Retrieves the next page of results.
+
+ patch(name, body=None, updateMask=None, x__xgafv=None)
Updates a DatasetVersion.
Restores a dataset version.
@@ -116,6 +119,7 @@patch(name, body=None, updateMask=None, x__xgafv=None)
+ Updates a DatasetVersion.
+
+Args:
+ name: string, Output only. The resource name of the DatasetVersion. (required)
+ body: object, The request body.
+ The object takes the form of:
+
+{ # Describes the dataset version.
+ "bigQueryDatasetName": "A String", # Output only. Name of the associated BigQuery dataset.
+ "createTime": "A String", # Output only. Timestamp when this DatasetVersion was created.
+ "displayName": "A String", # 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": "A String", # 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": "A String", # Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions.
+ "name": "A String", # Output only. The resource name of the DatasetVersion.
+ "updateTime": "A String", # Output only. Timestamp when this DatasetVersion was last updated.
+}
+
+ updateMask: string, Required. The update mask applies to the resource. For the `FieldMask` definition, see google.protobuf.FieldMask. Updatable fields: * `display_name`
+ x__xgafv: string, V1 error format.
+ Allowed values
+ 1 - v1 error format
+ 2 - v2 error format
+
+Returns:
+ An object of the form:
+
+ { # Describes the dataset version.
+ "bigQueryDatasetName": "A String", # Output only. Name of the associated BigQuery dataset.
+ "createTime": "A String", # Output only. Timestamp when this DatasetVersion was created.
+ "displayName": "A String", # 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": "A String", # 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": "A String", # Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions.
+ "name": "A String", # Output only. The resource name of the DatasetVersion.
+ "updateTime": "A String", # Output only. Timestamp when this DatasetVersion was last updated.
+}
+restore(name, x__xgafv=None)
Restores a dataset version. diff --git a/docs/dyn/aiplatform_v1.projects.locations.datasets.html b/docs/dyn/aiplatform_v1.projects.locations.datasets.html index 94da36599f7..039f667f418 100644 --- a/docs/dyn/aiplatform_v1.projects.locations.datasets.html +++ b/docs/dyn/aiplatform_v1.projects.locations.datasets.html @@ -162,6 +162,7 @@@@ -237,6 +242,7 @@Method Details
"metadata": "", # Required. Additional information about the Dataset. "metadataArtifact": "A String", # 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}/metadataStores/{metadata_store}/artifacts/{artifact}`. "metadataSchemaUri": "A String", # 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": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets. "name": "A String", # Output only. The resource name of the Dataset. "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. { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters. @@ -334,6 +335,7 @@Method Details
"metadata": "", # Required. Additional information about the Dataset. "metadataArtifact": "A String", # 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}/metadataStores/{metadata_store}/artifacts/{artifact}`. "metadataSchemaUri": "A String", # 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": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets. "name": "A String", # Output only. The resource name of the Dataset. "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. { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters. @@ -446,6 +448,7 @@Method Details
"metadata": "", # Required. Additional information about the Dataset. "metadataArtifact": "A String", # 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}/metadataStores/{metadata_store}/artifacts/{artifact}`. "metadataSchemaUri": "A String", # 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": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets. "name": "A String", # Output only. The resource name of the Dataset. "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. { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters. @@ -506,6 +509,7 @@Method Details
"metadata": "", # Required. Additional information about the Dataset. "metadataArtifact": "A String", # 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}/metadataStores/{metadata_store}/artifacts/{artifact}`. "metadataSchemaUri": "A String", # 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": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets. "name": "A String", # Output only. The resource name of the Dataset. "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. { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters. @@ -548,6 +552,7 @@Method Details
"metadata": "", # Required. Additional information about the Dataset. "metadataArtifact": "A String", # 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}/metadataStores/{metadata_store}/artifacts/{artifact}`. "metadataSchemaUri": "A String", # 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": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets. "name": "A String", # Output only. The resource name of the Dataset. "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. { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters. diff --git a/docs/dyn/aiplatform_v1.projects.locations.endpoints.html b/docs/dyn/aiplatform_v1.projects.locations.endpoints.html index e1c683d47b8..d135c63b46c 100644 --- a/docs/dyn/aiplatform_v1.projects.locations.endpoints.html +++ b/docs/dyn/aiplatform_v1.projects.locations.endpoints.html @@ -1105,6 +1105,7 @@Method Details
"maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message. "presencePenalty": 3.14, # Optional. Positive penalties. "responseMimeType": "A String", # 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. + "responseStyle": "A String", # Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED "stopSequences": [ # Optional. Stop sequences. "A String", ], @@ -2605,6 +2606,7 @@Method Details
"maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message. "presencePenalty": 3.14, # Optional. Positive penalties. "responseMimeType": "A String", # 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. + "responseStyle": "A String", # Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED "stopSequences": [ # Optional. Stop sequences. "A String", ], diff --git a/docs/dyn/aiplatform_v1.projects.locations.featureGroups.html b/docs/dyn/aiplatform_v1.projects.locations.featureGroups.html index 9c7f5396d92..0920690ca2a 100644 --- a/docs/dyn/aiplatform_v1.projects.locations.featureGroups.html +++ b/docs/dyn/aiplatform_v1.projects.locations.featureGroups.html @@ -121,7 +121,7 @@Method Details
The object takes the form of: { # Vertex AI Feature Group. - "bigQuery": { # Input source type for BigQuery Tables and Views. # Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entity_id and a feature_timestamp column in the source. + "bigQuery": { # Input source type for BigQuery Tables and Views. # 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`. "bigQuerySource": { # The BigQuery location for the input content. # Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View. "inputUri": "A String", # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`. }, @@ -220,7 +220,7 @@Method Details
An object of the form: { # Vertex AI Feature Group. - "bigQuery": { # Input source type for BigQuery Tables and Views. # Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entity_id and a feature_timestamp column in the source. + "bigQuery": { # Input source type for BigQuery Tables and Views. # 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`. "bigQuerySource": { # The BigQuery location for the input content. # Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View. "inputUri": "A String", # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`. }, @@ -260,7 +260,7 @@Method Details
{ # Response message for FeatureRegistryService.ListFeatureGroups. "featureGroups": [ # The FeatureGroups matching the request. { # Vertex AI Feature Group. - "bigQuery": { # Input source type for BigQuery Tables and Views. # Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entity_id and a feature_timestamp column in the source. + "bigQuery": { # Input source type for BigQuery Tables and Views. # 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`. "bigQuerySource": { # The BigQuery location for the input content. # Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View. "inputUri": "A String", # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`. }, @@ -306,7 +306,7 @@Method Details
The object takes the form of: { # Vertex AI Feature Group. - "bigQuery": { # Input source type for BigQuery Tables and Views. # Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entity_id and a feature_timestamp column in the source. + "bigQuery": { # Input source type for BigQuery Tables and Views. # 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`. "bigQuerySource": { # The BigQuery location for the input content. # Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View. "inputUri": "A String", # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`. }, diff --git a/docs/dyn/aiplatform_v1.projects.locations.indexEndpoints.html b/docs/dyn/aiplatform_v1.projects.locations.indexEndpoints.html index 32f76907faa..413d1af6def 100644 --- a/docs/dyn/aiplatform_v1.projects.locations.indexEndpoints.html +++ b/docs/dyn/aiplatform_v1.projects.locations.indexEndpoints.html @@ -411,10 +411,10 @@Method Details
}, ], "sparseEmbedding": { # Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions. # Optional. Feature embedding vector for sparse index. - "dimensions": [ # Optional. The list of indexes for the embedding values of the sparse vector. + "dimensions": [ # Required. The list of indexes for the embedding values of the sparse vector. "A String", ], - "values": [ # Optional. The list of embedding values of the sparse vector. + "values": [ # Required. The list of embedding values of the sparse vector. 3.14, ], }, @@ -473,10 +473,10 @@Method Details
}, ], "sparseEmbedding": { # Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions. # Optional. Feature embedding vector for sparse index. - "dimensions": [ # Optional. The list of indexes for the embedding values of the sparse vector. + "dimensions": [ # Required. The list of indexes for the embedding values of the sparse vector. "A String", ], - "values": [ # Optional. The list of embedding values of the sparse vector. + "values": [ # Required. The list of embedding values of the sparse vector. 3.14, ], }, @@ -1029,10 +1029,10 @@Method Details
}, ], "sparseEmbedding": { # Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions. # Optional. Feature embedding vector for sparse index. - "dimensions": [ # Optional. The list of indexes for the embedding values of the sparse vector. + "dimensions": [ # Required. The list of indexes for the embedding values of the sparse vector. "A String", ], - "values": [ # Optional. The list of embedding values of the sparse vector. + "values": [ # Required. The list of embedding values of the sparse vector. 3.14, ], }, diff --git a/docs/dyn/aiplatform_v1.projects.locations.indexes.html b/docs/dyn/aiplatform_v1.projects.locations.indexes.html index c5ec9a7dd28..8d81a7674f2 100644 --- a/docs/dyn/aiplatform_v1.projects.locations.indexes.html +++ b/docs/dyn/aiplatform_v1.projects.locations.indexes.html @@ -464,10 +464,10 @@Method Details
}, ], "sparseEmbedding": { # Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions. # Optional. Feature embedding vector for sparse index. - "dimensions": [ # Optional. The list of indexes for the embedding values of the sparse vector. + "dimensions": [ # Required. The list of indexes for the embedding values of the sparse vector. "A String", ], - "values": [ # Optional. The list of embedding values of the sparse vector. + "values": [ # Required. The list of embedding values of the sparse vector. 3.14, ], }, diff --git a/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimes.html b/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimes.html index 91e49376c98..ad194aae5d3 100644 --- a/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimes.html +++ b/docs/dyn/aiplatform_v1.projects.locations.notebookRuntimes.html @@ -137,6 +137,8 @@Method Details
}, "runtimeState": "A String", # Output only. The runtime (instance) state of the NotebookRuntime. "runtimeUser": "A String", # Required. The user email of the NotebookRuntime. + "satisfiesPzi": True or False, # Output only. Reserved for future use. + "satisfiesPzs": True or False, # Output only. Reserved for future use. "serviceAccount": "A String", # Output only. The service account that the NotebookRuntime workload runs as. "updateTime": "A String", # Output only. Timestamp when this NotebookRuntime was most recently updated. "version": "A String", # Output only. The VM os image version of NotebookRuntime. @@ -256,6 +258,8 @@Method Details
}, "runtimeState": "A String", # Output only. The runtime (instance) state of the NotebookRuntime. "runtimeUser": "A String", # Required. The user email of the NotebookRuntime. + "satisfiesPzi": True or False, # Output only. Reserved for future use. + "satisfiesPzs": True or False, # Output only. Reserved for future use. "serviceAccount": "A String", # Output only. The service account that the NotebookRuntime workload runs as. "updateTime": "A String", # Output only. Timestamp when this NotebookRuntime was most recently updated. "version": "A String", # Output only. The VM os image version of NotebookRuntime. @@ -312,6 +316,8 @@Method Details
}, "runtimeState": "A String", # Output only. The runtime (instance) state of the NotebookRuntime. "runtimeUser": "A String", # Required. The user email of the NotebookRuntime. + "satisfiesPzi": True or False, # Output only. Reserved for future use. + "satisfiesPzs": True or False, # Output only. Reserved for future use. "serviceAccount": "A String", # Output only. The service account that the NotebookRuntime workload runs as. "updateTime": "A String", # Output only. Timestamp when this NotebookRuntime was most recently updated. "version": "A String", # Output only. The VM os image version of NotebookRuntime. diff --git a/docs/dyn/aiplatform_v1.projects.locations.publishers.models.html b/docs/dyn/aiplatform_v1.projects.locations.publishers.models.html index 676fec7de38..4274fe9f508 100644 --- a/docs/dyn/aiplatform_v1.projects.locations.publishers.models.html +++ b/docs/dyn/aiplatform_v1.projects.locations.publishers.models.html @@ -258,6 +258,7 @@Method Details
"maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message. "presencePenalty": 3.14, # Optional. Positive penalties. "responseMimeType": "A String", # 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. + "responseStyle": "A String", # Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED "stopSequences": [ # Optional. Stop sequences. "A String", ], @@ -769,6 +770,7 @@Method Details
"maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message. "presencePenalty": 3.14, # Optional. Positive penalties. "responseMimeType": "A String", # 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. + "responseStyle": "A String", # Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED "stopSequences": [ # Optional. Stop sequences. "A String", ], diff --git a/docs/dyn/aiplatform_v1.projects.locations.tuningJobs.html b/docs/dyn/aiplatform_v1.projects.locations.tuningJobs.html index 1628f239617..34bb882cdff 100644 --- a/docs/dyn/aiplatform_v1.projects.locations.tuningJobs.html +++ b/docs/dyn/aiplatform_v1.projects.locations.tuningJobs.html @@ -140,6 +140,9 @@Method Details
"baseModel": "A String", # The base model that is being tuned, e.g., "gemini-1.0-pro-002". "createTime": "A String", # Output only. Time when the TuningJob was created. "description": "A String", # Optional. The description of the TuningJob. + "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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. + "kmsKeyName": "A String", # 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. + }, "endTime": "A String", # 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": { # 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). # Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. "code": 42, # The status code, which should be an enum value of google.rpc.Code. @@ -276,6 +279,9 @@Method Details
"baseModel": "A String", # The base model that is being tuned, e.g., "gemini-1.0-pro-002". "createTime": "A String", # Output only. Time when the TuningJob was created. "description": "A String", # Optional. The description of the TuningJob. + "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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. + "kmsKeyName": "A String", # 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. + }, "endTime": "A String", # 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": { # 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). # Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. "code": 42, # The status code, which should be an enum value of google.rpc.Code. @@ -419,6 +425,9 @@Method Details
"baseModel": "A String", # The base model that is being tuned, e.g., "gemini-1.0-pro-002". "createTime": "A String", # Output only. Time when the TuningJob was created. "description": "A String", # Optional. The description of the TuningJob. + "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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. + "kmsKeyName": "A String", # 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. + }, "endTime": "A String", # 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": { # 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). # Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. "code": 42, # The status code, which should be an enum value of google.rpc.Code. @@ -568,6 +577,9 @@Method Details
"baseModel": "A String", # The base model that is being tuned, e.g., "gemini-1.0-pro-002". "createTime": "A String", # Output only. Time when the TuningJob was created. "description": "A String", # Optional. The description of the TuningJob. + "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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. + "kmsKeyName": "A String", # 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. + }, "endTime": "A String", # 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": { # 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). # Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. "code": 42, # The status code, which should be an enum value of google.rpc.Code. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.datasetVersions.html b/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.datasetVersions.html index 14cef7f6696..254b37f6acf 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.datasetVersions.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.datasetVersions.html @@ -92,6 +92,9 @@Instance Methods
Retrieves the next page of results.
++
+patch(name, body=None, updateMask=None, x__xgafv=None)Updates a DatasetVersion.
Restores a dataset version.
@@ -116,6 +119,7 @@Method Details
"displayName": "A String", # 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": "A String", # 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": "A String", # Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions. "name": "A String", # Output only. The resource name of the DatasetVersion. "updateTime": "A String", # Output only. Timestamp when this DatasetVersion was last updated. } @@ -205,6 +209,7 @@Method Details
"displayName": "A String", # 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": "A String", # 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": "A String", # Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions. "name": "A String", # Output only. The resource name of the DatasetVersion. "updateTime": "A String", # Output only. Timestamp when this DatasetVersion was last updated. }
patch(name, body=None, updateMask=None, x__xgafv=None)
+ Updates a DatasetVersion.
+
+Args:
+ name: string, Output only. The resource name of the DatasetVersion. (required)
+ body: object, The request body.
+ The object takes the form of:
+
+{ # Describes the dataset version.
+ "bigQueryDatasetName": "A String", # Output only. Name of the associated BigQuery dataset.
+ "createTime": "A String", # Output only. Timestamp when this DatasetVersion was created.
+ "displayName": "A String", # 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": "A String", # 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": "A String", # Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions.
+ "name": "A String", # Output only. The resource name of the DatasetVersion.
+ "updateTime": "A String", # Output only. Timestamp when this DatasetVersion was last updated.
+}
+
+ updateMask: string, Required. The update mask applies to the resource. For the `FieldMask` definition, see google.protobuf.FieldMask. Updatable fields: * `display_name`
+ x__xgafv: string, V1 error format.
+ Allowed values
+ 1 - v1 error format
+ 2 - v2 error format
+
+Returns:
+ An object of the form:
+
+ { # Describes the dataset version.
+ "bigQueryDatasetName": "A String", # Output only. Name of the associated BigQuery dataset.
+ "createTime": "A String", # Output only. Timestamp when this DatasetVersion was created.
+ "displayName": "A String", # 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": "A String", # 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": "A String", # Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions.
+ "name": "A String", # Output only. The resource name of the DatasetVersion.
+ "updateTime": "A String", # Output only. Timestamp when this DatasetVersion was last updated.
+}
+restore(name, x__xgafv=None)
Restores a dataset version. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html b/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html index 42a02a6d576..9f4aba83646 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html @@ -162,6 +162,7 @@Method Details
"metadata": "", # Required. Additional information about the Dataset. "metadataArtifact": "A String", # 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}/metadataStores/{metadata_store}/artifacts/{artifact}`. "metadataSchemaUri": "A String", # 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": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets. "name": "A String", # Output only. The resource name of the Dataset. "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. { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters. @@ -326,6 +327,7 @@Method Details
"metadata": "", # Required. Additional information about the Dataset. "metadataArtifact": "A String", # 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}/metadataStores/{metadata_store}/artifacts/{artifact}`. "metadataSchemaUri": "A String", # 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": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets. "name": "A String", # Output only. The resource name of the Dataset. "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. { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters. @@ -438,6 +440,7 @@Method Details
"metadata": "", # Required. Additional information about the Dataset. "metadataArtifact": "A String", # 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}/metadataStores/{metadata_store}/artifacts/{artifact}`. "metadataSchemaUri": "A String", # 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": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets. "name": "A String", # Output only. The resource name of the Dataset. "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. { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters. @@ -498,6 +501,7 @@Method Details
"metadata": "", # Required. Additional information about the Dataset. "metadataArtifact": "A String", # 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}/metadataStores/{metadata_store}/artifacts/{artifact}`. "metadataSchemaUri": "A String", # 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": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets. "name": "A String", # Output only. The resource name of the Dataset. "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. { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters. @@ -540,6 +544,7 @@Method Details
"metadata": "", # Required. Additional information about the Dataset. "metadataArtifact": "A String", # 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}/metadataStores/{metadata_store}/artifacts/{artifact}`. "metadataSchemaUri": "A String", # 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": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets. "name": "A String", # Output only. The resource name of the Dataset. "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. { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html b/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html index 52783d10e6d..911ca68a312 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html @@ -1249,6 +1249,7 @@Method Details
"maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message. "presencePenalty": 3.14, # Optional. Positive penalties. "responseMimeType": "A String", # 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. + "responseStyle": "A String", # Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED "stopSequences": [ # Optional. Stop sequences. "A String", ], @@ -2920,6 +2921,7 @@Method Details
"maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message. "presencePenalty": 3.14, # Optional. Positive penalties. "responseMimeType": "A String", # 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. + "responseStyle": "A String", # Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED "stopSequences": [ # Optional. Stop sequences. "A String", ], diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.featureGroups.html b/docs/dyn/aiplatform_v1beta1.projects.locations.featureGroups.html index 760fe375b0d..d7d45aeaa1d 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.featureGroups.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.featureGroups.html @@ -121,7 +121,7 @@Method Details
The object takes the form of: { # Vertex AI Feature Group. - "bigQuery": { # Input source type for BigQuery Tables and Views. # Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entity_id and a feature_timestamp column in the source. + "bigQuery": { # Input source type for BigQuery Tables and Views. # 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`. "bigQuerySource": { # The BigQuery location for the input content. # Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View. "inputUri": "A String", # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`. }, @@ -220,7 +220,7 @@Method Details
An object of the form: { # Vertex AI Feature Group. - "bigQuery": { # Input source type for BigQuery Tables and Views. # Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entity_id and a feature_timestamp column in the source. + "bigQuery": { # Input source type for BigQuery Tables and Views. # 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`. "bigQuerySource": { # The BigQuery location for the input content. # Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View. "inputUri": "A String", # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`. }, @@ -260,7 +260,7 @@Method Details
{ # Response message for FeatureRegistryService.ListFeatureGroups. "featureGroups": [ # The FeatureGroups matching the request. { # Vertex AI Feature Group. - "bigQuery": { # Input source type for BigQuery Tables and Views. # Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entity_id and a feature_timestamp column in the source. + "bigQuery": { # Input source type for BigQuery Tables and Views. # 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`. "bigQuerySource": { # The BigQuery location for the input content. # Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View. "inputUri": "A String", # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`. }, @@ -306,7 +306,7 @@Method Details
The object takes the form of: { # Vertex AI Feature Group. - "bigQuery": { # Input source type for BigQuery Tables and Views. # Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entity_id and a feature_timestamp column in the source. + "bigQuery": { # Input source type for BigQuery Tables and Views. # 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`. "bigQuerySource": { # The BigQuery location for the input content. # Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View. "inputUri": "A String", # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`. }, diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.html b/docs/dyn/aiplatform_v1beta1.projects.locations.html index 8fe7d5c50a5..7f10d808271 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.html @@ -274,6 +274,11 @@Instance Methods
Returns the trainingPipelines Resource.
++
+tuningJobs()+Returns the tuningJobs Resource.
+Close httplib2 connections.
diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.indexEndpoints.html b/docs/dyn/aiplatform_v1beta1.projects.locations.indexEndpoints.html index 7d496c10ac7..065ee37350e 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.indexEndpoints.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.indexEndpoints.html @@ -411,10 +411,10 @@Method Details
}, ], "sparseEmbedding": { # Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions. # Optional. Feature embedding vector for sparse index. - "dimensions": [ # Optional. The list of indexes for the embedding values of the sparse vector. + "dimensions": [ # Required. The list of indexes for the embedding values of the sparse vector. "A String", ], - "values": [ # Optional. The list of embedding values of the sparse vector. + "values": [ # Required. The list of embedding values of the sparse vector. 3.14, ], }, @@ -473,10 +473,10 @@Method Details
}, ], "sparseEmbedding": { # Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions. # Optional. Feature embedding vector for sparse index. - "dimensions": [ # Optional. The list of indexes for the embedding values of the sparse vector. + "dimensions": [ # Required. The list of indexes for the embedding values of the sparse vector. "A String", ], - "values": [ # Optional. The list of embedding values of the sparse vector. + "values": [ # Required. The list of embedding values of the sparse vector. 3.14, ], }, @@ -1029,10 +1029,10 @@Method Details
}, ], "sparseEmbedding": { # Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions. # Optional. Feature embedding vector for sparse index. - "dimensions": [ # Optional. The list of indexes for the embedding values of the sparse vector. + "dimensions": [ # Required. The list of indexes for the embedding values of the sparse vector. "A String", ], - "values": [ # Optional. The list of embedding values of the sparse vector. + "values": [ # Required. The list of embedding values of the sparse vector. 3.14, ], }, diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.indexes.html b/docs/dyn/aiplatform_v1beta1.projects.locations.indexes.html index cd18fa25c2e..b0a32c11f81 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.indexes.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.indexes.html @@ -464,10 +464,10 @@Method Details
}, ], "sparseEmbedding": { # Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions. # Optional. Feature embedding vector for sparse index. - "dimensions": [ # Optional. The list of indexes for the embedding values of the sparse vector. + "dimensions": [ # Required. The list of indexes for the embedding values of the sparse vector. "A String", ], - "values": [ # Optional. The list of embedding values of the sparse vector. + "values": [ # Required. The list of embedding values of the sparse vector. 3.14, ], }, diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.notebookExecutionJobs.html b/docs/dyn/aiplatform_v1beta1.projects.locations.notebookExecutionJobs.html index 9fb3986037f..e287ddb9501 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.notebookExecutionJobs.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.notebookExecutionJobs.html @@ -213,12 +213,15 @@Method Details
"displayName": "A String", # The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. "executionTimeout": "A String", # Max running time of the execution job in seconds (default 86400s / 24 hrs). "executionUser": "A String", # The user email to run the execution as. Only supported by Colab runtimes. - "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The GCS url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` + "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The Cloud Storage url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` "generation": "A String", # 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": "A String", # The Cloud Storage uri pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` }, - "gcsOutputUri": "A String", # The GCS location to upload the result to. Format: `gs://bucket-name` + "gcsOutputUri": "A String", # The Cloud Storage location to upload the result to. Format: `gs://bucket-name` "jobState": "A String", # Output only. The state of the NotebookExecutionJob. + "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. + "a_key": "A String", + }, "name": "A String", # Output only. The resource name of this NotebookExecutionJob. Format: `projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}` "notebookRuntimeTemplateResourceName": "A String", # The NotebookRuntimeTemplate to source compute configuration from. "scheduleResourceName": "A String", # Output only. The Schedule resource name if this job is triggered by one. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` @@ -291,12 +294,15 @@Method Details
"displayName": "A String", # The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. "executionTimeout": "A String", # Max running time of the execution job in seconds (default 86400s / 24 hrs). "executionUser": "A String", # The user email to run the execution as. Only supported by Colab runtimes. - "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The GCS url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` + "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The Cloud Storage url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` "generation": "A String", # 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": "A String", # The Cloud Storage uri pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` }, - "gcsOutputUri": "A String", # The GCS location to upload the result to. Format: `gs://bucket-name` + "gcsOutputUri": "A String", # The Cloud Storage location to upload the result to. Format: `gs://bucket-name` "jobState": "A String", # Output only. The state of the NotebookExecutionJob. + "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. + "a_key": "A String", + }, "name": "A String", # Output only. The resource name of this NotebookExecutionJob. Format: `projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}` "notebookRuntimeTemplateResourceName": "A String", # The NotebookRuntimeTemplate to source compute configuration from. "scheduleResourceName": "A String", # Output only. The Schedule resource name if this job is triggered by one. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.notebookRuntimes.html b/docs/dyn/aiplatform_v1beta1.projects.locations.notebookRuntimes.html index 72b81702728..c47321353de 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.notebookRuntimes.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.notebookRuntimes.html @@ -143,6 +143,8 @@Method Details
}, "runtimeState": "A String", # Output only. The runtime (instance) state of the NotebookRuntime. "runtimeUser": "A String", # Required. The user email of the NotebookRuntime. + "satisfiesPzi": True or False, # Output only. Reserved for future use. + "satisfiesPzs": True or False, # Output only. Reserved for future use. "serviceAccount": "A String", # Output only. The service account that the NotebookRuntime workload runs as. "updateTime": "A String", # Output only. Timestamp when this NotebookRuntime was most recently updated. "version": "A String", # Output only. The VM os image version of NotebookRuntime. @@ -291,6 +293,8 @@Method Details
}, "runtimeState": "A String", # Output only. The runtime (instance) state of the NotebookRuntime. "runtimeUser": "A String", # Required. The user email of the NotebookRuntime. + "satisfiesPzi": True or False, # Output only. Reserved for future use. + "satisfiesPzs": True or False, # Output only. Reserved for future use. "serviceAccount": "A String", # Output only. The service account that the NotebookRuntime workload runs as. "updateTime": "A String", # Output only. Timestamp when this NotebookRuntime was most recently updated. "version": "A String", # Output only. The VM os image version of NotebookRuntime. @@ -347,6 +351,8 @@Method Details
}, "runtimeState": "A String", # Output only. The runtime (instance) state of the NotebookRuntime. "runtimeUser": "A String", # Required. The user email of the NotebookRuntime. + "satisfiesPzi": True or False, # Output only. Reserved for future use. + "satisfiesPzs": True or False, # Output only. Reserved for future use. "serviceAccount": "A String", # Output only. The service account that the NotebookRuntime workload runs as. "updateTime": "A String", # Output only. Timestamp when this NotebookRuntime was most recently updated. "version": "A String", # Output only. The VM os image version of NotebookRuntime. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.publishers.models.html b/docs/dyn/aiplatform_v1beta1.projects.locations.publishers.models.html index 441f68ebcf6..20cd2d3712e 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.publishers.models.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.publishers.models.html @@ -258,6 +258,7 @@Method Details
"maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message. "presencePenalty": 3.14, # Optional. Positive penalties. "responseMimeType": "A String", # 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. + "responseStyle": "A String", # Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED "stopSequences": [ # Optional. Stop sequences. "A String", ], @@ -858,6 +859,7 @@Method Details
"maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message. "presencePenalty": 3.14, # Optional. Positive penalties. "responseMimeType": "A String", # 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. + "responseStyle": "A String", # Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED "stopSequences": [ # Optional. Stop sequences. "A String", ], diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.schedules.html b/docs/dyn/aiplatform_v1beta1.projects.locations.schedules.html index caee99f2e90..c55afbb193f 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.schedules.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.schedules.html @@ -463,12 +463,15 @@Method Details
"displayName": "A String", # The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. "executionTimeout": "A String", # Max running time of the execution job in seconds (default 86400s / 24 hrs). "executionUser": "A String", # The user email to run the execution as. Only supported by Colab runtimes. - "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The GCS url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` + "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The Cloud Storage url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` "generation": "A String", # 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": "A String", # The Cloud Storage uri pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` }, - "gcsOutputUri": "A String", # The GCS location to upload the result to. Format: `gs://bucket-name` + "gcsOutputUri": "A String", # The Cloud Storage location to upload the result to. Format: `gs://bucket-name` "jobState": "A String", # Output only. The state of the NotebookExecutionJob. + "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. + "a_key": "A String", + }, "name": "A String", # Output only. The resource name of this NotebookExecutionJob. Format: `projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}` "notebookRuntimeTemplateResourceName": "A String", # The NotebookRuntimeTemplate to source compute configuration from. "scheduleResourceName": "A String", # Output only. The Schedule resource name if this job is triggered by one. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` @@ -1076,12 +1079,15 @@Method Details
"displayName": "A String", # The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. "executionTimeout": "A String", # Max running time of the execution job in seconds (default 86400s / 24 hrs). "executionUser": "A String", # The user email to run the execution as. Only supported by Colab runtimes. - "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The GCS url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` + "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The Cloud Storage url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` "generation": "A String", # 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": "A String", # The Cloud Storage uri pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` }, - "gcsOutputUri": "A String", # The GCS location to upload the result to. Format: `gs://bucket-name` + "gcsOutputUri": "A String", # The Cloud Storage location to upload the result to. Format: `gs://bucket-name` "jobState": "A String", # Output only. The state of the NotebookExecutionJob. + "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. + "a_key": "A String", + }, "name": "A String", # Output only. The resource name of this NotebookExecutionJob. Format: `projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}` "notebookRuntimeTemplateResourceName": "A String", # The NotebookRuntimeTemplate to source compute configuration from. "scheduleResourceName": "A String", # Output only. The Schedule resource name if this job is triggered by one. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` @@ -1731,12 +1737,15 @@Method Details
"displayName": "A String", # The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. "executionTimeout": "A String", # Max running time of the execution job in seconds (default 86400s / 24 hrs). "executionUser": "A String", # The user email to run the execution as. Only supported by Colab runtimes. - "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The GCS url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` + "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The Cloud Storage url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` "generation": "A String", # 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": "A String", # The Cloud Storage uri pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` }, - "gcsOutputUri": "A String", # The GCS location to upload the result to. Format: `gs://bucket-name` + "gcsOutputUri": "A String", # The Cloud Storage location to upload the result to. Format: `gs://bucket-name` "jobState": "A String", # Output only. The state of the NotebookExecutionJob. + "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. + "a_key": "A String", + }, "name": "A String", # Output only. The resource name of this NotebookExecutionJob. Format: `projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}` "notebookRuntimeTemplateResourceName": "A String", # The NotebookRuntimeTemplate to source compute configuration from. "scheduleResourceName": "A String", # Output only. The Schedule resource name if this job is triggered by one. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` @@ -2358,12 +2367,15 @@Method Details
"displayName": "A String", # The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. "executionTimeout": "A String", # Max running time of the execution job in seconds (default 86400s / 24 hrs). "executionUser": "A String", # The user email to run the execution as. Only supported by Colab runtimes. - "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The GCS url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` + "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The Cloud Storage url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` "generation": "A String", # 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": "A String", # The Cloud Storage uri pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` }, - "gcsOutputUri": "A String", # The GCS location to upload the result to. Format: `gs://bucket-name` + "gcsOutputUri": "A String", # The Cloud Storage location to upload the result to. Format: `gs://bucket-name` "jobState": "A String", # Output only. The state of the NotebookExecutionJob. + "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. + "a_key": "A String", + }, "name": "A String", # Output only. The resource name of this NotebookExecutionJob. Format: `projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}` "notebookRuntimeTemplateResourceName": "A String", # The NotebookRuntimeTemplate to source compute configuration from. "scheduleResourceName": "A String", # Output only. The Schedule resource name if this job is triggered by one. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` @@ -2989,12 +3001,15 @@Method Details
"displayName": "A String", # The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. "executionTimeout": "A String", # Max running time of the execution job in seconds (default 86400s / 24 hrs). "executionUser": "A String", # The user email to run the execution as. Only supported by Colab runtimes. - "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The GCS url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` + "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The Cloud Storage url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` "generation": "A String", # 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": "A String", # The Cloud Storage uri pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` }, - "gcsOutputUri": "A String", # The GCS location to upload the result to. Format: `gs://bucket-name` + "gcsOutputUri": "A String", # The Cloud Storage location to upload the result to. Format: `gs://bucket-name` "jobState": "A String", # Output only. The state of the NotebookExecutionJob. + "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. + "a_key": "A String", + }, "name": "A String", # Output only. The resource name of this NotebookExecutionJob. Format: `projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}` "notebookRuntimeTemplateResourceName": "A String", # The NotebookRuntimeTemplate to source compute configuration from. "scheduleResourceName": "A String", # Output only. The Schedule resource name if this job is triggered by one. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` @@ -3603,12 +3618,15 @@Method Details
"displayName": "A String", # The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. "executionTimeout": "A String", # Max running time of the execution job in seconds (default 86400s / 24 hrs). "executionUser": "A String", # The user email to run the execution as. Only supported by Colab runtimes. - "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The GCS url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` + "gcsNotebookSource": { # The Cloud Storage uri for the input notebook. # The Cloud Storage url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` "generation": "A String", # 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": "A String", # The Cloud Storage uri pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb` }, - "gcsOutputUri": "A String", # The GCS location to upload the result to. Format: `gs://bucket-name` + "gcsOutputUri": "A String", # The Cloud Storage location to upload the result to. Format: `gs://bucket-name` "jobState": "A String", # Output only. The state of the NotebookExecutionJob. + "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. + "a_key": "A String", + }, "name": "A String", # Output only. The resource name of this NotebookExecutionJob. Format: `projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}` "notebookRuntimeTemplateResourceName": "A String", # The NotebookRuntimeTemplate to source compute configuration from. "scheduleResourceName": "A String", # Output only. The Schedule resource name if this job is triggered by one. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.tuningJobs.html b/docs/dyn/aiplatform_v1beta1.projects.locations.tuningJobs.html new file mode 100644 index 00000000000..6dea72a0c3c --- /dev/null +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.tuningJobs.html @@ -0,0 +1,719 @@ + + + +Vertex AI API . projects . locations . tuningJobs
+Instance Methods
++
+cancel(name, body=None, x__xgafv=None)Cancels a TuningJob. Starts asynchronous cancellation on the TuningJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use GenAiTuningService.GetTuningJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the TuningJob is not deleted; instead it becomes a job with a TuningJob.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`, and TuningJob.state is set to `CANCELLED`.
++
+close()Close httplib2 connections.
++
+create(parent, body=None, x__xgafv=None)Creates a TuningJob. A created TuningJob right away will be attempted to be run.
+ +Gets a TuningJob.
++
+list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)Lists TuningJobs in a Location.
+ +Retrieves the next page of results.
+Method Details
+++ +cancel(name, body=None, x__xgafv=None)+Cancels a TuningJob. Starts asynchronous cancellation on the TuningJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use GenAiTuningService.GetTuningJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the TuningJob is not deleted; instead it becomes a job with a TuningJob.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`, and TuningJob.state is set to `CANCELLED`. + +Args: + name: string, Required. The name of the TuningJob to cancel. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}` (required) + body: object, The request body. + The object takes the form of: + +{ # Request message for GenAiTuningService.CancelTuningJob. +} + + x__xgafv: string, V1 error format. + Allowed values + 1 - v1 error format + 2 - v2 error format + +Returns: + An object of the form: + + { # 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); } +}+++ +close()+Close httplib2 connections.+++ +create(parent, body=None, x__xgafv=None)+Creates a TuningJob. A created TuningJob right away will be attempted to be run. + +Args: + parent: string, Required. The resource name of the Location to create the TuningJob in. Format: `projects/{project}/locations/{location}` (required) + body: object, The request body. + The object takes the form of: + +{ # Represents a TuningJob that runs with Google owned models. + "baseModel": "A String", # The base model that is being tuned, e.g., "gemini-1.0-pro-002". + "createTime": "A String", # Output only. Time when the TuningJob was created. + "description": "A String", # Optional. The description of the TuningJob. + "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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. + "kmsKeyName": "A String", # 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. + }, + "endTime": "A String", # 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": { # 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). # Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. + "code": 42, # 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. + { + "a_key": "", # Properties of the object. Contains field @type with type URL. + }, + ], + "message": "A String", # 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. + }, + "experiment": "A String", # 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. + "a_key": "A String", + }, + "name": "A String", # Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}` + "startTime": "A String", # Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state. + "state": "A String", # Output only. The detailed state of the job. + "supervisedTuningSpec": { # Tuning Spec for Supervised Tuning. # Tuning Spec for Supervised Fine Tuning. + "hyperParameters": { # Hyperparameters for SFT. # Optional. Hyperparameters for SFT. + "adapterSize": "A String", # Optional. Adapter size for tuning. + "epochCount": "A String", # Optional. Number of complete passes the model makes over the entire training dataset during training. + "learningRateMultiplier": 3.14, # Optional. Multiplier for adjusting the default learning rate. + }, + "trainingDatasetUri": "A String", # Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file. + "validationDatasetUri": "A String", # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file. + }, + "tunedModel": { # The Model Registry Model and Online Prediction Endpoint assiociated with this TuningJob. # Output only. The tuned model resources assiociated with this TuningJob. + "endpoint": "A String", # Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`. + "model": "A String", # Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}`. + }, + "tunedModelDisplayName": "A String", # 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": { # The tuning data statistic values for TuningJob. # Output only. The tuning data statistics associated with this TuningJob. + "supervisedTuningDataStats": { # Tuning data statistics for Supervised Tuning. # The SFT Tuning data stats. + "totalBillableCharacterCount": "A String", # Output only. Number of billable characters in the tuning dataset. + "totalTuningCharacterCount": "A String", # Output only. Number of tuning characters in the tuning dataset. + "tuningDatasetExampleCount": "A String", # Output only. Number of examples in the tuning dataset. + "tuningStepCount": "A String", # Output only. Number of tuning steps for this Tuning Job. + "userDatasetExamples": [ # Output only. Sample user messages in the training dataset uri. + { # 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. + "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. + { # 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. + "fileData": { # URI based data. # Optional. URI based data. + "fileUri": "A String", # Required. URI. + "mimeType": "A String", # Required. The IANA standard MIME type of the source data. + }, + "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. + "args": { # Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. + "a_key": "", # Properties of the object. + }, + "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name]. + }, + "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # 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. + "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. + "response": { # Required. The function response in JSON object format. + "a_key": "", # Properties of the object. + }, + }, + "inlineData": { # Content blob. It's preferred to send as text directly rather than raw bytes. # Optional. Inlined bytes data. + "data": "A String", # Required. Raw bytes. + "mimeType": "A String", # Required. The IANA standard MIME type of the source data. + }, + "text": "A String", # Optional. Text part (can be code). + "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. + "endOffset": "A String", # Optional. The end offset of the video. + "startOffset": "A String", # Optional. The start offset of the video. + }, + }, + ], + "role": "A String", # 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. + }, + ], + "userInputTokenDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user input tokens. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + "userMessagePerExampleDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the messages per example. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + "userOutputTokenDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user output tokens. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + }, + }, + "updateTime": "A String", # Output only. Time when the TuningJob was most recently updated. +} + + x__xgafv: string, V1 error format. + Allowed values + 1 - v1 error format + 2 - v2 error format + +Returns: + An object of the form: + + { # Represents a TuningJob that runs with Google owned models. + "baseModel": "A String", # The base model that is being tuned, e.g., "gemini-1.0-pro-002". + "createTime": "A String", # Output only. Time when the TuningJob was created. + "description": "A String", # Optional. The description of the TuningJob. + "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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. + "kmsKeyName": "A String", # 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. + }, + "endTime": "A String", # 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": { # 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). # Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. + "code": 42, # 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. + { + "a_key": "", # Properties of the object. Contains field @type with type URL. + }, + ], + "message": "A String", # 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. + }, + "experiment": "A String", # 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. + "a_key": "A String", + }, + "name": "A String", # Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}` + "startTime": "A String", # Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state. + "state": "A String", # Output only. The detailed state of the job. + "supervisedTuningSpec": { # Tuning Spec for Supervised Tuning. # Tuning Spec for Supervised Fine Tuning. + "hyperParameters": { # Hyperparameters for SFT. # Optional. Hyperparameters for SFT. + "adapterSize": "A String", # Optional. Adapter size for tuning. + "epochCount": "A String", # Optional. Number of complete passes the model makes over the entire training dataset during training. + "learningRateMultiplier": 3.14, # Optional. Multiplier for adjusting the default learning rate. + }, + "trainingDatasetUri": "A String", # Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file. + "validationDatasetUri": "A String", # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file. + }, + "tunedModel": { # The Model Registry Model and Online Prediction Endpoint assiociated with this TuningJob. # Output only. The tuned model resources assiociated with this TuningJob. + "endpoint": "A String", # Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`. + "model": "A String", # Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}`. + }, + "tunedModelDisplayName": "A String", # 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": { # The tuning data statistic values for TuningJob. # Output only. The tuning data statistics associated with this TuningJob. + "supervisedTuningDataStats": { # Tuning data statistics for Supervised Tuning. # The SFT Tuning data stats. + "totalBillableCharacterCount": "A String", # Output only. Number of billable characters in the tuning dataset. + "totalTuningCharacterCount": "A String", # Output only. Number of tuning characters in the tuning dataset. + "tuningDatasetExampleCount": "A String", # Output only. Number of examples in the tuning dataset. + "tuningStepCount": "A String", # Output only. Number of tuning steps for this Tuning Job. + "userDatasetExamples": [ # Output only. Sample user messages in the training dataset uri. + { # 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. + "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. + { # 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. + "fileData": { # URI based data. # Optional. URI based data. + "fileUri": "A String", # Required. URI. + "mimeType": "A String", # Required. The IANA standard MIME type of the source data. + }, + "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. + "args": { # Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. + "a_key": "", # Properties of the object. + }, + "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name]. + }, + "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # 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. + "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. + "response": { # Required. The function response in JSON object format. + "a_key": "", # Properties of the object. + }, + }, + "inlineData": { # Content blob. It's preferred to send as text directly rather than raw bytes. # Optional. Inlined bytes data. + "data": "A String", # Required. Raw bytes. + "mimeType": "A String", # Required. The IANA standard MIME type of the source data. + }, + "text": "A String", # Optional. Text part (can be code). + "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. + "endOffset": "A String", # Optional. The end offset of the video. + "startOffset": "A String", # Optional. The start offset of the video. + }, + }, + ], + "role": "A String", # 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. + }, + ], + "userInputTokenDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user input tokens. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + "userMessagePerExampleDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the messages per example. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + "userOutputTokenDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user output tokens. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + }, + }, + "updateTime": "A String", # Output only. Time when the TuningJob was most recently updated. +}+++ +get(name, x__xgafv=None)+Gets a TuningJob. + +Args: + name: string, Required. The name of the TuningJob resource. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}` (required) + x__xgafv: string, V1 error format. + Allowed values + 1 - v1 error format + 2 - v2 error format + +Returns: + An object of the form: + + { # Represents a TuningJob that runs with Google owned models. + "baseModel": "A String", # The base model that is being tuned, e.g., "gemini-1.0-pro-002". + "createTime": "A String", # Output only. Time when the TuningJob was created. + "description": "A String", # Optional. The description of the TuningJob. + "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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. + "kmsKeyName": "A String", # 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. + }, + "endTime": "A String", # 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": { # 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). # Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. + "code": 42, # 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. + { + "a_key": "", # Properties of the object. Contains field @type with type URL. + }, + ], + "message": "A String", # 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. + }, + "experiment": "A String", # 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. + "a_key": "A String", + }, + "name": "A String", # Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}` + "startTime": "A String", # Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state. + "state": "A String", # Output only. The detailed state of the job. + "supervisedTuningSpec": { # Tuning Spec for Supervised Tuning. # Tuning Spec for Supervised Fine Tuning. + "hyperParameters": { # Hyperparameters for SFT. # Optional. Hyperparameters for SFT. + "adapterSize": "A String", # Optional. Adapter size for tuning. + "epochCount": "A String", # Optional. Number of complete passes the model makes over the entire training dataset during training. + "learningRateMultiplier": 3.14, # Optional. Multiplier for adjusting the default learning rate. + }, + "trainingDatasetUri": "A String", # Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file. + "validationDatasetUri": "A String", # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file. + }, + "tunedModel": { # The Model Registry Model and Online Prediction Endpoint assiociated with this TuningJob. # Output only. The tuned model resources assiociated with this TuningJob. + "endpoint": "A String", # Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`. + "model": "A String", # Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}`. + }, + "tunedModelDisplayName": "A String", # 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": { # The tuning data statistic values for TuningJob. # Output only. The tuning data statistics associated with this TuningJob. + "supervisedTuningDataStats": { # Tuning data statistics for Supervised Tuning. # The SFT Tuning data stats. + "totalBillableCharacterCount": "A String", # Output only. Number of billable characters in the tuning dataset. + "totalTuningCharacterCount": "A String", # Output only. Number of tuning characters in the tuning dataset. + "tuningDatasetExampleCount": "A String", # Output only. Number of examples in the tuning dataset. + "tuningStepCount": "A String", # Output only. Number of tuning steps for this Tuning Job. + "userDatasetExamples": [ # Output only. Sample user messages in the training dataset uri. + { # 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. + "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. + { # 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. + "fileData": { # URI based data. # Optional. URI based data. + "fileUri": "A String", # Required. URI. + "mimeType": "A String", # Required. The IANA standard MIME type of the source data. + }, + "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. + "args": { # Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. + "a_key": "", # Properties of the object. + }, + "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name]. + }, + "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # 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. + "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. + "response": { # Required. The function response in JSON object format. + "a_key": "", # Properties of the object. + }, + }, + "inlineData": { # Content blob. It's preferred to send as text directly rather than raw bytes. # Optional. Inlined bytes data. + "data": "A String", # Required. Raw bytes. + "mimeType": "A String", # Required. The IANA standard MIME type of the source data. + }, + "text": "A String", # Optional. Text part (can be code). + "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. + "endOffset": "A String", # Optional. The end offset of the video. + "startOffset": "A String", # Optional. The start offset of the video. + }, + }, + ], + "role": "A String", # 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. + }, + ], + "userInputTokenDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user input tokens. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + "userMessagePerExampleDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the messages per example. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + "userOutputTokenDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user output tokens. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + }, + }, + "updateTime": "A String", # Output only. Time when the TuningJob was most recently updated. +}+++ +list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)+Lists TuningJobs in a Location. + +Args: + parent: string, Required. The resource name of the Location to list the TuningJobs from. Format: `projects/{project}/locations/{location}` (required) + filter: string, Optional. The standard list filter. + pageSize: integer, Optional. The standard list page size. + pageToken: string, Optional. The standard list page token. Typically obtained via ListTuningJob.next_page_token of the previous GenAiTuningService.ListTuningJob][] call. + x__xgafv: string, V1 error format. + Allowed values + 1 - v1 error format + 2 - v2 error format + +Returns: + An object of the form: + + { # Response message for GenAiTuningService.ListTuningJobs + "nextPageToken": "A String", # 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. + { # Represents a TuningJob that runs with Google owned models. + "baseModel": "A String", # The base model that is being tuned, e.g., "gemini-1.0-pro-002". + "createTime": "A String", # Output only. Time when the TuningJob was created. + "description": "A String", # Optional. The description of the TuningJob. + "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # 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. + "kmsKeyName": "A String", # 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. + }, + "endTime": "A String", # 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": { # 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). # Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`. + "code": 42, # 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. + { + "a_key": "", # Properties of the object. Contains field @type with type URL. + }, + ], + "message": "A String", # 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. + }, + "experiment": "A String", # 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. + "a_key": "A String", + }, + "name": "A String", # Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}` + "startTime": "A String", # Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state. + "state": "A String", # Output only. The detailed state of the job. + "supervisedTuningSpec": { # Tuning Spec for Supervised Tuning. # Tuning Spec for Supervised Fine Tuning. + "hyperParameters": { # Hyperparameters for SFT. # Optional. Hyperparameters for SFT. + "adapterSize": "A String", # Optional. Adapter size for tuning. + "epochCount": "A String", # Optional. Number of complete passes the model makes over the entire training dataset during training. + "learningRateMultiplier": 3.14, # Optional. Multiplier for adjusting the default learning rate. + }, + "trainingDatasetUri": "A String", # Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file. + "validationDatasetUri": "A String", # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file. + }, + "tunedModel": { # The Model Registry Model and Online Prediction Endpoint assiociated with this TuningJob. # Output only. The tuned model resources assiociated with this TuningJob. + "endpoint": "A String", # Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`. + "model": "A String", # Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}`. + }, + "tunedModelDisplayName": "A String", # 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": { # The tuning data statistic values for TuningJob. # Output only. The tuning data statistics associated with this TuningJob. + "supervisedTuningDataStats": { # Tuning data statistics for Supervised Tuning. # The SFT Tuning data stats. + "totalBillableCharacterCount": "A String", # Output only. Number of billable characters in the tuning dataset. + "totalTuningCharacterCount": "A String", # Output only. Number of tuning characters in the tuning dataset. + "tuningDatasetExampleCount": "A String", # Output only. Number of examples in the tuning dataset. + "tuningStepCount": "A String", # Output only. Number of tuning steps for this Tuning Job. + "userDatasetExamples": [ # Output only. Sample user messages in the training dataset uri. + { # 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. + "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types. + { # 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. + "fileData": { # URI based data. # Optional. URI based data. + "fileUri": "A String", # Required. URI. + "mimeType": "A String", # Required. The IANA standard MIME type of the source data. + }, + "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values. + "args": { # Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details. + "a_key": "", # Properties of the object. + }, + "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name]. + }, + "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # 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. + "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name]. + "response": { # Required. The function response in JSON object format. + "a_key": "", # Properties of the object. + }, + }, + "inlineData": { # Content blob. It's preferred to send as text directly rather than raw bytes. # Optional. Inlined bytes data. + "data": "A String", # Required. Raw bytes. + "mimeType": "A String", # Required. The IANA standard MIME type of the source data. + }, + "text": "A String", # Optional. Text part (can be code). + "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data. + "endOffset": "A String", # Optional. The end offset of the video. + "startOffset": "A String", # Optional. The start offset of the video. + }, + }, + ], + "role": "A String", # 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. + }, + ], + "userInputTokenDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user input tokens. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + "userMessagePerExampleDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the messages per example. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + "userOutputTokenDistribution": { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user output tokens. + "buckets": [ # Output only. Defines the histogram bucket. + { # Dataset bucket used to create a histogram for the distribution given a population of values. + "count": 3.14, # Output only. Number of values in the bucket. + "left": 3.14, # Output only. Left bound of the bucket. + "right": 3.14, # Output only. Right bound of the bucket. + }, + ], + "max": 3.14, # Output only. The maximum of the population values. + "mean": 3.14, # Output only. The arithmetic mean of the values in the population. + "median": 3.14, # Output only. The median of the values in the population. + "min": 3.14, # Output only. The minimum of the population values. + "p5": 3.14, # Output only. The 5th percentile of the values in the population. + "p95": 3.14, # Output only. The 95th percentile of the values in the population. + "sum": "A String", # Output only. Sum of a given population of values. + }, + }, + }, + "updateTime": "A String", # Output only. Time when the TuningJob was most recently updated. + }, + ], +}+++ + \ No newline at end of file diff --git a/googleapiclient/discovery_cache/documents/aiplatform.v1.json b/googleapiclient/discovery_cache/documents/aiplatform.v1.json index 9af87a7d6c1..a33ccc3462d 100644 --- a/googleapiclient/discovery_cache/documents/aiplatform.v1.json +++ b/googleapiclient/discovery_cache/documents/aiplatform.v1.json @@ -2230,6 +2230,40 @@ "https://www.googleapis.com/auth/cloud-platform" ] }, +"patch": { +"description": "Updates a DatasetVersion.", +"flatPath": "v1/projects/{projectsId}/locations/{locationsId}/datasets/{datasetsId}/datasetVersions/{datasetVersionsId}", +"httpMethod": "PATCH", +"id": "aiplatform.projects.locations.datasets.datasetVersions.patch", +"parameterOrder": [ +"name" +], +"parameters": { +"name": { +"description": "Output only. The resource name of the DatasetVersion.", +"location": "path", +"pattern": "^projects/[^/]+/locations/[^/]+/datasets/[^/]+/datasetVersions/[^/]+$", +"required": true, +"type": "string" +}, +"updateMask": { +"description": "Required. The update mask applies to the resource. For the `FieldMask` definition, see google.protobuf.FieldMask. Updatable fields: * `display_name`", +"format": "google-fieldmask", +"location": "query", +"type": "string" +} +}, +"path": "v1/{+name}", +"request": { +"$ref": "GoogleCloudAiplatformV1DatasetVersion" +}, +"response": { +"$ref": "GoogleCloudAiplatformV1DatasetVersion" +}, +"scopes": [ +"https://www.googleapis.com/auth/cloud-platform" +] +}, "restore": { "description": "Restores a dataset version.", "flatPath": "v1/projects/{projectsId}/locations/{locationsId}/datasets/{datasetsId}/datasetVersions/{datasetVersionsId}:restore", @@ -16204,23 +16238,9 @@ } } }, -"revision": "20240501", +"revision": "20240510", "rootUrl": "https://aiplatform.googleapis.com/", "schemas": { -"CloudAiLargeModelsVisionEmbedVideoResponse": { -"description": "Video embedding response.", -"id": "CloudAiLargeModelsVisionEmbedVideoResponse", -"properties": { -"videoEmbeddings": { -"description": "The embedding vector for the video.", -"items": { -"type": "any" -}, -"type": "array" -} -}, -"type": "object" -}, "CloudAiLargeModelsVisionFilteredText": { "description": "Details for filtered input text.", "id": "CloudAiLargeModelsVisionFilteredText", @@ -16430,17 +16450,6 @@ }, "type": "object" }, -"CloudAiLargeModelsVisionMediaGenerateContentResponse": { -"description": "Generate media content response", -"id": "CloudAiLargeModelsVisionMediaGenerateContentResponse", -"properties": { -"response": { -"$ref": "CloudAiNlLlmProtoServiceGenerateMultiModalResponse", -"description": "Response to the user's request." -} -}, -"type": "object" -}, "CloudAiLargeModelsVisionNamedBoundingBox": { "id": "CloudAiLargeModelsVisionNamedBoundingBox", "properties": { @@ -16503,52 +16512,6 @@ }, "type": "object" }, -"CloudAiLargeModelsVisionReasonVideoResponse": { -"description": "Video reasoning response.", -"id": "CloudAiLargeModelsVisionReasonVideoResponse", -"properties": { -"responses": { -"description": "Generated text responses. The generated responses for different segments within the same video.", -"items": { -"$ref": "CloudAiLargeModelsVisionReasonVideoResponseTextResponse" -}, -"type": "array" -} -}, -"type": "object" -}, -"CloudAiLargeModelsVisionReasonVideoResponseTextResponse": { -"description": "Contains text that is the response of the video captioning.", -"id": "CloudAiLargeModelsVisionReasonVideoResponseTextResponse", -"properties": { -"relativeTemporalPartition": { -"$ref": "CloudAiLargeModelsVisionRelativeTemporalPartition", -"description": "Partition of the caption's video in time. This field is intended for video captioning. To represent the start time and end time of the caption's video." -}, -"text": { -"description": "Text information", -"type": "string" -} -}, -"type": "object" -}, -"CloudAiLargeModelsVisionRelativeTemporalPartition": { -"description": "For ease of use, assume that the start_offset is inclusive and the end_offset is exclusive. In mathematical terms, the partition would be written as [start_offset, end_offset).", -"id": "CloudAiLargeModelsVisionRelativeTemporalPartition", -"properties": { -"endOffset": { -"description": "End time offset of the partition.", -"format": "google-duration", -"type": "string" -}, -"startOffset": { -"description": "Start time offset of the partition.", -"format": "google-duration", -"type": "string" -} -}, -"type": "object" -}, "CloudAiLargeModelsVisionSemanticFilterResponse": { "id": "CloudAiLargeModelsVisionSemanticFilterResponse", "properties": { @@ -16582,1890 +16545,1137 @@ }, "type": "object" }, -"CloudAiNlLlmProtoServiceCandidate": { -"id": "CloudAiNlLlmProtoServiceCandidate", +"GoogleApiHttpBody": { +"description": "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.", +"id": "GoogleApiHttpBody", "properties": { -"citationMetadata": { -"$ref": "CloudAiNlLlmProtoServiceCitationMetadata", -"description": "Source attribution of the generated content." -}, -"content": { -"$ref": "CloudAiNlLlmProtoServiceContent", -"description": "Content of the candidate." -}, -"finishMessage": { -"description": "A string that describes the filtering behavior in more detail. Only filled when reason is set.", +"contentType": { +"description": "The HTTP Content-Type header value specifying the content type of the body.", "type": "string" }, -"finishReason": { -"description": "The reason why the model stopped generating tokens.", -"enum": [ -"FINISH_REASON_UNSPECIFIED", -"FINISH_REASON_STOP", -"FINISH_REASON_MAX_TOKENS", -"FINISH_REASON_SAFETY", -"FINISH_REASON_RECITATION", -"FINISH_REASON_OTHER", -"FINISH_REASON_BLOCKLIST", -"FINISH_REASON_PROHIBITED_CONTENT", -"FINISH_REASON_SPII" -], -"enumDescriptions": [ -"The finish reason is unspecified.", -"Natural stop point of the model or provided stop sequence.", -"The maximum number of tokens as specified in the request was reached.", -"The token generation was stopped as the response was flagged for safety reasons. NOTE: When streaming the Candidate.content will be empty if content filters blocked the output.", -"The token generation was stopped as the response was flagged for unauthorized citations.", -"All other reasons that stopped the token generation (currently only language filter).", -"The token generation was stopped as the response was flagged for the terms which are included from the terminology blocklist.", -"The token generation was stopped as the response was flagged for the prohibited contents (currently only CSAM).", -"The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents." -], +"data": { +"description": "The HTTP request/response body as raw binary.", +"format": "byte", "type": "string" }, -"groundingMetadata": { -"$ref": "LearningGenaiRootGroundingMetadata", -"description": "Grounding metadata. Combine with the facts list from response to generate grounding citations for this choice." -}, -"index": { -"description": "Index of the candidate.", -"format": "int32", -"type": "integer" -}, -"safetyRatings": { -"description": "Safety ratings of the generated content.", +"extensions": { +"description": "Application specific response metadata. Must be set in the first response for streaming APIs.", "items": { -"$ref": "CloudAiNlLlmProtoServiceSafetyRating" +"additionalProperties": { +"description": "Properties of the object. Contains field @type with type URL.", +"type": "any" +}, +"type": "object" }, "type": "array" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceCitation": { -"description": "Source attributions for content.", -"id": "CloudAiNlLlmProtoServiceCitation", +"GoogleCloudAiplatformV1ActiveLearningConfig": { +"description": "Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy.", +"id": "GoogleCloudAiplatformV1ActiveLearningConfig", "properties": { -"endIndex": { -"description": "End index into the content.", +"maxDataItemCount": { +"description": "Max number of human labeled DataItems.", +"format": "int64", +"type": "string" +}, +"maxDataItemPercentage": { +"description": "Max percent of total DataItems for human labeling.", "format": "int32", "type": "integer" }, -"license": { -"description": "License of the attribution.", -"type": "string" +"sampleConfig": { +"$ref": "GoogleCloudAiplatformV1SampleConfig", +"description": "Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy." }, -"publicationDate": { -"$ref": "GoogleTypeDate", -"description": "Publication date of the attribution." +"trainingConfig": { +"$ref": "GoogleCloudAiplatformV1TrainingConfig", +"description": "CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems." +} }, -"startIndex": { -"description": "Start index into the content.", -"format": "int32", -"type": "integer" +"type": "object" }, -"title": { -"description": "Title of the attribution.", +"GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsRequest": { +"description": "Request message for MetadataService.AddContextArtifactsAndExecutions.", +"id": "GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsRequest", +"properties": { +"artifacts": { +"description": "The resource names of the Artifacts to attribute to the Context. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}`", +"items": { "type": "string" }, -"uri": { -"description": "Url reference of the attribution.", +"type": "array" +}, +"executions": { +"description": "The resource names of the Executions to associate with the Context. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}`", +"items": { "type": "string" +}, +"type": "array" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceCitationMetadata": { -"description": "A collection of source attributions for a piece of content.", -"id": "CloudAiNlLlmProtoServiceCitationMetadata", +"GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsResponse": { +"description": "Response message for MetadataService.AddContextArtifactsAndExecutions.", +"id": "GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsResponse", +"properties": {}, +"type": "object" +}, +"GoogleCloudAiplatformV1AddContextChildrenRequest": { +"description": "Request message for MetadataService.AddContextChildren.", +"id": "GoogleCloudAiplatformV1AddContextChildrenRequest", "properties": { -"citations": { -"description": "List of citations.", +"childContexts": { +"description": "The resource names of the child Contexts.", "items": { -"$ref": "CloudAiNlLlmProtoServiceCitation" +"type": "string" }, "type": "array" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceContent": { -"description": "The content of a single message from a participant.", -"id": "CloudAiNlLlmProtoServiceContent", +"GoogleCloudAiplatformV1AddContextChildrenResponse": { +"description": "Response message for MetadataService.AddContextChildren.", +"id": "GoogleCloudAiplatformV1AddContextChildrenResponse", +"properties": {}, +"type": "object" +}, +"GoogleCloudAiplatformV1AddExecutionEventsRequest": { +"description": "Request message for MetadataService.AddExecutionEvents.", +"id": "GoogleCloudAiplatformV1AddExecutionEventsRequest", "properties": { -"parts": { -"description": "The parts of the message.", +"events": { +"description": "The Events to create and add.", "items": { -"$ref": "CloudAiNlLlmProtoServicePart" +"$ref": "GoogleCloudAiplatformV1Event" }, "type": "array" +} }, -"role": { -"description": "The role of the current conversation participant.", -"type": "string" +"type": "object" +}, +"GoogleCloudAiplatformV1AddExecutionEventsResponse": { +"description": "Response message for MetadataService.AddExecutionEvents.", +"id": "GoogleCloudAiplatformV1AddExecutionEventsResponse", +"properties": {}, +"type": "object" +}, +"GoogleCloudAiplatformV1AddTrialMeasurementRequest": { +"description": "Request message for VizierService.AddTrialMeasurement.", +"id": "GoogleCloudAiplatformV1AddTrialMeasurementRequest", +"properties": { +"measurement": { +"$ref": "GoogleCloudAiplatformV1Measurement", +"description": "Required. The measurement to be added to a Trial." } }, "type": "object" }, -"CloudAiNlLlmProtoServiceFact": { -"description": "A condense version of WorldFact (assistant/boq/lamda/factuality/proto/factuality.proto) to propagate the essential information about the fact used in factuality to the upstream caller.", -"id": "CloudAiNlLlmProtoServiceFact", +"GoogleCloudAiplatformV1Annotation": { +"description": "Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.", +"id": "GoogleCloudAiplatformV1Annotation", "properties": { -"query": { -"description": "Query that is used to retrieve this fact.", -"type": "string" +"annotationSource": { +"$ref": "GoogleCloudAiplatformV1UserActionReference", +"description": "Output only. The source of the Annotation.", +"readOnly": true }, -"summary": { -"description": "If present, the summary/snippet of the fact.", +"createTime": { +"description": "Output only. Timestamp when this Annotation was created.", +"format": "google-datetime", +"readOnly": true, "type": "string" }, -"title": { -"description": "If present, it refers to the title of this fact.", +"etag": { +"description": "Optional. Used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", "type": "string" }, -"url": { -"description": "If present, this URL links to the webpage of the fact.", +"labels": { +"additionalProperties": { "type": "string" -} }, +"description": "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.", "type": "object" }, -"CloudAiNlLlmProtoServiceFunctionCall": { -"description": "Function call details.", -"id": "CloudAiNlLlmProtoServiceFunctionCall", -"properties": { -"args": { -"additionalProperties": { -"description": "Properties of the object.", +"name": { +"description": "Output only. Resource name of the Annotation.", +"readOnly": true, +"type": "string" +}, +"payload": { +"description": "Required. The schema of the payload can be found in payload_schema.", "type": "any" }, -"description": "The function parameters and values in JSON format.", -"type": "object" +"payloadSchemaUri": { +"description": "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.", +"type": "string" }, -"name": { -"description": "Required. The name of the function to call.", +"updateTime": { +"description": "Output only. Timestamp when this Annotation was last updated.", +"format": "google-datetime", +"readOnly": true, "type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceFunctionResponse": { -"description": "Function response details.", -"id": "CloudAiNlLlmProtoServiceFunctionResponse", +"GoogleCloudAiplatformV1AnnotationSpec": { +"description": "Identifies a concept with which DataItems may be annotated with.", +"id": "GoogleCloudAiplatformV1AnnotationSpec", "properties": { -"name": { -"description": "Required. The name of the function to call.", +"createTime": { +"description": "Output only. Timestamp when this AnnotationSpec was created.", +"format": "google-datetime", +"readOnly": true, "type": "string" }, -"response": { -"additionalProperties": { -"description": "Properties of the object.", -"type": "any" +"displayName": { +"description": "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.", +"type": "string" }, -"description": "Required. The function response in JSON object format.", -"type": "object" +"etag": { +"description": "Optional. Used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", +"type": "string" +}, +"name": { +"description": "Output only. Resource name of the AnnotationSpec.", +"readOnly": true, +"type": "string" +}, +"updateTime": { +"description": "Output only. Timestamp when AnnotationSpec was last updated.", +"format": "google-datetime", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceGenerateMultiModalResponse": { -"id": "CloudAiNlLlmProtoServiceGenerateMultiModalResponse", +"GoogleCloudAiplatformV1Artifact": { +"description": "Instance of a general artifact.", +"id": "GoogleCloudAiplatformV1Artifact", "properties": { -"candidates": { -"description": "Possible candidate responses to the conversation up until this point.", -"items": { -"$ref": "CloudAiNlLlmProtoServiceCandidate" -}, -"type": "array" +"createTime": { +"description": "Output only. Timestamp when this Artifact was created.", +"format": "google-datetime", +"readOnly": true, +"type": "string" }, -"debugMetadata": { -"$ref": "CloudAiNlLlmProtoServiceMessageMetadata", -"description": "Debug information containing message metadata. Clients should not consume this field, and this is only populated for Flow Runner path." +"description": { +"description": "Description of the Artifact", +"type": "string" }, -"facts": { -"description": "External facts retrieved for factuality/grounding.", -"items": { -"$ref": "CloudAiNlLlmProtoServiceFact" +"displayName": { +"description": "User provided display name of the Artifact. May be up to 128 Unicode characters.", +"type": "string" }, -"type": "array" +"etag": { +"description": "An eTag used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", +"type": "string" }, -"promptFeedback": { -"$ref": "CloudAiNlLlmProtoServicePromptFeedback", -"description": "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." +"labels": { +"additionalProperties": { +"type": "string" }, -"reportingMetrics": { -"$ref": "IntelligenceCloudAutomlXpsReportingMetrics", -"description": "Billable prediction metrics." +"description": "The labels with user-defined metadata to organize your Artifacts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Artifact (System labels are excluded).", +"type": "object" }, -"usageMetadata": { -"$ref": "CloudAiNlLlmProtoServiceUsageMetadata", -"description": "Usage metadata about the response(s)." -} +"metadata": { +"additionalProperties": { +"description": "Properties of the object.", +"type": "any" }, +"description": "Properties of the Artifact. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.", "type": "object" }, -"CloudAiNlLlmProtoServiceMessageMetadata": { -"id": "CloudAiNlLlmProtoServiceMessageMetadata", -"properties": { -"factualityDebugMetadata": { -"$ref": "LearningGenaiRootPerRequestProcessorDebugMetadataFactualityDebugMetadata", -"description": "Factuality-related debug metadata." +"name": { +"description": "Output only. The resource name of the Artifact.", +"readOnly": true, +"type": "string" +}, +"schemaTitle": { +"description": "The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.", +"type": "string" }, -"inputFilterInfo": { -"$ref": "LearningServingLlmMessageMetadata", -"description": "Filter metadata of the input messages." +"schemaVersion": { +"description": "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.", +"type": "string" }, -"modelRoutingDecision": { -"$ref": "LearningGenaiRootRoutingDecision", -"description": "This score is generated by the router model to decide which model to use" +"state": { +"description": "The state of this Artifact. This is a property of the Artifact, and does not imply or capture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines), and the system does not prescribe or check the validity of state transitions.", +"enum": [ +"STATE_UNSPECIFIED", +"PENDING", +"LIVE" +], +"enumDescriptions": [ +"Unspecified state for the Artifact.", +"A state used by systems like Vertex AI Pipelines to indicate that the underlying data item represented by this Artifact is being created.", +"A state indicating that the Artifact should exist, unless something external to the system deletes it." +], +"type": "string" }, -"outputFilterInfo": { -"description": "Filter metadata of the output messages.", -"items": { -"$ref": "LearningServingLlmMessageMetadata" +"updateTime": { +"description": "Output only. Timestamp when this Artifact was last updated.", +"format": "google-datetime", +"readOnly": true, +"type": "string" }, -"type": "array" +"uri": { +"description": "The uniform resource identifier of the artifact file. May be empty if there is no actual artifact file.", +"type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServicePart": { -"description": "A single part of a message.", -"id": "CloudAiNlLlmProtoServicePart", +"GoogleCloudAiplatformV1AssignNotebookRuntimeOperationMetadata": { +"description": "Metadata information for NotebookService.AssignNotebookRuntime.", +"id": "GoogleCloudAiplatformV1AssignNotebookRuntimeOperationMetadata", "properties": { -"documentMetadata": { -"$ref": "CloudAiNlLlmProtoServicePartDocumentMetadata", -"description": "Document metadata. The metadata should only be used by the Cloud LLM when supporting document mime types. It will only be populated when this image input part is converted from a document input part." -}, -"fileData": { -"$ref": "CloudAiNlLlmProtoServicePartFileData", -"description": "URI-based data." -}, -"functionCall": { -"$ref": "CloudAiNlLlmProtoServiceFunctionCall", -"description": "Function call data." +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "The operation generic information." }, -"functionResponse": { -"$ref": "CloudAiNlLlmProtoServiceFunctionResponse", -"description": "Function response data." +"progressMessage": { +"description": "A human-readable message that shows the intermediate progress details of NotebookRuntime.", +"type": "string" +} }, -"inlineData": { -"$ref": "CloudAiNlLlmProtoServicePartBlob", -"description": "Inline bytes data" +"type": "object" }, -"lmRootMetadata": { -"$ref": "CloudAiNlLlmProtoServicePartLMRootMetadata", -"description": "Metadata provides extra info for building the LM Root request. Note: High enough tag number for internal only fields." +"GoogleCloudAiplatformV1AssignNotebookRuntimeRequest": { +"description": "Request message for NotebookService.AssignNotebookRuntime.", +"id": "GoogleCloudAiplatformV1AssignNotebookRuntimeRequest", +"properties": { +"notebookRuntime": { +"$ref": "GoogleCloudAiplatformV1NotebookRuntime", +"description": "Required. Provide runtime specific information (e.g. runtime owner, notebook id) used for NotebookRuntime assignment." }, -"text": { -"description": "Text input.", +"notebookRuntimeId": { +"description": "Optional. User specified ID for the notebook runtime.", "type": "string" }, -"videoMetadata": { -"$ref": "CloudAiNlLlmProtoServicePartVideoMetadata", -"description": "Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data." +"notebookRuntimeTemplate": { +"description": "Required. The resource name of the NotebookRuntimeTemplate based on which a NotebookRuntime will be assigned (reuse or create a new one).", +"type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServicePartBlob": { -"description": "Represents arbitrary blob data input.", -"id": "CloudAiNlLlmProtoServicePartBlob", +"GoogleCloudAiplatformV1Attribution": { +"description": "Attribution that explains a particular prediction output.", +"id": "GoogleCloudAiplatformV1Attribution", "properties": { -"data": { -"description": "Inline data.", -"format": "byte", -"type": "string" +"approximationError": { +"description": "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.", +"format": "double", +"readOnly": true, +"type": "number" }, -"mimeType": { -"description": "The mime type corresponding to this input.", +"baselineOutputValue": { +"description": "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.", +"format": "double", +"readOnly": true, +"type": "number" +}, +"featureAttributions": { +"description": "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).", +"readOnly": true, +"type": "any" +}, +"instanceOutputValue": { +"description": "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.", +"format": "double", +"readOnly": true, +"type": "number" +}, +"outputDisplayName": { +"description": "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.", +"readOnly": true, "type": "string" }, -"originalFileData": { -"$ref": "CloudAiNlLlmProtoServicePartFileData", -"description": "Original file data where the blob comes from." +"outputIndex": { +"description": "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.", +"items": { +"format": "int32", +"type": "integer" +}, +"readOnly": true, +"type": "array" +}, +"outputName": { +"description": "Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs.", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServicePartDocumentMetadata": { -"description": "Metadata describes the original input document content.", -"id": "CloudAiNlLlmProtoServicePartDocumentMetadata", +"GoogleCloudAiplatformV1AutomaticResources": { +"description": "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.", +"id": "GoogleCloudAiplatformV1AutomaticResources", "properties": { -"originalDocumentBlob": { -"$ref": "CloudAiNlLlmProtoServicePartBlob", -"description": "The original document blob." +"maxReplicaCount": { +"description": "Immutable. The maximum number of replicas this DeployedModel 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 the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel 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.", +"format": "int32", +"type": "integer" }, -"pageNumber": { -"description": "The (1-indexed) page number of the image in the original document. The first page carries the original document content and mime type.", +"minReplicaCount": { +"description": "Immutable. The minimum number of replicas this DeployedModel 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.", "format": "int32", "type": "integer" } }, "type": "object" }, -"CloudAiNlLlmProtoServicePartFileData": { -"description": "Represents file data.", -"id": "CloudAiNlLlmProtoServicePartFileData", +"GoogleCloudAiplatformV1AutoscalingMetricSpec": { +"description": "The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.", +"id": "GoogleCloudAiplatformV1AutoscalingMetricSpec", "properties": { -"fileUri": { -"description": "Inline data.", +"metricName": { +"description": "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`", "type": "string" }, -"mimeType": { -"description": "The mime type corresponding to this input.", -"type": "string" +"target": { +"description": "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.", +"format": "int32", +"type": "integer" } }, "type": "object" }, -"CloudAiNlLlmProtoServicePartLMRootMetadata": { -"description": "Metadata provides extra info for building the LM Root request.", -"id": "CloudAiNlLlmProtoServicePartLMRootMetadata", +"GoogleCloudAiplatformV1AvroSource": { +"description": "The storage details for Avro input content.", +"id": "GoogleCloudAiplatformV1AvroSource", "properties": { -"chunkId": { -"description": "Chunk id that will be used when mapping the part to the LM Root's chunk.", -"type": "string" +"gcsSource": { +"$ref": "GoogleCloudAiplatformV1GcsSource", +"description": "Required. Google Cloud Storage location." } }, "type": "object" }, -"CloudAiNlLlmProtoServicePartVideoMetadata": { -"description": "Metadata describes the input video content.", -"id": "CloudAiNlLlmProtoServicePartVideoMetadata", +"GoogleCloudAiplatformV1BatchCancelPipelineJobsRequest": { +"description": "Request message for PipelineService.BatchCancelPipelineJobs.", +"id": "GoogleCloudAiplatformV1BatchCancelPipelineJobsRequest", "properties": { -"endOffset": { -"description": "The end offset of the video.", -"format": "google-duration", +"names": { +"description": "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}`", +"items": { "type": "string" }, -"startOffset": { -"description": "The start offset of the video.", -"format": "google-duration", -"type": "string" +"type": "array" } }, "type": "object" }, -"CloudAiNlLlmProtoServicePromptFeedback": { -"description": "Content filter results for a prompt sent in the request.", -"id": "CloudAiNlLlmProtoServicePromptFeedback", +"GoogleCloudAiplatformV1BatchCreateFeaturesOperationMetadata": { +"description": "Details of operations that perform batch create Features.", +"id": "GoogleCloudAiplatformV1BatchCreateFeaturesOperationMetadata", "properties": { -"blockReason": { -"description": "Blocked reason.", -"enum": [ -"BLOCKED_REASON_UNSPECIFIED", -"SAFETY", -"OTHER", -"BLOCKLIST", -"PROHIBITED_CONTENT" -], -"enumDescriptions": [ -"Unspecified blocked reason.", -"Candidates blocked due to safety.", -"Candidates blocked due to other reason (currently only language filter).", -"Candidates blocked due to the terms which are included from the terminology blocklist.", -"Candidates blocked due to prohibited content (currently only CSAM)." -], -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "Operation metadata for Feature." +} }, -"blockReasonMessage": { -"description": "A readable block reason message.", -"type": "string" +"type": "object" }, -"safetyRatings": { -"description": "Safety ratings.", +"GoogleCloudAiplatformV1BatchCreateFeaturesRequest": { +"description": "Request message for FeaturestoreService.BatchCreateFeatures.", +"id": "GoogleCloudAiplatformV1BatchCreateFeaturesRequest", +"properties": { +"requests": { +"description": "Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType. The `parent` field in each child request message can be omitted. If `parent` is set in a child request, then the value must match the `parent` value in this request message.", "items": { -"$ref": "CloudAiNlLlmProtoServiceSafetyRating" +"$ref": "GoogleCloudAiplatformV1CreateFeatureRequest" }, "type": "array" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceRaiResult": { -"description": "The RAI results for a given text.", -"id": "CloudAiNlLlmProtoServiceRaiResult", +"GoogleCloudAiplatformV1BatchCreateFeaturesResponse": { +"description": "Response message for FeaturestoreService.BatchCreateFeatures.", +"id": "GoogleCloudAiplatformV1BatchCreateFeaturesResponse", "properties": { -"aidaRecitationResult": { -"$ref": "LanguageLabsAidaTrustRecitationProtoRecitationResult", -"description": "Recitation result from Aida recitation checker." -}, -"blocked": { -"deprecated": true, -"description": "Use `triggered_blocklist`.", -"type": "boolean" -}, -"errorCodes": { -"description": "The error codes indicate which RAI filters block the response.", +"features": { +"description": "The Features created.", "items": { -"format": "int32", -"type": "integer" +"$ref": "GoogleCloudAiplatformV1Feature" }, "type": "array" +} }, -"filtered": { -"description": "Whether the text should be filtered and not shown to the end user. This is determined based on a combination of `triggered_recitation`, `triggered_blocklist`, `language_filter_result`, and `triggered_safety_filter`.", -"type": "boolean" -}, -"languageFilterResult": { -"$ref": "LearningGenaiRootLanguageFilterResult", -"description": "Language filter result from SAFT LangId." +"type": "object" }, -"raiSignals": { -"description": "The RAI signals for the text.", +"GoogleCloudAiplatformV1BatchCreateTensorboardRunsRequest": { +"description": "Request message for TensorboardService.BatchCreateTensorboardRuns.", +"id": "GoogleCloudAiplatformV1BatchCreateTensorboardRunsRequest", +"properties": { +"requests": { +"description": "Required. The request message specifying the TensorboardRuns to create. A maximum of 1000 TensorboardRuns can be created in a batch.", "items": { -"$ref": "CloudAiNlLlmProtoServiceRaiSignal" +"$ref": "GoogleCloudAiplatformV1CreateTensorboardRunRequest" }, "type": "array" +} }, -"translationRequestInfos": { -"description": "Translation request info during RAI for debugging purpose. Each TranslationRequestInfo corresponds to a request sent to the translation server.", +"type": "object" +}, +"GoogleCloudAiplatformV1BatchCreateTensorboardRunsResponse": { +"description": "Response message for TensorboardService.BatchCreateTensorboardRuns.", +"id": "GoogleCloudAiplatformV1BatchCreateTensorboardRunsResponse", +"properties": { +"tensorboardRuns": { +"description": "The created TensorboardRuns.", "items": { -"$ref": "LearningGenaiRootTranslationRequestInfo" +"$ref": "GoogleCloudAiplatformV1TensorboardRun" }, "type": "array" +} }, -"triggeredBlocklist": { -"description": "Whether the text triggered the blocklist.", -"type": "boolean" +"type": "object" }, -"triggeredRecitation": { -"description": "Whether the text should be blocked by the recitation result from Aida recitation checker. It is determined from aida_recitation_result.", -"type": "boolean" +"GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesRequest": { +"description": "Request message for TensorboardService.BatchCreateTensorboardTimeSeries.", +"id": "GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesRequest", +"properties": { +"requests": { +"description": "Required. The request message specifying the TensorboardTimeSeries to create. A maximum of 1000 TensorboardTimeSeries can be created in a batch.", +"items": { +"$ref": "GoogleCloudAiplatformV1CreateTensorboardTimeSeriesRequest" }, -"triggeredSafetyFilter": { -"description": "Whether the text triggered the safety filter. Currently, this is due to CSAI triggering or one of four categories (derogatory, sexual, toxic, violent) having a score over the filter threshold.", -"type": "boolean" +"type": "array" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceRaiSignal": { -"description": "An RAI signal for a single category.", -"id": "CloudAiNlLlmProtoServiceRaiSignal", +"GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesResponse": { +"description": "Response message for TensorboardService.BatchCreateTensorboardTimeSeries.", +"id": "GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesResponse", "properties": { -"confidence": { -"description": "The confidence level for the RAI category.", -"enum": [ -"CONFIDENCE_UNSPECIFIED", -"CONFIDENCE_NONE", -"CONFIDENCE_LOW", -"CONFIDENCE_MEDIUM", -"CONFIDENCE_HIGH" -], -"enumDescriptions": [ -"", -"", -"", -"", -"" -], -"type": "string" -}, -"flagged": { -"description": "Whether the category is flagged as being present. Currently, this is set to true if score >= 0.5.", -"type": "boolean" -}, -"influentialTerms": { -"description": "The influential terms that could potentially block the response.", +"tensorboardTimeSeries": { +"description": "The created TensorboardTimeSeries.", "items": { -"$ref": "CloudAiNlLlmProtoServiceRaiSignalInfluentialTerm" +"$ref": "GoogleCloudAiplatformV1TensorboardTimeSeries" }, "type": "array" -}, -"raiCategory": { -"description": "The RAI category.", -"enum": [ -"RAI_CATEGORY_UNSPECIFIED", -"TOXIC", -"SEXUALLY_EXPLICIT", -"HATE_SPEECH", -"VIOLENT", -"PROFANITY", -"HARASSMENT", -"DEATH_HARM_TRAGEDY", -"FIREARMS_WEAPONS", -"PUBLIC_SAFETY", -"HEALTH", -"RELIGIOUS_BELIEF", -"ILLICIT_DRUGS", -"WAR_CONFLICT", -"POLITICS", -"FINANCE", -"LEGAL", -"CSAI", -"FRINGE", -"THREAT", -"SEVERE_TOXICITY", -"TOXICITY", -"SEXUAL", -"INSULT", -"DEROGATORY", -"IDENTITY_ATTACK", -"VIOLENCE_ABUSE", -"OBSCENE", -"DRUGS", -"CSAM", -"SPII", -"DANGEROUS_CONTENT", -"DANGEROUS_CONTENT_SEVERITY", -"INSULT_SEVERITY", -"DEROGATORY_SEVERITY", -"SEXUAL_SEVERITY" -], -"enumDescriptions": [ -"", -"SafetyCat categories.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"GRAIL categories that can't be exposed to end users.", -"", -"Unused categories.", -"", -"Old category names.", -"", -"", -"", -"", -"", -"", -"", -"CSAM V2", -"SPII", -"New SafetyCat v3 categories", -"", -"", -"", -"" -], -"type": "string" -}, -"score": { -"description": "The score for the category, in the range [0.0, 1.0].", -"format": "float", -"type": "number" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceRaiSignalInfluentialTerm": { -"description": "The influential term that could potentially block the response.", -"id": "CloudAiNlLlmProtoServiceRaiSignalInfluentialTerm", +"GoogleCloudAiplatformV1BatchDedicatedResources": { +"description": "A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.", +"id": "GoogleCloudAiplatformV1BatchDedicatedResources", "properties": { -"beginOffset": { -"description": "The beginning offset of the influential term.", +"machineSpec": { +"$ref": "GoogleCloudAiplatformV1MachineSpec", +"description": "Required. Immutable. The specification of a single machine." +}, +"maxReplicaCount": { +"description": "Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.", "format": "int32", "type": "integer" }, -"confidence": { -"description": "The confidence score of the influential term.", -"format": "float", -"type": "number" -}, -"source": { -"description": "The source of the influential term, prompt or response.", -"enum": [ -"SOURCE_UNSPECIFIED", -"PROMPT", -"RESPONSE" -], -"enumDescriptions": [ -"Unspecified source.", -"The influential term comes from the prompt.", -"The influential term comes from the response." -], -"type": "string" -}, -"term": { -"description": "The influential term.", -"type": "string" +"startingReplicaCount": { +"description": "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", +"format": "int32", +"type": "integer" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceSafetyRating": { -"description": "Safety rating corresponding to the generated content.", -"id": "CloudAiNlLlmProtoServiceSafetyRating", +"GoogleCloudAiplatformV1BatchDeletePipelineJobsRequest": { +"description": "Request message for PipelineService.BatchDeletePipelineJobs.", +"id": "GoogleCloudAiplatformV1BatchDeletePipelineJobsRequest", "properties": { -"blocked": { -"description": "Indicates whether the content was filtered out because of this rating.", -"type": "boolean" -}, -"category": { -"description": "Harm category.", -"enum": [ -"HARM_CATEGORY_UNSPECIFIED", -"HARM_CATEGORY_HATE_SPEECH", -"HARM_CATEGORY_DANGEROUS_CONTENT", -"HARM_CATEGORY_HARASSMENT", -"HARM_CATEGORY_SEXUALLY_EXPLICIT" -], -"enumDescriptions": [ -"The harm category is unspecified.", -"The harm category is hate speech.", -"The harm category is dengerous content.", -"The harm category is harassment.", -"The harm category is sexually explicit." -], -"type": "string" -}, -"influentialTerms": { -"description": "The influential terms that could potentially block the response.", +"names": { +"description": "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}`", "items": { -"$ref": "CloudAiNlLlmProtoServiceSafetyRatingInfluentialTerm" -}, -"type": "array" -}, -"probability": { -"description": "Harm probability levels in the content.", -"enum": [ -"HARM_PROBABILITY_UNSPECIFIED", -"NEGLIGIBLE", -"LOW", -"MEDIUM", -"HIGH" -], -"enumDescriptions": [ -"Harm probability unspecified.", -"Negligible level of harm.", -"Low level of harm.", -"Medium level of harm.", -"High level of harm." -], -"type": "string" -}, -"probabilityScore": { -"description": "Harm probability score.", -"format": "float", -"type": "number" -}, -"severity": { -"description": "Harm severity levels in the content.", -"enum": [ -"HARM_SEVERITY_UNSPECIFIED", -"HARM_SEVERITY_NEGLIGIBLE", -"HARM_SEVERITY_LOW", -"HARM_SEVERITY_MEDIUM", -"HARM_SEVERITY_HIGH" -], -"enumDescriptions": [ -"Harm severity unspecified.", -"Negligible level of harm severity.", -"Low level of harm severity.", -"Medium level of harm severity.", -"High level of harm severity." -], "type": "string" }, -"severityScore": { -"description": "Harm severity score.", -"format": "float", -"type": "number" +"type": "array" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceSafetyRatingInfluentialTerm": { -"description": "The influential term that could potentially block the response.", -"id": "CloudAiNlLlmProtoServiceSafetyRatingInfluentialTerm", +"GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsRequest": { +"description": "Request message for ModelService.BatchImportEvaluatedAnnotations", +"id": "GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsRequest", "properties": { -"beginOffset": { -"description": "The beginning offset of the influential term.", -"format": "int32", -"type": "integer" -}, -"confidence": { -"description": "The confidence score of the influential term.", -"format": "float", -"type": "number" -}, -"source": { -"description": "The source of the influential term, prompt or response.", -"enum": [ -"SOURCE_UNSPECIFIED", -"PROMPT", -"RESPONSE" -], -"enumDescriptions": [ -"Unspecified source.", -"The influential term comes from the prompt.", -"The influential term comes from the response." -], -"type": "string" +"evaluatedAnnotations": { +"description": "Required. Evaluated annotations resource to be imported.", +"items": { +"$ref": "GoogleCloudAiplatformV1EvaluatedAnnotation" }, -"term": { -"description": "The influential term.", -"type": "string" +"type": "array" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceUsageMetadata": { -"description": "Usage metadata about response(s).", -"id": "CloudAiNlLlmProtoServiceUsageMetadata", +"GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsResponse": { +"description": "Response message for ModelService.BatchImportEvaluatedAnnotations", +"id": "GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsResponse", "properties": { -"candidatesTokenCount": { -"description": "Number of tokens in the response(s).", -"format": "int32", -"type": "integer" -}, -"promptTokenCount": { -"description": "Number of tokens in the request.", -"format": "int32", -"type": "integer" -}, -"totalTokenCount": { +"importedEvaluatedAnnotationsCount": { +"description": "Output only. Number of EvaluatedAnnotations imported.", "format": "int32", +"readOnly": true, "type": "integer" } }, "type": "object" }, -"GoogleApiHttpBody": { -"description": "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.", -"id": "GoogleApiHttpBody", +"GoogleCloudAiplatformV1BatchImportModelEvaluationSlicesRequest": { +"description": "Request message for ModelService.BatchImportModelEvaluationSlices", +"id": "GoogleCloudAiplatformV1BatchImportModelEvaluationSlicesRequest", "properties": { -"contentType": { -"description": "The HTTP Content-Type header value specifying the content type of the body.", -"type": "string" -}, -"data": { -"description": "The HTTP request/response body as raw binary.", -"format": "byte", -"type": "string" -}, -"extensions": { -"description": "Application specific response metadata. Must be set in the first response for streaming APIs.", +"modelEvaluationSlices": { +"description": "Required. Model evaluation slice resource to be imported.", "items": { -"additionalProperties": { -"description": "Properties of the object. Contains field @type with type URL.", -"type": "any" -}, -"type": "object" +"$ref": "GoogleCloudAiplatformV1ModelEvaluationSlice" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1ActiveLearningConfig": { -"description": "Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy.", -"id": "GoogleCloudAiplatformV1ActiveLearningConfig", +"GoogleCloudAiplatformV1BatchImportModelEvaluationSlicesResponse": { +"description": "Response message for ModelService.BatchImportModelEvaluationSlices", +"id": "GoogleCloudAiplatformV1BatchImportModelEvaluationSlicesResponse", "properties": { -"maxDataItemCount": { -"description": "Max number of human labeled DataItems.", -"format": "int64", +"importedModelEvaluationSlices": { +"description": "Output only. List of imported ModelEvaluationSlice.name.", +"items": { "type": "string" }, -"maxDataItemPercentage": { -"description": "Max percent of total DataItems for human labeling.", -"format": "int32", -"type": "integer" -}, -"sampleConfig": { -"$ref": "GoogleCloudAiplatformV1SampleConfig", -"description": "Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy." -}, -"trainingConfig": { -"$ref": "GoogleCloudAiplatformV1TrainingConfig", -"description": "CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems." +"readOnly": true, +"type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsRequest": { -"description": "Request message for MetadataService.AddContextArtifactsAndExecutions.", -"id": "GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsRequest", +"GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadata": { +"description": "Runtime operation information for MigrationService.BatchMigrateResources.", +"id": "GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadata", "properties": { -"artifacts": { -"description": "The resource names of the Artifacts to attribute to the Context. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}`", -"items": { -"type": "string" -}, -"type": "array" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "The common part of the operation metadata." }, -"executions": { -"description": "The resource names of the Executions to associate with the Context. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}`", +"partialResults": { +"description": "Partial results that reflect the latest migration operation progress.", "items": { -"type": "string" +"$ref": "GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadataPartialResult" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsResponse": { -"description": "Response message for MetadataService.AddContextArtifactsAndExecutions.", -"id": "GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1AddContextChildrenRequest": { -"description": "Request message for MetadataService.AddContextChildren.", -"id": "GoogleCloudAiplatformV1AddContextChildrenRequest", +"GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadataPartialResult": { +"description": "Represents a partial result in batch migration operation for one MigrateResourceRequest.", +"id": "GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadataPartialResult", "properties": { -"childContexts": { -"description": "The resource names of the child Contexts.", -"items": { +"dataset": { +"description": "Migrated dataset resource name.", "type": "string" }, -"type": "array" -} +"error": { +"$ref": "GoogleRpcStatus", +"description": "The error result of the migration request in case of failure." }, -"type": "object" +"model": { +"description": "Migrated model resource name.", +"type": "string" +}, +"request": { +"$ref": "GoogleCloudAiplatformV1MigrateResourceRequest", +"description": "It's the same as the value in MigrateResourceRequest.migrate_resource_requests." +} }, -"GoogleCloudAiplatformV1AddContextChildrenResponse": { -"description": "Response message for MetadataService.AddContextChildren.", -"id": "GoogleCloudAiplatformV1AddContextChildrenResponse", -"properties": {}, "type": "object" }, -"GoogleCloudAiplatformV1AddExecutionEventsRequest": { -"description": "Request message for MetadataService.AddExecutionEvents.", -"id": "GoogleCloudAiplatformV1AddExecutionEventsRequest", +"GoogleCloudAiplatformV1BatchMigrateResourcesRequest": { +"description": "Request message for MigrationService.BatchMigrateResources.", +"id": "GoogleCloudAiplatformV1BatchMigrateResourcesRequest", "properties": { -"events": { -"description": "The Events to create and add.", +"migrateResourceRequests": { +"description": "Required. The request messages specifying the resources to migrate. They must be in the same location as the destination. Up to 50 resources can be migrated in one batch.", "items": { -"$ref": "GoogleCloudAiplatformV1Event" +"$ref": "GoogleCloudAiplatformV1MigrateResourceRequest" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1AddExecutionEventsResponse": { -"description": "Response message for MetadataService.AddExecutionEvents.", -"id": "GoogleCloudAiplatformV1AddExecutionEventsResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1AddTrialMeasurementRequest": { -"description": "Request message for VizierService.AddTrialMeasurement.", -"id": "GoogleCloudAiplatformV1AddTrialMeasurementRequest", +"GoogleCloudAiplatformV1BatchMigrateResourcesResponse": { +"description": "Response message for MigrationService.BatchMigrateResources.", +"id": "GoogleCloudAiplatformV1BatchMigrateResourcesResponse", "properties": { -"measurement": { -"$ref": "GoogleCloudAiplatformV1Measurement", -"description": "Required. The measurement to be added to a Trial." +"migrateResourceResponses": { +"description": "Successfully migrated resources.", +"items": { +"$ref": "GoogleCloudAiplatformV1MigrateResourceResponse" +}, +"type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1Annotation": { -"description": "Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.", -"id": "GoogleCloudAiplatformV1Annotation", +"GoogleCloudAiplatformV1BatchPredictionJob": { +"description": "A job that uses a Model to produce predictions on multiple input instances. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.", +"id": "GoogleCloudAiplatformV1BatchPredictionJob", "properties": { -"annotationSource": { -"$ref": "GoogleCloudAiplatformV1UserActionReference", -"description": "Output only. The source of the Annotation.", +"completionStats": { +"$ref": "GoogleCloudAiplatformV1CompletionStats", +"description": "Output only. Statistics on completed and failed prediction instances.", "readOnly": true }, "createTime": { -"description": "Output only. Timestamp when this Annotation was created.", +"description": "Output only. Time when the BatchPredictionJob was created.", "format": "google-datetime", "readOnly": true, "type": "string" }, -"etag": { -"description": "Optional. Used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", +"dedicatedResources": { +"$ref": "GoogleCloudAiplatformV1BatchDedicatedResources", +"description": "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": { +"description": "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.", +"type": "boolean" +}, +"displayName": { +"description": "Required. The user-defined name of this BatchPredictionJob.", +"type": "string" +}, +"encryptionSpec": { +"$ref": "GoogleCloudAiplatformV1EncryptionSpec", +"description": "Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key." +}, +"endTime": { +"description": "Output only. Time when the BatchPredictionJob entered any of the following states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`.", +"format": "google-datetime", +"readOnly": true, "type": "string" }, +"error": { +"$ref": "GoogleRpcStatus", +"description": "Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.", +"readOnly": true +}, +"explanationSpec": { +"$ref": "GoogleCloudAiplatformV1ExplanationSpec", +"description": "Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to `true`. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited." +}, +"generateExplanation": { +"description": "Generate explanation with the batch prediction results. When set to `true`, the batch prediction output changes based on the `predictions_format` field of the BatchPredictionJob.output_config object: * `bigquery`: output includes a column named `explanation`. The value is a struct that conforms to the Explanation object. * `jsonl`: The JSON objects on each line include an additional entry keyed `explanation`. The value of the entry is a JSON object that conforms to the Explanation object. * `csv`: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.", +"type": "boolean" +}, +"inputConfig": { +"$ref": "GoogleCloudAiplatformV1BatchPredictionJobInputConfig", +"description": "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": { +"$ref": "GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig", +"description": "Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model." +}, "labels": { "additionalProperties": { "type": "string" }, -"description": "Optional. The labels with user-defined metadata to organize your Annotations. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Annotation(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with \"aiplatform.googleapis.com/\" and are immutable. Following system labels exist for each Annotation: * \"aiplatform.googleapis.com/annotation_set_name\": optional, name of the UI's annotation set this Annotation belongs to. If not set, the Annotation is not visible in the UI. * \"aiplatform.googleapis.com/payload_schema\": output only, its value is the payload_schema's title.", +"description": "The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.", "type": "object" }, -"name": { -"description": "Output only. Resource name of the Annotation.", -"readOnly": true, +"manualBatchTuningParameters": { +"$ref": "GoogleCloudAiplatformV1ManualBatchTuningParameters", +"description": "Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself)." +}, +"model": { +"description": "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}`", "type": "string" }, -"payload": { -"description": "Required. The schema of the payload can be found in payload_schema.", +"modelParameters": { +"description": "The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.", "type": "any" }, -"payloadSchemaUri": { -"description": "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.", -"type": "string" -}, -"updateTime": { -"description": "Output only. Timestamp when this Annotation was last updated.", -"format": "google-datetime", +"modelVersionId": { +"description": "Output only. The version ID of the Model that produces the predictions via this job.", "readOnly": true, "type": "string" -} -}, -"type": "object" }, -"GoogleCloudAiplatformV1AnnotationSpec": { -"description": "Identifies a concept with which DataItems may be annotated with.", -"id": "GoogleCloudAiplatformV1AnnotationSpec", -"properties": { -"createTime": { -"description": "Output only. Timestamp when this AnnotationSpec was created.", -"format": "google-datetime", +"name": { +"description": "Output only. Resource name of the BatchPredictionJob.", "readOnly": true, "type": "string" }, -"displayName": { -"description": "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.", -"type": "string" +"outputConfig": { +"$ref": "GoogleCloudAiplatformV1BatchPredictionJobOutputConfig", +"description": "Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri." }, -"etag": { -"description": "Optional. Used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", -"type": "string" +"outputInfo": { +"$ref": "GoogleCloudAiplatformV1BatchPredictionJobOutputInfo", +"description": "Output only. Information further describing the output of this job.", +"readOnly": true +}, +"partialFailures": { +"description": "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.", +"items": { +"$ref": "GoogleRpcStatus" }, -"name": { -"description": "Output only. Resource name of the AnnotationSpec.", "readOnly": true, +"type": "array" +}, +"resourcesConsumed": { +"$ref": "GoogleCloudAiplatformV1ResourcesConsumed", +"description": "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.", +"readOnly": true +}, +"serviceAccount": { +"description": "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.", "type": "string" }, -"updateTime": { -"description": "Output only. Timestamp when AnnotationSpec was last updated.", +"startTime": { +"description": "Output only. Time when the BatchPredictionJob for the first time entered the `JOB_STATE_RUNNING` state.", "format": "google-datetime", "readOnly": true, "type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1Artifact": { -"description": "Instance of a general artifact.", -"id": "GoogleCloudAiplatformV1Artifact", -"properties": { -"createTime": { -"description": "Output only. Timestamp when this Artifact was created.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -}, -"description": { -"description": "Description of the Artifact", -"type": "string" -}, -"displayName": { -"description": "User provided display name of the Artifact. May be up to 128 Unicode characters.", -"type": "string" -}, -"etag": { -"description": "An eTag used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", -"type": "string" -}, -"labels": { -"additionalProperties": { -"type": "string" -}, -"description": "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).", -"type": "object" -}, -"metadata": { -"additionalProperties": { -"description": "Properties of the object.", -"type": "any" -}, -"description": "Properties of the Artifact. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.", -"type": "object" -}, -"name": { -"description": "Output only. The resource name of the Artifact.", -"readOnly": true, -"type": "string" -}, -"schemaTitle": { -"description": "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.", -"type": "string" -}, -"schemaVersion": { -"description": "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.", -"type": "string" }, "state": { -"description": "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.", +"description": "Output only. The detailed state of the job.", "enum": [ -"STATE_UNSPECIFIED", -"PENDING", -"LIVE" +"JOB_STATE_UNSPECIFIED", +"JOB_STATE_QUEUED", +"JOB_STATE_PENDING", +"JOB_STATE_RUNNING", +"JOB_STATE_SUCCEEDED", +"JOB_STATE_FAILED", +"JOB_STATE_CANCELLING", +"JOB_STATE_CANCELLED", +"JOB_STATE_PAUSED", +"JOB_STATE_EXPIRED", +"JOB_STATE_UPDATING", +"JOB_STATE_PARTIALLY_SUCCEEDED" ], "enumDescriptions": [ -"Unspecified state for the Artifact.", -"A state used by systems like Vertex AI Pipelines to indicate that the underlying data item represented by this Artifact is being created.", -"A state indicating that the Artifact should exist, unless something external to the system deletes it." +"The job state is unspecified.", +"The job has been just created or resumed and processing has not yet begun.", +"The service is preparing to run the job.", +"The job is in progress.", +"The job completed successfully.", +"The job failed.", +"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`.", +"The job has been cancelled.", +"The job has been stopped, and can be resumed.", +"The job has expired.", +"The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state.", +"The job is partially succeeded, some results may be missing due to errors." ], +"readOnly": true, "type": "string" }, +"unmanagedContainerModel": { +"$ref": "GoogleCloudAiplatformV1UnmanagedContainerModel", +"description": "Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set." +}, "updateTime": { -"description": "Output only. Timestamp when this Artifact was last updated.", +"description": "Output only. Time when the BatchPredictionJob was most recently updated.", "format": "google-datetime", "readOnly": true, "type": "string" -}, -"uri": { -"description": "The uniform resource identifier of the artifact file. May be empty if there is no actual artifact file.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1AssignNotebookRuntimeOperationMetadata": { -"description": "Metadata information for NotebookService.AssignNotebookRuntime.", -"id": "GoogleCloudAiplatformV1AssignNotebookRuntimeOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "The operation generic information." -}, -"progressMessage": { -"description": "A human-readable message that shows the intermediate progress details of NotebookRuntime.", -"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1AssignNotebookRuntimeRequest": { -"description": "Request message for NotebookService.AssignNotebookRuntime.", -"id": "GoogleCloudAiplatformV1AssignNotebookRuntimeRequest", +"GoogleCloudAiplatformV1BatchPredictionJobInputConfig": { +"description": "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.", +"id": "GoogleCloudAiplatformV1BatchPredictionJobInputConfig", "properties": { -"notebookRuntime": { -"$ref": "GoogleCloudAiplatformV1NotebookRuntime", -"description": "Required. Provide runtime specific information (e.g. runtime owner, notebook id) used for NotebookRuntime assignment." +"bigquerySource": { +"$ref": "GoogleCloudAiplatformV1BigQuerySource", +"description": "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." }, -"notebookRuntimeId": { -"description": "Optional. User specified ID for the notebook runtime.", -"type": "string" +"gcsSource": { +"$ref": "GoogleCloudAiplatformV1GcsSource", +"description": "The Cloud Storage location for the input instances." }, -"notebookRuntimeTemplate": { -"description": "Required. The resource name of the NotebookRuntimeTemplate based on which a NotebookRuntime will be assigned (reuse or create a new one).", +"instancesFormat": { +"description": "Required. The format in which instances are given, must be one of the Model's supported_input_storage_formats.", "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1Attribution": { -"description": "Attribution that explains a particular prediction output.", -"id": "GoogleCloudAiplatformV1Attribution", +"GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig": { +"description": "Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.", +"id": "GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig", "properties": { -"approximationError": { -"description": "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.", -"format": "double", -"readOnly": true, -"type": "number" -}, -"baselineOutputValue": { -"description": "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.", -"format": "double", -"readOnly": true, -"type": "number" -}, -"featureAttributions": { -"description": "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).", -"readOnly": true, -"type": "any" -}, -"instanceOutputValue": { -"description": "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.", -"format": "double", -"readOnly": true, -"type": "number" -}, -"outputDisplayName": { -"description": "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.", -"readOnly": true, +"excludedFields": { +"description": "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.", +"items": { "type": "string" }, -"outputIndex": { -"description": "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.", +"type": "array" +}, +"includedFields": { +"description": "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.", "items": { -"format": "int32", -"type": "integer" +"type": "string" }, -"readOnly": true, "type": "array" }, -"outputName": { -"description": "Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs.", -"readOnly": true, +"instanceType": { +"description": "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.", +"type": "string" +}, +"keyField": { +"description": "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.", "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1AutomaticResources": { -"description": "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.", -"id": "GoogleCloudAiplatformV1AutomaticResources", +"GoogleCloudAiplatformV1BatchPredictionJobOutputConfig": { +"description": "Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.", +"id": "GoogleCloudAiplatformV1BatchPredictionJobOutputConfig", "properties": { -"maxReplicaCount": { -"description": "Immutable. The maximum number of replicas this DeployedModel 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 the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel 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.", -"format": "int32", -"type": "integer" -}, -"minReplicaCount": { -"description": "Immutable. The minimum number of replicas this DeployedModel 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.", -"format": "int32", -"type": "integer" -} +"bigqueryDestination": { +"$ref": "GoogleCloudAiplatformV1BigQueryDestination", +"description": "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`." }, -"type": "object" +"gcsDestination": { +"$ref": "GoogleCloudAiplatformV1GcsDestination", +"description": "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." }, -"GoogleCloudAiplatformV1AutoscalingMetricSpec": { -"description": "The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.", -"id": "GoogleCloudAiplatformV1AutoscalingMetricSpec", -"properties": { -"metricName": { -"description": "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`", +"predictionsFormat": { +"description": "Required. The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.", "type": "string" -}, -"target": { -"description": "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.", -"format": "int32", -"type": "integer" } }, "type": "object" }, -"GoogleCloudAiplatformV1AvroSource": { -"description": "The storage details for Avro input content.", -"id": "GoogleCloudAiplatformV1AvroSource", +"GoogleCloudAiplatformV1BatchPredictionJobOutputInfo": { +"description": "Further describes this job's output. Supplements output_config.", +"id": "GoogleCloudAiplatformV1BatchPredictionJobOutputInfo", "properties": { -"gcsSource": { -"$ref": "GoogleCloudAiplatformV1GcsSource", -"description": "Required. Google Cloud Storage location." -} -}, -"type": "object" +"bigqueryOutputDataset": { +"description": "Output only. The path of the BigQuery dataset created, in `bq://projectId.bqDatasetId` format, into which the prediction output is written.", +"readOnly": true, +"type": "string" }, -"GoogleCloudAiplatformV1BatchCancelPipelineJobsRequest": { -"description": "Request message for PipelineService.BatchCancelPipelineJobs.", -"id": "GoogleCloudAiplatformV1BatchCancelPipelineJobsRequest", -"properties": { -"names": { -"description": "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}`", -"items": { +"bigqueryOutputTable": { +"description": "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.", +"readOnly": true, "type": "string" }, -"type": "array" +"gcsOutputDirectory": { +"description": "Output only. The full path of the Cloud Storage directory created, into which the prediction output is written.", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1BatchCreateFeaturesOperationMetadata": { -"description": "Details of operations that perform batch create Features.", -"id": "GoogleCloudAiplatformV1BatchCreateFeaturesOperationMetadata", +"GoogleCloudAiplatformV1BatchReadFeatureValuesOperationMetadata": { +"description": "Details of operations that batch reads Feature values.", +"id": "GoogleCloudAiplatformV1BatchReadFeatureValuesOperationMetadata", "properties": { "genericMetadata": { "$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "Operation metadata for Feature." +"description": "Operation metadata for Featurestore batch read Features values." } }, "type": "object" }, -"GoogleCloudAiplatformV1BatchCreateFeaturesRequest": { -"description": "Request message for FeaturestoreService.BatchCreateFeatures.", -"id": "GoogleCloudAiplatformV1BatchCreateFeaturesRequest", +"GoogleCloudAiplatformV1BatchReadFeatureValuesRequest": { +"description": "Request message for FeaturestoreService.BatchReadFeatureValues.", +"id": "GoogleCloudAiplatformV1BatchReadFeatureValuesRequest", "properties": { -"requests": { -"description": "Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType. The `parent` field in each child request message can be omitted. If `parent` is set in a child request, then the value must match the `parent` value in this request message.", -"items": { -"$ref": "GoogleCloudAiplatformV1CreateFeatureRequest" +"bigqueryReadInstances": { +"$ref": "GoogleCloudAiplatformV1BigQuerySource", +"description": "Similar to csv_read_instances, but from BigQuery source." }, -"type": "array" -} +"csvReadInstances": { +"$ref": "GoogleCloudAiplatformV1CsvSource", +"description": "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`." }, -"type": "object" +"destination": { +"$ref": "GoogleCloudAiplatformV1FeatureValueDestination", +"description": "Required. Specifies output location and format." }, -"GoogleCloudAiplatformV1BatchCreateFeaturesResponse": { -"description": "Response message for FeaturestoreService.BatchCreateFeatures.", -"id": "GoogleCloudAiplatformV1BatchCreateFeaturesResponse", -"properties": { -"features": { -"description": "The Features created.", +"entityTypeSpecs": { +"description": "Required. Specifies EntityType grouping Features to read values of and settings.", "items": { -"$ref": "GoogleCloudAiplatformV1Feature" +"$ref": "GoogleCloudAiplatformV1BatchReadFeatureValuesRequestEntityTypeSpec" }, "type": "array" -} -}, -"type": "object" }, -"GoogleCloudAiplatformV1BatchCreateTensorboardRunsRequest": { -"description": "Request message for TensorboardService.BatchCreateTensorboardRuns.", -"id": "GoogleCloudAiplatformV1BatchCreateTensorboardRunsRequest", -"properties": { -"requests": { -"description": "Required. The request message specifying the TensorboardRuns to create. A maximum of 1000 TensorboardRuns can be created in a batch.", +"passThroughFields": { +"description": "When not empty, the specified fields in the *_read_instances source will be joined as-is in the output, in addition to those fields from the Featurestore Entity. For BigQuery source, the type of the pass-through values will be automatically inferred. For CSV source, the pass-through values will be passed as opaque bytes.", "items": { -"$ref": "GoogleCloudAiplatformV1CreateTensorboardRunRequest" +"$ref": "GoogleCloudAiplatformV1BatchReadFeatureValuesRequestPassThroughField" }, "type": "array" +}, +"startTime": { +"description": "Optional. Excludes Feature values with feature generation timestamp before this timestamp. If not set, retrieve oldest values kept in Feature Store. Timestamp, if present, must not have higher than millisecond precision.", +"format": "google-datetime", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1BatchCreateTensorboardRunsResponse": { -"description": "Response message for TensorboardService.BatchCreateTensorboardRuns.", -"id": "GoogleCloudAiplatformV1BatchCreateTensorboardRunsResponse", +"GoogleCloudAiplatformV1BatchReadFeatureValuesRequestEntityTypeSpec": { +"description": "Selects Features of an EntityType to read values of and specifies read settings.", +"id": "GoogleCloudAiplatformV1BatchReadFeatureValuesRequestEntityTypeSpec", "properties": { -"tensorboardRuns": { -"description": "The created TensorboardRuns.", -"items": { -"$ref": "GoogleCloudAiplatformV1TensorboardRun" -}, -"type": "array" -} +"entityTypeId": { +"description": "Required. ID of the EntityType to select Features. The EntityType id is the entity_type_id specified during EntityType creation.", +"type": "string" }, -"type": "object" +"featureSelector": { +"$ref": "GoogleCloudAiplatformV1FeatureSelector", +"description": "Required. Selectors choosing which Feature values to read from the EntityType." }, -"GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesRequest": { -"description": "Request message for TensorboardService.BatchCreateTensorboardTimeSeries.", -"id": "GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesRequest", -"properties": { -"requests": { -"description": "Required. The request message specifying the TensorboardTimeSeries to create. A maximum of 1000 TensorboardTimeSeries can be created in a batch.", +"settings": { +"description": "Per-Feature settings for the batch read.", "items": { -"$ref": "GoogleCloudAiplatformV1CreateTensorboardTimeSeriesRequest" +"$ref": "GoogleCloudAiplatformV1DestinationFeatureSetting" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesResponse": { -"description": "Response message for TensorboardService.BatchCreateTensorboardTimeSeries.", -"id": "GoogleCloudAiplatformV1BatchCreateTensorboardTimeSeriesResponse", +"GoogleCloudAiplatformV1BatchReadFeatureValuesRequestPassThroughField": { +"description": "Describe pass-through fields in read_instance source.", +"id": "GoogleCloudAiplatformV1BatchReadFeatureValuesRequestPassThroughField", "properties": { -"tensorboardTimeSeries": { -"description": "The created TensorboardTimeSeries.", -"items": { -"$ref": "GoogleCloudAiplatformV1TensorboardTimeSeries" -}, -"type": "array" +"fieldName": { +"description": "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.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1BatchDedicatedResources": { -"description": "A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.", -"id": "GoogleCloudAiplatformV1BatchDedicatedResources", -"properties": { -"machineSpec": { -"$ref": "GoogleCloudAiplatformV1MachineSpec", -"description": "Required. Immutable. The specification of a single machine." -}, -"maxReplicaCount": { -"description": "Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.", -"format": "int32", -"type": "integer" -}, -"startingReplicaCount": { -"description": "Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count", -"format": "int32", -"type": "integer" -} -}, +"GoogleCloudAiplatformV1BatchReadFeatureValuesResponse": { +"description": "Response message for FeaturestoreService.BatchReadFeatureValues.", +"id": "GoogleCloudAiplatformV1BatchReadFeatureValuesResponse", +"properties": {}, "type": "object" }, -"GoogleCloudAiplatformV1BatchDeletePipelineJobsRequest": { -"description": "Request message for PipelineService.BatchDeletePipelineJobs.", -"id": "GoogleCloudAiplatformV1BatchDeletePipelineJobsRequest", +"GoogleCloudAiplatformV1BatchReadTensorboardTimeSeriesDataResponse": { +"description": "Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.", +"id": "GoogleCloudAiplatformV1BatchReadTensorboardTimeSeriesDataResponse", "properties": { -"names": { -"description": "Required. The names of the PipelineJobs to delete. A maximum of 32 PipelineJobs can be deleted in a batch. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipelineJob}`", +"timeSeriesData": { +"description": "The returned time series data.", "items": { -"type": "string" +"$ref": "GoogleCloudAiplatformV1TimeSeriesData" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsRequest": { -"description": "Request message for ModelService.BatchImportEvaluatedAnnotations", -"id": "GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsRequest", +"GoogleCloudAiplatformV1BigQueryDestination": { +"description": "The BigQuery location for the output content.", +"id": "GoogleCloudAiplatformV1BigQueryDestination", "properties": { -"evaluatedAnnotations": { -"description": "Required. Evaluated annotations resource to be imported.", -"items": { -"$ref": "GoogleCloudAiplatformV1EvaluatedAnnotation" -}, -"type": "array" +"outputUri": { +"description": "Required. BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: `bq://projectId` or `bq://projectId.bqDatasetId` or `bq://projectId.bqDatasetId.bqTableId`.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsResponse": { -"description": "Response message for ModelService.BatchImportEvaluatedAnnotations", -"id": "GoogleCloudAiplatformV1BatchImportEvaluatedAnnotationsResponse", +"GoogleCloudAiplatformV1BigQuerySource": { +"description": "The BigQuery location for the input content.", +"id": "GoogleCloudAiplatformV1BigQuerySource", "properties": { -"importedEvaluatedAnnotationsCount": { -"description": "Output only. Number of EvaluatedAnnotations imported.", -"format": "int32", -"readOnly": true, -"type": "integer" +"inputUri": { +"description": "Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1BatchImportModelEvaluationSlicesRequest": { -"description": "Request message for ModelService.BatchImportModelEvaluationSlices", -"id": "GoogleCloudAiplatformV1BatchImportModelEvaluationSlicesRequest", +"GoogleCloudAiplatformV1Blob": { +"description": "Content blob. It's preferred to send as text directly rather than raw bytes.", +"id": "GoogleCloudAiplatformV1Blob", "properties": { -"modelEvaluationSlices": { -"description": "Required. Model evaluation slice resource to be imported.", -"items": { -"$ref": "GoogleCloudAiplatformV1ModelEvaluationSlice" +"data": { +"description": "Required. Raw bytes.", +"format": "byte", +"type": "string" }, -"type": "array" +"mimeType": { +"description": "Required. The IANA standard MIME type of the source data.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1BatchImportModelEvaluationSlicesResponse": { -"description": "Response message for ModelService.BatchImportModelEvaluationSlices", -"id": "GoogleCloudAiplatformV1BatchImportModelEvaluationSlicesResponse", +"GoogleCloudAiplatformV1BlurBaselineConfig": { +"description": "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", +"id": "GoogleCloudAiplatformV1BlurBaselineConfig", "properties": { -"importedModelEvaluationSlices": { -"description": "Output only. List of imported ModelEvaluationSlice.name.", -"items": { -"type": "string" -}, -"readOnly": true, -"type": "array" +"maxBlurSigma": { +"description": "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.", +"format": "float", +"type": "number" } }, "type": "object" }, -"GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadata": { -"description": "Runtime operation information for MigrationService.BatchMigrateResources.", -"id": "GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadata", +"GoogleCloudAiplatformV1BoolArray": { +"description": "A list of boolean values.", +"id": "GoogleCloudAiplatformV1BoolArray", "properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "The common part of the operation metadata." -}, -"partialResults": { -"description": "Partial results that reflect the latest migration operation progress.", +"values": { +"description": "A list of bool values.", "items": { -"$ref": "GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadataPartialResult" +"type": "boolean" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadataPartialResult": { -"description": "Represents a partial result in batch migration operation for one MigrateResourceRequest.", -"id": "GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadataPartialResult", -"properties": { -"dataset": { -"description": "Migrated dataset resource name.", -"type": "string" +"GoogleCloudAiplatformV1CancelBatchPredictionJobRequest": { +"description": "Request message for JobService.CancelBatchPredictionJob.", +"id": "GoogleCloudAiplatformV1CancelBatchPredictionJobRequest", +"properties": {}, +"type": "object" }, -"error": { -"$ref": "GoogleRpcStatus", -"description": "The error result of the migration request in case of failure." +"GoogleCloudAiplatformV1CancelCustomJobRequest": { +"description": "Request message for JobService.CancelCustomJob.", +"id": "GoogleCloudAiplatformV1CancelCustomJobRequest", +"properties": {}, +"type": "object" }, -"model": { -"description": "Migrated model resource name.", -"type": "string" +"GoogleCloudAiplatformV1CancelDataLabelingJobRequest": { +"description": "Request message for JobService.CancelDataLabelingJob.", +"id": "GoogleCloudAiplatformV1CancelDataLabelingJobRequest", +"properties": {}, +"type": "object" }, -"request": { -"$ref": "GoogleCloudAiplatformV1MigrateResourceRequest", -"description": "It's the same as the value in MigrateResourceRequest.migrate_resource_requests." -} +"GoogleCloudAiplatformV1CancelHyperparameterTuningJobRequest": { +"description": "Request message for JobService.CancelHyperparameterTuningJob.", +"id": "GoogleCloudAiplatformV1CancelHyperparameterTuningJobRequest", +"properties": {}, +"type": "object" }, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchMigrateResourcesRequest": { -"description": "Request message for MigrationService.BatchMigrateResources.", -"id": "GoogleCloudAiplatformV1BatchMigrateResourcesRequest", -"properties": { -"migrateResourceRequests": { -"description": "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.", -"items": { -"$ref": "GoogleCloudAiplatformV1MigrateResourceRequest" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchMigrateResourcesResponse": { -"description": "Response message for MigrationService.BatchMigrateResources.", -"id": "GoogleCloudAiplatformV1BatchMigrateResourcesResponse", -"properties": { -"migrateResourceResponses": { -"description": "Successfully migrated resources.", -"items": { -"$ref": "GoogleCloudAiplatformV1MigrateResourceResponse" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchPredictionJob": { -"description": "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.", -"id": "GoogleCloudAiplatformV1BatchPredictionJob", -"properties": { -"completionStats": { -"$ref": "GoogleCloudAiplatformV1CompletionStats", -"description": "Output only. Statistics on completed and failed prediction instances.", -"readOnly": true -}, -"createTime": { -"description": "Output only. Time when the BatchPredictionJob was created.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -}, -"dedicatedResources": { -"$ref": "GoogleCloudAiplatformV1BatchDedicatedResources", -"description": "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": { -"description": "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.", -"type": "boolean" -}, -"displayName": { -"description": "Required. The user-defined name of this BatchPredictionJob.", -"type": "string" -}, -"encryptionSpec": { -"$ref": "GoogleCloudAiplatformV1EncryptionSpec", -"description": "Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key." -}, -"endTime": { -"description": "Output only. Time when the BatchPredictionJob entered any of the following states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -}, -"error": { -"$ref": "GoogleRpcStatus", -"description": "Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.", -"readOnly": true -}, -"explanationSpec": { -"$ref": "GoogleCloudAiplatformV1ExplanationSpec", -"description": "Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to `true`. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited." -}, -"generateExplanation": { -"description": "Generate explanation with the batch prediction results. When set to `true`, the batch prediction output changes based on the `predictions_format` field of the BatchPredictionJob.output_config object: * `bigquery`: output includes a column named `explanation`. The value is a struct that conforms to the Explanation object. * `jsonl`: The JSON objects on each line include an additional entry keyed `explanation`. The value of the entry is a JSON object that conforms to the Explanation object. * `csv`: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.", -"type": "boolean" -}, -"inputConfig": { -"$ref": "GoogleCloudAiplatformV1BatchPredictionJobInputConfig", -"description": "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": { -"$ref": "GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig", -"description": "Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model." -}, -"labels": { -"additionalProperties": { -"type": "string" -}, -"description": "The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.", -"type": "object" -}, -"manualBatchTuningParameters": { -"$ref": "GoogleCloudAiplatformV1ManualBatchTuningParameters", -"description": "Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself)." -}, -"model": { -"description": "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}`", -"type": "string" -}, -"modelParameters": { -"description": "The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.", -"type": "any" -}, -"modelVersionId": { -"description": "Output only. The version ID of the Model that produces the predictions via this job.", -"readOnly": true, -"type": "string" -}, -"name": { -"description": "Output only. Resource name of the BatchPredictionJob.", -"readOnly": true, -"type": "string" -}, -"outputConfig": { -"$ref": "GoogleCloudAiplatformV1BatchPredictionJobOutputConfig", -"description": "Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri." -}, -"outputInfo": { -"$ref": "GoogleCloudAiplatformV1BatchPredictionJobOutputInfo", -"description": "Output only. Information further describing the output of this job.", -"readOnly": true -}, -"partialFailures": { -"description": "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.", -"items": { -"$ref": "GoogleRpcStatus" -}, -"readOnly": true, -"type": "array" -}, -"resourcesConsumed": { -"$ref": "GoogleCloudAiplatformV1ResourcesConsumed", -"description": "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.", -"readOnly": true -}, -"serviceAccount": { -"description": "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.", -"type": "string" -}, -"startTime": { -"description": "Output only. Time when the BatchPredictionJob for the first time entered the `JOB_STATE_RUNNING` state.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -}, -"state": { -"description": "Output only. The detailed state of the job.", -"enum": [ -"JOB_STATE_UNSPECIFIED", -"JOB_STATE_QUEUED", -"JOB_STATE_PENDING", -"JOB_STATE_RUNNING", -"JOB_STATE_SUCCEEDED", -"JOB_STATE_FAILED", -"JOB_STATE_CANCELLING", -"JOB_STATE_CANCELLED", -"JOB_STATE_PAUSED", -"JOB_STATE_EXPIRED", -"JOB_STATE_UPDATING", -"JOB_STATE_PARTIALLY_SUCCEEDED" -], -"enumDescriptions": [ -"The job state is unspecified.", -"The job has been just created or resumed and processing has not yet begun.", -"The service is preparing to run the job.", -"The job is in progress.", -"The job completed successfully.", -"The job failed.", -"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`.", -"The job has been cancelled.", -"The job has been stopped, and can be resumed.", -"The job has expired.", -"The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state.", -"The job is partially succeeded, some results may be missing due to errors." -], -"readOnly": true, -"type": "string" -}, -"unmanagedContainerModel": { -"$ref": "GoogleCloudAiplatformV1UnmanagedContainerModel", -"description": "Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set." -}, -"updateTime": { -"description": "Output only. Time when the BatchPredictionJob was most recently updated.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchPredictionJobInputConfig": { -"description": "Configures the input to BatchPredictionJob. See Model.supported_input_storage_formats for Model's supported input formats, and how instances should be expressed via any of them.", -"id": "GoogleCloudAiplatformV1BatchPredictionJobInputConfig", -"properties": { -"bigquerySource": { -"$ref": "GoogleCloudAiplatformV1BigQuerySource", -"description": "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": { -"$ref": "GoogleCloudAiplatformV1GcsSource", -"description": "The Cloud Storage location for the input instances." -}, -"instancesFormat": { -"description": "Required. The format in which instances are given, must be one of the Model's supported_input_storage_formats.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig": { -"description": "Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.", -"id": "GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig", -"properties": { -"excludedFields": { -"description": "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.", -"items": { -"type": "string" -}, -"type": "array" -}, -"includedFields": { -"description": "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.", -"items": { -"type": "string" -}, -"type": "array" -}, -"instanceType": { -"description": "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.", -"type": "string" -}, -"keyField": { -"description": "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.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchPredictionJobOutputConfig": { -"description": "Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.", -"id": "GoogleCloudAiplatformV1BatchPredictionJobOutputConfig", -"properties": { -"bigqueryDestination": { -"$ref": "GoogleCloudAiplatformV1BigQueryDestination", -"description": "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": { -"$ref": "GoogleCloudAiplatformV1GcsDestination", -"description": "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": { -"description": "Required. The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchPredictionJobOutputInfo": { -"description": "Further describes this job's output. Supplements output_config.", -"id": "GoogleCloudAiplatformV1BatchPredictionJobOutputInfo", -"properties": { -"bigqueryOutputDataset": { -"description": "Output only. The path of the BigQuery dataset created, in `bq://projectId.bqDatasetId` format, into which the prediction output is written.", -"readOnly": true, -"type": "string" -}, -"bigqueryOutputTable": { -"description": "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.", -"readOnly": true, -"type": "string" -}, -"gcsOutputDirectory": { -"description": "Output only. The full path of the Cloud Storage directory created, into which the prediction output is written.", -"readOnly": true, -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchReadFeatureValuesOperationMetadata": { -"description": "Details of operations that batch reads Feature values.", -"id": "GoogleCloudAiplatformV1BatchReadFeatureValuesOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "Operation metadata for Featurestore batch read Features values." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchReadFeatureValuesRequest": { -"description": "Request message for FeaturestoreService.BatchReadFeatureValues.", -"id": "GoogleCloudAiplatformV1BatchReadFeatureValuesRequest", -"properties": { -"bigqueryReadInstances": { -"$ref": "GoogleCloudAiplatformV1BigQuerySource", -"description": "Similar to csv_read_instances, but from BigQuery source." -}, -"csvReadInstances": { -"$ref": "GoogleCloudAiplatformV1CsvSource", -"description": "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": { -"$ref": "GoogleCloudAiplatformV1FeatureValueDestination", -"description": "Required. Specifies output location and format." -}, -"entityTypeSpecs": { -"description": "Required. Specifies EntityType grouping Features to read values of and settings.", -"items": { -"$ref": "GoogleCloudAiplatformV1BatchReadFeatureValuesRequestEntityTypeSpec" -}, -"type": "array" -}, -"passThroughFields": { -"description": "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.", -"items": { -"$ref": "GoogleCloudAiplatformV1BatchReadFeatureValuesRequestPassThroughField" -}, -"type": "array" -}, -"startTime": { -"description": "Optional. Excludes Feature values with feature generation timestamp before this timestamp. If not set, retrieve oldest values kept in Feature Store. Timestamp, if present, must not have higher than millisecond precision.", -"format": "google-datetime", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchReadFeatureValuesRequestEntityTypeSpec": { -"description": "Selects Features of an EntityType to read values of and specifies read settings.", -"id": "GoogleCloudAiplatformV1BatchReadFeatureValuesRequestEntityTypeSpec", -"properties": { -"entityTypeId": { -"description": "Required. ID of the EntityType to select Features. The EntityType id is the entity_type_id specified during EntityType creation.", -"type": "string" -}, -"featureSelector": { -"$ref": "GoogleCloudAiplatformV1FeatureSelector", -"description": "Required. Selectors choosing which Feature values to read from the EntityType." -}, -"settings": { -"description": "Per-Feature settings for the batch read.", -"items": { -"$ref": "GoogleCloudAiplatformV1DestinationFeatureSetting" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchReadFeatureValuesRequestPassThroughField": { -"description": "Describe pass-through fields in read_instance source.", -"id": "GoogleCloudAiplatformV1BatchReadFeatureValuesRequestPassThroughField", -"properties": { -"fieldName": { -"description": "Required. The name of the field in the CSV header or the name of the column in BigQuery table. The naming restriction is the same as Feature.name.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchReadFeatureValuesResponse": { -"description": "Response message for FeaturestoreService.BatchReadFeatureValues.", -"id": "GoogleCloudAiplatformV1BatchReadFeatureValuesResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1BatchReadTensorboardTimeSeriesDataResponse": { -"description": "Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.", -"id": "GoogleCloudAiplatformV1BatchReadTensorboardTimeSeriesDataResponse", -"properties": { -"timeSeriesData": { -"description": "The returned time series data.", -"items": { -"$ref": "GoogleCloudAiplatformV1TimeSeriesData" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BigQueryDestination": { -"description": "The BigQuery location for the output content.", -"id": "GoogleCloudAiplatformV1BigQueryDestination", -"properties": { -"outputUri": { -"description": "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`.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BigQuerySource": { -"description": "The BigQuery location for the input content.", -"id": "GoogleCloudAiplatformV1BigQuerySource", -"properties": { -"inputUri": { -"description": "Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1Blob": { -"description": "Content blob. It's preferred to send as text directly rather than raw bytes.", -"id": "GoogleCloudAiplatformV1Blob", -"properties": { -"data": { -"description": "Required. Raw bytes.", -"format": "byte", -"type": "string" -}, -"mimeType": { -"description": "Required. The IANA standard MIME type of the source data.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BlurBaselineConfig": { -"description": "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", -"id": "GoogleCloudAiplatformV1BlurBaselineConfig", -"properties": { -"maxBlurSigma": { -"description": "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.", -"format": "float", -"type": "number" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1BoolArray": { -"description": "A list of boolean values.", -"id": "GoogleCloudAiplatformV1BoolArray", -"properties": { -"values": { -"description": "A list of bool values.", -"items": { -"type": "boolean" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1CancelBatchPredictionJobRequest": { -"description": "Request message for JobService.CancelBatchPredictionJob.", -"id": "GoogleCloudAiplatformV1CancelBatchPredictionJobRequest", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1CancelCustomJobRequest": { -"description": "Request message for JobService.CancelCustomJob.", -"id": "GoogleCloudAiplatformV1CancelCustomJobRequest", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1CancelDataLabelingJobRequest": { -"description": "Request message for JobService.CancelDataLabelingJob.", -"id": "GoogleCloudAiplatformV1CancelDataLabelingJobRequest", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1CancelHyperparameterTuningJobRequest": { -"description": "Request message for JobService.CancelHyperparameterTuningJob.", -"id": "GoogleCloudAiplatformV1CancelHyperparameterTuningJobRequest", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1CancelNasJobRequest": { -"description": "Request message for JobService.CancelNasJob.", -"id": "GoogleCloudAiplatformV1CancelNasJobRequest", -"properties": {}, +"GoogleCloudAiplatformV1CancelNasJobRequest": { +"description": "Request message for JobService.CancelNasJob.", +"id": "GoogleCloudAiplatformV1CancelNasJobRequest", +"properties": {}, "type": "object" }, "GoogleCloudAiplatformV1CancelPipelineJobRequest": { @@ -19676,6 +18886,10 @@ "description": "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/.", "type": "string" }, +"modelReference": { +"description": "Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.", +"type": "string" +}, "name": { "description": "Output only. The resource name of the Dataset.", "readOnly": true, @@ -19725,6 +18939,11 @@ "readOnly": true, "type": "any" }, +"modelReference": { +"description": "Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions.", +"readOnly": true, +"type": "string" +}, "name": { "description": "Output only. The resource name of the DatasetVersion.", "readOnly": true, @@ -21677,7 +20896,7 @@ "properties": { "bigQuery": { "$ref": "GoogleCloudAiplatformV1FeatureGroupBigQuery", -"description": "Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entity_id and a feature_timestamp column in the source." +"description": "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": { "description": "Output only. Timestamp when this FeatureGroup was created.", @@ -22989,6 +22208,22 @@ "description": "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.", "type": "string" }, +"responseStyle": { +"description": "Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED", +"enum": [ +"RESPONSE_STYLE_UNSPECIFIED", +"RESPONSE_STYLE_PRECISE", +"RESPONSE_STYLE_BALANCED", +"RESPONSE_STYLE_CREATIVE" +], +"enumDescriptions": [ +"response style unspecified.", +"Precise response.", +"Default response style.", +"Creative response style." +], +"type": "string" +}, "stopSequences": { "description": "Optional. Stop sequences.", "items": { @@ -23624,7 +22859,7 @@ "id": "GoogleCloudAiplatformV1IndexDatapointSparseEmbedding", "properties": { "dimensions": { -"description": "Optional. The list of indexes for the embedding values of the sparse vector.", +"description": "Required. The list of indexes for the embedding values of the sparse vector.", "items": { "format": "int64", "type": "string" @@ -23632,7 +22867,7 @@ "type": "array" }, "values": { -"description": "Optional. The list of embedding values of the sparse vector.", +"description": "Required. The list of embedding values of the sparse vector.", "items": { "format": "float", "type": "number" @@ -26856,6 +26091,11 @@ "format": "int64", "type": "string" }, +"invalidSparseRecordCount": { +"description": "Number of sparse records in this file we skipped due to validate errors.", +"format": "int64", +"type": "string" +}, "partialErrors": { "description": "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.", "items": { @@ -26871,6 +26111,11 @@ "description": "Number of records in this file that were successfully processed.", "format": "int64", "type": "string" +}, +"validSparseRecordCount": { +"description": "Number of sparse records in this file that were successfully processed.", +"format": "int64", +"type": "string" } }, "type": "object" @@ -27217,6 +26462,16 @@ "description": "Required. The user email of the NotebookRuntime.", "type": "string" }, +"satisfiesPzi": { +"description": "Output only. Reserved for future use.", +"readOnly": true, +"type": "boolean" +}, +"satisfiesPzs": { +"description": "Output only. Reserved for future use.", +"readOnly": true, +"type": "boolean" +}, "serviceAccount": { "description": "Output only. The service account that the NotebookRuntime workload runs as.", "readOnly": true, @@ -35521,6189 +34776,993 @@ false }, "type": "object" }, -"GoogleCloudAiplatformV1TuningDataStats": { -"description": "The tuning data statistic values for TuningJob.", -"id": "GoogleCloudAiplatformV1TuningDataStats", -"properties": { -"supervisedTuningDataStats": { -"$ref": "GoogleCloudAiplatformV1SupervisedTuningDataStats", -"description": "The SFT Tuning data stats." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1TuningJob": { -"description": "Represents a TuningJob that runs with Google owned models.", -"id": "GoogleCloudAiplatformV1TuningJob", -"properties": { -"baseModel": { -"description": "The base model that is being tuned, e.g., \"gemini-1.0-pro-002\".", -"type": "string" -}, -"createTime": { -"description": "Output only. Time when the TuningJob was created.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -}, -"description": { -"description": "Optional. The description of the TuningJob.", -"type": "string" -}, -"endTime": { -"description": "Output only. Time when the TuningJob entered any of the following JobStates: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`, `JOB_STATE_EXPIRED`.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -}, -"error": { -"$ref": "GoogleRpcStatus", -"description": "Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.", -"readOnly": true -}, -"experiment": { -"description": "Output only. The Experiment associated with this TuningJob.", -"readOnly": true, -"type": "string" -}, -"labels": { -"additionalProperties": { -"type": "string" -}, -"description": "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.", -"type": "object" -}, -"name": { -"description": "Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}`", -"readOnly": true, -"type": "string" -}, -"startTime": { -"description": "Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -}, -"state": { -"description": "Output only. The detailed state of the job.", -"enum": [ -"JOB_STATE_UNSPECIFIED", -"JOB_STATE_QUEUED", -"JOB_STATE_PENDING", -"JOB_STATE_RUNNING", -"JOB_STATE_SUCCEEDED", -"JOB_STATE_FAILED", -"JOB_STATE_CANCELLING", -"JOB_STATE_CANCELLED", -"JOB_STATE_PAUSED", -"JOB_STATE_EXPIRED", -"JOB_STATE_UPDATING", -"JOB_STATE_PARTIALLY_SUCCEEDED" -], -"enumDescriptions": [ -"The job state is unspecified.", -"The job has been just created or resumed and processing has not yet begun.", -"The service is preparing to run the job.", -"The job is in progress.", -"The job completed successfully.", -"The job failed.", -"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`.", -"The job has been cancelled.", -"The job has been stopped, and can be resumed.", -"The job has expired.", -"The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state.", -"The job is partially succeeded, some results may be missing due to errors." -], -"readOnly": true, -"type": "string" -}, -"supervisedTuningSpec": { -"$ref": "GoogleCloudAiplatformV1SupervisedTuningSpec", -"description": "Tuning Spec for Supervised Fine Tuning." -}, -"tunedModel": { -"$ref": "GoogleCloudAiplatformV1TunedModel", -"description": "Output only. The tuned model resources assiociated with this TuningJob.", -"readOnly": true -}, -"tunedModelDisplayName": { -"description": "Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters.", -"type": "string" -}, -"tuningDataStats": { -"$ref": "GoogleCloudAiplatformV1TuningDataStats", -"description": "Output only. The tuning data statistics associated with this TuningJob.", -"readOnly": true -}, -"updateTime": { -"description": "Output only. Time when the TuningJob was most recently updated.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UndeployIndexOperationMetadata": { -"description": "Runtime operation information for IndexEndpointService.UndeployIndex.", -"id": "GoogleCloudAiplatformV1UndeployIndexOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UndeployIndexRequest": { -"description": "Request message for IndexEndpointService.UndeployIndex.", -"id": "GoogleCloudAiplatformV1UndeployIndexRequest", -"properties": { -"deployedIndexId": { -"description": "Required. The ID of the DeployedIndex to be undeployed from the IndexEndpoint.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UndeployIndexResponse": { -"description": "Response message for IndexEndpointService.UndeployIndex.", -"id": "GoogleCloudAiplatformV1UndeployIndexResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1UndeployModelOperationMetadata": { -"description": "Runtime operation information for EndpointService.UndeployModel.", -"id": "GoogleCloudAiplatformV1UndeployModelOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UndeployModelRequest": { -"description": "Request message for EndpointService.UndeployModel.", -"id": "GoogleCloudAiplatformV1UndeployModelRequest", -"properties": { -"deployedModelId": { -"description": "Required. The ID of the DeployedModel to be undeployed from the Endpoint.", -"type": "string" -}, -"trafficSplit": { -"additionalProperties": { -"format": "int32", -"type": "integer" -}, -"description": "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.", -"type": "object" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UndeployModelResponse": { -"description": "Response message for EndpointService.UndeployModel.", -"id": "GoogleCloudAiplatformV1UndeployModelResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1UnmanagedContainerModel": { -"description": "Contains model information necessary to perform batch prediction without requiring a full model import.", -"id": "GoogleCloudAiplatformV1UnmanagedContainerModel", -"properties": { -"artifactUri": { -"description": "The path to the directory containing the Model artifact and any of its supporting files.", -"type": "string" -}, -"containerSpec": { -"$ref": "GoogleCloudAiplatformV1ModelContainerSpec", -"description": "Input only. The specification of the container that is to be used when deploying this Model." -}, -"predictSchemata": { -"$ref": "GoogleCloudAiplatformV1PredictSchemata", -"description": "Contains the schemata used in Model's predictions and explanations" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateDeploymentResourcePoolOperationMetadata": { -"description": "Runtime operation information for UpdateDeploymentResourcePool method.", -"id": "GoogleCloudAiplatformV1UpdateDeploymentResourcePoolOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateExplanationDatasetOperationMetadata": { -"description": "Runtime operation information for ModelService.UpdateExplanationDataset.", -"id": "GoogleCloudAiplatformV1UpdateExplanationDatasetOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "The common part of the operation metadata." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateExplanationDatasetRequest": { -"description": "Request message for ModelService.UpdateExplanationDataset.", -"id": "GoogleCloudAiplatformV1UpdateExplanationDatasetRequest", -"properties": { -"examples": { -"$ref": "GoogleCloudAiplatformV1Examples", -"description": "The example config containing the location of the dataset." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateExplanationDatasetResponse": { -"description": "Response message of ModelService.UpdateExplanationDataset operation.", -"id": "GoogleCloudAiplatformV1UpdateExplanationDatasetResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateFeatureGroupOperationMetadata": { -"description": "Details of operations that perform update FeatureGroup.", -"id": "GoogleCloudAiplatformV1UpdateFeatureGroupOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "Operation metadata for FeatureGroup." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateFeatureOnlineStoreOperationMetadata": { -"description": "Details of operations that perform update FeatureOnlineStore.", -"id": "GoogleCloudAiplatformV1UpdateFeatureOnlineStoreOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "Operation metadata for FeatureOnlineStore." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateFeatureOperationMetadata": { -"description": "Details of operations that perform update Feature.", -"id": "GoogleCloudAiplatformV1UpdateFeatureOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "Operation metadata for Feature Update." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateFeatureViewOperationMetadata": { -"description": "Details of operations that perform update FeatureView.", -"id": "GoogleCloudAiplatformV1UpdateFeatureViewOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "Operation metadata for FeatureView Update." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateFeaturestoreOperationMetadata": { -"description": "Details of operations that perform update Featurestore.", -"id": "GoogleCloudAiplatformV1UpdateFeaturestoreOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "Operation metadata for Featurestore." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateIndexOperationMetadata": { -"description": "Runtime operation information for IndexService.UpdateIndex.", -"id": "GoogleCloudAiplatformV1UpdateIndexOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "The operation generic information." -}, -"nearestNeighborSearchOperationMetadata": { -"$ref": "GoogleCloudAiplatformV1NearestNeighborSearchOperationMetadata", -"description": "The operation metadata with regard to Matching Engine Index operation." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateModelDeploymentMonitoringJobOperationMetadata": { -"description": "Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob.", -"id": "GoogleCloudAiplatformV1UpdateModelDeploymentMonitoringJobOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdatePersistentResourceOperationMetadata": { -"description": "Details of operations that perform update PersistentResource.", -"id": "GoogleCloudAiplatformV1UpdatePersistentResourceOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "Operation metadata for PersistentResource." -}, -"progressMessage": { -"description": "Progress Message for Update LRO", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateSpecialistPoolOperationMetadata": { -"description": "Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.", -"id": "GoogleCloudAiplatformV1UpdateSpecialistPoolOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "The operation generic information." -}, -"specialistPool": { -"description": "Output only. The name of the SpecialistPool to which the specialists are being added. Format: `projects/{project_id}/locations/{location_id}/specialistPools/{specialist_pool}`", -"readOnly": true, -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpdateTensorboardOperationMetadata": { -"description": "Details of operations that perform update Tensorboard.", -"id": "GoogleCloudAiplatformV1UpdateTensorboardOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "Operation metadata for Tensorboard." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpgradeNotebookRuntimeOperationMetadata": { -"description": "Metadata information for NotebookService.UpgradeNotebookRuntime.", -"id": "GoogleCloudAiplatformV1UpgradeNotebookRuntimeOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "The operation generic information." -}, -"progressMessage": { -"description": "A human-readable message that shows the intermediate progress details of NotebookRuntime.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpgradeNotebookRuntimeRequest": { -"description": "Request message for NotebookService.UpgradeNotebookRuntime.", -"id": "GoogleCloudAiplatformV1UpgradeNotebookRuntimeRequest", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1UploadModelOperationMetadata": { -"description": "Details of ModelService.UploadModel operation.", -"id": "GoogleCloudAiplatformV1UploadModelOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", -"description": "The common part of the operation metadata." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UploadModelRequest": { -"description": "Request message for ModelService.UploadModel.", -"id": "GoogleCloudAiplatformV1UploadModelRequest", -"properties": { -"model": { -"$ref": "GoogleCloudAiplatformV1Model", -"description": "Required. The Model to create." -}, -"modelId": { -"description": "Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are `[a-z0-9_-]`. The first character cannot be a number or hyphen.", -"type": "string" -}, -"parentModel": { -"description": "Optional. The resource name of the model into which to upload the version. Only specify this field when uploading a new version.", -"type": "string" -}, -"serviceAccount": { -"description": "Optional. The user-provided custom service account to use to do the model upload. If empty, [Vertex AI Service Agent](https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) will be used to access resources needed to upload the model. This account must belong to the target project where the model is uploaded to, i.e., the project specified in the `parent` field of this request and have necessary read permissions (to Google Cloud Storage, Artifact Registry, etc.).", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UploadModelResponse": { -"description": "Response message of ModelService.UploadModel operation.", -"id": "GoogleCloudAiplatformV1UploadModelResponse", -"properties": { -"model": { -"description": "The name of the uploaded Model resource. Format: `projects/{project}/locations/{location}/models/{model}`", -"type": "string" -}, -"modelVersionId": { -"description": "Output only. The version ID of the model that is uploaded.", -"readOnly": true, -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpsertDatapointsRequest": { -"description": "Request message for IndexService.UpsertDatapoints", -"id": "GoogleCloudAiplatformV1UpsertDatapointsRequest", -"properties": { -"datapoints": { -"description": "A list of datapoints to be created/updated.", -"items": { -"$ref": "GoogleCloudAiplatformV1IndexDatapoint" -}, -"type": "array" -}, -"updateMask": { -"description": "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.", -"format": "google-fieldmask", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1UpsertDatapointsResponse": { -"description": "Response message for IndexService.UpsertDatapoints", -"id": "GoogleCloudAiplatformV1UpsertDatapointsResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1UserActionReference": { -"description": "References an API call. It contains more information about long running operation and Jobs that are triggered by the API call.", -"id": "GoogleCloudAiplatformV1UserActionReference", -"properties": { -"dataLabelingJob": { -"description": "For API calls that start a LabelingJob. Resource name of the LabelingJob. Format: `projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}`", -"type": "string" -}, -"method": { -"description": "The method name of the API RPC call. For example, \"/google.cloud.aiplatform.{apiVersion}.DatasetService.CreateDataset\"", -"type": "string" -}, -"operation": { -"description": "For API calls that return a long running operation. Resource name of the long running operation. Format: `projects/{project}/locations/{location}/operations/{operation}`", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1Value": { -"description": "Value is the value of the field.", -"id": "GoogleCloudAiplatformV1Value", -"properties": { -"doubleValue": { -"description": "A double value.", -"format": "double", -"type": "number" -}, -"intValue": { -"description": "An integer value.", -"format": "int64", -"type": "string" -}, -"stringValue": { -"description": "A string value.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1VertexAISearch": { -"description": "Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation", -"id": "GoogleCloudAiplatformV1VertexAISearch", -"properties": { -"datastore": { -"description": "Required. Fully-qualified Vertex AI Search's datastore resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1VideoMetadata": { -"description": "Metadata describes the input video content.", -"id": "GoogleCloudAiplatformV1VideoMetadata", -"properties": { -"endOffset": { -"description": "Optional. The end offset of the video.", -"format": "google-duration", -"type": "string" -}, -"startOffset": { -"description": "Optional. The start offset of the video.", -"format": "google-duration", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1WorkerPoolSpec": { -"description": "Represents the spec of a worker pool in a job.", -"id": "GoogleCloudAiplatformV1WorkerPoolSpec", -"properties": { -"containerSpec": { -"$ref": "GoogleCloudAiplatformV1ContainerSpec", -"description": "The custom container task." -}, -"diskSpec": { -"$ref": "GoogleCloudAiplatformV1DiskSpec", -"description": "Disk spec." -}, -"machineSpec": { -"$ref": "GoogleCloudAiplatformV1MachineSpec", -"description": "Optional. Immutable. The specification of a single machine." -}, -"nfsMounts": { -"description": "Optional. List of NFS mount spec.", -"items": { -"$ref": "GoogleCloudAiplatformV1NfsMount" -}, -"type": "array" -}, -"pythonPackageSpec": { -"$ref": "GoogleCloudAiplatformV1PythonPackageSpec", -"description": "The Python packaged task." -}, -"replicaCount": { -"description": "Optional. The number of worker replicas to use for this worker pool.", -"format": "int64", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1WriteFeatureValuesPayload": { -"description": "Contains Feature values to be written for a specific entity.", -"id": "GoogleCloudAiplatformV1WriteFeatureValuesPayload", -"properties": { -"entityId": { -"description": "Required. The ID of the entity.", -"type": "string" -}, -"featureValues": { -"additionalProperties": { -"$ref": "GoogleCloudAiplatformV1FeatureValue" -}, -"description": "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.", -"type": "object" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1WriteFeatureValuesRequest": { -"description": "Request message for FeaturestoreOnlineServingService.WriteFeatureValues.", -"id": "GoogleCloudAiplatformV1WriteFeatureValuesRequest", -"properties": { -"payloads": { -"description": "Required. The entities to be written. Up to 100,000 feature values can be written across all `payloads`.", -"items": { -"$ref": "GoogleCloudAiplatformV1WriteFeatureValuesPayload" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1WriteFeatureValuesResponse": { -"description": "Response message for FeaturestoreOnlineServingService.WriteFeatureValues.", -"id": "GoogleCloudAiplatformV1WriteFeatureValuesResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1WriteTensorboardExperimentDataRequest": { -"description": "Request message for TensorboardService.WriteTensorboardExperimentData.", -"id": "GoogleCloudAiplatformV1WriteTensorboardExperimentDataRequest", -"properties": { -"writeRunDataRequests": { -"description": "Required. Requests containing per-run TensorboardTimeSeries data to write.", -"items": { -"$ref": "GoogleCloudAiplatformV1WriteTensorboardRunDataRequest" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1WriteTensorboardExperimentDataResponse": { -"description": "Response message for TensorboardService.WriteTensorboardExperimentData.", -"id": "GoogleCloudAiplatformV1WriteTensorboardExperimentDataResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1WriteTensorboardRunDataRequest": { -"description": "Request message for TensorboardService.WriteTensorboardRunData.", -"id": "GoogleCloudAiplatformV1WriteTensorboardRunDataRequest", -"properties": { -"tensorboardRun": { -"description": "Required. The resource name of the TensorboardRun to write data to. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}`", -"type": "string" -}, -"timeSeriesData": { -"description": "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.", -"items": { -"$ref": "GoogleCloudAiplatformV1TimeSeriesData" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1WriteTensorboardRunDataResponse": { -"description": "Response message for TensorboardService.WriteTensorboardRunData.", -"id": "GoogleCloudAiplatformV1WriteTensorboardRunDataResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1XraiAttribution": { -"description": "An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Supported only by image Models.", -"id": "GoogleCloudAiplatformV1XraiAttribution", -"properties": { -"blurBaselineConfig": { -"$ref": "GoogleCloudAiplatformV1BlurBaselineConfig", -"description": "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": { -"$ref": "GoogleCloudAiplatformV1SmoothGradConfig", -"description": "Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf" -}, -"stepCount": { -"description": "Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"GoogleCloudLocationListLocationsResponse": { -"description": "The response message for Locations.ListLocations.", -"id": "GoogleCloudLocationListLocationsResponse", -"properties": { -"locations": { -"description": "A list of locations that matches the specified filter in the request.", -"items": { -"$ref": "GoogleCloudLocationLocation" -}, -"type": "array" -}, -"nextPageToken": { -"description": "The standard List next-page token.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudLocationLocation": { -"description": "A resource that represents a Google Cloud location.", -"id": "GoogleCloudLocationLocation", -"properties": { -"displayName": { -"description": "The friendly name for this location, typically a nearby city name. For example, \"Tokyo\".", -"type": "string" -}, -"labels": { -"additionalProperties": { -"type": "string" -}, -"description": "Cross-service attributes for the location. For example {\"cloud.googleapis.com/region\": \"us-east1\"}", -"type": "object" -}, -"locationId": { -"description": "The canonical id for this location. For example: `\"us-east1\"`.", -"type": "string" -}, -"metadata": { -"additionalProperties": { -"description": "Properties of the object. Contains field @type with type URL.", -"type": "any" -}, -"description": "Service-specific metadata. For example the available capacity at the given location.", -"type": "object" -}, -"name": { -"description": "Resource name for the location, which may vary between implementations. For example: `\"projects/example-project/locations/us-east1\"`", -"type": "string" -} -}, -"type": "object" -}, -"GoogleIamV1Binding": { -"description": "Associates `members`, or principals, with a `role`.", -"id": "GoogleIamV1Binding", -"properties": { -"condition": { -"$ref": "GoogleTypeExpr", -"description": "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": { -"description": "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/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workforce identity pool. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}`: All workforce identities in a group. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All workforce identities with a specific attribute value. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*`: All identities in a workforce identity pool. * `principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workload identity pool. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}`: A workload identity pool group. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{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/global/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.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: Deleted single identity in a workforce identity pool. For example, `deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value`.", -"items": { -"type": "string" -}, -"type": "array" -}, -"role": { -"description": "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).", -"type": "string" -} -}, -"type": "object" -}, -"GoogleIamV1Policy": { -"description": "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/).", -"id": "GoogleIamV1Policy", -"properties": { -"bindings": { -"description": "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`.", -"items": { -"$ref": "GoogleIamV1Binding" -}, -"type": "array" -}, -"etag": { -"description": "`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.", -"format": "byte", -"type": "string" -}, -"version": { -"description": "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).", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"GoogleIamV1SetIamPolicyRequest": { -"description": "Request message for `SetIamPolicy` method.", -"id": "GoogleIamV1SetIamPolicyRequest", -"properties": { -"policy": { -"$ref": "GoogleIamV1Policy", -"description": "REQUIRED: The complete policy to be applied to the `resource`. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Google Cloud services (such as Projects) might reject them." -} -}, -"type": "object" -}, -"GoogleIamV1TestIamPermissionsResponse": { -"description": "Response message for `TestIamPermissions` method.", -"id": "GoogleIamV1TestIamPermissionsResponse", -"properties": { -"permissions": { -"description": "A subset of `TestPermissionsRequest.permissions` that the caller is allowed.", -"items": { -"type": "string" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleLongrunningListOperationsResponse": { -"description": "The response message for Operations.ListOperations.", -"id": "GoogleLongrunningListOperationsResponse", -"properties": { -"nextPageToken": { -"description": "The standard List next-page token.", -"type": "string" -}, -"operations": { -"description": "A list of operations that matches the specified filter in the request.", -"items": { -"$ref": "GoogleLongrunningOperation" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleLongrunningOperation": { -"description": "This resource represents a long-running operation that is the result of a network API call.", -"id": "GoogleLongrunningOperation", -"properties": { -"done": { -"description": "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.", -"type": "boolean" -}, -"error": { -"$ref": "GoogleRpcStatus", -"description": "The error result of the operation in case of failure or cancellation." -}, -"metadata": { -"additionalProperties": { -"description": "Properties of the object. Contains field @type with type URL.", -"type": "any" -}, -"description": "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.", -"type": "object" -}, -"name": { -"description": "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}`.", -"type": "string" -}, -"response": { -"additionalProperties": { -"description": "Properties of the object. Contains field @type with type URL.", -"type": "any" -}, -"description": "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`.", -"type": "object" -} -}, -"type": "object" -}, -"GoogleProtobufEmpty": { -"description": "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); }", -"id": "GoogleProtobufEmpty", -"properties": {}, -"type": "object" -}, -"GoogleRpcStatus": { -"description": "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).", -"id": "GoogleRpcStatus", -"properties": { -"code": { -"description": "The status code, which should be an enum value of google.rpc.Code.", -"format": "int32", -"type": "integer" -}, -"details": { -"description": "A list of messages that carry the error details. There is a common set of message types for APIs to use.", -"items": { -"additionalProperties": { -"description": "Properties of the object. Contains field @type with type URL.", -"type": "any" -}, -"type": "object" -}, -"type": "array" -}, -"message": { -"description": "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.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleTypeColor": { -"description": "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\u2014for 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(''); }; // ...", -"id": "GoogleTypeColor", -"properties": { -"alpha": { -"description": "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).", -"format": "float", -"type": "number" -}, -"blue": { -"description": "The amount of blue in the color as a value in the interval [0, 1].", -"format": "float", -"type": "number" -}, -"green": { -"description": "The amount of green in the color as a value in the interval [0, 1].", -"format": "float", -"type": "number" -}, -"red": { -"description": "The amount of red in the color as a value in the interval [0, 1].", -"format": "float", -"type": "number" -} -}, -"type": "object" -}, -"GoogleTypeDate": { -"description": "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", -"id": "GoogleTypeDate", -"properties": { -"day": { -"description": "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.", -"format": "int32", -"type": "integer" -}, -"month": { -"description": "Month of a year. Must be from 1 to 12, or 0 to specify a year without a month and day.", -"format": "int32", -"type": "integer" -}, -"year": { -"description": "Year of the date. Must be from 1 to 9999, or 0 to specify a date without a year.", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"GoogleTypeExpr": { -"description": "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.", -"id": "GoogleTypeExpr", -"properties": { -"description": { -"description": "Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.", -"type": "string" -}, -"expression": { -"description": "Textual representation of an expression in Common Expression Language syntax.", -"type": "string" -}, -"location": { -"description": "Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.", -"type": "string" -}, -"title": { -"description": "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.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleTypeInterval": { -"description": "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.", -"id": "GoogleTypeInterval", -"properties": { -"endTime": { -"description": "Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end.", -"format": "google-datetime", -"type": "string" -}, -"startTime": { -"description": "Optional. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start.", -"format": "google-datetime", -"type": "string" -} -}, -"type": "object" -}, -"GoogleTypeMoney": { -"description": "Represents an amount of money with its currency type.", -"id": "GoogleTypeMoney", -"properties": { -"currencyCode": { -"description": "The three-letter currency code defined in ISO 4217.", -"type": "string" -}, -"nanos": { -"description": "Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If `units` is positive, `nanos` must be positive or zero. If `units` is zero, `nanos` can be positive, zero, or negative. If `units` is negative, `nanos` must be negative or zero. For example $-1.75 is represented as `units`=-1 and `nanos`=-750,000,000.", -"format": "int32", -"type": "integer" -}, -"units": { -"description": "The whole units of the amount. For example if `currencyCode` is `\"USD\"`, then 1 unit is one US dollar.", -"format": "int64", -"type": "string" -} -}, -"type": "object" -}, -"IntelligenceCloudAutomlXpsMetricEntry": { -"id": "IntelligenceCloudAutomlXpsMetricEntry", -"properties": { -"argentumMetricId": { -"description": "For billing metrics that are using legacy sku's, set the legacy billing metric id here. This will be sent to Chemist as the \"cloudbilling.googleapis.com/argentum_metric_id\" label. Otherwise leave empty.", -"type": "string" -}, -"doubleValue": { -"description": "A double value.", -"format": "double", -"type": "number" -}, -"int64Value": { -"description": "A signed 64-bit integer value.", -"format": "int64", -"type": "string" -}, -"metricName": { -"description": "The metric name defined in the service configuration.", -"type": "string" -}, -"systemLabels": { -"description": "Billing system labels for this (metric, value) pair.", -"items": { -"$ref": "IntelligenceCloudAutomlXpsMetricEntryLabel" -}, -"type": "array" -} -}, -"type": "object" -}, -"IntelligenceCloudAutomlXpsMetricEntryLabel": { -"id": "IntelligenceCloudAutomlXpsMetricEntryLabel", -"properties": { -"labelName": { -"description": "The name of the label.", -"type": "string" -}, -"labelValue": { -"description": "The value of the label.", -"type": "string" -} -}, -"type": "object" -}, -"IntelligenceCloudAutomlXpsReportingMetrics": { -"id": "IntelligenceCloudAutomlXpsReportingMetrics", -"properties": { -"effectiveTrainingDuration": { -"deprecated": true, -"description": "The effective time training used. If set, this is used for quota management and billing. Deprecated. AutoML BE doesn't use this. Don't set.", -"format": "google-duration", -"type": "string" -}, -"metricEntries": { -"description": "One entry per metric name. The values must be aggregated per metric name.", -"items": { -"$ref": "IntelligenceCloudAutomlXpsMetricEntry" -}, -"type": "array" -} -}, -"type": "object" -}, -"LanguageLabsAidaTrustRecitationProtoDocAttribution": { -"description": "The proto defines the attribution information for a document using whatever fields are most applicable for that document's datasource. For example, a Wikipedia article's attribution is in the form of its article title, a website is in the form of a URL, and a Github repo is in the form of a repo name. Next id: 30", -"id": "LanguageLabsAidaTrustRecitationProtoDocAttribution", -"properties": { -"amarnaId": { -"type": "string" -}, -"arxivId": { -"type": "string" -}, -"author": { -"type": "string" -}, -"bibkey": { -"type": "string" -}, -"biorxivId": { -"description": "ID of the paper in bioarxiv like ddoi.org/{biorxiv_id} eg: https://doi.org/10.1101/343517", -"type": "string" -}, -"bookTitle": { -"type": "string" -}, -"bookVolumeId": { -"description": "The Oceanographers full-view books dataset uses a 'volume id' as the unique ID of a book. There is a deterministic function from a volume id to a URL under the books.google.com domain. Marked as 'optional' since a volume ID of zero is potentially possible and we want to distinguish that from the volume ID not being set.", -"format": "int64", -"type": "string" -}, -"category": { -"enum": [ -"CATEGORY_UNSPECIFIED", -"CATEGORY_NEWS", -"CATEGORY_NON_NEWS_WEBDOC", -"CATEGORY_UNKNOWN_MISSING_SIGNAL" -], -"enumDescriptions": [ -"", -"The doc has a url and the news classifier has classified this doc as news.", -"The doc has a url and the news classifier classified this doc as non-news.", -"The doc has a url but the url was missing from the news classifier URL table." -], -"type": "string" -}, -"conversationId": { -"type": "string" -}, -"dataset": { -"description": "The dataset this document comes from.", -"enum": [ -"DATASET_UNSPECIFIED", -"WIKIPEDIA", -"WEBDOCS", -"WEBDOCS_FINETUNE", -"GITHUB_MIRROR", -"BOOKS_FULL_VIEW", -"BOOKS_PRIVATE", -"GNEWS", -"ULM_DOCJOINS", -"ULM_DOCJOINS_DEDUPED", -"MEENA_FC", -"PODCAST", -"AQUA", -"WEB_ASR", -"BARD_GOLDEN", -"COMMON_SENSE_REASONING", -"MATH", -"MATH_REASONING", -"CLEAN_ARXIV", -"LAMDA_FACTUALITY_E2E_QUERY_GENERATION", -"LAMDA_FACTUALITY_E2E_RESPONSE_GENERATION", -"MASSIVE_FORUM_THREAD_SCORED_BARD", -"MASSIVE_FORUM_THREAD_SCORED_LONG_200", -"MASSIVE_FORUM_THREAD_SCORED_LONG_500", -"DOCUMENT_CHUNKS", -"MEENA_RESEARCH_PHASE_GOLDEN_MARKDOWN", -"MEENA_RESEARCH_PHASE_GOOGLERS", -"MEENA_RESPONSE_SAFETY_HUMAN_GEN", -"MEENA_RESPONSE_SAFETY_SCHEMA_NO_BROADCAST", -"MEENA_RESPONSE_SAFETY_V3_HUMAN_GEN2", -"MEENA_RESPONSE_SAFETY_V3_SCHEMA_NO_BROADCAST", -"LAMDA_FACTUALITY_TRIGGER", -"LAMDA_SAFETY_V2_SCHEMA_NO_BROADCAST", -"LAMDA_SSI_DISCRIMINATIVE", -"ASSISTANT_PERSONALITY_SAFETY", -"PODCAST_FINETUNE_DIALOG", -"WORLD_QUERY_GENERATOR", -"C4_JOINED_DOCJOINS", -"HOL4_THEORIES", -"HOL_LIGHT_THEORIES", -"HOLSTEPS", -"ISABELLE_STEP", -"ISABELLE_THEORIES", -"LEAN_MATHLIB_THEORIES", -"LEAN_STEP", -"MIZAR_THEORIES", -"COQ_STEP", -"COQ_THEORIES", -"AMPS_KHAN", -"AMPS_MATHEMATICA", -"CODEY_CODE", -"CODE_QA_SE", -"CODE_QA_SO", -"CODE_QA_FT_FORMAT", -"CODE_QA_FT_KNOWLEDGE", -"CODE_QA_GITHUB_FILTERED_CODE", -"BARD_PERSONALITY_GOLDEN", -"ULM_DOCJOINS_WITH_URLS_EN", -"ULM_DOCJOINS_WITH_URLS_I18N", -"GOODALL_MTV5_GITHUB", -"GOODALL_MTV5_BOOKS", -"GOODALL_MTV5_C4", -"GOODALL_MTV5_WIKIPEDIA", -"GOODALL_MW_TOP_100B", -"GOODALL_MW_STACK_EXCHANGE", -"GOODALL_MW_TOP_0_10B", -"GOODALL_MW_TOP_10B_20B", -"CODEY_NOTEBOOK_LM_PRETRAINING", -"VERTEX_SAFE_FLAN", -"GITHUB_MIRROR_V1_0_1", -"GITHUB_MIRROR_V2_1_0", -"CMS_WIKIPEDIA_LANG_FILTERED", -"CMS_STACKOVERFLOW_MULTILINGUAL", -"CMS_STACKEXCHANGE", -"PUBMED", -"GEMINI_DOCJOINS_EN_TOP10B_GCC", -"GEMINI_DOCJOINS_EN_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_EN_TOP20B_TOP100B_GCC", -"GEMINI_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_I18N_TOP20B_TOP100B_GCC", -"SIMPLIFIED_HTML_V1_GCC", -"GEMINI_DOCJOINS_TOXICITY_TAGGED_GCC", -"CMS_GITHUB_V4", -"GITHUB_HTML_V4", -"GITHUB_OTHER_V4", -"GITHUB_LONG_TAIL_V4", -"CMS_GITHUB_MULTIFILE_V4", -"GITHUB_DIFFS_WITH_COMMIT_MESSAGE", -"ULM_ARXIV", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_ENONLY", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_NONENONLY", -"QUORA", -"PODCASTS_ROBOTSTXT", -"COMBINED_REDDIT", -"CANARIES_SHUFFLED", -"CLM_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"TECHDOCS_DATA_SOURCE", -"SCIENCE_PDF_70M_DOCS_FILTERED", -"GEMINI_V1_CMS_WIKIPEDIA_LANG_FILTERED", -"GEMINI_V1_WIKIPEDIA_DIFFS", -"GEMINI_V1_DOCJOINS_EN_TOP10B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP10B_TOP20B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP20B_TOP100B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_TOP20B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP20B_TOP100B_GCC_050523", -"GEMINI_V1_SIMPLIFIED_HTML_V2_GCC", -"GEMINI_V1_CMS_STACKOVERFLOW_MULTILINGUAL_V2", -"GEMINI_V1_CMS_STACKEXCHANGE_DECONT", -"GEMINI_V1_QUORA", -"GEMINI_V1_COMBINED_REDDIT", -"GEMINI_V1_DOCJOIN_100B_EN_TOXICITY_TAGGED_GCC_FIXED_TAGS", -"GEMINI_V1_PUBMED", -"GEMINI_V1_WEB_MATH_V2", -"GEMINI_V1_CMS_GITHUB_V7", -"GEMINI_V1_CMS_GITHUB_DECONTAMINATED_V_7", -"GEMINI_V1_GITHUB_DIFF_WITH_COMMIT_MESSAGE_V2", -"GEMINI_V1_GITHUB_HTML_CSS_XML_V4", -"GEMINI_V1_GITHUB_OTHER_V4", -"GEMINI_V1_GITHUB_LONG_TAIL_V4", -"GEMINI_V1_GITHUB_JUPTYER_NOTEBOOKS_SSTABLE", -"GEMINI_V1_ULM_ARXIV_SSTABLE", -"GEMINI_V1_PODCASTS_ROBOTSTXT", -"GEMINI_V1_SCIENCE_PDF_68M_HQ_DOCS_GCC", -"GEMINI_V1_GITHUB_TECHDOCS_V2", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_EN", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_NONEN", -"GEMINI_V1_STEM_BOOKS_650K_TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_M3W_V2_FILTERED", -"GEMINI_V1_VQCOCA_1B_MULTIRES_WEBLI_EN_V4_350M_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_SCREENAI_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CULTURE_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_EN_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_I18N_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_NON_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_VTP_4F_VIDEO2TEXT_PREFIX", -"GEMINI_V1_FORMAL_MATH_WITHOUT_HOLSTEPS_AND_MIZAR", -"GEMINI_V1_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"GEMINI_V1_CANARIES_SHUFFLED_DOCJOIN_EN_NONEN_CODE_ARXIV_TRANSLATE", -"DUET_CLOUD_SECURITY_DOCS", -"DUET_GITHUB_CODE_SNIPPETS", -"DUET_GITHUB_FILES", -"DUET_GOBYEXAMPLE", -"DUET_GOLANG_DOCS", -"DUET_CLOUD_DOCS_TROUBLESHOOTING_TABLES", -"DUET_DEVSITE_DOCS", -"DUET_CLOUD_BLOG_POSTS", -"DUET_CLOUD_PODCAST_EPISODES", -"DUET_YOUTUBE_VIDEOS", -"DUET_CLOUD_SKILLS_BOOST", -"DUET_CLOUD_DOCS", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_GENERATED", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_HANDWRITTEN", -"DUET_GOOGLESQL_GENERATION", -"DUET_CLOUD_IX_PROMPTS", -"DUET_RAD", -"DUET_STACKOVERFLOW_ISSUES", -"DUET_STACKOVERFLOW_ANSWERS", -"BARD_ARCADE_GITHUB", -"MOBILE_ASSISTANT_MAGI_FILTERED_0825_373K", -"MOBILE_ASSISTANT_PALM24B_FILTERED_400K", -"GENESIS_NEWS_INSIGHTS", -"CLOUD_SECURITY_PRETRAINING", -"CLOUD_SECURITY_FINETUNING", -"CLOUD_SECURITY_RAG_CISA", -"LABS_AQA_DSCOUT", -"LABS_AQA_TAILWIND", -"LABS_AQA_DELEWARE", -"GEMINI_MULTIMODAL_FT_URL", -"GEMINI_MULTIMODAL_FT_YT", -"GEMINI_MULTIMODAL_FT_SHUTTERSTOCK", -"GEMINI_MULTIMODAL_FT_NONE", -"GEMINI_MULTIMODAL_FT_OTHER", -"GEMINI_MULTIMODAL_FT_INK", -"GEMINI_MULTIMODAL_IT", -"GEMINI_IT_SHUTTERSTOCK", -"GEMINI_IT_M3W", -"GEMINI_IT_HEDGING", -"GEMINI_IT_DSCOUT_FACTUALITY", -"GEMINI_IT_AQUAMUSE", -"GEMINI_IT_SHOTGUN", -"GEMINI_IT_ACI_BENCH", -"GEMINI_IT_SPIDER_FILTERED", -"GEMINI_IT_TAB_SUM_BQ", -"GEMINI_IT_QA_WITH_URL", -"GEMINI_IT_CODE_INSTRUCT", -"GEMINI_IT_MED_PALM", -"GEMINI_IT_TASK_ORIENTED_DIALOG", -"GEMINI_IT_NIMBUS_GROUNDING_TO_PROMPT", -"GEMINI_IT_EITL_GEN", -"GEMINI_IT_HITL_GEN", -"GEMINI_IT_MECH", -"GEMINI_IT_TABLE_GEN", -"GEMINI_IT_NIMBUS_DECIBEL", -"GEMINI_IT_CLOUD_CODE_IF", -"GEMINI_IT_CLOUD_EUR_LEX_JSON", -"GEMINI_IT_CLOUD_OASST", -"GEMINI_IT_CLOUD_SELF_INSTRUCT", -"GEMINI_IT_CLOUD_UCS_AQUAMUSE", -"GEMIT_BRIDGE_SUFFIX_FT", -"GEMINI_GOOSE_PUBLIC", -"GEMINI_GOOSE_SILOED", -"GEMINI_V2_CMS_WIKIPEDIA_LANG_FILTERED_GCC_PII", -"GEMINI_V2_WIKIPEDIA_DIFFS_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_QUORA_COMPLIANT", -"GEMINI_V2_FORUMS_V2_COMPLIANT", -"GEMINI_V2_CMS_STACKOVERFLOW_MULTILINGUAL_V2_COMPLIANT", -"GEMINI_V2_SIMPLIFIED_HTML_V2_CORRECT_FORMAT_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_TOXICITY_TAGGED_FIXED_TAGS_COMPLIANT", -"GEMINI_V2_CODEWEB_V1_COMPLIANT", -"GEMINI_V2_LEETCODE_GCC_PII", -"GEMINI_V2_CODE_CONTESTS_COMPLIANT", -"GEMINI_V2_CMS_GITHUB_MULTI_FILE_FOR_FIM_GEMBAGZ_FIXED_BYTES_LENGTHS", -"GEMINI_V2_GITHUB_EVALED_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_NON_EVAL_HIGH_PRI_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_LOW_PRI_LANGUAGES_AND_CONFIGS_COMPLIANT", -"GEMINI_V2_GITHUB_LONG_TAIL_AND_STRUCTURED_DATA_COMPLIANT", -"GEMINI_V2_GITHUB_PYTHON_NOTEBOOKS_COMPLIANT", -"GEMINI_V2_GITHUB_DIFFS_COMPLIANT", -"GEMINI_V2_GITHUB_TECHDOCS_COMPLIANT", -"GEMINI_V2_HIGH_QUALITY_CODE_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_SCIENCE_PDF_68M_HQ_DOCS_DEDUP_COMPLIANT_CLEAN_TEX", -"GEMINI_V2_ARXIV_2023_COMPLIANT", -"GEMINI_V2_FORMAL_COMPLIANT", -"GEMINI_V2_CMS_STACKEXCHANGE_COMPLIANT", -"GEMINI_V2_PUBMED_COMPLIANT", -"GEMINI_V2_WEB_MATH_V3_COMPLIANT", -"GEMINI_V2_SCIENCEWEB_V0_GCC_PII", -"GEMINI_V2_WEB_POLYMATH_V1_COMPLIANT", -"GEMINI_V2_MATH_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_BIOLOGY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_PHYSICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHEMISTRY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MACHINE_LEARNING_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_QA_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_ECONOMICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MEDICAL_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHESS_COMPLIANT", -"GEMINI_V2_YOUTUBE_SCIENCE_V4_FILTERED_COMPLIANT", -"GEMINI_V2_GOALDMINE_XL_GENERATED_PLUS_GT_NO_DM_MATH_COMPLIANT", -"GEMINI_V2_FIRSTTIMES_SCIENCE_PDF_DEDUP_HQ_LENGTH_FILTERED_COMPLIANT", -"GEMINI_V2_PODCASTS_COMPLIANT", -"GEMINI_V2_EN_NONSCIENCE_PDF_DEDUP_46M_DOCS_COMPLIANT", -"GEMINI_V2_NONPUB_COPYRIGHT_BOOKS_V3_70_CONF_082323_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_COPYRIGHT_BOOKS_V3_111823_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_BOOKS_318K_TEXT_COMPLIANT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_T2I_TEXT_TO_IMAGE_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_I18N_680M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_FULL_HTML_75M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_V1_1_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_DOC_240M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_VIDEO_VIDEO_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M4W_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CULTURE_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_DETECTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_ALT_TEXT_NONEN_500M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SPATIAL_AWARE_PALI_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_3D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_NON_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_3D_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CC3M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_INFOGRAPHICS_LARGE_WEB_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_BIORXIV_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PLOT2TABLE_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TIKZ_DERENDERING_MERGED_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_2D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WIKIPEDIA_EQUATIONS_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_EQ2LATEX_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_ARXIV_EQUATIONS_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_HEAD_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_CLM_TRANSLATE_DATAV3_WEB_UNWMT_INCR_MIX", -"GEMINI_V2_NTL_NTLV4A_MONOLINGUAL_DEDUP_N5", -"GEMINI_V2_NTL_STT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_TRANSLIT_BILEX_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_BT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_FT_FIXED_TRANSLATE_DEDUP_N5", -"GEMINI_V2_CANARIES_SHUFFLED_COMPLIANT", -"CLOUD_GEMIT_CLOUD_FACTUALITY_GROUNDING_MAGI", -"CLOUD_GEMIT_MT_DIALGUE_LMSYS", -"CLOUD_GEMIT_MTS_DIALOGUE_V3", -"CLOUD_GEMIT_COMMIT_MSG_GEN_V3", -"CLOUD_GEMIT_CODE_IF_V1", -"CLOUD_GEMIT_CODE_SELF_REPAIR", -"CLOUD_GEMIT_IDENTITY", -"CLOUD_GEMIT_SEARCH_AUGMENTED_RESPONSE_GENERATION", -"CLOUD_GEMIT_AMPS", -"CLOUD_GEMIT_AQUA", -"CLOUD_GEMIT_COMMON_SENSE_REASONING_SCHEMA", -"CLOUD_GEMIT_GSM8K_SCHEMA", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_UN", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_EUROPARL", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_NEWSCOMMENTARY", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_2021_INCR", -"GEMINI_V1_TAIL_PATCH_GOALDMINE", -"GEMINI_V1_TAIL_PATCH_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION", -"GEMINI_V1_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V1_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V1_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V2_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V2_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_PMC", -"GEMINI_V2_TAIL_PATCH_VOXPOPULI", -"GEMINI_V2_TAIL_PATCH_FLEURS", -"GEMINI_V2_SSFS", -"GEMINI_V2_CODE_TRANSFORM_SYNTHETIC_ERROR_FIX", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_COMMITS", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_PR", -"GEMINI_V2_SQL_REPAIR_SFT", -"GEMINI_V2_JSON_MODE_SYS_INSTRUCTION", -"YT_CONTENT_INSPIRATION" -], -"enumDescriptions": [ -"", -"Wikipedia article Tensorflow datasets used by Tarzan and maintained by TFDS team.", -"Webdocs that have been filtered from the docjoins by the Tarzan team for use in the Tarzan training set.", -"", -"", -"'Full view' books dataset maintained by Oceanographers team, meaning 'ok to view the book in full in all localities'. Largely the same as 'public domain', but with potentially subtle distinction.", -"Filtered private books used by ULM: http://google3/learning/multipod/pax/lm/params/ulm/tasks.py;l=123;rcl=494241309. which corresponds with /cns/mf-d/home/multipod-language-data/private_books/books_filtered_en_resharded@50000", -"Google news dataset referenced in: http://google3/learning/brain/research/conversation/meena/t5/pretrain_tasks.py;l=922;rcl=496534668", -"The docjoins data for ULM /cns/yo-d/home/multipod-language-data/docjoins/rs=6.3/20220728/100B_docstructure_split/examples_en.tfrecord_lattice_05_score_01_HFV13@3929", -"", -"Meena full conversations. http://google3/learning/brain/research/conversation/meena/t5/pretrain_mixtures.py;l=675;rcl=496583228", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Academic dataset of math text. http://google3/learning/brain/research/conversation/meena/seqio/mixtures/experimental/bard.py;rcl=500222380", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Datasets managed by the Goodall team: deepmind-goodall@google.com", -"", -"", -"", -"", -"", -"", -"", -"Datasets used by Codepoet", -"Datasets used by Vertex", -"", -"", -"Datasets used by Gemini Public data", -"", -"", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"Github", -"", -"", -"", -"", -"", -"Arxiv", -"Others", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V1, order by precedence. Wikipedia", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Github dataset with license info. We prefer this to help cite proper licenses for code recitation.", -"", -"", -"", -"", -"", -"", -"ArXiv", -"Citable misc", -"", -"", -"Non-public books", -"", -"", -"Other", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Duet AI finetune datasets, order by precedence.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Bard ARCADE finetune dataset.", -"Mobile assistant finetune datasets.", -"", -"Genesis fine-tune datasets.", -"Cloud Security fine-tune datasets.", -"", -"", -"LABS AQA fine-tune datasets.", -"", -"", -"Gemini multimodal instruction tune(IT) and fine tune(FT) datasets datasets.", -"", -"", -"", -"", -"", -"", -"Gemini IT 1.2.7 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemit Bridge ULM FT dataset", -"Gemini Goose FT datasets.", -"", -"Gemini V2 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Cloud gemit pro FT datasets.", -"", -"", -"", -"", -"", -"", -"Cloud gemit ultra FT datasets.", -"", -"", -"", -"", -"Gemini V1 tail patch translation.", -"", -"", -"", -"Gemini V1 tail patch others.", -"", -"Gemini V1 and V2 shared tail patch.