8) items = _messages.MessageField('GoogleCloudAiplatformV1Schema', 9) maxItems = _messages.IntegerField(10) maxLength = _messages.IntegerField(11) maxProperties = _messages.IntegerField(12) maximum = _messages.FloatField(13) minItems = _messages.IntegerField(14) minLength = _messages.IntegerField(15) minProperties = _messages.IntegerField(16) minimum = _messages.FloatField(17) nullable = _messages.BooleanField(18) pattern = _messages.StringField(19) properties = _messages.MessageField('PropertiesValue', 20) propertyOrdering = _messages.StringField(21, repeated=True) ref = _messages.StringField(22) required = _messages.StringField(23, repeated=True) title = _messages.StringField(24) type = _messages.EnumField('TypeValueValuesEnum', 25) class GoogleCloudAiplatformV1SchemaAnnotationSpecColor(_messages.Message): r"""An entry of mapping between color and AnnotationSpec. The mapping is used in segmentation mask. Fields: color: The color of the AnnotationSpec in a segmentation mask. displayName: The display name of the AnnotationSpec represented by the color in the segmentation mask. id: The ID of the AnnotationSpec represented by the color in the segmentation mask. """ color = _messages.MessageField('GoogleTypeColor', 1) displayName = _messages.StringField(2) id = _messages.StringField(3) class GoogleCloudAiplatformV1SchemaImageBoundingBoxAnnotation(_messages.Message): r"""Annotation details specific to image object detection. Fields: annotationSpecId: The resource Id of the AnnotationSpec that this Annotation pertains to. displayName: The display name of the AnnotationSpec that this Annotation pertains to. xMax: The rightmost coordinate of the bounding box. xMin: The leftmost coordinate of the bounding box. yMax: The bottommost coordinate of the bounding box. yMin: The topmost coordinate of the bounding box. """ annotationSpecId = _messages.StringField(1) displayName = _messages.StringField(2) xMax = _messages.FloatField(3) xMin = _messages.FloatField(4) yMax = _messages.FloatField(5) yMin = _messages.FloatField(6) class GoogleCloudAiplatformV1SchemaImageClassificationAnnotation(_messages.Message): r"""Annotation details specific to image classification. Fields: annotationSpecId: The resource Id of the AnnotationSpec that this Annotation pertains to. displayName: The display name of the AnnotationSpec that this Annotation pertains to. """ annotationSpecId = _messages.StringField(1) displayName = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaImageDataItem(_messages.Message): r"""Payload of Image DataItem. Fields: gcsUri: Required. Google Cloud Storage URI points to the original image in user's bucket. The image is up to 30MB in size. mimeType: Output only. The mime type of the content of the image. Only the images in below listed mime types are supported. - image/jpeg - image/gif - image/png - image/webp - image/bmp - image/tiff - image/vnd.microsoft.icon """ gcsUri = _messages.StringField(1) mimeType = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaImageDatasetMetadata(_messages.Message): r"""The metadata of Datasets that contain Image DataItems. Fields: dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage describing payload of the Image DataItems that belong to this Dataset. gcsBucket: Google Cloud Storage Bucket name that contains the blob data of this Dataset. """ dataItemSchemaUri = _messages.StringField(1) gcsBucket = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaImageSegmentationAnnotation(_messages.Message): r"""Annotation details specific to image segmentation. Fields: maskAnnotation: Mask based segmentation annotation. Only one mask annotation can exist for one image. polygonAnnotation: Polygon annotation. polylineAnnotation: Polyline annotation. """ maskAnnotation = _messages.MessageField('GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationMaskAnnotation', 1) polygonAnnotation = _messages.MessageField('GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationPolygonAnnotation', 2) polylineAnnotation = _messages.MessageField('GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationPolylineAnnotation', 3) class GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationMaskAnnotation(_messages.Message): r"""The mask based segmentation annotation. Fields: annotationSpecColors: The mapping between color and AnnotationSpec for this Annotation. maskGcsUri: Google Cloud Storage URI that points to the mask image. The image must be in PNG format. It must have the same size as the DataItem's image. Each pixel in the image mask represents the AnnotationSpec which the pixel in the image DataItem belong to. Each color is mapped to one AnnotationSpec based on annotation_spec_colors. """ annotationSpecColors = _messages.MessageField('GoogleCloudAiplatformV1SchemaAnnotationSpecColor', 1, repeated=True) maskGcsUri = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationPolygonAnnotation(_messages.Message): r"""Represents a polygon in image. Fields: annotationSpecId: The resource Id of the AnnotationSpec that this Annotation pertains to. displayName: The display name of the AnnotationSpec that this Annotation pertains to. vertexes: The vertexes are connected one by one and the last vertex is connected to the first one to represent a polygon. """ annotationSpecId = _messages.StringField(1) displayName = _messages.StringField(2) vertexes = _messages.MessageField('GoogleCloudAiplatformV1SchemaVertex', 3, repeated=True) class GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationPolylineAnnotation(_messages.Message): r"""Represents a polyline in image. Fields: annotationSpecId: The resource Id of the AnnotationSpec that this Annotation pertains to. displayName: The display name of the AnnotationSpec that this Annotation pertains to. vertexes: The vertexes are connected one by one and the last vertex in not connected to the first one. """ annotationSpecId = _messages.StringField(1) displayName = _messages.StringField(2) vertexes = _messages.MessageField('GoogleCloudAiplatformV1SchemaVertex', 3, repeated=True) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsBoundingBoxMetrics(_messages.Message): r"""Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds. Fields: confidenceMetrics: Metrics for each label-match confidence_threshold from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is derived from them. iouThreshold: The intersection-over-union threshold value used to compute this metrics entry. meanAveragePrecision: The mean average precision, most often close to `auPrc`. """ confidenceMetrics = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsBoundingBoxMetricsConfidenceMetrics', 1, repeated=True) iouThreshold = _messages.FloatField(2, variant=_messages.Variant.FLOAT) meanAveragePrecision = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsBoundingBoxMetricsConfidenceMetrics(_messages.Message): r"""Metrics for a single confidence threshold. Fields: confidenceThreshold: The confidence threshold value used to compute the metrics. f1Score: The harmonic mean of recall and precision. precision: Precision under the given confidence threshold. recall: Recall under the given confidence threshold. """ confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) f1Score = _messages.FloatField(2, variant=_messages.Variant.FLOAT) precision = _messages.FloatField(3, variant=_messages.Variant.FLOAT) recall = _messages.FloatField(4, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsClassificationEvaluationMetrics(_messages.Message): r"""Metrics for classification evaluation results. Fields: auPrc: The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation. auRoc: The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation. confidenceMetrics: Metrics for each `confidenceThreshold` in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and `positionThreshold` = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of `positionThreshold`, but from these no aggregated metrics are computed. confusionMatrix: Confusion matrix of the evaluation. logLoss: The Log Loss metric. """ auPrc = _messages.FloatField(1, variant=_messages.Variant.FLOAT) auRoc = _messages.FloatField(2, variant=_messages.Variant.FLOAT) confidenceMetrics = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics', 3, repeated=True) confusionMatrix = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrix', 4) logLoss = _messages.FloatField(5, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaModelevaluationMetricsClassificationEvalu ationMetricsConfidenceMetrics object. Fields: confidenceThreshold: Metrics are computed with an assumption that the Model never returns predictions with score lower than this value. confusionMatrix: Confusion matrix of the evaluation for this confidence_threshold. f1Score: The harmonic mean of recall and precision. For summary metrics, it computes the micro-averaged F1 score. f1ScoreAt1: The harmonic mean of recallAt1 and precisionAt1. f1ScoreMacro: Macro-averaged F1 Score. f1ScoreMicro: Micro-averaged F1 Score. falseNegativeCount: The number of ground truth labels that are not matched by a Model created label. falsePositiveCount: The number of Model created labels that do not match a ground truth label. falsePositiveRate: False Positive Rate for the given confidence threshold. falsePositiveRateAt1: The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem. maxPredictions: Metrics are computed with an assumption that the Model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the `confidenceThreshold`. precision: Precision for the given confidence threshold. precisionAt1: The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem. recall: Recall (True Positive Rate) for the given confidence threshold. recallAt1: The Recall (True Positive Rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem. trueNegativeCount: The number of labels that were not created by the Model, but if they would, they would not match a ground truth label. truePositiveCount: The number of Model created labels that match a ground truth label. """ confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) confusionMatrix = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrix', 2) f1Score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) f1ScoreAt1 = _messages.FloatField(4, variant=_messages.Variant.FLOAT) f1ScoreMacro = _messages.FloatField(5, variant=_messages.Variant.FLOAT) f1ScoreMicro = _messages.FloatField(6, variant=_messages.Variant.FLOAT) falseNegativeCount = _messages.IntegerField(7) falsePositiveCount = _messages.IntegerField(8) falsePositiveRate = _messages.FloatField(9, variant=_messages.Variant.FLOAT) falsePositiveRateAt1 = _messages.FloatField(10, variant=_messages.Variant.FLOAT) maxPredictions = _messages.IntegerField(11, variant=_messages.Variant.INT32) precision = _messages.FloatField(12, variant=_messages.Variant.FLOAT) precisionAt1 = _messages.FloatField(13, variant=_messages.Variant.FLOAT) recall = _messages.FloatField(14, variant=_messages.Variant.FLOAT) recallAt1 = _messages.FloatField(15, variant=_messages.Variant.FLOAT) trueNegativeCount = _messages.IntegerField(16) truePositiveCount = _messages.IntegerField(17) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrix(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrix object. Messages: RowsValueListEntry: Single entry in a RowsValue. Fields: annotationSpecs: AnnotationSpecs used in the confusion matrix. For AutoML Text Extraction, a special negative AnnotationSpec with empty `id` and `displayName` of "NULL" will be added as the last element. rows: Rows in the confusion matrix. The number of rows is equal to the size of `annotationSpecs`. `rowsi` is the number of DataItems that have ground truth of the `annotationSpecs[i]` and are predicted as `annotationSpecs[j]` by the Model being evaluated. For Text Extraction, when `annotationSpecs[i]` is the last element in `annotationSpecs`, i.e. the special negative AnnotationSpec, `rowsi` is the number of predicted entities of `annoatationSpec[j]` that are not labeled as any of the ground truth AnnotationSpec. When annotationSpecs[j] is the special negative AnnotationSpec, `rowsi` is the number of entities have ground truth of `annotationSpec[i]` that are not predicted as an entity by the Model. The value of the last cell, i.e. `rowi` where i == j and `annotationSpec[i]` is the special negative AnnotationSpec, is always 0. """ class RowsValueListEntry(_messages.Message): r"""Single entry in a RowsValue. Fields: entry: A extra_types.JsonValue attribute. """ entry = _messages.MessageField('extra_types.JsonValue', 1, repeated=True) annotationSpecs = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrixAnnotationSpecRef', 1, repeated=True) rows = _messages.MessageField('RowsValueListEntry', 2, repeated=True) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrixAnnotationSpecRef(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrixAnno tationSpecRef object. Fields: displayName: Display name of the AnnotationSpec. id: ID of the AnnotationSpec. """ displayName = _messages.StringField(1) id = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsForecastingEvaluationMetrics(_messages.Message): r"""Metrics for forecasting evaluation results. Fields: meanAbsoluteError: Mean Absolute Error (MAE). meanAbsolutePercentageError: Mean absolute percentage error. Infinity when there are zeros in the ground truth. quantileMetrics: The quantile metrics entries for each quantile. rSquared: Coefficient of determination as Pearson correlation coefficient. Undefined when ground truth or predictions are constant or near constant. rootMeanSquaredError: Root Mean Squared Error (RMSE). rootMeanSquaredLogError: Root mean squared log error. Undefined when there are negative ground truth values or predictions. rootMeanSquaredPercentageError: Root Mean Square Percentage Error. Square root of MSPE. Undefined/imaginary when MSPE is negative. weightedAbsolutePercentageError: Weighted Absolute Percentage Error. Does not use weights, this is just what the metric is called. Undefined if actual values sum to zero. Will be very large if actual values sum to a very small number. """ meanAbsoluteError = _messages.FloatField(1, variant=_messages.Variant.FLOAT) meanAbsolutePercentageError = _messages.FloatField(2, variant=_messages.Variant.FLOAT) quantileMetrics = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsForecastingEvaluationMetricsQuantileMetricsEntry', 3, repeated=True) rSquared = _messages.FloatField(4, variant=_messages.Variant.FLOAT) rootMeanSquaredError = _messages.FloatField(5, variant=_messages.Variant.FLOAT) rootMeanSquaredLogError = _messages.FloatField(6, variant=_messages.Variant.FLOAT) rootMeanSquaredPercentageError = _messages.FloatField(7, variant=_messages.Variant.FLOAT) weightedAbsolutePercentageError = _messages.FloatField(8, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsForecastingEvaluationMetricsQuantileMetricsEntry(_messages.Message): r"""Entry for the Quantiles loss type optimization objective. Fields: observedQuantile: This is a custom metric that calculates the percentage of true values that were less than the predicted value for that quantile. Only populated when optimization_objective is minimize- quantile-loss and each entry corresponds to an entry in quantiles The percent value can be used to compare with the quantile value, which is the target value. quantile: The quantile for this entry. scaledPinballLoss: The scaled pinball loss of this quantile. """ observedQuantile = _messages.FloatField(1) quantile = _messages.FloatField(2) scaledPinballLoss = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsGeneralTextGenerationEvaluationMetrics(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaModelevaluationMetricsGeneralTextGenerati onEvaluationMetrics object. Fields: bleu: BLEU (bilingual evaluation understudy) scores based on sacrebleu implementation. rougeLSum: ROUGE-L (Longest Common Subsequence) scoring at summary level. """ bleu = _messages.FloatField(1, variant=_messages.Variant.FLOAT) rougeLSum = _messages.FloatField(2, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsImageObjectDetectionEvaluationMetrics(_messages.Message): r"""Metrics for image object detection evaluation results. Fields: boundingBoxMeanAveragePrecision: The single metric for bounding boxes evaluation: the `meanAveragePrecision` averaged over all `boundingBoxMetricsEntries`. boundingBoxMetrics: The bounding boxes match metrics for each intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair. evaluatedBoundingBoxCount: The total number of bounding boxes (i.e. summed over all images) the ground truth used to create this evaluation had. """ boundingBoxMeanAveragePrecision = _messages.FloatField(1, variant=_messages.Variant.FLOAT) boundingBoxMetrics = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsBoundingBoxMetrics', 2, repeated=True) evaluatedBoundingBoxCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsImageSegmentationEvaluationMetrics(_messages.Message): r"""Metrics for image segmentation evaluation results. Fields: confidenceMetricsEntries: Metrics for each confidenceThreshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 Precision-recall curve can be derived from it. """ confidenceMetricsEntries = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsImageSegmentationEvaluationMetricsConfidenceMetricsEntry', 1, repeated=True) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsImageSegmentationEvaluationMetricsConfidenceMetricsEntry(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaModelevaluationMetricsImageSegmentationEv aluationMetricsConfidenceMetricsEntry object. Fields: confidenceThreshold: Metrics are computed with an assumption that the model never returns predictions with score lower than this value. confusionMatrix: Confusion matrix for the given confidence threshold. diceScoreCoefficient: DSC or the F1 score, The harmonic mean of recall and precision. iouScore: The intersection-over-union score. The measure of overlap of the annotation's category mask with ground truth category mask on the DataItem. precision: Precision for the given confidence threshold. recall: Recall (True Positive Rate) for the given confidence threshold. """ confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) confusionMatrix = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrix', 2) diceScoreCoefficient = _messages.FloatField(3, variant=_messages.Variant.FLOAT) iouScore = _messages.FloatField(4, variant=_messages.Variant.FLOAT) precision = _messages.FloatField(5, variant=_messages.Variant.FLOAT) recall = _messages.FloatField(6, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsPairwiseTextGenerationEvaluationMetrics(_messages.Message): r"""Metrics for general pairwise text generation evaluation results. Fields: accuracy: Fraction of cases where the autorater agreed with the human raters. baselineModelWinRate: Percentage of time the autorater decided the baseline model had the better response. cohensKappa: A measurement of agreement between the autorater and human raters that takes the likelihood of random agreement into account. f1Score: Harmonic mean of precision and recall. falseNegativeCount: Number of examples where the autorater chose the baseline model, but humans preferred the model. falsePositiveCount: Number of examples where the autorater chose the model, but humans preferred the baseline model. humanPreferenceBaselineModelWinRate: Percentage of time humans decided the baseline model had the better response. humanPreferenceModelWinRate: Percentage of time humans decided the model had the better response. modelWinRate: Percentage of time the autorater decided the model had the better response. precision: Fraction of cases where the autorater and humans thought the model had a better response out of all cases where the autorater thought the model had a better response. True positive divided by all positive. recall: Fraction of cases where the autorater and humans thought the model had a better response out of all cases where the humans thought the model had a better response. trueNegativeCount: Number of examples where both the autorater and humans decided that the model had the worse response. truePositiveCount: Number of examples where both the autorater and humans decided that the model had the better response. """ accuracy = _messages.FloatField(1, variant=_messages.Variant.FLOAT) baselineModelWinRate = _messages.FloatField(2, variant=_messages.Variant.FLOAT) cohensKappa = _messages.FloatField(3, variant=_messages.Variant.FLOAT) f1Score = _messages.FloatField(4, variant=_messages.Variant.FLOAT) falseNegativeCount = _messages.IntegerField(5) falsePositiveCount = _messages.IntegerField(6) humanPreferenceBaselineModelWinRate = _messages.FloatField(7, variant=_messages.Variant.FLOAT) humanPreferenceModelWinRate = _messages.FloatField(8, variant=_messages.Variant.FLOAT) modelWinRate = _messages.FloatField(9, variant=_messages.Variant.FLOAT) precision = _messages.FloatField(10, variant=_messages.Variant.FLOAT) recall = _messages.FloatField(11, variant=_messages.Variant.FLOAT) trueNegativeCount = _messages.IntegerField(12) truePositiveCount = _messages.IntegerField(13) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsQuestionAnsweringEvaluationMetrics(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaModelevaluationMetricsQuestionAnsweringEv aluationMetrics object. Fields: exactMatch: The rate at which the input predicted strings exactly match their references. """ exactMatch = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsRegressionEvaluationMetrics(_messages.Message): r"""Metrics for regression evaluation results. Fields: meanAbsoluteError: Mean Absolute Error (MAE). meanAbsolutePercentageError: Mean absolute percentage error. Infinity when there are zeros in the ground truth. rSquared: Coefficient of determination as Pearson correlation coefficient. Undefined when ground truth or predictions are constant or near constant. rootMeanSquaredError: Root Mean Squared Error (RMSE). rootMeanSquaredLogError: Root mean squared log error. Undefined when there are negative ground truth values or predictions. """ meanAbsoluteError = _messages.FloatField(1, variant=_messages.Variant.FLOAT) meanAbsolutePercentageError = _messages.FloatField(2, variant=_messages.Variant.FLOAT) rSquared = _messages.FloatField(3, variant=_messages.Variant.FLOAT) rootMeanSquaredError = _messages.FloatField(4, variant=_messages.Variant.FLOAT) rootMeanSquaredLogError = _messages.FloatField(5, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsSummarizationEvaluationMetrics(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaModelevaluationMetricsSummarizationEvalua tionMetrics object. Fields: rougeLSum: ROUGE-L (Longest Common Subsequence) scoring at summary level. """ rougeLSum = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsTextExtractionEvaluationMetrics(_messages.Message): r"""Metrics for text extraction evaluation results. Fields: confidenceMetrics: Metrics that have confidence thresholds. Precision- recall curve can be derived from them. confusionMatrix: Confusion matrix of the evaluation. Only set for Models where number of AnnotationSpecs is no more than 10. Only set for ModelEvaluations, not for ModelEvaluationSlices. """ confidenceMetrics = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsTextExtractionEvaluationMetricsConfidenceMetrics', 1, repeated=True) confusionMatrix = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrix', 2) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsTextExtractionEvaluationMetricsConfidenceMetrics(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaModelevaluationMetricsTextExtractionEvalu ationMetricsConfidenceMetrics object. Fields: confidenceThreshold: Metrics are computed with an assumption that the Model never returns predictions with score lower than this value. f1Score: The harmonic mean of recall and precision. precision: Precision for the given confidence threshold. recall: Recall (True Positive Rate) for the given confidence threshold. """ confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) f1Score = _messages.FloatField(2, variant=_messages.Variant.FLOAT) precision = _messages.FloatField(3, variant=_messages.Variant.FLOAT) recall = _messages.FloatField(4, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsTextSentimentEvaluationMetrics(_messages.Message): r"""Model evaluation metrics for text sentiment problems. Fields: confusionMatrix: Confusion matrix of the evaluation. Only set for ModelEvaluations, not for ModelEvaluationSlices. f1Score: The harmonic mean of recall and precision. linearKappa: Linear weighted kappa. Only set for ModelEvaluations, not for ModelEvaluationSlices. meanAbsoluteError: Mean absolute error. Only set for ModelEvaluations, not for ModelEvaluationSlices. meanSquaredError: Mean squared error. Only set for ModelEvaluations, not for ModelEvaluationSlices. precision: Precision. quadraticKappa: Quadratic weighted kappa. Only set for ModelEvaluations, not for ModelEvaluationSlices. recall: Recall. """ confusionMatrix = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrix', 1) f1Score = _messages.FloatField(2, variant=_messages.Variant.FLOAT) linearKappa = _messages.FloatField(3, variant=_messages.Variant.FLOAT) meanAbsoluteError = _messages.FloatField(4, variant=_messages.Variant.FLOAT) meanSquaredError = _messages.FloatField(5, variant=_messages.Variant.FLOAT) precision = _messages.FloatField(6, variant=_messages.Variant.FLOAT) quadraticKappa = _messages.FloatField(7, variant=_messages.Variant.FLOAT) recall = _messages.FloatField(8, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsTrackMetrics(_messages.Message): r"""UNIMPLEMENTED. Track matching model metrics for a single track match threshold and multiple label match confidence thresholds. Fields: confidenceMetrics: Metrics for each label-match `confidenceThreshold` from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is derived from them. iouThreshold: The intersection-over-union threshold value between bounding boxes across frames used to compute this metric entry. meanBoundingBoxIou: The mean bounding box iou over all confidence thresholds. meanMismatchRate: The mean mismatch rate over all confidence thresholds. meanTrackingAveragePrecision: The mean average precision over all confidence thresholds. """ confidenceMetrics = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsTrackMetricsConfidenceMetrics', 1, repeated=True) iouThreshold = _messages.FloatField(2, variant=_messages.Variant.FLOAT) meanBoundingBoxIou = _messages.FloatField(3, variant=_messages.Variant.FLOAT) meanMismatchRate = _messages.FloatField(4, variant=_messages.Variant.FLOAT) meanTrackingAveragePrecision = _messages.FloatField(5, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsTrackMetricsConfidenceMetrics(_messages.Message): r"""Metrics for a single confidence threshold. Fields: boundingBoxIou: Bounding box intersection-over-union precision. Measures how well the bounding boxes overlap between each other (e.g. complete overlap or just barely above iou_threshold). confidenceThreshold: The confidence threshold value used to compute the metrics. mismatchRate: Mismatch rate, which measures the tracking consistency, i.e. correctness of instance ID continuity. trackingPrecision: Tracking precision. trackingRecall: Tracking recall. """ boundingBoxIou = _messages.FloatField(1, variant=_messages.Variant.FLOAT) confidenceThreshold = _messages.FloatField(2, variant=_messages.Variant.FLOAT) mismatchRate = _messages.FloatField(3, variant=_messages.Variant.FLOAT) trackingPrecision = _messages.FloatField(4, variant=_messages.Variant.FLOAT) trackingRecall = _messages.FloatField(5, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsVideoActionMetrics(_messages.Message): r"""The Evaluation metrics given a specific precision_window_length. Fields: confidenceMetrics: Metrics for each label-match confidence_threshold from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. meanAveragePrecision: The mean average precision. precisionWindowLength: This VideoActionMetrics is calculated based on this prediction window length. If the predicted action's timestamp is inside the time window whose center is the ground truth action's timestamp with this specific length, the prediction result is treated as a true positive. """ confidenceMetrics = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsVideoActionMetricsConfidenceMetrics', 1, repeated=True) meanAveragePrecision = _messages.FloatField(2, variant=_messages.Variant.FLOAT) precisionWindowLength = _messages.StringField(3) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsVideoActionMetricsConfidenceMetrics(_messages.Message): r"""Metrics for a single confidence threshold. Fields: confidenceThreshold: Output only. The confidence threshold value used to compute the metrics. f1Score: Output only. The harmonic mean of recall and precision. precision: Output only. Precision for the given confidence threshold. recall: Output only. Recall for the given confidence threshold. """ confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) f1Score = _messages.FloatField(2, variant=_messages.Variant.FLOAT) precision = _messages.FloatField(3, variant=_messages.Variant.FLOAT) recall = _messages.FloatField(4, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsVideoActionRecognitionMetrics(_messages.Message): r"""Model evaluation metrics for video action recognition. Fields: evaluatedActionCount: The number of ground truth actions used to create this evaluation. videoActionMetrics: The metric entries for precision window lengths: 1s,2s,3s. """ evaluatedActionCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) videoActionMetrics = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsVideoActionMetrics', 2, repeated=True) class GoogleCloudAiplatformV1SchemaModelevaluationMetricsVideoObjectTrackingMetrics(_messages.Message): r"""Model evaluation metrics for video object tracking problems. Evaluates prediction quality of both labeled bounding boxes and labeled tracks (i.e. series of bounding boxes sharing same label and instance ID). Fields: boundingBoxMeanAveragePrecision: The single metric for bounding boxes evaluation: the `meanAveragePrecision` averaged over all `boundingBoxMetrics`. boundingBoxMetrics: The bounding boxes match metrics for each intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair. evaluatedBoundingBoxCount: UNIMPLEMENTED. The total number of bounding boxes (i.e. summed over all frames) the ground truth used to create this evaluation had. evaluatedFrameCount: UNIMPLEMENTED. The number of video frames used to create this evaluation. evaluatedTrackCount: UNIMPLEMENTED. The total number of tracks (i.e. as seen across all frames) the ground truth used to create this evaluation had. trackMeanAveragePrecision: UNIMPLEMENTED. The single metric for tracks accuracy evaluation: the `meanAveragePrecision` averaged over all `trackMetrics`. trackMeanBoundingBoxIou: UNIMPLEMENTED. The single metric for tracks bounding box iou evaluation: the `meanBoundingBoxIou` averaged over all `trackMetrics`. trackMeanMismatchRate: UNIMPLEMENTED. The single metric for tracking consistency evaluation: the `meanMismatchRate` averaged over all `trackMetrics`. trackMetrics: UNIMPLEMENTED. The tracks match metrics for each intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair. """ boundingBoxMeanAveragePrecision = _messages.FloatField(1, variant=_messages.Variant.FLOAT) boundingBoxMetrics = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsBoundingBoxMetrics', 2, repeated=True) evaluatedBoundingBoxCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) evaluatedFrameCount = _messages.IntegerField(4, variant=_messages.Variant.INT32) evaluatedTrackCount = _messages.IntegerField(5, variant=_messages.Variant.INT32) trackMeanAveragePrecision = _messages.FloatField(6, variant=_messages.Variant.FLOAT) trackMeanBoundingBoxIou = _messages.FloatField(7, variant=_messages.Variant.FLOAT) trackMeanMismatchRate = _messages.FloatField(8, variant=_messages.Variant.FLOAT) trackMetrics = _messages.MessageField('GoogleCloudAiplatformV1SchemaModelevaluationMetricsTrackMetrics', 9, repeated=True) class GoogleCloudAiplatformV1SchemaPredictInstanceImageClassificationPredictionInstance(_messages.Message): r"""Prediction input format for Image Classification. Fields: content: The image bytes or Cloud Storage URI to make the prediction on. mimeType: The MIME type of the content of the image. Only the images in below listed MIME types are supported. - image/jpeg - image/gif - image/png - image/webp - image/bmp - image/tiff - image/vnd.microsoft.icon """ content = _messages.StringField(1) mimeType = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaPredictInstanceImageObjectDetectionPredictionInstance(_messages.Message): r"""Prediction input format for Image Object Detection. Fields: content: The image bytes or Cloud Storage URI to make the prediction on. mimeType: The MIME type of the content of the image. Only the images in below listed MIME types are supported. - image/jpeg - image/gif - image/png - image/webp - image/bmp - image/tiff - image/vnd.microsoft.icon """ content = _messages.StringField(1) mimeType = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaPredictInstanceImageSegmentationPredictionInstance(_messages.Message): r"""Prediction input format for Image Segmentation. Fields: content: The image bytes to make the predictions on. mimeType: The MIME type of the content of the image. Only the images in below listed MIME types are supported. - image/jpeg - image/png """ content = _messages.StringField(1) mimeType = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaPredictInstanceTextClassificationPredictionInstance(_messages.Message): r"""Prediction input format for Text Classification. Fields: content: The text snippet to make the predictions on. mimeType: The MIME type of the text snippet. The supported MIME types are listed below. - text/plain """ content = _messages.StringField(1) mimeType = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaPredictInstanceTextExtractionPredictionInstance(_messages.Message): r"""Prediction input format for Text Extraction. Fields: content: The text snippet to make the predictions on. key: This field is only used for batch prediction. If a key is provided, the batch prediction result will by mapped to this key. If omitted, then the batch prediction result will contain the entire input instance. Vertex AI will not check if keys in the request are duplicates, so it is up to the caller to ensure the keys are unique. mimeType: The MIME type of the text snippet. The supported MIME types are listed below. - text/plain """ content = _messages.StringField(1) key = _messages.StringField(2) mimeType = _messages.StringField(3) class GoogleCloudAiplatformV1SchemaPredictInstanceTextSentimentPredictionInstance(_messages.Message): r"""Prediction input format for Text Sentiment. Fields: content: The text snippet to make the predictions on. mimeType: The MIME type of the text snippet. The supported MIME types are listed below. - text/plain """ content = _messages.StringField(1) mimeType = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaPredictInstanceVideoActionRecognitionPredictionInstance(_messages.Message): r"""Prediction input format for Video Action Recognition. Fields: content: The Google Cloud Storage location of the video on which to perform the prediction. mimeType: The MIME type of the content of the video. Only the following are supported: video/mp4 video/avi video/quicktime timeSegmentEnd: The end, exclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision, and "inf" or "Infinity" is allowed, which means the end of the video. timeSegmentStart: The beginning, inclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision. """ content = _messages.StringField(1) mimeType = _messages.StringField(2) timeSegmentEnd = _messages.StringField(3) timeSegmentStart = _messages.StringField(4) class GoogleCloudAiplatformV1SchemaPredictInstanceVideoClassificationPredictionInstance(_messages.Message): r"""Prediction input format for Video Classification. Fields: content: The Google Cloud Storage location of the video on which to perform the prediction. mimeType: The MIME type of the content of the video. Only the following are supported: video/mp4 video/avi video/quicktime timeSegmentEnd: The end, exclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision, and "inf" or "Infinity" is allowed, which means the end of the video. timeSegmentStart: The beginning, inclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision. """ content = _messages.StringField(1) mimeType = _messages.StringField(2) timeSegmentEnd = _messages.StringField(3) timeSegmentStart = _messages.StringField(4) class GoogleCloudAiplatformV1SchemaPredictInstanceVideoObjectTrackingPredictionInstance(_messages.Message): r"""Prediction input format for Video Object Tracking. Fields: content: The Google Cloud Storage location of the video on which to perform the prediction. mimeType: The MIME type of the content of the video. Only the following are supported: video/mp4 video/avi video/quicktime timeSegmentEnd: The end, exclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision, and "inf" or "Infinity" is allowed, which means the end of the video. timeSegmentStart: The beginning, inclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision. """ content = _messages.StringField(1) mimeType = _messages.StringField(2) timeSegmentEnd = _messages.StringField(3) timeSegmentStart = _messages.StringField(4) class GoogleCloudAiplatformV1SchemaPredictParamsImageClassificationPredictionParams(_messages.Message): r"""Prediction model parameters for Image Classification. Fields: confidenceThreshold: The Model only returns predictions with at least this confidence score. Default value is 0.0 maxPredictions: The Model only returns up to that many top, by confidence score, predictions per instance. If this number is very high, the Model may return fewer predictions. Default value is 10. """ confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SchemaPredictParamsImageObjectDetectionPredictionParams(_messages.Message): r"""Prediction model parameters for Image Object Detection. Fields: confidenceThreshold: The Model only returns predictions with at least this confidence score. Default value is 0.0 maxPredictions: The Model only returns up to that many top, by confidence score, predictions per instance. Note that number of returned predictions is also limited by metadata's predictionsLimit. Default value is 10. """ confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SchemaPredictParamsImageSegmentationPredictionParams(_messages.Message): r"""Prediction model parameters for Image Segmentation. Fields: confidenceThreshold: When the model predicts category of pixels of the image, it will only provide predictions for pixels that it is at least this much confident about. All other pixels will be classified as background. Default value is 0.5. """ confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaPredictParamsVideoActionRecognitionPredictionParams(_messages.Message): r"""Prediction model parameters for Video Action Recognition. Fields: confidenceThreshold: The Model only returns predictions with at least this confidence score. Default value is 0.0 maxPredictions: The model only returns up to that many top, by confidence score, predictions per frame of the video. If this number is very high, the Model may return fewer predictions per frame. Default value is 50. """ confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SchemaPredictParamsVideoClassificationPredictionParams(_messages.Message): r"""Prediction model parameters for Video Classification. Fields: confidenceThreshold: The Model only returns predictions with at least this confidence score. Default value is 0.0 maxPredictions: The Model only returns up to that many top, by confidence score, predictions per instance. If this number is very high, the Model may return fewer predictions. Default value is 10,000. oneSecIntervalClassification: Set to true to request classification for a video at one-second intervals. Vertex AI returns labels and their confidence scores for each second of the entire time segment of the video that user specified in the input WARNING: Model evaluation is not done for this classification type, the quality of it depends on the training data, but there are no metrics provided to describe that quality. Default value is false segmentClassification: Set to true to request segment-level classification. Vertex AI returns labels and their confidence scores for the entire time segment of the video that user specified in the input instance. Default value is true shotClassification: Set to true to request shot-level classification. Vertex AI determines the boundaries for each camera shot in the entire time segment of the video that user specified in the input instance. Vertex AI then returns labels and their confidence scores for each detected shot, along with the start and end time of the shot. WARNING: Model evaluation is not done for this classification type, the quality of it depends on the training data, but there are no metrics provided to describe that quality. Default value is false """ confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32) oneSecIntervalClassification = _messages.BooleanField(3) segmentClassification = _messages.BooleanField(4) shotClassification = _messages.BooleanField(5) class GoogleCloudAiplatformV1SchemaPredictParamsVideoObjectTrackingPredictionParams(_messages.Message): r"""Prediction model parameters for Video Object Tracking. Fields: confidenceThreshold: The Model only returns predictions with at least this confidence score. Default value is 0.0 maxPredictions: The model only returns up to that many top, by confidence score, predictions per frame of the video. If this number is very high, the Model may return fewer predictions per frame. Default value is 50. minBoundingBoxSize: Only bounding boxes with shortest edge at least that long as a relative value of video frame size are returned. Default value is 0.0. """ confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT) maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32) minBoundingBoxSize = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaPredictPredictionClassificationPredictionResult(_messages.Message): r"""Prediction output format for Image and Text Classification. Fields: confidences: The Model's confidences in correctness of the predicted IDs, higher value means higher confidence. Order matches the Ids. displayNames: The display names of the AnnotationSpecs that had been identified, order matches the IDs. ids: The resource IDs of the AnnotationSpecs that had been identified. """ confidences = _messages.FloatField(1, repeated=True, variant=_messages.Variant.FLOAT) displayNames = _messages.StringField(2, repeated=True) ids = _messages.IntegerField(3, repeated=True) class GoogleCloudAiplatformV1SchemaPredictPredictionImageObjectDetectionPredictionResult(_messages.Message): r"""Prediction output format for Image Object Detection. Messages: BboxesValueListEntry: Single entry in a BboxesValue. Fields: bboxes: Bounding boxes, i.e. the rectangles over the image, that pinpoint the found AnnotationSpecs. Given in order that matches the IDs. Each bounding box is an array of 4 numbers `xMin`, `xMax`, `yMin`, and `yMax`, which represent the extremal coordinates of the box. They are relative to the image size, and the point 0,0 is in the top left of the image. confidences: The Model's confidences in correctness of the predicted IDs, higher value means higher confidence. Order matches the Ids. displayNames: The display names of the AnnotationSpecs that had been identified, order matches the IDs. ids: The resource IDs of the AnnotationSpecs that had been identified, ordered by the confidence score descendingly. """ class BboxesValueListEntry(_messages.Message): r"""Single entry in a BboxesValue. Fields: entry: A extra_types.JsonValue attribute. """ entry = _messages.MessageField('extra_types.JsonValue', 1, repeated=True) bboxes = _messages.MessageField('BboxesValueListEntry', 1, repeated=True) confidences = _messages.FloatField(2, repeated=True, variant=_messages.Variant.FLOAT) displayNames = _messages.StringField(3, repeated=True) ids = _messages.IntegerField(4, repeated=True) class GoogleCloudAiplatformV1SchemaPredictPredictionImageSegmentationPredictionResult(_messages.Message): r"""Prediction output format for Image Segmentation. Fields: categoryMask: A PNG image where each pixel in the mask represents the category in which the pixel in the original image was predicted to belong to. The size of this image will be the same as the original image. The mapping between the AnntoationSpec and the color can be found in model's metadata. The model will choose the most likely category and if none of the categories reach the confidence threshold, the pixel will be marked as background. confidenceMask: A one channel image which is encoded as an 8bit lossless PNG. The size of the image will be the same as the original image. For a specific pixel, darker color means less confidence in correctness of the cateogry in the categoryMask for the corresponding pixel. Black means no confidence and white means complete confidence. """ categoryMask = _messages.StringField(1) confidenceMask = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaPredictPredictionTabularClassificationPredictionResult(_messages.Message): r"""Prediction output format for Tabular Classification. Fields: classes: The name of the classes being classified, contains all possible values of the target column. scores: The model's confidence in each class being correct, higher value means higher confidence. The N-th score corresponds to the N-th class in classes. """ classes = _messages.StringField(1, repeated=True) scores = _messages.FloatField(2, repeated=True, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaPredictPredictionTabularRegressionPredictionResult(_messages.Message): r"""Prediction output format for Tabular Regression. Fields: lowerBound: The lower bound of the prediction interval. quantilePredictions: Quantile predictions, in 1-1 correspondence with quantile_values. quantileValues: Quantile values. upperBound: The upper bound of the prediction interval. value: The regression value. """ lowerBound = _messages.FloatField(1, variant=_messages.Variant.FLOAT) quantilePredictions = _messages.FloatField(2, repeated=True, variant=_messages.Variant.FLOAT) quantileValues = _messages.FloatField(3, repeated=True, variant=_messages.Variant.FLOAT) upperBound = _messages.FloatField(4, variant=_messages.Variant.FLOAT) value = _messages.FloatField(5, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaPredictPredictionTextExtractionPredictionResult(_messages.Message): r"""Prediction output format for Text Extraction. Fields: confidences: The Model's confidences in correctness of the predicted IDs, higher value means higher confidence. Order matches the Ids. displayNames: The display names of the AnnotationSpecs that had been identified, order matches the IDs. ids: The resource IDs of the AnnotationSpecs that had been identified, ordered by the confidence score descendingly. textSegmentEndOffsets: The end offsets, inclusive, of the text segment in which the AnnotationSpec has been identified. Expressed as a zero-based number of characters as measured from the start of the text snippet. textSegmentStartOffsets: The start offsets, inclusive, of the text segment in which the AnnotationSpec has been identified. Expressed as a zero- based number of characters as measured from the start of the text snippet. """ confidences = _messages.FloatField(1, repeated=True, variant=_messages.Variant.FLOAT) displayNames = _messages.StringField(2, repeated=True) ids = _messages.IntegerField(3, repeated=True) textSegmentEndOffsets = _messages.IntegerField(4, repeated=True) textSegmentStartOffsets = _messages.IntegerField(5, repeated=True) class GoogleCloudAiplatformV1SchemaPredictPredictionTextSentimentPredictionResult(_messages.Message): r"""Prediction output format for Text Sentiment Fields: sentiment: The integer sentiment labels between 0 (inclusive) and sentimentMax label (inclusive), while 0 maps to the least positive sentiment and sentimentMax maps to the most positive one. The higher the score is, the more positive the sentiment in the text snippet is. Note: sentimentMax is an integer value between 1 (inclusive) and 10 (inclusive). """ sentiment = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SchemaPredictPredictionTftFeatureImportance(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaPredictPredictionTftFeatureImportance object. Fields: attributeColumns: A string attribute. attributeWeights: A number attribute. contextColumns: A string attribute. contextWeights: TFT feature importance values. Each pair for {context/horizon/attribute} should have the same shape since the weight corresponds to the column names. horizonColumns: A string attribute. horizonWeights: A number attribute. """ attributeColumns = _messages.StringField(1, repeated=True) attributeWeights = _messages.FloatField(2, repeated=True, variant=_messages.Variant.FLOAT) contextColumns = _messages.StringField(3, repeated=True) contextWeights = _messages.FloatField(4, repeated=True, variant=_messages.Variant.FLOAT) horizonColumns = _messages.StringField(5, repeated=True) horizonWeights = _messages.FloatField(6, repeated=True, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaPredictPredictionTimeSeriesForecastingPredictionResult(_messages.Message): r"""Prediction output format for Time Series Forecasting. Fields: quantilePredictions: Quantile predictions, in 1-1 correspondence with quantile_values. quantileValues: Quantile values. tftFeatureImportance: Only use these if TFt is enabled. value: The regression value. """ quantilePredictions = _messages.FloatField(1, repeated=True, variant=_messages.Variant.FLOAT) quantileValues = _messages.FloatField(2, repeated=True, variant=_messages.Variant.FLOAT) tftFeatureImportance = _messages.MessageField('GoogleCloudAiplatformV1SchemaPredictPredictionTftFeatureImportance', 3) value = _messages.FloatField(4, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaPredictPredictionVideoActionRecognitionPredictionResult(_messages.Message): r"""Prediction output format for Video Action Recognition. Fields: confidence: The Model's confidence in correction of this prediction, higher value means higher confidence. displayName: The display name of the AnnotationSpec that had been identified. id: The resource ID of the AnnotationSpec that had been identified. timeSegmentEnd: The end, exclusive, of the video's time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end. timeSegmentStart: The beginning, inclusive, of the video's time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) displayName = _messages.StringField(2) id = _messages.StringField(3) timeSegmentEnd = _messages.StringField(4) timeSegmentStart = _messages.StringField(5) class GoogleCloudAiplatformV1SchemaPredictPredictionVideoClassificationPredictionResult(_messages.Message): r"""Prediction output format for Video Classification. Fields: confidence: The Model's confidence in correction of this prediction, higher value means higher confidence. displayName: The display name of the AnnotationSpec that had been identified. id: The resource ID of the AnnotationSpec that had been identified. timeSegmentEnd: The end, exclusive, of the video's time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end. Note that for 'segment-classification' prediction type, this equals the original 'timeSegmentEnd' from the input instance, for other types it is the end of a shot or a 1 second interval respectively. timeSegmentStart: The beginning, inclusive, of the video's time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end. Note that for 'segment-classification' prediction type, this equals the original 'timeSegmentStart' from the input instance, for other types it is the start of a shot or a 1 second interval respectively. type: The type of the prediction. The requested types can be configured via parameters. This will be one of - segment-classification - shot- classification - one-sec-interval-classification """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) displayName = _messages.StringField(2) id = _messages.StringField(3) timeSegmentEnd = _messages.StringField(4) timeSegmentStart = _messages.StringField(5) type = _messages.StringField(6) class GoogleCloudAiplatformV1SchemaPredictPredictionVideoObjectTrackingPredictionResult(_messages.Message): r"""Prediction output format for Video Object Tracking. Fields: confidence: The Model's confidence in correction of this prediction, higher value means higher confidence. displayName: The display name of the AnnotationSpec that had been identified. frames: All of the frames of the video in which a single object instance has been detected. The bounding boxes in the frames identify the same object. id: The resource ID of the AnnotationSpec that had been identified. timeSegmentEnd: The end, inclusive, of the video's time segment in which the object instance has been detected. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end. timeSegmentStart: The beginning, inclusive, of the video's time segment in which the object instance has been detected. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) displayName = _messages.StringField(2) frames = _messages.MessageField('GoogleCloudAiplatformV1SchemaPredictPredictionVideoObjectTrackingPredictionResultFrame', 3, repeated=True) id = _messages.StringField(4) timeSegmentEnd = _messages.StringField(5) timeSegmentStart = _messages.StringField(6) class GoogleCloudAiplatformV1SchemaPredictPredictionVideoObjectTrackingPredictionResultFrame(_messages.Message): r"""The fields `xMin`, `xMax`, `yMin`, and `yMax` refer to a bounding box, i.e. the rectangle over the video frame pinpointing the found AnnotationSpec. The coordinates are relative to the frame size, and the point 0,0 is in the top left of the frame. Fields: timeOffset: A time (frame) of a video in which the object has been detected. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end. xMax: The rightmost coordinate of the bounding box. xMin: The leftmost coordinate of the bounding box. yMax: The bottommost coordinate of the bounding box. yMin: The topmost coordinate of the bounding box. """ timeOffset = _messages.StringField(1) xMax = _messages.FloatField(2, variant=_messages.Variant.FLOAT) xMin = _messages.FloatField(3, variant=_messages.Variant.FLOAT) yMax = _messages.FloatField(4, variant=_messages.Variant.FLOAT) yMin = _messages.FloatField(5, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SchemaPredictionResult(_messages.Message): r"""Represents a line of JSONL in the batch prediction output file. Messages: InstanceValue: User's input instance. Struct is used here instead of Any so that JsonFormat does not append an extra "@type" field when we convert the proto to JSON. Fields: error: The error result. Do not set prediction if this is set. instance: User's input instance. Struct is used here instead of Any so that JsonFormat does not append an extra "@type" field when we convert the proto to JSON. key: Optional user-provided key from the input instance. prediction: The prediction result. Value is used here instead of Any so that JsonFormat does not append an extra "@type" field when we convert the proto to JSON and so we can represent array of objects. Do not set error if this is set. """ @encoding.MapUnrecognizedFields('additionalProperties') class InstanceValue(_messages.Message): r"""User's input instance. Struct is used here instead of Any so that JsonFormat does not append an extra "@type" field when we convert the proto to JSON. Messages: AdditionalProperty: An additional property for a InstanceValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a InstanceValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) error = _messages.MessageField('GoogleCloudAiplatformV1SchemaPredictionResultError', 1) instance = _messages.MessageField('InstanceValue', 2) key = _messages.StringField(3) prediction = _messages.MessageField('extra_types.JsonValue', 4) class GoogleCloudAiplatformV1SchemaPredictionResultError(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaPredictionResultError object. Enums: StatusValueValuesEnum: Error status. This will be serialized into the enum name e.g. "NOT_FOUND". Fields: message: Error message with additional details. status: Error status. This will be serialized into the enum name e.g. "NOT_FOUND". """ class StatusValueValuesEnum(_messages.Enum): r"""Error status. This will be serialized into the enum name e.g. "NOT_FOUND". Values: OK: Not an error; returned on success. HTTP Mapping: 200 OK CANCELLED: The operation was cancelled, typically by the caller. HTTP Mapping: 499 Client Closed Request UNKNOWN: Unknown error. For example, this error may be returned when a `Status` value received from another address space belongs to an error space that is not known in this address space. Also errors raised by APIs that do not return enough error information may be converted to this error. HTTP Mapping: 500 Internal Server Error INVALID_ARGUMENT: The client specified an invalid argument. Note that this differs from `FAILED_PRECONDITION`. `INVALID_ARGUMENT` indicates arguments that are problematic regardless of the state of the system (e.g., a malformed file name). HTTP Mapping: 400 Bad Request DEADLINE_EXCEEDED: The deadline expired before the operation could complete. For operations that change the state of the system, this error may be returned even if the operation has completed successfully. For example, a successful response from a server could have been delayed long enough for the deadline to expire. HTTP Mapping: 504 Gateway Timeout NOT_FOUND: Some requested entity (e.g., file or directory) was not found. Note to server developers: if a request is denied for an entire class of users, such as gradual feature rollout or undocumented allowlist, `NOT_FOUND` may be used. If a request is denied for some users within a class of users, such as user-based access control, `PERMISSION_DENIED` must be used. HTTP Mapping: 404 Not Found ALREADY_EXISTS: The entity that a client attempted to create (e.g., file or directory) already exists. HTTP Mapping: 409 Conflict PERMISSION_DENIED: The caller does not have permission to execute the specified operation. `PERMISSION_DENIED` must not be used for rejections caused by exhausting some resource (use `RESOURCE_EXHAUSTED` instead for those errors). `PERMISSION_DENIED` must not be used if the caller can not be identified (use `UNAUTHENTICATED` instead for those errors). This error code does not imply the request is valid or the requested entity exists or satisfies other pre-conditions. HTTP Mapping: 403 Forbidden UNAUTHENTICATED: The request does not have valid authentication credentials for the operation. HTTP Mapping: 401 Unauthorized RESOURCE_EXHAUSTED: Some resource has been exhausted, perhaps a per-user quota, or perhaps the entire file system is out of space. HTTP Mapping: 429 Too Many Requests FAILED_PRECONDITION: The operation was rejected because the system is not in a state required for the operation's execution. For example, the directory to be deleted is non-empty, an rmdir operation is applied to a non-directory, etc. Service implementors can use the following guidelines to decide between `FAILED_PRECONDITION`, `ABORTED`, and `UNAVAILABLE`: (a) Use `UNAVAILABLE` if the client can retry just the failing call. (b) Use `ABORTED` if the client should retry at a higher level. For example, when a client-specified test- and-set fails, indicating the client should restart a read-modify- write sequence. (c) Use `FAILED_PRECONDITION` if the client should not retry until the system state has been explicitly fixed. For example, if an "rmdir" fails because the directory is non-empty, `FAILED_PRECONDITION` should be returned since the client should not retry unless the files are deleted from the directory. HTTP Mapping: 400 Bad Request ABORTED: The operation was aborted, typically due to a concurrency issue such as a sequencer check failure or transaction abort. See the guidelines above for deciding between `FAILED_PRECONDITION`, `ABORTED`, and `UNAVAILABLE`. HTTP Mapping: 409 Conflict OUT_OF_RANGE: The operation was attempted past the valid range. E.g., seeking or reading past end-of-file. Unlike `INVALID_ARGUMENT`, this error indicates a problem that may be fixed if the system state changes. For example, a 32-bit file system will generate `INVALID_ARGUMENT` if asked to read at an offset that is not in the range [0,2^32-1], but it will generate `OUT_OF_RANGE` if asked to read from an offset past the current file size. There is a fair bit of overlap between `FAILED_PRECONDITION` and `OUT_OF_RANGE`. We recommend using `OUT_OF_RANGE` (the more specific error) when it applies so that callers who are iterating through a space can easily look for an `OUT_OF_RANGE` error to detect when they are done. HTTP Mapping: 400 Bad Request UNIMPLEMENTED: The operation is not implemented or is not supported/enabled in this service. HTTP Mapping: 501 Not Implemented INTERNAL: Internal errors. This means that some invariants expected by the underlying system have been broken. This error code is reserved for serious errors. HTTP Mapping: 500 Internal Server Error UNAVAILABLE: The service is currently unavailable. This is most likely a transient condition, which can be corrected by retrying with a backoff. Note that it is not always safe to retry non-idempotent operations. See the guidelines above for deciding between `FAILED_PRECONDITION`, `ABORTED`, and `UNAVAILABLE`. HTTP Mapping: 503 Service Unavailable DATA_LOSS: Unrecoverable data loss or corruption. HTTP Mapping: 500 Internal Server Error """ OK = 0 CANCELLED = 1 UNKNOWN = 2 INVALID_ARGUMENT = 3 DEADLINE_EXCEEDED = 4 NOT_FOUND = 5 ALREADY_EXISTS = 6 PERMISSION_DENIED = 7 UNAUTHENTICATED = 8 RESOURCE_EXHAUSTED = 9 FAILED_PRECONDITION = 10 ABORTED = 11 OUT_OF_RANGE = 12 UNIMPLEMENTED = 13 INTERNAL = 14 UNAVAILABLE = 15 DATA_LOSS = 16 message = _messages.StringField(1) status = _messages.EnumField('StatusValueValuesEnum', 2) class GoogleCloudAiplatformV1SchemaTablesDatasetMetadata(_messages.Message): r"""The metadata of Datasets that contain tables data. Fields: inputConfig: A GoogleCloudAiplatformV1SchemaTablesDatasetMetadataInputConfig attribute. """ inputConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTablesDatasetMetadataInputConfig', 1) class GoogleCloudAiplatformV1SchemaTablesDatasetMetadataBigQuerySource(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTablesDatasetMetadataBigQuerySource object. Fields: uri: The URI of a BigQuery table. e.g. bq://projectId.bqDatasetId.bqTableId """ uri = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTablesDatasetMetadataGcsSource(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTablesDatasetMetadataGcsSource object. Fields: uri: Cloud Storage URI of one or more files. Only CSV files are supported. The first line of the CSV file is used as the header. If there are multiple files, the header is the first line of the lexicographically first file, the other files must either contain the exact same header or omit the header. """ uri = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1SchemaTablesDatasetMetadataInputConfig(_messages.Message): r"""The tables Dataset's data source. The Dataset doesn't store the data directly, but only pointer(s) to its data. Fields: bigquerySource: A GoogleCloudAiplatformV1SchemaTablesDatasetMetadataBigQuerySource attribute. gcsSource: A GoogleCloudAiplatformV1SchemaTablesDatasetMetadataGcsSource attribute. """ bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1SchemaTablesDatasetMetadataBigQuerySource', 1) gcsSource = _messages.MessageField('GoogleCloudAiplatformV1SchemaTablesDatasetMetadataGcsSource', 2) class GoogleCloudAiplatformV1SchemaTextClassificationAnnotation(_messages.Message): r"""Annotation details specific to text classification. Fields: annotationSpecId: The resource Id of the AnnotationSpec that this Annotation pertains to. displayName: The display name of the AnnotationSpec that this Annotation pertains to. """ annotationSpecId = _messages.StringField(1) displayName = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaTextDataItem(_messages.Message): r"""Payload of Text DataItem. Fields: gcsUri: Output only. Google Cloud Storage URI points to a copy of the original text in the Vertex-managed bucket in the user's project. The text file is up to 10MB in size. """ gcsUri = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTextDatasetMetadata(_messages.Message): r"""The metadata of Datasets that contain Text DataItems. Fields: dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage describing payload of the Text DataItems that belong to this Dataset. gcsBucket: Google Cloud Storage Bucket name that contains the blob data of this Dataset. """ dataItemSchemaUri = _messages.StringField(1) gcsBucket = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaTextExtractionAnnotation(_messages.Message): r"""Annotation details specific to text extraction. Fields: annotationSpecId: The resource Id of the AnnotationSpec that this Annotation pertains to. displayName: The display name of the AnnotationSpec that this Annotation pertains to. textSegment: The segment of the text content. """ annotationSpecId = _messages.StringField(1) displayName = _messages.StringField(2) textSegment = _messages.MessageField('GoogleCloudAiplatformV1SchemaTextSegment', 3) class GoogleCloudAiplatformV1SchemaTextSegment(_messages.Message): r"""The text segment inside of DataItem. Fields: content: The text content in the segment for output only. endOffset: Zero-based character index of the first character past the end of the text segment (counting character from the beginning of the text). The character at the end_offset is NOT included in the text segment. startOffset: Zero-based character index of the first character of the text segment (counting characters from the beginning of the text). """ content = _messages.StringField(1) endOffset = _messages.IntegerField(2, variant=_messages.Variant.UINT64) startOffset = _messages.IntegerField(3, variant=_messages.Variant.UINT64) class GoogleCloudAiplatformV1SchemaTextSentimentAnnotation(_messages.Message): r"""Annotation details specific to text sentiment. Fields: annotationSpecId: The resource Id of the AnnotationSpec that this Annotation pertains to. displayName: The display name of the AnnotationSpec that this Annotation pertains to. sentiment: The sentiment score for text. sentimentMax: The sentiment max score for text. """ annotationSpecId = _messages.StringField(1) displayName = _messages.StringField(2) sentiment = _messages.IntegerField(3, variant=_messages.Variant.INT32) sentimentMax = _messages.IntegerField(4, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SchemaTextSentimentSavedQueryMetadata(_messages.Message): r"""The metadata of SavedQuery contains TextSentiment Annotations. Fields: sentimentMax: The maximum sentiment of sentiment Anntoation in this SavedQuery. """ sentimentMax = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SchemaTimeSegment(_messages.Message): r"""A time period inside of a DataItem that has a time dimension (e.g. video). Fields: endTimeOffset: End of the time segment (exclusive), represented as the duration since the start of the DataItem. startTimeOffset: Start of the time segment (inclusive), represented as the duration since the start of the DataItem. """ endTimeOffset = _messages.StringField(1) startTimeOffset = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadata(_messages.Message): r"""The metadata of Datasets that contain time series data. Fields: inputConfig: A GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataInputConfig attribute. timeColumn: The column name of the time column that identifies time order in the time series. timeSeriesIdentifierColumn: The column name of the time series identifier column that identifies the time series. """ inputConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataInputConfig', 1) timeColumn = _messages.StringField(2) timeSeriesIdentifierColumn = _messages.StringField(3) class GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataBigQuerySource(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataBigQuerySource object. Fields: uri: The URI of a BigQuery table. """ uri = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataGcsSource(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataGcsSource object. Fields: uri: Cloud Storage URI of one or more files. Only CSV files are supported. The first line of the CSV file is used as the header. If there are multiple files, the header is the first line of the lexicographically first file, the other files must either contain the exact same header or omit the header. """ uri = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataInputConfig(_messages.Message): r"""The time series Dataset's data source. The Dataset doesn't store the data directly, but only pointer(s) to its data. Fields: bigquerySource: A GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataBigQuerySource attribute. gcsSource: A GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataGcsSource attribute. """ bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataBigQuerySource', 1) gcsSource = _messages.MessageField('GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataGcsSource', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecasting(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Forecasting Model. Fields: inputs: The input parameters of this TrainingJob. metadata: The metadata information. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputs', 1) metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingMetadata', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputs object. Fields: additionalExperiments: Additional experiment flags for the time series forcasting training. availableAtForecastColumns: Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day. contextWindow: The amount of time into the past training and prediction data is used for model training and prediction respectively. Expressed in number of units defined by the `data_granularity` field. dataGranularity: Expected difference in time granularity between rows in the data. enableProbabilisticInference: If probabilistic inference is enabled, the model will fit a distribution that captures the uncertainty of a prediction. At inference time, the predictive distribution is used to make a point prediction that minimizes the optimization objective. For example, the mean of a predictive distribution is the point prediction that minimizes RMSE loss. If quantiles are specified, then the quantiles of the distribution are also returned. The optimization objective cannot be minimize-quantile-loss. exportEvaluatedDataItemsConfig: Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed. forecastHorizon: The amount of time into the future for which forecasted values for the target are returned. Expressed in number of units defined by the `data_granularity` field. hierarchyConfig: Configuration that defines the hierarchical relationship of time series and parameters for hierarchical forecasting strategies. holidayRegions: The geographical region based on which the holiday effect is applied in modeling by adding holiday categorical array feature that include all holidays matching the date. This option only allowed when data_granularity is day. By default, holiday effect modeling is disabled. To turn it on, specify the holiday region using this option. optimizationObjective: Objective function the model is optimizing towards. The training process creates a model that optimizes the value of the objective function over the validation set. The supported optimization objectives: * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). * "minimize-mae" - Minimize mean-absolute error (MAE). * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE). * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE). * "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and mean-absolute-error (MAE). * "minimize- quantile-loss" - Minimize the quantile loss at the quantiles defined in `quantiles`. * "minimize-mape" - Minimize the mean absolute percentage error. quantiles: Quantiles to use for minimize-quantile-loss `optimization_objective`, or for probabilistic inference. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Required if the value of optimization_objective is minimize-quantile-loss. Represents the percent quantiles to use for that objective. Quantiles must be unique. targetColumn: The name of the column that the Model is to predict values for. This column must be unavailable at forecast. timeColumn: The name of the column that identifies time order in the time series. This column must be available at forecast. timeSeriesAttributeColumns: Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color. timeSeriesIdentifierColumn: The name of the column that identifies the time series. trainBudgetMilliNodeHours: Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive. transformations: Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter. unavailableAtForecastColumns: Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day. validationOptions: Validation options for the data validation component. The available options are: * "fail-pipeline" - default, will validate against the validation and fail the pipeline if it fails. * "ignore- validation" - ignore the results of the validation and continue weightColumn: Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1. windowConfig: Config containing strategy for generating sliding windows. """ additionalExperiments = _messages.StringField(1, repeated=True) availableAtForecastColumns = _messages.StringField(2, repeated=True) contextWindow = _messages.IntegerField(3) dataGranularity = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsGranularity', 4) enableProbabilisticInference = _messages.BooleanField(5) exportEvaluatedDataItemsConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig', 6) forecastHorizon = _messages.IntegerField(7) hierarchyConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHierarchyConfig', 8) holidayRegions = _messages.StringField(9, repeated=True) optimizationObjective = _messages.StringField(10) quantiles = _messages.FloatField(11, repeated=True) targetColumn = _messages.StringField(12) timeColumn = _messages.StringField(13) timeSeriesAttributeColumns = _messages.StringField(14, repeated=True) timeSeriesIdentifierColumn = _messages.StringField(15) trainBudgetMilliNodeHours = _messages.IntegerField(16) transformations = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation', 17, repeated=True) unavailableAtForecastColumns = _messages.StringField(18, repeated=True) validationOptions = _messages.StringField(19) weightColumn = _messages.StringField(20) windowConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionWindowConfig', 21) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsGranularity(_messages.Message): r"""A duration of time expressed in time granularity units. Fields: quantity: The number of granularity_units between data points in the training data. If `granularity_unit` is `minute`, can be 1, 5, 10, 15, or 30. For all other values of `granularity_unit`, must be 1. unit: The time granularity unit of this time period. The supported units are: * "minute" * "hour" * "day" * "week" * "month" * "year" """ quantity = _messages.IntegerField(1) unit = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInp utsTransformation object. Fields: auto: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastin gInputsTransformationAutoTransformation attribute. categorical: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlFor ecastingInputsTransformationCategoricalTransformation attribute. numeric: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecas tingInputsTransformationNumericTransformation attribute. text: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastin gInputsTransformationTextTransformation attribute. timestamp: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForec astingInputsTransformationTimestampTransformation attribute. """ auto = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationAutoTransformation', 1) categorical = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalTransformation', 2) numeric = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericTransformation', 3) text = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextTransformation', 4) timestamp = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTimestampTransformation', 5) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationAutoTransformation(_messages.Message): r"""Training pipeline will infer the proper transformation based on the statistic of dataset. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * The categorical string as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index. * Categories that appear less than 5 times in the training dataset are treated as the "unknown" category. The "unknown" category gets its own special lookup index and resulting embedding. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * The value converted to float32. * The z_score of the value. * log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. * z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. * A boolean value that indicates whether the value is valid. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * The text as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTimestampTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * Apply the transformation functions for Numerical columns. * Determine the year, month, day,and weekday. Treat each value from the timestamp as a Categorical column. * Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed. Fields: columnName: A string attribute. timeFormat: The format in which that time field is expressed. The time_format must either be one of: * `unix-seconds` * `unix- milliseconds` * `unix-microseconds` * `unix-nanoseconds` (for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written in `strftime` syntax. If time_format is not set, then the default format is RFC 3339 `date-time` format, where `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z) """ columnName = _messages.StringField(1) timeFormat = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingMetadata(_messages.Message): r"""Model metadata specific to AutoML Forecasting. Fields: evaluatedDataItemsBigqueryUri: BigQuery destination uri for exported evaluated examples. trainCostMilliNodeHours: Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget. """ evaluatedDataItemsBigqueryUri = _messages.StringField(1) trainCostMilliNodeHours = _messages.IntegerField(2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassification(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Image Classification Model. Fields: inputs: The input parameters of this TrainingJob. metadata: The metadata information. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassificationInputs', 1) metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassificationMetadata', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassificationInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassific ationInputs object. Enums: ModelTypeValueValuesEnum: Fields: baseModelId: The ID of the `base` model. If it is specified, the new model will be trained based on the `base` model. Otherwise, the new model will be trained from scratch. The `base` model must be in the same Project and Location as the new Model to train, and have the same modelType. budgetMilliNodeHours: The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be `model-converged`. Note, node_hour = actual_hour * number_of_nodes_involved. For modelType `cloud`(default), the budget must be between 8,000 and 800,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time, considering 8 nodes are used. For model types `mobile- tf-low-latency-1`, `mobile-tf-versatile-1`, `mobile-tf-high-accuracy-1`, the training budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24,000 which represents one day in wall time on a single node that is used. disableEarlyStopping: Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Classification might stop training before the entire training budget has been used. modelType: A ModelTypeValueValuesEnum attribute. multiLabel: If false, a single-label (multi-class) Model will be trained (i.e. assuming that for each image just up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e. assuming that for each image multiple annotations may be applicable). uptrainBaseModelId: The ID of `base` model for upTraining. If it is specified, the new model will be upTrained based on the `base` model for upTraining. Otherwise, the new model will be trained from scratch. The `base` model for upTraining must be in the same Project and Location as the new Model to train, and have the same modelType. """ class ModelTypeValueValuesEnum(_messages.Enum): r"""ModelTypeValueValuesEnum enum type. Values: MODEL_TYPE_UNSPECIFIED: Should not be set. CLOUD: A Model best tailored to be used within Google Cloud, and which cannot be exported. Default. CLOUD_1: A model type best tailored to be used within Google Cloud, which cannot be exported externally. Compared to the CLOUD model above, it is expected to have higher prediction accuracy. MOBILE_TF_LOW_LATENCY_1: A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models. MOBILE_TF_VERSATILE_1: A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device with afterwards. MOBILE_TF_HIGH_ACCURACY_1: A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other mobile models. """ MODEL_TYPE_UNSPECIFIED = 0 CLOUD = 1 CLOUD_1 = 2 MOBILE_TF_LOW_LATENCY_1 = 3 MOBILE_TF_VERSATILE_1 = 4 MOBILE_TF_HIGH_ACCURACY_1 = 5 baseModelId = _messages.StringField(1) budgetMilliNodeHours = _messages.IntegerField(2) disableEarlyStopping = _messages.BooleanField(3) modelType = _messages.EnumField('ModelTypeValueValuesEnum', 4) multiLabel = _messages.BooleanField(5) uptrainBaseModelId = _messages.StringField(6) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassificationMetadata(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassific ationMetadata object. Enums: SuccessfulStopReasonValueValuesEnum: For successful job completions, this is the reason why the job has finished. Fields: costMilliNodeHours: The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours. successfulStopReason: For successful job completions, this is the reason why the job has finished. """ class SuccessfulStopReasonValueValuesEnum(_messages.Enum): r"""For successful job completions, this is the reason why the job has finished. Values: SUCCESSFUL_STOP_REASON_UNSPECIFIED: Should not be set. BUDGET_REACHED: The inputs.budgetMilliNodeHours had been reached. MODEL_CONVERGED: Further training of the Model ceased to increase its quality, since it already has converged. """ SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0 BUDGET_REACHED = 1 MODEL_CONVERGED = 2 costMilliNodeHours = _messages.IntegerField(1) successfulStopReason = _messages.EnumField('SuccessfulStopReasonValueValuesEnum', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDetection(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Image Object Detection Model. Fields: inputs: The input parameters of this TrainingJob. metadata: The metadata information """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionInputs', 1) metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionMetadata', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDet ectionInputs object. Enums: ModelTypeValueValuesEnum: Fields: budgetMilliNodeHours: The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be `model-converged`. Note, node_hour = actual_hour * number_of_nodes_involved. For modelType `cloud`(default), the budget must be between 20,000 and 900,000 milli node hours, inclusive. The default value is 216,000 which represents one day in wall time, considering 9 nodes are used. For model types `mobile- tf-low-latency-1`, `mobile-tf-versatile-1`, `mobile-tf-high-accuracy-1` the training budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24,000 which represents one day in wall time on a single node that is used. disableEarlyStopping: Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Object Detection might stop training before the entire training budget has been used. modelType: A ModelTypeValueValuesEnum attribute. uptrainBaseModelId: The ID of `base` model for upTraining. If it is specified, the new model will be upTrained based on the `base` model for upTraining. Otherwise, the new model will be trained from scratch. The `base` model for upTraining must be in the same Project and Location as the new Model to train, and have the same modelType. """ class ModelTypeValueValuesEnum(_messages.Enum): r"""ModelTypeValueValuesEnum enum type. Values: MODEL_TYPE_UNSPECIFIED: Should not be set. CLOUD_HIGH_ACCURACY_1: A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to have a higher latency, but should also have a higher prediction quality than other cloud models. CLOUD_LOW_LATENCY_1: A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to have a low latency, but may have lower prediction quality than other cloud models. CLOUD_1: A model best tailored to be used within Google Cloud, and which cannot be exported. Compared to the CLOUD_HIGH_ACCURACY_1 and CLOUD_LOW_LATENCY_1 models above, it is expected to have higher prediction quality and lower latency. MOBILE_TF_LOW_LATENCY_1: A model that, in addition to being available within Google Cloud can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models. MOBILE_TF_VERSATILE_1: A model that, in addition to being available within Google Cloud can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. MOBILE_TF_HIGH_ACCURACY_1: A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other mobile models. CLOUD_STREAMING_1: A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to best support predictions in streaming with lower latency and lower prediction quality than other cloud models. """ MODEL_TYPE_UNSPECIFIED = 0 CLOUD_HIGH_ACCURACY_1 = 1 CLOUD_LOW_LATENCY_1 = 2 CLOUD_1 = 3 MOBILE_TF_LOW_LATENCY_1 = 4 MOBILE_TF_VERSATILE_1 = 5 MOBILE_TF_HIGH_ACCURACY_1 = 6 CLOUD_STREAMING_1 = 7 budgetMilliNodeHours = _messages.IntegerField(1) disableEarlyStopping = _messages.BooleanField(2) modelType = _messages.EnumField('ModelTypeValueValuesEnum', 3) uptrainBaseModelId = _messages.StringField(4) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionMetadata(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDet ectionMetadata object. Enums: SuccessfulStopReasonValueValuesEnum: For successful job completions, this is the reason why the job has finished. Fields: costMilliNodeHours: The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours. successfulStopReason: For successful job completions, this is the reason why the job has finished. """ class SuccessfulStopReasonValueValuesEnum(_messages.Enum): r"""For successful job completions, this is the reason why the job has finished. Values: SUCCESSFUL_STOP_REASON_UNSPECIFIED: Should not be set. BUDGET_REACHED: The inputs.budgetMilliNodeHours had been reached. MODEL_CONVERGED: Further training of the Model ceased to increase its quality, since it already has converged. """ SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0 BUDGET_REACHED = 1 MODEL_CONVERGED = 2 costMilliNodeHours = _messages.IntegerField(1) successfulStopReason = _messages.EnumField('SuccessfulStopReasonValueValuesEnum', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentation(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Image Segmentation Model. Fields: inputs: The input parameters of this TrainingJob. metadata: The metadata information. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentationInputs', 1) metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentationMetadata', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentationInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentat ionInputs object. Enums: ModelTypeValueValuesEnum: Fields: baseModelId: The ID of the `base` model. If it is specified, the new model will be trained based on the `base` model. Otherwise, the new model will be trained from scratch. The `base` model must be in the same Project and Location as the new Model to train, and have the same modelType. budgetMilliNodeHours: The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be `model-converged`. Note, node_hour = actual_hour * number_of_nodes_involved. Or actual_wall_clock_hours = train_budget_milli_node_hours / (number_of_nodes_involved * 1000) For modelType `cloud-high-accuracy-1`(default), the budget must be between 20,000 and 2,000,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time (1000 milli * 24 hours * 8 nodes). modelType: A ModelTypeValueValuesEnum attribute. """ class ModelTypeValueValuesEnum(_messages.Enum): r"""ModelTypeValueValuesEnum enum type. Values: MODEL_TYPE_UNSPECIFIED: Should not be set. CLOUD_HIGH_ACCURACY_1: A model to be used via prediction calls to uCAIP API. Expected to have a higher latency, but should also have a higher prediction quality than other models. CLOUD_LOW_ACCURACY_1: A model to be used via prediction calls to uCAIP API. Expected to have a lower latency but relatively lower prediction quality. MOBILE_TF_LOW_LATENCY_1: A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow model and used on a mobile or edge device afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models. """ MODEL_TYPE_UNSPECIFIED = 0 CLOUD_HIGH_ACCURACY_1 = 1 CLOUD_LOW_ACCURACY_1 = 2 MOBILE_TF_LOW_LATENCY_1 = 3 baseModelId = _messages.StringField(1) budgetMilliNodeHours = _messages.IntegerField(2) modelType = _messages.EnumField('ModelTypeValueValuesEnum', 3) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentationMetadata(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentat ionMetadata object. Enums: SuccessfulStopReasonValueValuesEnum: For successful job completions, this is the reason why the job has finished. Fields: costMilliNodeHours: The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours. successfulStopReason: For successful job completions, this is the reason why the job has finished. """ class SuccessfulStopReasonValueValuesEnum(_messages.Enum): r"""For successful job completions, this is the reason why the job has finished. Values: SUCCESSFUL_STOP_REASON_UNSPECIFIED: Should not be set. BUDGET_REACHED: The inputs.budgetMilliNodeHours had been reached. MODEL_CONVERGED: Further training of the Model ceased to increase its quality, since it already has converged. """ SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0 BUDGET_REACHED = 1 MODEL_CONVERGED = 2 costMilliNodeHours = _messages.IntegerField(1) successfulStopReason = _messages.EnumField('SuccessfulStopReasonValueValuesEnum', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTables(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Tables Model. Fields: inputs: The input parameters of this TrainingJob. metadata: The metadata information. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs', 1) metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesMetadata', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs object. Fields: additionalExperiments: Additional experiment flags for the Tables training pipeline. disableEarlyStopping: Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used. exportEvaluatedDataItemsConfig: Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed. optimizationObjective: Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set. The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used. classification (binary): "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "minimize-log-loss" - Minimize log loss. "maximize-au-prc" - Maximize the area under the precision-recall curve. "maximize-precision-at-recall" - Maximize precision for a specified recall value. "maximize-recall-at-precision" - Maximize recall for a specified precision value. classification (multi-class): "minimize-log-loss" (default) - Minimize log loss. regression: "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE). optimizationObjectivePrecisionValue: Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive. optimizationObjectiveRecallValue: Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive. predictionType: The type of prediction the Model is to produce. "classification" - Predict one out of multiple target values is picked for each row. "regression" - Predict a value based on its relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings. targetColumn: The column name of the target column that the model is to predict. trainBudgetMilliNodeHours: Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive. transformations: Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter. weightColumnName: Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1. """ additionalExperiments = _messages.StringField(1, repeated=True) disableEarlyStopping = _messages.BooleanField(2) exportEvaluatedDataItemsConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig', 3) optimizationObjective = _messages.StringField(4) optimizationObjectivePrecisionValue = _messages.FloatField(5, variant=_messages.Variant.FLOAT) optimizationObjectiveRecallValue = _messages.FloatField(6, variant=_messages.Variant.FLOAT) predictionType = _messages.StringField(7) targetColumn = _messages.StringField(8) trainBudgetMilliNodeHours = _messages.IntegerField(9) transformations = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation', 10, repeated=True) weightColumnName = _messages.StringField(11) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTr ansformation object. Fields: auto: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInpu tsTransformationAutoTransformation attribute. categorical: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTab lesInputsTransformationCategoricalTransformation attribute. numeric: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesI nputsTransformationNumericTransformation attribute. repeatedCategorical: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionA utoMlTablesInputsTransformationCategoricalArrayTransformation attribute. repeatedNumeric: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoM lTablesInputsTransformationNumericArrayTransformation attribute. repeatedText: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTa blesInputsTransformationTextArrayTransformation attribute. text: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInpu tsTransformationTextTransformation attribute. timestamp: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTable sInputsTransformationTimestampTransformation attribute. """ auto = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationAutoTransformation', 1) categorical = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalTransformation', 2) numeric = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericTransformation', 3) repeatedCategorical = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalArrayTransformation', 4) repeatedNumeric = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericArrayTransformation', 5) repeatedText = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextArrayTransformation', 6) text = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextTransformation', 7) timestamp = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTimestampTransformation', 8) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationAutoTransformation(_messages.Message): r"""Training pipeline will infer the proper transformation based on the statistic of dataset. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalArrayTransformation(_messages.Message): r"""Treats the column as categorical array and performs following transformation functions. * For each element in the array, convert the category name to a dictionary lookup index and generate an embedding for each index. Combine the embedding of all elements into a single embedding using the mean. * Empty arrays treated as an embedding of zeroes. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * The categorical string as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index. * Categories that appear less than 5 times in the training dataset are treated as the "unknown" category. The "unknown" category gets its own special lookup index and resulting embedding. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericArrayTransformation(_messages.Message): r"""Treats the column as numerical array and performs following transformation functions. * All transformations for Numerical types applied to the average of the all elements. * The average of empty arrays is treated as zero. Fields: columnName: A string attribute. invalidValuesAllowed: If invalid values is allowed, the training pipeline will create a boolean feature that indicated whether the value is valid. Otherwise, the training pipeline will discard the input row from trainining data. """ columnName = _messages.StringField(1) invalidValuesAllowed = _messages.BooleanField(2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * The value converted to float32. * The z_score of the value. * log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. * z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. * A boolean value that indicates whether the value is valid. Fields: columnName: A string attribute. invalidValuesAllowed: If invalid values is allowed, the training pipeline will create a boolean feature that indicated whether the value is valid. Otherwise, the training pipeline will discard the input row from trainining data. """ columnName = _messages.StringField(1) invalidValuesAllowed = _messages.BooleanField(2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextArrayTransformation(_messages.Message): r"""Treats the column as text array and performs following transformation functions. * Concatenate all text values in the array into a single text value using a space (" ") as a delimiter, and then treat the result as a single text value. Apply the transformations for Text columns. * Empty arrays treated as an empty text. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * The text as is--no change to case, punctuation, spelling, tense, and so on. * Tokenize text to words. Convert each words to a dictionary lookup index and generate an embedding for each index. Combine the embedding of all elements into a single embedding using the mean. * Tokenization is based on unicode script boundaries. * Missing values get their own lookup index and resulting embedding. * Stop-words receive no special treatment and are not removed. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTimestampTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * Apply the transformation functions for Numerical columns. * Determine the year, month, day,and weekday. Treat each value from the * timestamp as a Categorical column. * Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed. Fields: columnName: A string attribute. invalidValuesAllowed: If invalid values is allowed, the training pipeline will create a boolean feature that indicated whether the value is valid. Otherwise, the training pipeline will discard the input row from trainining data. timeFormat: The format in which that time field is expressed. The time_format must either be one of: * `unix-seconds` * `unix- milliseconds` * `unix-microseconds` * `unix-nanoseconds` (for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written in `strftime` syntax. If time_format is not set, then the default format is RFC 3339 `date-time` format, where `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z) """ columnName = _messages.StringField(1) invalidValuesAllowed = _messages.BooleanField(2) timeFormat = _messages.StringField(3) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesMetadata(_messages.Message): r"""Model metadata specific to AutoML Tables. Fields: evaluatedDataItemsBigqueryUri: BigQuery destination uri for exported evaluated examples. trainCostMilliNodeHours: Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget. """ evaluatedDataItemsBigqueryUri = _messages.StringField(1) trainCostMilliNodeHours = _messages.IntegerField(2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextClassification(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Text Classification Model. Fields: inputs: The input parameters of this TrainingJob. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextClassificationInputs', 1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextClassificationInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextClassifica tionInputs object. Fields: multiLabel: A boolean attribute. """ multiLabel = _messages.BooleanField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextExtraction(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Text Extraction Model. Fields: inputs: The input parameters of this TrainingJob. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextExtractionInputs', 1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextExtractionInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextExtractionInputs object. """ class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextSentiment(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Text Sentiment Model. Fields: inputs: The input parameters of this TrainingJob. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextSentimentInputs', 1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextSentimentInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextSentimentInputs object. Fields: sentimentMax: A sentiment is expressed as an integer ordinal, where higher value means a more positive sentiment. The range of sentiments that will be used is between 0 and sentimentMax (inclusive on both ends), and all the values in the range must be represented in the dataset before a model can be created. Only the Annotations with this sentimentMax will be used for training. sentimentMax value must be between 1 and 10 (inclusive). """ sentimentMax = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoActionRecognition(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Video Action Recognition Model. Fields: inputs: The input parameters of this TrainingJob. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoActionRecognitionInputs', 1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoActionRecognitionInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoActionRec ognitionInputs object. Enums: ModelTypeValueValuesEnum: Fields: modelType: A ModelTypeValueValuesEnum attribute. """ class ModelTypeValueValuesEnum(_messages.Enum): r"""ModelTypeValueValuesEnum enum type. Values: MODEL_TYPE_UNSPECIFIED: Should not be set. CLOUD: A model best tailored to be used within Google Cloud, and which c annot be exported. Default. MOBILE_VERSATILE_1: A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards. MOBILE_JETSON_VERSATILE_1: A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) to a Jetson device afterwards. MOBILE_CORAL_VERSATILE_1: A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a Coral device afterwards. """ MODEL_TYPE_UNSPECIFIED = 0 CLOUD = 1 MOBILE_VERSATILE_1 = 2 MOBILE_JETSON_VERSATILE_1 = 3 MOBILE_CORAL_VERSATILE_1 = 4 modelType = _messages.EnumField('ModelTypeValueValuesEnum', 1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoClassification(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Video Classification Model. Fields: inputs: The input parameters of this TrainingJob. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoClassificationInputs', 1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoClassificationInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoClassific ationInputs object. Enums: ModelTypeValueValuesEnum: Fields: modelType: A ModelTypeValueValuesEnum attribute. """ class ModelTypeValueValuesEnum(_messages.Enum): r"""ModelTypeValueValuesEnum enum type. Values: MODEL_TYPE_UNSPECIFIED: Should not be set. CLOUD: A model best tailored to be used within Google Cloud, and which cannot be exported. Default. MOBILE_VERSATILE_1: A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards. MOBILE_JETSON_VERSATILE_1: A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) to a Jetson device afterwards. """ MODEL_TYPE_UNSPECIFIED = 0 CLOUD = 1 MOBILE_VERSATILE_1 = 2 MOBILE_JETSON_VERSATILE_1 = 3 modelType = _messages.EnumField('ModelTypeValueValuesEnum', 1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoObjectTracking(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Video ObjectTracking Model. Fields: inputs: The input parameters of this TrainingJob. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoObjectTrackingInputs', 1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoObjectTrackingInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoObjectTra ckingInputs object. Enums: ModelTypeValueValuesEnum: Fields: modelType: A ModelTypeValueValuesEnum attribute. """ class ModelTypeValueValuesEnum(_messages.Enum): r"""ModelTypeValueValuesEnum enum type. Values: MODEL_TYPE_UNSPECIFIED: Should not be set. CLOUD: A model best tailored to be used within Google Cloud, and which c annot be exported. Default. MOBILE_VERSATILE_1: A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards. MOBILE_CORAL_VERSATILE_1: A versatile model that is meant to be exported (see ModelService.ExportModel) and used on a Google Coral device. MOBILE_CORAL_LOW_LATENCY_1: A model that trades off quality for low latency, to be exported (see ModelService.ExportModel) and used on a Google Coral device. MOBILE_JETSON_VERSATILE_1: A versatile model that is meant to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device. MOBILE_JETSON_LOW_LATENCY_1: A model that trades off quality for low latency, to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device. """ MODEL_TYPE_UNSPECIFIED = 0 CLOUD = 1 MOBILE_VERSATILE_1 = 2 MOBILE_CORAL_VERSATILE_1 = 3 MOBILE_CORAL_LOW_LATENCY_1 = 4 MOBILE_JETSON_VERSATILE_1 = 5 MOBILE_JETSON_LOW_LATENCY_1 = 6 modelType = _messages.EnumField('ModelTypeValueValuesEnum', 1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionCustomJobMetadata(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionCustomJobMetadata object. Fields: backingCustomJob: The resource name of the CustomJob that has been created to carry out this custom task. """ backingCustomJob = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionCustomTask(_messages.Message): r"""A TrainingJob that trains a custom code Model. Fields: inputs: The input parameters of this CustomTask. metadata: The metadata information. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1CustomJobSpec', 1) metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionCustomJobMetadata', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig(_messages.Message): r"""Configuration for exporting test set predictions to a BigQuery table. Fields: destinationBigqueryUri: URI of desired destination BigQuery table. Expected format: `bq://{project_id}:{dataset_id}:{table}` If not specified, then results are exported to the following auto-created BigQuery table: `{project_id}:export_evaluated_examples_{model_name}_{yy yy_MM_dd'T'HH_mm_ss_SSS'Z'}.evaluated_examples` overrideExistingTable: If true and an export destination is specified, then the contents of the destination are overwritten. Otherwise, if the export destination already exists, then the export operation fails. """ destinationBigqueryUri = _messages.StringField(1) overrideExistingTable = _messages.BooleanField(2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHierarchyConfig(_messages.Message): r"""Configuration that defines the hierarchical relationship of time series and parameters for hierarchical forecasting strategies. Fields: groupColumns: A list of time series attribute column names that define the time series hierarchy. Only one level of hierarchy is supported, ex. 'region' for a hierarchy of stores or 'department' for a hierarchy of products. If multiple columns are specified, time series will be grouped by their combined values, ex. ('blue', 'large') for 'color' and 'size', up to 5 columns are accepted. If no group columns are specified, all time series are considered to be part of the same group. groupTemporalTotalWeight: The weight of the loss for predictions aggregated over both the horizon and time series in the same hierarchy group. groupTotalWeight: The weight of the loss for predictions aggregated over time series in the same group. temporalTotalWeight: The weight of the loss for predictions aggregated over the horizon for a single time series. """ groupColumns = _messages.StringField(1, repeated=True) groupTemporalTotalWeight = _messages.FloatField(2) groupTotalWeight = _messages.FloatField(3) temporalTotalWeight = _messages.FloatField(4) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuningJobMetadata(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuning JobMetadata object. Fields: backingHyperparameterTuningJob: The resource name of the HyperparameterTuningJob that has been created to carry out this HyperparameterTuning task. bestTrialBackingCustomJob: The resource name of the CustomJob that has been created to run the best Trial of this HyperparameterTuning task. """ backingHyperparameterTuningJob = _messages.StringField(1) bestTrialBackingCustomJob = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuningJobSpec(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuning JobSpec object. Fields: maxFailedTrialCount: The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails. maxTrialCount: The desired total number of Trials. parallelTrialCount: The desired number of Trials to run in parallel. studySpec: Study configuration of the HyperparameterTuningJob. trialJobSpec: The spec of a trial job. The same spec applies to the CustomJobs created in all the trials. """ maxFailedTrialCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) maxTrialCount = _messages.IntegerField(2, variant=_messages.Variant.INT32) parallelTrialCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) studySpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpec', 4) trialJobSpec = _messages.MessageField('GoogleCloudAiplatformV1CustomJobSpec', 5) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuningTask(_messages.Message): r"""A TrainingJob that tunes Hypererparameters of a custom code Model. Fields: inputs: The input parameters of this HyperparameterTuningTask. metadata: The metadata information. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuningJobSpec', 1) metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuningJobMetadata', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecasting(_messages.Message): r"""A TrainingJob that trains and uploads an AutoML Forecasting Model. Fields: inputs: The input parameters of this TrainingJob. metadata: The metadata information. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputs', 1) metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingMetadata', 2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputs(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecasti ngInputs object. Fields: additionalExperiments: Additional experiment flags for the time series forcasting training. availableAtForecastColumns: Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day. contextWindow: The amount of time into the past training and prediction data is used for model training and prediction respectively. Expressed in number of units defined by the `data_granularity` field. dataGranularity: Expected difference in time granularity between rows in the data. exportEvaluatedDataItemsConfig: Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed. forecastHorizon: The amount of time into the future for which forecasted values for the target are returned. Expressed in number of units defined by the `data_granularity` field. hierarchyConfig: Configuration that defines the hierarchical relationship of time series and parameters for hierarchical forecasting strategies. holidayRegions: The geographical region based on which the holiday effect is applied in modeling by adding holiday categorical array feature that include all holidays matching the date. This option only allowed when data_granularity is day. By default, holiday effect modeling is disabled. To turn it on, specify the holiday region using this option. optimizationObjective: Objective function the model is optimizing towards. The training process creates a model that optimizes the value of the objective function over the validation set. The supported optimization objectives: * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). * "minimize-mae" - Minimize mean-absolute error (MAE). * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE). * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE). * "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and mean-absolute-error (MAE). * "minimize- quantile-loss" - Minimize the quantile loss at the quantiles defined in `quantiles`. * "minimize-mape" - Minimize the mean absolute percentage error. quantiles: Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Required if the value of optimization_objective is minimize-quantile-loss. Represents the percent quantiles to use for that objective. Quantiles must be unique. targetColumn: The name of the column that the Model is to predict values for. This column must be unavailable at forecast. timeColumn: The name of the column that identifies time order in the time series. This column must be available at forecast. timeSeriesAttributeColumns: Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color. timeSeriesIdentifierColumn: The name of the column that identifies the time series. trainBudgetMilliNodeHours: Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive. transformations: Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter. unavailableAtForecastColumns: Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day. validationOptions: Validation options for the data validation component. The available options are: * "fail-pipeline" - default, will validate against the validation and fail the pipeline if it fails. * "ignore- validation" - ignore the results of the validation and continue weightColumn: Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1. This column must be available at forecast. windowConfig: Config containing strategy for generating sliding windows. """ additionalExperiments = _messages.StringField(1, repeated=True) availableAtForecastColumns = _messages.StringField(2, repeated=True) contextWindow = _messages.IntegerField(3) dataGranularity = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsGranularity', 4) exportEvaluatedDataItemsConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig', 5) forecastHorizon = _messages.IntegerField(6) hierarchyConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHierarchyConfig', 7) holidayRegions = _messages.StringField(8, repeated=True) optimizationObjective = _messages.StringField(9) quantiles = _messages.FloatField(10, repeated=True) targetColumn = _messages.StringField(11) timeColumn = _messages.StringField(12) timeSeriesAttributeColumns = _messages.StringField(13, repeated=True) timeSeriesIdentifierColumn = _messages.StringField(14) trainBudgetMilliNodeHours = _messages.IntegerField(15) transformations = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformation', 16, repeated=True) unavailableAtForecastColumns = _messages.StringField(17, repeated=True) validationOptions = _messages.StringField(18) weightColumn = _messages.StringField(19) windowConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionWindowConfig', 20) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsGranularity(_messages.Message): r"""A duration of time expressed in time granularity units. Fields: quantity: The number of granularity_units between data points in the training data. If `granularity_unit` is `minute`, can be 1, 5, 10, 15, or 30. For all other values of `granularity_unit`, must be 1. unit: The time granularity unit of this time period. The supported units are: * "minute" * "hour" * "day" * "week" * "month" * "year" """ quantity = _messages.IntegerField(1) unit = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformation(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecasti ngInputsTransformation object. Fields: auto: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForec astingInputsTransformationAutoTransformation attribute. categorical: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPl usForecastingInputsTransformationCategoricalTransformation attribute. numeric: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusFo recastingInputsTransformationNumericTransformation attribute. text: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForec astingInputsTransformationTextTransformation attribute. timestamp: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlus ForecastingInputsTransformationTimestampTransformation attribute. """ auto = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationAutoTransformation', 1) categorical = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationCategoricalTransformation', 2) numeric = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationNumericTransformation', 3) text = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationTextTransformation', 4) timestamp = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationTimestampTransformation', 5) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationAutoTransformation(_messages.Message): r"""Training pipeline will infer the proper transformation based on the statistic of dataset. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationCategoricalTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * The categorical string as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index. * Categories that appear less than 5 times in the training dataset are treated as the "unknown" category. The "unknown" category gets its own special lookup index and resulting embedding. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationNumericTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * The value converted to float32. * The z_score of the value. * log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. * z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationTextTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * The text as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index. Fields: columnName: A string attribute. """ columnName = _messages.StringField(1) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationTimestampTransformation(_messages.Message): r"""Training pipeline will perform following transformation functions. * Apply the transformation functions for Numerical columns. * Determine the year, month, day,and weekday. Treat each value from the timestamp as a Categorical column. * Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed. Fields: columnName: A string attribute. timeFormat: The format in which that time field is expressed. The time_format must either be one of: * `unix-seconds` * `unix- milliseconds` * `unix-microseconds` * `unix-nanoseconds` (for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written in `strftime` syntax. If time_format is not set, then the default format is RFC 3339 `date-time` format, where `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z) """ columnName = _messages.StringField(1) timeFormat = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingMetadata(_messages.Message): r"""Model metadata specific to Seq2Seq Plus Forecasting. Fields: evaluatedDataItemsBigqueryUri: BigQuery destination uri for exported evaluated examples. trainCostMilliNodeHours: Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget. """ evaluatedDataItemsBigqueryUri = _messages.StringField(1) trainCostMilliNodeHours = _messages.IntegerField(2) class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionWindowConfig(_messages.Message): r"""Config that contains the strategy used to generate sliding windows in time series training. A window is a series of rows that comprise the context up to the time of prediction, and the horizon following. The corresponding row for each window marks the start of the forecast horizon. Each window is used as an input example for training/evaluation. Fields: column: Name of the column that should be used to generate sliding windows. The column should contain either booleans or string booleans; if the value of the row is True, generate a sliding window with the horizon starting at that row. The column will not be used as a feature in training. maxCount: Maximum number of windows that should be generated across all time series. strideLength: Stride length used to generate input examples. Within one time series, every {$STRIDE_LENGTH} rows will be used to generate a sliding window. """ column = _messages.StringField(1) maxCount = _messages.IntegerField(2) strideLength = _messages.IntegerField(3) class GoogleCloudAiplatformV1SchemaVertex(_messages.Message): r"""A vertex represents a 2D point in the image. NOTE: the normalized vertex coordinates are relative to the original image and range from 0 to 1. Fields: x: X coordinate. y: Y coordinate. """ x = _messages.FloatField(1) y = _messages.FloatField(2) class GoogleCloudAiplatformV1SchemaVideoActionRecognitionAnnotation(_messages.Message): r"""Annotation details specific to video action recognition. Fields: annotationSpecId: The resource Id of the AnnotationSpec that this Annotation pertains to. displayName: The display name of the AnnotationSpec that this Annotation pertains to. timeSegment: This Annotation applies to the time period represented by the TimeSegment. If it's not set, the Annotation applies to the whole video. """ annotationSpecId = _messages.StringField(1) displayName = _messages.StringField(2) timeSegment = _messages.MessageField('GoogleCloudAiplatformV1SchemaTimeSegment', 3) class GoogleCloudAiplatformV1SchemaVideoClassificationAnnotation(_messages.Message): r"""Annotation details specific to video classification. Fields: annotationSpecId: The resource Id of the AnnotationSpec that this Annotation pertains to. displayName: The display name of the AnnotationSpec that this Annotation pertains to. timeSegment: This Annotation applies to the time period represented by the TimeSegment. If it's not set, the Annotation applies to the whole video. """ annotationSpecId = _messages.StringField(1) displayName = _messages.StringField(2) timeSegment = _messages.MessageField('GoogleCloudAiplatformV1SchemaTimeSegment', 3) class GoogleCloudAiplatformV1SchemaVideoDataItem(_messages.Message): r"""Payload of Video DataItem. Fields: gcsUri: Required. Google Cloud Storage URI points to the original video in user's bucket. The video is up to 50 GB in size and up to 3 hour in duration. mimeType: Output only. The mime type of the content of the video. Only the videos in below listed mime types are supported. Supported mime_type: - video/mp4 - video/avi - video/quicktime """ gcsUri = _messages.StringField(1) mimeType = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaVideoDatasetMetadata(_messages.Message): r"""The metadata of Datasets that contain Video DataItems. Fields: dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage describing payload of the Video DataItems that belong to this Dataset. gcsBucket: Google Cloud Storage Bucket name that contains the blob data of this Dataset. """ dataItemSchemaUri = _messages.StringField(1) gcsBucket = _messages.StringField(2) class GoogleCloudAiplatformV1SchemaVideoObjectTrackingAnnotation(_messages.Message): r"""Annotation details specific to video object tracking. Fields: annotationSpecId: The resource Id of the AnnotationSpec that this Annotation pertains to. displayName: The display name of the AnnotationSpec that this Annotation pertains to. instanceId: The instance of the object, expressed as a positive integer. Used to track the same object across different frames. timeOffset: A time (frame) of a video to which this annotation pertains. Represented as the duration since the video's start. xMax: The rightmost coordinate of the bounding box. xMin: The leftmost coordinate of the bounding box. yMax: The bottommost coordinate of the bounding box. yMin: The topmost coordinate of the bounding box. """ annotationSpecId = _messages.StringField(1) displayName = _messages.StringField(2) instanceId = _messages.IntegerField(3) timeOffset = _messages.StringField(4) xMax = _messages.FloatField(5) xMin = _messages.FloatField(6) yMax = _messages.FloatField(7) yMin = _messages.FloatField(8) class GoogleCloudAiplatformV1SchemaVisualInspectionClassificationLabelSavedQueryMetadata(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaVisualInspectionClassificationLabelSavedQ ueryMetadata object. Fields: multiLabel: Whether or not the classification label is multi_label. """ multiLabel = _messages.BooleanField(1) class GoogleCloudAiplatformV1SchemaVisualInspectionMaskSavedQueryMetadata(_messages.Message): r"""A GoogleCloudAiplatformV1SchemaVisualInspectionMaskSavedQueryMetadata object. """ class GoogleCloudAiplatformV1SearchDataItemsResponse(_messages.Message): r"""Response message for DatasetService.SearchDataItems. Fields: dataItemViews: The DataItemViews read. nextPageToken: A token to retrieve next page of results. Pass to SearchDataItemsRequest.page_token to obtain that page. """ dataItemViews = _messages.MessageField('GoogleCloudAiplatformV1DataItemView', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1SearchEntryPoint(_messages.Message): r"""Google search entry point. Fields: renderedContent: Optional. Web content snippet that can be embedded in a web page or an app webview. sdkBlob: Optional. Base64 encoded JSON representing array of tuple. """ renderedContent = _messages.StringField(1) sdkBlob = _messages.BytesField(2) class GoogleCloudAiplatformV1SearchFeaturesResponse(_messages.Message): r"""Response message for FeaturestoreService.SearchFeatures. Fields: features: The Features matching the request. Fields returned: * `name` * `description` * `labels` * `create_time` * `update_time` nextPageToken: A token, which can be sent as SearchFeaturesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages. """ features = _messages.MessageField('GoogleCloudAiplatformV1Feature', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1SearchMigratableResourcesRequest(_messages.Message): r"""Request message for MigrationService.SearchMigratableResources. Fields: filter: A filter for your search. You can use the following types of filters: * Resource type filters. The following strings filter for a specific type of MigratableResource: * `ml_engine_model_version:*` * `automl_model:*` * `automl_dataset:*` * `data_labeling_dataset:*` * "Migrated or not" filters. The following strings filter for resources that either have or have not already been migrated: * `last_migrate_time:*` filters for migrated resources. * `NOT last_migrate_time:*` filters for not yet migrated resources. pageSize: The standard page size. The default and maximum value is 100. pageToken: The standard page token. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) class GoogleCloudAiplatformV1SearchMigratableResourcesResponse(_messages.Message): r"""Response message for MigrationService.SearchMigratableResources. Fields: migratableResources: All migratable resources that can be migrated to the location specified in the request. nextPageToken: The standard next-page token. The migratable_resources may not fill page_size in SearchMigratableResourcesRequest even when there are subsequent pages. """ migratableResources = _messages.MessageField('GoogleCloudAiplatformV1MigratableResource', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1SearchModelDeploymentMonitoringStatsAnomaliesRequest(_messages.Message): r"""Request message for JobService.SearchModelDeploymentMonitoringStatsAnomalies. Fields: deployedModelId: Required. The DeployedModel ID of the [ModelDeploymentMonitoringObjectiveConfig.deployed_model_id]. endTime: The latest timestamp of stats being generated. If not set, indicates feching stats till the latest possible one. featureDisplayName: The feature display name. If specified, only return the stats belonging to this feature. Format: ModelMonitoringStatsAnomali es.FeatureHistoricStatsAnomalies.feature_display_name, example: "user_destination". objectives: Required. Objectives of the stats to retrieve. pageSize: The standard list page size. pageToken: A page token received from a previous JobService.SearchModelDeploymentMonitoringStatsAnomalies call. startTime: The earliest timestamp of stats being generated. If not set, indicates fetching stats till the earliest possible one. """ deployedModelId = _messages.StringField(1) endTime = _messages.StringField(2) featureDisplayName = _messages.StringField(3) objectives = _messages.MessageField('GoogleCloudAiplatformV1SearchModelDeploymentMonitoringStatsAnomaliesRequestStatsAnomaliesObjective', 4, repeated=True) pageSize = _messages.IntegerField(5, variant=_messages.Variant.INT32) pageToken = _messages.StringField(6) startTime = _messages.StringField(7) class GoogleCloudAiplatformV1SearchModelDeploymentMonitoringStatsAnomaliesRequestStatsAnomaliesObjective(_messages.Message): r"""Stats requested for specific objective. Enums: TypeValueValuesEnum: Fields: topFeatureCount: If set, all attribution scores between SearchModelDeploymentMonitoringStatsAnomaliesRequest.start_time and SearchModelDeploymentMonitoringStatsAnomaliesRequest.end_time are fetched, and page token doesn't take effect in this case. Only used to retrieve attribution score for the top Features which has the highest attribution score in the latest monitoring run. type: A TypeValueValuesEnum attribute. """ class TypeValueValuesEnum(_messages.Enum): r"""TypeValueValuesEnum enum type. Values: MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED: Default value, should not be set. RAW_FEATURE_SKEW: Raw feature values' stats to detect skew between Training-Prediction datasets. RAW_FEATURE_DRIFT: Raw feature values' stats to detect drift between Serving-Prediction datasets. FEATURE_ATTRIBUTION_SKEW: Feature attribution scores to detect skew between Training-Prediction datasets. FEATURE_ATTRIBUTION_DRIFT: Feature attribution scores to detect skew between Prediction datasets collected within different time windows. """ MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED = 0 RAW_FEATURE_SKEW = 1 RAW_FEATURE_DRIFT = 2 FEATURE_ATTRIBUTION_SKEW = 3 FEATURE_ATTRIBUTION_DRIFT = 4 topFeatureCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) type = _messages.EnumField('TypeValueValuesEnum', 2) class GoogleCloudAiplatformV1SearchModelDeploymentMonitoringStatsAnomaliesResponse(_messages.Message): r"""Response message for JobService.SearchModelDeploymentMonitoringStatsAnomalies. Fields: monitoringStats: Stats retrieved for requested objectives. There are at most 1000 ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.pr ediction_stats in the response. nextPageToken: The page token that can be used by the next JobService.SearchModelDeploymentMonitoringStatsAnomalies call. """ monitoringStats = _messages.MessageField('GoogleCloudAiplatformV1ModelMonitoringStatsAnomalies', 1, repeated=True) nextPageToken = _messages.StringField(2) class GoogleCloudAiplatformV1SearchNearestEntitiesRequest(_messages.Message): r"""The request message for FeatureOnlineStoreService.SearchNearestEntities. Fields: query: Required. The query. returnFullEntity: Optional. If set to true, the full entities (including all vector values and metadata) of the nearest neighbors are returned; otherwise only entity id of the nearest neighbors will be returned. Note that returning full entities will significantly increase the latency and cost of the query. """ query = _messages.MessageField('GoogleCloudAiplatformV1NearestNeighborQuery', 1) returnFullEntity = _messages.BooleanField(2) class GoogleCloudAiplatformV1SearchNearestEntitiesResponse(_messages.Message): r"""Response message for FeatureOnlineStoreService.SearchNearestEntities Fields: nearestNeighbors: The nearest neighbors of the query entity. """ nearestNeighbors = _messages.MessageField('GoogleCloudAiplatformV1NearestNeighbors', 1) class GoogleCloudAiplatformV1SecretEnvVar(_messages.Message): r"""Represents an environment variable where the value is a secret in Cloud Secret Manager. Fields: name: Required. Name of the secret environment variable. secretRef: Required. Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable. """ name = _messages.StringField(1) secretRef = _messages.MessageField('GoogleCloudAiplatformV1SecretRef', 2) class GoogleCloudAiplatformV1SecretRef(_messages.Message): r"""Reference to a secret stored in the Cloud Secret Manager that will provide the value for this environment variable. Fields: secret: Required. The name of the secret in Cloud Secret Manager. Format: {secret_name}. version: The Cloud Secret Manager secret version. Can be 'latest' for the latest version, an integer for a specific version, or a version alias. """ secret = _messages.StringField(1) version = _messages.StringField(2) class GoogleCloudAiplatformV1Segment(_messages.Message): r"""Segment of the content. Fields: endIndex: Output only. End index in the given Part, measured in bytes. Offset from the start of the Part, exclusive, starting at zero. partIndex: Output only. The index of a Part object within its parent Content object. startIndex: Output only. Start index in the given Part, measured in bytes. Offset from the start of the Part, inclusive, starting at zero. text: Output only. The text corresponding to the segment from the response. """ endIndex = _messages.IntegerField(1, variant=_messages.Variant.INT32) partIndex = _messages.IntegerField(2, variant=_messages.Variant.INT32) startIndex = _messages.IntegerField(3, variant=_messages.Variant.INT32) text = _messages.StringField(4) class GoogleCloudAiplatformV1ServiceAccountSpec(_messages.Message): r"""Configuration for the use of custom service account to run the workloads. Fields: enableCustomServiceAccount: Required. If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the [Vertex AI Custom Code Service Agent](https://cloud.google.com/vertex-ai/docs/general/access- control#service-agents). serviceAccount: Optional. Required when all below conditions are met * `enable_custom_service_account` is true; * any runtime is specified via `ResourceRuntimeSpec` on creation time, for example, Ray The users must have `iam.serviceAccounts.actAs` permission on this service account and then the specified runtime containers will run as it. Do not set this field if you want to submit jobs using custom service account to this PersistentResource after creation, but only specify the `service_account` inside the job. """ enableCustomServiceAccount = _messages.BooleanField(1) serviceAccount = _messages.StringField(2) class GoogleCloudAiplatformV1SharePointSources(_messages.Message): r"""The SharePointSources to pass to ImportRagFiles. Fields: sharePointSources: The SharePoint sources. """ sharePointSources = _messages.MessageField('GoogleCloudAiplatformV1SharePointSourcesSharePointSource', 1, repeated=True) class GoogleCloudAiplatformV1SharePointSourcesSharePointSource(_messages.Message): r"""An individual SharePointSource. Fields: clientId: The Application ID for the app registered in Microsoft Azure Portal. The application must also be configured with MS Graph permissions "Files.ReadAll", "Sites.ReadAll" and BrowserSiteLists.Read.All. clientSecret: The application secret for the app registered in Azure. driveId: The ID of the drive to download from. driveName: The name of the drive to download from. fileId: Output only. The SharePoint file id. Output only. sharepointFolderId: The ID of the SharePoint folder to download from. sharepointFolderPath: The path of the SharePoint folder to download from. sharepointSiteName: The name of the SharePoint site to download from. This can be the site name or the site id. tenantId: Unique identifier of the Azure Active Directory Instance. """ clientId = _messages.StringField(1) clientSecret = _messages.MessageField('GoogleCloudAiplatformV1ApiAuthApiKeyConfig', 2) driveId = _messages.StringField(3) driveName = _messages.StringField(4) fileId = _messages.StringField(5) sharepointFolderId = _messages.StringField(6) sharepointFolderPath = _messages.StringField(7) sharepointSiteName = _messages.StringField(8) tenantId = _messages.StringField(9) class GoogleCloudAiplatformV1ShieldedVmConfig(_messages.Message): r"""A set of Shielded Instance options. See [Images using supported Shielded VM features](https://cloud.google.com/compute/docs/instances/modifying- shielded-vm). Fields: enableSecureBoot: Defines whether the instance has [Secure Boot](https://cloud.google.com/compute/shielded-vm/docs/shielded- vm#secure-boot) enabled. Secure Boot helps ensure that the system only runs authentic software by verifying the digital signature of all boot components, and halting the boot process if signature verification fails. """ enableSecureBoot = _messages.BooleanField(1) class GoogleCloudAiplatformV1SlackSource(_messages.Message): r"""The Slack source for the ImportRagFilesRequest. Fields: channels: Required. The Slack channels. """ channels = _messages.MessageField('GoogleCloudAiplatformV1SlackSourceSlackChannels', 1, repeated=True) class GoogleCloudAiplatformV1SlackSourceSlackChannels(_messages.Message): r"""SlackChannels contains the Slack channels and corresponding access token. Fields: apiKeyConfig: Required. The SecretManager secret version resource name (e.g. projects/{project}/secrets/{secret}/versions/{version}) storing the Slack channel access token that has access to the slack channel IDs. See: https://api.slack.com/tutorials/tracks/getting-a-token. channels: Required. The Slack channel IDs. """ apiKeyConfig = _messages.MessageField('GoogleCloudAiplatformV1ApiAuthApiKeyConfig', 1) channels = _messages.MessageField('GoogleCloudAiplatformV1SlackSourceSlackChannelsSlackChannel', 2, repeated=True) class GoogleCloudAiplatformV1SlackSourceSlackChannelsSlackChannel(_messages.Message): r"""SlackChannel contains the Slack channel ID and the time range to import. Fields: channelId: Required. The Slack channel ID. endTime: Optional. The ending timestamp for messages to import. startTime: Optional. The starting timestamp for messages to import. """ channelId = _messages.StringField(1) endTime = _messages.StringField(2) startTime = _messages.StringField(3) class GoogleCloudAiplatformV1SmoothGradConfig(_messages.Message): r"""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 Fields: featureNoiseSigma: This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features. noiseSigma: This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about [normalization](https://developers.google.com/machine- learning/data-prep/transform/normalization). For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature. noisySampleCount: The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3. """ featureNoiseSigma = _messages.MessageField('GoogleCloudAiplatformV1FeatureNoiseSigma', 1) noiseSigma = _messages.FloatField(2, variant=_messages.Variant.FLOAT) noisySampleCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SpecialistPool(_messages.Message): r"""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. Fields: displayName: Required. The user-defined name of the SpecialistPool. The name can be up to 128 characters long and can consist of any UTF-8 characters. This field should be unique on project-level. name: Required. The resource name of the SpecialistPool. pendingDataLabelingJobs: Output only. The resource name of the pending data labeling jobs. specialistManagerEmails: The email addresses of the managers in the SpecialistPool. specialistManagersCount: Output only. The number of managers in this SpecialistPool. specialistWorkerEmails: The email addresses of workers in the SpecialistPool. """ displayName = _messages.StringField(1) name = _messages.StringField(2) pendingDataLabelingJobs = _messages.StringField(3, repeated=True) specialistManagerEmails = _messages.StringField(4, repeated=True) specialistManagersCount = _messages.IntegerField(5, variant=_messages.Variant.INT32) specialistWorkerEmails = _messages.StringField(6, repeated=True) class GoogleCloudAiplatformV1SpeculativeDecodingSpec(_messages.Message): r"""Configuration for Speculative Decoding. Fields: draftModelSpeculation: draft model speculation. ngramSpeculation: N-Gram speculation. speculativeTokenCount: The number of speculative tokens to generate at each step. """ draftModelSpeculation = _messages.MessageField('GoogleCloudAiplatformV1SpeculativeDecodingSpecDraftModelSpeculation', 1) ngramSpeculation = _messages.MessageField('GoogleCloudAiplatformV1SpeculativeDecodingSpecNgramSpeculation', 2) speculativeTokenCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SpeculativeDecodingSpecDraftModelSpeculation(_messages.Message): r"""Draft model speculation works by using the smaller model to generate candidate tokens for speculative decoding. Fields: draftModel: Required. The resource name of the draft model. """ draftModel = _messages.StringField(1) class GoogleCloudAiplatformV1SpeculativeDecodingSpecNgramSpeculation(_messages.Message): r"""N-Gram speculation works by trying to find matching tokens in the previous prompt sequence and use those as speculation for generating new tokens. Fields: ngramSize: The number of last N input tokens used as ngram to search/match against the previous prompt sequence. This is equal to the N in N-Gram. The default value is 3 if not specified. """ ngramSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1StartNotebookRuntimeOperationMetadata(_messages.Message): r"""Metadata information for NotebookService.StartNotebookRuntime. Fields: genericMetadata: The operation generic information. progressMessage: A human-readable message that shows the intermediate progress details of NotebookRuntime. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) progressMessage = _messages.StringField(2) class GoogleCloudAiplatformV1StartNotebookRuntimeRequest(_messages.Message): r"""Request message for NotebookService.StartNotebookRuntime.""" class GoogleCloudAiplatformV1StopNotebookRuntimeRequest(_messages.Message): r"""Request message for NotebookService.StopNotebookRuntime.""" class GoogleCloudAiplatformV1StopTrialRequest(_messages.Message): r"""Request message for VizierService.StopTrial.""" class GoogleCloudAiplatformV1StratifiedSplit(_messages.Message): r"""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. Fields: key: Required. The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column. testFraction: The fraction of the input data that is to be used to evaluate the Model. trainingFraction: The fraction of the input data that is to be used to train the Model. validationFraction: The fraction of the input data that is to be used to validate the Model. """ key = _messages.StringField(1) testFraction = _messages.FloatField(2) trainingFraction = _messages.FloatField(3) validationFraction = _messages.FloatField(4) class GoogleCloudAiplatformV1StreamQueryReasoningEngineRequest(_messages.Message): r"""Request message for ReasoningEngineExecutionService.StreamQuery. Messages: InputValue: Optional. Input content provided by users in JSON object format. Examples include text query, function calling parameters, media bytes, etc. Fields: classMethod: Optional. Class method to be used for the stream query. It is optional and defaults to "stream_query" if unspecified. input: Optional. Input content provided by users in JSON object format. Examples include text query, function calling parameters, media bytes, etc. """ @encoding.MapUnrecognizedFields('additionalProperties') class InputValue(_messages.Message): r"""Optional. Input content provided by users in JSON object format. Examples include text query, function calling parameters, media bytes, etc. Messages: AdditionalProperty: An additional property for a InputValue object. Fields: additionalProperties: Properties of the object. """ class AdditionalProperty(_messages.Message): r"""An additional property for a InputValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) classMethod = _messages.StringField(1) input = _messages.MessageField('InputValue', 2) class GoogleCloudAiplatformV1StreamRawPredictRequest(_messages.Message): r"""Request message for PredictionService.StreamRawPredict. Fields: httpBody: The prediction input. Supports HTTP headers and arbitrary data payload. """ httpBody = _messages.MessageField('GoogleApiHttpBody', 1) class GoogleCloudAiplatformV1StreamingPredictRequest(_messages.Message): r"""Request message for PredictionService.StreamingPredict. The first message must contain endpoint field and optionally input. The subsequent messages must contain input. Fields: inputs: The prediction input. parameters: The parameters that govern the prediction. """ inputs = _messages.MessageField('GoogleCloudAiplatformV1Tensor', 1, repeated=True) parameters = _messages.MessageField('GoogleCloudAiplatformV1Tensor', 2) class GoogleCloudAiplatformV1StreamingPredictResponse(_messages.Message): r"""Response message for PredictionService.StreamingPredict. Fields: outputs: The prediction output. parameters: The parameters that govern the prediction. """ outputs = _messages.MessageField('GoogleCloudAiplatformV1Tensor', 1, repeated=True) parameters = _messages.MessageField('GoogleCloudAiplatformV1Tensor', 2) class GoogleCloudAiplatformV1StreamingReadFeatureValuesRequest(_messages.Message): r"""Request message for FeaturestoreOnlineServingService.StreamingReadFeatureValues. Fields: entityIds: Required. IDs of entities to read Feature values of. The maximum number of IDs is 100. For example, for a machine learning model predicting user clicks on a website, an entity ID could be `user_123`. featureSelector: Required. Selector choosing Features of the target EntityType. Feature IDs will be deduplicated. """ entityIds = _messages.StringField(1, repeated=True) featureSelector = _messages.MessageField('GoogleCloudAiplatformV1FeatureSelector', 2) class GoogleCloudAiplatformV1StringArray(_messages.Message): r"""A list of string values. Fields: values: A list of string values. """ values = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1StructFieldValue(_messages.Message): r"""One field of a Struct (or object) type feature value. Fields: name: Name of the field in the struct feature. value: The value for this field. """ name = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1FeatureValue', 2) class GoogleCloudAiplatformV1StructValue(_messages.Message): r"""Struct (or object) type feature value. Fields: values: A list of field values. """ values = _messages.MessageField('GoogleCloudAiplatformV1StructFieldValue', 1, repeated=True) class GoogleCloudAiplatformV1Study(_messages.Message): r"""A message representing a Study. Enums: StateValueValuesEnum: Output only. The detailed state of a Study. Fields: createTime: Output only. Time at which the study was created. displayName: Required. Describes the Study, default value is empty string. inactiveReason: Output only. A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED. name: Output only. The name of a study. The study's globally unique identifier. Format: `projects/{project}/locations/{location}/studies/{study}` state: Output only. The detailed state of a Study. studySpec: Required. Configuration of the Study. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of a Study. Values: STATE_UNSPECIFIED: The study state is unspecified. ACTIVE: The study is active. INACTIVE: The study is stopped due to an internal error. COMPLETED: The study is done when the service exhausts the parameter search space or max_trial_count is reached. """ STATE_UNSPECIFIED = 0 ACTIVE = 1 INACTIVE = 2 COMPLETED = 3 createTime = _messages.StringField(1) displayName = _messages.StringField(2) inactiveReason = _messages.StringField(3) name = _messages.StringField(4) state = _messages.EnumField('StateValueValuesEnum', 5) studySpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpec', 6) class GoogleCloudAiplatformV1StudySpec(_messages.Message): r"""Represents specification of a Study. Enums: AlgorithmValueValuesEnum: The search algorithm specified for the Study. MeasurementSelectionTypeValueValuesEnum: Describe which measurement selection type will be used ObservationNoiseValueValuesEnum: The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. Fields: algorithm: The search algorithm specified for the Study. convexAutomatedStoppingSpec: The automated early stopping spec using convex stopping rule. decayCurveStoppingSpec: The automated early stopping spec using decay curve rule. measurementSelectionType: Describe which measurement selection type will be used medianAutomatedStoppingSpec: The automated early stopping spec using median rule. metrics: Required. Metric specs for the Study. observationNoise: The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. parameters: Required. The set of parameters to tune. studyStoppingConfig: Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition. """ class AlgorithmValueValuesEnum(_messages.Enum): r"""The search algorithm specified for the Study. Values: ALGORITHM_UNSPECIFIED: The default algorithm used by Vertex AI for [hyperparameter tuning](https://cloud.google.com/vertex- ai/docs/training/hyperparameter-tuning-overview) and [Vertex AI Vizier](https://cloud.google.com/vertex-ai/docs/vizier). GRID_SEARCH: Simple grid search within the feasible space. To use grid search, all parameters must be `INTEGER`, `CATEGORICAL`, or `DISCRETE`. RANDOM_SEARCH: Simple random search within the feasible space. """ ALGORITHM_UNSPECIFIED = 0 GRID_SEARCH = 1 RANDOM_SEARCH = 2 class MeasurementSelectionTypeValueValuesEnum(_messages.Enum): r"""Describe which measurement selection type will be used Values: MEASUREMENT_SELECTION_TYPE_UNSPECIFIED: Will be treated as LAST_MEASUREMENT. LAST_MEASUREMENT: Use the last measurement reported. BEST_MEASUREMENT: Use the best measurement reported. """ MEASUREMENT_SELECTION_TYPE_UNSPECIFIED = 0 LAST_MEASUREMENT = 1 BEST_MEASUREMENT = 2 class ObservationNoiseValueValuesEnum(_messages.Enum): r"""The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. Values: OBSERVATION_NOISE_UNSPECIFIED: The default noise level chosen by Vertex AI. LOW: Vertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters. HIGH: Vertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once. """ OBSERVATION_NOISE_UNSPECIFIED = 0 LOW = 1 HIGH = 2 algorithm = _messages.EnumField('AlgorithmValueValuesEnum', 1) convexAutomatedStoppingSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpec', 2) decayCurveStoppingSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpec', 3) measurementSelectionType = _messages.EnumField('MeasurementSelectionTypeValueValuesEnum', 4) medianAutomatedStoppingSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpec', 5) metrics = _messages.MessageField('GoogleCloudAiplatformV1StudySpecMetricSpec', 6, repeated=True) observationNoise = _messages.EnumField('ObservationNoiseValueValuesEnum', 7) parameters = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpec', 8, repeated=True) studyStoppingConfig = _messages.MessageField('GoogleCloudAiplatformV1StudySpecStudyStoppingConfig', 9) class GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpec(_messages.Message): r"""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. Fields: learningRateParameterName: The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial. maxStepCount: Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds. minMeasurementCount: The minimal number of measurements in a Trial. Early- stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5. minStepCount: Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds. updateAllStoppedTrials: ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their `final_measurement`. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. useElapsedDuration: This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds. """ learningRateParameterName = _messages.StringField(1) maxStepCount = _messages.IntegerField(2) minMeasurementCount = _messages.IntegerField(3) minStepCount = _messages.IntegerField(4) updateAllStoppedTrials = _messages.BooleanField(5) useElapsedDuration = _messages.BooleanField(6) class GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpec(_messages.Message): r"""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. Fields: useElapsedDuration: True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis. """ useElapsedDuration = _messages.BooleanField(1) class GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpec(_messages.Message): r"""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. Fields: useElapsedDuration: True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials. """ useElapsedDuration = _messages.BooleanField(1) class GoogleCloudAiplatformV1StudySpecMetricSpec(_messages.Message): r"""Represents a metric to optimize. Enums: GoalValueValuesEnum: Required. The optimization goal of the metric. Fields: goal: Required. The optimization goal of the metric. metricId: Required. The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs. safetyConfig: Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric. """ class GoalValueValuesEnum(_messages.Enum): r"""Required. The optimization goal of the metric. Values: GOAL_TYPE_UNSPECIFIED: Goal Type will default to maximize. MAXIMIZE: Maximize the goal metric. MINIMIZE: Minimize the goal metric. """ GOAL_TYPE_UNSPECIFIED = 0 MAXIMIZE = 1 MINIMIZE = 2 goal = _messages.EnumField('GoalValueValuesEnum', 1) metricId = _messages.StringField(2) safetyConfig = _messages.MessageField('GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfig', 3) class GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfig(_messages.Message): r"""Used in safe optimization to specify threshold levels and risk tolerance. Fields: desiredMinSafeTrialsFraction: Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials. safetyThreshold: Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used. """ desiredMinSafeTrialsFraction = _messages.FloatField(1) safetyThreshold = _messages.FloatField(2) class GoogleCloudAiplatformV1StudySpecParameterSpec(_messages.Message): r"""Represents a single parameter to optimize. Enums: ScaleTypeValueValuesEnum: How the parameter should be scaled. Leave unset for `CATEGORICAL` parameters. Fields: categoricalValueSpec: The value spec for a 'CATEGORICAL' parameter. conditionalParameterSpecs: A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition. discreteValueSpec: The value spec for a 'DISCRETE' parameter. doubleValueSpec: The value spec for a 'DOUBLE' parameter. integerValueSpec: The value spec for an 'INTEGER' parameter. parameterId: Required. The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs. scaleType: How the parameter should be scaled. Leave unset for `CATEGORICAL` parameters. """ class ScaleTypeValueValuesEnum(_messages.Enum): r"""How the parameter should be scaled. Leave unset for `CATEGORICAL` parameters. Values: SCALE_TYPE_UNSPECIFIED: By default, no scaling is applied. UNIT_LINEAR_SCALE: Scales the feasible space to (0, 1) linearly. UNIT_LOG_SCALE: Scales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive. UNIT_REVERSE_LOG_SCALE: Scales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive. """ SCALE_TYPE_UNSPECIFIED = 0 UNIT_LINEAR_SCALE = 1 UNIT_LOG_SCALE = 2 UNIT_REVERSE_LOG_SCALE = 3 categoricalValueSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpec', 1) conditionalParameterSpecs = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpec', 2, repeated=True) discreteValueSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpec', 3) doubleValueSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpec', 4) integerValueSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpec', 5) parameterId = _messages.StringField(6) scaleType = _messages.EnumField('ScaleTypeValueValuesEnum', 7) class GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpec(_messages.Message): r"""Value specification for a parameter in `CATEGORICAL` type. Fields: defaultValue: A default value for a `CATEGORICAL` parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. values: Required. The list of possible categories. """ defaultValue = _messages.StringField(1) values = _messages.StringField(2, repeated=True) class GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpec(_messages.Message): r"""Represents a parameter spec with condition from its parent parameter. Fields: parameterSpec: Required. The spec for a conditional parameter. parentCategoricalValues: The spec for matching values from a parent parameter of `CATEGORICAL` type. parentDiscreteValues: The spec for matching values from a parent parameter of `DISCRETE` type. parentIntValues: The spec for matching values from a parent parameter of `INTEGER` type. """ parameterSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpec', 1) parentCategoricalValues = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueCondition', 2) parentDiscreteValues = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueCondition', 3) parentIntValues = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueCondition', 4) class GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueCondition(_messages.Message): r"""Represents the spec to match categorical values from parent parameter. Fields: values: Required. Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in `categorical_value_spec` of parent parameter. """ values = _messages.StringField(1, repeated=True) class GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueCondition(_messages.Message): r"""Represents the spec to match discrete values from parent parameter. Fields: values: Required. Matches values of the parent parameter of 'DISCRETE' type. All values must exist in `discrete_value_spec` of parent parameter. The Epsilon of the value matching is 1e-10. """ values = _messages.FloatField(1, repeated=True) class GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueCondition(_messages.Message): r"""Represents the spec to match integer values from parent parameter. Fields: values: Required. Matches values of the parent parameter of 'INTEGER' type. All values must lie in `integer_value_spec` of parent parameter. """ values = _messages.IntegerField(1, repeated=True) class GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpec(_messages.Message): r"""Value specification for a parameter in `DISCRETE` type. Fields: defaultValue: A default value for a `DISCRETE` parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. values: Required. A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values. """ defaultValue = _messages.FloatField(1) values = _messages.FloatField(2, repeated=True) class GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpec(_messages.Message): r"""Value specification for a parameter in `DOUBLE` type. Fields: defaultValue: A default value for a `DOUBLE` parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. maxValue: Required. Inclusive maximum value of the parameter. minValue: Required. Inclusive minimum value of the parameter. """ defaultValue = _messages.FloatField(1) maxValue = _messages.FloatField(2) minValue = _messages.FloatField(3) class GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpec(_messages.Message): r"""Value specification for a parameter in `INTEGER` type. Fields: defaultValue: A default value for an `INTEGER` parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. maxValue: Required. Inclusive maximum value of the parameter. minValue: Required. Inclusive minimum value of the parameter. """ defaultValue = _messages.IntegerField(1) maxValue = _messages.IntegerField(2) minValue = _messages.IntegerField(3) class GoogleCloudAiplatformV1StudySpecStudyStoppingConfig(_messages.Message): r"""The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection. Fields: maxDurationNoProgress: If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies. maxNumTrials: If there are more than this many trials, stop the study. maxNumTrialsNoProgress: If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies. maximumRuntimeConstraint: If the specified time or duration has passed, stop the study. minNumTrials: If there are fewer than this many COMPLETED trials, do not stop the study. minimumRuntimeConstraint: Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting `min_num_trials=5` and `always_stop_after= 1 hour` means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to _resume_ a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. shouldStopAsap: If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth). """ maxDurationNoProgress = _messages.StringField(1) maxNumTrials = _messages.IntegerField(2, variant=_messages.Variant.INT32) maxNumTrialsNoProgress = _messages.IntegerField(3, variant=_messages.Variant.INT32) maximumRuntimeConstraint = _messages.MessageField('GoogleCloudAiplatformV1StudyTimeConstraint', 4) minNumTrials = _messages.IntegerField(5, variant=_messages.Variant.INT32) minimumRuntimeConstraint = _messages.MessageField('GoogleCloudAiplatformV1StudyTimeConstraint', 6) shouldStopAsap = _messages.BooleanField(7) class GoogleCloudAiplatformV1StudyTimeConstraint(_messages.Message): r"""Time-based Constraint for Study Fields: endTime: Compares the wallclock time to this time. Must use UTC timezone. maxDuration: Counts the wallclock time passed since the creation of this Study. """ endTime = _messages.StringField(1) maxDuration = _messages.StringField(2) class GoogleCloudAiplatformV1SuggestTrialsMetadata(_messages.Message): r"""Details of operations that perform Trials suggestion. Fields: clientId: The identifier of the client that is requesting the suggestion. If multiple SuggestTrialsRequests have the same `client_id`, the service will return the identical suggested Trial if the Trial is pending, and provide a new Trial if the last suggested Trial was completed. genericMetadata: Operation metadata for suggesting Trials. """ clientId = _messages.StringField(1) genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 2) class GoogleCloudAiplatformV1SuggestTrialsRequest(_messages.Message): r"""Request message for VizierService.SuggestTrials. Fields: clientId: Required. The identifier of the client that is requesting the suggestion. If multiple SuggestTrialsRequests have the same `client_id`, the service will return the identical suggested Trial if the Trial is pending, and provide a new Trial if the last suggested Trial was completed. contexts: Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of- week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace. suggestionCount: Required. The number of suggestions requested. It must be positive. """ clientId = _messages.StringField(1) contexts = _messages.MessageField('GoogleCloudAiplatformV1TrialContext', 2, repeated=True) suggestionCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SuggestTrialsResponse(_messages.Message): r"""Response message for VizierService.SuggestTrials. Enums: StudyStateValueValuesEnum: The state of the Study. Fields: endTime: The time at which operation processing completed. startTime: The time at which the operation was started. studyState: The state of the Study. trials: A list of Trials. """ class StudyStateValueValuesEnum(_messages.Enum): r"""The state of the Study. Values: STATE_UNSPECIFIED: The study state is unspecified. ACTIVE: The study is active. INACTIVE: The study is stopped due to an internal error. COMPLETED: The study is done when the service exhausts the parameter search space or max_trial_count is reached. """ STATE_UNSPECIFIED = 0 ACTIVE = 1 INACTIVE = 2 COMPLETED = 3 endTime = _messages.StringField(1) startTime = _messages.StringField(2) studyState = _messages.EnumField('StudyStateValueValuesEnum', 3) trials = _messages.MessageField('GoogleCloudAiplatformV1Trial', 4, repeated=True) class GoogleCloudAiplatformV1SummarizationHelpfulnessInput(_messages.Message): r"""Input for summarization helpfulness metric. Fields: instance: Required. Summarization helpfulness instance. metricSpec: Required. Spec for summarization helpfulness score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1SummarizationHelpfulnessInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1SummarizationHelpfulnessSpec', 2) class GoogleCloudAiplatformV1SummarizationHelpfulnessInstance(_messages.Message): r"""Spec for summarization helpfulness instance. Fields: context: Required. Text to be summarized. instruction: Optional. Summarization prompt for LLM. prediction: Required. Output of the evaluated model. reference: Optional. Ground truth used to compare against the prediction. """ context = _messages.StringField(1) instruction = _messages.StringField(2) prediction = _messages.StringField(3) reference = _messages.StringField(4) class GoogleCloudAiplatformV1SummarizationHelpfulnessResult(_messages.Message): r"""Spec for summarization helpfulness result. Fields: confidence: Output only. Confidence for summarization helpfulness score. explanation: Output only. Explanation for summarization helpfulness score. score: Output only. Summarization Helpfulness score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SummarizationHelpfulnessSpec(_messages.Message): r"""Spec for summarization helpfulness score metric. Fields: useReference: Optional. Whether to use instance.reference to compute summarization helpfulness. version: Optional. Which version to use for evaluation. """ useReference = _messages.BooleanField(1) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SummarizationQualityInput(_messages.Message): r"""Input for summarization quality metric. Fields: instance: Required. Summarization quality instance. metricSpec: Required. Spec for summarization quality score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1SummarizationQualityInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1SummarizationQualitySpec', 2) class GoogleCloudAiplatformV1SummarizationQualityInstance(_messages.Message): r"""Spec for summarization quality instance. Fields: context: Required. Text to be summarized. instruction: Required. Summarization prompt for LLM. prediction: Required. Output of the evaluated model. reference: Optional. Ground truth used to compare against the prediction. """ context = _messages.StringField(1) instruction = _messages.StringField(2) prediction = _messages.StringField(3) reference = _messages.StringField(4) class GoogleCloudAiplatformV1SummarizationQualityResult(_messages.Message): r"""Spec for summarization quality result. Fields: confidence: Output only. Confidence for summarization quality score. explanation: Output only. Explanation for summarization quality score. score: Output only. Summarization Quality score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SummarizationQualitySpec(_messages.Message): r"""Spec for summarization quality score metric. Fields: useReference: Optional. Whether to use instance.reference to compute summarization quality. version: Optional. Which version to use for evaluation. """ useReference = _messages.BooleanField(1) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SummarizationVerbosityInput(_messages.Message): r"""Input for summarization verbosity metric. Fields: instance: Required. Summarization verbosity instance. metricSpec: Required. Spec for summarization verbosity score metric. """ instance = _messages.MessageField('GoogleCloudAiplatformV1SummarizationVerbosityInstance', 1) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1SummarizationVerbositySpec', 2) class GoogleCloudAiplatformV1SummarizationVerbosityInstance(_messages.Message): r"""Spec for summarization verbosity instance. Fields: context: Required. Text to be summarized. instruction: Optional. Summarization prompt for LLM. prediction: Required. Output of the evaluated model. reference: Optional. Ground truth used to compare against the prediction. """ context = _messages.StringField(1) instruction = _messages.StringField(2) prediction = _messages.StringField(3) reference = _messages.StringField(4) class GoogleCloudAiplatformV1SummarizationVerbosityResult(_messages.Message): r"""Spec for summarization verbosity result. Fields: confidence: Output only. Confidence for summarization verbosity score. explanation: Output only. Explanation for summarization verbosity score. score: Output only. Summarization Verbosity score. """ confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT) explanation = _messages.StringField(2) score = _messages.FloatField(3, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1SummarizationVerbositySpec(_messages.Message): r"""Spec for summarization verbosity score metric. Fields: useReference: Optional. Whether to use instance.reference to compute summarization verbosity. version: Optional. Which version to use for evaluation. """ useReference = _messages.BooleanField(1) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1SupervisedHyperParameters(_messages.Message): r"""Hyperparameters for SFT. Enums: AdapterSizeValueValuesEnum: Optional. Adapter size for tuning. Fields: adapterSize: Optional. Adapter size for tuning. epochCount: Optional. Number of complete passes the model makes over the entire training dataset during training. learningRateMultiplier: Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models. """ class AdapterSizeValueValuesEnum(_messages.Enum): r"""Optional. Adapter size for tuning. Values: ADAPTER_SIZE_UNSPECIFIED: Adapter size is unspecified. ADAPTER_SIZE_ONE: Adapter size 1. ADAPTER_SIZE_FOUR: Adapter size 4. ADAPTER_SIZE_EIGHT: Adapter size 8. ADAPTER_SIZE_SIXTEEN: Adapter size 16. ADAPTER_SIZE_THIRTY_TWO: Adapter size 32. """ ADAPTER_SIZE_UNSPECIFIED = 0 ADAPTER_SIZE_ONE = 1 ADAPTER_SIZE_FOUR = 2 ADAPTER_SIZE_EIGHT = 3 ADAPTER_SIZE_SIXTEEN = 4 ADAPTER_SIZE_THIRTY_TWO = 5 adapterSize = _messages.EnumField('AdapterSizeValueValuesEnum', 1) epochCount = _messages.IntegerField(2) learningRateMultiplier = _messages.FloatField(3) class GoogleCloudAiplatformV1SupervisedTuningDataStats(_messages.Message): r"""Tuning data statistics for Supervised Tuning. Fields: totalBillableCharacterCount: Output only. Number of billable characters in the tuning dataset. totalBillableTokenCount: Output only. Number of billable tokens in the tuning dataset. totalTruncatedExampleCount: Output only. The number of examples in the dataset that have been dropped. An