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V2 only tail patch.", -"", -"", -"Gemini V2 rev10", -"", -"", -"", -"", -"", -"Youtube Content Inpsiration." -], -"type": "string" -}, -"filepath": { -"type": "string" -}, -"geminiId": { -"type": "string" -}, -"gnewsArticleTitle": { -"type": "string" -}, -"goodallExampleId": { -"type": "string" -}, -"isOptOut": { -"description": "Whether the document is opted out.", -"type": "boolean" -}, -"isPrompt": { -"type": "boolean" -}, -"lamdaExampleId": { -"type": "string" -}, -"license": { -"type": "string" -}, -"meenaConversationId": { -"type": "string" -}, -"naturalLanguageCode": { -"description": "Natural (not programming) language of the document. Language code as defined by http://www.unicode.org/reports/tr35/#Identifiers and https://tools.ietf.org/html/bcp47. Currently applicable to full-view books. Use docinfo-util.h to set & read language fields. See go/iii.", -"type": "string" -}, -"noAttribution": { -"description": "True if this doc has no attribution information available. We use an explicit field for this instead of just implicitly leaving all the DocAttribution fields blank to distinguish a case where a bug/oversight has left the attribution information empty vs when we really have no attribution information available.", -"type": "boolean" -}, -"podcastUtteranceId": { -"type": "string" -}, -"publicationDate": { -"$ref": "GoogleTypeDate" -}, -"qualityScoreExperimentOnly": { -"description": "This field is for opt-out experiment only, MUST never be used during actual production/serving. ", -"format": "double", -"type": "number" -}, -"repo": { -"description": "Github repository", -"type": "string" -}, -"url": { -"description": "URL of a webdoc", -"type": "string" -}, -"volumeId": { -"type": "string" -}, -"wikipediaArticleTitle": { -"description": "Wikipedia article title. The Wikipedia TFDS dataset includes article titles but not URLs. While a URL is to the best of our knowledge a deterministic function of the title, we store the original title to reflect the information in the original dataset.", -"type": "string" -}, -"youtubeVideoId": { -"description": "The unique video id from Youtube. Example: AkoGsW52Ir0", -"type": "string" -} -}, -"type": "object" -}, -"LanguageLabsAidaTrustRecitationProtoRecitationResult": { -"description": "The recitation result for one input", -"id": "LanguageLabsAidaTrustRecitationProtoRecitationResult", -"properties": { -"dynamicSegmentResults": { -"items": { -"$ref": "LanguageLabsAidaTrustRecitationProtoSegmentResult" -}, -"type": "array" -}, -"recitationAction": { -"description": "The recitation action for one given input. When its segments contain different actions, the overall action will be returned in the precedence of BLOCK > CITE > NO_ACTION. When the given input is not found in any source, the recitation action will not be specified.", -"enum": [ -"ACTION_UNSPECIFIED", -"CITE", -"BLOCK", -"NO_ACTION", -"EXEMPT_FOUND_IN_PROMPT" -], -"enumDescriptions": [ -"", -"indicate that attribution must be shown for a Segment", -"indicate that a Segment should be blocked from being used", -"for tagging high-frequency code snippets", -"The recitation was found in prompt and is exempted from overall results" -], -"type": "string" -}, -"trainingSegmentResults": { -"items": { -"$ref": "LanguageLabsAidaTrustRecitationProtoSegmentResult" -}, -"type": "array" -} -}, -"type": "object" -}, -"LanguageLabsAidaTrustRecitationProtoSegmentResult": { -"description": "The recitation result for each segment in a given input.", -"id": "LanguageLabsAidaTrustRecitationProtoSegmentResult", -"properties": { -"attributionDataset": { -"description": "The dataset the segment came from. Datasets change often as model evolves. Treat this field as informational only and avoid depending on it directly.", -"enum": [ -"DATASET_UNSPECIFIED", -"WIKIPEDIA", -"WEBDOCS", -"WEBDOCS_FINETUNE", -"GITHUB_MIRROR", -"BOOKS_FULL_VIEW", -"BOOKS_PRIVATE", -"GNEWS", -"ULM_DOCJOINS", -"ULM_DOCJOINS_DEDUPED", -"MEENA_FC", -"PODCAST", -"AQUA", -"WEB_ASR", -"BARD_GOLDEN", -"COMMON_SENSE_REASONING", -"MATH", -"MATH_REASONING", -"CLEAN_ARXIV", -"LAMDA_FACTUALITY_E2E_QUERY_GENERATION", -"LAMDA_FACTUALITY_E2E_RESPONSE_GENERATION", -"MASSIVE_FORUM_THREAD_SCORED_BARD", -"MASSIVE_FORUM_THREAD_SCORED_LONG_200", -"MASSIVE_FORUM_THREAD_SCORED_LONG_500", -"DOCUMENT_CHUNKS", -"MEENA_RESEARCH_PHASE_GOLDEN_MARKDOWN", -"MEENA_RESEARCH_PHASE_GOOGLERS", -"MEENA_RESPONSE_SAFETY_HUMAN_GEN", -"MEENA_RESPONSE_SAFETY_SCHEMA_NO_BROADCAST", -"MEENA_RESPONSE_SAFETY_V3_HUMAN_GEN2", -"MEENA_RESPONSE_SAFETY_V3_SCHEMA_NO_BROADCAST", -"LAMDA_FACTUALITY_TRIGGER", -"LAMDA_SAFETY_V2_SCHEMA_NO_BROADCAST", -"LAMDA_SSI_DISCRIMINATIVE", -"ASSISTANT_PERSONALITY_SAFETY", -"PODCAST_FINETUNE_DIALOG", -"WORLD_QUERY_GENERATOR", -"C4_JOINED_DOCJOINS", -"HOL4_THEORIES", -"HOL_LIGHT_THEORIES", -"HOLSTEPS", -"ISABELLE_STEP", -"ISABELLE_THEORIES", -"LEAN_MATHLIB_THEORIES", -"LEAN_STEP", -"MIZAR_THEORIES", -"COQ_STEP", -"COQ_THEORIES", -"AMPS_KHAN", -"AMPS_MATHEMATICA", -"CODEY_CODE", -"CODE_QA_SE", -"CODE_QA_SO", -"CODE_QA_FT_FORMAT", -"CODE_QA_FT_KNOWLEDGE", -"CODE_QA_GITHUB_FILTERED_CODE", -"BARD_PERSONALITY_GOLDEN", -"ULM_DOCJOINS_WITH_URLS_EN", -"ULM_DOCJOINS_WITH_URLS_I18N", -"GOODALL_MTV5_GITHUB", -"GOODALL_MTV5_BOOKS", -"GOODALL_MTV5_C4", -"GOODALL_MTV5_WIKIPEDIA", -"GOODALL_MW_TOP_100B", -"GOODALL_MW_STACK_EXCHANGE", -"GOODALL_MW_TOP_0_10B", -"GOODALL_MW_TOP_10B_20B", -"CODEY_NOTEBOOK_LM_PRETRAINING", -"VERTEX_SAFE_FLAN", -"GITHUB_MIRROR_V1_0_1", -"GITHUB_MIRROR_V2_1_0", -"CMS_WIKIPEDIA_LANG_FILTERED", -"CMS_STACKOVERFLOW_MULTILINGUAL", -"CMS_STACKEXCHANGE", -"PUBMED", -"GEMINI_DOCJOINS_EN_TOP10B_GCC", -"GEMINI_DOCJOINS_EN_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_EN_TOP20B_TOP100B_GCC", -"GEMINI_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_I18N_TOP20B_TOP100B_GCC", -"SIMPLIFIED_HTML_V1_GCC", -"GEMINI_DOCJOINS_TOXICITY_TAGGED_GCC", -"CMS_GITHUB_V4", -"GITHUB_HTML_V4", -"GITHUB_OTHER_V4", -"GITHUB_LONG_TAIL_V4", -"CMS_GITHUB_MULTIFILE_V4", -"GITHUB_DIFFS_WITH_COMMIT_MESSAGE", -"ULM_ARXIV", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_ENONLY", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_NONENONLY", -"QUORA", -"PODCASTS_ROBOTSTXT", -"COMBINED_REDDIT", -"CANARIES_SHUFFLED", -"CLM_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"TECHDOCS_DATA_SOURCE", -"SCIENCE_PDF_70M_DOCS_FILTERED", -"GEMINI_V1_CMS_WIKIPEDIA_LANG_FILTERED", -"GEMINI_V1_WIKIPEDIA_DIFFS", -"GEMINI_V1_DOCJOINS_EN_TOP10B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP10B_TOP20B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP20B_TOP100B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_TOP20B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP20B_TOP100B_GCC_050523", -"GEMINI_V1_SIMPLIFIED_HTML_V2_GCC", -"GEMINI_V1_CMS_STACKOVERFLOW_MULTILINGUAL_V2", -"GEMINI_V1_CMS_STACKEXCHANGE_DECONT", -"GEMINI_V1_QUORA", -"GEMINI_V1_COMBINED_REDDIT", -"GEMINI_V1_DOCJOIN_100B_EN_TOXICITY_TAGGED_GCC_FIXED_TAGS", -"GEMINI_V1_PUBMED", -"GEMINI_V1_WEB_MATH_V2", -"GEMINI_V1_CMS_GITHUB_V7", -"GEMINI_V1_CMS_GITHUB_DECONTAMINATED_V_7", -"GEMINI_V1_GITHUB_DIFF_WITH_COMMIT_MESSAGE_V2", -"GEMINI_V1_GITHUB_HTML_CSS_XML_V4", -"GEMINI_V1_GITHUB_OTHER_V4", -"GEMINI_V1_GITHUB_LONG_TAIL_V4", -"GEMINI_V1_GITHUB_JUPTYER_NOTEBOOKS_SSTABLE", -"GEMINI_V1_ULM_ARXIV_SSTABLE", -"GEMINI_V1_PODCASTS_ROBOTSTXT", -"GEMINI_V1_SCIENCE_PDF_68M_HQ_DOCS_GCC", -"GEMINI_V1_GITHUB_TECHDOCS_V2", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_EN", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_NONEN", -"GEMINI_V1_STEM_BOOKS_650K_TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_M3W_V2_FILTERED", -"GEMINI_V1_VQCOCA_1B_MULTIRES_WEBLI_EN_V4_350M_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_SCREENAI_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CULTURE_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_EN_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_I18N_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_NON_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_VTP_4F_VIDEO2TEXT_PREFIX", -"GEMINI_V1_FORMAL_MATH_WITHOUT_HOLSTEPS_AND_MIZAR", -"GEMINI_V1_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"GEMINI_V1_CANARIES_SHUFFLED_DOCJOIN_EN_NONEN_CODE_ARXIV_TRANSLATE", -"DUET_CLOUD_SECURITY_DOCS", -"DUET_GITHUB_CODE_SNIPPETS", -"DUET_GITHUB_FILES", -"DUET_GOBYEXAMPLE", -"DUET_GOLANG_DOCS", -"DUET_CLOUD_DOCS_TROUBLESHOOTING_TABLES", -"DUET_DEVSITE_DOCS", -"DUET_CLOUD_BLOG_POSTS", -"DUET_CLOUD_PODCAST_EPISODES", -"DUET_YOUTUBE_VIDEOS", -"DUET_CLOUD_SKILLS_BOOST", -"DUET_CLOUD_DOCS", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_GENERATED", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_HANDWRITTEN", -"DUET_GOOGLESQL_GENERATION", -"DUET_CLOUD_IX_PROMPTS", -"DUET_RAD", -"DUET_STACKOVERFLOW_ISSUES", -"DUET_STACKOVERFLOW_ANSWERS", -"BARD_ARCADE_GITHUB", -"MOBILE_ASSISTANT_MAGI_FILTERED_0825_373K", -"MOBILE_ASSISTANT_PALM24B_FILTERED_400K", -"GENESIS_NEWS_INSIGHTS", -"CLOUD_SECURITY_PRETRAINING", -"CLOUD_SECURITY_FINETUNING", -"CLOUD_SECURITY_RAG_CISA", -"LABS_AQA_DSCOUT", -"LABS_AQA_TAILWIND", -"LABS_AQA_DELEWARE", -"GEMINI_MULTIMODAL_FT_URL", -"GEMINI_MULTIMODAL_FT_YT", -"GEMINI_MULTIMODAL_FT_SHUTTERSTOCK", -"GEMINI_MULTIMODAL_FT_NONE", -"GEMINI_MULTIMODAL_FT_OTHER", -"GEMINI_MULTIMODAL_FT_INK", -"GEMINI_MULTIMODAL_IT", -"GEMINI_IT_SHUTTERSTOCK", -"GEMINI_IT_M3W", -"GEMINI_IT_HEDGING", -"GEMINI_IT_DSCOUT_FACTUALITY", -"GEMINI_IT_AQUAMUSE", -"GEMINI_IT_SHOTGUN", -"GEMINI_IT_ACI_BENCH", -"GEMINI_IT_SPIDER_FILTERED", -"GEMINI_IT_TAB_SUM_BQ", -"GEMINI_IT_QA_WITH_URL", -"GEMINI_IT_CODE_INSTRUCT", -"GEMINI_IT_MED_PALM", -"GEMINI_IT_TASK_ORIENTED_DIALOG", -"GEMINI_IT_NIMBUS_GROUNDING_TO_PROMPT", -"GEMINI_IT_EITL_GEN", -"GEMINI_IT_HITL_GEN", -"GEMINI_IT_MECH", -"GEMINI_IT_TABLE_GEN", -"GEMINI_IT_NIMBUS_DECIBEL", -"GEMINI_IT_CLOUD_CODE_IF", -"GEMINI_IT_CLOUD_EUR_LEX_JSON", -"GEMINI_IT_CLOUD_OASST", -"GEMINI_IT_CLOUD_SELF_INSTRUCT", -"GEMINI_IT_CLOUD_UCS_AQUAMUSE", -"GEMIT_BRIDGE_SUFFIX_FT", -"GEMINI_GOOSE_PUBLIC", -"GEMINI_GOOSE_SILOED", -"GEMINI_V2_CMS_WIKIPEDIA_LANG_FILTERED_GCC_PII", -"GEMINI_V2_WIKIPEDIA_DIFFS_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_QUORA_COMPLIANT", -"GEMINI_V2_FORUMS_V2_COMPLIANT", -"GEMINI_V2_CMS_STACKOVERFLOW_MULTILINGUAL_V2_COMPLIANT", -"GEMINI_V2_SIMPLIFIED_HTML_V2_CORRECT_FORMAT_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_TOXICITY_TAGGED_FIXED_TAGS_COMPLIANT", -"GEMINI_V2_CODEWEB_V1_COMPLIANT", -"GEMINI_V2_LEETCODE_GCC_PII", -"GEMINI_V2_CODE_CONTESTS_COMPLIANT", -"GEMINI_V2_CMS_GITHUB_MULTI_FILE_FOR_FIM_GEMBAGZ_FIXED_BYTES_LENGTHS", -"GEMINI_V2_GITHUB_EVALED_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_NON_EVAL_HIGH_PRI_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_LOW_PRI_LANGUAGES_AND_CONFIGS_COMPLIANT", -"GEMINI_V2_GITHUB_LONG_TAIL_AND_STRUCTURED_DATA_COMPLIANT", -"GEMINI_V2_GITHUB_PYTHON_NOTEBOOKS_COMPLIANT", -"GEMINI_V2_GITHUB_DIFFS_COMPLIANT", -"GEMINI_V2_GITHUB_TECHDOCS_COMPLIANT", -"GEMINI_V2_HIGH_QUALITY_CODE_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_SCIENCE_PDF_68M_HQ_DOCS_DEDUP_COMPLIANT_CLEAN_TEX", -"GEMINI_V2_ARXIV_2023_COMPLIANT", -"GEMINI_V2_FORMAL_COMPLIANT", -"GEMINI_V2_CMS_STACKEXCHANGE_COMPLIANT", -"GEMINI_V2_PUBMED_COMPLIANT", -"GEMINI_V2_WEB_MATH_V3_COMPLIANT", -"GEMINI_V2_SCIENCEWEB_V0_GCC_PII", -"GEMINI_V2_WEB_POLYMATH_V1_COMPLIANT", -"GEMINI_V2_MATH_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_BIOLOGY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_PHYSICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHEMISTRY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MACHINE_LEARNING_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_QA_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_ECONOMICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MEDICAL_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHESS_COMPLIANT", -"GEMINI_V2_YOUTUBE_SCIENCE_V4_FILTERED_COMPLIANT", -"GEMINI_V2_GOALDMINE_XL_GENERATED_PLUS_GT_NO_DM_MATH_COMPLIANT", -"GEMINI_V2_FIRSTTIMES_SCIENCE_PDF_DEDUP_HQ_LENGTH_FILTERED_COMPLIANT", -"GEMINI_V2_PODCASTS_COMPLIANT", -"GEMINI_V2_EN_NONSCIENCE_PDF_DEDUP_46M_DOCS_COMPLIANT", -"GEMINI_V2_NONPUB_COPYRIGHT_BOOKS_V3_70_CONF_082323_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_COPYRIGHT_BOOKS_V3_111823_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_BOOKS_318K_TEXT_COMPLIANT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_T2I_TEXT_TO_IMAGE_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_I18N_680M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_FULL_HTML_75M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_V1_1_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_DOC_240M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_VIDEO_VIDEO_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M4W_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CULTURE_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_DETECTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_ALT_TEXT_NONEN_500M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SPATIAL_AWARE_PALI_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_3D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_NON_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_3D_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CC3M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_INFOGRAPHICS_LARGE_WEB_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_BIORXIV_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PLOT2TABLE_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TIKZ_DERENDERING_MERGED_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_2D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WIKIPEDIA_EQUATIONS_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_EQ2LATEX_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_ARXIV_EQUATIONS_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_HEAD_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_CLM_TRANSLATE_DATAV3_WEB_UNWMT_INCR_MIX", -"GEMINI_V2_NTL_NTLV4A_MONOLINGUAL_DEDUP_N5", -"GEMINI_V2_NTL_STT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_TRANSLIT_BILEX_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_BT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_FT_FIXED_TRANSLATE_DEDUP_N5", -"GEMINI_V2_CANARIES_SHUFFLED_COMPLIANT", -"CLOUD_GEMIT_CLOUD_FACTUALITY_GROUNDING_MAGI", -"CLOUD_GEMIT_MT_DIALGUE_LMSYS", -"CLOUD_GEMIT_MTS_DIALOGUE_V3", -"CLOUD_GEMIT_COMMIT_MSG_GEN_V3", -"CLOUD_GEMIT_CODE_IF_V1", -"CLOUD_GEMIT_CODE_SELF_REPAIR", -"CLOUD_GEMIT_IDENTITY", -"CLOUD_GEMIT_SEARCH_AUGMENTED_RESPONSE_GENERATION", -"CLOUD_GEMIT_AMPS", -"CLOUD_GEMIT_AQUA", -"CLOUD_GEMIT_COMMON_SENSE_REASONING_SCHEMA", -"CLOUD_GEMIT_GSM8K_SCHEMA", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_UN", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_EUROPARL", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_NEWSCOMMENTARY", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_2021_INCR", -"GEMINI_V1_TAIL_PATCH_GOALDMINE", -"GEMINI_V1_TAIL_PATCH_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION", -"GEMINI_V1_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V1_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V1_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V2_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V2_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_PMC", -"GEMINI_V2_TAIL_PATCH_VOXPOPULI", -"GEMINI_V2_TAIL_PATCH_FLEURS", -"GEMINI_V2_SSFS", -"GEMINI_V2_CODE_TRANSFORM_SYNTHETIC_ERROR_FIX", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_COMMITS", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_PR", -"GEMINI_V2_SQL_REPAIR_SFT", -"GEMINI_V2_JSON_MODE_SYS_INSTRUCTION", -"YT_CONTENT_INSPIRATION" -], -"enumDescriptions": [ -"", -"Wikipedia article Tensorflow datasets used by Tarzan and maintained by TFDS team.", -"Webdocs that have been filtered from the docjoins by the Tarzan team for use in the Tarzan training set.", -"", -"", -"'Full view' books dataset maintained by Oceanographers team, meaning 'ok to view the book in full in all localities'. Largely the same as 'public domain', but with potentially subtle distinction.", -"Filtered private books used by ULM: http://google3/learning/multipod/pax/lm/params/ulm/tasks.py;l=123;rcl=494241309. which corresponds with /cns/mf-d/home/multipod-language-data/private_books/books_filtered_en_resharded@50000", -"Google news dataset referenced in: http://google3/learning/brain/research/conversation/meena/t5/pretrain_tasks.py;l=922;rcl=496534668", -"The docjoins data for ULM /cns/yo-d/home/multipod-language-data/docjoins/rs=6.3/20220728/100B_docstructure_split/examples_en.tfrecord_lattice_05_score_01_HFV13@3929", -"", -"Meena full conversations. http://google3/learning/brain/research/conversation/meena/t5/pretrain_mixtures.py;l=675;rcl=496583228", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Academic dataset of math text. http://google3/learning/brain/research/conversation/meena/seqio/mixtures/experimental/bard.py;rcl=500222380", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Datasets managed by the Goodall team: deepmind-goodall@google.com", -"", -"", -"", -"", -"", -"", -"", -"Datasets used by Codepoet", -"Datasets used by Vertex", -"", -"", -"Datasets used by Gemini Public data", -"", -"", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"Github", -"", -"", -"", -"", -"", -"Arxiv", -"Others", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V1, order by precedence. Wikipedia", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Github dataset with license info. We prefer this to help cite proper licenses for code recitation.", -"", -"", -"", -"", -"", -"", -"ArXiv", -"Citable misc", -"", -"", -"Non-public books", -"", -"", -"Other", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Duet AI finetune datasets, order by precedence.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Bard ARCADE finetune dataset.", -"Mobile assistant finetune datasets.", -"", -"Genesis fine-tune datasets.", -"Cloud Security fine-tune datasets.", -"", -"", -"LABS AQA fine-tune datasets.", -"", -"", -"Gemini multimodal instruction tune(IT) and fine tune(FT) datasets datasets.", -"", -"", -"", -"", -"", -"", -"Gemini IT 1.2.7 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemit Bridge ULM FT dataset", -"Gemini Goose FT datasets.", -"", -"Gemini V2 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Cloud gemit pro FT datasets.", -"", -"", -"", -"", -"", -"", -"Cloud gemit ultra FT datasets.", -"", -"", -"", -"", -"Gemini V1 tail patch translation.", -"", -"", -"", -"Gemini V1 tail patch others.", -"", -"Gemini V1 and V2 shared tail patch.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V2 only tail patch.", -"", -"", -"Gemini V2 rev10", -"", -"", -"", -"", -"", -"Youtube Content Inpsiration." -], -"type": "string" -}, -"displayAttributionMessage": { -"description": "human-friendly string that contains information from doc_attribution which could be shown by clients", -"type": "string" -}, -"docAttribution": { -"$ref": "LanguageLabsAidaTrustRecitationProtoDocAttribution" -}, -"docOccurrences": { -"description": "number of documents that contained this segment", -"format": "int32", -"type": "integer" -}, -"endIndex": { -"format": "int32", -"type": "integer" -}, -"rawText": { -"description": "The raw text in the given input that is corresponding to the segment. It will be available only when 'return_segment_raw_text' is enabled in the request options.", -"type": "string" -}, -"segmentRecitationAction": { -"enum": [ -"ACTION_UNSPECIFIED", -"CITE", -"BLOCK", -"NO_ACTION", -"EXEMPT_FOUND_IN_PROMPT" -], -"enumDescriptions": [ -"", -"indicate that attribution must be shown for a Segment", -"indicate that a Segment should be blocked from being used", -"for tagging high-frequency code snippets", -"The recitation was found in prompt and is exempted from overall results" -], -"type": "string" -}, -"sourceCategory": { -"description": "The category of the source dataset where the segment came from. This is more stable than Dataset.", -"enum": [ -"SOURCE_CATEGORY_UNSPECIFIED", -"SOURCE_CATEGORY_WIKIPEDIA", -"SOURCE_CATEGORY_WEBDOCS", -"SOURCE_CATEGORY_GITHUB", -"SOURCE_CATEGORY_ARXIV", -"SOURCE_CATEGORY_PRIVATE_BOOKS", -"SOURCE_CATEGORY_OTHERS", -"SOURCE_CATEGORY_PUBLIC_BOOKS", -"SOURCE_CATEGORY_GNEWS" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"", -"", -"", -"" -], -"type": "string" -}, -"startIndex": { -"description": "The segment boundary start (inclusive) and end index (exclusive) in the given text. In the streaming RPC, the indexes always start from the beginning of the first text in the entire stream. The indexes are measured in UTF-16 code units.", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"LanguageLabsAidaTrustRecitationProtoStreamRecitationResult": { -"description": "The recitation result for one stream input", -"id": "LanguageLabsAidaTrustRecitationProtoStreamRecitationResult", -"properties": { -"dynamicSegmentResults": { -"description": "The recitation result against the given dynamic data source.", -"items": { -"$ref": "LanguageLabsAidaTrustRecitationProtoSegmentResult" -}, -"type": "array" -}, -"fullyCheckedTextIndex": { -"description": "Last index of input text fully checked for recitation in the entire streaming context. Would return `-1` if no Input was checked for recitation.", -"format": "int32", -"type": "integer" -}, -"recitationAction": { -"description": "The recitation action for one given input. When its segments contain different actions, the overall action will be returned in the precedence of BLOCK > CITE > NO_ACTION.", -"enum": [ -"ACTION_UNSPECIFIED", -"CITE", -"BLOCK", -"NO_ACTION", -"EXEMPT_FOUND_IN_PROMPT" -], -"enumDescriptions": [ -"", -"indicate that attribution must be shown for a Segment", -"indicate that a Segment should be blocked from being used", -"for tagging high-frequency code snippets", -"The recitation was found in prompt and is exempted from overall results" -], -"type": "string" -}, -"trainingSegmentResults": { -"description": "The recitation result against model training data.", -"items": { -"$ref": "LanguageLabsAidaTrustRecitationProtoSegmentResult" -}, -"type": "array" -} -}, -"type": "object" -}, -"LearningGenaiRecitationDocAttribution": { -"description": "The proto defines the attribution information for a document using whatever fields are most applicable for that document's datasource. For example, a Wikipedia article's attribution is in the form of its article title, a website is in the form of a URL, and a Github repo is in the form of a repo name. Next id: 30", -"id": "LearningGenaiRecitationDocAttribution", -"properties": { -"amarnaId": { -"type": "string" -}, -"arxivId": { -"type": "string" -}, -"author": { -"type": "string" -}, -"bibkey": { -"type": "string" -}, -"biorxivId": { -"description": "ID of the paper in bioarxiv like ddoi.org/{biorxiv_id} eg: https://doi.org/10.1101/343517", -"type": "string" -}, -"bookTitle": { -"type": "string" -}, -"bookVolumeId": { -"description": "The Oceanographers full-view books dataset uses a 'volume id' as the unique ID of a book. There is a deterministic function from a volume id to a URL under the books.google.com domain. Marked as 'optional' since a volume ID of zero is potentially possible and we want to distinguish that from the volume ID not being set.", -"format": "int64", -"type": "string" -}, -"conversationId": { -"type": "string" -}, -"dataset": { -"description": "The dataset this document comes from.", -"enum": [ -"DATASET_UNSPECIFIED", -"WIKIPEDIA", -"WEBDOCS", -"WEBDOCS_FINETUNE", -"GITHUB_MIRROR", -"BOOKS_FULL_VIEW", -"BOOKS_PRIVATE", -"GNEWS", -"ULM_DOCJOINS", -"ULM_DOCJOINS_DEDUPED", -"MEENA_FC", -"PODCAST", -"AQUA", -"WEB_ASR", -"BARD_GOLDEN", -"COMMON_SENSE_REASONING", -"MATH", -"MATH_REASONING", -"CLEAN_ARXIV", -"LAMDA_FACTUALITY_E2E_QUERY_GENERATION", -"LAMDA_FACTUALITY_E2E_RESPONSE_GENERATION", -"MASSIVE_FORUM_THREAD_SCORED_BARD", -"MASSIVE_FORUM_THREAD_SCORED_LONG_200", -"MASSIVE_FORUM_THREAD_SCORED_LONG_500", -"DOCUMENT_CHUNKS", -"MEENA_RESEARCH_PHASE_GOLDEN_MARKDOWN", -"MEENA_RESEARCH_PHASE_GOOGLERS", -"MEENA_RESPONSE_SAFETY_HUMAN_GEN", -"MEENA_RESPONSE_SAFETY_SCHEMA_NO_BROADCAST", -"MEENA_RESPONSE_SAFETY_V3_HUMAN_GEN2", -"MEENA_RESPONSE_SAFETY_V3_SCHEMA_NO_BROADCAST", -"LAMDA_FACTUALITY_TRIGGER", -"LAMDA_SAFETY_V2_SCHEMA_NO_BROADCAST", -"LAMDA_SSI_DISCRIMINATIVE", -"ASSISTANT_PERSONALITY_SAFETY", -"PODCAST_FINETUNE_DIALOG", -"WORLD_QUERY_GENERATOR", -"C4_JOINED_DOCJOINS", -"HOL4_THEORIES", -"HOL_LIGHT_THEORIES", -"HOLSTEPS", -"ISABELLE_STEP", -"ISABELLE_THEORIES", -"LEAN_MATHLIB_THEORIES", -"LEAN_STEP", -"MIZAR_THEORIES", -"COQ_STEP", -"COQ_THEORIES", -"AMPS_KHAN", -"AMPS_MATHEMATICA", -"CODEY_CODE", -"CODE_QA_SE", -"CODE_QA_SO", -"CODE_QA_FT_FORMAT", -"CODE_QA_FT_KNOWLEDGE", -"CODE_QA_GITHUB_FILTERED_CODE", -"BARD_PERSONALITY_GOLDEN", -"ULM_DOCJOINS_WITH_URLS_EN", -"ULM_DOCJOINS_WITH_URLS_I18N", -"GOODALL_MTV5_GITHUB", -"GOODALL_MTV5_BOOKS", -"GOODALL_MTV5_C4", -"GOODALL_MTV5_WIKIPEDIA", -"GOODALL_MW_TOP_100B", -"GOODALL_MW_STACK_EXCHANGE", -"GOODALL_MW_TOP_0_10B", -"GOODALL_MW_TOP_10B_20B", -"CODEY_NOTEBOOK_LM_PRETRAINING", -"VERTEX_SAFE_FLAN", -"GITHUB_MIRROR_V1_0_1", -"GITHUB_MIRROR_V2_1_0", -"CMS_WIKIPEDIA_LANG_FILTERED", -"CMS_STACKOVERFLOW_MULTILINGUAL", -"CMS_STACKEXCHANGE", -"PUBMED", -"GEMINI_DOCJOINS_EN_TOP10B_GCC", -"GEMINI_DOCJOINS_EN_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_EN_TOP20B_TOP100B_GCC", -"GEMINI_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_I18N_TOP20B_TOP100B_GCC", -"SIMPLIFIED_HTML_V1_GCC", -"GEMINI_DOCJOINS_TOXICITY_TAGGED_GCC", -"CMS_GITHUB_V4", -"GITHUB_HTML_V4", -"GITHUB_OTHER_V4", -"GITHUB_LONG_TAIL_V4", -"CMS_GITHUB_MULTIFILE_V4", -"GITHUB_DIFFS_WITH_COMMIT_MESSAGE", -"ULM_ARXIV", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_ENONLY", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_NONENONLY", -"QUORA", -"PODCASTS_ROBOTSTXT", -"COMBINED_REDDIT", -"CANARIES_SHUFFLED", -"CLM_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"TECHDOCS_DATA_SOURCE", -"SCIENCE_PDF_70M_DOCS_FILTERED", -"GEMINI_V1_CMS_WIKIPEDIA_LANG_FILTERED", -"GEMINI_V1_WIKIPEDIA_DIFFS", -"GEMINI_V1_DOCJOINS_EN_TOP10B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP10B_TOP20B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP20B_TOP100B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_TOP20B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP20B_TOP100B_GCC_050523", -"GEMINI_V1_SIMPLIFIED_HTML_V2_GCC", -"GEMINI_V1_CMS_STACKOVERFLOW_MULTILINGUAL_V2", -"GEMINI_V1_CMS_STACKEXCHANGE_DECONT", -"GEMINI_V1_QUORA", -"GEMINI_V1_COMBINED_REDDIT", -"GEMINI_V1_DOCJOIN_100B_EN_TOXICITY_TAGGED_GCC_FIXED_TAGS", -"GEMINI_V1_PUBMED", -"GEMINI_V1_WEB_MATH_V2", -"GEMINI_V1_CMS_GITHUB_V7", -"GEMINI_V1_CMS_GITHUB_DECONTAMINATED_V_7", -"GEMINI_V1_GITHUB_DIFF_WITH_COMMIT_MESSAGE_V2", -"GEMINI_V1_GITHUB_HTML_CSS_XML_V4", -"GEMINI_V1_GITHUB_OTHER_V4", -"GEMINI_V1_GITHUB_LONG_TAIL_V4", -"GEMINI_V1_GITHUB_JUPTYER_NOTEBOOKS_SSTABLE", -"GEMINI_V1_ULM_ARXIV_SSTABLE", -"GEMINI_V1_PODCASTS_ROBOTSTXT", -"GEMINI_V1_SCIENCE_PDF_68M_HQ_DOCS_GCC", -"GEMINI_V1_GITHUB_TECHDOCS_V2", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_EN", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_NONEN", -"GEMINI_V1_STEM_BOOKS_650K_TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_M3W_V2_FILTERED", -"GEMINI_V1_VQCOCA_1B_MULTIRES_WEBLI_EN_V4_350M_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_SCREENAI_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CULTURE_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_EN_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_I18N_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_NON_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_VTP_4F_VIDEO2TEXT_PREFIX", -"GEMINI_V1_FORMAL_MATH_WITHOUT_HOLSTEPS_AND_MIZAR", -"GEMINI_V1_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"GEMINI_V1_CANARIES_SHUFFLED_DOCJOIN_EN_NONEN_CODE_ARXIV_TRANSLATE", -"DUET_CLOUD_SECURITY_DOCS", -"DUET_GITHUB_CODE_SNIPPETS", -"DUET_GITHUB_FILES", -"DUET_GOBYEXAMPLE", -"DUET_GOLANG_DOCS", -"DUET_CLOUD_DOCS_TROUBLESHOOTING_TABLES", -"DUET_DEVSITE_DOCS", -"DUET_CLOUD_BLOG_POSTS", -"DUET_CLOUD_PODCAST_EPISODES", -"DUET_YOUTUBE_VIDEOS", -"DUET_CLOUD_SKILLS_BOOST", -"DUET_CLOUD_DOCS", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_GENERATED", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_HANDWRITTEN", -"DUET_GOOGLESQL_GENERATION", -"DUET_CLOUD_IX_PROMPTS", -"DUET_RAD", -"DUET_STACKOVERFLOW_ISSUES", -"DUET_STACKOVERFLOW_ANSWERS", -"BARD_ARCADE_GITHUB", -"MOBILE_ASSISTANT_MAGI_FILTERED_0825_373K", -"MOBILE_ASSISTANT_PALM24B_FILTERED_400K", -"GENESIS_NEWS_INSIGHTS", -"LABS_AQA_DSCOUT", -"LABS_AQA_TAILWIND", -"LABS_AQA_DELEWARE", -"GEMINI_MULTIMODAL_FT_URL", -"GEMINI_MULTIMODAL_FT_YT", -"GEMINI_MULTIMODAL_FT_SHUTTERSTOCK", -"GEMINI_MULTIMODAL_FT_NONE", -"GEMINI_MULTIMODAL_FT_OTHER", -"GEMINI_MULTIMODAL_FT_INK", -"GEMINI_MULTIMODAL_IT", -"GEMINI_IT_SHUTTERSTOCK", -"GEMINI_IT_M3W", -"GEMINI_IT_HEDGING", -"GEMINI_IT_DSCOUT_FACTUALITY", -"GEMINI_IT_AQUAMUSE", -"GEMINI_IT_SHOTGUN", -"GEMINI_IT_ACI_BENCH", -"GEMINI_IT_SPIDER_FILTERED", -"GEMINI_IT_TAB_SUM_BQ", -"GEMINI_IT_QA_WITH_URL", -"GEMINI_IT_CODE_INSTRUCT", -"GEMINI_IT_MED_PALM", -"GEMINI_IT_TASK_ORIENTED_DIALOG", -"GEMINI_IT_NIMBUS_GROUNDING_TO_PROMPT", -"GEMINI_IT_EITL_GEN", -"GEMINI_IT_HITL_GEN", -"GEMINI_IT_MECH", -"GEMINI_IT_TABLE_GEN", -"GEMINI_IT_NIMBUS_DECIBEL", -"GEMINI_IT_CLOUD_CODE_IF", -"GEMINI_IT_CLOUD_EUR_LEX_JSON", -"GEMINI_IT_CLOUD_OASST", -"GEMINI_IT_CLOUD_SELF_INSTRUCT", -"GEMINI_IT_CLOUD_UCS_AQUAMUSE", -"GEMIT_BRIDGE_SUFFIX_FT", -"CLOUD_SECURITY_PRETRAINING", -"CLOUD_SECURITY_FINETUNING", -"CLOUD_SECURITY_RAG_CISA", -"GEMINI_GOOSE_PUBLIC", -"GEMINI_GOOSE_SILOED", -"GEMINI_V2_CMS_WIKIPEDIA_LANG_FILTERED_GCC_PII", -"GEMINI_V2_WIKIPEDIA_DIFFS_COMPLIANT", 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-"GEMINI_V2_FORMAL_COMPLIANT", -"GEMINI_V2_CMS_STACKEXCHANGE_COMPLIANT", -"GEMINI_V2_PUBMED_COMPLIANT", -"GEMINI_V2_WEB_MATH_V3_COMPLIANT", -"GEMINI_V2_SCIENCEWEB_V0_GCC_PII", -"GEMINI_V2_WEB_POLYMATH_V1_COMPLIANT", -"GEMINI_V2_MATH_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_BIOLOGY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_PHYSICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHEMISTRY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MACHINE_LEARNING_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_QA_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_ECONOMICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MEDICAL_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHESS_COMPLIANT", -"GEMINI_V2_YOUTUBE_SCIENCE_V4_FILTERED_COMPLIANT", -"GEMINI_V2_GOALDMINE_XL_GENERATED_PLUS_GT_NO_DM_MATH_COMPLIANT", -"GEMINI_V2_FIRSTTIMES_SCIENCE_PDF_DEDUP_HQ_LENGTH_FILTERED_COMPLIANT", -"GEMINI_V2_PODCASTS_COMPLIANT", -"GEMINI_V2_EN_NONSCIENCE_PDF_DEDUP_46M_DOCS_COMPLIANT", -"GEMINI_V2_NONPUB_COPYRIGHT_BOOKS_V3_70_CONF_082323_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_COPYRIGHT_BOOKS_V3_111823_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_BOOKS_318K_TEXT_COMPLIANT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_T2I_TEXT_TO_IMAGE_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_I18N_680M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_FULL_HTML_75M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_V1_1_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_DOC_240M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_VIDEO_VIDEO_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M4W_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CULTURE_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_DETECTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_ALT_TEXT_NONEN_500M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SPATIAL_AWARE_PALI_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_3D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_NON_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_3D_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CC3M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_INFOGRAPHICS_LARGE_WEB_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_BIORXIV_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PLOT2TABLE_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TIKZ_DERENDERING_MERGED_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_2D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WIKIPEDIA_EQUATIONS_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_EQ2LATEX_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_ARXIV_EQUATIONS_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_HEAD_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_CLM_TRANSLATE_DATAV3_WEB_UNWMT_INCR_MIX", -"GEMINI_V2_NTL_NTLV4A_MONOLINGUAL_DEDUP_N5", -"GEMINI_V2_NTL_STT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_TRANSLIT_BILEX_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_BT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_FT_FIXED_TRANSLATE_DEDUP_N5", -"GEMINI_V2_CANARIES_SHUFFLED_COMPLIANT", -"CLOUD_GEMIT_CLOUD_FACTUALITY_GROUNDING_MAGI", -"CLOUD_GEMIT_MT_DIALGUE_LMSYS", -"CLOUD_GEMIT_MTS_DIALOGUE_V3", -"CLOUD_GEMIT_COMMIT_MSG_GEN_V3", -"CLOUD_GEMIT_CODE_IF_V1", -"CLOUD_GEMIT_CODE_SELF_REPAIR", -"CLOUD_GEMIT_IDENTITY", -"CLOUD_GEMIT_SEARCH_AUGMENTED_RESPONSE_GENERATION", -"CLOUD_GEMIT_AMPS", -"CLOUD_GEMIT_AQUA", -"CLOUD_GEMIT_COMMON_SENSE_REASONING_SCHEMA", -"CLOUD_GEMIT_GSM8K_SCHEMA", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_UN", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_EUROPARL", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_NEWSCOMMENTARY", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_2021_INCR", -"GEMINI_V1_TAIL_PATCH_GOALDMINE", -"GEMINI_V1_TAIL_PATCH_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION", -"GEMINI_V1_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V1_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V1_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V2_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V2_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_PMC", -"GEMINI_V2_TAIL_PATCH_VOXPOPULI", -"GEMINI_V2_TAIL_PATCH_FLEURS", -"GEMINI_V2_SSFS", -"GEMINI_V2_CODE_TRANSFORM_SYNTHETIC_ERROR_FIX", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_COMMITS", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_PR", -"GEMINI_V2_SQL_REPAIR_SFT", -"GEMINI_V2_JSON_MODE_SYS_INSTRUCTION", -"YT_CONTENT_INSPIRATION" -], -"enumDescriptions": [ -"", -"Wikipedia article Tensorflow datasets used by Tarzan and maintained by TFDS team.", -"Webdocs that have been filtered from the docjoins by the Tarzan team for use in the Tarzan training set.", -"", -"", -"'Full view' books dataset maintained by Oceanographers team, meaning 'ok to view the book in full in all localities'. Largely the same as 'public domain', but with potentially subtle distinction.", -"Filtered private books used by ULM: http://google3/learning/multipod/pax/lm/params/ulm/tasks.py;l=123;rcl=494241309. which corresponds with /cns/mf-d/home/multipod-language-data/private_books/books_filtered_en_resharded@50000", -"Google news dataset referenced in: http://google3/learning/brain/research/conversation/meena/t5/pretrain_tasks.py;l=922;rcl=496534668", -"The docjoins data for ULM /cns/yo-d/home/multipod-language-data/docjoins/rs=6.3/20220728/100B_docstructure_split/examples_en.tfrecord_lattice_05_score_01_HFV13@3929", -"", -"Meena full conversations. http://google3/learning/brain/research/conversation/meena/t5/pretrain_mixtures.py;l=675;rcl=496583228", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Academic dataset of math text. http://google3/learning/brain/research/conversation/meena/seqio/mixtures/experimental/bard.py;rcl=500222380", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Datasets managed by the Goodall team: deepmind-goodall@google.com", -"", -"", -"", -"", -"", -"", -"", -"Datasets used by Codepoet", -"Datasets used by Vertex", -"", -"", -"Datasets used by Gemini Public data", -"", -"", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"Github", -"", -"", -"", -"", -"", -"Arxiv", -"Others", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V1, order by precedence. Wikipedia", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"GitHub dataset with license info. We prefer this to help cite proper licenses for code recitation.", -"", -"", -"", -"", -"", -"", -"ArXiv", -"Citable misc", -"", -"", -"Non-public books", -"", -"", -"Other", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Duet AI finetune datasets, order by precedence.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Bard ARCADE finetune dataset", -"Mobile assistant finetune datasets.", -"", -"Genesis fine tuned datasets.", -"LABS AQA fine-tune datasets.", -"", -"", -"Gemini multimodal instruction tune(IT) and fine tune(FT) datasets datasets.", -"", -"", -"", -"", -"", -"", -"Gemini IT 1.2.7 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemit Bridge ULM FT dataset", -"Cloud Security fine tuned datasets.", -"", -"", -"Gemini Goose FT datasets.", -"", -"Gemini V2 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Cloud gemit pro FT datasets.", -"", -"", -"", -"", -"", -"", -"Cloud gemit ultra FT datasets.", -"", -"", -"", -"", -"Gemini V1 tail patch translation.", -"", -"", -"", -"Gemini V1 tail patch others.", -"", -"Gemini V1 and V2 shared tail patch.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V2 only tail patch.", -"", -"", -"Gemini V2 rev10", -"", -"", -"", -"", -"", -"Youtube Content Inspiration FT datasets." -], -"type": "string" -}, -"filepath": { -"type": "string" -}, -"geminiId": { -"type": "string" -}, -"gnewsArticleTitle": { -"type": "string" -}, -"goodallExampleId": { -"type": "string" -}, -"isOptOut": { -"description": "Whether the document is opted out.", -"type": "boolean" -}, -"isPrompt": { -"description": "When true, this attribution came from the user's prompt.", -"type": "boolean" -}, -"lamdaExampleId": { -"type": "string" -}, -"license": { -"type": "string" -}, -"meenaConversationId": { -"type": "string" -}, -"naturalLanguageCode": { -"description": "Natural (not programming) language of the document. Language code as defined by http://www.unicode.org/reports/tr35/#Identifiers and https://tools.ietf.org/html/bcp47. Currently applicable to full-view books. Use docinfo-util.h to set & read language fields. See go/iii.", -"type": "string" -}, -"noAttribution": { -"description": "True if this doc has no attribution information available. We use an explicit field for this instead of just implicitly leaving all the DocAttribution fields blank to distinguish a case where a bug/oversight has left the attribution information empty vs when we really have no attribution information available.", -"type": "boolean" -}, -"podcastUtteranceId": { -"type": "string" -}, -"publicationDate": { -"$ref": "GoogleTypeDate" -}, -"qualityScoreExperimentOnly": { -"description": "This field is for opt-out experiment only, MUST never be used during actual production/serving. ", -"format": "double", -"type": "number" -}, -"repo": { -"description": "Github repository", -"type": "string" -}, -"url": { -"description": "URL of a webdoc", -"type": "string" -}, -"volumeId": { -"type": "string" -}, -"wikipediaArticleTitle": { -"description": "Wikipedia article title. The Wikipedia TFDS dataset includes article titles but not URLs. While a URL is to the best of our knowledge a deterministic function of the title, we store the original title to reflect the information in the original dataset.", -"type": "string" -}, -"youtubeVideoId": { -"type": "string" -} -}, -"type": "object" -}, -"LearningGenaiRecitationRecitationResult": { -"description": "The recitation result for one input", -"id": "LearningGenaiRecitationRecitationResult", -"properties": { -"dynamicSegmentResults": { -"items": { -"$ref": "LearningGenaiRecitationSegmentResult" -}, -"type": "array" -}, -"recitationAction": { -"description": "The recitation action for one given input. When its segments contain different actions, the overall action will be returned in the precedence of BLOCK > CITE > NO_ACTION. When the given input is not found in any source, the recitation action will be NO_ACTION.", -"enum": [ -"ACTION_UNSPECIFIED", -"CITE", -"BLOCK", -"NO_ACTION", -"EXEMPT_FOUND_IN_PROMPT" -], -"enumDescriptions": [ -"", -"indicate that attribution must be shown for a Segment", -"indicate that a Segment should be blocked from being used", -"for tagging high-frequency code snippets", -"The recitation was found in prompt and is exempted from overall results" -], -"type": "string" -}, -"trainingSegmentResults": { -"items": { -"$ref": "LearningGenaiRecitationSegmentResult" -}, -"type": "array" -} -}, -"type": "object" -}, -"LearningGenaiRecitationSegmentResult": { -"description": "The recitation result for each segment in a given input.", -"id": "LearningGenaiRecitationSegmentResult", -"properties": { -"attributionDataset": { -"description": "The dataset the segment came from. Datasets change often as model evolves. Treat this field as informational only and avoid depending on it directly.", -"enum": [ -"DATASET_UNSPECIFIED", -"WIKIPEDIA", -"WEBDOCS", -"WEBDOCS_FINETUNE", -"GITHUB_MIRROR", -"BOOKS_FULL_VIEW", -"BOOKS_PRIVATE", -"GNEWS", -"ULM_DOCJOINS", -"ULM_DOCJOINS_DEDUPED", -"MEENA_FC", -"PODCAST", -"AQUA", -"WEB_ASR", -"BARD_GOLDEN", -"COMMON_SENSE_REASONING", -"MATH", -"MATH_REASONING", -"CLEAN_ARXIV", -"LAMDA_FACTUALITY_E2E_QUERY_GENERATION", -"LAMDA_FACTUALITY_E2E_RESPONSE_GENERATION", -"MASSIVE_FORUM_THREAD_SCORED_BARD", -"MASSIVE_FORUM_THREAD_SCORED_LONG_200", -"MASSIVE_FORUM_THREAD_SCORED_LONG_500", -"DOCUMENT_CHUNKS", -"MEENA_RESEARCH_PHASE_GOLDEN_MARKDOWN", -"MEENA_RESEARCH_PHASE_GOOGLERS", -"MEENA_RESPONSE_SAFETY_HUMAN_GEN", -"MEENA_RESPONSE_SAFETY_SCHEMA_NO_BROADCAST", -"MEENA_RESPONSE_SAFETY_V3_HUMAN_GEN2", -"MEENA_RESPONSE_SAFETY_V3_SCHEMA_NO_BROADCAST", -"LAMDA_FACTUALITY_TRIGGER", -"LAMDA_SAFETY_V2_SCHEMA_NO_BROADCAST", -"LAMDA_SSI_DISCRIMINATIVE", -"ASSISTANT_PERSONALITY_SAFETY", -"PODCAST_FINETUNE_DIALOG", -"WORLD_QUERY_GENERATOR", -"C4_JOINED_DOCJOINS", -"HOL4_THEORIES", -"HOL_LIGHT_THEORIES", -"HOLSTEPS", -"ISABELLE_STEP", -"ISABELLE_THEORIES", -"LEAN_MATHLIB_THEORIES", -"LEAN_STEP", -"MIZAR_THEORIES", -"COQ_STEP", -"COQ_THEORIES", -"AMPS_KHAN", -"AMPS_MATHEMATICA", -"CODEY_CODE", -"CODE_QA_SE", -"CODE_QA_SO", -"CODE_QA_FT_FORMAT", -"CODE_QA_FT_KNOWLEDGE", -"CODE_QA_GITHUB_FILTERED_CODE", -"BARD_PERSONALITY_GOLDEN", -"ULM_DOCJOINS_WITH_URLS_EN", -"ULM_DOCJOINS_WITH_URLS_I18N", -"GOODALL_MTV5_GITHUB", -"GOODALL_MTV5_BOOKS", -"GOODALL_MTV5_C4", -"GOODALL_MTV5_WIKIPEDIA", -"GOODALL_MW_TOP_100B", -"GOODALL_MW_STACK_EXCHANGE", -"GOODALL_MW_TOP_0_10B", -"GOODALL_MW_TOP_10B_20B", -"CODEY_NOTEBOOK_LM_PRETRAINING", -"VERTEX_SAFE_FLAN", -"GITHUB_MIRROR_V1_0_1", -"GITHUB_MIRROR_V2_1_0", -"CMS_WIKIPEDIA_LANG_FILTERED", -"CMS_STACKOVERFLOW_MULTILINGUAL", -"CMS_STACKEXCHANGE", -"PUBMED", -"GEMINI_DOCJOINS_EN_TOP10B_GCC", -"GEMINI_DOCJOINS_EN_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_EN_TOP20B_TOP100B_GCC", -"GEMINI_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_I18N_TOP20B_TOP100B_GCC", -"SIMPLIFIED_HTML_V1_GCC", -"GEMINI_DOCJOINS_TOXICITY_TAGGED_GCC", -"CMS_GITHUB_V4", -"GITHUB_HTML_V4", -"GITHUB_OTHER_V4", -"GITHUB_LONG_TAIL_V4", -"CMS_GITHUB_MULTIFILE_V4", -"GITHUB_DIFFS_WITH_COMMIT_MESSAGE", -"ULM_ARXIV", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_ENONLY", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_NONENONLY", -"QUORA", -"PODCASTS_ROBOTSTXT", -"COMBINED_REDDIT", -"CANARIES_SHUFFLED", -"CLM_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"TECHDOCS_DATA_SOURCE", -"SCIENCE_PDF_70M_DOCS_FILTERED", -"GEMINI_V1_CMS_WIKIPEDIA_LANG_FILTERED", -"GEMINI_V1_WIKIPEDIA_DIFFS", -"GEMINI_V1_DOCJOINS_EN_TOP10B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP10B_TOP20B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP20B_TOP100B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_TOP20B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP20B_TOP100B_GCC_050523", -"GEMINI_V1_SIMPLIFIED_HTML_V2_GCC", -"GEMINI_V1_CMS_STACKOVERFLOW_MULTILINGUAL_V2", -"GEMINI_V1_CMS_STACKEXCHANGE_DECONT", -"GEMINI_V1_QUORA", -"GEMINI_V1_COMBINED_REDDIT", -"GEMINI_V1_DOCJOIN_100B_EN_TOXICITY_TAGGED_GCC_FIXED_TAGS", -"GEMINI_V1_PUBMED", -"GEMINI_V1_WEB_MATH_V2", -"GEMINI_V1_CMS_GITHUB_V7", -"GEMINI_V1_CMS_GITHUB_DECONTAMINATED_V_7", -"GEMINI_V1_GITHUB_DIFF_WITH_COMMIT_MESSAGE_V2", -"GEMINI_V1_GITHUB_HTML_CSS_XML_V4", -"GEMINI_V1_GITHUB_OTHER_V4", -"GEMINI_V1_GITHUB_LONG_TAIL_V4", -"GEMINI_V1_GITHUB_JUPTYER_NOTEBOOKS_SSTABLE", -"GEMINI_V1_ULM_ARXIV_SSTABLE", -"GEMINI_V1_PODCASTS_ROBOTSTXT", -"GEMINI_V1_SCIENCE_PDF_68M_HQ_DOCS_GCC", -"GEMINI_V1_GITHUB_TECHDOCS_V2", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_EN", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_NONEN", -"GEMINI_V1_STEM_BOOKS_650K_TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_M3W_V2_FILTERED", -"GEMINI_V1_VQCOCA_1B_MULTIRES_WEBLI_EN_V4_350M_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_SCREENAI_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CULTURE_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_EN_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_I18N_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_NON_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_VTP_4F_VIDEO2TEXT_PREFIX", -"GEMINI_V1_FORMAL_MATH_WITHOUT_HOLSTEPS_AND_MIZAR", -"GEMINI_V1_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"GEMINI_V1_CANARIES_SHUFFLED_DOCJOIN_EN_NONEN_CODE_ARXIV_TRANSLATE", -"DUET_CLOUD_SECURITY_DOCS", -"DUET_GITHUB_CODE_SNIPPETS", -"DUET_GITHUB_FILES", -"DUET_GOBYEXAMPLE", -"DUET_GOLANG_DOCS", -"DUET_CLOUD_DOCS_TROUBLESHOOTING_TABLES", -"DUET_DEVSITE_DOCS", -"DUET_CLOUD_BLOG_POSTS", -"DUET_CLOUD_PODCAST_EPISODES", -"DUET_YOUTUBE_VIDEOS", -"DUET_CLOUD_SKILLS_BOOST", -"DUET_CLOUD_DOCS", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_GENERATED", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_HANDWRITTEN", -"DUET_GOOGLESQL_GENERATION", -"DUET_CLOUD_IX_PROMPTS", -"DUET_RAD", -"DUET_STACKOVERFLOW_ISSUES", -"DUET_STACKOVERFLOW_ANSWERS", -"BARD_ARCADE_GITHUB", -"MOBILE_ASSISTANT_MAGI_FILTERED_0825_373K", -"MOBILE_ASSISTANT_PALM24B_FILTERED_400K", -"GENESIS_NEWS_INSIGHTS", -"LABS_AQA_DSCOUT", -"LABS_AQA_TAILWIND", -"LABS_AQA_DELEWARE", -"GEMINI_MULTIMODAL_FT_URL", -"GEMINI_MULTIMODAL_FT_YT", -"GEMINI_MULTIMODAL_FT_SHUTTERSTOCK", -"GEMINI_MULTIMODAL_FT_NONE", -"GEMINI_MULTIMODAL_FT_OTHER", -"GEMINI_MULTIMODAL_FT_INK", -"GEMINI_MULTIMODAL_IT", -"GEMINI_IT_SHUTTERSTOCK", -"GEMINI_IT_M3W", -"GEMINI_IT_HEDGING", -"GEMINI_IT_DSCOUT_FACTUALITY", -"GEMINI_IT_AQUAMUSE", -"GEMINI_IT_SHOTGUN", -"GEMINI_IT_ACI_BENCH", -"GEMINI_IT_SPIDER_FILTERED", -"GEMINI_IT_TAB_SUM_BQ", -"GEMINI_IT_QA_WITH_URL", -"GEMINI_IT_CODE_INSTRUCT", -"GEMINI_IT_MED_PALM", -"GEMINI_IT_TASK_ORIENTED_DIALOG", -"GEMINI_IT_NIMBUS_GROUNDING_TO_PROMPT", -"GEMINI_IT_EITL_GEN", -"GEMINI_IT_HITL_GEN", -"GEMINI_IT_MECH", -"GEMINI_IT_TABLE_GEN", -"GEMINI_IT_NIMBUS_DECIBEL", -"GEMINI_IT_CLOUD_CODE_IF", -"GEMINI_IT_CLOUD_EUR_LEX_JSON", -"GEMINI_IT_CLOUD_OASST", -"GEMINI_IT_CLOUD_SELF_INSTRUCT", -"GEMINI_IT_CLOUD_UCS_AQUAMUSE", -"GEMIT_BRIDGE_SUFFIX_FT", -"CLOUD_SECURITY_PRETRAINING", -"CLOUD_SECURITY_FINETUNING", -"CLOUD_SECURITY_RAG_CISA", -"GEMINI_GOOSE_PUBLIC", -"GEMINI_GOOSE_SILOED", -"GEMINI_V2_CMS_WIKIPEDIA_LANG_FILTERED_GCC_PII", -"GEMINI_V2_WIKIPEDIA_DIFFS_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_QUORA_COMPLIANT", -"GEMINI_V2_FORUMS_V2_COMPLIANT", -"GEMINI_V2_CMS_STACKOVERFLOW_MULTILINGUAL_V2_COMPLIANT", -"GEMINI_V2_SIMPLIFIED_HTML_V2_CORRECT_FORMAT_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_TOXICITY_TAGGED_FIXED_TAGS_COMPLIANT", -"GEMINI_V2_CODEWEB_V1_COMPLIANT", -"GEMINI_V2_LEETCODE_GCC_PII", -"GEMINI_V2_CODE_CONTESTS_COMPLIANT", -"GEMINI_V2_CMS_GITHUB_MULTI_FILE_FOR_FIM_GEMBAGZ_FIXED_BYTES_LENGTHS", -"GEMINI_V2_GITHUB_EVALED_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_NON_EVAL_HIGH_PRI_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_LOW_PRI_LANGUAGES_AND_CONFIGS_COMPLIANT", -"GEMINI_V2_GITHUB_LONG_TAIL_AND_STRUCTURED_DATA_COMPLIANT", -"GEMINI_V2_GITHUB_PYTHON_NOTEBOOKS_COMPLIANT", -"GEMINI_V2_GITHUB_DIFFS_COMPLIANT", -"GEMINI_V2_GITHUB_TECHDOCS_COMPLIANT", -"GEMINI_V2_HIGH_QUALITY_CODE_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_SCIENCE_PDF_68M_HQ_DOCS_DEDUP_COMPLIANT_CLEAN_TEX", -"GEMINI_V2_ARXIV_2023_COMPLIANT", -"GEMINI_V2_FORMAL_COMPLIANT", -"GEMINI_V2_CMS_STACKEXCHANGE_COMPLIANT", -"GEMINI_V2_PUBMED_COMPLIANT", 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maintained by Oceanographers team, meaning 'ok to view the book in full in all localities'. Largely the same as 'public domain', but with potentially subtle distinction.", -"Filtered private books used by ULM: http://google3/learning/multipod/pax/lm/params/ulm/tasks.py;l=123;rcl=494241309. which corresponds with /cns/mf-d/home/multipod-language-data/private_books/books_filtered_en_resharded@50000", -"Google news dataset referenced in: http://google3/learning/brain/research/conversation/meena/t5/pretrain_tasks.py;l=922;rcl=496534668", -"The docjoins data for ULM /cns/yo-d/home/multipod-language-data/docjoins/rs=6.3/20220728/100B_docstructure_split/examples_en.tfrecord_lattice_05_score_01_HFV13@3929", -"", -"Meena full conversations. http://google3/learning/brain/research/conversation/meena/t5/pretrain_mixtures.py;l=675;rcl=496583228", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Academic dataset of math text. http://google3/learning/brain/research/conversation/meena/seqio/mixtures/experimental/bard.py;rcl=500222380", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Datasets managed by the Goodall team: deepmind-goodall@google.com", -"", -"", -"", -"", -"", -"", -"", -"Datasets used by Codepoet", -"Datasets used by Vertex", -"", -"", -"Datasets used by Gemini Public data", -"", -"", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"Github", -"", -"", -"", -"", -"", -"Arxiv", -"Others", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V1, order by precedence. Wikipedia", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"GitHub dataset with license info. We prefer this to help cite proper licenses for code recitation.", -"", -"", -"", -"", -"", -"", -"ArXiv", -"Citable misc", -"", -"", -"Non-public books", -"", -"", -"Other", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Duet AI finetune datasets, order by precedence.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Bard ARCADE finetune dataset", -"Mobile assistant finetune datasets.", -"", -"Genesis fine tuned datasets.", -"LABS AQA fine-tune datasets.", -"", -"", -"Gemini multimodal instruction tune(IT) and fine tune(FT) datasets datasets.", -"", -"", -"", -"", -"", -"", -"Gemini IT 1.2.7 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemit Bridge ULM FT dataset", -"Cloud Security fine tuned datasets.", -"", -"", -"Gemini Goose FT datasets.", -"", -"Gemini V2 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Cloud gemit pro FT datasets.", -"", -"", -"", -"", -"", -"", -"Cloud gemit ultra FT datasets.", -"", -"", -"", -"", -"Gemini V1 tail patch translation.", -"", -"", -"", -"Gemini V1 tail patch others.", -"", -"Gemini V1 and V2 shared tail patch.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V2 only tail patch.", -"", -"", -"Gemini V2 rev10", -"", -"", -"", -"", -"", -"Youtube Content Inspiration FT datasets." -], -"type": "string" -}, -"displayAttributionMessage": { -"description": "human-friendly string that contains information from doc_attribution which could be shown by clients", -"type": "string" -}, -"docAttribution": { -"$ref": "LearningGenaiRecitationDocAttribution" -}, -"docOccurrences": { -"description": "number of documents that contained this segment", -"format": "int32", -"type": "integer" -}, -"endIndex": { -"format": "int32", -"type": "integer" -}, -"rawText": { -"description": "The raw text in the given input that is corresponding to the segment. It will be available only when 'return_segment_raw_text' is enabled in the request options.", -"type": "string" -}, -"segmentRecitationAction": { -"enum": [ -"ACTION_UNSPECIFIED", -"CITE", -"BLOCK", -"NO_ACTION", -"EXEMPT_FOUND_IN_PROMPT" -], -"enumDescriptions": [ -"", -"indicate that attribution must be shown for a Segment", -"indicate that a Segment should be blocked from being used", -"for tagging high-frequency code snippets", -"The recitation was found in prompt and is exempted from overall results" -], -"type": "string" -}, -"sourceCategory": { -"description": "The category of the source dataset where the segment came from. This is more stable than Dataset.", -"enum": [ -"SOURCE_CATEGORY_UNSPECIFIED", -"SOURCE_CATEGORY_WIKIPEDIA", -"SOURCE_CATEGORY_WEBDOCS", -"SOURCE_CATEGORY_GITHUB", -"SOURCE_CATEGORY_ARXIV", -"SOURCE_CATEGORY_PRIVATE_BOOKS", -"SOURCE_CATEGORY_OTHERS", -"SOURCE_CATEGORY_PUBLIC_BOOKS", -"SOURCE_CATEGORY_GNEWS" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"", -"", -"", -"" -], -"type": "string" -}, -"startIndex": { -"description": "The segment boundary start (inclusive) and end index (exclusive) in the given text. In the streaming RPC, the indexes always start from the beginning of the first text in the entire stream. The indexes are measured in UTF-16 code units.", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"LearningGenaiRootCalculationType": { -"description": "The type used for final weights calculation.", -"id": "LearningGenaiRootCalculationType", -"properties": { -"scoreType": { -"enum": [ -"TYPE_UNKNOWN", -"TYPE_SAFE", -"TYPE_POLICY", -"TYPE_GENERATION" -], -"enumDescriptions": [ -"Unknown scorer type.", -"Safety scorer.", -"Policy scorer.", -"Generation scorer." -], -"type": "string" -}, -"weights": { -"format": "double", -"type": "number" -} -}, -"type": "object" -}, -"LearningGenaiRootClassifierOutput": { -"id": "LearningGenaiRootClassifierOutput", -"properties": { -"ruleOutput": { -"$ref": "LearningGenaiRootRuleOutput", -"deprecated": true, -"description": "If set, this is the output of the first matching rule." -}, -"ruleOutputs": { -"description": "outputs of all matching rule.", -"items": { -"$ref": "LearningGenaiRootRuleOutput" -}, -"type": "array" -}, -"state": { -"$ref": "LearningGenaiRootClassifierState", -"description": "The results of data_providers and metrics." -} -}, -"type": "object" -}, -"LearningGenaiRootClassifierOutputSummary": { -"id": "LearningGenaiRootClassifierOutputSummary", -"properties": { -"metrics": { -"items": { -"$ref": "LearningGenaiRootMetricOutput" -}, -"type": "array" -}, -"ruleOutput": { -"$ref": "LearningGenaiRootRuleOutput", -"deprecated": true, -"description": "Output of the first matching rule." -}, -"ruleOutputs": { -"description": "outputs of all matching rule.", -"items": { -"$ref": "LearningGenaiRootRuleOutput" -}, -"type": "array" -} -}, -"type": "object" -}, -"LearningGenaiRootClassifierState": { -"description": "DataProviderOutput and MetricOutput can be saved between calls to the Classifier framework. For instance, you can run the query classifier, get outputs from those metrics, then use them in a result classifier as well. Example rule based on this idea: and_rules { rule { metric_name: 'query_safesearch_v2' ... } rule { metric_name: 'response_safesearch_v2' ... } }", -"id": "LearningGenaiRootClassifierState", -"properties": { -"dataProviderOutput": { -"items": { -"$ref": "LearningGenaiRootDataProviderOutput" -}, -"type": "array" -}, -"metricOutput": { -"items": { -"$ref": "LearningGenaiRootMetricOutput" -}, -"type": "array" -} -}, -"type": "object" -}, -"LearningGenaiRootCodeyChatMetadata": { -"description": "Stores all metadata relating to AIDA DoConversation.", -"id": "LearningGenaiRootCodeyChatMetadata", -"properties": { -"codeLanguage": { -"description": "Indicates the programming language of the code if the message is a code chunk.", -"enum": [ -"UNSPECIFIED", -"ALL", -"TEXT", -"CPP", -"PYTHON", -"KOTLIN", -"JAVA", -"JAVASCRIPT", -"GO", -"R", -"JUPYTER_NOTEBOOK", -"TYPESCRIPT", -"HTML", -"SQL", -"BASH", -"C", -"DART", -"GRADLE", -"GROOVY", -"JAVADOC", -"JSON", -"MAKEFILE", -"MARKDOWN", -"PROTO", -"XML", -"YAML" -], -"enumDescriptions": [ -"Unspecified Language.", -"All languages.", -"Not code.", -"The most common, well-supported languages. C++ code.", -"Python code.", -"Kotlin code.", -"Java code.", -"JavaScript code.", -"Go code.", -"R code.", -"Jupyter notebook.", -"TypeScript code.", -"HTML code.", -"SQL code.", -"Other languages in alphabetical order. BASH code.", -"C code.", -"Dart code.", -"Gradle code.", -"Groovy code.", -"API documentation.", -"JSON code.", -"Makefile code.", -"Markdown code.", -"Protocol buffer.", -"XML code.", -"YAML code." -], -"type": "string" -} -}, -"type": "object" -}, -"LearningGenaiRootCodeyCheckpoint": { -"description": "Describes a sample at a checkpoint for post-processing.", -"id": "LearningGenaiRootCodeyCheckpoint", -"properties": { -"codeyTruncatorMetadata": { -"$ref": "LearningGenaiRootCodeyTruncatorMetadata", -"description": "Metadata that describes what was truncated at this checkpoint." -}, -"currentSample": { -"description": "Current state of the sample after truncator.", -"type": "string" -}, -"postInferenceStep": { -"description": "Postprocessor run that yielded this checkpoint.", -"enum": [ -"STEP_POST_PROCESSING_STEP_UNSPECIFIED", -"STEP_ORIGINAL_MODEL_OUTPUT", -"STEP_MODEL_OUTPUT_DEDUPLICATION", -"STEP_STOP_SEQUENCE_TRUNCATION", -"STEP_HEURISTIC_TRUNCATION", -"STEP_WALD_TRUNCATION", -"STEP_WHITESPACE_TRUNCATION", -"STEP_FINAL_DEDUPLICATION", -"STEP_TOXICITY_CHECK", -"STEP_RECITATION_CHECK", -"STEP_RETURNED", -"STEP_WALKBACK_CORRECTION", -"STEP_SCORE_THRESHOLDING", -"STEP_MODEL_CONFIG_STOP_SEQUENCE_TRUNCATION", -"STEP_CUSTOM_STOP_SEQUENCE_TRUNCATION", -"STEP_EXPECTED_SAMPLE_SIZE", -"STEP_TREE_TRIM_TRUNCATION" -], -"enumDeprecated": [ -false, -false, -false, -true, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false -], -"enumDescriptions": [ -"", -"Original model outputs as-is.", -"Original model outputs after deduplication.", -"StopSequencePostProcessor.", -"Heuristic SuffixTruncator step.", -"Go service post-processor.", -"Truncate trailing whitespace and filter whitespace-only completions.", -"Deduplicate after all truncations.", -"Toxicity returns true.", -"Recitation causes BLOCK.", -"Return the response to the API.", -"Correcting walkback constraint (samples are dropped if they don't match the prefix constraint).", -"Thresholding samples based on a minimum score.", -"StopSequencePostProcessor.", -"StopSequencePostProcessor.", -"Drop extra number of samples that exceeds expected_samples.", -"Truncated by highest end token score." -], -"type": "string" -} -}, -"type": "object" -}, -"LearningGenaiRootCodeyCompletionMetadata": { -"description": "Stores all metadata relating to Completion.", -"id": "LearningGenaiRootCodeyCompletionMetadata", -"properties": { -"checkpoints": { -"items": { -"$ref": "LearningGenaiRootCodeyCheckpoint" -}, -"type": "array" -} -}, -"type": "object" -}, -"LearningGenaiRootCodeyGenerationMetadata": { -"description": "Stores all metadata relating to GenerateCode.", -"id": "LearningGenaiRootCodeyGenerationMetadata", -"properties": { -"output": { -"description": "Last state of the sample before getting dropped/returned.", -"type": "string" -}, -"postInferenceStep": { -"description": "Last Codey postprocessing step for this sample before getting dropped/returned.", -"enum": [ -"STEP_POST_PROCESSING_STEP_UNSPECIFIED", -"STEP_ORIGINAL_MODEL_OUTPUT", -"STEP_MODEL_OUTPUT_DEDUPLICATION", -"STEP_STOP_SEQUENCE_TRUNCATION", -"STEP_HEURISTIC_TRUNCATION", -"STEP_WALD_TRUNCATION", -"STEP_WHITESPACE_TRUNCATION", -"STEP_FINAL_DEDUPLICATION", -"STEP_TOXICITY_CHECK", -"STEP_RECITATION_CHECK", -"STEP_RETURNED", -"STEP_WALKBACK_CORRECTION", -"STEP_SCORE_THRESHOLDING", -"STEP_MODEL_CONFIG_STOP_SEQUENCE_TRUNCATION", -"STEP_CUSTOM_STOP_SEQUENCE_TRUNCATION", -"STEP_EXPECTED_SAMPLE_SIZE", -"STEP_TREE_TRIM_TRUNCATION" -], -"enumDeprecated": [ -false, -false, -false, -true, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false -], -"enumDescriptions": [ -"", -"Original model outputs as-is.", -"Original model outputs after deduplication.", -"StopSequencePostProcessor.", -"Heuristic SuffixTruncator step.", -"Go service post-processor.", -"Truncate trailing whitespace and filter whitespace-only completions.", -"Deduplicate after all truncations.", -"Toxicity returns true.", -"Recitation causes BLOCK.", -"Return the response to the API.", -"Correcting walkback constraint (samples are dropped if they don't match the prefix constraint).", -"Thresholding samples based on a minimum score.", -"StopSequencePostProcessor.", -"StopSequencePostProcessor.", -"Drop extra number of samples that exceeds expected_samples.", -"Truncated by highest end token score." -], -"type": "string" -} -}, -"type": "object" -}, -"LearningGenaiRootCodeyOutput": { -"description": "Top-level wrapper used to store all things codey-related.", -"id": "LearningGenaiRootCodeyOutput", -"properties": { -"codeyChatMetadata": { -"$ref": "LearningGenaiRootCodeyChatMetadata" -}, -"codeyCompletionMetadata": { -"$ref": "LearningGenaiRootCodeyCompletionMetadata" -}, -"codeyGenerationMetadata": { -"$ref": "LearningGenaiRootCodeyGenerationMetadata" -} -}, -"type": "object" -}, -"LearningGenaiRootCodeyTruncatorMetadata": { -"description": "Metadata describing what was truncated at each checkpoint.", -"id": "LearningGenaiRootCodeyTruncatorMetadata", -"properties": { -"cutoffIndex": { -"description": "Index of the current sample that trims off truncated text.", -"format": "int32", -"type": "integer" -}, -"truncatedText": { -"description": "Text that was truncated at a specific checkpoint.", -"type": "string" -} -}, -"type": "object" -}, -"LearningGenaiRootControlDecodingConfigThreshold": { -"description": "Score threshold for a category.", -"id": "LearningGenaiRootControlDecodingConfigThreshold", -"properties": { -"policy": { -"enum": [ -"UNSPECIFIED", -"DANGEROUS_CONTENT", -"HARASSMENT", -"HATE_SPEECH", -"SEXUALLY_EXPLICIT" -], -"enumDescriptions": [ -"", -"", -"", -"", -"" -], -"type": "string" -}, -"scoreMax": { -"format": "float", -"type": "number" -} -}, -"type": "object" -}, -"LearningGenaiRootControlDecodingRecord": { -"description": "Holds one control decoding record.", -"id": "LearningGenaiRootControlDecodingRecord", -"properties": { -"prefixes": { -"description": "Prefixes feeded into scorer.", -"type": "string" -}, -"scores": { -"description": "Per policy scores returned from Scorer. Expect to have the same number of scores as in `thresholds`.", -"items": { -"$ref": "LearningGenaiRootControlDecodingRecordPolicyScore" -}, -"type": "array" -}, -"suffiexes": { -"description": "Suffixes feeded into scorer.", -"type": "string" -}, -"thresholds": { -"description": "Per policy thresholds from user config.", -"items": { -"$ref": "LearningGenaiRootControlDecodingConfigThreshold" -}, -"type": "array" -} -}, -"type": "object" -}, -"LearningGenaiRootControlDecodingRecordPolicyScore": { -"id": "LearningGenaiRootControlDecodingRecordPolicyScore", -"properties": { -"policy": { -"enum": [ -"UNSPECIFIED", -"DANGEROUS_CONTENT", -"HARASSMENT", -"HATE_SPEECH", -"SEXUALLY_EXPLICIT" -], -"enumDescriptions": [ -"", -"", -"", -"", -"" -], -"type": "string" -}, -"score": { -"format": "float", -"type": "number" -} -}, -"type": "object" -}, -"LearningGenaiRootControlDecodingRecords": { -"id": "LearningGenaiRootControlDecodingRecords", +"GoogleCloudAiplatformV1TuningDataStats": { +"description": "The tuning data statistic values for TuningJob.", +"id": "GoogleCloudAiplatformV1TuningDataStats", "properties": { -"records": { -"description": "One ControlDecodingRecord record maps to one rewind.", -"items": { -"$ref": "LearningGenaiRootControlDecodingRecord" -}, -"type": "array" +"supervisedTuningDataStats": { +"$ref": "GoogleCloudAiplatformV1SupervisedTuningDataStats", +"description": "The SFT Tuning data stats." } }, "type": "object" }, -"LearningGenaiRootDataProviderOutput": { -"id": "LearningGenaiRootDataProviderOutput", +"GoogleCloudAiplatformV1TuningJob": { +"description": "Represents a TuningJob that runs with Google owned models.", +"id": "GoogleCloudAiplatformV1TuningJob", "properties": { -"name": { +"baseModel": { +"description": "The base model that is being tuned, e.g., \"gemini-1.0-pro-002\".", "type": "string" }, -"status": { -"$ref": "UtilStatusProto", -"description": "If set, this DataProvider failed and this is the error message." -} +"createTime": { +"description": "Output only. Time when the TuningJob was created.", +"format": "google-datetime", +"readOnly": true, +"type": "string" }, -"type": "object" +"description": { +"description": "Optional. The description of the TuningJob.", +"type": "string" }, -"LearningGenaiRootFilterMetadata": { -"id": "LearningGenaiRootFilterMetadata", -"properties": { -"confidence": { -"description": "Filter confidence.", -"enum": [ -"FILTER_CONFIDENCE_UNKNOWN", -"FILTER_CONFIDENCE_VERY_LOW", -"FILTER_CONFIDENCE_LOW", -"FILTER_CONFIDENCE_MEDIUM", -"FILTER_CONFIDENCE_HIGH", -"FILTER_CONFIDENCE_VERY_HIGH" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"" -], +"encryptionSpec": { +"$ref": "GoogleCloudAiplatformV1EncryptionSpec", +"description": "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": { +"description": "Output only. Time when the TuningJob entered any of the following JobStates: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`, `JOB_STATE_EXPIRED`.", +"format": "google-datetime", +"readOnly": true, "type": "string" }, -"debugInfo": { -"$ref": "LearningGenaiRootFilterMetadataFilterDebugInfo", -"description": "Debug info for the message." +"error": { +"$ref": "GoogleRpcStatus", +"description": "Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.", +"readOnly": true }, -"fallback": { -"description": "A fallback message chosen by the applied filter.", +"experiment": { +"description": "Output only. The Experiment associated with this TuningJob.", +"readOnly": true, "type": "string" }, -"info": { -"description": "Additional info for the filter.", +"labels": { +"additionalProperties": { "type": "string" }, +"description": "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.", +"type": "object" +}, "name": { -"description": "Name of the filter that triggered.", +"description": "Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}`", +"readOnly": true, +"type": "string" +}, +"startTime": { +"description": "Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state.", +"format": "google-datetime", +"readOnly": true, "type": "string" }, -"reason": { -"description": "Filter reason.", +"state": { +"description": "Output only. The detailed state of the job.", "enum": [ -"FILTER_REASON_UNKNOWN", -"FILTER_REASON_NOT_FILTERED", -"FILTER_REASON_SENSITIVE", -"FILTER_REASON_RECITATION", -"FILTER_REASON_LANGUAGE", -"FILTER_REASON_TAKEDOWN", -"FILTER_REASON_CLASSIFIER", -"FILTER_REASON_EMPTY_RESPONSE", -"FILTER_REASON_SIMILARITY_TAKEDOWN", -"FILTER_REASON_UNSAFE", -"FILTER_REASON_PAIRWISE_CLASSIFIER", -"FILTER_REASON_CODEY", -"FILTER_REASON_URL", -"FILTER_REASON_EMAIL", -"FILTER_REASON_SAFETY_CAT", -"FILTER_REASON_REQUEST_RESPONSE_TAKEDOWN", -"FILTER_REASON_RAI_PQC", -"FILTER_REASON_ATLAS", -"FILTER_REASON_RAI_CSAM", -"FILTER_REASON_RAI_FRINGE", -"FILTER_REASON_RAI_SPII", -"FILTER_REASON_RAI_IMAGE_VIOLENCE", -"FILTER_REASON_RAI_IMAGE_PORN", -"FILTER_REASON_RAI_IMAGE_CSAM", -"FILTER_REASON_RAI_IMAGE_PEDO", -"FILTER_REASON_RAI_IMAGE_CHILD", -"FILTER_REASON_RAI_VIDEO_FRAME_VIOLENCE", -"FILTER_REASON_RAI_VIDEO_FRAME_PORN", -"FILTER_REASON_RAI_VIDEO_FRAME_CSAM", -"FILTER_REASON_RAI_VIDEO_FRAME_PEDO", -"FILTER_REASON_RAI_VIDEO_FRAME_CHILD", -"FILTER_REASON_RAI_CONTEXTUAL_DANGEROUS", -"FILTER_REASON_RAI_GRAIL_TEXT", -"FILTER_REASON_RAI_GRAIL_IMAGE", -"FILTER_REASON_RAI_SAFETYCAT", -"FILTER_REASON_TOXICITY", -"FILTER_REASON_ATLAS_PRICING", -"FILTER_REASON_ATLAS_BILLING", -"FILTER_REASON_ATLAS_NON_ENGLISH_QUESTION", -"FILTER_REASON_ATLAS_NOT_RELATED_TO_GCP", -"FILTER_REASON_ATLAS_AWS_AZURE_RELATED", -"FILTER_REASON_XAI", -"FILTER_CONTROL_DECODING" +"JOB_STATE_UNSPECIFIED", +"JOB_STATE_QUEUED", +"JOB_STATE_PENDING", +"JOB_STATE_RUNNING", +"JOB_STATE_SUCCEEDED", +"JOB_STATE_FAILED", +"JOB_STATE_CANCELLING", +"JOB_STATE_CANCELLED", +"JOB_STATE_PAUSED", +"JOB_STATE_EXPIRED", +"JOB_STATE_UPDATING", +"JOB_STATE_PARTIALLY_SUCCEEDED" ], "enumDescriptions": [ -"Unknown filter reason.", -"Input not filtered.", -"Sensitive content.", -"Recited content.", -"Language filtering", -"Takedown policy", -"Classifier Module", -"Empty response message.", -"Similarity takedown.", -"Unsafe responses from scorers.", -"Pairwise classifier.", -"Codey Filter.", -"URLs Filter.", -"Emails Filter.", -"SafetyCat filter.", -"Request Response takedown.", -"RAI Filter.", -"Atlas specific topic filter", -"RAI Filter.", -"RAI Filter.", -"RAI Filter.", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"Grail Text", -"Grail Image", -"SafetyCat.", -"Toxic content.", -"Atlas specific topic filter for pricing questions.", -"Atlas specific topic filter for billing questions.", -"Atlas specific topic filter for non english questions.", -"Atlas specific topic filter for non GCP questions.", -"Atlas specific topic filter aws/azure related questions.", -"Right now we don't do any filtering for XAI. Adding this just want to differentiatiat the XAI output metadata from other SafetyCat RAI output metadata", -"The response are filtered because it could not pass the control decoding thresholds and the maximum rewind attempts is reached." +"The job state is unspecified.", +"The job has been just created or resumed and processing has not yet begun.", +"The service is preparing to run the job.", +"The job is in progress.", +"The job completed successfully.", +"The job failed.", +"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`.", +"The job has been cancelled.", +"The job has been stopped, and can be resumed.", +"The job has expired.", +"The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state.", +"The job is partially succeeded, some results may be missing due to errors." ], +"readOnly": true, "type": "string" }, -"text": { -"description": "The input query or generated response that is getting filtered.", -"type": "string" -} -}, -"type": "object" +"supervisedTuningSpec": { +"$ref": "GoogleCloudAiplatformV1SupervisedTuningSpec", +"description": "Tuning Spec for Supervised Fine Tuning." }, -"LearningGenaiRootFilterMetadataFilterDebugInfo": { -"id": "LearningGenaiRootFilterMetadataFilterDebugInfo", -"properties": { -"classifierOutput": { -"$ref": "LearningGenaiRootClassifierOutput" +"tunedModel": { +"$ref": "GoogleCloudAiplatformV1TunedModel", +"description": "Output only. The tuned model resources assiociated with this TuningJob.", +"readOnly": true }, -"defaultMetadata": { +"tunedModelDisplayName": { +"description": "Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters.", "type": "string" }, -"languageFilterResult": { -"$ref": "LearningGenaiRootLanguageFilterResult" -}, -"raiOutput": { -"$ref": "LearningGenaiRootRAIOutput", -"description": "Safety filter output information for LLM Root RAI harm check." -}, -"raiResult": { -"$ref": "CloudAiNlLlmProtoServiceRaiResult" -}, -"raiSignal": { -"$ref": "CloudAiNlLlmProtoServiceRaiSignal", -"deprecated": true -}, -"records": { -"$ref": "LearningGenaiRootControlDecodingRecords", -"description": "Number of rewinds by controlled decoding." +"tuningDataStats": { +"$ref": "GoogleCloudAiplatformV1TuningDataStats", +"description": "Output only. The tuning data statistics associated with this TuningJob.", +"readOnly": true }, -"streamRecitationResult": { -"$ref": "LanguageLabsAidaTrustRecitationProtoStreamRecitationResult", -"deprecated": true +"updateTime": { +"description": "Output only. Time when the TuningJob was most recently updated.", +"format": "google-datetime", +"readOnly": true, +"type": "string" +} }, -"takedownResult": { -"$ref": "LearningGenaiRootTakedownResult" +"type": "object" }, -"toxicityResult": { -"$ref": "LearningGenaiRootToxicityResult" +"GoogleCloudAiplatformV1UndeployIndexOperationMetadata": { +"description": "Runtime operation information for IndexEndpointService.UndeployIndex.", +"id": "GoogleCloudAiplatformV1UndeployIndexOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "The operation generic information." } }, "type": "object" }, -"LearningGenaiRootGroundingMetadata": { -"id": "LearningGenaiRootGroundingMetadata", +"GoogleCloudAiplatformV1UndeployIndexRequest": { +"description": "Request message for IndexEndpointService.UndeployIndex.", +"id": "GoogleCloudAiplatformV1UndeployIndexRequest", "properties": { -"citations": { -"items": { -"$ref": "LearningGenaiRootGroundingMetadataCitation" -}, -"type": "array" +"deployedIndexId": { +"description": "Required. The ID of the DeployedIndex to be undeployed from the IndexEndpoint.", +"type": "string" +} }, -"groundingCancelled": { -"description": "True if grounding is cancelled, for example, no facts being retrieved.", -"type": "boolean" +"type": "object" }, -"searchQueries": { -"items": { -"type": "string" +"GoogleCloudAiplatformV1UndeployIndexResponse": { +"description": "Response message for IndexEndpointService.UndeployIndex.", +"id": "GoogleCloudAiplatformV1UndeployIndexResponse", +"properties": {}, +"type": "object" }, -"type": "array" +"GoogleCloudAiplatformV1UndeployModelOperationMetadata": { +"description": "Runtime operation information for EndpointService.UndeployModel.", +"id": "GoogleCloudAiplatformV1UndeployModelOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "The operation generic information." } }, "type": "object" }, -"LearningGenaiRootGroundingMetadataCitation": { -"id": "LearningGenaiRootGroundingMetadataCitation", +"GoogleCloudAiplatformV1UndeployModelRequest": { +"description": "Request message for EndpointService.UndeployModel.", +"id": "GoogleCloudAiplatformV1UndeployModelRequest", "properties": { -"endIndex": { -"description": "Index in the prediction output where the citation ends (exclusive). Must be > start_index and <= len(output).", -"format": "int32", -"type": "integer" +"deployedModelId": { +"description": "Required. The ID of the DeployedModel to be undeployed from the Endpoint.", +"type": "string" }, -"factIndex": { -"description": "Index of the fact supporting this claim. Should be within the range of the `world_facts` in the GenerateResponse.", +"trafficSplit": { +"additionalProperties": { "format": "int32", "type": "integer" }, -"score": { -"description": "Confidence score of this entailment. Value is [0,1] with 1 is the most confidence.", -"format": "double", -"type": "number" -}, -"startIndex": { -"description": "Index in the prediction output where the citation starts (inclusive). Must be >= 0 and < end_index.", -"format": "int32", -"type": "integer" +"description": "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.", +"type": "object" } }, "type": "object" }, -"LearningGenaiRootHarm": { -"id": "LearningGenaiRootHarm", -"properties": { -"contextualDangerous": { -"description": "Please do not use, this is still under development.", -"type": "boolean" -}, -"csam": { -"type": "boolean" -}, -"fringe": { -"type": "boolean" -}, -"grailImageHarmType": { -"$ref": "LearningGenaiRootHarmGrailImageHarmType" -}, -"grailTextHarmType": { -"$ref": "LearningGenaiRootHarmGrailTextHarmType" -}, -"imageChild": { -"type": "boolean" -}, -"imageCsam": { -"type": "boolean" -}, -"imagePedo": { -"type": "boolean" -}, -"imagePorn": { -"description": "Image signals", -"type": "boolean" -}, -"imageViolence": { -"type": "boolean" -}, -"pqc": { -"type": "boolean" -}, -"safetycat": { -"$ref": "LearningGenaiRootHarmSafetyCatCategories" -}, -"spii": { -"$ref": "LearningGenaiRootHarmSpiiFilter", -"description": "Spii Filter uses buckets http://google3/google/privacy/dlp/v2/storage.proto;l=77;rcl=584719820 to classify the input. LMRoot converts the bucket into double score. For example the score for \"POSSIBLE\" is 3 / 5 = 0.6 ." -}, -"threshold": { -"format": "double", -"type": "number" -}, -"videoFrameChild": { -"type": "boolean" -}, -"videoFrameCsam": { -"type": "boolean" -}, -"videoFramePedo": { -"type": "boolean" -}, -"videoFramePorn": { -"description": "Video frame signals", -"type": "boolean" -}, -"videoFrameViolence": { -"type": "boolean" -} -}, +"GoogleCloudAiplatformV1UndeployModelResponse": { +"description": "Response message for EndpointService.UndeployModel.", +"id": "GoogleCloudAiplatformV1UndeployModelResponse", +"properties": {}, "type": "object" }, -"LearningGenaiRootHarmGrailImageHarmType": { -"description": "Harm type for images", -"id": "LearningGenaiRootHarmGrailImageHarmType", +"GoogleCloudAiplatformV1UnmanagedContainerModel": { +"description": "Contains model information necessary to perform batch prediction without requiring a full model import.", +"id": "GoogleCloudAiplatformV1UnmanagedContainerModel", "properties": { -"imageHarmType": { -"items": { -"enum": [ -"IMAGE_HARM_TYPE_UNSPECIFIED", -"IMAGE_HARM_TYPE_PORN", -"IMAGE_HARM_TYPE_VIOLENCE", -"IMAGE_HARM_TYPE_CSAI", -"IMAGE_HARM_TYPE_PEDO", -"IMAGE_HARM_TYPE_MINORS", -"IMAGE_HARM_TYPE_DANGEROUS", -"IMAGE_HARM_TYPE_MEDICAL", -"IMAGE_HARM_TYPE_RACY", -"IMAGE_HARM_TYPE_OBSCENE", -"IMAGE_HARM_TYPE_MINOR_PRESENCE", -"IMAGE_HARM_TYPE_GENERATIVE_MINOR_PRESENCE", -"IMAGE_HARM_TYPE_GENERATIVE_REALISTIC_VISIBLE_FACE" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"" -], +"artifactUri": { +"description": "The path to the directory containing the Model artifact and any of its supporting files.", "type": "string" }, -"type": "array" +"containerSpec": { +"$ref": "GoogleCloudAiplatformV1ModelContainerSpec", +"description": "Input only. The specification of the container that is to be used when deploying this Model." +}, +"predictSchemata": { +"$ref": "GoogleCloudAiplatformV1PredictSchemata", +"description": "Contains the schemata used in Model's predictions and explanations" } }, "type": "object" }, -"LearningGenaiRootHarmGrailTextHarmType": { -"description": "Harm type for text", -"id": "LearningGenaiRootHarmGrailTextHarmType", +"GoogleCloudAiplatformV1UpdateDeploymentResourcePoolOperationMetadata": { +"description": "Runtime operation information for UpdateDeploymentResourcePool method.", +"id": "GoogleCloudAiplatformV1UpdateDeploymentResourcePoolOperationMetadata", "properties": { -"harmType": { -"items": { -"enum": [ -"HARM_TYPE_UNSPECIFIED", -"HARM_TYPE_HATE", -"HARM_TYPE_TOXICITY", -"HARM_TYPE_VIOLENCE", -"HARM_TYPE_CSAI", -"HARM_TYPE_SEXUAL", -"HARM_TYPE_FRINGE", -"HARM_TYPE_POLITICAL", -"HARM_TYPE_MEMORIZATION", -"HARM_TYPE_SPII", -"HARM_TYPE_NEW_DANGEROUS", -"HARM_TYPE_MEDICAL", -"HARM_TYPE_HARASSMENT" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"New definition of dangerous.", -"", -"" -], -"type": "string" -}, -"type": "array" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "The operation generic information." } }, "type": "object" }, -"LearningGenaiRootHarmSafetyCatCategories": { -"id": "LearningGenaiRootHarmSafetyCatCategories", +"GoogleCloudAiplatformV1UpdateExplanationDatasetOperationMetadata": { +"description": "Runtime operation information for ModelService.UpdateExplanationDataset.", +"id": "GoogleCloudAiplatformV1UpdateExplanationDatasetOperationMetadata", "properties": { -"categories": { -"items": { -"enum": [ -"SAFETYCAT_CATEGORY_UNSPECIFIED", -"TOXICITY", -"OBSCENE", -"SEXUAL", -"INSULT", -"IDENTITY_HATE", -"DEATH_HARM_TRAGEDY", -"VIOLENCE_ABUSE", -"FIREARMS_WEAPONS", -"PUBLIC_SAFETY", -"HEALTH", -"RELIGION_BELIEF", -"DRUGS", -"WAR_CONFLICT", -"POLITICS", -"FINANCE", -"LEGAL", -"DANGEROUS", -"DANGEROUS_SEVERITY", -"HARASSMENT_SEVERITY", -"HATE_SEVERITY", -"SEXUAL_SEVERITY" -], -"enumDescriptions": [ -"", -"SafetyCat categories.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Following categories are only supported in SAFETY_CAT_TEXT_V3_PAX model", -"", -"", -"", -"" -], -"type": "string" -}, -"type": "array" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "The common part of the operation metadata." } }, "type": "object" }, -"LearningGenaiRootHarmSpiiFilter": { -"id": "LearningGenaiRootHarmSpiiFilter", +"GoogleCloudAiplatformV1UpdateExplanationDatasetRequest": { +"description": "Request message for ModelService.UpdateExplanationDataset.", +"id": "GoogleCloudAiplatformV1UpdateExplanationDatasetRequest", "properties": { -"usBankRoutingMicr": { -"type": "boolean" -}, -"usEmployerIdentificationNumber": { -"type": "boolean" -}, -"usSocialSecurityNumber": { -"type": "boolean" +"examples": { +"$ref": "GoogleCloudAiplatformV1Examples", +"description": "The example config containing the location of the dataset." } }, "type": "object" }, -"LearningGenaiRootInternalMetadata": { -"id": "LearningGenaiRootInternalMetadata", -"properties": { -"scoredTokens": { -"items": { -"$ref": "LearningGenaiRootScoredToken" +"GoogleCloudAiplatformV1UpdateExplanationDatasetResponse": { +"description": "Response message of ModelService.UpdateExplanationDataset operation.", +"id": "GoogleCloudAiplatformV1UpdateExplanationDatasetResponse", +"properties": {}, +"type": "object" }, -"type": "array" +"GoogleCloudAiplatformV1UpdateFeatureGroupOperationMetadata": { +"description": "Details of operations that perform update FeatureGroup.", +"id": "GoogleCloudAiplatformV1UpdateFeatureGroupOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "Operation metadata for FeatureGroup." } }, "type": "object" }, -"LearningGenaiRootLanguageFilterResult": { -"id": "LearningGenaiRootLanguageFilterResult", +"GoogleCloudAiplatformV1UpdateFeatureOnlineStoreOperationMetadata": { +"description": "Details of operations that perform update FeatureOnlineStore.", +"id": "GoogleCloudAiplatformV1UpdateFeatureOnlineStoreOperationMetadata", "properties": { -"allowed": { -"description": "False when query or response should be filtered out due to unsupported language.", -"type": "boolean" -}, -"detectedLanguage": { -"description": "Language of the query or response.", -"type": "string" -}, -"detectedLanguageProbability": { -"description": "Probability of the language predicted as returned by LangID.", -"format": "float", -"type": "number" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "Operation metadata for FeatureOnlineStore." } }, "type": "object" }, -"LearningGenaiRootMetricOutput": { -"id": "LearningGenaiRootMetricOutput", +"GoogleCloudAiplatformV1UpdateFeatureOperationMetadata": { +"description": "Details of operations that perform update Feature.", +"id": "GoogleCloudAiplatformV1UpdateFeatureOperationMetadata", "properties": { -"debug": { -"type": "string" -}, -"name": { -"description": "Name of the metric.", -"type": "string" -}, -"numericValue": { -"format": "double", -"type": "number" -}, -"status": { -"$ref": "UtilStatusProto" -}, -"stringValue": { -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "Operation metadata for Feature Update." } }, "type": "object" }, -"LearningGenaiRootPerRequestProcessorDebugMetadataFactualityDebugMetadata": { -"id": "LearningGenaiRootPerRequestProcessorDebugMetadataFactualityDebugMetadata", +"GoogleCloudAiplatformV1UpdateFeatureViewOperationMetadata": { +"description": "Details of operations that perform update FeatureView.", +"id": "GoogleCloudAiplatformV1UpdateFeatureViewOperationMetadata", "properties": { -"factRetrievalMillisecondsByProvider": { -"additionalProperties": { -"format": "int64", -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "Operation metadata for FeatureView Update." +} }, -"description": "Latency spent on fact retrievals. There might be multiple retrievals from different fact providers.", "type": "object" }, -"prompt2queryMilliseconds": { -"description": "Latency spent on prompt2query. The procedure generates a search-friendly query given the original prompt.", -"format": "int64", -"type": "string" +"GoogleCloudAiplatformV1UpdateFeaturestoreOperationMetadata": { +"description": "Details of operations that perform update Featurestore.", +"id": "GoogleCloudAiplatformV1UpdateFeaturestoreOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "Operation metadata for Featurestore." } }, "type": "object" }, -"LearningGenaiRootRAIOutput": { -"description": "This is per harm.", -"id": "LearningGenaiRootRAIOutput", +"GoogleCloudAiplatformV1UpdateIndexOperationMetadata": { +"description": "Runtime operation information for IndexService.UpdateIndex.", +"id": "GoogleCloudAiplatformV1UpdateIndexOperationMetadata", "properties": { -"allowed": { -"type": "boolean" -}, -"harm": { -"$ref": "LearningGenaiRootHarm" -}, -"name": { -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "The operation generic information." }, -"score": { -"format": "double", -"type": "number" +"nearestNeighborSearchOperationMetadata": { +"$ref": "GoogleCloudAiplatformV1NearestNeighborSearchOperationMetadata", +"description": "The operation metadata with regard to Matching Engine Index operation." } }, "type": "object" }, -"LearningGenaiRootRegexTakedownResult": { -"id": "LearningGenaiRootRegexTakedownResult", +"GoogleCloudAiplatformV1UpdateModelDeploymentMonitoringJobOperationMetadata": { +"description": "Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob.", +"id": "GoogleCloudAiplatformV1UpdateModelDeploymentMonitoringJobOperationMetadata", "properties": { -"allowed": { -"description": "False when query or response should be taken down due to match with a blocked regex, true otherwise.", -"type": "boolean" -}, -"takedownRegex": { -"description": "Regex used to decide that query or response should be taken down. Empty when query or response is kept.", -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "The operation generic information." } }, "type": "object" }, -"LearningGenaiRootRequestResponseTakedownResult": { -"id": "LearningGenaiRootRequestResponseTakedownResult", +"GoogleCloudAiplatformV1UpdatePersistentResourceOperationMetadata": { +"description": "Details of operations that perform update PersistentResource.", +"id": "GoogleCloudAiplatformV1UpdatePersistentResourceOperationMetadata", "properties": { -"allowed": { -"description": "False when response has to be taken down per above config.", -"type": "boolean" -}, -"requestTakedownRegex": { -"description": "Regex used to match the request.", -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "Operation metadata for PersistentResource." }, -"responseTakedownRegex": { -"description": "Regex used to decide that response should be taken down. Empty when response is kept.", +"progressMessage": { +"description": "Progress Message for Update LRO", "type": "string" } }, "type": "object" }, -"LearningGenaiRootRoutingDecision": { -"description": "Holds the final routing decision, by storing the model_config_id. And individual scores each model got.", -"id": "LearningGenaiRootRoutingDecision", +"GoogleCloudAiplatformV1UpdateSpecialistPoolOperationMetadata": { +"description": "Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.", +"id": "GoogleCloudAiplatformV1UpdateSpecialistPoolOperationMetadata", "properties": { -"metadata": { -"$ref": "LearningGenaiRootRoutingDecisionMetadata" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "The operation generic information." }, -"modelConfigId": { -"description": "The selected model to route traffic to.", +"specialistPool": { +"description": "Output only. The name of the SpecialistPool to which the specialists are being added. Format: `projects/{project_id}/locations/{location_id}/specialistPools/{specialist_pool}`", +"readOnly": true, "type": "string" } }, "type": "object" }, -"LearningGenaiRootRoutingDecisionMetadata": { -"description": "Debug metadata about the routing decision.", -"id": "LearningGenaiRootRoutingDecisionMetadata", +"GoogleCloudAiplatformV1UpdateTensorboardOperationMetadata": { +"description": "Details of operations that perform update Tensorboard.", +"id": "GoogleCloudAiplatformV1UpdateTensorboardOperationMetadata", "properties": { -"scoreBasedRoutingMetadata": { -"$ref": "LearningGenaiRootRoutingDecisionMetadataScoreBased" -}, -"tokenLengthBasedRoutingMetadata": { -"$ref": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBased" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "Operation metadata for Tensorboard." } }, "type": "object" }, -"LearningGenaiRootRoutingDecisionMetadataScoreBased": { -"description": "If we are routing using scored based configuration, then the metadata about that is available in this proto.", -"id": "LearningGenaiRootRoutingDecisionMetadataScoreBased", +"GoogleCloudAiplatformV1UpgradeNotebookRuntimeOperationMetadata": { +"description": "Metadata information for NotebookService.UpgradeNotebookRuntime.", +"id": "GoogleCloudAiplatformV1UpgradeNotebookRuntimeOperationMetadata", "properties": { -"matchedRule": { -"$ref": "LearningGenaiRootScoreBasedRoutingConfigRule", -"description": "The rule that was matched." -}, -"score": { -"$ref": "LearningGenaiRootScore", -"description": "The score that was generated by the router i.e. the model." +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "The operation generic information." }, -"usedDefaultFallback": { -"description": "No rules were matched & therefore used the default fallback.", -"type": "boolean" +"progressMessage": { +"description": "A human-readable message that shows the intermediate progress details of NotebookRuntime.", +"type": "string" } }, "type": "object" }, -"LearningGenaiRootRoutingDecisionMetadataTokenLengthBased": { -"id": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBased", -"properties": { -"modelInputTokenMetadata": { -"items": { -"$ref": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelInputTokenMetadata" -}, -"type": "array" -}, -"modelMaxTokenMetadata": { -"items": { -"$ref": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelMaxTokenMetadata" -}, -"type": "array" -} -}, +"GoogleCloudAiplatformV1UpgradeNotebookRuntimeRequest": { +"description": "Request message for NotebookService.UpgradeNotebookRuntime.", +"id": "GoogleCloudAiplatformV1UpgradeNotebookRuntimeRequest", +"properties": {}, "type": "object" }, -"LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelInputTokenMetadata": { -"id": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelInputTokenMetadata", +"GoogleCloudAiplatformV1UploadModelOperationMetadata": { +"description": "Details of ModelService.UploadModel operation.", +"id": "GoogleCloudAiplatformV1UploadModelOperationMetadata", "properties": { -"computedInputTokenLength": { -"description": "The length computed by backends using the formatter & tokenizer specific to the model", -"format": "int32", -"type": "integer" -}, -"modelId": { -"type": "string" -}, -"pickedAsFallback": { -"description": "If true, the model was selected as a fallback, since no model met requirements.", -"type": "boolean" -}, -"selected": { -"description": "If true, the model was selected since it met the requriements.", -"type": "boolean" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1GenericOperationMetadata", +"description": "The common part of the operation metadata." } }, "type": "object" }, -"LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelMaxTokenMetadata": { -"id": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelMaxTokenMetadata", +"GoogleCloudAiplatformV1UploadModelRequest": { +"description": "Request message for ModelService.UploadModel.", +"id": "GoogleCloudAiplatformV1UploadModelRequest", "properties": { -"maxNumInputTokens": { -"format": "int32", -"type": "integer" -}, -"maxNumOutputTokens": { -"format": "int32", -"type": "integer" +"model": { +"$ref": "GoogleCloudAiplatformV1Model", +"description": "Required. The Model to create." }, "modelId": { +"description": "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.", "type": "string" -} }, -"type": "object" -}, -"LearningGenaiRootRuleOutput": { -"id": "LearningGenaiRootRuleOutput", -"properties": { -"decision": { -"enum": [ -"NO_MATCH", -"MATCH" -], -"enumDescriptions": [ -"This rule was not matched. When used in a ClassifierOutput, this means that no rules were matched.", -"This is a generic \"match\" message, indicating that a rule was triggered. Usually you would use this for a categorization classifier." -], +"parentModel": { +"description": "Optional. The resource name of the model into which to upload the version. Only specify this field when uploading a new version.", "type": "string" }, -"name": { +"serviceAccount": { +"description": "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.).", "type": "string" } }, "type": "object" }, -"LearningGenaiRootScore": { -"id": "LearningGenaiRootScore", +"GoogleCloudAiplatformV1UploadModelResponse": { +"description": "Response message of ModelService.UploadModel operation.", +"id": "GoogleCloudAiplatformV1UploadModelResponse", "properties": { -"calculationType": { -"$ref": "LearningGenaiRootCalculationType" -}, -"internalMetadata": { -"$ref": "LearningGenaiRootInternalMetadata", -"description": "The internal_metadata is intended to be used by internal processors and will be cleared before returns." -}, -"thresholdType": { -"$ref": "LearningGenaiRootThresholdType" -}, -"tokensAndLogprobPerDecodingStep": { -"$ref": "LearningGenaiRootTokensAndLogProbPerDecodingStep", -"description": "Top candidate tokens and log probabilities at each decoding step." +"model": { +"description": "The name of the uploaded Model resource. Format: `projects/{project}/locations/{location}/models/{model}`", +"type": "string" }, -"value": { -"format": "double", -"type": "number" +"modelVersionId": { +"description": "Output only. The version ID of the model that is uploaded.", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"LearningGenaiRootScoreBasedRoutingConfigRule": { -"id": "LearningGenaiRootScoreBasedRoutingConfigRule", +"GoogleCloudAiplatformV1UpsertDatapointsRequest": { +"description": "Request message for IndexService.UpsertDatapoints", +"id": "GoogleCloudAiplatformV1UpsertDatapointsRequest", "properties": { -"equalOrGreaterThan": { -"$ref": "LearningGenaiRootScore", -"description": "NOTE: Hardest examples have smaller values in their routing scores." +"datapoints": { +"description": "A list of datapoints to be created/updated.", +"items": { +"$ref": "GoogleCloudAiplatformV1IndexDatapoint" }, -"lessThan": { -"$ref": "LearningGenaiRootScore" +"type": "array" }, -"modelConfigId": { -"description": "This model_config_id points to ModelConfig::id which allows us to find the ModelConfig to route to. This is part of the banks specified in the ModelBankConfig.", +"updateMask": { +"description": "Optional. Update mask is used to specify the fields to be overwritten in the datapoints by the update. The fields specified in the update_mask are relative to each IndexDatapoint inside datapoints, not the full request. Updatable fields: * Use `all_restricts` to update both restricts and numeric_restricts.", +"format": "google-fieldmask", "type": "string" } }, "type": "object" }, -"LearningGenaiRootScoredSimilarityTakedownPhrase": { -"description": "Proto containing the results from the Universal Sentence Encoder / Other models", -"id": "LearningGenaiRootScoredSimilarityTakedownPhrase", +"GoogleCloudAiplatformV1UpsertDatapointsResponse": { +"description": "Response message for IndexService.UpsertDatapoints", +"id": "GoogleCloudAiplatformV1UpsertDatapointsResponse", +"properties": {}, +"type": "object" +}, +"GoogleCloudAiplatformV1UserActionReference": { +"description": "References an API call. It contains more information about long running operation and Jobs that are triggered by the API call.", +"id": "GoogleCloudAiplatformV1UserActionReference", "properties": { -"phrase": { -"$ref": "LearningGenaiRootSimilarityTakedownPhrase" +"dataLabelingJob": { +"description": "For API calls that start a LabelingJob. Resource name of the LabelingJob. Format: `projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}`", +"type": "string" }, -"similarityScore": { -"format": "float", -"type": "number" +"method": { +"description": "The method name of the API RPC call. For example, \"/google.cloud.aiplatform.{apiVersion}.DatasetService.CreateDataset\"", +"type": "string" +}, +"operation": { +"description": "For API calls that return a long running operation. Resource name of the long running operation. Format: `projects/{project}/locations/{location}/operations/{operation}`", +"type": "string" } }, "type": "object" }, -"LearningGenaiRootScoredToken": { -"description": "A token with its own score.", -"id": "LearningGenaiRootScoredToken", +"GoogleCloudAiplatformV1Value": { +"description": "Value is the value of the field.", +"id": "GoogleCloudAiplatformV1Value", "properties": { -"endTokenScore": { -"description": "Each end_token_score is a logprob for how well the completion would end at a particular token. See http://google3/labs/language/aida/config/proto/model_config.proto;l=376;rcl=573039459", -"format": "float", +"doubleValue": { +"description": "A double value.", +"format": "double", "type": "number" }, -"score": { -"description": "Each score is the logprob for the token in model response.", -"format": "float", -"type": "number" +"intValue": { +"description": "An integer value.", +"format": "int64", +"type": "string" }, -"token": { +"stringValue": { +"description": "A string value.", "type": "string" } }, "type": "object" }, -"LearningGenaiRootSimilarityTakedownPhrase": { -"description": "Each SimilarityTakedownPhrase treats a logical group of blocked and allowed phrases together along with a corresponding punt If the closest matching response is of the allowed type, we allow the response If the closest matching response is of the blocked type, we block the response. eg: Blocked phrase - \"All lives matter\"", -"id": "LearningGenaiRootSimilarityTakedownPhrase", +"GoogleCloudAiplatformV1VertexAISearch": { +"description": "Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation", +"id": "GoogleCloudAiplatformV1VertexAISearch", "properties": { -"blockedPhrase": { +"datastore": { +"description": "Required. Fully-qualified Vertex AI Search's datastore resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`", "type": "string" } }, "type": "object" }, -"LearningGenaiRootSimilarityTakedownResult": { -"id": "LearningGenaiRootSimilarityTakedownResult", +"GoogleCloudAiplatformV1VideoMetadata": { +"description": "Metadata describes the input video content.", +"id": "GoogleCloudAiplatformV1VideoMetadata", "properties": { -"allowed": { -"description": "False when query or response should be taken down by any of the takedown rules, true otherwise.", -"type": "boolean" -}, -"scoredPhrases": { -"description": "List of similar phrases with score. Set only if allowed=false.", -"items": { -"$ref": "LearningGenaiRootScoredSimilarityTakedownPhrase" +"endOffset": { +"description": "Optional. The end offset of the video.", +"format": "google-duration", +"type": "string" }, -"type": "array" +"startOffset": { +"description": "Optional. The start offset of the video.", +"format": "google-duration", +"type": "string" } }, "type": "object" }, -"LearningGenaiRootTakedownResult": { -"id": "LearningGenaiRootTakedownResult", +"GoogleCloudAiplatformV1WorkerPoolSpec": { +"description": "Represents the spec of a worker pool in a job.", +"id": "GoogleCloudAiplatformV1WorkerPoolSpec", "properties": { -"allowed": { -"description": "False when query or response should be taken down by any of the takedown rules, true otherwise.", -"type": "boolean" +"containerSpec": { +"$ref": "GoogleCloudAiplatformV1ContainerSpec", +"description": "The custom container task." +}, +"diskSpec": { +"$ref": "GoogleCloudAiplatformV1DiskSpec", +"description": "Disk spec." +}, +"machineSpec": { +"$ref": "GoogleCloudAiplatformV1MachineSpec", +"description": "Optional. Immutable. The specification of a single machine." +}, +"nfsMounts": { +"description": "Optional. List of NFS mount spec.", +"items": { +"$ref": "GoogleCloudAiplatformV1NfsMount" }, -"regexTakedownResult": { -"$ref": "LearningGenaiRootRegexTakedownResult" +"type": "array" }, -"requestResponseTakedownResult": { -"$ref": "LearningGenaiRootRequestResponseTakedownResult" +"pythonPackageSpec": { +"$ref": "GoogleCloudAiplatformV1PythonPackageSpec", +"description": "The Python packaged task." }, -"similarityTakedownResult": { -"$ref": "LearningGenaiRootSimilarityTakedownResult" +"replicaCount": { +"description": "Optional. The number of worker replicas to use for this worker pool.", +"format": "int64", +"type": "string" } }, "type": "object" }, -"LearningGenaiRootThresholdType": { -"description": "The type of score that bundled with a threshold, and will not be attending the final score calculation. How each score type uses the threshold can be implementation details.", -"id": "LearningGenaiRootThresholdType", +"GoogleCloudAiplatformV1WriteFeatureValuesPayload": { +"description": "Contains Feature values to be written for a specific entity.", +"id": "GoogleCloudAiplatformV1WriteFeatureValuesPayload", "properties": { -"scoreType": { -"enum": [ -"TYPE_UNKNOWN", -"TYPE_SAFE", -"TYPE_POLICY", -"TYPE_GENERATION" -], -"enumDescriptions": [ -"Unknown scorer type.", -"Safety scorer.", -"Policy scorer.", -"Generation scorer." -], +"entityId": { +"description": "Required. The ID of the entity.", "type": "string" }, -"threshold": { -"format": "double", -"type": "number" +"featureValues": { +"additionalProperties": { +"$ref": "GoogleCloudAiplatformV1FeatureValue" +}, +"description": "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.", +"type": "object" } }, "type": "object" }, -"LearningGenaiRootTokensAndLogProbPerDecodingStep": { -"description": "Results of RandomSamplingParams::top_k_logprob_per_decoding_step.", -"id": "LearningGenaiRootTokensAndLogProbPerDecodingStep", +"GoogleCloudAiplatformV1WriteFeatureValuesRequest": { +"description": "Request message for FeaturestoreOnlineServingService.WriteFeatureValues.", +"id": "GoogleCloudAiplatformV1WriteFeatureValuesRequest", "properties": { -"chosenCandidates": { -"description": "Length = total number of decoding steps. The chosen candidates may or may not be in top_candidates.", -"items": { -"$ref": "LearningGenaiRootTokensAndLogProbPerDecodingStepCandidate" -}, -"type": "array" -}, -"topCandidates": { -"description": "Length = total number of decoding steps.", +"payloads": { +"description": "Required. The entities to be written. Up to 100,000 feature values can be written across all `payloads`.", "items": { -"$ref": "LearningGenaiRootTokensAndLogProbPerDecodingStepTopCandidates" +"$ref": "GoogleCloudAiplatformV1WriteFeatureValuesPayload" }, "type": "array" } }, "type": "object" }, -"LearningGenaiRootTokensAndLogProbPerDecodingStepCandidate": { -"description": "A candidate at a decoding step.", -"id": "LearningGenaiRootTokensAndLogProbPerDecodingStepCandidate", -"properties": { -"logProbability": { -"description": "The candidate's log probability.", -"format": "float", -"type": "number" -}, -"token": { -"description": "The candidate\u2019s token value.", -"type": "string" -} -}, +"GoogleCloudAiplatformV1WriteFeatureValuesResponse": { +"description": "Response message for FeaturestoreOnlineServingService.WriteFeatureValues.", +"id": "GoogleCloudAiplatformV1WriteFeatureValuesResponse", +"properties": {}, "type": "object" }, -"LearningGenaiRootTokensAndLogProbPerDecodingStepTopCandidates": { -"description": "Candidates with top log probabilities at each decoding step.", -"id": "LearningGenaiRootTokensAndLogProbPerDecodingStepTopCandidates", +"GoogleCloudAiplatformV1WriteTensorboardExperimentDataRequest": { +"description": "Request message for TensorboardService.WriteTensorboardExperimentData.", +"id": "GoogleCloudAiplatformV1WriteTensorboardExperimentDataRequest", "properties": { -"candidates": { -"description": "Sorted by log probability in descending order.", +"writeRunDataRequests": { +"description": "Required. Requests containing per-run TensorboardTimeSeries data to write.", "items": { -"$ref": "LearningGenaiRootTokensAndLogProbPerDecodingStepCandidate" +"$ref": "GoogleCloudAiplatformV1WriteTensorboardRunDataRequest" }, "type": "array" } }, "type": "object" }, -"LearningGenaiRootToxicityResult": { -"description": "A model can generate multiple signals and this captures all the generated signals for a single message.", -"id": "LearningGenaiRootToxicityResult", +"GoogleCloudAiplatformV1WriteTensorboardExperimentDataResponse": { +"description": "Response message for TensorboardService.WriteTensorboardExperimentData.", +"id": "GoogleCloudAiplatformV1WriteTensorboardExperimentDataResponse", +"properties": {}, +"type": "object" +}, +"GoogleCloudAiplatformV1WriteTensorboardRunDataRequest": { +"description": "Request message for TensorboardService.WriteTensorboardRunData.", +"id": "GoogleCloudAiplatformV1WriteTensorboardRunDataRequest", "properties": { -"signals": { +"tensorboardRun": { +"description": "Required. The resource name of the TensorboardRun to write data to. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}`", +"type": "string" +}, +"timeSeriesData": { +"description": "Required. The TensorboardTimeSeries data to write. Values with in a time series are indexed by their step value. Repeated writes to the same step will overwrite the existing value for that step. The upper limit of data points per write request is 5000.", "items": { -"$ref": "LearningGenaiRootToxicitySignal" +"$ref": "GoogleCloudAiplatformV1TimeSeriesData" }, "type": "array" } }, "type": "object" }, -"LearningGenaiRootToxicitySignal": { -"description": "Proto to capture a signal generated by the toxicity model.", -"id": "LearningGenaiRootToxicitySignal", +"GoogleCloudAiplatformV1WriteTensorboardRunDataResponse": { +"description": "Response message for TensorboardService.WriteTensorboardRunData.", +"id": "GoogleCloudAiplatformV1WriteTensorboardRunDataResponse", +"properties": {}, +"type": "object" +}, +"GoogleCloudAiplatformV1XraiAttribution": { +"description": "An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Supported only by image Models.", +"id": "GoogleCloudAiplatformV1XraiAttribution", "properties": { -"allowed": { -"type": "boolean" +"blurBaselineConfig": { +"$ref": "GoogleCloudAiplatformV1BlurBaselineConfig", +"description": "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" }, -"label": { -"enum": [ -"LABEL_UNSPECIFIED", -"NOT_SENSITIVE", -"SENSITIVE", -"ACCIDENTS_DISASTERS", -"ADULT", -"COMPUTER_SECURITY", -"CONTROVERSIAL_SOCIAL_ISSUES", -"DEATH_TRAGEDY", -"DRUGS", -"IDENTITY_ETHNICITY", -"FINANCIAL_HARDSHIP", -"FIREARMS_WEAPONS", -"HEALTH", -"INSULT", -"LEGAL", -"MENTAL_HEALTH", -"POLITICS", -"RELIGION_BELIEFS", -"SAFETY", -"SELF_HARM", -"SPECIAL_NEEDS", -"TERRORISM", -"TOXIC", -"TROUBLED_RELATIONSHIP", -"VIOLENCE_ABUSE", -"VULGAR", -"WAR_CONFLICT" -], -"enumDescriptions": [ -"Default label.", -"Input is not sensitive.", -"Input is sensitive.", -"Input is related to accidents or disasters.", -"Input contains adult content.", -"Input is related to computer security.", -"Input contains controversial social issues.", -"Input is related to death tragedy.", -"Input is related to drugs.", -"Input is related to identity or ethnicity.", -"Input is related to financial hardship.", -"Input is related to firearms or weapons.", -"Input contains health related information.", -"Input may be an insult.", -"Input is related to legal content.", -"Input contains mental health related information.", -"Input is related to politics.", -"Input is related to religions or beliefs.", -"Input is related to safety.", -"Input is related to self-harm.", -"Input is related to special needs.", -"Input is related to terrorism.", -"Input is toxic.", -"Input is related to troubled relationships.", -"Input contains content about violence or abuse.", -"Input is vulgar.", -"Input is related to war and conflict." -], -"type": "string" +"smoothGradConfig": { +"$ref": "GoogleCloudAiplatformV1SmoothGradConfig", +"description": "Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf" }, -"score": { -"format": "float", -"type": "number" +"stepCount": { +"description": "Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.", +"format": "int32", +"type": "integer" } }, "type": "object" }, -"LearningGenaiRootTranslationRequestInfo": { -"description": "Each TranslationRequestInfo corresponds to a request sent to the translation server.", -"id": "LearningGenaiRootTranslationRequestInfo", +"GoogleCloudLocationListLocationsResponse": { +"description": "The response message for Locations.ListLocations.", +"id": "GoogleCloudLocationListLocationsResponse", "properties": { -"detectedLanguageCodes": { -"description": "The ISO-639 language code of source text in the initial request, detected automatically, if no source language was passed within the initial request. If the source language was passed, auto-detection of the language does not occur and this field is empty.", +"locations": { +"description": "A list of locations that matches the specified filter in the request.", "items": { -"type": "string" +"$ref": "GoogleCloudLocationLocation" }, "type": "array" }, -"totalContentSize": { -"description": "The sum of the size of all the contents in the request.", -"format": "int64", +"nextPageToken": { +"description": "The standard List next-page token.", "type": "string" } }, "type": "object" }, -"LearningServingLlmAtlasOutputMetadata": { -"id": "LearningServingLlmAtlasOutputMetadata", +"GoogleCloudLocationLocation": { +"description": "A resource that represents a Google Cloud location.", +"id": "GoogleCloudLocationLocation", "properties": { -"requestTopic": { +"displayName": { +"description": "The friendly name for this location, typically a nearby city name. For example, \"Tokyo\".", "type": "string" }, -"source": { -"enum": [ -"UNKNOWN", -"FACTUALITY", -"INFOBOT", -"LLM" -], -"enumDescriptions": [ -"", -"", -"", -"" -], +"labels": { +"additionalProperties": { "type": "string" -} }, +"description": "Cross-service attributes for the location. For example {\"cloud.googleapis.com/region\": \"us-east1\"}", "type": "object" }, -"LearningServingLlmMessageMetadata": { -"description": "LINT.IfChange This metadata contains additional information required for debugging. Next ID: 28", -"id": "LearningServingLlmMessageMetadata", -"properties": { -"atlasMetadata": { -"$ref": "LearningServingLlmAtlasOutputMetadata" +"locationId": { +"description": "The canonical id for this location. For example: `\"us-east1\"`.", +"type": "string" }, -"classifierSummary": { -"$ref": "LearningGenaiRootClassifierOutputSummary", -"description": "Summary of classifier output. We attach this to all messages regardless of whether classification rules triggered or not." +"metadata": { +"additionalProperties": { +"description": "Properties of the object. Contains field @type with type URL.", +"type": "any" }, -"codeyOutput": { -"$ref": "LearningGenaiRootCodeyOutput", -"description": "Contains metadata related to Codey Processors." +"description": "Service-specific metadata. For example the available capacity at the given location.", +"type": "object" }, -"currentStreamTextLength": { -"format": "uint32", -"type": "integer" +"name": { +"description": "Resource name for the location, which may vary between implementations. For example: `\"projects/example-project/locations/us-east1\"`", +"type": "string" +} }, -"deleted": { -"description": "Whether the corresponding message has been deleted.", -"type": "boolean" +"type": "object" +}, +"GoogleIamV1Binding": { +"description": "Associates `members`, or principals, with a `role`.", +"id": "GoogleIamV1Binding", +"properties": { +"condition": { +"$ref": "GoogleTypeExpr", +"description": "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)." }, -"filterMeta": { -"description": "Metadata for filters that triggered.", +"members": { +"description": "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/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workforce identity pool. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}`: All workforce identities in a group. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All workforce identities with a specific attribute value. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*`: All identities in a workforce identity pool. * `principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workload identity pool. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}`: A workload identity pool group. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{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/global/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.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: Deleted single identity in a workforce identity pool. For example, `deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value`.", "items": { -"$ref": "LearningGenaiRootFilterMetadata" +"type": "string" }, "type": "array" }, -"finalMessageScore": { -"$ref": "LearningGenaiRootScore", -"description": "This score is finally used for ranking the message. This will be same as the score present in `Message.score` field." -}, -"finishReason": { -"description": "NOT YET IMPLEMENTED.", -"enum": [ -"UNSPECIFIED", -"RETURN", -"STOP", -"MAX_TOKENS", -"FILTER", -"TOP_N_FILTERED" -], -"enumDescriptions": [ -"", -"Return all the tokens back. This typically implies no filtering or stop sequence was triggered.", -"Finished due to provided stop sequence.", -"Model has emitted the maximum number of tokens as specified by max_decoding_steps.", -"Finished due to triggering some post-processing filter.", -"Filtered out due to Top_N < Response_Candidates.Size()" -], +"role": { +"description": "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).", "type": "string" +} }, -"groundingMetadata": { -"$ref": "LearningGenaiRootGroundingMetadata" -}, -"isCode": { -"description": "Applies to streaming response message only. Whether the message is a code.", -"type": "boolean" -}, -"isFallback": { -"description": "Applies to Response message only. Indicates whether the message is a fallback and the response would have otherwise been empty.", -"type": "boolean" -}, -"langidResult": { -"$ref": "NlpSaftLangIdResult", -"description": "Result from nlp_saft DetectLanguage method. Currently the predicted language code and language probability is used." +"type": "object" }, -"language": { -"description": "Detected language.", -"type": "string" +"GoogleIamV1Policy": { +"description": "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/).", +"id": "GoogleIamV1Policy", +"properties": { +"bindings": { +"description": "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`.", +"items": { +"$ref": "GoogleIamV1Binding" }, -"lmPrefix": { -"description": "The LM prefix used to generate this response.", -"type": "string" +"type": "array" }, -"originalText": { -"description": "The original text generated by LLM. This is the raw output for debugging purposes.", +"etag": { +"description": "`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.", +"format": "byte", "type": "string" }, -"perStreamDecodedTokenCount": { -"description": "Number of tokens decoded by the model as part of a stream. This count may be different from `per_stream_returned_token_count` which, is counted after any response rewriting or truncation. Applies to streaming response only.", +"version": { +"description": "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).", "format": "int32", "type": "integer" +} }, -"perStreamReturnedTokenCount": { -"description": "Number of tokens returned per stream in a response candidate after any response rewriting or truncation. Applies to streaming response only. Applies to Gemini models only.", -"format": "int32", -"type": "integer" +"type": "object" +}, +"GoogleIamV1SetIamPolicyRequest": { +"description": "Request message for `SetIamPolicy` method.", +"id": "GoogleIamV1SetIamPolicyRequest", +"properties": { +"policy": { +"$ref": "GoogleIamV1Policy", +"description": "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." +} +}, +"type": "object" }, -"raiOutputs": { -"description": "Results of running RAI on the query or this response candidate. One output per rai_config. It will be populated regardless of whether the threshold is exceeded or not.", +"GoogleIamV1TestIamPermissionsResponse": { +"description": "Response message for `TestIamPermissions` method.", +"id": "GoogleIamV1TestIamPermissionsResponse", +"properties": { +"permissions": { +"description": "A subset of `TestPermissionsRequest.permissions` that the caller is allowed.", "items": { -"$ref": "LearningGenaiRootRAIOutput" +"type": "string" }, "type": "array" +} }, -"recitationResult": { -"$ref": "LearningGenaiRecitationRecitationResult", -"description": "Recitation Results. It will be populated as long as Recitation processing is enabled, regardless of recitation outcome." +"type": "object" }, -"returnTokenCount": { -"description": "NOT IMPLEMENTED TODO (b/334187574) Remove this field after Labs migrates to per_stream_returned_token_count and total_returned_token_count.", -"format": "int32", -"type": "integer" +"GoogleLongrunningListOperationsResponse": { +"description": "The response message for Operations.ListOperations.", +"id": "GoogleLongrunningListOperationsResponse", +"properties": { +"nextPageToken": { +"description": "The standard List next-page token.", +"type": "string" }, -"scores": { -"description": "All the different scores for a message are logged here.", +"operations": { +"description": "A list of operations that matches the specified filter in the request.", "items": { -"$ref": "LearningGenaiRootScore" +"$ref": "GoogleLongrunningOperation" }, "type": "array" +} }, -"streamTerminated": { -"description": "Whether the response is terminated during streaming return. Only used for streaming requests.", +"type": "object" +}, +"GoogleLongrunningOperation": { +"description": "This resource represents a long-running operation that is the result of a network API call.", +"id": "GoogleLongrunningOperation", +"properties": { +"done": { +"description": "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.", "type": "boolean" }, -"totalDecodedTokenCount": { -"description": "Total tokens decoded so far per response_candidate. For streaming: Count of all the tokens decoded so far (aggregated count). For unary: Count of all the tokens decoded per response_candidate.", -"format": "int32", -"type": "integer" +"error": { +"$ref": "GoogleRpcStatus", +"description": "The error result of the operation in case of failure or cancellation." }, -"totalReturnedTokenCount": { -"description": "Total number of tokens returned in a response candidate. For streaming, it is the aggregated count (i.e. total so far) Applies to Gemini models only.", -"format": "int32", -"type": "integer" +"metadata": { +"additionalProperties": { +"description": "Properties of the object. Contains field @type with type URL.", +"type": "any" }, -"translatedUserPrompts": { -"description": "Translated user-prompt used for RAI post processing. This is for internal processing only. We will translate in pre-processor and pass the translated text to the post processor using this field. It will be empty if non of the signals requested need translation.", -"items": { +"description": "Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.", +"type": "object" +}, +"name": { +"description": "The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.", "type": "string" }, -"type": "array" +"response": { +"additionalProperties": { +"description": "Properties of the object. Contains field @type with type URL.", +"type": "any" }, -"vertexRaiResult": { -"$ref": "CloudAiNlLlmProtoServiceRaiResult", -"description": "The metadata from Vertex SafetyCat processors" +"description": "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`.", +"type": "object" } }, "type": "object" }, -"NlpSaftLangIdLocalesResult": { -"id": "NlpSaftLangIdLocalesResult", +"GoogleProtobufEmpty": { +"description": "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); }", +"id": "GoogleProtobufEmpty", +"properties": {}, +"type": "object" +}, +"GoogleRpcStatus": { +"description": "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).", +"id": "GoogleRpcStatus", "properties": { -"predictions": { -"description": "List of locales in which the text would be considered acceptable. Sorted in descending order according to each locale's respective likelihood. For example, if a Portuguese text is acceptable in both Brazil and Portugal, but is more strongly associated with Brazil, then the predictions would be [\"pt-BR\", \"pt-PT\"], in that order. May be empty, indicating that the model did not predict any acceptable locales.", -"items": { -"$ref": "NlpSaftLangIdLocalesResultLocale" +"code": { +"description": "The status code, which should be an enum value of google.rpc.Code.", +"format": "int32", +"type": "integer" }, -"type": "array" -} +"details": { +"description": "A list of messages that carry the error details. There is a common set of message types for APIs to use.", +"items": { +"additionalProperties": { +"description": "Properties of the object. Contains field @type with type URL.", +"type": "any" }, "type": "object" }, -"NlpSaftLangIdLocalesResultLocale": { -"id": "NlpSaftLangIdLocalesResultLocale", -"properties": { -"languageCode": { -"description": "A BCP 47 language code that includes region information. For example, \"pt-BR\" or \"pt-PT\". This field will always be populated.", +"type": "array" +}, +"message": { +"description": "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.", "type": "string" } }, "type": "object" }, -"NlpSaftLangIdResult": { -"id": "NlpSaftLangIdResult", +"GoogleTypeColor": { +"description": "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\u2014for 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(''); }; // ...", +"id": "GoogleTypeColor", "properties": { -"modelVersion": { -"description": "The version of the model used to create these annotations.", -"enum": [ -"VERSION_UNSPECIFIED", -"INDEXING_20181017", -"INDEXING_20191206", -"INDEXING_20200313", -"INDEXING_20210618", -"STANDARD_20220516" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"" -], -"type": "string" -}, -"predictions": { -"description": "This field stores the n-best list of possible BCP 47 language code strings for a given input sorted in descending order according to each code's respective probability.", -"items": { -"$ref": "NlpSaftLanguageSpan" +"alpha": { +"description": "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).", +"format": "float", +"type": "number" }, -"type": "array" +"blue": { +"description": "The amount of blue in the color as a value in the interval [0, 1].", +"format": "float", +"type": "number" }, -"spanPredictions": { -"description": "This field stores language predictions of subspans of the input, when available. Each LanguageSpanSequence is a sequence of LanguageSpans. A particular sequence of LanguageSpans has an associated probability, and need not necessarily cover the entire input. If no language could be predicted for any span, then this field may be empty.", -"items": { -"$ref": "NlpSaftLanguageSpanSequence" +"green": { +"description": "The amount of green in the color as a value in the interval [0, 1].", +"format": "float", +"type": "number" }, -"type": "array" +"red": { +"description": "The amount of red in the color as a value in the interval [0, 1].", +"format": "float", +"type": "number" } }, "type": "object" }, -"NlpSaftLanguageSpan": { -"id": "NlpSaftLanguageSpan", +"GoogleTypeDate": { +"description": "Represents a whole or partial calendar date, such as a birthday. The time of day and time zone are either specified elsewhere or are insignificant. The date is relative to the Gregorian Calendar. This can represent one of the following: * A full date, with non-zero year, month, and day values. * A month and day, with a zero year (for example, an anniversary). * A year on its own, with a zero month and a zero day. * A year and month, with a zero day (for example, a credit card expiration date). Related types: * google.type.TimeOfDay * google.type.DateTime * google.protobuf.Timestamp", +"id": "GoogleTypeDate", "properties": { -"end": { +"day": { +"description": "Day of a month. Must be from 1 to 31 and valid for the year and month, or 0 to specify a year by itself or a year and month where the day isn't significant.", "format": "int32", "type": "integer" }, -"languageCode": { -"description": "A BCP 47 language code for this span.", -"type": "string" -}, -"locales": { -"$ref": "NlpSaftLangIdLocalesResult", -"description": "Optional field containing any information that was predicted about the specific locale(s) of the span." -}, -"probability": { -"description": "A probability associated with this prediction.", -"format": "float", -"type": "number" +"month": { +"description": "Month of a year. Must be from 1 to 12, or 0 to specify a year without a month and day.", +"format": "int32", +"type": "integer" }, -"start": { -"description": "Start and end byte offsets, inclusive, within the given input string. A value of -1 implies that this field is not set. Both fields must either be set with a nonnegative value or both are unset. If both are unset then this LanguageSpan applies to the entire input.", +"year": { +"description": "Year of the date. Must be from 1 to 9999, or 0 to specify a date without a year.", "format": "int32", "type": "integer" } }, "type": "object" }, -"NlpSaftLanguageSpanSequence": { -"id": "NlpSaftLanguageSpanSequence", +"GoogleTypeExpr": { +"description": "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.", +"id": "GoogleTypeExpr", "properties": { -"languageSpans": { -"description": "A sequence of LanguageSpan objects, each assigning a language to a subspan of the input.", -"items": { -"$ref": "NlpSaftLanguageSpan" +"description": { +"description": "Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.", +"type": "string" }, -"type": "array" +"expression": { +"description": "Textual representation of an expression in Common Expression Language syntax.", +"type": "string" }, -"probability": { -"description": "The probability of this sequence of LanguageSpans.", -"format": "float", -"type": "number" +"location": { +"description": "Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.", +"type": "string" +}, +"title": { +"description": "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.", +"type": "string" } }, "type": "object" }, -"Proto2BridgeMessageSet": { -"description": "This is proto2's version of MessageSet.", -"id": "Proto2BridgeMessageSet", -"properties": {}, +"GoogleTypeInterval": { +"description": "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.", +"id": "GoogleTypeInterval", +"properties": { +"endTime": { +"description": "Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end.", +"format": "google-datetime", +"type": "string" +}, +"startTime": { +"description": "Optional. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start.", +"format": "google-datetime", +"type": "string" +} +}, "type": "object" }, -"UtilStatusProto": { -"description": "Wire-format for a Status object", -"id": "UtilStatusProto", +"GoogleTypeMoney": { +"description": "Represents an amount of money with its currency type.", +"id": "GoogleTypeMoney", "properties": { -"canonicalCode": { -"description": "The canonical error code (see codes.proto) that most closely corresponds to this status. This may be missing, and in the common case of the generic space, it definitely will be.", -"format": "int32", -"type": "integer" +"currencyCode": { +"description": "The three-letter currency code defined in ISO 4217.", +"type": "string" }, -"code": { -"description": "Numeric code drawn from the space specified below. Often, this is the canonical error space, and code is drawn from google3/util/task/codes.proto", +"nanos": { +"description": "Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If `units` is positive, `nanos` must be positive or zero. If `units` is zero, `nanos` can be positive, zero, or negative. If `units` is negative, `nanos` must be negative or zero. For example $-1.75 is represented as `units`=-1 and `nanos`=-750,000,000.", "format": "int32", "type": "integer" }, -"message": { -"description": "Detail message", -"type": "string" -}, -"messageSet": { -"$ref": "Proto2BridgeMessageSet", -"description": "message_set associates an arbitrary proto message with the status." -}, -"space": { -"description": "The following are usually only present when code != 0 Space to which this status belongs", +"units": { +"description": "The whole units of the amount. For example if `currencyCode` is `\"USD\"`, then 1 unit is one US dollar.", +"format": "int64", "type": "string" } }, diff --git a/googleapiclient/discovery_cache/documents/aiplatform.v1beta1.json b/googleapiclient/discovery_cache/documents/aiplatform.v1beta1.json index 47343070162..15cb0fc6e3c 100644 --- a/googleapiclient/discovery_cache/documents/aiplatform.v1beta1.json +++ b/googleapiclient/discovery_cache/documents/aiplatform.v1beta1.json @@ -2695,6 +2695,40 @@ "https://www.googleapis.com/auth/cloud-platform" ] }, +"patch": { +"description": "Updates a DatasetVersion.", +"flatPath": "v1beta1/projects/{projectsId}/locations/{locationsId}/datasets/{datasetsId}/datasetVersions/{datasetVersionsId}", +"httpMethod": "PATCH", +"id": "aiplatform.projects.locations.datasets.datasetVersions.patch", +"parameterOrder": [ +"name" +], +"parameters": { +"name": { +"description": "Output only. The resource name of the DatasetVersion.", +"location": "path", +"pattern": "^projects/[^/]+/locations/[^/]+/datasets/[^/]+/datasetVersions/[^/]+$", +"required": true, +"type": "string" +}, +"updateMask": { +"description": "Required. The update mask applies to the resource. For the `FieldMask` definition, see google.protobuf.FieldMask. Updatable fields: * `display_name`", +"format": "google-fieldmask", +"location": "query", +"type": "string" +} +}, +"path": "v1beta1/{+name}", +"request": { +"$ref": "GoogleCloudAiplatformV1beta1DatasetVersion" +}, +"response": { +"$ref": "GoogleCloudAiplatformV1beta1DatasetVersion" +}, +"scopes": [ +"https://www.googleapis.com/auth/cloud-platform" +] +}, "restore": { "description": "Restores a dataset version.", "flatPath": "v1beta1/projects/{projectsId}/locations/{locationsId}/datasets/{datasetsId}/datasetVersions/{datasetVersionsId}:restore", @@ -19306,6 +19340,132 @@ } } } +}, +"tuningJobs": { +"methods": { +"cancel": { +"description": "Cancels a TuningJob. Starts asynchronous cancellation on the TuningJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use GenAiTuningService.GetTuningJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the TuningJob is not deleted; instead it becomes a job with a TuningJob.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`, and TuningJob.state is set to `CANCELLED`.", +"flatPath": "v1beta1/projects/{projectsId}/locations/{locationsId}/tuningJobs/{tuningJobsId}:cancel", +"httpMethod": "POST", +"id": "aiplatform.projects.locations.tuningJobs.cancel", +"parameterOrder": [ +"name" +], +"parameters": { +"name": { +"description": "Required. The name of the TuningJob to cancel. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}`", +"location": "path", +"pattern": "^projects/[^/]+/locations/[^/]+/tuningJobs/[^/]+$", +"required": true, +"type": "string" +} +}, +"path": "v1beta1/{+name}:cancel", +"request": { +"$ref": "GoogleCloudAiplatformV1beta1CancelTuningJobRequest" +}, +"response": { +"$ref": "GoogleProtobufEmpty" +}, +"scopes": [ +"https://www.googleapis.com/auth/cloud-platform" +] +}, +"create": { +"description": "Creates a TuningJob. A created TuningJob right away will be attempted to be run.", +"flatPath": "v1beta1/projects/{projectsId}/locations/{locationsId}/tuningJobs", +"httpMethod": "POST", +"id": "aiplatform.projects.locations.tuningJobs.create", +"parameterOrder": [ +"parent" +], +"parameters": { +"parent": { +"description": "Required. The resource name of the Location to create the TuningJob in. Format: `projects/{project}/locations/{location}`", +"location": "path", +"pattern": "^projects/[^/]+/locations/[^/]+$", +"required": true, +"type": "string" +} +}, +"path": "v1beta1/{+parent}/tuningJobs", +"request": { +"$ref": "GoogleCloudAiplatformV1beta1TuningJob" +}, +"response": { +"$ref": "GoogleCloudAiplatformV1beta1TuningJob" +}, +"scopes": [ +"https://www.googleapis.com/auth/cloud-platform" +] +}, +"get": { +"description": "Gets a TuningJob.", +"flatPath": "v1beta1/projects/{projectsId}/locations/{locationsId}/tuningJobs/{tuningJobsId}", +"httpMethod": "GET", +"id": "aiplatform.projects.locations.tuningJobs.get", +"parameterOrder": [ +"name" +], +"parameters": { +"name": { +"description": "Required. The name of the TuningJob resource. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}`", +"location": "path", +"pattern": "^projects/[^/]+/locations/[^/]+/tuningJobs/[^/]+$", +"required": true, +"type": "string" +} +}, +"path": "v1beta1/{+name}", +"response": { +"$ref": "GoogleCloudAiplatformV1beta1TuningJob" +}, +"scopes": [ +"https://www.googleapis.com/auth/cloud-platform" +] +}, +"list": { +"description": "Lists TuningJobs in a Location.", +"flatPath": "v1beta1/projects/{projectsId}/locations/{locationsId}/tuningJobs", +"httpMethod": "GET", +"id": "aiplatform.projects.locations.tuningJobs.list", +"parameterOrder": [ +"parent" +], +"parameters": { +"filter": { +"description": "Optional. The standard list filter.", +"location": "query", +"type": "string" +}, +"pageSize": { +"description": "Optional. The standard list page size.", +"format": "int32", +"location": "query", +"type": "integer" +}, +"pageToken": { +"description": "Optional. The standard list page token. Typically obtained via ListTuningJob.next_page_token of the previous GenAiTuningService.ListTuningJob][] call.", +"location": "query", +"type": "string" +}, +"parent": { +"description": "Required. The resource name of the Location to list the TuningJobs from. Format: `projects/{project}/locations/{location}`", +"location": "path", +"pattern": "^projects/[^/]+/locations/[^/]+$", +"required": true, +"type": "string" +} +}, +"path": "v1beta1/{+parent}/tuningJobs", +"response": { +"$ref": "GoogleCloudAiplatformV1beta1ListTuningJobsResponse" +}, +"scopes": [ +"https://www.googleapis.com/auth/cloud-platform" +] +} +} } } } @@ -19435,23 +19595,9 @@ } } }, -"revision": "20240501", +"revision": "20240510", "rootUrl": "https://aiplatform.googleapis.com/", "schemas": { -"CloudAiLargeModelsVisionEmbedVideoResponse": { -"description": "Video embedding response.", -"id": "CloudAiLargeModelsVisionEmbedVideoResponse", -"properties": { -"videoEmbeddings": { -"description": "The embedding vector for the video.", -"items": { -"type": "any" -}, -"type": "array" -} -}, -"type": "object" -}, "CloudAiLargeModelsVisionFilteredText": { "description": "Details for filtered input text.", "id": "CloudAiLargeModelsVisionFilteredText", @@ -19661,17 +19807,6 @@ }, "type": "object" }, -"CloudAiLargeModelsVisionMediaGenerateContentResponse": { -"description": "Generate media content response", -"id": "CloudAiLargeModelsVisionMediaGenerateContentResponse", -"properties": { -"response": { -"$ref": "CloudAiNlLlmProtoServiceGenerateMultiModalResponse", -"description": "Response to the user's request." -} -}, -"type": "object" -}, "CloudAiLargeModelsVisionNamedBoundingBox": { "id": "CloudAiLargeModelsVisionNamedBoundingBox", "properties": { @@ -19734,52 +19869,6 @@ }, "type": "object" }, -"CloudAiLargeModelsVisionReasonVideoResponse": { -"description": "Video reasoning response.", -"id": "CloudAiLargeModelsVisionReasonVideoResponse", -"properties": { -"responses": { -"description": "Generated text responses. The generated responses for different segments within the same video.", -"items": { -"$ref": "CloudAiLargeModelsVisionReasonVideoResponseTextResponse" -}, -"type": "array" -} -}, -"type": "object" -}, -"CloudAiLargeModelsVisionReasonVideoResponseTextResponse": { -"description": "Contains text that is the response of the video captioning.", -"id": "CloudAiLargeModelsVisionReasonVideoResponseTextResponse", -"properties": { -"relativeTemporalPartition": { -"$ref": "CloudAiLargeModelsVisionRelativeTemporalPartition", -"description": "Partition of the caption's video in time. This field is intended for video captioning. To represent the start time and end time of the caption's video." -}, -"text": { -"description": "Text information", -"type": "string" -} -}, -"type": "object" -}, -"CloudAiLargeModelsVisionRelativeTemporalPartition": { -"description": "For ease of use, assume that the start_offset is inclusive and the end_offset is exclusive. In mathematical terms, the partition would be written as [start_offset, end_offset).", -"id": "CloudAiLargeModelsVisionRelativeTemporalPartition", -"properties": { -"endOffset": { -"description": "End time offset of the partition.", -"format": "google-duration", -"type": "string" -}, -"startOffset": { -"description": "Start time offset of the partition.", -"format": "google-duration", -"type": "string" -} -}, -"type": "object" -}, "CloudAiLargeModelsVisionSemanticFilterResponse": { "id": "CloudAiLargeModelsVisionSemanticFilterResponse", "properties": { @@ -19813,2964 +19902,2217 @@ }, "type": "object" }, -"CloudAiNlLlmProtoServiceCandidate": { -"id": "CloudAiNlLlmProtoServiceCandidate", +"GoogleApiHttpBody": { +"description": "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.", +"id": "GoogleApiHttpBody", "properties": { -"citationMetadata": { -"$ref": "CloudAiNlLlmProtoServiceCitationMetadata", -"description": "Source attribution of the generated content." -}, -"content": { -"$ref": "CloudAiNlLlmProtoServiceContent", -"description": "Content of the candidate." -}, -"finishMessage": { -"description": "A string that describes the filtering behavior in more detail. Only filled when reason is set.", +"contentType": { +"description": "The HTTP Content-Type header value specifying the content type of the body.", "type": "string" }, -"finishReason": { -"description": "The reason why the model stopped generating tokens.", -"enum": [ -"FINISH_REASON_UNSPECIFIED", -"FINISH_REASON_STOP", -"FINISH_REASON_MAX_TOKENS", -"FINISH_REASON_SAFETY", -"FINISH_REASON_RECITATION", -"FINISH_REASON_OTHER", -"FINISH_REASON_BLOCKLIST", -"FINISH_REASON_PROHIBITED_CONTENT", -"FINISH_REASON_SPII" -], -"enumDescriptions": [ -"The finish reason is unspecified.", -"Natural stop point of the model or provided stop sequence.", -"The maximum number of tokens as specified in the request was reached.", -"The token generation was stopped as the response was flagged for safety reasons. NOTE: When streaming the Candidate.content will be empty if content filters blocked the output.", -"The token generation was stopped as the response was flagged for unauthorized citations.", -"All other reasons that stopped the token generation (currently only language filter).", -"The token generation was stopped as the response was flagged for the terms which are included from the terminology blocklist.", -"The token generation was stopped as the response was flagged for the prohibited contents (currently only CSAM).", -"The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents." -], +"data": { +"description": "The HTTP request/response body as raw binary.", +"format": "byte", "type": "string" }, -"groundingMetadata": { -"$ref": "LearningGenaiRootGroundingMetadata", -"description": "Grounding metadata. Combine with the facts list from response to generate grounding citations for this choice." -}, -"index": { -"description": "Index of the candidate.", -"format": "int32", -"type": "integer" -}, -"safetyRatings": { -"description": "Safety ratings of the generated content.", +"extensions": { +"description": "Application specific response metadata. Must be set in the first response for streaming APIs.", "items": { -"$ref": "CloudAiNlLlmProtoServiceSafetyRating" +"additionalProperties": { +"description": "Properties of the object. Contains field @type with type URL.", +"type": "any" +}, +"type": "object" }, "type": "array" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceCitation": { -"description": "Source attributions for content.", -"id": "CloudAiNlLlmProtoServiceCitation", +"GoogleCloudAiplatformV1beta1ActiveLearningConfig": { +"description": "Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy.", +"id": "GoogleCloudAiplatformV1beta1ActiveLearningConfig", "properties": { -"endIndex": { -"description": "End index into the content.", -"format": "int32", -"type": "integer" -}, -"license": { -"description": "License of the attribution.", +"maxDataItemCount": { +"description": "Max number of human labeled DataItems.", +"format": "int64", "type": "string" }, -"publicationDate": { -"$ref": "GoogleTypeDate", -"description": "Publication date of the attribution." -}, -"startIndex": { -"description": "Start index into the content.", +"maxDataItemPercentage": { +"description": "Max percent of total DataItems for human labeling.", "format": "int32", "type": "integer" }, -"title": { -"description": "Title of the attribution.", -"type": "string" +"sampleConfig": { +"$ref": "GoogleCloudAiplatformV1beta1SampleConfig", +"description": "Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy." }, -"uri": { -"description": "Url reference of the attribution.", -"type": "string" +"trainingConfig": { +"$ref": "GoogleCloudAiplatformV1beta1TrainingConfig", +"description": "CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems." } }, "type": "object" }, -"CloudAiNlLlmProtoServiceCitationMetadata": { -"description": "A collection of source attributions for a piece of content.", -"id": "CloudAiNlLlmProtoServiceCitationMetadata", +"GoogleCloudAiplatformV1beta1AddContextArtifactsAndExecutionsRequest": { +"description": "Request message for MetadataService.AddContextArtifactsAndExecutions.", +"id": "GoogleCloudAiplatformV1beta1AddContextArtifactsAndExecutionsRequest", "properties": { -"citations": { -"description": "List of citations.", +"artifacts": { +"description": "The resource names of the Artifacts to attribute to the Context. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}`", "items": { -"$ref": "CloudAiNlLlmProtoServiceCitation" +"type": "string" }, "type": "array" -} -}, -"type": "object" }, -"CloudAiNlLlmProtoServiceContent": { -"description": "The content of a single message from a participant.", -"id": "CloudAiNlLlmProtoServiceContent", -"properties": { -"parts": { -"description": "The parts of the message.", +"executions": { +"description": "The resource names of the Executions to associate with the Context. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}`", "items": { -"$ref": "CloudAiNlLlmProtoServicePart" -}, -"type": "array" -}, -"role": { -"description": "The role of the current conversation participant.", -"type": "string" -} -}, -"type": "object" -}, -"CloudAiNlLlmProtoServiceFact": { -"description": "A condense version of WorldFact (assistant/boq/lamda/factuality/proto/factuality.proto) to propagate the essential information about the fact used in factuality to the upstream caller.", -"id": "CloudAiNlLlmProtoServiceFact", -"properties": { -"query": { -"description": "Query that is used to retrieve this fact.", -"type": "string" -}, -"summary": { -"description": "If present, the summary/snippet of the fact.", -"type": "string" -}, -"title": { -"description": "If present, it refers to the title of this fact.", "type": "string" }, -"url": { -"description": "If present, this URL links to the webpage of the fact.", -"type": "string" +"type": "array" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceFunctionCall": { -"description": "Function call details.", -"id": "CloudAiNlLlmProtoServiceFunctionCall", -"properties": { -"args": { -"additionalProperties": { -"description": "Properties of the object.", -"type": "any" -}, -"description": "The function parameters and values in JSON format.", -"type": "object" -}, -"name": { -"description": "Required. The name of the function to call.", -"type": "string" -} -}, +"GoogleCloudAiplatformV1beta1AddContextArtifactsAndExecutionsResponse": { +"description": "Response message for MetadataService.AddContextArtifactsAndExecutions.", +"id": "GoogleCloudAiplatformV1beta1AddContextArtifactsAndExecutionsResponse", +"properties": {}, "type": "object" }, -"CloudAiNlLlmProtoServiceFunctionResponse": { -"description": "Function response details.", -"id": "CloudAiNlLlmProtoServiceFunctionResponse", +"GoogleCloudAiplatformV1beta1AddContextChildrenRequest": { +"description": "Request message for MetadataService.AddContextChildren.", +"id": "GoogleCloudAiplatformV1beta1AddContextChildrenRequest", "properties": { -"name": { -"description": "Required. The name of the function to call.", +"childContexts": { +"description": "The resource names of the child Contexts.", +"items": { "type": "string" }, -"response": { -"additionalProperties": { -"description": "Properties of the object.", -"type": "any" +"type": "array" +} }, -"description": "Required. The function response in JSON object format.", "type": "object" -} }, +"GoogleCloudAiplatformV1beta1AddContextChildrenResponse": { +"description": "Response message for MetadataService.AddContextChildren.", +"id": "GoogleCloudAiplatformV1beta1AddContextChildrenResponse", +"properties": {}, "type": "object" }, -"CloudAiNlLlmProtoServiceGenerateMultiModalResponse": { -"id": "CloudAiNlLlmProtoServiceGenerateMultiModalResponse", +"GoogleCloudAiplatformV1beta1AddExecutionEventsRequest": { +"description": "Request message for MetadataService.AddExecutionEvents.", +"id": "GoogleCloudAiplatformV1beta1AddExecutionEventsRequest", "properties": { -"candidates": { -"description": "Possible candidate responses to the conversation up until this point.", -"items": { -"$ref": "CloudAiNlLlmProtoServiceCandidate" -}, -"type": "array" -}, -"debugMetadata": { -"$ref": "CloudAiNlLlmProtoServiceMessageMetadata", -"description": "Debug information containing message metadata. Clients should not consume this field, and this is only populated for Flow Runner path." -}, -"facts": { -"description": "External facts retrieved for factuality/grounding.", +"events": { +"description": "The Events to create and add.", "items": { -"$ref": "CloudAiNlLlmProtoServiceFact" +"$ref": "GoogleCloudAiplatformV1beta1Event" }, "type": "array" -}, -"promptFeedback": { -"$ref": "CloudAiNlLlmProtoServicePromptFeedback", -"description": "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." -}, -"reportingMetrics": { -"$ref": "IntelligenceCloudAutomlXpsReportingMetrics", -"description": "Billable prediction metrics." -}, -"usageMetadata": { -"$ref": "CloudAiNlLlmProtoServiceUsageMetadata", -"description": "Usage metadata about the response(s)." } }, "type": "object" }, -"CloudAiNlLlmProtoServiceMessageMetadata": { -"id": "CloudAiNlLlmProtoServiceMessageMetadata", -"properties": { -"factualityDebugMetadata": { -"$ref": "LearningGenaiRootPerRequestProcessorDebugMetadataFactualityDebugMetadata", -"description": "Factuality-related debug metadata." -}, -"inputFilterInfo": { -"$ref": "LearningServingLlmMessageMetadata", -"description": "Filter metadata of the input messages." -}, -"modelRoutingDecision": { -"$ref": "LearningGenaiRootRoutingDecision", -"description": "This score is generated by the router model to decide which model to use" -}, -"outputFilterInfo": { -"description": "Filter metadata of the output messages.", -"items": { -"$ref": "LearningServingLlmMessageMetadata" +"GoogleCloudAiplatformV1beta1AddExecutionEventsResponse": { +"description": "Response message for MetadataService.AddExecutionEvents.", +"id": "GoogleCloudAiplatformV1beta1AddExecutionEventsResponse", +"properties": {}, +"type": "object" }, -"type": "array" +"GoogleCloudAiplatformV1beta1AddTrialMeasurementRequest": { +"description": "Request message for VizierService.AddTrialMeasurement.", +"id": "GoogleCloudAiplatformV1beta1AddTrialMeasurementRequest", +"properties": { +"measurement": { +"$ref": "GoogleCloudAiplatformV1beta1Measurement", +"description": "Required. The measurement to be added to a Trial." } }, "type": "object" }, -"CloudAiNlLlmProtoServicePart": { -"description": "A single part of a message.", -"id": "CloudAiNlLlmProtoServicePart", +"GoogleCloudAiplatformV1beta1Annotation": { +"description": "Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.", +"id": "GoogleCloudAiplatformV1beta1Annotation", "properties": { -"documentMetadata": { -"$ref": "CloudAiNlLlmProtoServicePartDocumentMetadata", -"description": "Document metadata. The metadata should only be used by the Cloud LLM when supporting document mime types. It will only be populated when this image input part is converted from a document input part." +"annotationSource": { +"$ref": "GoogleCloudAiplatformV1beta1UserActionReference", +"description": "Output only. The source of the Annotation.", +"readOnly": true }, -"fileData": { -"$ref": "CloudAiNlLlmProtoServicePartFileData", -"description": "URI-based data." +"createTime": { +"description": "Output only. Timestamp when this Annotation was created.", +"format": "google-datetime", +"readOnly": true, +"type": "string" }, -"functionCall": { -"$ref": "CloudAiNlLlmProtoServiceFunctionCall", -"description": "Function call data." +"etag": { +"description": "Optional. Used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", +"type": "string" }, -"functionResponse": { -"$ref": "CloudAiNlLlmProtoServiceFunctionResponse", -"description": "Function response data." +"labels": { +"additionalProperties": { +"type": "string" }, -"inlineData": { -"$ref": "CloudAiNlLlmProtoServicePartBlob", -"description": "Inline bytes data" +"description": "Optional. The labels with user-defined metadata to organize your Annotations. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Annotation(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with \"aiplatform.googleapis.com/\" and are immutable. Following system labels exist for each Annotation: * \"aiplatform.googleapis.com/annotation_set_name\": optional, name of the UI's annotation set this Annotation belongs to. If not set, the Annotation is not visible in the UI. * \"aiplatform.googleapis.com/payload_schema\": output only, its value is the payload_schema's title.", +"type": "object" }, -"lmRootMetadata": { -"$ref": "CloudAiNlLlmProtoServicePartLMRootMetadata", -"description": "Metadata provides extra info for building the LM Root request. Note: High enough tag number for internal only fields." +"name": { +"description": "Output only. Resource name of the Annotation.", +"readOnly": true, +"type": "string" }, -"text": { -"description": "Text input.", +"payload": { +"description": "Required. The schema of the payload can be found in payload_schema.", +"type": "any" +}, +"payloadSchemaUri": { +"description": "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.", "type": "string" }, -"videoMetadata": { -"$ref": "CloudAiNlLlmProtoServicePartVideoMetadata", -"description": "Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data." +"updateTime": { +"description": "Output only. Timestamp when this Annotation was last updated.", +"format": "google-datetime", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServicePartBlob": { -"description": "Represents arbitrary blob data input.", -"id": "CloudAiNlLlmProtoServicePartBlob", +"GoogleCloudAiplatformV1beta1AnnotationSpec": { +"description": "Identifies a concept with which DataItems may be annotated with.", +"id": "GoogleCloudAiplatformV1beta1AnnotationSpec", "properties": { -"data": { -"description": "Inline data.", -"format": "byte", +"createTime": { +"description": "Output only. Timestamp when this AnnotationSpec was created.", +"format": "google-datetime", +"readOnly": true, "type": "string" }, -"mimeType": { -"description": "The mime type corresponding to this input.", +"displayName": { +"description": "Required. The user-defined name of the AnnotationSpec. The name can be up to 128 characters long and can consist of any UTF-8 characters.", +"type": "string" +}, +"etag": { +"description": "Optional. Used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", +"type": "string" +}, +"name": { +"description": "Output only. Resource name of the AnnotationSpec.", +"readOnly": true, "type": "string" }, -"originalFileData": { -"$ref": "CloudAiNlLlmProtoServicePartFileData", -"description": "Original file data where the blob comes from." +"updateTime": { +"description": "Output only. Timestamp when AnnotationSpec was last updated.", +"format": "google-datetime", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServicePartDocumentMetadata": { -"description": "Metadata describes the original input document content.", -"id": "CloudAiNlLlmProtoServicePartDocumentMetadata", +"GoogleCloudAiplatformV1beta1Artifact": { +"description": "Instance of a general artifact.", +"id": "GoogleCloudAiplatformV1beta1Artifact", "properties": { -"originalDocumentBlob": { -"$ref": "CloudAiNlLlmProtoServicePartBlob", -"description": "The original document blob." +"createTime": { +"description": "Output only. Timestamp when this Artifact was created.", +"format": "google-datetime", +"readOnly": true, +"type": "string" }, -"pageNumber": { -"description": "The (1-indexed) page number of the image in the original document. The first page carries the original document content and mime type.", -"format": "int32", -"type": "integer" -} +"description": { +"description": "Description of the Artifact", +"type": "string" }, -"type": "object" +"displayName": { +"description": "User provided display name of the Artifact. May be up to 128 Unicode characters.", +"type": "string" }, -"CloudAiNlLlmProtoServicePartFileData": { -"description": "Represents file data.", -"id": "CloudAiNlLlmProtoServicePartFileData", -"properties": { -"fileUri": { -"description": "Inline data.", +"etag": { +"description": "An eTag used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", "type": "string" }, -"mimeType": { -"description": "The mime type corresponding to this input.", +"labels": { +"additionalProperties": { "type": "string" -} }, +"description": "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).", "type": "object" }, -"CloudAiNlLlmProtoServicePartLMRootMetadata": { -"description": "Metadata provides extra info for building the LM Root request.", -"id": "CloudAiNlLlmProtoServicePartLMRootMetadata", -"properties": { -"chunkId": { -"description": "Chunk id that will be used when mapping the part to the LM Root's chunk.", -"type": "string" -} +"metadata": { +"additionalProperties": { +"description": "Properties of the object.", +"type": "any" }, +"description": "Properties of the Artifact. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.", "type": "object" }, -"CloudAiNlLlmProtoServicePartVideoMetadata": { -"description": "Metadata describes the input video content.", -"id": "CloudAiNlLlmProtoServicePartVideoMetadata", -"properties": { -"endOffset": { -"description": "The end offset of the video.", -"format": "google-duration", +"name": { +"description": "Output only. The resource name of the Artifact.", +"readOnly": true, "type": "string" }, -"startOffset": { -"description": "The start offset of the video.", -"format": "google-duration", +"schemaTitle": { +"description": "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.", "type": "string" -} }, -"type": "object" +"schemaVersion": { +"description": "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.", +"type": "string" }, -"CloudAiNlLlmProtoServicePromptFeedback": { -"description": "Content filter results for a prompt sent in the request.", -"id": "CloudAiNlLlmProtoServicePromptFeedback", -"properties": { -"blockReason": { -"description": "Blocked reason.", +"state": { +"description": "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.", "enum": [ -"BLOCKED_REASON_UNSPECIFIED", -"SAFETY", -"OTHER", -"BLOCKLIST", -"PROHIBITED_CONTENT" +"STATE_UNSPECIFIED", +"PENDING", +"LIVE" ], "enumDescriptions": [ -"Unspecified blocked reason.", -"Candidates blocked due to safety.", -"Candidates blocked due to other reason (currently only language filter).", -"Candidates blocked due to the terms which are included from the terminology blocklist.", -"Candidates blocked due to prohibited content (currently only CSAM)." +"Unspecified state for the Artifact.", +"A state used by systems like Vertex AI Pipelines to indicate that the underlying data item represented by this Artifact is being created.", +"A state indicating that the Artifact should exist, unless something external to the system deletes it." ], "type": "string" }, -"blockReasonMessage": { -"description": "A readable block reason message.", +"updateTime": { +"description": "Output only. Timestamp when this Artifact was last updated.", +"format": "google-datetime", +"readOnly": true, "type": "string" }, -"safetyRatings": { -"description": "Safety ratings.", -"items": { -"$ref": "CloudAiNlLlmProtoServiceSafetyRating" -}, -"type": "array" +"uri": { +"description": "The uniform resource identifier of the artifact file. May be empty if there is no actual artifact file.", +"type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceRaiResult": { -"description": "The RAI results for a given text.", -"id": "CloudAiNlLlmProtoServiceRaiResult", +"GoogleCloudAiplatformV1beta1AssignNotebookRuntimeOperationMetadata": { +"description": "Metadata information for NotebookService.AssignNotebookRuntime.", +"id": "GoogleCloudAiplatformV1beta1AssignNotebookRuntimeOperationMetadata", "properties": { -"aidaRecitationResult": { -"$ref": "LanguageLabsAidaTrustRecitationProtoRecitationResult", -"description": "Recitation result from Aida recitation checker." +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." }, -"blocked": { -"deprecated": true, -"description": "Use `triggered_blocklist`.", -"type": "boolean" +"progressMessage": { +"description": "A human-readable message that shows the intermediate progress details of NotebookRuntime.", +"type": "string" +} }, -"errorCodes": { -"description": "The error codes indicate which RAI filters block the response.", -"items": { -"format": "int32", -"type": "integer" +"type": "object" }, -"type": "array" +"GoogleCloudAiplatformV1beta1AssignNotebookRuntimeRequest": { +"description": "Request message for NotebookService.AssignNotebookRuntime.", +"id": "GoogleCloudAiplatformV1beta1AssignNotebookRuntimeRequest", +"properties": { +"notebookRuntime": { +"$ref": "GoogleCloudAiplatformV1beta1NotebookRuntime", +"description": "Required. Provide runtime specific information (e.g. runtime owner, notebook id) used for NotebookRuntime assignment." }, -"filtered": { -"description": "Whether the text should be filtered and not shown to the end user. This is determined based on a combination of `triggered_recitation`, `triggered_blocklist`, `language_filter_result`, and `triggered_safety_filter`.", -"type": "boolean" +"notebookRuntimeId": { +"description": "Optional. User specified ID for the notebook runtime.", +"type": "string" }, -"languageFilterResult": { -"$ref": "LearningGenaiRootLanguageFilterResult", -"description": "Language filter result from SAFT LangId." +"notebookRuntimeTemplate": { +"description": "Required. The resource name of the NotebookRuntimeTemplate based on which a NotebookRuntime will be assigned (reuse or create a new one).", +"type": "string" +} }, -"raiSignals": { -"description": "The RAI signals for the text.", -"items": { -"$ref": "CloudAiNlLlmProtoServiceRaiSignal" +"type": "object" }, -"type": "array" +"GoogleCloudAiplatformV1beta1Attribution": { +"description": "Attribution that explains a particular prediction output.", +"id": "GoogleCloudAiplatformV1beta1Attribution", +"properties": { +"approximationError": { +"description": "Output only. Error of feature_attributions caused by approximation used in the explanation method. Lower value means more precise attributions. * For Sampled Shapley attribution, increasing path_count might reduce the error. * For Integrated Gradients attribution, increasing step_count might reduce the error. * For XRAI attribution, increasing step_count might reduce the error. See [this introduction](/vertex-ai/docs/explainable-ai/overview) for more information.", +"format": "double", +"readOnly": true, +"type": "number" }, -"translationRequestInfos": { -"description": "Translation request info during RAI for debugging purpose. Each TranslationRequestInfo corresponds to a request sent to the translation server.", -"items": { -"$ref": "LearningGenaiRootTranslationRequestInfo" +"baselineOutputValue": { +"description": "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.", +"format": "double", +"readOnly": true, +"type": "number" }, -"type": "array" +"featureAttributions": { +"description": "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).", +"readOnly": true, +"type": "any" }, -"triggeredBlocklist": { -"description": "Whether the text triggered the blocklist.", -"type": "boolean" +"instanceOutputValue": { +"description": "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.", +"format": "double", +"readOnly": true, +"type": "number" }, -"triggeredRecitation": { -"description": "Whether the text should be blocked by the recitation result from Aida recitation checker. It is determined from aida_recitation_result.", -"type": "boolean" +"outputDisplayName": { +"description": "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.", +"readOnly": true, +"type": "string" }, -"triggeredSafetyFilter": { -"description": "Whether the text triggered the safety filter. Currently, this is due to CSAI triggering or one of four categories (derogatory, sexual, toxic, violent) having a score over the filter threshold.", -"type": "boolean" +"outputIndex": { +"description": "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.", +"items": { +"format": "int32", +"type": "integer" +}, +"readOnly": true, +"type": "array" +}, +"outputName": { +"description": "Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs.", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceRaiSignal": { -"description": "An RAI signal for a single category.", -"id": "CloudAiNlLlmProtoServiceRaiSignal", +"GoogleCloudAiplatformV1beta1AuthConfig": { +"description": "Auth configuration to run the extension.", +"id": "GoogleCloudAiplatformV1beta1AuthConfig", "properties": { -"confidence": { -"description": "The confidence level for the RAI category.", +"apiKeyConfig": { +"$ref": "GoogleCloudAiplatformV1beta1AuthConfigApiKeyConfig", +"description": "Config for API key auth." +}, +"authType": { +"description": "Type of auth scheme.", "enum": [ -"CONFIDENCE_UNSPECIFIED", -"CONFIDENCE_NONE", -"CONFIDENCE_LOW", -"CONFIDENCE_MEDIUM", -"CONFIDENCE_HIGH" +"AUTH_TYPE_UNSPECIFIED", +"NO_AUTH", +"API_KEY_AUTH", +"HTTP_BASIC_AUTH", +"GOOGLE_SERVICE_ACCOUNT_AUTH", +"OAUTH", +"OIDC_AUTH" ], "enumDescriptions": [ "", -"", -"", -"", -"" +"No Auth.", +"API Key Auth.", +"HTTP Basic Auth.", +"Google Service Account Auth.", +"OAuth auth.", +"OpenID Connect (OIDC) Auth." ], "type": "string" }, -"flagged": { -"description": "Whether the category is flagged as being present. Currently, this is set to true if score >= 0.5.", -"type": "boolean" -}, -"influentialTerms": { -"description": "The influential terms that could potentially block the response.", -"items": { -"$ref": "CloudAiNlLlmProtoServiceRaiSignalInfluentialTerm" +"googleServiceAccountConfig": { +"$ref": "GoogleCloudAiplatformV1beta1AuthConfigGoogleServiceAccountConfig", +"description": "Config for Google Service Account auth." }, -"type": "array" +"httpBasicAuthConfig": { +"$ref": "GoogleCloudAiplatformV1beta1AuthConfigHttpBasicAuthConfig", +"description": "Config for HTTP Basic auth." }, -"raiCategory": { -"description": "The RAI category.", -"enum": [ -"RAI_CATEGORY_UNSPECIFIED", -"TOXIC", -"SEXUALLY_EXPLICIT", -"HATE_SPEECH", -"VIOLENT", -"PROFANITY", -"HARASSMENT", -"DEATH_HARM_TRAGEDY", -"FIREARMS_WEAPONS", -"PUBLIC_SAFETY", -"HEALTH", -"RELIGIOUS_BELIEF", -"ILLICIT_DRUGS", -"WAR_CONFLICT", -"POLITICS", -"FINANCE", -"LEGAL", -"CSAI", -"FRINGE", -"THREAT", -"SEVERE_TOXICITY", -"TOXICITY", -"SEXUAL", -"INSULT", -"DEROGATORY", -"IDENTITY_ATTACK", -"VIOLENCE_ABUSE", -"OBSCENE", -"DRUGS", -"CSAM", -"SPII", -"DANGEROUS_CONTENT", -"DANGEROUS_CONTENT_SEVERITY", -"INSULT_SEVERITY", -"DEROGATORY_SEVERITY", -"SEXUAL_SEVERITY" -], -"enumDescriptions": [ -"", -"SafetyCat categories.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"GRAIL categories that can't be exposed to end users.", -"", -"Unused categories.", -"", -"Old category names.", -"", -"", -"", -"", -"", -"", -"", -"CSAM V2", -"SPII", -"New SafetyCat v3 categories", -"", -"", -"", -"" -], -"type": "string" +"oauthConfig": { +"$ref": "GoogleCloudAiplatformV1beta1AuthConfigOauthConfig", +"description": "Config for user oauth." }, -"score": { -"description": "The score for the category, in the range [0.0, 1.0].", -"format": "float", -"type": "number" +"oidcConfig": { +"$ref": "GoogleCloudAiplatformV1beta1AuthConfigOidcConfig", +"description": "Config for user OIDC auth." } }, "type": "object" }, -"CloudAiNlLlmProtoServiceRaiSignalInfluentialTerm": { -"description": "The influential term that could potentially block the response.", -"id": "CloudAiNlLlmProtoServiceRaiSignalInfluentialTerm", +"GoogleCloudAiplatformV1beta1AuthConfigApiKeyConfig": { +"description": "Config for authentication with API key.", +"id": "GoogleCloudAiplatformV1beta1AuthConfigApiKeyConfig", "properties": { -"beginOffset": { -"description": "The beginning offset of the influential term.", -"format": "int32", -"type": "integer" -}, -"confidence": { -"description": "The confidence score of the influential term.", -"format": "float", -"type": "number" +"apiKeySecret": { +"description": "Required. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.", +"type": "string" }, -"source": { -"description": "The source of the influential term, prompt or response.", +"httpElementLocation": { +"description": "Required. The location of the API key.", "enum": [ -"SOURCE_UNSPECIFIED", -"PROMPT", -"RESPONSE" +"HTTP_IN_UNSPECIFIED", +"HTTP_IN_QUERY", +"HTTP_IN_HEADER", +"HTTP_IN_PATH", +"HTTP_IN_BODY", +"HTTP_IN_COOKIE" ], "enumDescriptions": [ -"Unspecified source.", -"The influential term comes from the prompt.", -"The influential term comes from the response." +"", +"Element is in the HTTP request query.", +"Element is in the HTTP request header.", +"Element is in the HTTP request path.", +"Element is in the HTTP request body.", +"Element is in the HTTP request cookie." ], "type": "string" }, -"term": { -"description": "The influential term.", +"name": { +"description": "Required. The parameter name of the API key. E.g. If the API request is \"https://example.com/act?api_key=\", \"api_key\" would be the parameter name.", "type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceSafetyRating": { -"description": "Safety rating corresponding to the generated content.", -"id": "CloudAiNlLlmProtoServiceSafetyRating", +"GoogleCloudAiplatformV1beta1AuthConfigGoogleServiceAccountConfig": { +"description": "Config for Google Service Account Authentication.", +"id": "GoogleCloudAiplatformV1beta1AuthConfigGoogleServiceAccountConfig", "properties": { -"blocked": { -"description": "Indicates whether the content was filtered out because of this rating.", -"type": "boolean" -}, -"category": { -"description": "Harm category.", -"enum": [ -"HARM_CATEGORY_UNSPECIFIED", -"HARM_CATEGORY_HATE_SPEECH", -"HARM_CATEGORY_DANGEROUS_CONTENT", -"HARM_CATEGORY_HARASSMENT", -"HARM_CATEGORY_SEXUALLY_EXPLICIT" -], -"enumDescriptions": [ -"The harm category is unspecified.", -"The harm category is hate speech.", -"The harm category is dengerous content.", -"The harm category is harassment.", -"The harm category is sexually explicit." -], +"serviceAccount": { +"description": "Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.", "type": "string" +} }, -"influentialTerms": { -"description": "The influential terms that could potentially block the response.", -"items": { -"$ref": "CloudAiNlLlmProtoServiceSafetyRatingInfluentialTerm" -}, -"type": "array" +"type": "object" }, -"probability": { -"description": "Harm probability levels in the content.", -"enum": [ -"HARM_PROBABILITY_UNSPECIFIED", -"NEGLIGIBLE", -"LOW", -"MEDIUM", -"HIGH" -], -"enumDescriptions": [ -"Harm probability unspecified.", -"Negligible level of harm.", -"Low level of harm.", -"Medium level of harm.", -"High level of harm." -], +"GoogleCloudAiplatformV1beta1AuthConfigHttpBasicAuthConfig": { +"description": "Config for HTTP Basic Authentication.", +"id": "GoogleCloudAiplatformV1beta1AuthConfigHttpBasicAuthConfig", +"properties": { +"credentialSecret": { +"description": "Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.", "type": "string" +} }, -"probabilityScore": { -"description": "Harm probability score.", -"format": "float", -"type": "number" +"type": "object" }, -"severity": { -"description": "Harm severity levels in the content.", -"enum": [ -"HARM_SEVERITY_UNSPECIFIED", -"HARM_SEVERITY_NEGLIGIBLE", -"HARM_SEVERITY_LOW", -"HARM_SEVERITY_MEDIUM", -"HARM_SEVERITY_HIGH" -], -"enumDescriptions": [ -"Harm severity unspecified.", -"Negligible level of harm severity.", -"Low level of harm severity.", -"Medium level of harm severity.", -"High level of harm severity." -], +"GoogleCloudAiplatformV1beta1AuthConfigOauthConfig": { +"description": "Config for user oauth.", +"id": "GoogleCloudAiplatformV1beta1AuthConfigOauthConfig", +"properties": { +"accessToken": { +"description": "Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.", "type": "string" }, -"severityScore": { -"description": "Harm severity score.", -"format": "float", -"type": "number" +"serviceAccount": { +"description": "The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.", +"type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceSafetyRatingInfluentialTerm": { -"description": "The influential term that could potentially block the response.", -"id": "CloudAiNlLlmProtoServiceSafetyRatingInfluentialTerm", +"GoogleCloudAiplatformV1beta1AuthConfigOidcConfig": { +"description": "Config for user OIDC auth.", +"id": "GoogleCloudAiplatformV1beta1AuthConfigOidcConfig", "properties": { -"beginOffset": { -"description": "The beginning offset of the influential term.", -"format": "int32", -"type": "integer" -}, -"confidence": { -"description": "The confidence score of the influential term.", -"format": "float", -"type": "number" -}, -"source": { -"description": "The source of the influential term, prompt or response.", -"enum": [ -"SOURCE_UNSPECIFIED", -"PROMPT", -"RESPONSE" -], -"enumDescriptions": [ -"Unspecified source.", -"The influential term comes from the prompt.", -"The influential term comes from the response." -], +"idToken": { +"description": "OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.", "type": "string" }, -"term": { -"description": "The influential term.", +"serviceAccount": { +"description": "The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).", "type": "string" } }, "type": "object" }, -"CloudAiNlLlmProtoServiceUsageMetadata": { -"description": "Usage metadata about response(s).", -"id": "CloudAiNlLlmProtoServiceUsageMetadata", +"GoogleCloudAiplatformV1beta1AutomaticResources": { +"description": "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.", +"id": "GoogleCloudAiplatformV1beta1AutomaticResources", "properties": { -"candidatesTokenCount": { -"description": "Number of tokens in the response(s).", +"maxReplicaCount": { +"description": "Immutable. The maximum number of replicas this DeployedModel 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 the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel 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.", "format": "int32", "type": "integer" }, -"promptTokenCount": { -"description": "Number of tokens in the request.", +"minReplicaCount": { +"description": "Immutable. The minimum number of replicas this DeployedModel 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.", "format": "int32", "type": "integer" +} }, -"totalTokenCount": { +"type": "object" +}, +"GoogleCloudAiplatformV1beta1AutoscalingMetricSpec": { +"description": "The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.", +"id": "GoogleCloudAiplatformV1beta1AutoscalingMetricSpec", +"properties": { +"metricName": { +"description": "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`", +"type": "string" +}, +"target": { +"description": "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.", "format": "int32", "type": "integer" } }, "type": "object" }, -"GoogleApiHttpBody": { -"description": "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.", -"id": "GoogleApiHttpBody", +"GoogleCloudAiplatformV1beta1AvroSource": { +"description": "The storage details for Avro input content.", +"id": "GoogleCloudAiplatformV1beta1AvroSource", "properties": { -"contentType": { -"description": "The HTTP Content-Type header value specifying the content type of the body.", -"type": "string" +"gcsSource": { +"$ref": "GoogleCloudAiplatformV1beta1GcsSource", +"description": "Required. Google Cloud Storage location." +} }, -"data": { -"description": "The HTTP request/response body as raw binary.", -"format": "byte", -"type": "string" +"type": "object" }, -"extensions": { -"description": "Application specific response metadata. Must be set in the first response for streaming APIs.", +"GoogleCloudAiplatformV1beta1BatchCancelPipelineJobsRequest": { +"description": "Request message for PipelineService.BatchCancelPipelineJobs.", +"id": "GoogleCloudAiplatformV1beta1BatchCancelPipelineJobsRequest", +"properties": { +"names": { +"description": "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}`", "items": { -"additionalProperties": { -"description": "Properties of the object. Contains field @type with type URL.", -"type": "any" -}, -"type": "object" +"type": "string" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1ActiveLearningConfig": { -"description": "Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy.", -"id": "GoogleCloudAiplatformV1beta1ActiveLearningConfig", +"GoogleCloudAiplatformV1beta1BatchCancelPipelineJobsResponse": { +"description": "Response message for PipelineService.BatchCancelPipelineJobs.", +"id": "GoogleCloudAiplatformV1beta1BatchCancelPipelineJobsResponse", "properties": { -"maxDataItemCount": { -"description": "Max number of human labeled DataItems.", -"format": "int64", -"type": "string" +"pipelineJobs": { +"description": "PipelineJobs cancelled.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1PipelineJob" }, -"maxDataItemPercentage": { -"description": "Max percent of total DataItems for human labeling.", -"format": "int32", -"type": "integer" +"type": "array" +} }, -"sampleConfig": { -"$ref": "GoogleCloudAiplatformV1beta1SampleConfig", -"description": "Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy." +"type": "object" }, -"trainingConfig": { -"$ref": "GoogleCloudAiplatformV1beta1TrainingConfig", -"description": "CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems." +"GoogleCloudAiplatformV1beta1BatchCreateFeaturesOperationMetadata": { +"description": "Details of operations that perform batch create Features.", +"id": "GoogleCloudAiplatformV1beta1BatchCreateFeaturesOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for Feature." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1AddContextArtifactsAndExecutionsRequest": { -"description": "Request message for MetadataService.AddContextArtifactsAndExecutions.", -"id": "GoogleCloudAiplatformV1beta1AddContextArtifactsAndExecutionsRequest", +"GoogleCloudAiplatformV1beta1BatchCreateFeaturesRequest": { +"description": "Request message for FeaturestoreService.BatchCreateFeatures.", +"id": "GoogleCloudAiplatformV1beta1BatchCreateFeaturesRequest", "properties": { -"artifacts": { -"description": "The resource names of the Artifacts to attribute to the Context. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}`", +"requests": { +"description": "Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType. 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.", "items": { -"type": "string" +"$ref": "GoogleCloudAiplatformV1beta1CreateFeatureRequest" }, "type": "array" +} }, -"executions": { -"description": "The resource names of the Executions to associate with the Context. Format: `projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}`", +"type": "object" +}, +"GoogleCloudAiplatformV1beta1BatchCreateFeaturesResponse": { +"description": "Response message for FeaturestoreService.BatchCreateFeatures.", +"id": "GoogleCloudAiplatformV1beta1BatchCreateFeaturesResponse", +"properties": { +"features": { +"description": "The Features created.", "items": { -"type": "string" +"$ref": "GoogleCloudAiplatformV1beta1Feature" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1AddContextArtifactsAndExecutionsResponse": { -"description": "Response message for MetadataService.AddContextArtifactsAndExecutions.", -"id": "GoogleCloudAiplatformV1beta1AddContextArtifactsAndExecutionsResponse", -"properties": {}, +"GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsRequest": { +"description": "Request message for TensorboardService.BatchCreateTensorboardRuns.", +"id": "GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsRequest", +"properties": { +"requests": { +"description": "Required. The request message specifying the TensorboardRuns to create. A maximum of 1000 TensorboardRuns can be created in a batch.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1CreateTensorboardRunRequest" +}, +"type": "array" +} +}, "type": "object" }, -"GoogleCloudAiplatformV1beta1AddContextChildrenRequest": { -"description": "Request message for MetadataService.AddContextChildren.", -"id": "GoogleCloudAiplatformV1beta1AddContextChildrenRequest", +"GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsResponse": { +"description": "Response message for TensorboardService.BatchCreateTensorboardRuns.", +"id": "GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsResponse", "properties": { -"childContexts": { -"description": "The resource names of the child Contexts.", +"tensorboardRuns": { +"description": "The created TensorboardRuns.", "items": { -"type": "string" +"$ref": "GoogleCloudAiplatformV1beta1TensorboardRun" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1AddContextChildrenResponse": { -"description": "Response message for MetadataService.AddContextChildren.", -"id": "GoogleCloudAiplatformV1beta1AddContextChildrenResponse", -"properties": {}, +"GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesRequest": { +"description": "Request message for TensorboardService.BatchCreateTensorboardTimeSeries.", +"id": "GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesRequest", +"properties": { +"requests": { +"description": "Required. The request message specifying the TensorboardTimeSeries to create. A maximum of 1000 TensorboardTimeSeries can be created in a batch.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1CreateTensorboardTimeSeriesRequest" +}, +"type": "array" +} +}, "type": "object" }, -"GoogleCloudAiplatformV1beta1AddExecutionEventsRequest": { -"description": "Request message for MetadataService.AddExecutionEvents.", -"id": "GoogleCloudAiplatformV1beta1AddExecutionEventsRequest", +"GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesResponse": { +"description": "Response message for TensorboardService.BatchCreateTensorboardTimeSeries.", +"id": "GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesResponse", "properties": { -"events": { -"description": "The Events to create and add.", +"tensorboardTimeSeries": { +"description": "The created TensorboardTimeSeries.", "items": { -"$ref": "GoogleCloudAiplatformV1beta1Event" +"$ref": "GoogleCloudAiplatformV1beta1TensorboardTimeSeries" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1AddExecutionEventsResponse": { -"description": "Response message for MetadataService.AddExecutionEvents.", -"id": "GoogleCloudAiplatformV1beta1AddExecutionEventsResponse", -"properties": {}, +"GoogleCloudAiplatformV1beta1BatchDedicatedResources": { +"description": "A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.", +"id": "GoogleCloudAiplatformV1beta1BatchDedicatedResources", +"properties": { +"machineSpec": { +"$ref": "GoogleCloudAiplatformV1beta1MachineSpec", +"description": "Required. Immutable. The specification of a single machine." +}, +"maxReplicaCount": { +"description": "Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.", +"format": "int32", +"type": "integer" +}, +"startingReplicaCount": { +"description": "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", +"format": "int32", +"type": "integer" +} +}, "type": "object" }, -"GoogleCloudAiplatformV1beta1AddTrialMeasurementRequest": { -"description": "Request message for VizierService.AddTrialMeasurement.", -"id": "GoogleCloudAiplatformV1beta1AddTrialMeasurementRequest", +"GoogleCloudAiplatformV1beta1BatchDeletePipelineJobsRequest": { +"description": "Request message for PipelineService.BatchDeletePipelineJobs.", +"id": "GoogleCloudAiplatformV1beta1BatchDeletePipelineJobsRequest", "properties": { -"measurement": { -"$ref": "GoogleCloudAiplatformV1beta1Measurement", -"description": "Required. The measurement to be added to a Trial." +"names": { +"description": "Required. The names of the PipelineJobs to delete. A maximum of 32 PipelineJobs can be deleted in a batch. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipelineJob}`", +"items": { +"type": "string" +}, +"type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1Annotation": { -"description": "Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.", -"id": "GoogleCloudAiplatformV1beta1Annotation", +"GoogleCloudAiplatformV1beta1BatchDeletePipelineJobsResponse": { +"description": "Response message for PipelineService.BatchDeletePipelineJobs.", +"id": "GoogleCloudAiplatformV1beta1BatchDeletePipelineJobsResponse", "properties": { -"annotationSource": { -"$ref": "GoogleCloudAiplatformV1beta1UserActionReference", -"description": "Output only. The source of the Annotation.", -"readOnly": true +"pipelineJobs": { +"description": "PipelineJobs deleted.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1PipelineJob" }, -"createTime": { -"description": "Output only. Timestamp when this Annotation was created.", -"format": "google-datetime", -"readOnly": true, -"type": "string" +"type": "array" +} }, -"etag": { -"description": "Optional. Used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", -"type": "string" +"type": "object" }, -"labels": { -"additionalProperties": { -"type": "string" +"GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsRequest": { +"description": "Request message for ModelService.BatchImportEvaluatedAnnotations", +"id": "GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsRequest", +"properties": { +"evaluatedAnnotations": { +"description": "Required. Evaluated annotations resource to be imported.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1EvaluatedAnnotation" +}, +"type": "array" +} }, -"description": "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.", "type": "object" }, -"name": { -"description": "Output only. Resource name of the Annotation.", +"GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsResponse": { +"description": "Response message for ModelService.BatchImportEvaluatedAnnotations", +"id": "GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsResponse", +"properties": { +"importedEvaluatedAnnotationsCount": { +"description": "Output only. Number of EvaluatedAnnotations imported.", +"format": "int32", "readOnly": true, -"type": "string" +"type": "integer" +} }, -"payload": { -"description": "Required. The schema of the payload can be found in payload_schema.", -"type": "any" +"type": "object" }, -"payloadSchemaUri": { -"description": "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.", -"type": "string" +"GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesRequest": { +"description": "Request message for ModelService.BatchImportModelEvaluationSlices", +"id": "GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesRequest", +"properties": { +"modelEvaluationSlices": { +"description": "Required. Model evaluation slice resource to be imported.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1ModelEvaluationSlice" }, -"updateTime": { -"description": "Output only. Timestamp when this Annotation was last updated.", -"format": "google-datetime", -"readOnly": true, -"type": "string" +"type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1AnnotationSpec": { -"description": "Identifies a concept with which DataItems may be annotated with.", -"id": "GoogleCloudAiplatformV1beta1AnnotationSpec", +"GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesResponse": { +"description": "Response message for ModelService.BatchImportModelEvaluationSlices", +"id": "GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesResponse", "properties": { -"createTime": { -"description": "Output only. Timestamp when this AnnotationSpec was created.", -"format": "google-datetime", -"readOnly": true, +"importedModelEvaluationSlices": { +"description": "Output only. List of imported ModelEvaluationSlice.name.", +"items": { "type": "string" }, -"displayName": { -"description": "Required. The user-defined name of the AnnotationSpec. The name can be up to 128 characters long and can consist of any UTF-8 characters.", -"type": "string" +"readOnly": true, +"type": "array" +} }, -"etag": { -"description": "Optional. Used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", -"type": "string" +"type": "object" }, -"name": { -"description": "Output only. Resource name of the AnnotationSpec.", -"readOnly": true, -"type": "string" +"GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadata": { +"description": "Runtime operation information for MigrationService.BatchMigrateResources.", +"id": "GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The common part of the operation metadata." }, -"updateTime": { -"description": "Output only. Timestamp when AnnotationSpec was last updated.", -"format": "google-datetime", -"readOnly": true, -"type": "string" +"partialResults": { +"description": "Partial results that reflect the latest migration operation progress.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult" +}, +"type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1Artifact": { -"description": "Instance of a general artifact.", -"id": "GoogleCloudAiplatformV1beta1Artifact", +"GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult": { +"description": "Represents a partial result in batch migration operation for one MigrateResourceRequest.", +"id": "GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult", "properties": { -"createTime": { -"description": "Output only. Timestamp when this Artifact was created.", -"format": "google-datetime", -"readOnly": true, +"dataset": { +"description": "Migrated dataset resource name.", "type": "string" }, -"description": { -"description": "Description of the Artifact", -"type": "string" +"error": { +"$ref": "GoogleRpcStatus", +"description": "The error result of the migration request in case of failure." }, -"displayName": { -"description": "User provided display name of the Artifact. May be up to 128 Unicode characters.", +"model": { +"description": "Migrated model resource name.", "type": "string" }, -"etag": { -"description": "An eTag used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", -"type": "string" +"request": { +"$ref": "GoogleCloudAiplatformV1beta1MigrateResourceRequest", +"description": "It's the same as the value in MigrateResourceRequest.migrate_resource_requests." +} }, -"labels": { -"additionalProperties": { -"type": "string" +"type": "object" +}, +"GoogleCloudAiplatformV1beta1BatchMigrateResourcesRequest": { +"description": "Request message for MigrationService.BatchMigrateResources.", +"id": "GoogleCloudAiplatformV1beta1BatchMigrateResourcesRequest", +"properties": { +"migrateResourceRequests": { +"description": "Required. The request messages specifying the resources to migrate. They must be in the same location as the destination. Up to 50 resources can be migrated in one batch.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1MigrateResourceRequest" +}, +"type": "array" +} }, -"description": "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).", "type": "object" }, -"metadata": { -"additionalProperties": { -"description": "Properties of the object.", -"type": "any" +"GoogleCloudAiplatformV1beta1BatchMigrateResourcesResponse": { +"description": "Response message for MigrationService.BatchMigrateResources.", +"id": "GoogleCloudAiplatformV1beta1BatchMigrateResourcesResponse", +"properties": { +"migrateResourceResponses": { +"description": "Successfully migrated resources.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1MigrateResourceResponse" +}, +"type": "array" +} }, -"description": "Properties of the Artifact. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.", "type": "object" }, -"name": { -"description": "Output only. The resource name of the Artifact.", +"GoogleCloudAiplatformV1beta1BatchPredictionJob": { +"description": "A job that uses a Model to produce predictions on multiple input instances. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.", +"id": "GoogleCloudAiplatformV1beta1BatchPredictionJob", +"properties": { +"completionStats": { +"$ref": "GoogleCloudAiplatformV1beta1CompletionStats", +"description": "Output only. Statistics on completed and failed prediction instances.", +"readOnly": true +}, +"createTime": { +"description": "Output only. Time when the BatchPredictionJob was created.", +"format": "google-datetime", "readOnly": true, "type": "string" }, -"schemaTitle": { -"description": "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.", -"type": "string" +"dedicatedResources": { +"$ref": "GoogleCloudAiplatformV1beta1BatchDedicatedResources", +"description": "The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided." }, -"schemaVersion": { -"description": "The version of the schema in schema_name to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.", -"type": "string" +"disableContainerLogging": { +"description": "For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.", +"type": "boolean" }, -"state": { -"description": "The state of this Artifact. This is a property of the Artifact, and does not imply or capture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines), and the system does not prescribe or check the validity of state transitions.", -"enum": [ -"STATE_UNSPECIFIED", -"PENDING", -"LIVE" -], -"enumDescriptions": [ -"Unspecified state for the Artifact.", -"A state used by systems like Vertex AI Pipelines to indicate that the underlying data item represented by this Artifact is being created.", -"A state indicating that the Artifact should exist, unless something external to the system deletes it." -], +"displayName": { +"description": "Required. The user-defined name of this BatchPredictionJob.", "type": "string" }, -"updateTime": { -"description": "Output only. Timestamp when this Artifact was last updated.", +"encryptionSpec": { +"$ref": "GoogleCloudAiplatformV1beta1EncryptionSpec", +"description": "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": { +"description": "Output only. Time when the BatchPredictionJob entered any of the following states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`.", "format": "google-datetime", "readOnly": true, "type": "string" }, -"uri": { -"description": "The uniform resource identifier of the artifact file. May be empty if there is no actual artifact file.", -"type": "string" -} +"error": { +"$ref": "GoogleRpcStatus", +"description": "Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.", +"readOnly": true }, -"type": "object" +"explanationSpec": { +"$ref": "GoogleCloudAiplatformV1beta1ExplanationSpec", +"description": "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." }, -"GoogleCloudAiplatformV1beta1AssignNotebookRuntimeOperationMetadata": { -"description": "Metadata information for NotebookService.AssignNotebookRuntime.", -"id": "GoogleCloudAiplatformV1beta1AssignNotebookRuntimeOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." +"generateExplanation": { +"description": "Generate explanation with the batch prediction results. When set to `true`, the batch prediction output changes based on the `predictions_format` field of the BatchPredictionJob.output_config object: * `bigquery`: output includes a column named `explanation`. The value is a struct that conforms to the Explanation object. * `jsonl`: The JSON objects on each line include an additional entry keyed `explanation`. The value of the entry is a JSON object that conforms to the Explanation object. * `csv`: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.", +"type": "boolean" }, -"progressMessage": { -"description": "A human-readable message that shows the intermediate progress details of NotebookRuntime.", +"inputConfig": { +"$ref": "GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig", +"description": "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": { +"$ref": "GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig", +"description": "Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model." +}, +"labels": { +"additionalProperties": { "type": "string" -} }, +"description": "The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.", "type": "object" }, -"GoogleCloudAiplatformV1beta1AssignNotebookRuntimeRequest": { -"description": "Request message for NotebookService.AssignNotebookRuntime.", -"id": "GoogleCloudAiplatformV1beta1AssignNotebookRuntimeRequest", -"properties": { -"notebookRuntime": { -"$ref": "GoogleCloudAiplatformV1beta1NotebookRuntime", -"description": "Required. Provide runtime specific information (e.g. runtime owner, notebook id) used for NotebookRuntime assignment." +"manualBatchTuningParameters": { +"$ref": "GoogleCloudAiplatformV1beta1ManualBatchTuningParameters", +"description": "Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself)." }, -"notebookRuntimeId": { -"description": "Optional. User specified ID for the notebook runtime.", +"model": { +"description": "The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: `projects/{project}/locations/{location}/models/{model}@2` or `projects/{project}/locations/{location}/models/{model}@golden` if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: `publishers/{publisher}/models/{model}` or `projects/{project}/locations/{location}/publishers/{publisher}/models/{model}`", "type": "string" }, -"notebookRuntimeTemplate": { -"description": "Required. The resource name of the NotebookRuntimeTemplate based on which a NotebookRuntime will be assigned (reuse or create a new one).", -"type": "string" -} +"modelMonitoringConfig": { +"$ref": "GoogleCloudAiplatformV1beta1ModelMonitoringConfig", +"description": "Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset." }, -"type": "object" +"modelMonitoringStatsAnomalies": { +"description": "Get batch prediction job monitoring statistics.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomalies" }, -"GoogleCloudAiplatformV1beta1Attribution": { -"description": "Attribution that explains a particular prediction output.", -"id": "GoogleCloudAiplatformV1beta1Attribution", -"properties": { -"approximationError": { -"description": "Output only. Error of feature_attributions caused by approximation used in the explanation method. Lower value means more precise attributions. * For Sampled Shapley attribution, increasing path_count might reduce the error. * For Integrated Gradients attribution, increasing step_count might reduce the error. * For XRAI attribution, increasing step_count might reduce the error. See [this introduction](/vertex-ai/docs/explainable-ai/overview) for more information.", -"format": "double", -"readOnly": true, -"type": "number" +"type": "array" }, -"baselineOutputValue": { -"description": "Output only. Model predicted output if the input instance is constructed from the baselines of all the features defined in ExplanationMetadata.inputs. The field name of the output is determined by the key in ExplanationMetadata.outputs. If the Model's predicted output has multiple dimensions (rank > 1), this is the value in the output located by output_index. If there are multiple baselines, their output values are averaged.", -"format": "double", -"readOnly": true, -"type": "number" +"modelMonitoringStatus": { +"$ref": "GoogleRpcStatus", +"description": "Output only. The running status of the model monitoring pipeline.", +"readOnly": true }, -"featureAttributions": { -"description": "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).", -"readOnly": true, +"modelParameters": { +"description": "The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.", "type": "any" }, -"instanceOutputValue": { -"description": "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.", -"format": "double", +"modelVersionId": { +"description": "Output only. The version ID of the Model that produces the predictions via this job.", "readOnly": true, -"type": "number" +"type": "string" }, -"outputDisplayName": { -"description": "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.", +"name": { +"description": "Output only. Resource name of the BatchPredictionJob.", "readOnly": true, "type": "string" }, -"outputIndex": { -"description": "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.", +"outputConfig": { +"$ref": "GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig", +"description": "Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri." +}, +"outputInfo": { +"$ref": "GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfo", +"description": "Output only. Information further describing the output of this job.", +"readOnly": true +}, +"partialFailures": { +"description": "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.", "items": { -"format": "int32", -"type": "integer" +"$ref": "GoogleRpcStatus" }, "readOnly": true, "type": "array" }, -"outputName": { -"description": "Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs.", -"readOnly": true, -"type": "string" -} +"resourcesConsumed": { +"$ref": "GoogleCloudAiplatformV1beta1ResourcesConsumed", +"description": "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.", +"readOnly": true }, -"type": "object" +"serviceAccount": { +"description": "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.", +"type": "string" }, -"GoogleCloudAiplatformV1beta1AuthConfig": { -"description": "Auth configuration to run the extension.", -"id": "GoogleCloudAiplatformV1beta1AuthConfig", -"properties": { -"apiKeyConfig": { -"$ref": "GoogleCloudAiplatformV1beta1AuthConfigApiKeyConfig", -"description": "Config for API key auth." +"startTime": { +"description": "Output only. Time when the BatchPredictionJob for the first time entered the `JOB_STATE_RUNNING` state.", +"format": "google-datetime", +"readOnly": true, +"type": "string" }, -"authType": { -"description": "Type of auth scheme.", +"state": { +"description": "Output only. The detailed state of the job.", "enum": [ -"AUTH_TYPE_UNSPECIFIED", -"NO_AUTH", -"API_KEY_AUTH", -"HTTP_BASIC_AUTH", -"GOOGLE_SERVICE_ACCOUNT_AUTH", -"OAUTH", -"OIDC_AUTH" +"JOB_STATE_UNSPECIFIED", +"JOB_STATE_QUEUED", +"JOB_STATE_PENDING", +"JOB_STATE_RUNNING", +"JOB_STATE_SUCCEEDED", +"JOB_STATE_FAILED", +"JOB_STATE_CANCELLING", +"JOB_STATE_CANCELLED", +"JOB_STATE_PAUSED", +"JOB_STATE_EXPIRED", +"JOB_STATE_UPDATING", +"JOB_STATE_PARTIALLY_SUCCEEDED" ], "enumDescriptions": [ -"", -"No Auth.", -"API Key Auth.", -"HTTP Basic Auth.", -"Google Service Account Auth.", -"OAuth auth.", -"OpenID Connect (OIDC) Auth." +"The job state is unspecified.", +"The job has been just created or resumed and processing has not yet begun.", +"The service is preparing to run the job.", +"The job is in progress.", +"The job completed successfully.", +"The job failed.", +"The job is being cancelled. From this state the job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.", +"The job has been cancelled.", +"The job has been stopped, and can be resumed.", +"The job has expired.", +"The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state.", +"The job is partially succeeded, some results may be missing due to errors." ], +"readOnly": true, "type": "string" }, -"googleServiceAccountConfig": { -"$ref": "GoogleCloudAiplatformV1beta1AuthConfigGoogleServiceAccountConfig", -"description": "Config for Google Service Account auth." -}, -"httpBasicAuthConfig": { -"$ref": "GoogleCloudAiplatformV1beta1AuthConfigHttpBasicAuthConfig", -"description": "Config for HTTP Basic auth." -}, -"oauthConfig": { -"$ref": "GoogleCloudAiplatformV1beta1AuthConfigOauthConfig", -"description": "Config for user oauth." +"unmanagedContainerModel": { +"$ref": "GoogleCloudAiplatformV1beta1UnmanagedContainerModel", +"description": "Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set." }, -"oidcConfig": { -"$ref": "GoogleCloudAiplatformV1beta1AuthConfigOidcConfig", -"description": "Config for user OIDC auth." +"updateTime": { +"description": "Output only. Time when the BatchPredictionJob was most recently updated.", +"format": "google-datetime", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1AuthConfigApiKeyConfig": { -"description": "Config for authentication with API key.", -"id": "GoogleCloudAiplatformV1beta1AuthConfigApiKeyConfig", +"GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig": { +"description": "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.", +"id": "GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig", "properties": { -"apiKeySecret": { -"description": "Required. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.", -"type": "string" +"bigquerySource": { +"$ref": "GoogleCloudAiplatformV1beta1BigQuerySource", +"description": "The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored." }, -"httpElementLocation": { -"description": "Required. The location of the API key.", -"enum": [ -"HTTP_IN_UNSPECIFIED", -"HTTP_IN_QUERY", -"HTTP_IN_HEADER", -"HTTP_IN_PATH", -"HTTP_IN_BODY", -"HTTP_IN_COOKIE" -], -"enumDescriptions": [ -"", -"Element is in the HTTP request query.", -"Element is in the HTTP request header.", -"Element is in the HTTP request path.", -"Element is in the HTTP request body.", -"Element is in the HTTP request cookie." -], -"type": "string" +"gcsSource": { +"$ref": "GoogleCloudAiplatformV1beta1GcsSource", +"description": "The Cloud Storage location for the input instances." }, -"name": { -"description": "Required. The parameter name of the API key. E.g. If the API request is \"https://example.com/act?api_key=\", \"api_key\" would be the parameter name.", +"instancesFormat": { +"description": "Required. The format in which instances are given, must be one of the Model's supported_input_storage_formats.", "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1AuthConfigGoogleServiceAccountConfig": { -"description": "Config for Google Service Account Authentication.", -"id": "GoogleCloudAiplatformV1beta1AuthConfigGoogleServiceAccountConfig", +"GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig": { +"description": "Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.", +"id": "GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig", "properties": { -"serviceAccount": { -"description": "Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.", +"excludedFields": { +"description": "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.", +"items": { "type": "string" -} }, -"type": "object" +"type": "array" }, -"GoogleCloudAiplatformV1beta1AuthConfigHttpBasicAuthConfig": { -"description": "Config for HTTP Basic Authentication.", -"id": "GoogleCloudAiplatformV1beta1AuthConfigHttpBasicAuthConfig", -"properties": { -"credentialSecret": { -"description": "Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.", +"includedFields": { +"description": "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.", +"items": { "type": "string" -} }, -"type": "object" +"type": "array" }, -"GoogleCloudAiplatformV1beta1AuthConfigOauthConfig": { -"description": "Config for user oauth.", -"id": "GoogleCloudAiplatformV1beta1AuthConfigOauthConfig", -"properties": { -"accessToken": { -"description": "Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.", +"instanceType": { +"description": "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.", "type": "string" }, -"serviceAccount": { -"description": "The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.", +"keyField": { +"description": "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.", "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1AuthConfigOidcConfig": { -"description": "Config for user OIDC auth.", -"id": "GoogleCloudAiplatformV1beta1AuthConfigOidcConfig", +"GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig": { +"description": "Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.", +"id": "GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig", "properties": { -"idToken": { -"description": "OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.", -"type": "string" -}, -"serviceAccount": { -"description": "The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).", -"type": "string" -} -}, -"type": "object" +"bigqueryDestination": { +"$ref": "GoogleCloudAiplatformV1beta1BigQueryDestination", +"description": "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`." }, -"GoogleCloudAiplatformV1beta1AutomaticResources": { -"description": "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.", -"id": "GoogleCloudAiplatformV1beta1AutomaticResources", -"properties": { -"maxReplicaCount": { -"description": "Immutable. The maximum number of replicas this DeployedModel 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 the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel 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.", -"format": "int32", -"type": "integer" +"gcsDestination": { +"$ref": "GoogleCloudAiplatformV1beta1GcsDestination", +"description": "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." }, -"minReplicaCount": { -"description": "Immutable. The minimum number of replicas this DeployedModel 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.", -"format": "int32", -"type": "integer" +"predictionsFormat": { +"description": "Required. The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1AutoscalingMetricSpec": { -"description": "The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.", -"id": "GoogleCloudAiplatformV1beta1AutoscalingMetricSpec", +"GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfo": { +"description": "Further describes this job's output. Supplements output_config.", +"id": "GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfo", "properties": { -"metricName": { -"description": "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`", +"bigqueryOutputDataset": { +"description": "Output only. The path of the BigQuery dataset created, in `bq://projectId.bqDatasetId` format, into which the prediction output is written.", +"readOnly": true, "type": "string" }, -"target": { -"description": "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.", -"format": "int32", -"type": "integer" +"bigqueryOutputTable": { +"description": "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.", +"readOnly": true, +"type": "string" +}, +"gcsOutputDirectory": { +"description": "Output only. The full path of the Cloud Storage directory created, into which the prediction output is written.", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1AvroSource": { -"description": "The storage details for Avro input content.", -"id": "GoogleCloudAiplatformV1beta1AvroSource", +"GoogleCloudAiplatformV1beta1BatchReadFeatureValuesOperationMetadata": { +"description": "Details of operations that batch reads Feature values.", +"id": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesOperationMetadata", "properties": { -"gcsSource": { -"$ref": "GoogleCloudAiplatformV1beta1GcsSource", -"description": "Required. Google Cloud Storage location." +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for Featurestore batch read Features values." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchCancelPipelineJobsRequest": { -"description": "Request message for PipelineService.BatchCancelPipelineJobs.", -"id": "GoogleCloudAiplatformV1beta1BatchCancelPipelineJobsRequest", +"GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequest": { +"description": "Request message for FeaturestoreService.BatchReadFeatureValues.", +"id": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequest", "properties": { -"names": { -"description": "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}`", +"bigqueryReadInstances": { +"$ref": "GoogleCloudAiplatformV1beta1BigQuerySource", +"description": "Similar to csv_read_instances, but from BigQuery source." +}, +"csvReadInstances": { +"$ref": "GoogleCloudAiplatformV1beta1CsvSource", +"description": "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": { +"$ref": "GoogleCloudAiplatformV1beta1FeatureValueDestination", +"description": "Required. Specifies output location and format." +}, +"entityTypeSpecs": { +"description": "Required. Specifies EntityType grouping Features to read values of and settings.", "items": { -"type": "string" +"$ref": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestEntityTypeSpec" }, "type": "array" -} -}, -"type": "object" }, -"GoogleCloudAiplatformV1beta1BatchCancelPipelineJobsResponse": { -"description": "Response message for PipelineService.BatchCancelPipelineJobs.", -"id": "GoogleCloudAiplatformV1beta1BatchCancelPipelineJobsResponse", -"properties": { -"pipelineJobs": { -"description": "PipelineJobs cancelled.", +"passThroughFields": { +"description": "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.", "items": { -"$ref": "GoogleCloudAiplatformV1beta1PipelineJob" +"$ref": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestPassThroughField" }, "type": "array" +}, +"startTime": { +"description": "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.", +"format": "google-datetime", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchCreateFeaturesOperationMetadata": { -"description": "Details of operations that perform batch create Features.", -"id": "GoogleCloudAiplatformV1beta1BatchCreateFeaturesOperationMetadata", +"GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestEntityTypeSpec": { +"description": "Selects Features of an EntityType to read values of and specifies read settings.", +"id": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestEntityTypeSpec", "properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for Feature." -} +"entityTypeId": { +"description": "Required. ID of the EntityType to select Features. The EntityType id is the entity_type_id specified during EntityType creation.", +"type": "string" }, -"type": "object" +"featureSelector": { +"$ref": "GoogleCloudAiplatformV1beta1FeatureSelector", +"description": "Required. Selectors choosing which Feature values to read from the EntityType." }, -"GoogleCloudAiplatformV1beta1BatchCreateFeaturesRequest": { -"description": "Request message for FeaturestoreService.BatchCreateFeatures.", -"id": "GoogleCloudAiplatformV1beta1BatchCreateFeaturesRequest", -"properties": { -"requests": { -"description": "Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType. 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.", +"settings": { +"description": "Per-Feature settings for the batch read.", "items": { -"$ref": "GoogleCloudAiplatformV1beta1CreateFeatureRequest" +"$ref": "GoogleCloudAiplatformV1beta1DestinationFeatureSetting" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchCreateFeaturesResponse": { -"description": "Response message for FeaturestoreService.BatchCreateFeatures.", -"id": "GoogleCloudAiplatformV1beta1BatchCreateFeaturesResponse", +"GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestPassThroughField": { +"description": "Describe pass-through fields in read_instance source.", +"id": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestPassThroughField", "properties": { -"features": { -"description": "The Features created.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1Feature" -}, -"type": "array" +"fieldName": { +"description": "Required. The name of the field in the CSV header or the name of the column in BigQuery table. The naming restriction is the same as Feature.name.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsRequest": { -"description": "Request message for TensorboardService.BatchCreateTensorboardRuns.", -"id": "GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsRequest", +"GoogleCloudAiplatformV1beta1BatchReadFeatureValuesResponse": { +"description": "Response message for FeaturestoreService.BatchReadFeatureValues.", +"id": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesResponse", +"properties": {}, +"type": "object" +}, +"GoogleCloudAiplatformV1beta1BatchReadTensorboardTimeSeriesDataResponse": { +"description": "Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.", +"id": "GoogleCloudAiplatformV1beta1BatchReadTensorboardTimeSeriesDataResponse", "properties": { -"requests": { -"description": "Required. The request message specifying the TensorboardRuns to create. A maximum of 1000 TensorboardRuns can be created in a batch.", +"timeSeriesData": { +"description": "The returned time series data.", "items": { -"$ref": "GoogleCloudAiplatformV1beta1CreateTensorboardRunRequest" +"$ref": "GoogleCloudAiplatformV1beta1TimeSeriesData" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsResponse": { -"description": "Response message for TensorboardService.BatchCreateTensorboardRuns.", -"id": "GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsResponse", +"GoogleCloudAiplatformV1beta1BigQueryDestination": { +"description": "The BigQuery location for the output content.", +"id": "GoogleCloudAiplatformV1beta1BigQueryDestination", "properties": { -"tensorboardRuns": { -"description": "The created TensorboardRuns.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1TensorboardRun" -}, -"type": "array" +"outputUri": { +"description": "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`.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesRequest": { -"description": "Request message for TensorboardService.BatchCreateTensorboardTimeSeries.", -"id": "GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesRequest", +"GoogleCloudAiplatformV1beta1BigQuerySource": { +"description": "The BigQuery location for the input content.", +"id": "GoogleCloudAiplatformV1beta1BigQuerySource", "properties": { -"requests": { -"description": "Required. The request message specifying the TensorboardTimeSeries to create. A maximum of 1000 TensorboardTimeSeries can be created in a batch.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1CreateTensorboardTimeSeriesRequest" -}, -"type": "array" +"inputUri": { +"description": "Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesResponse": { -"description": "Response message for TensorboardService.BatchCreateTensorboardTimeSeries.", -"id": "GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesResponse", +"GoogleCloudAiplatformV1beta1BleuInput": { +"description": "Input for bleu metric.", +"id": "GoogleCloudAiplatformV1beta1BleuInput", "properties": { -"tensorboardTimeSeries": { -"description": "The created TensorboardTimeSeries.", +"instances": { +"description": "Required. Repeated bleu instances.", "items": { -"$ref": "GoogleCloudAiplatformV1beta1TensorboardTimeSeries" +"$ref": "GoogleCloudAiplatformV1beta1BleuInstance" }, "type": "array" +}, +"metricSpec": { +"$ref": "GoogleCloudAiplatformV1beta1BleuSpec", +"description": "Required. Spec for bleu score metric." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchDedicatedResources": { -"description": "A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.", -"id": "GoogleCloudAiplatformV1beta1BatchDedicatedResources", +"GoogleCloudAiplatformV1beta1BleuInstance": { +"description": "Spec for bleu instance.", +"id": "GoogleCloudAiplatformV1beta1BleuInstance", "properties": { -"machineSpec": { -"$ref": "GoogleCloudAiplatformV1beta1MachineSpec", -"description": "Required. Immutable. The specification of a single machine." -}, -"maxReplicaCount": { -"description": "Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.", -"format": "int32", -"type": "integer" +"prediction": { +"description": "Required. Output of the evaluated model.", +"type": "string" }, -"startingReplicaCount": { -"description": "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", -"format": "int32", -"type": "integer" +"reference": { +"description": "Required. Ground truth used to compare against the prediction.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchDeletePipelineJobsRequest": { -"description": "Request message for PipelineService.BatchDeletePipelineJobs.", -"id": "GoogleCloudAiplatformV1beta1BatchDeletePipelineJobsRequest", +"GoogleCloudAiplatformV1beta1BleuMetricValue": { +"description": "Bleu metric value for an instance.", +"id": "GoogleCloudAiplatformV1beta1BleuMetricValue", "properties": { -"names": { -"description": "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}`", -"items": { -"type": "string" -}, -"type": "array" +"score": { +"description": "Output only. Bleu score.", +"format": "float", +"readOnly": true, +"type": "number" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchDeletePipelineJobsResponse": { -"description": "Response message for PipelineService.BatchDeletePipelineJobs.", -"id": "GoogleCloudAiplatformV1beta1BatchDeletePipelineJobsResponse", +"GoogleCloudAiplatformV1beta1BleuResults": { +"description": "Results for bleu metric.", +"id": "GoogleCloudAiplatformV1beta1BleuResults", "properties": { -"pipelineJobs": { -"description": "PipelineJobs deleted.", +"bleuMetricValues": { +"description": "Output only. Bleu metric values.", "items": { -"$ref": "GoogleCloudAiplatformV1beta1PipelineJob" +"$ref": "GoogleCloudAiplatformV1beta1BleuMetricValue" }, +"readOnly": true, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsRequest": { -"description": "Request message for ModelService.BatchImportEvaluatedAnnotations", -"id": "GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsRequest", +"GoogleCloudAiplatformV1beta1BleuSpec": { +"description": "Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1.", +"id": "GoogleCloudAiplatformV1beta1BleuSpec", +"properties": {}, +"type": "object" +}, +"GoogleCloudAiplatformV1beta1Blob": { +"description": "Content blob. It's preferred to send as text directly rather than raw bytes.", +"id": "GoogleCloudAiplatformV1beta1Blob", "properties": { -"evaluatedAnnotations": { -"description": "Required. Evaluated annotations resource to be imported.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1EvaluatedAnnotation" +"data": { +"description": "Required. Raw bytes.", +"format": "byte", +"type": "string" }, -"type": "array" +"mimeType": { +"description": "Required. The IANA standard MIME type of the source data.