example can be dropped for reasons including: too many tokens, contains an invalid image, contains too many images, etc. totalTuningCharacterCount: Output only. Number of tuning characters in the tuning dataset. truncatedExampleIndices: Output only. A partial sample of the indices (starting from 1) of the dropped examples. tuningDatasetExampleCount: Output only. Number of examples in the tuning dataset. tuningStepCount: Output only. Number of tuning steps for this Tuning Job. userDatasetExamples: Output only. Sample user messages in the training dataset uri. userInputTokenDistribution: Output only. Dataset distributions for the user input tokens. userMessagePerExampleDistribution: Output only. Dataset distributions for the messages per example. userOutputTokenDistribution: Output only. Dataset distributions for the user output tokens. """ totalBillableCharacterCount = _messages.IntegerField(1) totalBillableTokenCount = _messages.IntegerField(2) totalTruncatedExampleCount = _messages.IntegerField(3) totalTuningCharacterCount = _messages.IntegerField(4) truncatedExampleIndices = _messages.IntegerField(5, repeated=True) tuningDatasetExampleCount = _messages.IntegerField(6) tuningStepCount = _messages.IntegerField(7) userDatasetExamples = _messages.MessageField('GoogleCloudAiplatformV1Content', 8, repeated=True) userInputTokenDistribution = _messages.MessageField('GoogleCloudAiplatformV1SupervisedTuningDatasetDistribution', 9) userMessagePerExampleDistribution = _messages.MessageField('GoogleCloudAiplatformV1SupervisedTuningDatasetDistribution', 10) userOutputTokenDistribution = _messages.MessageField('GoogleCloudAiplatformV1SupervisedTuningDatasetDistribution', 11) class GoogleCloudAiplatformV1SupervisedTuningDatasetDistribution(_messages.Message): r"""Dataset distribution for Supervised Tuning. Fields: billableSum: Output only. Sum of a given population of values that are billable. buckets: Output only. Defines the histogram bucket. max: Output only. The maximum of the population values. mean: Output only. The arithmetic mean of the values in the population. median: Output only. The median of the values in the population. min: Output only. The minimum of the population values. p5: Output only. The 5th percentile of the values in the population. p95: Output only. The 95th percentile of the values in the population. sum: Output only. Sum of a given population of values. """ billableSum = _messages.IntegerField(1) buckets = _messages.MessageField('GoogleCloudAiplatformV1SupervisedTuningDatasetDistributionDatasetBucket', 2, repeated=True) max = _messages.FloatField(3) mean = _messages.FloatField(4) median = _messages.FloatField(5) min = _messages.FloatField(6) p5 = _messages.FloatField(7) p95 = _messages.FloatField(8) sum = _messages.IntegerField(9) class GoogleCloudAiplatformV1SupervisedTuningDatasetDistributionDatasetBucket(_messages.Message): r"""Dataset bucket used to create a histogram for the distribution given a population of values. Fields: count: Output only. Number of values in the bucket. left: Output only. Left bound of the bucket. right: Output only. Right bound of the bucket. """ count = _messages.FloatField(1) left = _messages.FloatField(2) right = _messages.FloatField(3) class GoogleCloudAiplatformV1SupervisedTuningSpec(_messages.Message): r"""Tuning Spec for Supervised Tuning for first party models. Fields: exportLastCheckpointOnly: Optional. If set to true, disable intermediate checkpoints for SFT and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for SFT. Default is false. hyperParameters: Optional. Hyperparameters for SFT. trainingDatasetUri: Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset. validationDatasetUri: Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset. """ exportLastCheckpointOnly = _messages.BooleanField(1) hyperParameters = _messages.MessageField('GoogleCloudAiplatformV1SupervisedHyperParameters', 2) trainingDatasetUri = _messages.StringField(3) validationDatasetUri = _messages.StringField(4) class GoogleCloudAiplatformV1SyncFeatureViewRequest(_messages.Message): r"""Request message for FeatureOnlineStoreAdminService.SyncFeatureView.""" class GoogleCloudAiplatformV1SyncFeatureViewResponse(_messages.Message): r"""Response message for FeatureOnlineStoreAdminService.SyncFeatureView. Fields: featureViewSync: Format: `projects/{project}/locations/{location}/featureO nlineStores/{feature_online_store}/featureViews/{feature_view}/featureVi ewSyncs/{feature_view_sync}` """ featureViewSync = _messages.StringField(1) class GoogleCloudAiplatformV1TFRecordDestination(_messages.Message): r"""The storage details for TFRecord output content. Fields: gcsDestination: Required. Google Cloud Storage location. """ gcsDestination = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 1) class GoogleCloudAiplatformV1Tensor(_messages.Message): r"""A tensor value type. Enums: DtypeValueValuesEnum: The data type of tensor. Messages: StructValValue: A map of string to tensor. Fields: boolVal: Type specific representations that make it easy to create tensor protos in all languages. Only the representation corresponding to "dtype" can be set. The values hold the flattened representation of the tensor in row major order. BOOL bytesVal: STRING doubleVal: DOUBLE dtype: The data type of tensor. floatVal: FLOAT int64Val: INT64 intVal: INT_8 INT_16 INT_32 listVal: A list of tensor values. shape: Shape of the tensor. stringVal: STRING structVal: A map of string to tensor. tensorVal: Serialized raw tensor content. uint64Val: UINT64 uintVal: UINT8 UINT16 UINT32 """ class DtypeValueValuesEnum(_messages.Enum): r"""The data type of tensor. Values: DATA_TYPE_UNSPECIFIED: Not a legal value for DataType. Used to indicate a DataType field has not been set. BOOL: Data types that all computation devices are expected to be capable to support. STRING: FLOAT: DOUBLE: INT8: INT16: INT32: INT64: UINT8: UINT16: UINT32: UINT64: """ DATA_TYPE_UNSPECIFIED = 0 BOOL = 1 STRING = 2 FLOAT = 3 DOUBLE = 4 INT8 = 5 INT16 = 6 INT32 = 7 INT64 = 8 UINT8 = 9 UINT16 = 10 UINT32 = 11 UINT64 = 12 @encoding.MapUnrecognizedFields('additionalProperties') class StructValValue(_messages.Message): r"""A map of string to tensor. Messages: AdditionalProperty: An additional property for a StructValValue object. Fields: additionalProperties: Additional properties of type StructValValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a StructValValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1Tensor attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1Tensor', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) boolVal = _messages.BooleanField(1, repeated=True) bytesVal = _messages.BytesField(2, repeated=True) doubleVal = _messages.FloatField(3, repeated=True) dtype = _messages.EnumField('DtypeValueValuesEnum', 4) floatVal = _messages.FloatField(5, repeated=True, variant=_messages.Variant.FLOAT) int64Val = _messages.IntegerField(6, repeated=True) intVal = _messages.IntegerField(7, repeated=True, variant=_messages.Variant.INT32) listVal = _messages.MessageField('GoogleCloudAiplatformV1Tensor', 8, repeated=True) shape = _messages.IntegerField(9, repeated=True) stringVal = _messages.StringField(10, repeated=True) structVal = _messages.MessageField('StructValValue', 11) tensorVal = _messages.BytesField(12) uint64Val = _messages.IntegerField(13, repeated=True, variant=_messages.Variant.UINT64) uintVal = _messages.IntegerField(14, repeated=True, variant=_messages.Variant.UINT32) class GoogleCloudAiplatformV1Tensorboard(_messages.Message): r"""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. Messages: LabelsValue: The labels with user-defined metadata to organize your Tensorboards. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Tensorboard (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Fields: blobStoragePathPrefix: Output only. Consumer project Cloud Storage path prefix used to store blob data, which can either be a bucket or directory. Does not end with a '/'. createTime: Output only. Timestamp when this Tensorboard was created. description: Description of this Tensorboard. displayName: Required. User provided name of this Tensorboard. encryptionSpec: Customer-managed encryption key spec for a Tensorboard. If set, this Tensorboard and all sub-resources of this Tensorboard will be secured by this key. etag: Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens. isDefault: Used to indicate if the TensorBoard instance is the default one. Each project & region can have at most one default TensorBoard instance. Creation of a default TensorBoard instance and updating an existing TensorBoard instance to be default will mark all other TensorBoard instances (if any) as non default. labels: The labels with user-defined metadata to organize your Tensorboards. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Tensorboard (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. name: Output only. Name of the Tensorboard. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}` runCount: Output only. The number of Runs stored in this Tensorboard. satisfiesPzi: Output only. Reserved for future use. satisfiesPzs: Output only. Reserved for future use. updateTime: Output only. Timestamp when this Tensorboard was last updated. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your Tensorboards. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Tensorboard (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) blobStoragePathPrefix = _messages.StringField(1) createTime = _messages.StringField(2) description = _messages.StringField(3) displayName = _messages.StringField(4) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 5) etag = _messages.StringField(6) isDefault = _messages.BooleanField(7) labels = _messages.MessageField('LabelsValue', 8) name = _messages.StringField(9) runCount = _messages.IntegerField(10, variant=_messages.Variant.INT32) satisfiesPzi = _messages.BooleanField(11) satisfiesPzs = _messages.BooleanField(12) updateTime = _messages.StringField(13) class GoogleCloudAiplatformV1TensorboardBlob(_messages.Message): r"""One blob (e.g, image, graph) viewable on a blob metric plot. Fields: data: Optional. The bytes of the blob is not present unless it's returned by the ReadTensorboardBlobData endpoint. id: Output only. A URI safe key uniquely identifying a blob. Can be used to locate the blob stored in the Cloud Storage bucket of the consumer project. """ data = _messages.BytesField(1) id = _messages.StringField(2) class GoogleCloudAiplatformV1TensorboardBlobSequence(_messages.Message): r"""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. Fields: values: List of blobs contained within the sequence. """ values = _messages.MessageField('GoogleCloudAiplatformV1TensorboardBlob', 1, repeated=True) class GoogleCloudAiplatformV1TensorboardExperiment(_messages.Message): r"""A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard. Messages: LabelsValue: The labels with user-defined metadata to organize your TensorboardExperiment. Label keys and values cannot be longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with `aiplatform.googleapis.com/` and are immutable. The following system labels exist for each Dataset: * `aiplatform.googleapis.com/dataset_metadata_schema`: output only. Its value is the metadata_schema's title. Fields: createTime: Output only. Timestamp when this TensorboardExperiment was created. description: Description of this TensorboardExperiment. displayName: User provided name of this TensorboardExperiment. etag: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: The labels with user-defined metadata to organize your TensorboardExperiment. Label keys and values cannot be longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with `aiplatform.googleapis.com/` and are immutable. The following system labels exist for each Dataset: * `aiplatform.googleapis.com/dataset_metadata_schema`: output only. Its value is the metadata_schema's title. name: Output only. Name of the TensorboardExperiment. Format: `projects/{p roject}/locations/{location}/tensorboards/{tensorboard}/experiments/{exp eriment}` source: Immutable. Source of the TensorboardExperiment. Example: a custom training job. updateTime: Output only. Timestamp when this TensorboardExperiment was last updated. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your TensorboardExperiment. Label keys and values cannot be longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with `aiplatform.googleapis.com/` and are immutable. The following system labels exist for each Dataset: * `aiplatform.googleapis.com/dataset_metadata_schema`: output only. Its value is the metadata_schema's title. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) description = _messages.StringField(2) displayName = _messages.StringField(3) etag = _messages.StringField(4) labels = _messages.MessageField('LabelsValue', 5) name = _messages.StringField(6) source = _messages.StringField(7) updateTime = _messages.StringField(8) class GoogleCloudAiplatformV1TensorboardRun(_messages.Message): r"""TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc Messages: LabelsValue: The labels with user-defined metadata to organize your TensorboardRuns. This field will be used to filter and visualize Runs in the Tensorboard UI. For example, a Vertex AI training job can set a label aiplatform.googleapis.com/training_job_id=xxxxx to all the runs created within that job. An end user can set a label experiment_id=xxxxx for all the runs produced in a Jupyter notebook. These runs can be grouped by a label value and visualized together in the Tensorboard UI. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one TensorboardRun (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Fields: createTime: Output only. Timestamp when this TensorboardRun was created. description: Description of this TensorboardRun. displayName: Required. User provided name of this TensorboardRun. This value must be unique among all TensorboardRuns belonging to the same parent TensorboardExperiment. etag: Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens. labels: The labels with user-defined metadata to organize your TensorboardRuns. This field will be used to filter and visualize Runs in the Tensorboard UI. For example, a Vertex AI training job can set a label aiplatform.googleapis.com/training_job_id=xxxxx to all the runs created within that job. An end user can set a label experiment_id=xxxxx for all the runs produced in a Jupyter notebook. These runs can be grouped by a label value and visualized together in the Tensorboard UI. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one TensorboardRun (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. name: Output only. Name of the TensorboardRun. Format: `projects/{project} /locations/{location}/tensorboards/{tensorboard}/experiments/{experiment }/runs/{run}` updateTime: Output only. Timestamp when this TensorboardRun was last updated. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize your TensorboardRuns. This field will be used to filter and visualize Runs in the Tensorboard UI. For example, a Vertex AI training job can set a label aiplatform.googleapis.com/training_job_id=xxxxx to all the runs created within that job. An end user can set a label experiment_id=xxxxx for all the runs produced in a Jupyter notebook. These runs can be grouped by a label value and visualized together in the Tensorboard UI. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one TensorboardRun (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) description = _messages.StringField(2) displayName = _messages.StringField(3) etag = _messages.StringField(4) labels = _messages.MessageField('LabelsValue', 5) name = _messages.StringField(6) updateTime = _messages.StringField(7) class GoogleCloudAiplatformV1TensorboardTensor(_messages.Message): r"""One point viewable on a tensor metric plot. Fields: value: Required. Serialized form of https://github.com/tensorflow/tensorfl ow/blob/master/tensorflow/core/framework/tensor.proto versionNumber: Optional. Version number of TensorProto used to serialize value. """ value = _messages.BytesField(1) versionNumber = _messages.IntegerField(2, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1TensorboardTimeSeries(_messages.Message): r"""TensorboardTimeSeries maps to times series produced in training runs Enums: ValueTypeValueValuesEnum: Required. Immutable. Type of TensorboardTimeSeries value. Fields: createTime: Output only. Timestamp when this TensorboardTimeSeries was created. description: Description of this TensorboardTimeSeries. displayName: Required. User provided name of this TensorboardTimeSeries. This value should be unique among all TensorboardTimeSeries resources belonging to the same TensorboardRun resource (parent resource). etag: Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens. metadata: Output only. Scalar, Tensor, or Blob metadata for this TensorboardTimeSeries. name: Output only. Name of the TensorboardTimeSeries. pluginData: Data of the current plugin, with the size limited to 65KB. pluginName: Immutable. Name of the plugin this time series pertain to. Such as Scalar, Tensor, Blob updateTime: Output only. Timestamp when this TensorboardTimeSeries was last updated. valueType: Required. Immutable. Type of TensorboardTimeSeries value. """ class ValueTypeValueValuesEnum(_messages.Enum): r"""Required. Immutable. Type of TensorboardTimeSeries value. Values: VALUE_TYPE_UNSPECIFIED: The value type is unspecified. SCALAR: Used for TensorboardTimeSeries that is a list of scalars. E.g. accuracy of a model over epochs/time. TENSOR: Used for TensorboardTimeSeries that is a list of tensors. E.g. histograms of weights of layer in a model over epoch/time. BLOB_SEQUENCE: Used for TensorboardTimeSeries that is a list of blob sequences. E.g. set of sample images with labels over epochs/time. """ VALUE_TYPE_UNSPECIFIED = 0 SCALAR = 1 TENSOR = 2 BLOB_SEQUENCE = 3 createTime = _messages.StringField(1) description = _messages.StringField(2) displayName = _messages.StringField(3) etag = _messages.StringField(4) metadata = _messages.MessageField('GoogleCloudAiplatformV1TensorboardTimeSeriesMetadata', 5) name = _messages.StringField(6) pluginData = _messages.BytesField(7) pluginName = _messages.StringField(8) updateTime = _messages.StringField(9) valueType = _messages.EnumField('ValueTypeValueValuesEnum', 10) class GoogleCloudAiplatformV1TensorboardTimeSeriesMetadata(_messages.Message): r"""Describes metadata for a TensorboardTimeSeries. Fields: maxBlobSequenceLength: Output only. The largest blob sequence length (number of blobs) of all data points in this time series, if its ValueType is BLOB_SEQUENCE. maxStep: Output only. Max step index of all data points within a TensorboardTimeSeries. maxWallTime: Output only. Max wall clock timestamp of all data points within a TensorboardTimeSeries. """ maxBlobSequenceLength = _messages.IntegerField(1) maxStep = _messages.IntegerField(2) maxWallTime = _messages.StringField(3) class GoogleCloudAiplatformV1ThresholdConfig(_messages.Message): r"""The config for feature monitoring threshold. Fields: value: Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen\u2013Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature. """ value = _messages.FloatField(1) class GoogleCloudAiplatformV1TimeSeriesData(_messages.Message): r"""All the data stored in a TensorboardTimeSeries. Enums: ValueTypeValueValuesEnum: Required. Immutable. The value type of this time series. All the values in this time series data must match this value type. Fields: tensorboardTimeSeriesId: Required. The ID of the TensorboardTimeSeries, which will become the final component of the TensorboardTimeSeries' resource name valueType: Required. Immutable. The value type of this time series. All the values in this time series data must match this value type. values: Required. Data points in this time series. """ class ValueTypeValueValuesEnum(_messages.Enum): r"""Required. Immutable. The value type of this time series. All the values in this time series data must match this value type. Values: VALUE_TYPE_UNSPECIFIED: The value type is unspecified. SCALAR: Used for TensorboardTimeSeries that is a list of scalars. E.g. accuracy of a model over epochs/time. TENSOR: Used for TensorboardTimeSeries that is a list of tensors. E.g. histograms of weights of layer in a model over epoch/time. BLOB_SEQUENCE: Used for TensorboardTimeSeries that is a list of blob sequences. E.g. set of sample images with labels over epochs/time. """ VALUE_TYPE_UNSPECIFIED = 0 SCALAR = 1 TENSOR = 2 BLOB_SEQUENCE = 3 tensorboardTimeSeriesId = _messages.StringField(1) valueType = _messages.EnumField('ValueTypeValueValuesEnum', 2) values = _messages.MessageField('GoogleCloudAiplatformV1TimeSeriesDataPoint', 3, repeated=True) class GoogleCloudAiplatformV1TimeSeriesDataPoint(_messages.Message): r"""A TensorboardTimeSeries data point. Fields: blobs: A blob sequence value. scalar: A scalar value. step: Step index of this data point within the run. tensor: A tensor value. wallTime: Wall clock timestamp when this data point is generated by the end user. """ blobs = _messages.MessageField('GoogleCloudAiplatformV1TensorboardBlobSequence', 1) scalar = _messages.MessageField('GoogleCloudAiplatformV1Scalar', 2) step = _messages.IntegerField(3) tensor = _messages.MessageField('GoogleCloudAiplatformV1TensorboardTensor', 4) wallTime = _messages.StringField(5) class GoogleCloudAiplatformV1TimestampSplit(_messages.Message): r"""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. Fields: key: Required. The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 `date- time` format, where `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. testFraction: The fraction of the input data that is to be used to evaluate the Model. trainingFraction: The fraction of the input data that is to be used to train the Model. validationFraction: The fraction of the input data that is to be used to validate the Model. """ key = _messages.StringField(1) testFraction = _messages.FloatField(2) trainingFraction = _messages.FloatField(3) validationFraction = _messages.FloatField(4) class GoogleCloudAiplatformV1TokensInfo(_messages.Message): r"""Tokens info with a list of tokens and the corresponding list of token ids. Fields: role: Optional. Optional fields for the role from the corresponding Content. tokenIds: A list of token ids from the input. tokens: A list of tokens from the input. """ role = _messages.StringField(1) tokenIds = _messages.IntegerField(2, repeated=True) tokens = _messages.BytesField(3, repeated=True) class GoogleCloudAiplatformV1Tool(_messages.Message): r"""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). Fields: codeExecution: Optional. CodeExecution tool type. Enables the model to execute code as part of generation. enterpriseWebSearch: Optional. Tool to support searching public web data, powered by Vertex AI Search and Sec4 compliance. functionDeclarations: Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided. googleSearch: Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. googleSearchRetrieval: Optional. GoogleSearchRetrieval tool type. Specialized retrieval tool that is powered by Google search. retrieval: Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation. urlContext: Optional. Tool to support URL context retrieval. """ codeExecution = _messages.MessageField('GoogleCloudAiplatformV1ToolCodeExecution', 1) enterpriseWebSearch = _messages.MessageField('GoogleCloudAiplatformV1EnterpriseWebSearch', 2) functionDeclarations = _messages.MessageField('GoogleCloudAiplatformV1FunctionDeclaration', 3, repeated=True) googleSearch = _messages.MessageField('GoogleCloudAiplatformV1ToolGoogleSearch', 4) googleSearchRetrieval = _messages.MessageField('GoogleCloudAiplatformV1GoogleSearchRetrieval', 5) retrieval = _messages.MessageField('GoogleCloudAiplatformV1Retrieval', 6) urlContext = _messages.MessageField('GoogleCloudAiplatformV1UrlContext', 7) class GoogleCloudAiplatformV1ToolCall(_messages.Message): r"""Spec for tool call. Fields: toolInput: Optional. Spec for tool input toolName: Required. Spec for tool name """ toolInput = _messages.StringField(1) toolName = _messages.StringField(2) class GoogleCloudAiplatformV1ToolCallValidInput(_messages.Message): r"""Input for tool call valid metric. Fields: instances: Required. Repeated tool call valid instances. metricSpec: Required. Spec for tool call valid metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1ToolCallValidInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1ToolCallValidSpec', 2) class GoogleCloudAiplatformV1ToolCallValidInstance(_messages.Message): r"""Spec for tool call valid instance. Fields: prediction: Required. Output of the evaluated model. reference: Required. Ground truth used to compare against the prediction. """ prediction = _messages.StringField(1) reference = _messages.StringField(2) class GoogleCloudAiplatformV1ToolCallValidMetricValue(_messages.Message): r"""Tool call valid metric value for an instance. Fields: score: Output only. Tool call valid score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1ToolCallValidResults(_messages.Message): r"""Results for tool call valid metric. Fields: toolCallValidMetricValues: Output only. Tool call valid metric values. """ toolCallValidMetricValues = _messages.MessageField('GoogleCloudAiplatformV1ToolCallValidMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1ToolCallValidSpec(_messages.Message): r"""Spec for tool call valid metric.""" class GoogleCloudAiplatformV1ToolCodeExecution(_messages.Message): r"""Tool that executes code generated by the model, and automatically returns the result to the model. See also [ExecutableCode]and [CodeExecutionResult] which are input and output to this tool. """ class GoogleCloudAiplatformV1ToolConfig(_messages.Message): r"""Tool config. This config is shared for all tools provided in the request. Fields: functionCallingConfig: Optional. Function calling config. retrievalConfig: Optional. Retrieval config. """ functionCallingConfig = _messages.MessageField('GoogleCloudAiplatformV1FunctionCallingConfig', 1) retrievalConfig = _messages.MessageField('GoogleCloudAiplatformV1RetrievalConfig', 2) class GoogleCloudAiplatformV1ToolGoogleSearch(_messages.Message): r"""GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. """ class GoogleCloudAiplatformV1ToolNameMatchInput(_messages.Message): r"""Input for tool name match metric. Fields: instances: Required. Repeated tool name match instances. metricSpec: Required. Spec for tool name match metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1ToolNameMatchInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1ToolNameMatchSpec', 2) class GoogleCloudAiplatformV1ToolNameMatchInstance(_messages.Message): r"""Spec for tool name match instance. Fields: prediction: Required. Output of the evaluated model. reference: Required. Ground truth used to compare against the prediction. """ prediction = _messages.StringField(1) reference = _messages.StringField(2) class GoogleCloudAiplatformV1ToolNameMatchMetricValue(_messages.Message): r"""Tool name match metric value for an instance. Fields: score: Output only. Tool name match score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1ToolNameMatchResults(_messages.Message): r"""Results for tool name match metric. Fields: toolNameMatchMetricValues: Output only. Tool name match metric values. """ toolNameMatchMetricValues = _messages.MessageField('GoogleCloudAiplatformV1ToolNameMatchMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1ToolNameMatchSpec(_messages.Message): r"""Spec for tool name match metric.""" class GoogleCloudAiplatformV1ToolParameterKVMatchInput(_messages.Message): r"""Input for tool parameter key value match metric. Fields: instances: Required. Repeated tool parameter key value match instances. metricSpec: Required. Spec for tool parameter key value match metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1ToolParameterKVMatchInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1ToolParameterKVMatchSpec', 2) class GoogleCloudAiplatformV1ToolParameterKVMatchInstance(_messages.Message): r"""Spec for tool parameter key value match instance. Fields: prediction: Required. Output of the evaluated model. reference: Required. Ground truth used to compare against the prediction. """ prediction = _messages.StringField(1) reference = _messages.StringField(2) class GoogleCloudAiplatformV1ToolParameterKVMatchMetricValue(_messages.Message): r"""Tool parameter key value match metric value for an instance. Fields: score: Output only. Tool parameter key value match score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1ToolParameterKVMatchResults(_messages.Message): r"""Results for tool parameter key value match metric. Fields: toolParameterKvMatchMetricValues: Output only. Tool parameter key value match metric values. """ toolParameterKvMatchMetricValues = _messages.MessageField('GoogleCloudAiplatformV1ToolParameterKVMatchMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1ToolParameterKVMatchSpec(_messages.Message): r"""Spec for tool parameter key value match metric. Fields: useStrictStringMatch: Optional. Whether to use STRICT string match on parameter values. """ useStrictStringMatch = _messages.BooleanField(1) class GoogleCloudAiplatformV1ToolParameterKeyMatchInput(_messages.Message): r"""Input for tool parameter key match metric. Fields: instances: Required. Repeated tool parameter key match instances. metricSpec: Required. Spec for tool parameter key match metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1ToolParameterKeyMatchInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1ToolParameterKeyMatchSpec', 2) class GoogleCloudAiplatformV1ToolParameterKeyMatchInstance(_messages.Message): r"""Spec for tool parameter key match instance. Fields: prediction: Required. Output of the evaluated model. reference: Required. Ground truth used to compare against the prediction. """ prediction = _messages.StringField(1) reference = _messages.StringField(2) class GoogleCloudAiplatformV1ToolParameterKeyMatchMetricValue(_messages.Message): r"""Tool parameter key match metric value for an instance. Fields: score: Output only. Tool parameter key match score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1ToolParameterKeyMatchResults(_messages.Message): r"""Results for tool parameter key match metric. Fields: toolParameterKeyMatchMetricValues: Output only. Tool parameter key match metric values. """ toolParameterKeyMatchMetricValues = _messages.MessageField('GoogleCloudAiplatformV1ToolParameterKeyMatchMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1ToolParameterKeyMatchSpec(_messages.Message): r"""Spec for tool parameter key match metric.""" class GoogleCloudAiplatformV1TrainingPipeline(_messages.Message): r"""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 the Model to Vertex AI, and evaluate the Model. Enums: StateValueValuesEnum: Output only. The detailed state of the pipeline. Messages: LabelsValue: The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Fields: createTime: Output only. Time when the TrainingPipeline was created. displayName: Required. The user-defined name of this TrainingPipeline. encryptionSpec: Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately. endTime: Output only. Time when the TrainingPipeline entered any of the following states: `PIPELINE_STATE_SUCCEEDED`, `PIPELINE_STATE_FAILED`, `PIPELINE_STATE_CANCELLED`. error: Output only. Only populated when the pipeline's state is `PIPELINE_STATE_FAILED` or `PIPELINE_STATE_CANCELLED`. inputDataConfig: Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline's training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration. labels: The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. modelId: Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are `[a-z0-9_-]`. The first character cannot be a number or hyphen. modelToUpload: Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline's training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline's state becomes `PIPELINE_STATE_SUCCEEDED` and the trained Model had been uploaded into Vertex AI, then the model_to_upload's resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is. name: Output only. Resource name of the TrainingPipeline. parentModel: Optional. When specify this field, the `model_to_upload` will not be uploaded as a new model, instead, it will become a new version of this `parent_model`. startTime: Output only. Time when the TrainingPipeline for the first time entered the `PIPELINE_STATE_RUNNING` state. state: Output only. The detailed state of the pipeline. trainingTaskDefinition: Required. A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud- aiplatform/schema/trainingjob/definition/. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. trainingTaskInputs: Required. The training task's parameter(s), as specified in the training_task_definition's `inputs`. trainingTaskMetadata: Output only. The metadata information as specified in the training_task_definition's `metadata`. This metadata is an auxiliary runtime and final information about the training task. While the pipeline is running this information is populated only at a best effort basis. Only present if the pipeline's training_task_definition contains `metadata` object. updateTime: Output only. Time when the TrainingPipeline was most recently updated. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of the pipeline. Values: PIPELINE_STATE_UNSPECIFIED: The pipeline state is unspecified. PIPELINE_STATE_QUEUED: The pipeline has been created or resumed, and processing has not yet begun. PIPELINE_STATE_PENDING: The service is preparing to run the pipeline. PIPELINE_STATE_RUNNING: The pipeline is in progress. PIPELINE_STATE_SUCCEEDED: The pipeline completed successfully. PIPELINE_STATE_FAILED: The pipeline failed. PIPELINE_STATE_CANCELLING: The pipeline is being cancelled. From this state, the pipeline may only go to either PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED. PIPELINE_STATE_CANCELLED: The pipeline has been cancelled. PIPELINE_STATE_PAUSED: The pipeline has been stopped, and can be resumed. """ PIPELINE_STATE_UNSPECIFIED = 0 PIPELINE_STATE_QUEUED = 1 PIPELINE_STATE_PENDING = 2 PIPELINE_STATE_RUNNING = 3 PIPELINE_STATE_SUCCEEDED = 4 PIPELINE_STATE_FAILED = 5 PIPELINE_STATE_CANCELLING = 6 PIPELINE_STATE_CANCELLED = 7 PIPELINE_STATE_PAUSED = 8 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) displayName = _messages.StringField(2) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 3) endTime = _messages.StringField(4) error = _messages.MessageField('GoogleRpcStatus', 5) inputDataConfig = _messages.MessageField('GoogleCloudAiplatformV1InputDataConfig', 6) labels = _messages.MessageField('LabelsValue', 7) modelId = _messages.StringField(8) modelToUpload = _messages.MessageField('GoogleCloudAiplatformV1Model', 9) name = _messages.StringField(10) parentModel = _messages.StringField(11) startTime = _messages.StringField(12) state = _messages.EnumField('StateValueValuesEnum', 13) trainingTaskDefinition = _messages.StringField(14) trainingTaskInputs = _messages.MessageField('extra_types.JsonValue', 15) trainingTaskMetadata = _messages.MessageField('extra_types.JsonValue', 16) updateTime = _messages.StringField(17) class GoogleCloudAiplatformV1Trajectory(_messages.Message): r"""Spec for trajectory. Fields: toolCalls: Required. Tool calls in the trajectory. """ toolCalls = _messages.MessageField('GoogleCloudAiplatformV1ToolCall', 1, repeated=True) class GoogleCloudAiplatformV1TrajectoryAnyOrderMatchInput(_messages.Message): r"""Instances and metric spec for TrajectoryAnyOrderMatch metric. Fields: instances: Required. Repeated TrajectoryAnyOrderMatch instance. metricSpec: Required. Spec for TrajectoryAnyOrderMatch metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryAnyOrderMatchInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryAnyOrderMatchSpec', 2) class GoogleCloudAiplatformV1TrajectoryAnyOrderMatchInstance(_messages.Message): r"""Spec for TrajectoryAnyOrderMatch instance. Fields: predictedTrajectory: Required. Spec for predicted tool call trajectory. referenceTrajectory: Required. Spec for reference tool call trajectory. """ predictedTrajectory = _messages.MessageField('GoogleCloudAiplatformV1Trajectory', 1) referenceTrajectory = _messages.MessageField('GoogleCloudAiplatformV1Trajectory', 2) class GoogleCloudAiplatformV1TrajectoryAnyOrderMatchMetricValue(_messages.Message): r"""TrajectoryAnyOrderMatch metric value for an instance. Fields: score: Output only. TrajectoryAnyOrderMatch score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1TrajectoryAnyOrderMatchResults(_messages.Message): r"""Results for TrajectoryAnyOrderMatch metric. Fields: trajectoryAnyOrderMatchMetricValues: Output only. TrajectoryAnyOrderMatch metric values. """ trajectoryAnyOrderMatchMetricValues = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryAnyOrderMatchMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1TrajectoryAnyOrderMatchSpec(_messages.Message): r"""Spec for TrajectoryAnyOrderMatch metric - returns 1 if all tool calls in the reference trajectory appear in the predicted trajectory in any order, else 0. """ class GoogleCloudAiplatformV1TrajectoryExactMatchInput(_messages.Message): r"""Instances and metric spec for TrajectoryExactMatch metric. Fields: instances: Required. Repeated TrajectoryExactMatch instance. metricSpec: Required. Spec for TrajectoryExactMatch metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryExactMatchInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryExactMatchSpec', 2) class GoogleCloudAiplatformV1TrajectoryExactMatchInstance(_messages.Message): r"""Spec for TrajectoryExactMatch instance. Fields: predictedTrajectory: Required. Spec for predicted tool call trajectory. referenceTrajectory: Required. Spec for reference tool call trajectory. """ predictedTrajectory = _messages.MessageField('GoogleCloudAiplatformV1Trajectory', 1) referenceTrajectory = _messages.MessageField('GoogleCloudAiplatformV1Trajectory', 2) class GoogleCloudAiplatformV1TrajectoryExactMatchMetricValue(_messages.Message): r"""TrajectoryExactMatch metric value for an instance. Fields: score: Output only. TrajectoryExactMatch score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1TrajectoryExactMatchResults(_messages.Message): r"""Results for TrajectoryExactMatch metric. Fields: trajectoryExactMatchMetricValues: Output only. TrajectoryExactMatch metric values. """ trajectoryExactMatchMetricValues = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryExactMatchMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1TrajectoryExactMatchSpec(_messages.Message): r"""Spec for TrajectoryExactMatch metric - returns 1 if tool calls in the reference trajectory exactly match the predicted trajectory, else 0. """ class GoogleCloudAiplatformV1TrajectoryInOrderMatchInput(_messages.Message): r"""Instances and metric spec for TrajectoryInOrderMatch metric. Fields: instances: Required. Repeated TrajectoryInOrderMatch instance. metricSpec: Required. Spec for TrajectoryInOrderMatch metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryInOrderMatchInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryInOrderMatchSpec', 2) class GoogleCloudAiplatformV1TrajectoryInOrderMatchInstance(_messages.Message): r"""Spec for TrajectoryInOrderMatch instance. Fields: predictedTrajectory: Required. Spec for predicted tool call trajectory. referenceTrajectory: Required. Spec for reference tool call trajectory. """ predictedTrajectory = _messages.MessageField('GoogleCloudAiplatformV1Trajectory', 1) referenceTrajectory = _messages.MessageField('GoogleCloudAiplatformV1Trajectory', 2) class GoogleCloudAiplatformV1TrajectoryInOrderMatchMetricValue(_messages.Message): r"""TrajectoryInOrderMatch metric value for an instance. Fields: score: Output only. TrajectoryInOrderMatch score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1TrajectoryInOrderMatchResults(_messages.Message): r"""Results for TrajectoryInOrderMatch metric. Fields: trajectoryInOrderMatchMetricValues: Output only. TrajectoryInOrderMatch metric values. """ trajectoryInOrderMatchMetricValues = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryInOrderMatchMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1TrajectoryInOrderMatchSpec(_messages.Message): r"""Spec for TrajectoryInOrderMatch metric - returns 1 if tool calls in the reference trajectory appear in the predicted trajectory in the same order, else 0. """ class GoogleCloudAiplatformV1TrajectoryPrecisionInput(_messages.Message): r"""Instances and metric spec for TrajectoryPrecision metric. Fields: instances: Required. Repeated TrajectoryPrecision instance. metricSpec: Required. Spec for TrajectoryPrecision metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryPrecisionInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryPrecisionSpec', 2) class GoogleCloudAiplatformV1TrajectoryPrecisionInstance(_messages.Message): r"""Spec for TrajectoryPrecision instance. Fields: predictedTrajectory: Required. Spec for predicted tool call trajectory. referenceTrajectory: Required. Spec for reference tool call trajectory. """ predictedTrajectory = _messages.MessageField('GoogleCloudAiplatformV1Trajectory', 1) referenceTrajectory = _messages.MessageField('GoogleCloudAiplatformV1Trajectory', 2) class GoogleCloudAiplatformV1TrajectoryPrecisionMetricValue(_messages.Message): r"""TrajectoryPrecision metric value for an instance. Fields: score: Output only. TrajectoryPrecision score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1TrajectoryPrecisionResults(_messages.Message): r"""Results for TrajectoryPrecision metric. Fields: trajectoryPrecisionMetricValues: Output only. TrajectoryPrecision metric values. """ trajectoryPrecisionMetricValues = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryPrecisionMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1TrajectoryPrecisionSpec(_messages.Message): r"""Spec for TrajectoryPrecision metric - returns a float score based on average precision of individual tool calls. """ class GoogleCloudAiplatformV1TrajectoryRecallInput(_messages.Message): r"""Instances and metric spec for TrajectoryRecall metric. Fields: instances: Required. Repeated TrajectoryRecall instance. metricSpec: Required. Spec for TrajectoryRecall metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryRecallInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryRecallSpec', 2) class GoogleCloudAiplatformV1TrajectoryRecallInstance(_messages.Message): r"""Spec for TrajectoryRecall instance. Fields: predictedTrajectory: Required. Spec for predicted tool call trajectory. referenceTrajectory: Required. Spec for reference tool call trajectory. """ predictedTrajectory = _messages.MessageField('GoogleCloudAiplatformV1Trajectory', 1) referenceTrajectory = _messages.MessageField('GoogleCloudAiplatformV1Trajectory', 2) class GoogleCloudAiplatformV1TrajectoryRecallMetricValue(_messages.Message): r"""TrajectoryRecall metric value for an instance. Fields: score: Output only. TrajectoryRecall score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1TrajectoryRecallResults(_messages.Message): r"""Results for TrajectoryRecall metric. Fields: trajectoryRecallMetricValues: Output only. TrajectoryRecall metric values. """ trajectoryRecallMetricValues = _messages.MessageField('GoogleCloudAiplatformV1TrajectoryRecallMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1TrajectoryRecallSpec(_messages.Message): r"""Spec for TrajectoryRecall metric - returns a float score based on average recall of individual tool calls. """ class GoogleCloudAiplatformV1TrajectorySingleToolUseInput(_messages.Message): r"""Instances and metric spec for TrajectorySingleToolUse metric. Fields: instances: Required. Repeated TrajectorySingleToolUse instance. metricSpec: Required. Spec for TrajectorySingleToolUse metric. """ instances = _messages.MessageField('GoogleCloudAiplatformV1TrajectorySingleToolUseInstance', 1, repeated=True) metricSpec = _messages.MessageField('GoogleCloudAiplatformV1TrajectorySingleToolUseSpec', 2) class GoogleCloudAiplatformV1TrajectorySingleToolUseInstance(_messages.Message): r"""Spec for TrajectorySingleToolUse instance. Fields: predictedTrajectory: Required. Spec for predicted tool call trajectory. """ predictedTrajectory = _messages.MessageField('GoogleCloudAiplatformV1Trajectory', 1) class GoogleCloudAiplatformV1TrajectorySingleToolUseMetricValue(_messages.Message): r"""TrajectorySingleToolUse metric value for an instance. Fields: score: Output only. TrajectorySingleToolUse score. """ score = _messages.FloatField(1, variant=_messages.Variant.FLOAT) class GoogleCloudAiplatformV1TrajectorySingleToolUseResults(_messages.Message): r"""Results for TrajectorySingleToolUse metric. Fields: trajectorySingleToolUseMetricValues: Output only. TrajectorySingleToolUse metric values. """ trajectorySingleToolUseMetricValues = _messages.MessageField('GoogleCloudAiplatformV1TrajectorySingleToolUseMetricValue', 1, repeated=True) class GoogleCloudAiplatformV1TrajectorySingleToolUseSpec(_messages.Message): r"""Spec for TrajectorySingleToolUse metric - returns 1 if tool is present in the predicted trajectory, else 0. Fields: toolName: Required. Spec for tool name to be checked for in the predicted trajectory. """ toolName = _messages.StringField(1) class GoogleCloudAiplatformV1Trial(_messages.Message): r"""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. Enums: StateValueValuesEnum: Output only. The detailed state of the Trial. Messages: WebAccessUrisValue: Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug- interactive-shell) (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is `true`. The keys are names of each node used for the trial; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell. Fields: clientId: Output only. The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial. customJob: Output only. The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial. endTime: Output only. Time when the Trial's status changed to `SUCCEEDED` or `INFEASIBLE`. finalMeasurement: Output only. The final measurement containing the objective value. id: Output only. The identifier of the Trial assigned by the service. infeasibleReason: Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is `INFEASIBLE`. measurements: Output only. A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations. name: Output only. Resource name of the Trial assigned by the service. parameters: Output only. The parameters of the Trial. startTime: Output only. Time when the Trial was started. state: Output only. The detailed state of the Trial. webAccessUris: Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug- interactive-shell) (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is `true`. The keys are names of each node used for the trial; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of the Trial. Values: STATE_UNSPECIFIED: The Trial state is unspecified. REQUESTED: Indicates that a specific Trial has been requested, but it has not yet been suggested by the service. ACTIVE: Indicates that the Trial has been suggested. STOPPING: Indicates that the Trial should stop according to the service. SUCCEEDED: Indicates that the Trial is completed successfully. INFEASIBLE: Indicates that the Trial should not be attempted again. The service will set a Trial to INFEASIBLE when it's done but missing the final_measurement. """ STATE_UNSPECIFIED = 0 REQUESTED = 1 ACTIVE = 2 STOPPING = 3 SUCCEEDED = 4 INFEASIBLE = 5 @encoding.MapUnrecognizedFields('additionalProperties') class WebAccessUrisValue(_messages.Message): r"""Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug- interactive-shell) (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is `true`. The keys are names of each node used for the trial; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell. Messages: AdditionalProperty: An additional property for a WebAccessUrisValue object. Fields: additionalProperties: Additional properties of type WebAccessUrisValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a WebAccessUrisValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clientId = _messages.StringField(1) customJob = _messages.StringField(2) endTime = _messages.StringField(3) finalMeasurement = _messages.MessageField('GoogleCloudAiplatformV1Measurement', 4) id = _messages.StringField(5) infeasibleReason = _messages.StringField(6) measurements = _messages.MessageField('GoogleCloudAiplatformV1Measurement', 7, repeated=True) name = _messages.StringField(8) parameters = _messages.MessageField('GoogleCloudAiplatformV1TrialParameter', 9, repeated=True) startTime = _messages.StringField(10) state = _messages.EnumField('StateValueValuesEnum', 11) webAccessUris = _messages.MessageField('WebAccessUrisValue', 12) class GoogleCloudAiplatformV1TrialContext(_messages.Message): r"""A GoogleCloudAiplatformV1TrialContext object. Fields: description: A human-readable field which can store a description of this context. This will become part of the resulting Trial's description field. parameters: If/when a Trial is generated or selected from this Context, its Parameters will match any parameters specified here. (I.e. if this context specifies parameter name:'a' int_value:3, then a resulting Trial will have int_value:3 for its parameter named 'a'.) Note that we first attempt to match existing REQUESTED Trials with contexts, and if there are no matches, we generate suggestions in the subspace defined by the parameters specified here. NOTE: a Context without any Parameters matches the entire feasible search space. """ description = _messages.StringField(1) parameters = _messages.MessageField('GoogleCloudAiplatformV1TrialParameter', 2, repeated=True) class GoogleCloudAiplatformV1TrialParameter(_messages.Message): r"""A message representing a parameter to be tuned. Fields: parameterId: Output only. The ID of the parameter. The parameter should be defined in StudySpec's Parameters. value: Output only. The value of the parameter. `number_value` will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. `string_value` will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'. """ parameterId = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) class GoogleCloudAiplatformV1TunedModel(_messages.Message): r"""The Model Registry Model and Online Prediction Endpoint associated with this TuningJob. Fields: checkpoints: Output only. The checkpoints associated with this TunedModel. This field is only populated for tuning jobs that enable intermediate checkpoints. endpoint: Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`. model: Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}@{version_id}` When tuning from a base model, the version_id will be 1. For continuous tuning, the version id will be incremented by 1 from the last version id in the parent model. E.g., `projects/{project}/locations/{location}/models/{model}@{last_version_id + 1}` """ checkpoints = _messages.MessageField('GoogleCloudAiplatformV1TunedModelCheckpoint', 1, repeated=True) endpoint = _messages.StringField(2) model = _messages.StringField(3) class GoogleCloudAiplatformV1TunedModelCheckpoint(_messages.Message): r"""TunedModelCheckpoint for the Tuned Model of a Tuning Job. Fields: checkpointId: The ID of the checkpoint. endpoint: The Endpoint resource name that the checkpoint is deployed to. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`. epoch: The epoch of the checkpoint. step: The step of the checkpoint. """ checkpointId = _messages.StringField(1) endpoint = _messages.StringField(2) epoch = _messages.IntegerField(3) step = _messages.IntegerField(4) class GoogleCloudAiplatformV1TunedModelRef(_messages.Message): r"""TunedModel Reference for legacy model migration. Fields: pipelineJob: Support migration from tuning job list page, from bison model to gemini model. tunedModel: Support migration from model registry. tuningJob: Support migration from tuning job list page, from gemini-1.0-pro-002 to 1.5 and above. """ pipelineJob = _messages.StringField(1) tunedModel = _messages.StringField(2) tuningJob = _messages.StringField(3) class GoogleCloudAiplatformV1TuningDataStats(_messages.Message): r"""The tuning data statistic values for TuningJob. Fields: supervisedTuningDataStats: The SFT Tuning data stats. """ supervisedTuningDataStats = _messages.MessageField('GoogleCloudAiplatformV1SupervisedTuningDataStats', 1) class GoogleCloudAiplatformV1TuningJob(_messages.Message): r"""Represents a TuningJob that runs with Google owned models. Enums: StateValueValuesEnum: Output only. The detailed state of the job. Messages: LabelsValue: Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Fields: baseModel: The base model that is being tuned. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model- reference/tuning#supported_models). createTime: Output only. Time when the TuningJob was created. description: Optional. The description of the TuningJob. encryptionSpec: Customer-managed encryption key options for a TuningJob. If this is set, then all resources created by the TuningJob will be encrypted with the provided encryption key. endTime: Output only. Time when the TuningJob entered any of the following JobStates: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`, `JOB_STATE_EXPIRED`. error: Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. experiment: Output only. The Experiment associated with this TuningJob. labels: Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. name: Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}` serviceAccount: The service account that the tuningJob workload runs as. If not specified, the Vertex AI Secure Fine-Tuned Service Agent in the project will be used. See https://cloud.google.com/iam/docs/service- agents#vertex-ai-secure-fine-tuning-service-agent Users starting the pipeline must have the `iam.serviceAccounts.actAs` permission on this service account. startTime: Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state. state: Output only. The detailed state of the job. supervisedTuningSpec: Tuning Spec for Supervised Fine Tuning. tunedModel: Output only. The tuned model resources associated with this TuningJob. tunedModelDisplayName: Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters. tuningDataStats: Output only. The tuning data statistics associated with this TuningJob. updateTime: Output only. Time when the TuningJob was most recently updated. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The detailed state of the job. Values: JOB_STATE_UNSPECIFIED: The job state is unspecified. JOB_STATE_QUEUED: The job has been just created or resumed and processing has not yet begun. JOB_STATE_PENDING: The service is preparing to run the job. JOB_STATE_RUNNING: The job is in progress. JOB_STATE_SUCCEEDED: The job completed successfully. JOB_STATE_FAILED: The job failed. JOB_STATE_CANCELLING: The job is being cancelled. From this state the job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. JOB_STATE_CANCELLED: The job has been cancelled. JOB_STATE_PAUSED: The job has been stopped, and can be resumed. JOB_STATE_EXPIRED: The job has expired. JOB_STATE_UPDATING: The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state. JOB_STATE_PARTIALLY_SUCCEEDED: The job is partially succeeded, some results may be missing due to errors. """ JOB_STATE_UNSPECIFIED = 0 JOB_STATE_QUEUED = 1 JOB_STATE_PENDING = 2 JOB_STATE_RUNNING = 3 JOB_STATE_SUCCEEDED = 4 JOB_STATE_FAILED = 5 JOB_STATE_CANCELLING = 6 JOB_STATE_CANCELLED = 7 JOB_STATE_PAUSED = 8 JOB_STATE_EXPIRED = 9 JOB_STATE_UPDATING = 10 JOB_STATE_PARTIALLY_SUCCEEDED = 11 @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) baseModel = _messages.StringField(1) createTime = _messages.StringField(2) description = _messages.StringField(3) encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 4) endTime = _messages.StringField(5) error = _messages.MessageField('GoogleRpcStatus', 6) experiment = _messages.StringField(7) labels = _messages.MessageField('LabelsValue', 8) name = _messages.StringField(9) serviceAccount = _messages.StringField(10) startTime = _messages.StringField(11) state = _messages.EnumField('StateValueValuesEnum', 12) supervisedTuningSpec = _messages.MessageField('GoogleCloudAiplatformV1SupervisedTuningSpec', 13) tunedModel = _messages.MessageField('GoogleCloudAiplatformV1TunedModel', 14) tunedModelDisplayName = _messages.StringField(15) tuningDataStats = _messages.MessageField('GoogleCloudAiplatformV1TuningDataStats', 16) updateTime = _messages.StringField(17) class GoogleCloudAiplatformV1UndeployIndexOperationMetadata(_messages.Message): r"""Runtime operation information for IndexEndpointService.UndeployIndex. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UndeployIndexRequest(_messages.Message): r"""Request message for IndexEndpointService.UndeployIndex. Fields: deployedIndexId: Required. The ID of the DeployedIndex to be undeployed from the IndexEndpoint. """ deployedIndexId = _messages.StringField(1) class GoogleCloudAiplatformV1UndeployIndexResponse(_messages.Message): r"""Response message for IndexEndpointService.UndeployIndex.""" class GoogleCloudAiplatformV1UndeployModelOperationMetadata(_messages.Message): r"""Runtime operation information for EndpointService.UndeployModel. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UndeployModelRequest(_messages.Message): r"""Request message for EndpointService.UndeployModel. Messages: TrafficSplitValue: If this field is provided, then the Endpoint's traffic_split will be overwritten with it. If last DeployedModel is being undeployed from the Endpoint, the [Endpoint.traffic_split] will always end up empty when this call returns. A DeployedModel will be successfully undeployed only if it doesn't have any traffic assigned to it when this method executes, or if this field unassigns any traffic to it. Fields: deployedModelId: Required. The ID of the DeployedModel to be undeployed from the Endpoint. trafficSplit: If this field is provided, then the Endpoint's traffic_split will be overwritten with it. If last DeployedModel is being undeployed from the Endpoint, the [Endpoint.traffic_split] will always end up empty when this call returns. A DeployedModel will be successfully undeployed only if it doesn't have any traffic assigned to it when this method executes, or if this field unassigns any traffic to it. """ @encoding.MapUnrecognizedFields('additionalProperties') class TrafficSplitValue(_messages.Message): r"""If this field is provided, then the Endpoint's traffic_split will be overwritten with it. If last DeployedModel is being undeployed from the Endpoint, the [Endpoint.traffic_split] will always end up empty when this call returns. A DeployedModel will be successfully undeployed only if it doesn't have any traffic assigned to it when this method executes, or if this field unassigns any traffic to it. Messages: AdditionalProperty: An additional property for a TrafficSplitValue object. Fields: additionalProperties: Additional properties of type TrafficSplitValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a TrafficSplitValue object. Fields: key: Name of the additional property. value: A integer attribute. """ key = _messages.StringField(1) value = _messages.IntegerField(2, variant=_messages.Variant.INT32) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) deployedModelId = _messages.StringField(1) trafficSplit = _messages.MessageField('TrafficSplitValue', 2) class GoogleCloudAiplatformV1UndeployModelResponse(_messages.Message): r"""Response message for EndpointService.UndeployModel.""" class GoogleCloudAiplatformV1UnmanagedContainerModel(_messages.Message): r"""Contains model information necessary to perform batch prediction without requiring a full model import. Fields: artifactUri: The path to the directory containing the Model artifact and any of its supporting files. containerSpec: Input only. The specification of the container that is to be used when deploying this Model. predictSchemata: Contains the schemata used in Model's predictions and explanations """ artifactUri = _messages.StringField(1) containerSpec = _messages.MessageField('GoogleCloudAiplatformV1ModelContainerSpec', 2) predictSchemata = _messages.MessageField('GoogleCloudAiplatformV1PredictSchemata', 3) class GoogleCloudAiplatformV1UpdateDeploymentResourcePoolOperationMetadata(_messages.Message): r"""Runtime operation information for UpdateDeploymentResourcePool method. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UpdateEndpointLongRunningRequest(_messages.Message): r"""Request message for EndpointService.UpdateEndpointLongRunning. Fields: endpoint: Required. The Endpoint which replaces the resource on the server. Currently we only support updating the `client_connection_config` field, all the other fields' update will be blocked. """ endpoint = _messages.MessageField('GoogleCloudAiplatformV1Endpoint', 1) class GoogleCloudAiplatformV1UpdateExplanationDatasetOperationMetadata(_messages.Message): r"""Runtime operation information for ModelService.UpdateExplanationDataset. Fields: genericMetadata: The common part of the operation metadata. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UpdateExplanationDatasetRequest(_messages.Message): r"""Request message for ModelService.UpdateExplanationDataset. Fields: examples: The example config containing the location of the dataset. """ examples = _messages.MessageField('GoogleCloudAiplatformV1Examples', 1) class GoogleCloudAiplatformV1UpdateExplanationDatasetResponse(_messages.Message): r"""Response message of ModelService.UpdateExplanationDataset operation.""" class GoogleCloudAiplatformV1UpdateFeatureGroupOperationMetadata(_messages.Message): r"""Details of operations that perform update FeatureGroup. Fields: genericMetadata: Operation metadata for FeatureGroup. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UpdateFeatureOnlineStoreOperationMetadata(_messages.Message): r"""Details of operations that perform update FeatureOnlineStore. Fields: genericMetadata: Operation metadata for FeatureOnlineStore. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UpdateFeatureOperationMetadata(_messages.Message): r"""Details of operations that perform update Feature. Fields: genericMetadata: Operation metadata for Feature Update. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UpdateFeatureViewOperationMetadata(_messages.Message): r"""Details of operations that perform update FeatureView. Fields: genericMetadata: Operation metadata for FeatureView Update. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UpdateFeaturestoreOperationMetadata(_messages.Message): r"""Details of operations that perform update Featurestore. Fields: genericMetadata: Operation metadata for Featurestore. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UpdateIndexOperationMetadata(_messages.Message): r"""Runtime operation information for IndexService.UpdateIndex. Fields: genericMetadata: The operation generic information. nearestNeighborSearchOperationMetadata: The operation metadata with regard to Matching Engine Index operation. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) nearestNeighborSearchOperationMetadata = _messages.MessageField('GoogleCloudAiplatformV1NearestNeighborSearchOperationMetadata', 2) class GoogleCloudAiplatformV1UpdateModelDeploymentMonitoringJobOperationMetadata(_messages.Message): r"""Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob. Fields: genericMetadata: The operation generic information. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UpdatePersistentResourceOperationMetadata(_messages.Message): r"""Details of operations that perform update PersistentResource. Fields: genericMetadata: Operation metadata for PersistentResource. progressMessage: Progress Message for Update LRO """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) progressMessage = _messages.StringField(2) class GoogleCloudAiplatformV1UpdateSpecialistPoolOperationMetadata(_messages.Message): r"""Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool. Fields: genericMetadata: The operation generic information. specialistPool: Output only. The name of the SpecialistPool to which the specialists are being added. Format: `projects/{project_id}/locations/{l ocation_id}/specialistPools/{specialist_pool}` """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) specialistPool = _messages.StringField(2) class GoogleCloudAiplatformV1UpdateTensorboardOperationMetadata(_messages.Message): r"""Details of operations that perform update Tensorboard. Fields: genericMetadata: Operation metadata for Tensorboard. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UpgradeNotebookRuntimeOperationMetadata(_messages.Message): r"""Metadata information for NotebookService.UpgradeNotebookRuntime. Fields: genericMetadata: The operation generic information. progressMessage: A human-readable message that shows the intermediate progress details of NotebookRuntime. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) progressMessage = _messages.StringField(2) class GoogleCloudAiplatformV1UpgradeNotebookRuntimeRequest(_messages.Message): r"""Request message for NotebookService.UpgradeNotebookRuntime.""" class GoogleCloudAiplatformV1UploadModelOperationMetadata(_messages.Message): r"""Details of ModelService.UploadModel operation. Fields: genericMetadata: The common part of the operation metadata. """ genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1) class GoogleCloudAiplatformV1UploadModelRequest(_messages.Message): r"""Request message for ModelService.UploadModel. Fields: model: Required. The Model to create. modelId: Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are `[a-z0-9_-]`. The first character cannot be a number or hyphen. parentModel: Optional. The resource name of the model into which to upload the version. Only specify this field when uploading a new version. serviceAccount: Optional. The user-provided custom service account to use to do the model upload. If empty, [Vertex AI Service Agent](https://cloud.google.com/vertex-ai/docs/general/access- control#service-agents) will be used to access resources needed to upload the model. This account must belong to the target project where the model is uploaded to, i.e., the project specified in the `parent` field of this request and have necessary read permissions (to Google Cloud Storage, Artifact Registry, etc.). """ model = _messages.MessageField('GoogleCloudAiplatformV1Model', 1) modelId = _messages.StringField(2) parentModel = _messages.StringField(3) serviceAccount = _messages.StringField(4) class GoogleCloudAiplatformV1UploadModelResponse(_messages.Message): r"""Response message of ModelService.UploadModel operation. Fields: model: The name of the uploaded Model resource. Format: `projects/{project}/locations/{location}/models/{model}` modelVersionId: Output only. The version ID of the model that is uploaded. """ model = _messages.StringField(1) modelVersionId = _messages.StringField(2) class GoogleCloudAiplatformV1UploadRagFileConfig(_messages.Message): r"""Config for uploading RagFile. Fields: ragFileTransformationConfig: Specifies the transformation config for RagFiles. """ ragFileTransformationConfig = _messages.MessageField('GoogleCloudAiplatformV1RagFileTransformationConfig', 1) class GoogleCloudAiplatformV1UploadRagFileRequest(_messages.Message): r"""Request message for VertexRagDataService.UploadRagFile. Fields: ragFile: Required. The RagFile to upload. uploadRagFileConfig: Required. The config for the RagFiles to be uploaded into the RagCorpus. VertexRagDataService.UploadRagFile. """ ragFile = _messages.MessageField('GoogleCloudAiplatformV1RagFile', 1) uploadRagFileConfig = _messages.MessageField('GoogleCloudAiplatformV1UploadRagFileConfig', 2) class GoogleCloudAiplatformV1UploadRagFileResponse(_messages.Message): r"""Response message for VertexRagDataService.UploadRagFile. Fields: error: The error that occurred while processing the RagFile. ragFile: The RagFile that had been uploaded into the RagCorpus. """ error = _messages.MessageField('GoogleRpcStatus', 1) ragFile = _messages.MessageField('GoogleCloudAiplatformV1RagFile', 2) class GoogleCloudAiplatformV1UpsertDatapointsRequest(_messages.Message): r"""Request message for IndexService.UpsertDatapoints Fields: datapoints: A list of datapoints to be created/updated. updateMask: Optional. Update mask is used to specify the fields to be overwritten in the datapoints by the update. The fields specified in the update_mask are relative to each IndexDatapoint inside datapoints, not the full request. Updatable fields: * Use `all_restricts` to update both restricts and numeric_restricts. """ datapoints = _messages.MessageField('GoogleCloudAiplatformV1IndexDatapoint', 1, repeated=True) updateMask = _messages.StringField(2) class GoogleCloudAiplatformV1UpsertDatapointsResponse(_messages.Message): r"""Response message for IndexService.UpsertDatapoints""" class GoogleCloudAiplatformV1UrlContext(_messages.Message): r"""Tool to support URL context.""" class GoogleCloudAiplatformV1UrlContextMetadata(_messages.Message): r"""Metadata related to url context retrieval tool. Fields: urlMetadata: Output only. List of url context. """ urlMetadata = _messages.MessageField('GoogleCloudAiplatformV1UrlMetadata', 1, repeated=True) class GoogleCloudAiplatformV1UrlMetadata(_messages.Message): r"""Context of the a single url retrieval. Enums: UrlRetrievalStatusValueValuesEnum: Status of the url retrieval. Fields: retrievedUrl: Retrieved url by the tool. urlRetrievalStatus: Status of the url retrieval. """ class UrlRetrievalStatusValueValuesEnum(_messages.Enum): r"""Status of the url retrieval. Values: URL_RETRIEVAL_STATUS_UNSPECIFIED: Default value. This value is unused. URL_RETRIEVAL_STATUS_SUCCESS: Url retrieval is successful. URL_RETRIEVAL_STATUS_ERROR: Url retrieval is failed due to error. """ URL_RETRIEVAL_STATUS_UNSPECIFIED = 0 URL_RETRIEVAL_STATUS_SUCCESS = 1 URL_RETRIEVAL_STATUS_ERROR = 2 retrievedUrl = _messages.StringField(1) urlRetrievalStatus = _messages.EnumField('UrlRetrievalStatusValueValuesEnum', 2) class GoogleCloudAiplatformV1UserActionReference(_messages.Message): r"""References an API call. It contains more information about long running operation and Jobs that are triggered by the API call. Fields: dataLabelingJob: For API calls that start a LabelingJob. Resource name of the LabelingJob. Format: `projects/{project}/locations/{location}/dataLa belingJobs/{data_labeling_job}` method: The method name of the API RPC call. For example, "/google.cloud.aiplatform.