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsResponse": { -"description": "Response message for ModelService.BatchImportEvaluatedAnnotations", -"id": "GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsResponse", +"GoogleCloudAiplatformV1beta1BlurBaselineConfig": { +"description": "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", +"id": "GoogleCloudAiplatformV1beta1BlurBaselineConfig", "properties": { -"importedEvaluatedAnnotationsCount": { -"description": "Output only. Number of EvaluatedAnnotations imported.", -"format": "int32", -"readOnly": true, -"type": "integer" +"maxBlurSigma": { +"description": "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.", +"format": "float", +"type": "number" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesRequest": { -"description": "Request message for ModelService.BatchImportModelEvaluationSlices", -"id": "GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesRequest", +"GoogleCloudAiplatformV1beta1BoolArray": { +"description": "A list of boolean values.", +"id": "GoogleCloudAiplatformV1beta1BoolArray", "properties": { -"modelEvaluationSlices": { -"description": "Required. Model evaluation slice resource to be imported.", +"values": { +"description": "A list of bool values.", "items": { -"$ref": "GoogleCloudAiplatformV1beta1ModelEvaluationSlice" +"type": "boolean" }, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesResponse": { -"description": "Response message for ModelService.BatchImportModelEvaluationSlices", -"id": "GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesResponse", +"GoogleCloudAiplatformV1beta1CacheConfig": { +"description": "Config of GenAI caching features. This is a singleton resource.", +"id": "GoogleCloudAiplatformV1beta1CacheConfig", "properties": { -"importedModelEvaluationSlices": { -"description": "Output only. List of imported ModelEvaluationSlice.name.", -"items": { -"type": "string" +"disableCache": { +"description": "If set to true, disables GenAI caching. Otherwise caching is enabled.", +"type": "boolean" }, -"readOnly": true, -"type": "array" +"name": { +"description": "Identifier. Name of the cache config. Format: - `projects/{project}/cacheConfig`.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadata": { -"description": "Runtime operation information for MigrationService.BatchMigrateResources.", -"id": "GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The common part of the operation metadata." -}, -"partialResults": { -"description": "Partial results that reflect the latest migration operation progress.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult" +"GoogleCloudAiplatformV1beta1CancelBatchPredictionJobRequest": { +"description": "Request message for JobService.CancelBatchPredictionJob.", +"id": "GoogleCloudAiplatformV1beta1CancelBatchPredictionJobRequest", +"properties": {}, +"type": "object" }, -"type": "array" -} +"GoogleCloudAiplatformV1beta1CancelCustomJobRequest": { +"description": "Request message for JobService.CancelCustomJob.", +"id": "GoogleCloudAiplatformV1beta1CancelCustomJobRequest", +"properties": {}, +"type": "object" }, +"GoogleCloudAiplatformV1beta1CancelDataLabelingJobRequest": { +"description": "Request message for JobService.CancelDataLabelingJob.", +"id": "GoogleCloudAiplatformV1beta1CancelDataLabelingJobRequest", +"properties": {}, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult": { -"description": "Represents a partial result in batch migration operation for one MigrateResourceRequest.", -"id": "GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult", -"properties": { -"dataset": { -"description": "Migrated dataset resource name.", -"type": "string" +"GoogleCloudAiplatformV1beta1CancelHyperparameterTuningJobRequest": { +"description": "Request message for JobService.CancelHyperparameterTuningJob.", +"id": "GoogleCloudAiplatformV1beta1CancelHyperparameterTuningJobRequest", +"properties": {}, +"type": "object" }, -"error": { -"$ref": "GoogleRpcStatus", -"description": "The error result of the migration request in case of failure." +"GoogleCloudAiplatformV1beta1CancelNasJobRequest": { +"description": "Request message for JobService.CancelNasJob.", +"id": "GoogleCloudAiplatformV1beta1CancelNasJobRequest", +"properties": {}, +"type": "object" }, -"model": { -"description": "Migrated model resource name.", -"type": "string" +"GoogleCloudAiplatformV1beta1CancelPipelineJobRequest": { +"description": "Request message for PipelineService.CancelPipelineJob.", +"id": "GoogleCloudAiplatformV1beta1CancelPipelineJobRequest", +"properties": {}, +"type": "object" }, -"request": { -"$ref": "GoogleCloudAiplatformV1beta1MigrateResourceRequest", -"description": "It's the same as the value in MigrateResourceRequest.migrate_resource_requests." -} +"GoogleCloudAiplatformV1beta1CancelTrainingPipelineRequest": { +"description": "Request message for PipelineService.CancelTrainingPipeline.", +"id": "GoogleCloudAiplatformV1beta1CancelTrainingPipelineRequest", +"properties": {}, +"type": "object" }, +"GoogleCloudAiplatformV1beta1CancelTuningJobRequest": { +"description": "Request message for GenAiTuningService.CancelTuningJob.", +"id": "GoogleCloudAiplatformV1beta1CancelTuningJobRequest", +"properties": {}, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchMigrateResourcesRequest": { -"description": "Request message for MigrationService.BatchMigrateResources.", -"id": "GoogleCloudAiplatformV1beta1BatchMigrateResourcesRequest", +"GoogleCloudAiplatformV1beta1Candidate": { +"description": "A response candidate generated from the model.", +"id": "GoogleCloudAiplatformV1beta1Candidate", "properties": { -"migrateResourceRequests": { -"description": "Required. The request messages specifying the resources to migrate. They must be in the same location as the destination. Up to 50 resources can be migrated in one batch.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1MigrateResourceRequest" +"citationMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1CitationMetadata", +"description": "Output only. Source attribution of the generated content.", +"readOnly": true }, -"type": "array" -} +"content": { +"$ref": "GoogleCloudAiplatformV1beta1Content", +"description": "Output only. Content parts of the candidate.", +"readOnly": true }, -"type": "object" +"finishMessage": { +"description": "Output only. Describes the reason the mode stopped generating tokens in more detail. This is only filled when `finish_reason` is set.", +"readOnly": true, +"type": "string" }, -"GoogleCloudAiplatformV1beta1BatchMigrateResourcesResponse": { -"description": "Response message for MigrationService.BatchMigrateResources.", -"id": "GoogleCloudAiplatformV1beta1BatchMigrateResourcesResponse", -"properties": { -"migrateResourceResponses": { -"description": "Successfully migrated resources.", +"finishReason": { +"description": "Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.", +"enum": [ +"FINISH_REASON_UNSPECIFIED", +"STOP", +"MAX_TOKENS", +"SAFETY", +"RECITATION", +"OTHER", +"BLOCKLIST", +"PROHIBITED_CONTENT", +"SPII" +], +"enumDescriptions": [ +"The finish reason is unspecified.", +"Natural stop point of the model or provided stop sequence.", +"The maximum number of tokens as specified in the request was reached.", +"The token generation was stopped as the response was flagged for safety reasons. NOTE: When streaming the Candidate.content will be empty if content filters blocked the output.", +"The token generation was stopped as the response was flagged for unauthorized citations.", +"All other reasons that stopped the token generation", +"The token generation was stopped as the response was flagged for the terms which are included from the terminology blocklist.", +"The token generation was stopped as the response was flagged for the prohibited contents.", +"The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents." +], +"readOnly": true, +"type": "string" +}, +"groundingMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GroundingMetadata", +"description": "Output only. Metadata specifies sources used to ground generated content.", +"readOnly": true +}, +"index": { +"description": "Output only. Index of the candidate.", +"format": "int32", +"readOnly": true, +"type": "integer" +}, +"safetyRatings": { +"description": "Output only. List of ratings for the safety of a response candidate. There is at most one rating per category.", "items": { -"$ref": "GoogleCloudAiplatformV1beta1MigrateResourceResponse" +"$ref": "GoogleCloudAiplatformV1beta1SafetyRating" }, +"readOnly": true, "type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchPredictionJob": { -"description": "A job that uses a Model to produce predictions on multiple input instances. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.", -"id": "GoogleCloudAiplatformV1beta1BatchPredictionJob", +"GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateMetatdata": { +"description": "This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.", +"id": "GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateMetatdata", "properties": { -"completionStats": { -"$ref": "GoogleCloudAiplatformV1beta1CompletionStats", -"description": "Output only. Statistics on completed and failed prediction instances.", -"readOnly": true +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for suggesting Trials." }, -"createTime": { -"description": "Output only. Time when the BatchPredictionJob was created.", -"format": "google-datetime", -"readOnly": true, +"study": { +"description": "The name of the Study that the Trial belongs to.", "type": "string" }, -"dedicatedResources": { -"$ref": "GoogleCloudAiplatformV1beta1BatchDedicatedResources", -"description": "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": { -"description": "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.", -"type": "boolean" -}, -"displayName": { -"description": "Required. The user-defined name of this BatchPredictionJob.", +"trial": { +"description": "The Trial name.", "type": "string" +} }, -"encryptionSpec": { -"$ref": "GoogleCloudAiplatformV1beta1EncryptionSpec", -"description": "Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key." +"type": "object" }, -"endTime": { -"description": "Output only. Time when the BatchPredictionJob entered any of the following states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -}, -"error": { -"$ref": "GoogleRpcStatus", -"description": "Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.", -"readOnly": true -}, -"explanationSpec": { -"$ref": "GoogleCloudAiplatformV1beta1ExplanationSpec", -"description": "Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to `true`. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited." +"GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateRequest": { +"description": "Request message for VizierService.CheckTrialEarlyStoppingState.", +"id": "GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateRequest", +"properties": {}, +"type": "object" }, -"generateExplanation": { -"description": "Generate explanation with the batch prediction results. When set to `true`, the batch prediction output changes based on the `predictions_format` field of the BatchPredictionJob.output_config object: * `bigquery`: output includes a column named `explanation`. The value is a struct that conforms to the Explanation object. * `jsonl`: The JSON objects on each line include an additional entry keyed `explanation`. The value of the entry is a JSON object that conforms to the Explanation object. * `csv`: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.", +"GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateResponse": { +"description": "Response message for VizierService.CheckTrialEarlyStoppingState.", +"id": "GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateResponse", +"properties": { +"shouldStop": { +"description": "True if the Trial should stop.", "type": "boolean" +} }, -"inputConfig": { -"$ref": "GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig", -"description": "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": { -"$ref": "GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig", -"description": "Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model." -}, -"labels": { -"additionalProperties": { -"type": "string" -}, -"description": "The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.", "type": "object" }, -"manualBatchTuningParameters": { -"$ref": "GoogleCloudAiplatformV1beta1ManualBatchTuningParameters", -"description": "Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself)." +"GoogleCloudAiplatformV1beta1Citation": { +"description": "Source attributions for content.", +"id": "GoogleCloudAiplatformV1beta1Citation", +"properties": { +"endIndex": { +"description": "Output only. End index into the content.", +"format": "int32", +"readOnly": true, +"type": "integer" }, -"model": { -"description": "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}`", +"license": { +"description": "Output only. License of the attribution.", +"readOnly": true, "type": "string" }, -"modelMonitoringConfig": { -"$ref": "GoogleCloudAiplatformV1beta1ModelMonitoringConfig", -"description": "Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset." -}, -"modelMonitoringStatsAnomalies": { -"description": "Get batch prediction job monitoring statistics.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomalies" -}, -"type": "array" -}, -"modelMonitoringStatus": { -"$ref": "GoogleRpcStatus", -"description": "Output only. The running status of the model monitoring pipeline.", +"publicationDate": { +"$ref": "GoogleTypeDate", +"description": "Output only. Publication date of the attribution.", "readOnly": true }, -"modelParameters": { -"description": "The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.", -"type": "any" +"startIndex": { +"description": "Output only. Start index into the content.", +"format": "int32", +"readOnly": true, +"type": "integer" }, -"modelVersionId": { -"description": "Output only. The version ID of the Model that produces the predictions via this job.", +"title": { +"description": "Output only. Title of the attribution.", "readOnly": true, "type": "string" }, -"name": { -"description": "Output only. Resource name of the BatchPredictionJob.", +"uri": { +"description": "Output only. Url reference of the attribution.", "readOnly": true, "type": "string" +} }, -"outputConfig": { -"$ref": "GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig", -"description": "Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri." -}, -"outputInfo": { -"$ref": "GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfo", -"description": "Output only. Information further describing the output of this job.", -"readOnly": true +"type": "object" }, -"partialFailures": { -"description": "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.", +"GoogleCloudAiplatformV1beta1CitationMetadata": { +"description": "A collection of source attributions for a piece of content.", +"id": "GoogleCloudAiplatformV1beta1CitationMetadata", +"properties": { +"citations": { +"description": "Output only. List of citations.", "items": { -"$ref": "GoogleRpcStatus" +"$ref": "GoogleCloudAiplatformV1beta1Citation" }, "readOnly": true, "type": "array" +} }, -"resourcesConsumed": { -"$ref": "GoogleCloudAiplatformV1beta1ResourcesConsumed", -"description": "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.", -"readOnly": true -}, -"serviceAccount": { -"description": "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.", -"type": "string" -}, -"startTime": { -"description": "Output only. Time when the BatchPredictionJob for the first time entered the `JOB_STATE_RUNNING` state.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -}, -"state": { -"description": "Output only. The detailed state of the job.", -"enum": [ -"JOB_STATE_UNSPECIFIED", -"JOB_STATE_QUEUED", -"JOB_STATE_PENDING", -"JOB_STATE_RUNNING", -"JOB_STATE_SUCCEEDED", -"JOB_STATE_FAILED", -"JOB_STATE_CANCELLING", -"JOB_STATE_CANCELLED", -"JOB_STATE_PAUSED", -"JOB_STATE_EXPIRED", -"JOB_STATE_UPDATING", -"JOB_STATE_PARTIALLY_SUCCEEDED" -], -"enumDescriptions": [ -"The job state is unspecified.", -"The job has been just created or resumed and processing has not yet begun.", -"The service is preparing to run the job.", -"The job is in progress.", -"The job completed successfully.", -"The job failed.", -"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`.", -"The job has been cancelled.", -"The job has been stopped, and can be resumed.", -"The job has expired.", -"The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state.", -"The job is partially succeeded, some results may be missing due to errors." -], -"readOnly": true, -"type": "string" +"type": "object" }, -"unmanagedContainerModel": { -"$ref": "GoogleCloudAiplatformV1beta1UnmanagedContainerModel", -"description": "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." +"GoogleCloudAiplatformV1beta1CoherenceInput": { +"description": "Input for coherence metric.", +"id": "GoogleCloudAiplatformV1beta1CoherenceInput", +"properties": { +"instance": { +"$ref": "GoogleCloudAiplatformV1beta1CoherenceInstance", +"description": "Required. Coherence instance." }, -"updateTime": { -"description": "Output only. Time when the BatchPredictionJob was most recently updated.", -"format": "google-datetime", -"readOnly": true, -"type": "string" +"metricSpec": { +"$ref": "GoogleCloudAiplatformV1beta1CoherenceSpec", +"description": "Required. Spec for coherence score metric." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig": { -"description": "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.", -"id": "GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig", +"GoogleCloudAiplatformV1beta1CoherenceInstance": { +"description": "Spec for coherence instance.", +"id": "GoogleCloudAiplatformV1beta1CoherenceInstance", "properties": { -"bigquerySource": { -"$ref": "GoogleCloudAiplatformV1beta1BigQuerySource", -"description": "The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored." -}, -"gcsSource": { -"$ref": "GoogleCloudAiplatformV1beta1GcsSource", -"description": "The Cloud Storage location for the input instances." -}, -"instancesFormat": { -"description": "Required. The format in which instances are given, must be one of the Model's supported_input_storage_formats.", +"prediction": { +"description": "Required. Output of the evaluated model.", "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig": { -"description": "Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.", -"id": "GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig", +"GoogleCloudAiplatformV1beta1CoherenceResult": { +"description": "Spec for coherence result.", +"id": "GoogleCloudAiplatformV1beta1CoherenceResult", "properties": { -"excludedFields": { -"description": "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.", -"items": { -"type": "string" -}, -"type": "array" +"confidence": { +"description": "Output only. Confidence for coherence score.", +"format": "float", +"readOnly": true, +"type": "number" }, -"includedFields": { -"description": "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.", -"items": { +"explanation": { +"description": "Output only. Explanation for coherence score.", +"readOnly": true, "type": "string" }, -"type": "array" +"score": { +"description": "Output only. Coherence score.", +"format": "float", +"readOnly": true, +"type": "number" +} }, -"instanceType": { -"description": "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.", -"type": "string" +"type": "object" }, -"keyField": { -"description": "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.", -"type": "string" +"GoogleCloudAiplatformV1beta1CoherenceSpec": { +"description": "Spec for coherence score metric.", +"id": "GoogleCloudAiplatformV1beta1CoherenceSpec", +"properties": { +"version": { +"description": "Optional. Which version to use for evaluation.", +"format": "int32", +"type": "integer" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig": { -"description": "Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.", -"id": "GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig", +"GoogleCloudAiplatformV1beta1CompleteTrialRequest": { +"description": "Request message for VizierService.CompleteTrial.", +"id": "GoogleCloudAiplatformV1beta1CompleteTrialRequest", "properties": { -"bigqueryDestination": { -"$ref": "GoogleCloudAiplatformV1beta1BigQueryDestination", -"description": "The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name `prediction__` where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ \"based on ISO-8601\" format. In the dataset two tables will be created, `predictions`, and `errors`. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The `predictions` table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The `errors` table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single \"errors\" column, which as values has google.rpc.Status represented as a STRUCT, and containing only `code` and `message`." -}, -"gcsDestination": { -"$ref": "GoogleCloudAiplatformV1beta1GcsDestination", -"description": "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." +"finalMeasurement": { +"$ref": "GoogleCloudAiplatformV1beta1Measurement", +"description": "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" }, -"predictionsFormat": { -"description": "Required. The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.", +"infeasibleReason": { +"description": "Optional. A human readable reason why the trial was infeasible. This should only be provided if `trial_infeasible` is true.", "type": "string" +}, +"trialInfeasible": { +"description": "Optional. True if the Trial cannot be run with the given Parameter, and final_measurement will be ignored.", +"type": "boolean" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfo": { -"description": "Further describes this job's output. Supplements output_config.", -"id": "GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfo", +"GoogleCloudAiplatformV1beta1CompletionStats": { +"description": "Success and error statistics of processing multiple entities (for example, DataItems or structured data rows) in batch.", +"id": "GoogleCloudAiplatformV1beta1CompletionStats", "properties": { -"bigqueryOutputDataset": { -"description": "Output only. The path of the BigQuery dataset created, in `bq://projectId.bqDatasetId` format, into which the prediction output is written.", +"failedCount": { +"description": "Output only. The number of entities for which any error was encountered.", +"format": "int64", "readOnly": true, "type": "string" }, -"bigqueryOutputTable": { -"description": "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.", +"incompleteCount": { +"description": "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).", +"format": "int64", "readOnly": true, "type": "string" }, -"gcsOutputDirectory": { -"description": "Output only. The full path of the Cloud Storage directory created, into which the prediction output is written.", +"successfulCount": { +"description": "Output only. The number of entities that had been processed successfully.", +"format": "int64", +"readOnly": true, +"type": "string" +}, +"successfulForecastPointCount": { +"description": "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.", +"format": "int64", "readOnly": true, "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchReadFeatureValuesOperationMetadata": { -"description": "Details of operations that batch reads Feature values.", -"id": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesOperationMetadata", +"GoogleCloudAiplatformV1beta1ComputeTokensRequest": { +"description": "Request message for ComputeTokens RPC call.", +"id": "GoogleCloudAiplatformV1beta1ComputeTokensRequest", "properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for Featurestore batch read Features values." +"instances": { +"description": "Required. 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.", +"items": { +"type": "any" +}, +"type": "array" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequest": { -"description": "Request message for FeaturestoreService.BatchReadFeatureValues.", -"id": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequest", +"GoogleCloudAiplatformV1beta1ComputeTokensResponse": { +"description": "Response message for ComputeTokens RPC call.", +"id": "GoogleCloudAiplatformV1beta1ComputeTokensResponse", "properties": { -"bigqueryReadInstances": { -"$ref": "GoogleCloudAiplatformV1beta1BigQuerySource", -"description": "Similar to csv_read_instances, but from BigQuery source." -}, -"csvReadInstances": { -"$ref": "GoogleCloudAiplatformV1beta1CsvSource", -"description": "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": { -"$ref": "GoogleCloudAiplatformV1beta1FeatureValueDestination", -"description": "Required. Specifies output location and format." -}, -"entityTypeSpecs": { -"description": "Required. Specifies EntityType grouping Features to read values of and settings.", +"tokensInfo": { +"description": "Lists of tokens info from the input. A ComputeTokensRequest could have multiple instances with a prompt in each instance. We also need to return lists of tokens info for the request with multiple instances.", "items": { -"$ref": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestEntityTypeSpec" +"$ref": "GoogleCloudAiplatformV1beta1TokensInfo" }, "type": "array" +} }, -"passThroughFields": { -"description": "When not empty, the specified fields in the *_read_instances source will be joined as-is in the output, in addition to those fields from the Featurestore Entity. For BigQuery source, the type of the pass-through values will be automatically inferred. For CSV source, the pass-through values will be passed as opaque bytes.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestPassThroughField" -}, -"type": "array" +"type": "object" }, -"startTime": { -"description": "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.", -"format": "google-datetime", +"GoogleCloudAiplatformV1beta1ContainerRegistryDestination": { +"description": "The Container Registry location for the container image.", +"id": "GoogleCloudAiplatformV1beta1ContainerRegistryDestination", +"properties": { +"outputUri": { +"description": "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.", "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestEntityTypeSpec": { -"description": "Selects Features of an EntityType to read values of and specifies read settings.", -"id": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestEntityTypeSpec", +"GoogleCloudAiplatformV1beta1ContainerSpec": { +"description": "The spec of a Container.", +"id": "GoogleCloudAiplatformV1beta1ContainerSpec", "properties": { -"entityTypeId": { -"description": "Required. ID of the EntityType to select Features. The EntityType id is the entity_type_id specified during EntityType creation.", +"args": { +"description": "The arguments to be passed when starting the container.", +"items": { "type": "string" }, -"featureSelector": { -"$ref": "GoogleCloudAiplatformV1beta1FeatureSelector", -"description": "Required. Selectors choosing which Feature values to read from the EntityType." +"type": "array" }, -"settings": { -"description": "Per-Feature settings for the batch read.", +"command": { +"description": "The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.", "items": { -"$ref": "GoogleCloudAiplatformV1beta1DestinationFeatureSetting" +"type": "string" }, "type": "array" -} }, -"type": "object" +"env": { +"description": "Environment variables to be passed to the container. Maximum limit is 100.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1EnvVar" }, -"GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestPassThroughField": { -"description": "Describe pass-through fields in read_instance source.", -"id": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestPassThroughField", -"properties": { -"fieldName": { -"description": "Required. The name of the field in the CSV header or the name of the column in BigQuery table. The naming restriction is the same as Feature.name.", +"type": "array" +}, +"imageUri": { +"description": "Required. The URI of a container image in the Container Registry that is to be run on each worker replica.", "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BatchReadFeatureValuesResponse": { -"description": "Response message for FeaturestoreService.BatchReadFeatureValues.", -"id": "GoogleCloudAiplatformV1beta1BatchReadFeatureValuesResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1BatchReadTensorboardTimeSeriesDataResponse": { -"description": "Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.", -"id": "GoogleCloudAiplatformV1beta1BatchReadTensorboardTimeSeriesDataResponse", +"GoogleCloudAiplatformV1beta1Content": { +"description": "The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn.", +"id": "GoogleCloudAiplatformV1beta1Content", "properties": { -"timeSeriesData": { -"description": "The returned time series data.", +"parts": { +"description": "Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.", "items": { -"$ref": "GoogleCloudAiplatformV1beta1TimeSeriesData" +"$ref": "GoogleCloudAiplatformV1beta1Part" }, "type": "array" +}, +"role": { +"description": "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.", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BigQueryDestination": { -"description": "The BigQuery location for the output content.", -"id": "GoogleCloudAiplatformV1beta1BigQueryDestination", +"GoogleCloudAiplatformV1beta1Context": { +"description": "Instance of a general context.", +"id": "GoogleCloudAiplatformV1beta1Context", "properties": { -"outputUri": { -"description": "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`.", +"createTime": { +"description": "Output only. Timestamp when this Context was created.", +"format": "google-datetime", +"readOnly": true, "type": "string" -} }, -"type": "object" +"description": { +"description": "Description of the Context", +"type": "string" }, -"GoogleCloudAiplatformV1beta1BigQuerySource": { -"description": "The BigQuery location for the input content.", -"id": "GoogleCloudAiplatformV1beta1BigQuerySource", -"properties": { -"inputUri": { -"description": "Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.", +"displayName": { +"description": "User provided display name of the Context. May be up to 128 Unicode characters.", "type": "string" -} }, -"type": "object" +"etag": { +"description": "An eTag used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", +"type": "string" }, -"GoogleCloudAiplatformV1beta1BleuInput": { -"description": "Input for bleu metric.", -"id": "GoogleCloudAiplatformV1beta1BleuInput", -"properties": { -"instances": { -"description": "Required. Repeated bleu instances.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1BleuInstance" +"labels": { +"additionalProperties": { +"type": "string" }, -"type": "array" +"description": "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).", +"type": "object" }, -"metricSpec": { -"$ref": "GoogleCloudAiplatformV1beta1BleuSpec", -"description": "Required. Spec for bleu score metric." -} +"metadata": { +"additionalProperties": { +"description": "Properties of the object.", +"type": "any" }, +"description": "Properties of the Context. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.", "type": "object" }, -"GoogleCloudAiplatformV1beta1BleuInstance": { -"description": "Spec for bleu instance.", -"id": "GoogleCloudAiplatformV1beta1BleuInstance", -"properties": { -"prediction": { -"description": "Required. Output of the evaluated model.", +"name": { +"description": "Immutable. The resource name of the Context.", "type": "string" }, -"reference": { -"description": "Required. Ground truth used to compare against the prediction.", +"parentContexts": { +"description": "Output only. A list of resource names of Contexts that are parents of this Context. A Context may have at most 10 parent_contexts.", +"items": { "type": "string" -} }, -"type": "object" +"readOnly": true, +"type": "array" }, -"GoogleCloudAiplatformV1beta1BleuMetricValue": { -"description": "Bleu metric value for an instance.", -"id": "GoogleCloudAiplatformV1beta1BleuMetricValue", -"properties": { -"score": { -"description": "Output only. Bleu score.", -"format": "float", +"schemaTitle": { +"description": "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.", +"type": "string" +}, +"schemaVersion": { +"description": "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.", +"type": "string" +}, +"updateTime": { +"description": "Output only. Timestamp when this Context was last updated.", +"format": "google-datetime", "readOnly": true, -"type": "number" +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BleuResults": { -"description": "Results for bleu metric.", -"id": "GoogleCloudAiplatformV1beta1BleuResults", +"GoogleCloudAiplatformV1beta1CopyModelOperationMetadata": { +"description": "Details of ModelService.CopyModel operation.", +"id": "GoogleCloudAiplatformV1beta1CopyModelOperationMetadata", "properties": { -"bleuMetricValues": { -"description": "Output only. Bleu metric values.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1BleuMetricValue" -}, -"readOnly": true, -"type": "array" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The common part of the operation metadata." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BleuSpec": { -"description": "Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1.", -"id": "GoogleCloudAiplatformV1beta1BleuSpec", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1Blob": { -"description": "Content blob. It's preferred to send as text directly rather than raw bytes.", -"id": "GoogleCloudAiplatformV1beta1Blob", +"GoogleCloudAiplatformV1beta1CopyModelRequest": { +"description": "Request message for ModelService.CopyModel.", +"id": "GoogleCloudAiplatformV1beta1CopyModelRequest", "properties": { -"data": { -"description": "Required. Raw bytes.", -"format": "byte", +"encryptionSpec": { +"$ref": "GoogleCloudAiplatformV1beta1EncryptionSpec", +"description": "Customer-managed encryption key options. If this is set, then the Model copy will be encrypted with the provided encryption key." +}, +"modelId": { +"description": "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.", "type": "string" }, -"mimeType": { -"description": "Required. The IANA standard MIME type of the source data.", +"parentModel": { +"description": "Optional. Specify this field to copy source_model into this existing Model as a new version. Format: `projects/{project}/locations/{location}/models/{model}`", +"type": "string" +}, +"sourceModel": { +"description": "Required. The resource name of the Model to copy. That Model must be in the same Project. Format: `projects/{project}/locations/{location}/models/{model}`", "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BlurBaselineConfig": { -"description": "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", -"id": "GoogleCloudAiplatformV1beta1BlurBaselineConfig", +"GoogleCloudAiplatformV1beta1CopyModelResponse": { +"description": "Response message of ModelService.CopyModel operation.", +"id": "GoogleCloudAiplatformV1beta1CopyModelResponse", "properties": { -"maxBlurSigma": { -"description": "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.", -"format": "float", -"type": "number" +"model": { +"description": "The name of the copied Model resource. Format: `projects/{project}/locations/{location}/models/{model}`", +"type": "string" +}, +"modelVersionId": { +"description": "Output only. The version ID of the model that is copied.", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1BoolArray": { -"description": "A list of boolean values.", -"id": "GoogleCloudAiplatformV1beta1BoolArray", +"GoogleCloudAiplatformV1beta1CountTokensRequest": { +"description": "Request message for PredictionService.CountTokens.", +"id": "GoogleCloudAiplatformV1beta1CountTokensRequest", "properties": { -"values": { -"description": "A list of bool values.", +"contents": { +"description": "Required. Input content.", "items": { -"type": "boolean" +"$ref": "GoogleCloudAiplatformV1beta1Content" }, "type": "array" -} }, -"type": "object" +"instances": { +"description": "Required. The instances that are the input to token counting call. Schema is identical to the prediction schema of the underlying model.", +"items": { +"type": "any" }, -"GoogleCloudAiplatformV1beta1CacheConfig": { -"description": "Config of GenAI caching features. This is a singleton resource.", -"id": "GoogleCloudAiplatformV1beta1CacheConfig", -"properties": { -"disableCache": { -"description": "If set to true, disables GenAI caching. Otherwise caching is enabled.", -"type": "boolean" +"type": "array" }, -"name": { -"description": "Identifier. Name of the cache config. Format: - `projects/{project}/cacheConfig`.", +"model": { +"description": "Required. The name of the publisher model requested to serve the prediction. Format: `projects/{project}/locations/{location}/publishers/*/models/*`", "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1CancelBatchPredictionJobRequest": { -"description": "Request message for JobService.CancelBatchPredictionJob.", -"id": "GoogleCloudAiplatformV1beta1CancelBatchPredictionJobRequest", -"properties": {}, -"type": "object" +"GoogleCloudAiplatformV1beta1CountTokensResponse": { +"description": "Response message for PredictionService.CountTokens.", +"id": "GoogleCloudAiplatformV1beta1CountTokensResponse", +"properties": { +"totalBillableCharacters": { +"description": "The total number of billable characters counted across all instances from the request.", +"format": "int32", +"type": "integer" }, -"GoogleCloudAiplatformV1beta1CancelCustomJobRequest": { -"description": "Request message for JobService.CancelCustomJob.", -"id": "GoogleCloudAiplatformV1beta1CancelCustomJobRequest", -"properties": {}, -"type": "object" +"totalTokens": { +"description": "The total number of tokens counted across all instances from the request.", +"format": "int32", +"type": "integer" +} }, -"GoogleCloudAiplatformV1beta1CancelDataLabelingJobRequest": { -"description": "Request message for JobService.CancelDataLabelingJob.", -"id": "GoogleCloudAiplatformV1beta1CancelDataLabelingJobRequest", -"properties": {}, "type": "object" }, -"GoogleCloudAiplatformV1beta1CancelHyperparameterTuningJobRequest": { -"description": "Request message for JobService.CancelHyperparameterTuningJob.", -"id": "GoogleCloudAiplatformV1beta1CancelHyperparameterTuningJobRequest", -"properties": {}, -"type": "object" +"GoogleCloudAiplatformV1beta1CreateDatasetOperationMetadata": { +"description": "Runtime operation information for DatasetService.CreateDataset.", +"id": "GoogleCloudAiplatformV1beta1CreateDatasetOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." +} }, -"GoogleCloudAiplatformV1beta1CancelNasJobRequest": { -"description": "Request message for JobService.CancelNasJob.", -"id": "GoogleCloudAiplatformV1beta1CancelNasJobRequest", -"properties": {}, "type": "object" }, -"GoogleCloudAiplatformV1beta1CancelPipelineJobRequest": { -"description": "Request message for PipelineService.CancelPipelineJob.", -"id": "GoogleCloudAiplatformV1beta1CancelPipelineJobRequest", -"properties": {}, -"type": "object" +"GoogleCloudAiplatformV1beta1CreateDatasetVersionOperationMetadata": { +"description": "Runtime operation information for DatasetService.CreateDatasetVersion.", +"id": "GoogleCloudAiplatformV1beta1CreateDatasetVersionOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The common part of the operation metadata." +} }, -"GoogleCloudAiplatformV1beta1CancelTrainingPipelineRequest": { -"description": "Request message for PipelineService.CancelTrainingPipeline.", -"id": "GoogleCloudAiplatformV1beta1CancelTrainingPipelineRequest", -"properties": {}, "type": "object" }, -"GoogleCloudAiplatformV1beta1Candidate": { -"description": "A response candidate generated from the model.", -"id": "GoogleCloudAiplatformV1beta1Candidate", +"GoogleCloudAiplatformV1beta1CreateDeploymentResourcePoolOperationMetadata": { +"description": "Runtime operation information for CreateDeploymentResourcePool method.", +"id": "GoogleCloudAiplatformV1beta1CreateDeploymentResourcePoolOperationMetadata", "properties": { -"citationMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1CitationMetadata", -"description": "Output only. Source attribution of the generated content.", -"readOnly": true +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." +} }, -"content": { -"$ref": "GoogleCloudAiplatformV1beta1Content", -"description": "Output only. Content parts of the candidate.", -"readOnly": true +"type": "object" }, -"finishMessage": { -"description": "Output only. Describes the reason the mode stopped generating tokens in more detail. This is only filled when `finish_reason` is set.", -"readOnly": true, -"type": "string" +"GoogleCloudAiplatformV1beta1CreateDeploymentResourcePoolRequest": { +"description": "Request message for CreateDeploymentResourcePool method.", +"id": "GoogleCloudAiplatformV1beta1CreateDeploymentResourcePoolRequest", +"properties": { +"deploymentResourcePool": { +"$ref": "GoogleCloudAiplatformV1beta1DeploymentResourcePool", +"description": "Required. The DeploymentResourcePool to create." }, -"finishReason": { -"description": "Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.", -"enum": [ -"FINISH_REASON_UNSPECIFIED", -"STOP", -"MAX_TOKENS", -"SAFETY", -"RECITATION", -"OTHER", -"BLOCKLIST", -"PROHIBITED_CONTENT", -"SPII" -], -"enumDescriptions": [ -"The finish reason is unspecified.", -"Natural stop point of the model or provided stop sequence.", -"The maximum number of tokens as specified in the request was reached.", -"The token generation was stopped as the response was flagged for safety reasons. NOTE: When streaming the Candidate.content will be empty if content filters blocked the output.", -"The token generation was stopped as the response was flagged for unauthorized citations.", -"All other reasons that stopped the token generation", -"The token generation was stopped as the response was flagged for the terms which are included from the terminology blocklist.", -"The token generation was stopped as the response was flagged for the prohibited contents.", -"The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents." -], -"readOnly": true, +"deploymentResourcePoolId": { +"description": "Required. The ID to use for the DeploymentResourcePool, which will become the final component of the DeploymentResourcePool's resource name. The maximum length is 63 characters, and valid characters are `/^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/`.", "type": "string" +} }, -"groundingMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GroundingMetadata", -"description": "Output only. Metadata specifies sources used to ground generated content.", -"readOnly": true -}, -"index": { -"description": "Output only. Index of the candidate.", -"format": "int32", -"readOnly": true, -"type": "integer" -}, -"safetyRatings": { -"description": "Output only. List of ratings for the safety of a response candidate. There is at most one rating per category.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1SafetyRating" +"type": "object" }, -"readOnly": true, -"type": "array" +"GoogleCloudAiplatformV1beta1CreateEndpointOperationMetadata": { +"description": "Runtime operation information for EndpointService.CreateEndpoint.", +"id": "GoogleCloudAiplatformV1beta1CreateEndpointOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateMetatdata": { -"description": "This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.", -"id": "GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateMetatdata", +"GoogleCloudAiplatformV1beta1CreateEntityTypeOperationMetadata": { +"description": "Details of operations that perform create EntityType.", +"id": "GoogleCloudAiplatformV1beta1CreateEntityTypeOperationMetadata", "properties": { "genericMetadata": { "$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for suggesting Trials." +"description": "Operation metadata for EntityType." +} }, -"study": { -"description": "The name of the Study that the Trial belongs to.", -"type": "string" +"type": "object" }, -"trial": { -"description": "The Trial name.", -"type": "string" +"GoogleCloudAiplatformV1beta1CreateExtensionControllerOperationMetadata": { +"description": "Details of ExtensionControllerService.CreateExtensionController operation.", +"id": "GoogleCloudAiplatformV1beta1CreateExtensionControllerOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The common part of the operation metadata." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateRequest": { -"description": "Request message for VizierService.CheckTrialEarlyStoppingState.", -"id": "GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateRequest", -"properties": {}, +"GoogleCloudAiplatformV1beta1CreateFeatureGroupOperationMetadata": { +"description": "Details of operations that perform create FeatureGroup.", +"id": "GoogleCloudAiplatformV1beta1CreateFeatureGroupOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for FeatureGroup." +} +}, "type": "object" }, -"GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateResponse": { -"description": "Response message for VizierService.CheckTrialEarlyStoppingState.", -"id": "GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateResponse", +"GoogleCloudAiplatformV1beta1CreateFeatureOnlineStoreOperationMetadata": { +"description": "Details of operations that perform create FeatureOnlineStore.", +"id": "GoogleCloudAiplatformV1beta1CreateFeatureOnlineStoreOperationMetadata", "properties": { -"shouldStop": { -"description": "True if the Trial should stop.", -"type": "boolean" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for FeatureOnlineStore." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1Citation": { -"description": "Source attributions for content.", -"id": "GoogleCloudAiplatformV1beta1Citation", +"GoogleCloudAiplatformV1beta1CreateFeatureOperationMetadata": { +"description": "Details of operations that perform create Feature.", +"id": "GoogleCloudAiplatformV1beta1CreateFeatureOperationMetadata", "properties": { -"endIndex": { -"description": "Output only. End index into the content.", -"format": "int32", -"readOnly": true, -"type": "integer" -}, -"license": { -"description": "Output only. License of the attribution.", -"readOnly": true, -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for Feature." +} }, -"publicationDate": { -"$ref": "GoogleTypeDate", -"description": "Output only. Publication date of the attribution.", -"readOnly": true +"type": "object" }, -"startIndex": { -"description": "Output only. Start index into the content.", -"format": "int32", -"readOnly": true, -"type": "integer" +"GoogleCloudAiplatformV1beta1CreateFeatureRequest": { +"description": "Request message for FeaturestoreService.CreateFeature. Request message for FeatureRegistryService.CreateFeature.", +"id": "GoogleCloudAiplatformV1beta1CreateFeatureRequest", +"properties": { +"feature": { +"$ref": "GoogleCloudAiplatformV1beta1Feature", +"description": "Required. The Feature to create." }, -"title": { -"description": "Output only. Title of the attribution.", -"readOnly": true, +"featureId": { +"description": "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.", "type": "string" }, -"uri": { -"description": "Output only. Url reference of the attribution.", -"readOnly": true, +"parent": { +"description": "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_type}` Format for feature_group as parent: `projects/{project}/locations/{location}/featureGroups/{feature_group}`", "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1CitationMetadata": { -"description": "A collection of source attributions for a piece of content.", -"id": "GoogleCloudAiplatformV1beta1CitationMetadata", +"GoogleCloudAiplatformV1beta1CreateFeatureViewOperationMetadata": { +"description": "Details of operations that perform create FeatureView.", +"id": "GoogleCloudAiplatformV1beta1CreateFeatureViewOperationMetadata", "properties": { -"citations": { -"description": "Output only. List of citations.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1Citation" -}, -"readOnly": true, -"type": "array" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for FeatureView Create." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1CoherenceInput": { -"description": "Input for coherence metric.", -"id": "GoogleCloudAiplatformV1beta1CoherenceInput", +"GoogleCloudAiplatformV1beta1CreateFeaturestoreOperationMetadata": { +"description": "Details of operations that perform create Featurestore.", +"id": "GoogleCloudAiplatformV1beta1CreateFeaturestoreOperationMetadata", "properties": { -"instance": { -"$ref": "GoogleCloudAiplatformV1beta1CoherenceInstance", -"description": "Required. Coherence instance." -}, -"metricSpec": { -"$ref": "GoogleCloudAiplatformV1beta1CoherenceSpec", -"description": "Required. Spec for coherence score metric." +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for Featurestore." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1CoherenceInstance": { -"description": "Spec for coherence instance.", -"id": "GoogleCloudAiplatformV1beta1CoherenceInstance", +"GoogleCloudAiplatformV1beta1CreateIndexEndpointOperationMetadata": { +"description": "Runtime operation information for IndexEndpointService.CreateIndexEndpoint.", +"id": "GoogleCloudAiplatformV1beta1CreateIndexEndpointOperationMetadata", "properties": { -"prediction": { -"description": "Required. Output of the evaluated model.", -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1CoherenceResult": { -"description": "Spec for coherence result.", -"id": "GoogleCloudAiplatformV1beta1CoherenceResult", +"GoogleCloudAiplatformV1beta1CreateIndexOperationMetadata": { +"description": "Runtime operation information for IndexService.CreateIndex.", +"id": "GoogleCloudAiplatformV1beta1CreateIndexOperationMetadata", "properties": { -"confidence": { -"description": "Output only. Confidence for coherence score.", -"format": "float", -"readOnly": true, -"type": "number" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." }, -"explanation": { -"description": "Output only. Explanation for coherence score.", -"readOnly": true, -"type": "string" +"nearestNeighborSearchOperationMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadata", +"description": "The operation metadata with regard to Matching Engine Index operation." +} }, -"score": { -"description": "Output only. Coherence score.", -"format": "float", -"readOnly": true, -"type": "number" +"type": "object" +}, +"GoogleCloudAiplatformV1beta1CreateMetadataStoreOperationMetadata": { +"description": "Details of operations that perform MetadataService.CreateMetadataStore.", +"id": "GoogleCloudAiplatformV1beta1CreateMetadataStoreOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for creating a MetadataStore." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1CoherenceSpec": { -"description": "Spec for coherence score metric.", -"id": "GoogleCloudAiplatformV1beta1CoherenceSpec", +"GoogleCloudAiplatformV1beta1CreateModelMonitorOperationMetadata": { +"description": "Runtime operation information for ModelMonitoringService.CreateModelMonitor.", +"id": "GoogleCloudAiplatformV1beta1CreateModelMonitorOperationMetadata", "properties": { -"version": { -"description": "Optional. Which version to use for evaluation.", -"format": "int32", -"type": "integer" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1CompleteTrialRequest": { -"description": "Request message for VizierService.CompleteTrial.", -"id": "GoogleCloudAiplatformV1beta1CompleteTrialRequest", +"GoogleCloudAiplatformV1beta1CreateModelMonitoringJobRequest": { +"description": "Request message for ModelMonitoringService.CreateModelMonitoringJob.", +"id": "GoogleCloudAiplatformV1beta1CreateModelMonitoringJobRequest", "properties": { -"finalMeasurement": { -"$ref": "GoogleCloudAiplatformV1beta1Measurement", -"description": "Optional. If provided, it will be used as the completed Trial's final_measurement; Otherwise, the service will auto-select a previously reported measurement as the final-measurement" +"modelMonitoringJob": { +"$ref": "GoogleCloudAiplatformV1beta1ModelMonitoringJob", +"description": "Required. The ModelMonitoringJob to create" }, -"infeasibleReason": { -"description": "Optional. A human readable reason why the trial was infeasible. This should only be provided if `trial_infeasible` is true.", +"modelMonitoringJobId": { +"description": "Optional. The ID to use for the Model Monitoring Job, which will become the final component of the model monitoring job resource name. The maximum length is 63 characters, and valid characters are `/^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/`.", "type": "string" }, -"trialInfeasible": { -"description": "Optional. True if the Trial cannot be run with the given Parameter, and final_measurement will be ignored.", -"type": "boolean" +"parent": { +"description": "Required. The parent of the ModelMonitoringJob. Format: `projects/{project}/locations/{location}/modelMoniitors/{model_monitor}`", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1CompletionStats": { -"description": "Success and error statistics of processing multiple entities (for example, DataItems or structured data rows) in batch.", -"id": "GoogleCloudAiplatformV1beta1CompletionStats", +"GoogleCloudAiplatformV1beta1CreateNotebookExecutionJobRequest": { +"description": "Request message for [NotebookService.CreateNotebookExecutionJob]", +"id": "GoogleCloudAiplatformV1beta1CreateNotebookExecutionJobRequest", "properties": { -"failedCount": { -"description": "Output only. The number of entities for which any error was encountered.", -"format": "int64", -"readOnly": true, -"type": "string" -}, -"incompleteCount": { -"description": "Output only. In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).", -"format": "int64", -"readOnly": true, -"type": "string" +"notebookExecutionJob": { +"$ref": "GoogleCloudAiplatformV1beta1NotebookExecutionJob", +"description": "Required. The NotebookExecutionJob to create." }, -"successfulCount": { -"description": "Output only. The number of entities that had been processed successfully.", -"format": "int64", -"readOnly": true, +"notebookExecutionJobId": { +"description": "Optional. User specified ID for the NotebookExecutionJob.", "type": "string" }, -"successfulForecastPointCount": { -"description": "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.", -"format": "int64", -"readOnly": true, +"parent": { +"description": "Required. The resource name of the Location to create the NotebookExecutionJob. Format: `projects/{project}/locations/{location}`", "type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1ComputeTokensRequest": { -"description": "Request message for ComputeTokens RPC call.", -"id": "GoogleCloudAiplatformV1beta1ComputeTokensRequest", +"GoogleCloudAiplatformV1beta1CreateNotebookRuntimeTemplateOperationMetadata": { +"description": "Metadata information for NotebookService.CreateNotebookRuntimeTemplate.", +"id": "GoogleCloudAiplatformV1beta1CreateNotebookRuntimeTemplateOperationMetadata", "properties": { -"instances": { -"description": "Required. 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.", -"items": { -"type": "any" -}, -"type": "array" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1ComputeTokensResponse": { -"description": "Response message for ComputeTokens RPC call.", -"id": "GoogleCloudAiplatformV1beta1ComputeTokensResponse", +"GoogleCloudAiplatformV1beta1CreatePersistentResourceOperationMetadata": { +"description": "Details of operations that perform create PersistentResource.", +"id": "GoogleCloudAiplatformV1beta1CreatePersistentResourceOperationMetadata", "properties": { -"tokensInfo": { -"description": "Lists of tokens info from the input. A ComputeTokensRequest could have multiple instances with a prompt in each instance. We also need to return lists of tokens info for the request with multiple instances.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1TokensInfo" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for PersistentResource." }, -"type": "array" +"progressMessage": { +"description": "Progress Message for Create LRO", +"type": "string" } }, "type": "object" }, -"GoogleCloudAiplatformV1beta1ContainerRegistryDestination": { -"description": "The Container Registry location for the container image.", -"id": "GoogleCloudAiplatformV1beta1ContainerRegistryDestination", +"GoogleCloudAiplatformV1beta1CreatePipelineJobRequest": { +"description": "Request message for PipelineService.CreatePipelineJob.", +"id": "GoogleCloudAiplatformV1beta1CreatePipelineJobRequest", "properties": { -"outputUri": { -"description": "Required. Container Registry URI of a container image. Only Google Container Registry and Artifact Registry are supported now. Accepted forms: * Google Container Registry path. For example: `gcr.io/projectId/imageName:tag`. * Artifact Registry path. For example: `us-central1-docker.pkg.dev/projectId/repoName/imageName:tag`. If a tag is not specified, \"latest\" will be used as the default tag.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1ContainerSpec": { -"description": "The spec of a Container.", -"id": "GoogleCloudAiplatformV1beta1ContainerSpec", -"properties": { -"args": { -"description": "The arguments to be passed when starting the container.", -"items": { -"type": "string" -}, -"type": "array" -}, -"command": { -"description": "The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.", -"items": { -"type": "string" -}, -"type": "array" -}, -"env": { -"description": "Environment variables to be passed to the container. Maximum limit is 100.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1EnvVar" -}, -"type": "array" -}, -"imageUri": { -"description": "Required. The URI of a container image in the Container Registry that is to be run on each worker replica.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1Content": { -"description": "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.", -"id": "GoogleCloudAiplatformV1beta1Content", -"properties": { -"parts": { -"description": "Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1Part" -}, -"type": "array" -}, -"role": { -"description": "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.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1Context": { -"description": "Instance of a general context.", -"id": "GoogleCloudAiplatformV1beta1Context", -"properties": { -"createTime": { -"description": "Output only. Timestamp when this Context was created.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -}, -"description": { -"description": "Description of the Context", -"type": "string" -}, -"displayName": { -"description": "User provided display name of the Context. May be up to 128 Unicode characters.", -"type": "string" -}, -"etag": { -"description": "An eTag used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.", -"type": "string" -}, -"labels": { -"additionalProperties": { -"type": "string" -}, -"description": "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).", -"type": "object" -}, -"metadata": { -"additionalProperties": { -"description": "Properties of the object.", -"type": "any" -}, -"description": "Properties of the Context. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.", -"type": "object" -}, -"name": { -"description": "Immutable. The resource name of the Context.", -"type": "string" -}, -"parentContexts": { -"description": "Output only. A list of resource names of Contexts that are parents of this Context. A Context may have at most 10 parent_contexts.", -"items": { -"type": "string" -}, -"readOnly": true, -"type": "array" -}, -"schemaTitle": { -"description": "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.", -"type": "string" -}, -"schemaVersion": { -"description": "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.", -"type": "string" -}, -"updateTime": { -"description": "Output only. Timestamp when this Context was last updated.", -"format": "google-datetime", -"readOnly": true, -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CopyModelOperationMetadata": { -"description": "Details of ModelService.CopyModel operation.", -"id": "GoogleCloudAiplatformV1beta1CopyModelOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The common part of the operation metadata." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CopyModelRequest": { -"description": "Request message for ModelService.CopyModel.", -"id": "GoogleCloudAiplatformV1beta1CopyModelRequest", -"properties": { -"encryptionSpec": { -"$ref": "GoogleCloudAiplatformV1beta1EncryptionSpec", -"description": "Customer-managed encryption key options. If this is set, then the Model copy will be encrypted with the provided encryption key." -}, -"modelId": { -"description": "Optional. Copy source_model into a new Model with this ID. The ID will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are `[a-z0-9_-]`. The first character cannot be a number or hyphen.", -"type": "string" -}, -"parentModel": { -"description": "Optional. Specify this field to copy source_model into this existing Model as a new version. Format: `projects/{project}/locations/{location}/models/{model}`", -"type": "string" -}, -"sourceModel": { -"description": "Required. The resource name of the Model to copy. That Model must be in the same Project. Format: `projects/{project}/locations/{location}/models/{model}`", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CopyModelResponse": { -"description": "Response message of ModelService.CopyModel operation.", -"id": "GoogleCloudAiplatformV1beta1CopyModelResponse", -"properties": { -"model": { -"description": "The name of the copied Model resource. Format: `projects/{project}/locations/{location}/models/{model}`", -"type": "string" -}, -"modelVersionId": { -"description": "Output only. The version ID of the model that is copied.", -"readOnly": true, -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CountTokensRequest": { -"description": "Request message for PredictionService.CountTokens.", -"id": "GoogleCloudAiplatformV1beta1CountTokensRequest", -"properties": { -"contents": { -"description": "Required. Input content.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1Content" -}, -"type": "array" -}, -"instances": { -"description": "Required. The instances that are the input to token counting call. Schema is identical to the prediction schema of the underlying model.", -"items": { -"type": "any" -}, -"type": "array" -}, -"model": { -"description": "Required. The name of the publisher model requested to serve the prediction. Format: `projects/{project}/locations/{location}/publishers/*/models/*`", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CountTokensResponse": { -"description": "Response message for PredictionService.CountTokens.", -"id": "GoogleCloudAiplatformV1beta1CountTokensResponse", -"properties": { -"totalBillableCharacters": { -"description": "The total number of billable characters counted across all instances from the request.", -"format": "int32", -"type": "integer" -}, -"totalTokens": { -"description": "The total number of tokens counted across all instances from the request.", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateDatasetOperationMetadata": { -"description": "Runtime operation information for DatasetService.CreateDataset.", -"id": "GoogleCloudAiplatformV1beta1CreateDatasetOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateDatasetVersionOperationMetadata": { -"description": "Runtime operation information for DatasetService.CreateDatasetVersion.", -"id": "GoogleCloudAiplatformV1beta1CreateDatasetVersionOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The common part of the operation metadata." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateDeploymentResourcePoolOperationMetadata": { -"description": "Runtime operation information for CreateDeploymentResourcePool method.", -"id": "GoogleCloudAiplatformV1beta1CreateDeploymentResourcePoolOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateDeploymentResourcePoolRequest": { -"description": "Request message for CreateDeploymentResourcePool method.", -"id": "GoogleCloudAiplatformV1beta1CreateDeploymentResourcePoolRequest", -"properties": { -"deploymentResourcePool": { -"$ref": "GoogleCloudAiplatformV1beta1DeploymentResourcePool", -"description": "Required. The DeploymentResourcePool to create." -}, -"deploymentResourcePoolId": { -"description": "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])?$/`.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateEndpointOperationMetadata": { -"description": "Runtime operation information for EndpointService.CreateEndpoint.", -"id": "GoogleCloudAiplatformV1beta1CreateEndpointOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateEntityTypeOperationMetadata": { -"description": "Details of operations that perform create EntityType.", -"id": "GoogleCloudAiplatformV1beta1CreateEntityTypeOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for EntityType." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateExtensionControllerOperationMetadata": { -"description": "Details of ExtensionControllerService.CreateExtensionController operation.", -"id": "GoogleCloudAiplatformV1beta1CreateExtensionControllerOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The common part of the operation metadata." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateFeatureGroupOperationMetadata": { -"description": "Details of operations that perform create FeatureGroup.", -"id": "GoogleCloudAiplatformV1beta1CreateFeatureGroupOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for FeatureGroup." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateFeatureOnlineStoreOperationMetadata": { -"description": "Details of operations that perform create FeatureOnlineStore.", -"id": "GoogleCloudAiplatformV1beta1CreateFeatureOnlineStoreOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for FeatureOnlineStore." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateFeatureOperationMetadata": { -"description": "Details of operations that perform create Feature.", -"id": "GoogleCloudAiplatformV1beta1CreateFeatureOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for Feature." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateFeatureRequest": { -"description": "Request message for FeaturestoreService.CreateFeature. Request message for FeatureRegistryService.CreateFeature.", -"id": "GoogleCloudAiplatformV1beta1CreateFeatureRequest", -"properties": { -"feature": { -"$ref": "GoogleCloudAiplatformV1beta1Feature", -"description": "Required. The Feature to create." -}, -"featureId": { -"description": "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.", -"type": "string" -}, -"parent": { -"description": "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_type}` Format for feature_group as parent: `projects/{project}/locations/{location}/featureGroups/{feature_group}`", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateFeatureViewOperationMetadata": { -"description": "Details of operations that perform create FeatureView.", -"id": "GoogleCloudAiplatformV1beta1CreateFeatureViewOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for FeatureView Create." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateFeaturestoreOperationMetadata": { -"description": "Details of operations that perform create Featurestore.", -"id": "GoogleCloudAiplatformV1beta1CreateFeaturestoreOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for Featurestore." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateIndexEndpointOperationMetadata": { -"description": "Runtime operation information for IndexEndpointService.CreateIndexEndpoint.", -"id": "GoogleCloudAiplatformV1beta1CreateIndexEndpointOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateIndexOperationMetadata": { -"description": "Runtime operation information for IndexService.CreateIndex.", -"id": "GoogleCloudAiplatformV1beta1CreateIndexOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -}, -"nearestNeighborSearchOperationMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadata", -"description": "The operation metadata with regard to Matching Engine Index operation." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateMetadataStoreOperationMetadata": { -"description": "Details of operations that perform MetadataService.CreateMetadataStore.", -"id": "GoogleCloudAiplatformV1beta1CreateMetadataStoreOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for creating a MetadataStore." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateModelMonitorOperationMetadata": { -"description": "Runtime operation information for ModelMonitoringService.CreateModelMonitor.", -"id": "GoogleCloudAiplatformV1beta1CreateModelMonitorOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateModelMonitoringJobRequest": { -"description": "Request message for ModelMonitoringService.CreateModelMonitoringJob.", -"id": "GoogleCloudAiplatformV1beta1CreateModelMonitoringJobRequest", -"properties": { -"modelMonitoringJob": { -"$ref": "GoogleCloudAiplatformV1beta1ModelMonitoringJob", -"description": "Required. The ModelMonitoringJob to create" -}, -"modelMonitoringJobId": { -"description": "Optional. The ID to use for the Model Monitoring Job, which will become the final component of the model monitoring job resource name. The maximum length is 63 characters, and valid characters are `/^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/`.", -"type": "string" -}, -"parent": { -"description": "Required. The parent of the ModelMonitoringJob. Format: `projects/{project}/locations/{location}/modelMoniitors/{model_monitor}`", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateNotebookExecutionJobRequest": { -"description": "Request message for [NotebookService.CreateNotebookExecutionJob]", -"id": "GoogleCloudAiplatformV1beta1CreateNotebookExecutionJobRequest", -"properties": { -"notebookExecutionJob": { -"$ref": "GoogleCloudAiplatformV1beta1NotebookExecutionJob", -"description": "Required. The NotebookExecutionJob to create." -}, -"notebookExecutionJobId": { -"description": "Optional. User specified ID for the NotebookExecutionJob.", -"type": "string" -}, -"parent": { -"description": "Required. The resource name of the Location to create the NotebookExecutionJob. Format: `projects/{project}/locations/{location}`", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreateNotebookRuntimeTemplateOperationMetadata": { -"description": "Metadata information for NotebookService.CreateNotebookRuntimeTemplate.", -"id": "GoogleCloudAiplatformV1beta1CreateNotebookRuntimeTemplateOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreatePersistentResourceOperationMetadata": { -"description": "Details of operations that perform create PersistentResource.", -"id": "GoogleCloudAiplatformV1beta1CreatePersistentResourceOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for PersistentResource." -}, -"progressMessage": { -"description": "Progress Message for Create LRO", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1CreatePipelineJobRequest": { -"description": "Request message for PipelineService.CreatePipelineJob.", -"id": "GoogleCloudAiplatformV1beta1CreatePipelineJobRequest", -"properties": { -"parent": { -"description": "Required. The resource name of the Location to create the PipelineJob in. Format: `projects/{project}/locations/{location}`", -"type": "string" -}, -"pipelineJob": { -"$ref": "GoogleCloudAiplatformV1beta1PipelineJob", -"description": "Required. The PipelineJob to create." -}, -"pipelineJobId": { -"description": "The ID to use for the PipelineJob, which will become the final component of the PipelineJob name. If not provided, an ID will be automatically generated. This value should be less than 128 characters, and valid characters are `/a-z-/`.", +"parent": { +"description": "Required. The resource name of the Location to create the PipelineJob in. Format: `projects/{project}/locations/{location}`", +"type": "string" +}, +"pipelineJob": { +"$ref": "GoogleCloudAiplatformV1beta1PipelineJob", +"description": "Required. The PipelineJob to create." +}, +"pipelineJobId": { +"description": "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-/`.", "type": "string" } }, @@ -23296,6 +22638,10 @@ "description": "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/.", "type": "string" }, +"modelReference": { +"description": "Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.", +"type": "string" +}, "name": { "description": "Output only. The resource name of the Dataset.", "readOnly": true, @@ -23345,6 +22691,11 @@ "readOnly": true, "type": "any" }, +"modelReference": { +"description": "Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions.", +"readOnly": true, +"type": "string" +}, "name": { "description": "Output only. The resource name of the DatasetVersion.", "readOnly": true, @@ -25722,7 +25073,7 @@ "properties": { "bigQuery": { "$ref": "GoogleCloudAiplatformV1beta1FeatureGroupBigQuery", -"description": "Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entity_id and a feature_timestamp column in the source." +"description": "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": { "description": "Output only. Timestamp when this FeatureGroup was created.", @@ -27379,6 +26730,22 @@ "description": "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.", "type": "string" }, +"responseStyle": { +"description": "Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED", +"enum": [ +"RESPONSE_STYLE_UNSPECIFIED", +"RESPONSE_STYLE_PRECISE", +"RESPONSE_STYLE_BALANCED", +"RESPONSE_STYLE_CREATIVE" +], +"enumDescriptions": [ +"response style unspecified.", +"Precise response.", +"Default response style.", +"Creative response style." +], +"type": "string" +}, "stopSequences": { "description": "Optional. Stop sequences.", "items": { @@ -28167,7 +27534,7 @@ "id": "GoogleCloudAiplatformV1beta1IndexDatapointSparseEmbedding", "properties": { "dimensions": { -"description": "Optional. The list of indexes for the embedding values of the sparse vector.", +"description": "Required. The list of indexes for the embedding values of the sparse vector.", "items": { "format": "int64", "type": "string" @@ -28175,7 +27542,7 @@ "type": "array" }, "values": { -"description": "Optional. The list of embedding values of the sparse vector.", +"description": "Required. The list of embedding values of the sparse vector.", "items": { "format": "float", "type": "number" @@ -29463,6 +28830,24 @@ }, "type": "object" }, +"GoogleCloudAiplatformV1beta1ListTuningJobsResponse": { +"description": "Response message for GenAiTuningService.ListTuningJobs", +"id": "GoogleCloudAiplatformV1beta1ListTuningJobsResponse", +"properties": { +"nextPageToken": { +"description": "A token to retrieve the next page of results. Pass to ListTuningJobsRequest.page_token to obtain that page.", +"type": "string" +}, +"tuningJobs": { +"description": "List of TuningJobs in the requested page.