{apiVersion}.DatasetService.CreateDataset" operation: For API calls that return a long running operation. Resource name of the long running operation. Format: `projects/{project}/locations/{location}/operations/{operation}` """ dataLabelingJob = _messages.StringField(1) method = _messages.StringField(2) operation = _messages.StringField(3) class GoogleCloudAiplatformV1Value(_messages.Message): r"""Value is the value of the field. Fields: doubleValue: A double value. intValue: An integer value. stringValue: A string value. """ doubleValue = _messages.FloatField(1) intValue = _messages.IntegerField(2) stringValue = _messages.StringField(3) class GoogleCloudAiplatformV1VertexAISearch(_messages.Message): r"""Retrieve from Vertex AI Search datastore or engine for grounding. datastore and engine are mutually exclusive. See https://cloud.google.com/products/agent-builder Fields: dataStoreSpecs: Specifications that define the specific DataStores to be searched, along with configurations for those data stores. This is only considered for Engines with multiple data stores. It should only be set if engine is used. datastore: Optional. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collec tion}/dataStores/{dataStore}` engine: Optional. Fully-qualified Vertex AI Search engine resource ID. Format: `projects/{project}/locations/{location}/collections/{collection }/engines/{engine}` filter: Optional. Filter strings to be passed to the search API. maxResults: Optional. Number of search results to return per query. The default value is 10. The maximumm allowed value is 10. """ dataStoreSpecs = _messages.MessageField('GoogleCloudAiplatformV1VertexAISearchDataStoreSpec', 1, repeated=True) datastore = _messages.StringField(2) engine = _messages.StringField(3) filter = _messages.StringField(4) maxResults = _messages.IntegerField(5, variant=_messages.Variant.INT32) class GoogleCloudAiplatformV1VertexAISearchDataStoreSpec(_messages.Message): r"""Define data stores within engine to filter on in a search call and configurations for those data stores. For more information, see https://cloud.google.com/generative-ai-app- builder/docs/reference/rpc/google.cloud.discoveryengine.v1#datastorespec Fields: dataStore: Full resource name of DataStore, such as Format: `projects/{pro ject}/locations/{location}/collections/{collection}/dataStores/{dataStor e}` filter: Optional. Filter specification to filter documents in the data store specified by data_store field. For more information on filtering, see [Filtering](https://cloud.google.com/generative-ai-app- builder/docs/filter-search-metadata) """ dataStore = _messages.StringField(1) filter = _messages.StringField(2) class GoogleCloudAiplatformV1VertexAiSearchConfig(_messages.Message): r"""Config for the Vertex AI Search. Fields: servingConfig: Vertex AI Search Serving Config resource full name. For example, `projects/{project}/locations/{location}/collections/{collectio n}/engines/{engine}/servingConfigs/{serving_config}` or `projects/{proje ct}/locations/{location}/collections/{collection}/dataStores/{data_store }/servingConfigs/{serving_config}`. """ servingConfig = _messages.StringField(1) class GoogleCloudAiplatformV1VertexRagStore(_messages.Message): r"""Retrieve from Vertex RAG Store for grounding. Fields: ragResources: Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support. ragRetrievalConfig: Optional. The retrieval config for the Rag query. similarityTopK: Optional. Number of top k results to return from the selected corpora. vectorDistanceThreshold: Optional. Only return results with vector distance smaller than the threshold. """ ragResources = _messages.MessageField('GoogleCloudAiplatformV1VertexRagStoreRagResource', 1, repeated=True) ragRetrievalConfig = _messages.MessageField('GoogleCloudAiplatformV1RagRetrievalConfig', 2) similarityTopK = _messages.IntegerField(3, variant=_messages.Variant.INT32) vectorDistanceThreshold = _messages.FloatField(4) class GoogleCloudAiplatformV1VertexRagStoreRagResource(_messages.Message): r"""The definition of the Rag resource. Fields: ragCorpus: Optional. RagCorpora resource name. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}` ragFileIds: Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field. """ ragCorpus = _messages.StringField(1) ragFileIds = _messages.StringField(2, repeated=True) class GoogleCloudAiplatformV1VideoMetadata(_messages.Message): r"""Metadata describes the input video content. Fields: endOffset: Optional. The end offset of the video. fps: Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0]. startOffset: Optional. The start offset of the video. """ endOffset = _messages.StringField(1) fps = _messages.FloatField(2) startOffset = _messages.StringField(3) class GoogleCloudAiplatformV1WorkerPoolSpec(_messages.Message): r"""Represents the spec of a worker pool in a job. Fields: containerSpec: The custom container task. diskSpec: Disk spec. machineSpec: Optional. Immutable. The specification of a single machine. nfsMounts: Optional. List of NFS mount spec. pythonPackageSpec: The Python packaged task. replicaCount: Optional. The number of worker replicas to use for this worker pool. """ containerSpec = _messages.MessageField('GoogleCloudAiplatformV1ContainerSpec', 1) diskSpec = _messages.MessageField('GoogleCloudAiplatformV1DiskSpec', 2) machineSpec = _messages.MessageField('GoogleCloudAiplatformV1MachineSpec', 3) nfsMounts = _messages.MessageField('GoogleCloudAiplatformV1NfsMount', 4, repeated=True) pythonPackageSpec = _messages.MessageField('GoogleCloudAiplatformV1PythonPackageSpec', 5) replicaCount = _messages.IntegerField(6) class GoogleCloudAiplatformV1WriteFeatureValuesPayload(_messages.Message): r"""Contains Feature values to be written for a specific entity. Messages: FeatureValuesValue: Required. Feature values to be written, mapping from Feature ID to value. Up to 100,000 `feature_values` entries may be written across all payloads. The feature generation time, aligned by days, must be no older than five years (1825 days) and no later than one year (366 days) in the future. Fields: entityId: Required. The ID of the entity. featureValues: Required. Feature values to be written, mapping from Feature ID to value. Up to 100,000 `feature_values` entries may be written across all payloads. The feature generation time, aligned by days, must be no older than five years (1825 days) and no later than one year (366 days) in the future. """ @encoding.MapUnrecognizedFields('additionalProperties') class FeatureValuesValue(_messages.Message): r"""Required. Feature values to be written, mapping from Feature ID to value. Up to 100,000 `feature_values` entries may be written across all payloads. The feature generation time, aligned by days, must be no older than five years (1825 days) and no later than one year (366 days) in the future. Messages: AdditionalProperty: An additional property for a FeatureValuesValue object. Fields: additionalProperties: Additional properties of type FeatureValuesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a FeatureValuesValue object. Fields: key: Name of the additional property. value: A GoogleCloudAiplatformV1FeatureValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('GoogleCloudAiplatformV1FeatureValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) entityId = _messages.StringField(1) featureValues = _messages.MessageField('FeatureValuesValue', 2) class GoogleCloudAiplatformV1WriteFeatureValuesRequest(_messages.Message): r"""Request message for FeaturestoreOnlineServingService.WriteFeatureValues. Fields: payloads: Required. The entities to be written. Up to 100,000 feature values can be written across all `payloads`. """ payloads = _messages.MessageField('GoogleCloudAiplatformV1WriteFeatureValuesPayload', 1, repeated=True) class GoogleCloudAiplatformV1WriteFeatureValuesResponse(_messages.Message): r"""Response message for FeaturestoreOnlineServingService.WriteFeatureValues. """ class GoogleCloudAiplatformV1WriteTensorboardExperimentDataRequest(_messages.Message): r"""Request message for TensorboardService.WriteTensorboardExperimentData. Fields: writeRunDataRequests: Required. Requests containing per-run TensorboardTimeSeries data to write. """ writeRunDataRequests = _messages.MessageField('GoogleCloudAiplatformV1WriteTensorboardRunDataRequest', 1, repeated=True) class GoogleCloudAiplatformV1WriteTensorboardExperimentDataResponse(_messages.Message): r"""Response message for TensorboardService.WriteTensorboardExperimentData. """ class GoogleCloudAiplatformV1WriteTensorboardRunDataRequest(_messages.Message): r"""Request message for TensorboardService.WriteTensorboardRunData. Fields: tensorboardRun: Required. The resource name of the TensorboardRun to write data to. Format: `projects/{project}/locations/{location}/tensorboards/{ tensorboard}/experiments/{experiment}/runs/{run}` timeSeriesData: Required. The TensorboardTimeSeries data to write. Values with in a time series are indexed by their step value. Repeated writes to the same step will overwrite the existing value for that step. The upper limit of data points per write request is 5000. """ tensorboardRun = _messages.StringField(1) timeSeriesData = _messages.MessageField('GoogleCloudAiplatformV1TimeSeriesData', 2, repeated=True) class GoogleCloudAiplatformV1WriteTensorboardRunDataResponse(_messages.Message): r"""Response message for TensorboardService.WriteTensorboardRunData.""" class GoogleCloudAiplatformV1XraiAttribution(_messages.Message): r"""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. Fields: blurBaselineConfig: Config for XRAI with 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 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 stepCount: Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively. """ blurBaselineConfig = _messages.MessageField('GoogleCloudAiplatformV1BlurBaselineConfig', 1) smoothGradConfig = _messages.MessageField('GoogleCloudAiplatformV1SmoothGradConfig', 2) stepCount = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleIamV1Binding(_messages.Message): r"""Associates `members`, or principals, with a `role`. Fields: condition: The condition that is associated with this binding. If the condition evaluates to `true`, then this binding applies to the current request. If the condition evaluates to `false`, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). members: Specifies the principals requesting access for a Google Cloud resource. `members` can have the following values: * `allUsers`: A special identifier that represents anyone who is on the internet; with or without a Google account. * `allAuthenticatedUsers`: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. * `user:{emailid}`: An email address that represents a specific Google account. For example, `alice@example.com` . * `serviceAccount:{emailid}`: An email address that represents a Google service account. For example, `my-other- app@appspot.gserviceaccount.com`. * `serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]`: An identifier for a [Kubernetes service account](https://cloud.google.com/kubernetes-engine/docs/how- to/kubernetes-service-accounts). For example, `my- project.svc.id.goog[my-namespace/my-kubernetes-sa]`. * `group:{emailid}`: An email address that represents a Google group. For example, `admins@example.com`. * `domain:{domain}`: The G Suite domain (primary) that represents all the users of that domain. For example, `google.com` or `example.com`. * `principal://iam.googleapis.com/locatio ns/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workforce identity pool. * `principalSet://iam.goog leapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}`: All workforce identities in a group. * `principalSet://iam.googleapis.co m/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{ attribute_value}`: All workforce identities with a specific attribute value. * `principalSet://iam.googleapis.com/locations/global/workforcePo ols/{pool_id}/*`: All identities in a workforce identity pool. * `princi pal://iam.googleapis.com/projects/{project_number}/locations/global/work loadIdentityPools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workload identity pool. * `principalSet://iam.googleapis.c om/projects/{project_number}/locations/global/workloadIdentityPools/{poo l_id}/group/{group_id}`: A workload identity pool group. * `principalSet ://iam.googleapis.com/projects/{project_number}/locations/global/workloa dIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All identities in a workload identity pool with a certain attribute. * ` principalSet://iam.googleapis.com/projects/{project_number}/locations/gl obal/workloadIdentityPools/{pool_id}/*`: All identities in a workload identity pool. * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a user that has been recently deleted. For example, `alice@example.com?uid=123456789012345678901`. If the user is recovered, this value reverts to `user:{emailid}` and the recovered user retains the role in the binding. * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, `my-other- app@appspot.gserviceaccount.com?uid=123456789012345678901`. If the service account is undeleted, this value reverts to `serviceAccount:{emailid}` and the undeleted service account retains the role in the binding. * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, `admins@example.com?uid=123456789012345678901`. If the group is recovered, this value reverts to `group:{emailid}` and the recovered group retains the role in the binding. * `deleted:principal://iam.google apis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attr ibute_value}`: Deleted single identity in a workforce identity pool. For example, `deleted:principal://iam.googleapis.com/locations/global/workfo rcePools/my-pool-id/subject/my-subject-attribute-value`. role: Role that is assigned to the list of `members`, or principals. For example, `roles/viewer`, `roles/editor`, or `roles/owner`. For an overview of the IAM roles and permissions, see the [IAM documentation](https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see [here](https://cloud.google.com/iam/docs/understanding-roles). """ condition = _messages.MessageField('GoogleTypeExpr', 1) members = _messages.StringField(2, repeated=True) role = _messages.StringField(3) class GoogleIamV1Policy(_messages.Message): r"""An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources. A `Policy` is a collection of `bindings`. A `binding` binds one or more `members`, or principals, to a single `role`. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A `role` is a named list of permissions; each `role` can be an IAM predefined role or a user-created custom role. For some types of Google Cloud resources, a `binding` can also specify a `condition`, which is a logical expression that allows access to a resource only if the expression evaluates to `true`. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). **JSON example:** ``` { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } ``` **YAML example:** ``` bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 ``` For a description of IAM and its features, see the [IAM documentation](https://cloud.google.com/iam/docs/). Fields: bindings: Associates a list of `members`, or principals, with a `role`. Optionally, may specify a `condition` that determines how and when the `bindings` are applied. Each of the `bindings` must contain at least one principal. The `bindings` in a `Policy` can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the `bindings` grant 50 different roles to `user:alice@example.com`, and not to any other principal, then you can add another 1,450 principals to the `bindings` in the `Policy`. etag: `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read- modify-write cycle to perform policy updates in order to avoid race conditions: An `etag` is returned in the response to `getIamPolicy`, and systems are expected to put that etag in the request to `setIamPolicy` to ensure that their change will be applied to the same version of the policy. **Important:** If you use IAM Conditions, you must include the `etag` field whenever you call `setIamPolicy`. If you omit this field, then IAM allows you to overwrite a version `3` policy with a version `1` policy, and all of the conditions in the version `3` policy are lost. version: Specifies the format of the policy. Valid values are `0`, `1`, and `3`. Requests that specify an invalid value are rejected. Any operation that affects conditional role bindings must specify version `3`. This requirement applies to the following operations: * Getting a policy that includes a conditional role binding * Adding a conditional role binding to a policy * Changing a conditional role binding in a policy * Removing any role binding, with or without a condition, from a policy that includes conditions **Important:** If you use IAM Conditions, you must include the `etag` field whenever you call `setIamPolicy`. If you omit this field, then IAM allows you to overwrite a version `3` policy with a version `1` policy, and all of the conditions in the version `3` policy are lost. If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource- policies). """ bindings = _messages.MessageField('GoogleIamV1Binding', 1, repeated=True) etag = _messages.BytesField(2) version = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleIamV1SetIamPolicyRequest(_messages.Message): r"""Request message for `SetIamPolicy` method. Fields: policy: REQUIRED: The complete policy to be applied to the `resource`. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Google Cloud services (such as Projects) might reject them. """ policy = _messages.MessageField('GoogleIamV1Policy', 1) class GoogleIamV1TestIamPermissionsResponse(_messages.Message): r"""Response message for `TestIamPermissions` method. Fields: permissions: A subset of `TestPermissionsRequest.permissions` that the caller is allowed. """ permissions = _messages.StringField(1, repeated=True) class GoogleLongrunningListOperationsResponse(_messages.Message): r"""The response message for Operations.ListOperations. Fields: nextPageToken: The standard List next-page token. operations: A list of operations that matches the specified filter in the request. """ nextPageToken = _messages.StringField(1) operations = _messages.MessageField('GoogleLongrunningOperation', 2, repeated=True) class GoogleLongrunningOperation(_messages.Message): r"""This resource represents a long-running operation that is the result of a network API call. Messages: MetadataValue: Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. ResponseValue: The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`. Fields: done: If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available. error: The error result of the operation in case of failure or cancellation. metadata: Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. name: The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`. response: The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`. """ @encoding.MapUnrecognizedFields('additionalProperties') class MetadataValue(_messages.Message): r"""Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. Messages: AdditionalProperty: An additional property for a MetadataValue object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a MetadataValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class ResponseValue(_messages.Message): r"""The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`. Messages: AdditionalProperty: An additional property for a ResponseValue object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a ResponseValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) done = _messages.BooleanField(1) error = _messages.MessageField('GoogleRpcStatus', 2) metadata = _messages.MessageField('MetadataValue', 3) name = _messages.StringField(4) response = _messages.MessageField('ResponseValue', 5) class GoogleProtobufEmpty(_messages.Message): r"""A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } """ class GoogleRpcStatus(_messages.Message): r"""The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). Messages: DetailsValueListEntry: A DetailsValueListEntry object. Fields: code: The status code, which should be an enum value of google.rpc.Code. details: A list of messages that carry the error details. There is a common set of message types for APIs to use. message: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. """ @encoding.MapUnrecognizedFields('additionalProperties') class DetailsValueListEntry(_messages.Message): r"""A DetailsValueListEntry object. Messages: AdditionalProperty: An additional property for a DetailsValueListEntry object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a DetailsValueListEntry object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) code = _messages.IntegerField(1, variant=_messages.Variant.INT32) details = _messages.MessageField('DetailsValueListEntry', 2, repeated=True) message = _messages.StringField(3) class GoogleTypeColor(_messages.Message): r"""Represents a color in the RGBA color space. This representation is designed for simplicity of conversion to and from color representations in various languages over compactness. For example, the fields of this representation can be trivially provided to the constructor of `java.awt.Color` in Java; it can also be trivially provided to UIColor's `+colorWithRed:green:blue:alpha` method in iOS; and, with just a little work, it can be easily formatted into a CSS `rgba()` string in JavaScript. This reference page doesn't have information about the absolute color space that should be used to interpret the RGB value-for example, sRGB, Adobe RGB, DCI-P3, and BT.2020. By default, applications should assume the sRGB color space. When color equality needs to be decided, implementations, unless documented otherwise, treat two colors as equal if all their red, green, blue, and alpha values each differ by at most `1e-5`. Example (Java): import com.google.type.Color; // ... public static java.awt.Color fromProto(Color protocolor) { float alpha = protocolor.hasAlpha() ? protocolor.getAlpha().getValue() : 1.0; return new java.awt.Color( protocolor.getRed(), protocolor.getGreen(), protocolor.getBlue(), alpha); } public static Color toProto(java.awt.Color color) { float red = (float) color.getRed(); float green = (float) color.getGreen(); float blue = (float) color.getBlue(); float denominator = 255.0; Color.Builder resultBuilder = Color .newBuilder() .setRed(red / denominator) .setGreen(green / denominator) .setBlue(blue / denominator); int alpha = color.getAlpha(); if (alpha != 255) { result.setAlpha( FloatValue .newBuilder() .setValue(((float) alpha) / denominator) .build()); } return resultBuilder.build(); } // ... Example (iOS / Obj-C): // ... static UIColor* fromProto(Color* protocolor) { float red = [protocolor red]; float green = [protocolor green]; float blue = [protocolor blue]; FloatValue* alpha_wrapper = [protocolor alpha]; float alpha = 1.0; if (alpha_wrapper != nil) { alpha = [alpha_wrapper value]; } return [UIColor colorWithRed:red green:green blue:blue alpha:alpha]; } static Color* toProto(UIColor* color) { CGFloat red, green, blue, alpha; if (![color getRed:&red green:&green blue:&blue alpha:&alpha]) { return nil; } Color* result = [[Color alloc] init]; [result setRed:red]; [result setGreen:green]; [result setBlue:blue]; if (alpha <= 0.9999) { [result setAlpha:floatWrapperWithValue(alpha)]; } [result autorelease]; return result; } // ... Example (JavaScript): // ... var protoToCssColor = function(rgb_color) { var redFrac = rgb_color.red || 0.0; var greenFrac = rgb_color.green || 0.0; var blueFrac = rgb_color.blue || 0.0; var red = Math.floor(redFrac * 255); var green = Math.floor(greenFrac * 255); var blue = Math.floor(blueFrac * 255); if (!('alpha' in rgb_color)) { return rgbToCssColor(red, green, blue); } var alphaFrac = rgb_color.alpha.value || 0.0; var rgbParams = [red, green, blue].join(','); return ['rgba(', rgbParams, ',', alphaFrac, ')'].join(''); }; var rgbToCssColor = function(red, green, blue) { var rgbNumber = new Number((red << 16) | (green << 8) | blue); var hexString = rgbNumber.toString(16); var missingZeros = 6 - hexString.length; var resultBuilder = ['#']; for (var i = 0; i < missingZeros; i++) { resultBuilder.push('0'); } resultBuilder.push(hexString); return resultBuilder.join(''); }; // ... Fields: alpha: The fraction of this color that should be applied to the pixel. That is, the final pixel color is defined by the equation: `pixel color = alpha * (this color) + (1.0 - alpha) * (background color)` This means that a value of 1.0 corresponds to a solid color, whereas a value of 0.0 corresponds to a completely transparent color. This uses a wrapper message rather than a simple float scalar so that it is possible to distinguish between a default value and the value being unset. If omitted, this color object is rendered as a solid color (as if the alpha value had been explicitly given a value of 1.0). blue: The amount of blue in the color as a value in the interval [0, 1]. green: The amount of green in the color as a value in the interval [0, 1]. red: The amount of red in the color as a value in the interval [0, 1]. """ alpha = _messages.FloatField(1, variant=_messages.Variant.FLOAT) blue = _messages.FloatField(2, variant=_messages.Variant.FLOAT) green = _messages.FloatField(3, variant=_messages.Variant.FLOAT) red = _messages.FloatField(4, variant=_messages.Variant.FLOAT) class GoogleTypeDate(_messages.Message): r"""Represents a whole or partial calendar date, such as a birthday. The time of day and time zone are either specified elsewhere or are insignificant. The date is relative to the Gregorian Calendar. This can represent one of the following: * A full date, with non-zero year, month, and day values. * A month and day, with a zero year (for example, an anniversary). * A year on its own, with a zero month and a zero day. * A year and month, with a zero day (for example, a credit card expiration date). Related types: * google.type.TimeOfDay * google.type.DateTime * google.protobuf.Timestamp Fields: day: Day of a month. Must be from 1 to 31 and valid for the year and month, or 0 to specify a year by itself or a year and month where the day isn't significant. month: Month of a year. Must be from 1 to 12, or 0 to specify a year without a month and day. year: Year of the date. Must be from 1 to 9999, or 0 to specify a date without a year. """ day = _messages.IntegerField(1, variant=_messages.Variant.INT32) month = _messages.IntegerField(2, variant=_messages.Variant.INT32) year = _messages.IntegerField(3, variant=_messages.Variant.INT32) class GoogleTypeExpr(_messages.Message): r"""Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec. Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. Fields: description: Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI. expression: Textual representation of an expression in Common Expression Language syntax. location: Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file. title: Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression. """ description = _messages.StringField(1) expression = _messages.StringField(2) location = _messages.StringField(3) title = _messages.StringField(4) class GoogleTypeInterval(_messages.Message): r"""Represents a time interval, encoded as a Timestamp start (inclusive) and a Timestamp end (exclusive). The start must be less than or equal to the end. When the start equals the end, the interval is empty (matches no time). When both start and end are unspecified, the interval matches any time. Fields: endTime: Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end. startTime: Required. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start. """ endTime = _messages.StringField(1) startTime = _messages.StringField(2) class GoogleTypeLatLng(_messages.Message): r"""An object that represents a latitude/longitude pair. This is expressed as a pair of doubles to represent degrees latitude and degrees longitude. Unless specified otherwise, this object must conform to the WGS84 standard. Values must be within normalized ranges. Fields: latitude: The latitude in degrees. It must be in the range [-90.0, +90.0]. longitude: The longitude in degrees. It must be in the range [-180.0, +180.0]. """ latitude = _messages.FloatField(1) longitude = _messages.FloatField(2) class StandardQueryParameters(_messages.Message): r"""Query parameters accepted by all methods. Enums: FXgafvValueValuesEnum: V1 error format. AltValueValuesEnum: Data format for response. Fields: f__xgafv: V1 error format. access_token: OAuth access token. alt: Data format for response. callback: JSONP fields: Selector specifying which fields to include in a partial response. key: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token. oauth_token: OAuth 2.0 token for the current user. prettyPrint: Returns response with indentations and line breaks. quotaUser: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters. trace: A tracing token of the form "token:" to include in api requests. uploadType: Legacy upload protocol for media (e.g. "media", "multipart"). upload_protocol: Upload protocol for media (e.g. "raw", "multipart"). """ class AltValueValuesEnum(_messages.Enum): r"""Data format for response. Values: json: Responses with Content-Type of application/json media: Media download with context-dependent Content-Type proto: Responses with Content-Type of application/x-protobuf """ json = 0 media = 1 proto = 2 class FXgafvValueValuesEnum(_messages.Enum): r"""V1 error format. Values: _1: v1 error format _2: v2 error format """ _1 = 0 _2 = 1 f__xgafv = _messages.EnumField('FXgafvValueValuesEnum', 1) access_token = _messages.StringField(2) alt = _messages.EnumField('AltValueValuesEnum', 3, default='json') callback = _messages.StringField(4) fields = _messages.StringField(5) key = _messages.StringField(6) oauth_token = _messages.StringField(7) prettyPrint = _messages.BooleanField(8, default=True) quotaUser = _messages.StringField(9) trace = _messages.StringField(10) uploadType = _messages.StringField(11) upload_protocol = _messages.StringField(12) encoding.AddCustomJsonFieldMapping( GoogleCloudAiplatformV1Probe, 'exec_', 'exec') encoding.AddCustomJsonFieldMapping( StandardQueryParameters, 'f__xgafv', '$.xgafv') encoding.AddCustomJsonEnumMapping( StandardQueryParameters.FXgafvValueValuesEnum, '_1', '1') encoding.AddCustomJsonEnumMapping( StandardQueryParameters.FXgafvValueValuesEnum, '_2', '2') encoding.AddCustomJsonFieldMapping( AiplatformProjectsLocationsDatasetsSearchDataItemsRequest, 'orderByAnnotation_orderBy', 'orderByAnnotation.orderBy') encoding.AddCustomJsonFieldMapping( AiplatformProjectsLocationsDatasetsSearchDataItemsRequest, 'orderByAnnotation_savedQuery', 'orderByAnnotation.savedQuery') encoding.AddCustomJsonFieldMapping( AiplatformProjectsLocationsFeatureGroupsGetIamPolicyRequest, 'options_requestedPolicyVersion', 'options.requestedPolicyVersion') encoding.AddCustomJsonFieldMapping( AiplatformProjectsLocationsFeatureOnlineStoresGetIamPolicyRequest, 'options_requestedPolicyVersion', 'options.requestedPolicyVersion') encoding.AddCustomJsonFieldMapping( AiplatformProjectsLocationsFeatureOnlineStoresFeatureViewsGetIamPolicyRequest, 'options_requestedPolicyVersion', 'options.requestedPolicyVersion') encoding.AddCustomJsonFieldMapping( AiplatformProjectsLocationsFeaturestoresGetIamPolicyRequest, 'options_requestedPolicyVersion', 'options.requestedPolicyVersion') encoding.AddCustomJsonFieldMapping( AiplatformProjectsLocationsFeaturestoresEntityTypesGetIamPolicyRequest, 'options_requestedPolicyVersion', 'options.requestedPolicyVersion') encoding.AddCustomJsonFieldMapping( AiplatformProjectsLocationsModelsGetIamPolicyRequest, 'options_requestedPolicyVersion', 'options.requestedPolicyVersion') encoding.AddCustomJsonFieldMapping( AiplatformProjectsLocationsNotebookRuntimeTemplatesGetIamPolicyRequest, 'options_requestedPolicyVersion', 'options.requestedPolicyVersion')