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1TuningJob" +}, +"type": "array" +} +}, +"type": "object" +}, "GoogleCloudAiplatformV1beta1LookupStudyRequest": { "description": "Request message for VizierService.LookupStudy.", "id": "GoogleCloudAiplatformV1beta1LookupStudyRequest", @@ -32331,6 +31716,11 @@ "format": "int64", "type": "string" }, +"invalidSparseRecordCount": { +"description": "Number of sparse records in this file we skipped due to validate errors.", +"format": "int64", +"type": "string" +}, "partialErrors": { "description": "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.", "items": { @@ -32346,6 +31736,11 @@ "description": "Number of records in this file that were successfully processed.", "format": "int64", "type": "string" +}, +"validSparseRecordCount": { +"description": "Number of sparse records in this file that were successfully processed.", +"format": "int64", +"type": "string" } }, "type": "object" @@ -32557,10 +31952,10 @@ }, "gcsNotebookSource": { "$ref": "GoogleCloudAiplatformV1beta1NotebookExecutionJobGcsNotebookSource", -"description": "The GCS url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb`" +"description": "The Cloud Storage url pointing to the ipynb file. Format: `gs://bucket/notebook_file.ipynb`" }, "gcsOutputUri": { -"description": "The GCS location to upload the result to. Format: `gs://bucket-name`", +"description": "The Cloud Storage location to upload the result to. Format: `gs://bucket-name`", "type": "string" }, "jobState": { @@ -32596,6 +31991,13 @@ "readOnly": true, "type": "string" }, +"labels": { +"additionalProperties": { +"type": "string" +}, +"description": "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.", +"type": "object" +}, "name": { "description": "Output only. The resource name of this NotebookExecutionJob. Format: `projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}`", "readOnly": true, @@ -32861,6 +32263,16 @@ "description": "Required. The user email of the NotebookRuntime.", "type": "string" }, +"satisfiesPzi": { +"description": "Output only. Reserved for future use.", +"readOnly": true, +"type": "boolean" +}, +"satisfiesPzs": { +"description": "Output only. Reserved for future use.", +"readOnly": true, +"type": "boolean" +}, "serviceAccount": { "description": "Output only. The service account that the NotebookRuntime workload runs as.", "readOnly": true, @@ -41694,6 +41106,196 @@ }, "type": "object" }, +"GoogleCloudAiplatformV1beta1SupervisedHyperParameters": { +"description": "Hyperparameters for SFT.", +"id": "GoogleCloudAiplatformV1beta1SupervisedHyperParameters", +"properties": { +"adapterSize": { +"description": "Optional. Adapter size for tuning.", +"enum": [ +"ADAPTER_SIZE_UNSPECIFIED", +"ADAPTER_SIZE_ONE", +"ADAPTER_SIZE_FOUR", +"ADAPTER_SIZE_EIGHT", +"ADAPTER_SIZE_SIXTEEN" +], +"enumDescriptions": [ +"Adapter size is unspecified.", +"Adapter size 1.", +"Adapter size 4.", +"Adapter size 8.", +"Adapter size 16." +], +"type": "string" +}, +"epochCount": { +"description": "Optional. Number of complete passes the model makes over the entire training dataset during training.", +"format": "int64", +"type": "string" +}, +"learningRateMultiplier": { +"description": "Optional. Multiplier for adjusting the default learning rate.", +"format": "double", +"type": "number" +} +}, +"type": "object" +}, +"GoogleCloudAiplatformV1beta1SupervisedTuningDataStats": { +"description": "Tuning data statistics for Supervised Tuning.", +"id": "GoogleCloudAiplatformV1beta1SupervisedTuningDataStats", +"properties": { +"totalBillableCharacterCount": { +"description": "Output only. Number of billable characters in the tuning dataset.", +"format": "int64", +"readOnly": true, +"type": "string" +}, +"totalTuningCharacterCount": { +"description": "Output only. Number of tuning characters in the tuning dataset.", +"format": "int64", +"readOnly": true, +"type": "string" +}, +"tuningDatasetExampleCount": { +"description": "Output only. Number of examples in the tuning dataset.", +"format": "int64", +"readOnly": true, +"type": "string" +}, +"tuningStepCount": { +"description": "Output only. Number of tuning steps for this Tuning Job.", +"format": "int64", +"readOnly": true, +"type": "string" +}, +"userDatasetExamples": { +"description": "Output only. Sample user messages in the training dataset uri.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1Content" +}, +"readOnly": true, +"type": "array" +}, +"userInputTokenDistribution": { +"$ref": "GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistribution", +"description": "Output only. Dataset distributions for the user input tokens.", +"readOnly": true +}, +"userMessagePerExampleDistribution": { +"$ref": "GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistribution", +"description": "Output only. Dataset distributions for the messages per example.", +"readOnly": true +}, +"userOutputTokenDistribution": { +"$ref": "GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistribution", +"description": "Output only. Dataset distributions for the user output tokens.", +"readOnly": true +} +}, +"type": "object" +}, +"GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistribution": { +"description": "Dataset distribution for Supervised Tuning.", +"id": "GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistribution", +"properties": { +"buckets": { +"description": "Output only. Defines the histogram bucket.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistributionDatasetBucket" +}, +"readOnly": true, +"type": "array" +}, +"max": { +"description": "Output only. The maximum of the population values.", +"format": "double", +"readOnly": true, +"type": "number" +}, +"mean": { +"description": "Output only. The arithmetic mean of the values in the population.", +"format": "double", +"readOnly": true, +"type": "number" +}, +"median": { +"description": "Output only. The median of the values in the population.", +"format": "double", +"readOnly": true, +"type": "number" +}, +"min": { +"description": "Output only. The minimum of the population values.", +"format": "double", +"readOnly": true, +"type": "number" +}, +"p5": { +"description": "Output only. The 5th percentile of the values in the population.", +"format": "double", +"readOnly": true, +"type": "number" +}, +"p95": { +"description": "Output only. The 95th percentile of the values in the population.", +"format": "double", +"readOnly": true, +"type": "number" +}, +"sum": { +"description": "Output only. Sum of a given population of values.", +"format": "int64", +"readOnly": true, +"type": "string" +} +}, +"type": "object" +}, +"GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistributionDatasetBucket": { +"description": "Dataset bucket used to create a histogram for the distribution given a population of values.", +"id": "GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistributionDatasetBucket", +"properties": { +"count": { +"description": "Output only. Number of values in the bucket.", +"format": "double", +"readOnly": true, +"type": "number" +}, +"left": { +"description": "Output only. Left bound of the bucket.", +"format": "double", +"readOnly": true, +"type": "number" +}, +"right": { +"description": "Output only. Right bound of the bucket.", +"format": "double", +"readOnly": true, +"type": "number" +} +}, +"type": "object" +}, +"GoogleCloudAiplatformV1beta1SupervisedTuningSpec": { +"description": "Tuning Spec for Supervised Tuning.", +"id": "GoogleCloudAiplatformV1beta1SupervisedTuningSpec", +"properties": { +"hyperParameters": { +"$ref": "GoogleCloudAiplatformV1beta1SupervisedHyperParameters", +"description": "Optional. Hyperparameters for SFT." +}, +"trainingDatasetUri": { +"description": "Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.", +"type": "string" +}, +"validationDatasetUri": { +"description": "Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.", +"type": "string" +} +}, +"type": "object" +}, "GoogleCloudAiplatformV1beta1SyncFeatureViewRequest": { "description": "Request message for FeatureOnlineStoreAdminService.SyncFeatureView.", "id": "GoogleCloudAiplatformV1beta1SyncFeatureViewRequest", @@ -42888,6 +42490,150 @@ }, "type": "object" }, +"GoogleCloudAiplatformV1beta1TunedModel": { +"description": "The Model Registry Model and Online Prediction Endpoint assiociated with this TuningJob.", +"id": "GoogleCloudAiplatformV1beta1TunedModel", +"properties": { +"endpoint": { +"description": "Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.", +"readOnly": true, +"type": "string" +}, +"model": { +"description": "Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}`.", +"readOnly": true, +"type": "string" +} +}, +"type": "object" +}, +"GoogleCloudAiplatformV1beta1TuningDataStats": { +"description": "The tuning data statistic values for TuningJob.", +"id": "GoogleCloudAiplatformV1beta1TuningDataStats", +"properties": { +"supervisedTuningDataStats": { +"$ref": "GoogleCloudAiplatformV1beta1SupervisedTuningDataStats", +"description": "The SFT Tuning data stats." +} +}, +"type": "object" +}, +"GoogleCloudAiplatformV1beta1TuningJob": { +"description": "Represents a TuningJob that runs with Google owned models.", +"id": "GoogleCloudAiplatformV1beta1TuningJob", +"properties": { +"baseModel": { +"description": "The base model that is being tuned, e.g., \"gemini-1.0-pro-002\".", +"type": "string" +}, +"createTime": { +"description": "Output only. Time when the TuningJob was created.", +"format": "google-datetime", +"readOnly": true, +"type": "string" +}, +"description": { +"description": "Optional. The description of the TuningJob.", +"type": "string" +}, +"encryptionSpec": { +"$ref": "GoogleCloudAiplatformV1beta1EncryptionSpec", +"description": "Customer-managed encryption key options for a TuningJob. If this is set, then all resources created by the TuningJob will be encrypted with the provided encryption key." +}, +"endTime": { +"description": "Output only. Time when the TuningJob entered any of the following JobStates: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`, `JOB_STATE_EXPIRED`.", +"format": "google-datetime", +"readOnly": true, +"type": "string" +}, +"error": { +"$ref": "GoogleRpcStatus", +"description": "Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.", +"readOnly": true +}, +"experiment": { +"description": "Output only. The Experiment associated with this TuningJob.", +"readOnly": true, +"type": "string" +}, +"labels": { +"additionalProperties": { +"type": "string" +}, +"description": "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.", +"type": "object" +}, +"name": { +"description": "Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}`", +"readOnly": true, +"type": "string" +}, +"startTime": { +"description": "Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state.", +"format": "google-datetime", +"readOnly": true, +"type": "string" +}, +"state": { +"description": "Output only. The detailed state of the job.", +"enum": [ +"JOB_STATE_UNSPECIFIED", +"JOB_STATE_QUEUED", +"JOB_STATE_PENDING", +"JOB_STATE_RUNNING", +"JOB_STATE_SUCCEEDED", +"JOB_STATE_FAILED", +"JOB_STATE_CANCELLING", +"JOB_STATE_CANCELLED", +"JOB_STATE_PAUSED", +"JOB_STATE_EXPIRED", +"JOB_STATE_UPDATING", +"JOB_STATE_PARTIALLY_SUCCEEDED" +], +"enumDescriptions": [ +"The job state is unspecified.", +"The job has been just created or resumed and processing has not yet begun.", +"The service is preparing to run the job.", +"The job is in progress.", +"The job completed successfully.", +"The job failed.", +"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`.", +"The job has been cancelled.", +"The job has been stopped, and can be resumed.", +"The job has expired.", +"The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state.", +"The job is partially succeeded, some results may be missing due to errors." +], +"readOnly": true, +"type": "string" +}, +"supervisedTuningSpec": { +"$ref": "GoogleCloudAiplatformV1beta1SupervisedTuningSpec", +"description": "Tuning Spec for Supervised Fine Tuning." +}, +"tunedModel": { +"$ref": "GoogleCloudAiplatformV1beta1TunedModel", +"description": "Output only. The tuned model resources assiociated with this TuningJob.", +"readOnly": true +}, +"tunedModelDisplayName": { +"description": "Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters.", +"type": "string" +}, +"tuningDataStats": { +"$ref": "GoogleCloudAiplatformV1beta1TuningDataStats", +"description": "Output only. The tuning data statistics associated with this TuningJob.", +"readOnly": true +}, +"updateTime": { +"description": "Output only. Time when the TuningJob was most recently updated.", +"format": "google-datetime", +"readOnly": true, +"type": "string" +} +}, +"type": "object" +}, "GoogleCloudAiplatformV1beta1UndeployIndexOperationMetadata": { "description": "Runtime operation information for IndexEndpointService.UndeployIndex.", "id": "GoogleCloudAiplatformV1beta1UndeployIndexOperationMetadata", @@ -43021,6082 +42767,883 @@ "properties": {}, "type": "object" }, -"GoogleCloudAiplatformV1beta1UpdateFeatureGroupOperationMetadata": { -"description": "Details of operations that perform update FeatureGroup.", -"id": "GoogleCloudAiplatformV1beta1UpdateFeatureGroupOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for FeatureGroup." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpdateFeatureOnlineStoreOperationMetadata": { -"description": "Details of operations that perform update FeatureOnlineStore.", -"id": "GoogleCloudAiplatformV1beta1UpdateFeatureOnlineStoreOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for FeatureOnlineStore." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpdateFeatureOperationMetadata": { -"description": "Details of operations that perform update Feature.", -"id": "GoogleCloudAiplatformV1beta1UpdateFeatureOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for Feature Update." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpdateFeatureViewOperationMetadata": { -"description": "Details of operations that perform update FeatureView.", -"id": "GoogleCloudAiplatformV1beta1UpdateFeatureViewOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for FeatureView Update." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpdateFeaturestoreOperationMetadata": { -"description": "Details of operations that perform update Featurestore.", -"id": "GoogleCloudAiplatformV1beta1UpdateFeaturestoreOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for Featurestore." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpdateIndexOperationMetadata": { -"description": "Runtime operation information for IndexService.UpdateIndex.", -"id": "GoogleCloudAiplatformV1beta1UpdateIndexOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -}, -"nearestNeighborSearchOperationMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadata", -"description": "The operation metadata with regard to Matching Engine Index operation." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpdateModelDeploymentMonitoringJobOperationMetadata": { -"description": "Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob.", -"id": "GoogleCloudAiplatformV1beta1UpdateModelDeploymentMonitoringJobOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpdateModelMonitorOperationMetadata": { -"description": "Runtime operation information for ModelMonitoringService.UpdateModelMonitor.", -"id": "GoogleCloudAiplatformV1beta1UpdateModelMonitorOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpdatePersistentResourceOperationMetadata": { -"description": "Details of operations that perform update PersistentResource.", -"id": "GoogleCloudAiplatformV1beta1UpdatePersistentResourceOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for PersistentResource." -}, -"progressMessage": { -"description": "Progress Message for Update LRO", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpdateSpecialistPoolOperationMetadata": { -"description": "Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.", -"id": "GoogleCloudAiplatformV1beta1UpdateSpecialistPoolOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -}, -"specialistPool": { -"description": "Output only. The name of the SpecialistPool to which the specialists are being added. Format: `projects/{project_id}/locations/{location_id}/specialistPools/{specialist_pool}`", -"readOnly": true, -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpdateTensorboardOperationMetadata": { -"description": "Details of operations that perform update Tensorboard.", -"id": "GoogleCloudAiplatformV1beta1UpdateTensorboardOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "Operation metadata for Tensorboard." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpgradeNotebookRuntimeOperationMetadata": { -"description": "Metadata information for NotebookService.UpgradeNotebookRuntime.", -"id": "GoogleCloudAiplatformV1beta1UpgradeNotebookRuntimeOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The operation generic information." -}, -"progressMessage": { -"description": "A human-readable message that shows the intermediate progress details of NotebookRuntime.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpgradeNotebookRuntimeRequest": { -"description": "Request message for NotebookService.UpgradeNotebookRuntime.", -"id": "GoogleCloudAiplatformV1beta1UpgradeNotebookRuntimeRequest", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UploadModelOperationMetadata": { -"description": "Details of ModelService.UploadModel operation.", -"id": "GoogleCloudAiplatformV1beta1UploadModelOperationMetadata", -"properties": { -"genericMetadata": { -"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", -"description": "The common part of the operation metadata." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UploadModelRequest": { -"description": "Request message for ModelService.UploadModel.", -"id": "GoogleCloudAiplatformV1beta1UploadModelRequest", -"properties": { -"model": { -"$ref": "GoogleCloudAiplatformV1beta1Model", -"description": "Required. The Model to create." -}, -"modelId": { -"description": "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.", -"type": "string" -}, -"parentModel": { -"description": "Optional. The resource name of the model into which to upload the version. Only specify this field when uploading a new version.", -"type": "string" -}, -"serviceAccount": { -"description": "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.).", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UploadModelResponse": { -"description": "Response message of ModelService.UploadModel operation.", -"id": "GoogleCloudAiplatformV1beta1UploadModelResponse", -"properties": { -"model": { -"description": "The name of the uploaded Model resource. Format: `projects/{project}/locations/{location}/models/{model}`", -"type": "string" -}, -"modelVersionId": { -"description": "Output only. The version ID of the model that is uploaded.", -"readOnly": true, -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UploadRagFileConfig": { -"description": "Config for uploading RagFile.", -"id": "GoogleCloudAiplatformV1beta1UploadRagFileConfig", -"properties": { -"ragFileChunkingConfig": { -"$ref": "GoogleCloudAiplatformV1beta1RagFileChunkingConfig", -"description": "Specifies the size and overlap of chunks after uploading RagFile." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UploadRagFileRequest": { -"description": "Request message for VertexRagDataService.UploadRagFile.", -"id": "GoogleCloudAiplatformV1beta1UploadRagFileRequest", -"properties": { -"ragFile": { -"$ref": "GoogleCloudAiplatformV1beta1RagFile", -"description": "Required. The RagFile to upload." -}, -"uploadRagFileConfig": { -"$ref": "GoogleCloudAiplatformV1beta1UploadRagFileConfig", -"description": "Required. The config for the RagFiles to be uploaded into the RagCorpus. VertexRagDataService.UploadRagFile." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UploadRagFileResponse": { -"description": "Response message for VertexRagDataService.UploadRagFile.", -"id": "GoogleCloudAiplatformV1beta1UploadRagFileResponse", -"properties": { -"error": { -"$ref": "GoogleRpcStatus", -"description": "The error that occurred while processing the RagFile." -}, -"ragFile": { -"$ref": "GoogleCloudAiplatformV1beta1RagFile", -"description": "The RagFile that had been uploaded into the RagCorpus." -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpsertDatapointsRequest": { -"description": "Request message for IndexService.UpsertDatapoints", -"id": "GoogleCloudAiplatformV1beta1UpsertDatapointsRequest", -"properties": { -"datapoints": { -"description": "A list of datapoints to be created/updated.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1IndexDatapoint" -}, -"type": "array" -}, -"updateMask": { -"description": "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.", -"format": "google-fieldmask", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UpsertDatapointsResponse": { -"description": "Response message for IndexService.UpsertDatapoints", -"id": "GoogleCloudAiplatformV1beta1UpsertDatapointsResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1UserActionReference": { -"description": "References an API call. It contains more information about long running operation and Jobs that are triggered by the API call.", -"id": "GoogleCloudAiplatformV1beta1UserActionReference", -"properties": { -"dataLabelingJob": { -"description": "For API calls that start a LabelingJob. Resource name of the LabelingJob. Format: `projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}`", -"type": "string" -}, -"method": { -"description": "The method name of the API RPC call. For example, \"/google.cloud.aiplatform.{apiVersion}.DatasetService.CreateDataset\"", -"type": "string" -}, -"operation": { -"description": "For API calls that return a long running operation. Resource name of the long running operation. Format: `projects/{project}/locations/{location}/operations/{operation}`", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1Value": { -"description": "Value is the value of the field.", -"id": "GoogleCloudAiplatformV1beta1Value", -"properties": { -"doubleValue": { -"description": "A double value.", -"format": "double", -"type": "number" -}, -"intValue": { -"description": "An integer value.", -"format": "int64", -"type": "string" -}, -"stringValue": { -"description": "A string value.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1VertexAISearch": { -"description": "Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation", -"id": "GoogleCloudAiplatformV1beta1VertexAISearch", -"properties": { -"datastore": { -"description": "Required. Fully-qualified Vertex AI Search's datastore resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1VertexRagStore": { -"description": "Retrieve from Vertex RAG Store for grounding.", -"id": "GoogleCloudAiplatformV1beta1VertexRagStore", -"properties": { -"ragCorpora": { -"description": "Optional. Deprecated. Please use rag_resources instead.", -"items": { -"type": "string" -}, -"type": "array" -}, -"ragResources": { -"description": "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.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1VertexRagStoreRagResource" -}, -"type": "array" -}, -"similarityTopK": { -"description": "Optional. Number of top k results to return from the selected corpora.", -"format": "int32", -"type": "integer" -}, -"vectorDistanceThreshold": { -"description": "Optional. Only return results with vector distance smaller than the threshold.", -"format": "double", -"type": "number" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1VertexRagStoreRagResource": { -"description": "The definition of the Rag resource.", -"id": "GoogleCloudAiplatformV1beta1VertexRagStoreRagResource", -"properties": { -"ragCorpus": { -"description": "Optional. RagCorpora resource name. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}`", -"type": "string" -}, -"ragFileIds": { -"description": "Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field.", -"items": { -"type": "string" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1VideoMetadata": { -"description": "Metadata describes the input video content.", -"id": "GoogleCloudAiplatformV1beta1VideoMetadata", -"properties": { -"endOffset": { -"description": "Optional. The end offset of the video.", -"format": "google-duration", -"type": "string" -}, -"startOffset": { -"description": "Optional. The start offset of the video.", -"format": "google-duration", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1WorkerPoolSpec": { -"description": "Represents the spec of a worker pool in a job.", -"id": "GoogleCloudAiplatformV1beta1WorkerPoolSpec", -"properties": { -"containerSpec": { -"$ref": "GoogleCloudAiplatformV1beta1ContainerSpec", -"description": "The custom container task." -}, -"diskSpec": { -"$ref": "GoogleCloudAiplatformV1beta1DiskSpec", -"description": "Disk spec." -}, -"machineSpec": { -"$ref": "GoogleCloudAiplatformV1beta1MachineSpec", -"description": "Optional. Immutable. The specification of a single machine." -}, -"nfsMounts": { -"description": "Optional. List of NFS mount spec.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1NfsMount" -}, -"type": "array" -}, -"pythonPackageSpec": { -"$ref": "GoogleCloudAiplatformV1beta1PythonPackageSpec", -"description": "The Python packaged task." -}, -"replicaCount": { -"description": "Optional. The number of worker replicas to use for this worker pool.", -"format": "int64", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1WriteFeatureValuesPayload": { -"description": "Contains Feature values to be written for a specific entity.", -"id": "GoogleCloudAiplatformV1beta1WriteFeatureValuesPayload", -"properties": { -"entityId": { -"description": "Required. The ID of the entity.", -"type": "string" -}, -"featureValues": { -"additionalProperties": { -"$ref": "GoogleCloudAiplatformV1beta1FeatureValue" -}, -"description": "Required. Feature values to be written, mapping from Feature ID to value. Up to 100,000 `feature_values` entries may be written across all payloads. The feature generation time, aligned by days, must be no older than five years (1825 days) and no later than one year (366 days) in the future.", -"type": "object" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1WriteFeatureValuesRequest": { -"description": "Request message for FeaturestoreOnlineServingService.WriteFeatureValues.", -"id": "GoogleCloudAiplatformV1beta1WriteFeatureValuesRequest", -"properties": { -"payloads": { -"description": "Required. The entities to be written. Up to 100,000 feature values can be written across all `payloads`.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1WriteFeatureValuesPayload" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1WriteFeatureValuesResponse": { -"description": "Response message for FeaturestoreOnlineServingService.WriteFeatureValues.", -"id": "GoogleCloudAiplatformV1beta1WriteFeatureValuesResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1WriteTensorboardExperimentDataRequest": { -"description": "Request message for TensorboardService.WriteTensorboardExperimentData.", -"id": "GoogleCloudAiplatformV1beta1WriteTensorboardExperimentDataRequest", -"properties": { -"writeRunDataRequests": { -"description": "Required. Requests containing per-run TensorboardTimeSeries data to write.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1WriteTensorboardRunDataRequest" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1WriteTensorboardExperimentDataResponse": { -"description": "Response message for TensorboardService.WriteTensorboardExperimentData.", -"id": "GoogleCloudAiplatformV1beta1WriteTensorboardExperimentDataResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1WriteTensorboardRunDataRequest": { -"description": "Request message for TensorboardService.WriteTensorboardRunData.", -"id": "GoogleCloudAiplatformV1beta1WriteTensorboardRunDataRequest", -"properties": { -"tensorboardRun": { -"description": "Required. The resource name of the TensorboardRun to write data to. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}`", -"type": "string" -}, -"timeSeriesData": { -"description": "Required. The TensorboardTimeSeries data to write. Values with in a time series are indexed by their step value. Repeated writes to the same step will overwrite the existing value for that step. The upper limit of data points per write request is 5000.", -"items": { -"$ref": "GoogleCloudAiplatformV1beta1TimeSeriesData" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1WriteTensorboardRunDataResponse": { -"description": "Response message for TensorboardService.WriteTensorboardRunData.", -"id": "GoogleCloudAiplatformV1beta1WriteTensorboardRunDataResponse", -"properties": {}, -"type": "object" -}, -"GoogleCloudAiplatformV1beta1XraiAttribution": { -"description": "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.", -"id": "GoogleCloudAiplatformV1beta1XraiAttribution", -"properties": { -"blurBaselineConfig": { -"$ref": "GoogleCloudAiplatformV1beta1BlurBaselineConfig", -"description": "Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383" -}, -"smoothGradConfig": { -"$ref": "GoogleCloudAiplatformV1beta1SmoothGradConfig", -"description": "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": { -"description": "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.", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"GoogleCloudLocationListLocationsResponse": { -"description": "The response message for Locations.ListLocations.", -"id": "GoogleCloudLocationListLocationsResponse", -"properties": { -"locations": { -"description": "A list of locations that matches the specified filter in the request.", -"items": { -"$ref": "GoogleCloudLocationLocation" -}, -"type": "array" -}, -"nextPageToken": { -"description": "The standard List next-page token.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleCloudLocationLocation": { -"description": "A resource that represents a Google Cloud location.", -"id": "GoogleCloudLocationLocation", -"properties": { -"displayName": { -"description": "The friendly name for this location, typically a nearby city name. For example, \"Tokyo\".", -"type": "string" -}, -"labels": { -"additionalProperties": { -"type": "string" -}, -"description": "Cross-service attributes for the location. For example {\"cloud.googleapis.com/region\": \"us-east1\"}", -"type": "object" -}, -"locationId": { -"description": "The canonical id for this location. For example: `\"us-east1\"`.", -"type": "string" -}, -"metadata": { -"additionalProperties": { -"description": "Properties of the object. Contains field @type with type URL.", -"type": "any" -}, -"description": "Service-specific metadata. For example the available capacity at the given location.", -"type": "object" -}, -"name": { -"description": "Resource name for the location, which may vary between implementations. For example: `\"projects/example-project/locations/us-east1\"`", -"type": "string" -} -}, -"type": "object" -}, -"GoogleIamV1Binding": { -"description": "Associates `members`, or principals, with a `role`.", -"id": "GoogleIamV1Binding", -"properties": { -"condition": { -"$ref": "GoogleTypeExpr", -"description": "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": { -"description": "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/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workforce identity pool. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}`: All workforce identities in a group. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All workforce identities with a specific attribute value. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*`: All identities in a workforce identity pool. * `principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workload identity pool. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}`: A workload identity pool group. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{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/global/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.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: Deleted single identity in a workforce identity pool. For example, `deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value`.", -"items": { -"type": "string" -}, -"type": "array" -}, -"role": { -"description": "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).", -"type": "string" -} -}, -"type": "object" -}, -"GoogleIamV1GetIamPolicyRequest": { -"description": "Request message for `GetIamPolicy` method.", -"id": "GoogleIamV1GetIamPolicyRequest", -"properties": { -"options": { -"$ref": "GoogleIamV1GetPolicyOptions", -"description": "OPTIONAL: A `GetPolicyOptions` object for specifying options to `GetIamPolicy`." -} -}, -"type": "object" -}, -"GoogleIamV1GetPolicyOptions": { -"description": "Encapsulates settings provided to GetIamPolicy.", -"id": "GoogleIamV1GetPolicyOptions", -"properties": { -"requestedPolicyVersion": { -"description": "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).", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"GoogleIamV1Policy": { -"description": "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/).", -"id": "GoogleIamV1Policy", -"properties": { -"bindings": { -"description": "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`.", -"items": { -"$ref": "GoogleIamV1Binding" -}, -"type": "array" -}, -"etag": { -"description": "`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.", -"format": "byte", -"type": "string" -}, -"version": { -"description": "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).", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"GoogleIamV1SetIamPolicyRequest": { -"description": "Request message for `SetIamPolicy` method.", -"id": "GoogleIamV1SetIamPolicyRequest", -"properties": { -"policy": { -"$ref": "GoogleIamV1Policy", -"description": "REQUIRED: The complete policy to be applied to the `resource`. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Google Cloud services (such as Projects) might reject them." -} -}, -"type": "object" -}, -"GoogleIamV1TestIamPermissionsRequest": { -"description": "Request message for `TestIamPermissions` method.", -"id": "GoogleIamV1TestIamPermissionsRequest", -"properties": { -"permissions": { -"description": "The set of permissions to check for the `resource`. Permissions with wildcards (such as `*` or `storage.*`) are not allowed. For more information see [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions).", -"items": { -"type": "string" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleIamV1TestIamPermissionsResponse": { -"description": "Response message for `TestIamPermissions` method.", -"id": "GoogleIamV1TestIamPermissionsResponse", -"properties": { -"permissions": { -"description": "A subset of `TestPermissionsRequest.permissions` that the caller is allowed.", -"items": { -"type": "string" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleLongrunningListOperationsResponse": { -"description": "The response message for Operations.ListOperations.", -"id": "GoogleLongrunningListOperationsResponse", -"properties": { -"nextPageToken": { -"description": "The standard List next-page token.", -"type": "string" -}, -"operations": { -"description": "A list of operations that matches the specified filter in the request.", -"items": { -"$ref": "GoogleLongrunningOperation" -}, -"type": "array" -} -}, -"type": "object" -}, -"GoogleLongrunningOperation": { -"description": "This resource represents a long-running operation that is the result of a network API call.", -"id": "GoogleLongrunningOperation", -"properties": { -"done": { -"description": "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.", -"type": "boolean" -}, -"error": { -"$ref": "GoogleRpcStatus", -"description": "The error result of the operation in case of failure or cancellation." -}, -"metadata": { -"additionalProperties": { -"description": "Properties of the object. Contains field @type with type URL.", -"type": "any" -}, -"description": "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.", -"type": "object" -}, -"name": { -"description": "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}`.", -"type": "string" -}, -"response": { -"additionalProperties": { -"description": "Properties of the object. Contains field @type with type URL.", -"type": "any" -}, -"description": "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`.", -"type": "object" -} -}, -"type": "object" -}, -"GoogleProtobufEmpty": { -"description": "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); }", -"id": "GoogleProtobufEmpty", -"properties": {}, -"type": "object" -}, -"GoogleRpcStatus": { -"description": "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).", -"id": "GoogleRpcStatus", -"properties": { -"code": { -"description": "The status code, which should be an enum value of google.rpc.Code.", -"format": "int32", -"type": "integer" -}, -"details": { -"description": "A list of messages that carry the error details. There is a common set of message types for APIs to use.", -"items": { -"additionalProperties": { -"description": "Properties of the object. Contains field @type with type URL.", -"type": "any" -}, -"type": "object" -}, -"type": "array" -}, -"message": { -"description": "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.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleTypeColor": { -"description": "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\u2014for 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(''); }; // ...", -"id": "GoogleTypeColor", -"properties": { -"alpha": { -"description": "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).", -"format": "float", -"type": "number" -}, -"blue": { -"description": "The amount of blue in the color as a value in the interval [0, 1].", -"format": "float", -"type": "number" -}, -"green": { -"description": "The amount of green in the color as a value in the interval [0, 1].", -"format": "float", -"type": "number" -}, -"red": { -"description": "The amount of red in the color as a value in the interval [0, 1].", -"format": "float", -"type": "number" -} -}, -"type": "object" -}, -"GoogleTypeDate": { -"description": "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", -"id": "GoogleTypeDate", -"properties": { -"day": { -"description": "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.", -"format": "int32", -"type": "integer" -}, -"month": { -"description": "Month of a year. Must be from 1 to 12, or 0 to specify a year without a month and day.", -"format": "int32", -"type": "integer" -}, -"year": { -"description": "Year of the date. Must be from 1 to 9999, or 0 to specify a date without a year.", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"GoogleTypeExpr": { -"description": "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.", -"id": "GoogleTypeExpr", -"properties": { -"description": { -"description": "Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.", -"type": "string" -}, -"expression": { -"description": "Textual representation of an expression in Common Expression Language syntax.", -"type": "string" -}, -"location": { -"description": "Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.", -"type": "string" -}, -"title": { -"description": "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.", -"type": "string" -} -}, -"type": "object" -}, -"GoogleTypeInterval": { -"description": "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.", -"id": "GoogleTypeInterval", -"properties": { -"endTime": { -"description": "Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end.", -"format": "google-datetime", -"type": "string" -}, -"startTime": { -"description": "Optional. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start.", -"format": "google-datetime", -"type": "string" -} -}, -"type": "object" -}, -"GoogleTypeMoney": { -"description": "Represents an amount of money with its currency type.", -"id": "GoogleTypeMoney", -"properties": { -"currencyCode": { -"description": "The three-letter currency code defined in ISO 4217.", -"type": "string" -}, -"nanos": { -"description": "Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If `units` is positive, `nanos` must be positive or zero. If `units` is zero, `nanos` can be positive, zero, or negative. If `units` is negative, `nanos` must be negative or zero. For example $-1.75 is represented as `units`=-1 and `nanos`=-750,000,000.", -"format": "int32", -"type": "integer" -}, -"units": { -"description": "The whole units of the amount. For example if `currencyCode` is `\"USD\"`, then 1 unit is one US dollar.", -"format": "int64", -"type": "string" -} -}, -"type": "object" -}, -"IntelligenceCloudAutomlXpsMetricEntry": { -"id": "IntelligenceCloudAutomlXpsMetricEntry", -"properties": { -"argentumMetricId": { -"description": "For billing metrics that are using legacy sku's, set the legacy billing metric id here. This will be sent to Chemist as the \"cloudbilling.googleapis.com/argentum_metric_id\" label. Otherwise leave empty.", -"type": "string" -}, -"doubleValue": { -"description": "A double value.", -"format": "double", -"type": "number" -}, -"int64Value": { -"description": "A signed 64-bit integer value.", -"format": "int64", -"type": "string" -}, -"metricName": { -"description": "The metric name defined in the service configuration.", -"type": "string" -}, -"systemLabels": { -"description": "Billing system labels for this (metric, value) pair.", -"items": { -"$ref": "IntelligenceCloudAutomlXpsMetricEntryLabel" -}, -"type": "array" -} -}, -"type": "object" -}, -"IntelligenceCloudAutomlXpsMetricEntryLabel": { -"id": "IntelligenceCloudAutomlXpsMetricEntryLabel", -"properties": { -"labelName": { -"description": "The name of the label.", -"type": "string" -}, -"labelValue": { -"description": "The value of the label.", -"type": "string" -} -}, -"type": "object" -}, -"IntelligenceCloudAutomlXpsReportingMetrics": { -"id": "IntelligenceCloudAutomlXpsReportingMetrics", -"properties": { -"effectiveTrainingDuration": { -"deprecated": true, -"description": "The effective time training used. If set, this is used for quota management and billing. Deprecated. AutoML BE doesn't use this. Don't set.", -"format": "google-duration", -"type": "string" -}, -"metricEntries": { -"description": "One entry per metric name. The values must be aggregated per metric name.", -"items": { -"$ref": "IntelligenceCloudAutomlXpsMetricEntry" -}, -"type": "array" -} -}, -"type": "object" -}, -"LanguageLabsAidaTrustRecitationProtoDocAttribution": { -"description": "The proto defines the attribution information for a document using whatever fields are most applicable for that document's datasource. For example, a Wikipedia article's attribution is in the form of its article title, a website is in the form of a URL, and a Github repo is in the form of a repo name. Next id: 30", -"id": "LanguageLabsAidaTrustRecitationProtoDocAttribution", -"properties": { -"amarnaId": { -"type": "string" -}, -"arxivId": { -"type": "string" -}, -"author": { -"type": "string" -}, -"bibkey": { -"type": "string" -}, -"biorxivId": { -"description": "ID of the paper in bioarxiv like ddoi.org/{biorxiv_id} eg: https://doi.org/10.1101/343517", -"type": "string" -}, -"bookTitle": { -"type": "string" -}, -"bookVolumeId": { -"description": "The Oceanographers full-view books dataset uses a 'volume id' as the unique ID of a book. There is a deterministic function from a volume id to a URL under the books.google.com domain. Marked as 'optional' since a volume ID of zero is potentially possible and we want to distinguish that from the volume ID not being set.", -"format": "int64", -"type": "string" -}, -"category": { -"enum": [ -"CATEGORY_UNSPECIFIED", -"CATEGORY_NEWS", -"CATEGORY_NON_NEWS_WEBDOC", -"CATEGORY_UNKNOWN_MISSING_SIGNAL" -], -"enumDescriptions": [ -"", -"The doc has a url and the news classifier has classified this doc as news.", -"The doc has a url and the news classifier classified this doc as non-news.", -"The doc has a url but the url was missing from the news classifier URL table." -], -"type": "string" -}, -"conversationId": { -"type": "string" -}, -"dataset": { -"description": "The dataset this document comes from.", -"enum": [ -"DATASET_UNSPECIFIED", -"WIKIPEDIA", -"WEBDOCS", -"WEBDOCS_FINETUNE", -"GITHUB_MIRROR", -"BOOKS_FULL_VIEW", -"BOOKS_PRIVATE", -"GNEWS", -"ULM_DOCJOINS", -"ULM_DOCJOINS_DEDUPED", -"MEENA_FC", -"PODCAST", -"AQUA", -"WEB_ASR", -"BARD_GOLDEN", -"COMMON_SENSE_REASONING", -"MATH", -"MATH_REASONING", -"CLEAN_ARXIV", -"LAMDA_FACTUALITY_E2E_QUERY_GENERATION", -"LAMDA_FACTUALITY_E2E_RESPONSE_GENERATION", -"MASSIVE_FORUM_THREAD_SCORED_BARD", -"MASSIVE_FORUM_THREAD_SCORED_LONG_200", -"MASSIVE_FORUM_THREAD_SCORED_LONG_500", -"DOCUMENT_CHUNKS", -"MEENA_RESEARCH_PHASE_GOLDEN_MARKDOWN", -"MEENA_RESEARCH_PHASE_GOOGLERS", -"MEENA_RESPONSE_SAFETY_HUMAN_GEN", -"MEENA_RESPONSE_SAFETY_SCHEMA_NO_BROADCAST", -"MEENA_RESPONSE_SAFETY_V3_HUMAN_GEN2", -"MEENA_RESPONSE_SAFETY_V3_SCHEMA_NO_BROADCAST", -"LAMDA_FACTUALITY_TRIGGER", -"LAMDA_SAFETY_V2_SCHEMA_NO_BROADCAST", -"LAMDA_SSI_DISCRIMINATIVE", -"ASSISTANT_PERSONALITY_SAFETY", -"PODCAST_FINETUNE_DIALOG", -"WORLD_QUERY_GENERATOR", -"C4_JOINED_DOCJOINS", -"HOL4_THEORIES", -"HOL_LIGHT_THEORIES", -"HOLSTEPS", -"ISABELLE_STEP", -"ISABELLE_THEORIES", -"LEAN_MATHLIB_THEORIES", -"LEAN_STEP", -"MIZAR_THEORIES", -"COQ_STEP", -"COQ_THEORIES", -"AMPS_KHAN", -"AMPS_MATHEMATICA", -"CODEY_CODE", -"CODE_QA_SE", -"CODE_QA_SO", -"CODE_QA_FT_FORMAT", -"CODE_QA_FT_KNOWLEDGE", -"CODE_QA_GITHUB_FILTERED_CODE", -"BARD_PERSONALITY_GOLDEN", -"ULM_DOCJOINS_WITH_URLS_EN", -"ULM_DOCJOINS_WITH_URLS_I18N", -"GOODALL_MTV5_GITHUB", -"GOODALL_MTV5_BOOKS", -"GOODALL_MTV5_C4", -"GOODALL_MTV5_WIKIPEDIA", -"GOODALL_MW_TOP_100B", -"GOODALL_MW_STACK_EXCHANGE", -"GOODALL_MW_TOP_0_10B", -"GOODALL_MW_TOP_10B_20B", -"CODEY_NOTEBOOK_LM_PRETRAINING", -"VERTEX_SAFE_FLAN", -"GITHUB_MIRROR_V1_0_1", -"GITHUB_MIRROR_V2_1_0", -"CMS_WIKIPEDIA_LANG_FILTERED", -"CMS_STACKOVERFLOW_MULTILINGUAL", -"CMS_STACKEXCHANGE", -"PUBMED", -"GEMINI_DOCJOINS_EN_TOP10B_GCC", -"GEMINI_DOCJOINS_EN_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_EN_TOP20B_TOP100B_GCC", -"GEMINI_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_I18N_TOP20B_TOP100B_GCC", -"SIMPLIFIED_HTML_V1_GCC", -"GEMINI_DOCJOINS_TOXICITY_TAGGED_GCC", -"CMS_GITHUB_V4", -"GITHUB_HTML_V4", -"GITHUB_OTHER_V4", -"GITHUB_LONG_TAIL_V4", -"CMS_GITHUB_MULTIFILE_V4", -"GITHUB_DIFFS_WITH_COMMIT_MESSAGE", -"ULM_ARXIV", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_ENONLY", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_NONENONLY", -"QUORA", -"PODCASTS_ROBOTSTXT", -"COMBINED_REDDIT", -"CANARIES_SHUFFLED", -"CLM_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"TECHDOCS_DATA_SOURCE", -"SCIENCE_PDF_70M_DOCS_FILTERED", -"GEMINI_V1_CMS_WIKIPEDIA_LANG_FILTERED", -"GEMINI_V1_WIKIPEDIA_DIFFS", -"GEMINI_V1_DOCJOINS_EN_TOP10B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP10B_TOP20B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP20B_TOP100B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_TOP20B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP20B_TOP100B_GCC_050523", -"GEMINI_V1_SIMPLIFIED_HTML_V2_GCC", -"GEMINI_V1_CMS_STACKOVERFLOW_MULTILINGUAL_V2", -"GEMINI_V1_CMS_STACKEXCHANGE_DECONT", -"GEMINI_V1_QUORA", -"GEMINI_V1_COMBINED_REDDIT", -"GEMINI_V1_DOCJOIN_100B_EN_TOXICITY_TAGGED_GCC_FIXED_TAGS", -"GEMINI_V1_PUBMED", -"GEMINI_V1_WEB_MATH_V2", -"GEMINI_V1_CMS_GITHUB_V7", -"GEMINI_V1_CMS_GITHUB_DECONTAMINATED_V_7", -"GEMINI_V1_GITHUB_DIFF_WITH_COMMIT_MESSAGE_V2", -"GEMINI_V1_GITHUB_HTML_CSS_XML_V4", -"GEMINI_V1_GITHUB_OTHER_V4", -"GEMINI_V1_GITHUB_LONG_TAIL_V4", -"GEMINI_V1_GITHUB_JUPTYER_NOTEBOOKS_SSTABLE", -"GEMINI_V1_ULM_ARXIV_SSTABLE", -"GEMINI_V1_PODCASTS_ROBOTSTXT", -"GEMINI_V1_SCIENCE_PDF_68M_HQ_DOCS_GCC", -"GEMINI_V1_GITHUB_TECHDOCS_V2", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_EN", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_NONEN", -"GEMINI_V1_STEM_BOOKS_650K_TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_M3W_V2_FILTERED", -"GEMINI_V1_VQCOCA_1B_MULTIRES_WEBLI_EN_V4_350M_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_SCREENAI_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CULTURE_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_EN_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_I18N_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_NON_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_VTP_4F_VIDEO2TEXT_PREFIX", -"GEMINI_V1_FORMAL_MATH_WITHOUT_HOLSTEPS_AND_MIZAR", -"GEMINI_V1_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"GEMINI_V1_CANARIES_SHUFFLED_DOCJOIN_EN_NONEN_CODE_ARXIV_TRANSLATE", -"DUET_CLOUD_SECURITY_DOCS", -"DUET_GITHUB_CODE_SNIPPETS", -"DUET_GITHUB_FILES", -"DUET_GOBYEXAMPLE", -"DUET_GOLANG_DOCS", -"DUET_CLOUD_DOCS_TROUBLESHOOTING_TABLES", -"DUET_DEVSITE_DOCS", -"DUET_CLOUD_BLOG_POSTS", -"DUET_CLOUD_PODCAST_EPISODES", -"DUET_YOUTUBE_VIDEOS", -"DUET_CLOUD_SKILLS_BOOST", -"DUET_CLOUD_DOCS", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_GENERATED", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_HANDWRITTEN", -"DUET_GOOGLESQL_GENERATION", -"DUET_CLOUD_IX_PROMPTS", -"DUET_RAD", -"DUET_STACKOVERFLOW_ISSUES", -"DUET_STACKOVERFLOW_ANSWERS", -"BARD_ARCADE_GITHUB", -"MOBILE_ASSISTANT_MAGI_FILTERED_0825_373K", -"MOBILE_ASSISTANT_PALM24B_FILTERED_400K", -"GENESIS_NEWS_INSIGHTS", -"CLOUD_SECURITY_PRETRAINING", -"CLOUD_SECURITY_FINETUNING", -"CLOUD_SECURITY_RAG_CISA", -"LABS_AQA_DSCOUT", -"LABS_AQA_TAILWIND", -"LABS_AQA_DELEWARE", -"GEMINI_MULTIMODAL_FT_URL", -"GEMINI_MULTIMODAL_FT_YT", -"GEMINI_MULTIMODAL_FT_SHUTTERSTOCK", -"GEMINI_MULTIMODAL_FT_NONE", -"GEMINI_MULTIMODAL_FT_OTHER", -"GEMINI_MULTIMODAL_FT_INK", -"GEMINI_MULTIMODAL_IT", -"GEMINI_IT_SHUTTERSTOCK", -"GEMINI_IT_M3W", -"GEMINI_IT_HEDGING", -"GEMINI_IT_DSCOUT_FACTUALITY", -"GEMINI_IT_AQUAMUSE", -"GEMINI_IT_SHOTGUN", -"GEMINI_IT_ACI_BENCH", -"GEMINI_IT_SPIDER_FILTERED", -"GEMINI_IT_TAB_SUM_BQ", -"GEMINI_IT_QA_WITH_URL", -"GEMINI_IT_CODE_INSTRUCT", -"GEMINI_IT_MED_PALM", -"GEMINI_IT_TASK_ORIENTED_DIALOG", -"GEMINI_IT_NIMBUS_GROUNDING_TO_PROMPT", -"GEMINI_IT_EITL_GEN", -"GEMINI_IT_HITL_GEN", -"GEMINI_IT_MECH", -"GEMINI_IT_TABLE_GEN", -"GEMINI_IT_NIMBUS_DECIBEL", -"GEMINI_IT_CLOUD_CODE_IF", -"GEMINI_IT_CLOUD_EUR_LEX_JSON", -"GEMINI_IT_CLOUD_OASST", -"GEMINI_IT_CLOUD_SELF_INSTRUCT", -"GEMINI_IT_CLOUD_UCS_AQUAMUSE", -"GEMIT_BRIDGE_SUFFIX_FT", -"GEMINI_GOOSE_PUBLIC", -"GEMINI_GOOSE_SILOED", -"GEMINI_V2_CMS_WIKIPEDIA_LANG_FILTERED_GCC_PII", -"GEMINI_V2_WIKIPEDIA_DIFFS_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_QUORA_COMPLIANT", -"GEMINI_V2_FORUMS_V2_COMPLIANT", -"GEMINI_V2_CMS_STACKOVERFLOW_MULTILINGUAL_V2_COMPLIANT", -"GEMINI_V2_SIMPLIFIED_HTML_V2_CORRECT_FORMAT_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_TOXICITY_TAGGED_FIXED_TAGS_COMPLIANT", -"GEMINI_V2_CODEWEB_V1_COMPLIANT", -"GEMINI_V2_LEETCODE_GCC_PII", -"GEMINI_V2_CODE_CONTESTS_COMPLIANT", -"GEMINI_V2_CMS_GITHUB_MULTI_FILE_FOR_FIM_GEMBAGZ_FIXED_BYTES_LENGTHS", -"GEMINI_V2_GITHUB_EVALED_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_NON_EVAL_HIGH_PRI_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_LOW_PRI_LANGUAGES_AND_CONFIGS_COMPLIANT", -"GEMINI_V2_GITHUB_LONG_TAIL_AND_STRUCTURED_DATA_COMPLIANT", -"GEMINI_V2_GITHUB_PYTHON_NOTEBOOKS_COMPLIANT", -"GEMINI_V2_GITHUB_DIFFS_COMPLIANT", -"GEMINI_V2_GITHUB_TECHDOCS_COMPLIANT", -"GEMINI_V2_HIGH_QUALITY_CODE_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_SCIENCE_PDF_68M_HQ_DOCS_DEDUP_COMPLIANT_CLEAN_TEX", -"GEMINI_V2_ARXIV_2023_COMPLIANT", -"GEMINI_V2_FORMAL_COMPLIANT", -"GEMINI_V2_CMS_STACKEXCHANGE_COMPLIANT", -"GEMINI_V2_PUBMED_COMPLIANT", -"GEMINI_V2_WEB_MATH_V3_COMPLIANT", -"GEMINI_V2_SCIENCEWEB_V0_GCC_PII", -"GEMINI_V2_WEB_POLYMATH_V1_COMPLIANT", -"GEMINI_V2_MATH_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_BIOLOGY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_PHYSICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHEMISTRY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MACHINE_LEARNING_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_QA_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_ECONOMICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MEDICAL_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHESS_COMPLIANT", -"GEMINI_V2_YOUTUBE_SCIENCE_V4_FILTERED_COMPLIANT", -"GEMINI_V2_GOALDMINE_XL_GENERATED_PLUS_GT_NO_DM_MATH_COMPLIANT", -"GEMINI_V2_FIRSTTIMES_SCIENCE_PDF_DEDUP_HQ_LENGTH_FILTERED_COMPLIANT", -"GEMINI_V2_PODCASTS_COMPLIANT", -"GEMINI_V2_EN_NONSCIENCE_PDF_DEDUP_46M_DOCS_COMPLIANT", -"GEMINI_V2_NONPUB_COPYRIGHT_BOOKS_V3_70_CONF_082323_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_COPYRIGHT_BOOKS_V3_111823_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_BOOKS_318K_TEXT_COMPLIANT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_T2I_TEXT_TO_IMAGE_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_I18N_680M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_FULL_HTML_75M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_V1_1_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_DOC_240M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_VIDEO_VIDEO_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M4W_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CULTURE_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_DETECTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_ALT_TEXT_NONEN_500M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SPATIAL_AWARE_PALI_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_3D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_NON_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_3D_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CC3M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_INFOGRAPHICS_LARGE_WEB_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_BIORXIV_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PLOT2TABLE_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TIKZ_DERENDERING_MERGED_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_2D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WIKIPEDIA_EQUATIONS_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_EQ2LATEX_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_ARXIV_EQUATIONS_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_HEAD_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_CLM_TRANSLATE_DATAV3_WEB_UNWMT_INCR_MIX", -"GEMINI_V2_NTL_NTLV4A_MONOLINGUAL_DEDUP_N5", -"GEMINI_V2_NTL_STT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_TRANSLIT_BILEX_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_BT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_FT_FIXED_TRANSLATE_DEDUP_N5", -"GEMINI_V2_CANARIES_SHUFFLED_COMPLIANT", -"CLOUD_GEMIT_CLOUD_FACTUALITY_GROUNDING_MAGI", -"CLOUD_GEMIT_MT_DIALGUE_LMSYS", -"CLOUD_GEMIT_MTS_DIALOGUE_V3", -"CLOUD_GEMIT_COMMIT_MSG_GEN_V3", -"CLOUD_GEMIT_CODE_IF_V1", -"CLOUD_GEMIT_CODE_SELF_REPAIR", -"CLOUD_GEMIT_IDENTITY", -"CLOUD_GEMIT_SEARCH_AUGMENTED_RESPONSE_GENERATION", -"CLOUD_GEMIT_AMPS", -"CLOUD_GEMIT_AQUA", -"CLOUD_GEMIT_COMMON_SENSE_REASONING_SCHEMA", -"CLOUD_GEMIT_GSM8K_SCHEMA", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_UN", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_EUROPARL", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_NEWSCOMMENTARY", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_2021_INCR", -"GEMINI_V1_TAIL_PATCH_GOALDMINE", -"GEMINI_V1_TAIL_PATCH_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION", -"GEMINI_V1_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V1_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V1_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V2_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V2_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_PMC", -"GEMINI_V2_TAIL_PATCH_VOXPOPULI", -"GEMINI_V2_TAIL_PATCH_FLEURS", -"GEMINI_V2_SSFS", -"GEMINI_V2_CODE_TRANSFORM_SYNTHETIC_ERROR_FIX", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_COMMITS", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_PR", -"GEMINI_V2_SQL_REPAIR_SFT", -"GEMINI_V2_JSON_MODE_SYS_INSTRUCTION", -"YT_CONTENT_INSPIRATION" -], -"enumDescriptions": [ -"", -"Wikipedia article Tensorflow datasets used by Tarzan and maintained by TFDS team.", -"Webdocs that have been filtered from the docjoins by the Tarzan team for use in the Tarzan training set.", -"", -"", -"'Full view' books dataset maintained by Oceanographers team, meaning 'ok to view the book in full in all localities'. Largely the same as 'public domain', but with potentially subtle distinction.", -"Filtered private books used by ULM: http://google3/learning/multipod/pax/lm/params/ulm/tasks.py;l=123;rcl=494241309. which corresponds with /cns/mf-d/home/multipod-language-data/private_books/books_filtered_en_resharded@50000", -"Google news dataset referenced in: http://google3/learning/brain/research/conversation/meena/t5/pretrain_tasks.py;l=922;rcl=496534668", -"The docjoins data for ULM /cns/yo-d/home/multipod-language-data/docjoins/rs=6.3/20220728/100B_docstructure_split/examples_en.tfrecord_lattice_05_score_01_HFV13@3929", -"", -"Meena full conversations. http://google3/learning/brain/research/conversation/meena/t5/pretrain_mixtures.py;l=675;rcl=496583228", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Academic dataset of math text. http://google3/learning/brain/research/conversation/meena/seqio/mixtures/experimental/bard.py;rcl=500222380", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Datasets managed by the Goodall team: deepmind-goodall@google.com", -"", -"", -"", -"", -"", -"", -"", -"Datasets used by Codepoet", -"Datasets used by Vertex", -"", -"", -"Datasets used by Gemini Public data", -"", -"", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"Github", -"", -"", -"", -"", -"", -"Arxiv", -"Others", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V1, order by precedence. Wikipedia", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Github dataset with license info. We prefer this to help cite proper licenses for code recitation.", -"", -"", -"", -"", -"", -"", -"ArXiv", -"Citable misc", -"", -"", -"Non-public books", -"", -"", -"Other", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Duet AI finetune datasets, order by precedence.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Bard ARCADE finetune dataset.", -"Mobile assistant finetune datasets.", -"", -"Genesis fine-tune datasets.", -"Cloud Security fine-tune datasets.", -"", -"", -"LABS AQA fine-tune datasets.", -"", -"", -"Gemini multimodal instruction tune(IT) and fine tune(FT) datasets datasets.", -"", -"", -"", -"", -"", -"", -"Gemini IT 1.2.7 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemit Bridge ULM FT dataset", -"Gemini Goose FT datasets.", -"", -"Gemini V2 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Cloud gemit pro FT datasets.", -"", -"", -"", -"", -"", -"", -"Cloud gemit ultra FT datasets.", -"", -"", -"", -"", -"Gemini V1 tail patch translation.", -"", -"", -"", -"Gemini V1 tail patch others.", -"", -"Gemini V1 and V2 shared tail patch.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V2 only tail patch.", -"", -"", -"Gemini V2 rev10", -"", -"", -"", -"", -"", -"Youtube Content Inpsiration." -], -"type": "string" -}, -"filepath": { -"type": "string" -}, -"geminiId": { -"type": "string" -}, -"gnewsArticleTitle": { -"type": "string" -}, -"goodallExampleId": { -"type": "string" -}, -"isOptOut": { -"description": "Whether the document is opted out.", -"type": "boolean" -}, -"isPrompt": { -"type": "boolean" -}, -"lamdaExampleId": { -"type": "string" -}, -"license": { -"type": "string" -}, -"meenaConversationId": { -"type": "string" -}, -"naturalLanguageCode": { -"description": "Natural (not programming) language of the document. Language code as defined by http://www.unicode.org/reports/tr35/#Identifiers and https://tools.ietf.org/html/bcp47. Currently applicable to full-view books. Use docinfo-util.h to set & read language fields. See go/iii.", -"type": "string" -}, -"noAttribution": { -"description": "True if this doc has no attribution information available. We use an explicit field for this instead of just implicitly leaving all the DocAttribution fields blank to distinguish a case where a bug/oversight has left the attribution information empty vs when we really have no attribution information available.", -"type": "boolean" -}, -"podcastUtteranceId": { -"type": "string" -}, -"publicationDate": { -"$ref": "GoogleTypeDate" -}, -"qualityScoreExperimentOnly": { -"description": "This field is for opt-out experiment only, MUST never be used during actual production/serving. ", -"format": "double", -"type": "number" -}, -"repo": { -"description": "Github repository", -"type": "string" -}, -"url": { -"description": "URL of a webdoc", -"type": "string" -}, -"volumeId": { -"type": "string" -}, -"wikipediaArticleTitle": { -"description": "Wikipedia article title. The Wikipedia TFDS dataset includes article titles but not URLs. While a URL is to the best of our knowledge a deterministic function of the title, we store the original title to reflect the information in the original dataset.", -"type": "string" -}, -"youtubeVideoId": { -"description": "The unique video id from Youtube. Example: AkoGsW52Ir0", -"type": "string" -} -}, -"type": "object" -}, -"LanguageLabsAidaTrustRecitationProtoRecitationResult": { -"description": "The recitation result for one input", -"id": "LanguageLabsAidaTrustRecitationProtoRecitationResult", -"properties": { -"dynamicSegmentResults": { -"items": { -"$ref": "LanguageLabsAidaTrustRecitationProtoSegmentResult" -}, -"type": "array" -}, -"recitationAction": { -"description": "The recitation action for one given input. When its segments contain different actions, the overall action will be returned in the precedence of BLOCK > CITE > NO_ACTION. When the given input is not found in any source, the recitation action will not be specified.", -"enum": [ -"ACTION_UNSPECIFIED", -"CITE", -"BLOCK", -"NO_ACTION", -"EXEMPT_FOUND_IN_PROMPT" -], -"enumDescriptions": [ -"", -"indicate that attribution must be shown for a Segment", -"indicate that a Segment should be blocked from being used", -"for tagging high-frequency code snippets", -"The recitation was found in prompt and is exempted from overall results" -], -"type": "string" -}, -"trainingSegmentResults": { -"items": { -"$ref": "LanguageLabsAidaTrustRecitationProtoSegmentResult" -}, -"type": "array" -} -}, -"type": "object" -}, -"LanguageLabsAidaTrustRecitationProtoSegmentResult": { -"description": "The recitation result for each segment in a given input.", -"id": "LanguageLabsAidaTrustRecitationProtoSegmentResult", -"properties": { -"attributionDataset": { -"description": "The dataset the segment came from. Datasets change often as model evolves. Treat this field as informational only and avoid depending on it directly.", -"enum": [ -"DATASET_UNSPECIFIED", -"WIKIPEDIA", -"WEBDOCS", -"WEBDOCS_FINETUNE", -"GITHUB_MIRROR", -"BOOKS_FULL_VIEW", -"BOOKS_PRIVATE", -"GNEWS", -"ULM_DOCJOINS", -"ULM_DOCJOINS_DEDUPED", -"MEENA_FC", -"PODCAST", -"AQUA", -"WEB_ASR", -"BARD_GOLDEN", -"COMMON_SENSE_REASONING", -"MATH", -"MATH_REASONING", -"CLEAN_ARXIV", -"LAMDA_FACTUALITY_E2E_QUERY_GENERATION", -"LAMDA_FACTUALITY_E2E_RESPONSE_GENERATION", -"MASSIVE_FORUM_THREAD_SCORED_BARD", -"MASSIVE_FORUM_THREAD_SCORED_LONG_200", -"MASSIVE_FORUM_THREAD_SCORED_LONG_500", -"DOCUMENT_CHUNKS", -"MEENA_RESEARCH_PHASE_GOLDEN_MARKDOWN", -"MEENA_RESEARCH_PHASE_GOOGLERS", -"MEENA_RESPONSE_SAFETY_HUMAN_GEN", -"MEENA_RESPONSE_SAFETY_SCHEMA_NO_BROADCAST", -"MEENA_RESPONSE_SAFETY_V3_HUMAN_GEN2", -"MEENA_RESPONSE_SAFETY_V3_SCHEMA_NO_BROADCAST", -"LAMDA_FACTUALITY_TRIGGER", -"LAMDA_SAFETY_V2_SCHEMA_NO_BROADCAST", -"LAMDA_SSI_DISCRIMINATIVE", -"ASSISTANT_PERSONALITY_SAFETY", -"PODCAST_FINETUNE_DIALOG", -"WORLD_QUERY_GENERATOR", -"C4_JOINED_DOCJOINS", -"HOL4_THEORIES", -"HOL_LIGHT_THEORIES", -"HOLSTEPS", -"ISABELLE_STEP", -"ISABELLE_THEORIES", -"LEAN_MATHLIB_THEORIES", -"LEAN_STEP", -"MIZAR_THEORIES", -"COQ_STEP", -"COQ_THEORIES", -"AMPS_KHAN", -"AMPS_MATHEMATICA", -"CODEY_CODE", -"CODE_QA_SE", -"CODE_QA_SO", -"CODE_QA_FT_FORMAT", -"CODE_QA_FT_KNOWLEDGE", -"CODE_QA_GITHUB_FILTERED_CODE", -"BARD_PERSONALITY_GOLDEN", -"ULM_DOCJOINS_WITH_URLS_EN", -"ULM_DOCJOINS_WITH_URLS_I18N", -"GOODALL_MTV5_GITHUB", -"GOODALL_MTV5_BOOKS", -"GOODALL_MTV5_C4", -"GOODALL_MTV5_WIKIPEDIA", -"GOODALL_MW_TOP_100B", -"GOODALL_MW_STACK_EXCHANGE", -"GOODALL_MW_TOP_0_10B", -"GOODALL_MW_TOP_10B_20B", -"CODEY_NOTEBOOK_LM_PRETRAINING", -"VERTEX_SAFE_FLAN", -"GITHUB_MIRROR_V1_0_1", -"GITHUB_MIRROR_V2_1_0", -"CMS_WIKIPEDIA_LANG_FILTERED", -"CMS_STACKOVERFLOW_MULTILINGUAL", -"CMS_STACKEXCHANGE", -"PUBMED", -"GEMINI_DOCJOINS_EN_TOP10B_GCC", -"GEMINI_DOCJOINS_EN_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_EN_TOP20B_TOP100B_GCC", -"GEMINI_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_I18N_TOP20B_TOP100B_GCC", -"SIMPLIFIED_HTML_V1_GCC", -"GEMINI_DOCJOINS_TOXICITY_TAGGED_GCC", -"CMS_GITHUB_V4", -"GITHUB_HTML_V4", -"GITHUB_OTHER_V4", -"GITHUB_LONG_TAIL_V4", -"CMS_GITHUB_MULTIFILE_V4", -"GITHUB_DIFFS_WITH_COMMIT_MESSAGE", -"ULM_ARXIV", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_ENONLY", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_NONENONLY", -"QUORA", -"PODCASTS_ROBOTSTXT", -"COMBINED_REDDIT", -"CANARIES_SHUFFLED", -"CLM_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"TECHDOCS_DATA_SOURCE", -"SCIENCE_PDF_70M_DOCS_FILTERED", -"GEMINI_V1_CMS_WIKIPEDIA_LANG_FILTERED", -"GEMINI_V1_WIKIPEDIA_DIFFS", -"GEMINI_V1_DOCJOINS_EN_TOP10B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP10B_TOP20B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP20B_TOP100B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_TOP20B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP20B_TOP100B_GCC_050523", -"GEMINI_V1_SIMPLIFIED_HTML_V2_GCC", -"GEMINI_V1_CMS_STACKOVERFLOW_MULTILINGUAL_V2", -"GEMINI_V1_CMS_STACKEXCHANGE_DECONT", -"GEMINI_V1_QUORA", -"GEMINI_V1_COMBINED_REDDIT", -"GEMINI_V1_DOCJOIN_100B_EN_TOXICITY_TAGGED_GCC_FIXED_TAGS", -"GEMINI_V1_PUBMED", -"GEMINI_V1_WEB_MATH_V2", -"GEMINI_V1_CMS_GITHUB_V7", -"GEMINI_V1_CMS_GITHUB_DECONTAMINATED_V_7", -"GEMINI_V1_GITHUB_DIFF_WITH_COMMIT_MESSAGE_V2", -"GEMINI_V1_GITHUB_HTML_CSS_XML_V4", -"GEMINI_V1_GITHUB_OTHER_V4", -"GEMINI_V1_GITHUB_LONG_TAIL_V4", -"GEMINI_V1_GITHUB_JUPTYER_NOTEBOOKS_SSTABLE", -"GEMINI_V1_ULM_ARXIV_SSTABLE", -"GEMINI_V1_PODCASTS_ROBOTSTXT", -"GEMINI_V1_SCIENCE_PDF_68M_HQ_DOCS_GCC", -"GEMINI_V1_GITHUB_TECHDOCS_V2", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_EN", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_NONEN", -"GEMINI_V1_STEM_BOOKS_650K_TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_M3W_V2_FILTERED", -"GEMINI_V1_VQCOCA_1B_MULTIRES_WEBLI_EN_V4_350M_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_SCREENAI_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CULTURE_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_EN_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_I18N_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_NON_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_VTP_4F_VIDEO2TEXT_PREFIX", -"GEMINI_V1_FORMAL_MATH_WITHOUT_HOLSTEPS_AND_MIZAR", -"GEMINI_V1_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"GEMINI_V1_CANARIES_SHUFFLED_DOCJOIN_EN_NONEN_CODE_ARXIV_TRANSLATE", -"DUET_CLOUD_SECURITY_DOCS", -"DUET_GITHUB_CODE_SNIPPETS", -"DUET_GITHUB_FILES", -"DUET_GOBYEXAMPLE", -"DUET_GOLANG_DOCS", -"DUET_CLOUD_DOCS_TROUBLESHOOTING_TABLES", -"DUET_DEVSITE_DOCS", -"DUET_CLOUD_BLOG_POSTS", -"DUET_CLOUD_PODCAST_EPISODES", -"DUET_YOUTUBE_VIDEOS", -"DUET_CLOUD_SKILLS_BOOST", -"DUET_CLOUD_DOCS", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_GENERATED", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_HANDWRITTEN", -"DUET_GOOGLESQL_GENERATION", -"DUET_CLOUD_IX_PROMPTS", -"DUET_RAD", -"DUET_STACKOVERFLOW_ISSUES", -"DUET_STACKOVERFLOW_ANSWERS", -"BARD_ARCADE_GITHUB", -"MOBILE_ASSISTANT_MAGI_FILTERED_0825_373K", -"MOBILE_ASSISTANT_PALM24B_FILTERED_400K", -"GENESIS_NEWS_INSIGHTS", -"CLOUD_SECURITY_PRETRAINING", -"CLOUD_SECURITY_FINETUNING", -"CLOUD_SECURITY_RAG_CISA", -"LABS_AQA_DSCOUT", -"LABS_AQA_TAILWIND", -"LABS_AQA_DELEWARE", -"GEMINI_MULTIMODAL_FT_URL", -"GEMINI_MULTIMODAL_FT_YT", -"GEMINI_MULTIMODAL_FT_SHUTTERSTOCK", -"GEMINI_MULTIMODAL_FT_NONE", -"GEMINI_MULTIMODAL_FT_OTHER", -"GEMINI_MULTIMODAL_FT_INK", -"GEMINI_MULTIMODAL_IT", -"GEMINI_IT_SHUTTERSTOCK", -"GEMINI_IT_M3W", -"GEMINI_IT_HEDGING", -"GEMINI_IT_DSCOUT_FACTUALITY", -"GEMINI_IT_AQUAMUSE", -"GEMINI_IT_SHOTGUN", -"GEMINI_IT_ACI_BENCH", -"GEMINI_IT_SPIDER_FILTERED", -"GEMINI_IT_TAB_SUM_BQ", -"GEMINI_IT_QA_WITH_URL", -"GEMINI_IT_CODE_INSTRUCT", -"GEMINI_IT_MED_PALM", -"GEMINI_IT_TASK_ORIENTED_DIALOG", -"GEMINI_IT_NIMBUS_GROUNDING_TO_PROMPT", -"GEMINI_IT_EITL_GEN", -"GEMINI_IT_HITL_GEN", -"GEMINI_IT_MECH", -"GEMINI_IT_TABLE_GEN", -"GEMINI_IT_NIMBUS_DECIBEL", -"GEMINI_IT_CLOUD_CODE_IF", -"GEMINI_IT_CLOUD_EUR_LEX_JSON", -"GEMINI_IT_CLOUD_OASST", -"GEMINI_IT_CLOUD_SELF_INSTRUCT", -"GEMINI_IT_CLOUD_UCS_AQUAMUSE", -"GEMIT_BRIDGE_SUFFIX_FT", -"GEMINI_GOOSE_PUBLIC", -"GEMINI_GOOSE_SILOED", -"GEMINI_V2_CMS_WIKIPEDIA_LANG_FILTERED_GCC_PII", -"GEMINI_V2_WIKIPEDIA_DIFFS_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_QUORA_COMPLIANT", -"GEMINI_V2_FORUMS_V2_COMPLIANT", -"GEMINI_V2_CMS_STACKOVERFLOW_MULTILINGUAL_V2_COMPLIANT", -"GEMINI_V2_SIMPLIFIED_HTML_V2_CORRECT_FORMAT_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_TOXICITY_TAGGED_FIXED_TAGS_COMPLIANT", -"GEMINI_V2_CODEWEB_V1_COMPLIANT", -"GEMINI_V2_LEETCODE_GCC_PII", -"GEMINI_V2_CODE_CONTESTS_COMPLIANT", -"GEMINI_V2_CMS_GITHUB_MULTI_FILE_FOR_FIM_GEMBAGZ_FIXED_BYTES_LENGTHS", -"GEMINI_V2_GITHUB_EVALED_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_NON_EVAL_HIGH_PRI_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_LOW_PRI_LANGUAGES_AND_CONFIGS_COMPLIANT", -"GEMINI_V2_GITHUB_LONG_TAIL_AND_STRUCTURED_DATA_COMPLIANT", -"GEMINI_V2_GITHUB_PYTHON_NOTEBOOKS_COMPLIANT", -"GEMINI_V2_GITHUB_DIFFS_COMPLIANT", -"GEMINI_V2_GITHUB_TECHDOCS_COMPLIANT", -"GEMINI_V2_HIGH_QUALITY_CODE_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_SCIENCE_PDF_68M_HQ_DOCS_DEDUP_COMPLIANT_CLEAN_TEX", -"GEMINI_V2_ARXIV_2023_COMPLIANT", -"GEMINI_V2_FORMAL_COMPLIANT", -"GEMINI_V2_CMS_STACKEXCHANGE_COMPLIANT", -"GEMINI_V2_PUBMED_COMPLIANT", -"GEMINI_V2_WEB_MATH_V3_COMPLIANT", -"GEMINI_V2_SCIENCEWEB_V0_GCC_PII", -"GEMINI_V2_WEB_POLYMATH_V1_COMPLIANT", -"GEMINI_V2_MATH_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_BIOLOGY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_PHYSICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHEMISTRY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MACHINE_LEARNING_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_QA_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_ECONOMICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MEDICAL_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHESS_COMPLIANT", -"GEMINI_V2_YOUTUBE_SCIENCE_V4_FILTERED_COMPLIANT", -"GEMINI_V2_GOALDMINE_XL_GENERATED_PLUS_GT_NO_DM_MATH_COMPLIANT", -"GEMINI_V2_FIRSTTIMES_SCIENCE_PDF_DEDUP_HQ_LENGTH_FILTERED_COMPLIANT", -"GEMINI_V2_PODCASTS_COMPLIANT", -"GEMINI_V2_EN_NONSCIENCE_PDF_DEDUP_46M_DOCS_COMPLIANT", -"GEMINI_V2_NONPUB_COPYRIGHT_BOOKS_V3_70_CONF_082323_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_COPYRIGHT_BOOKS_V3_111823_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_BOOKS_318K_TEXT_COMPLIANT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_T2I_TEXT_TO_IMAGE_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_I18N_680M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_FULL_HTML_75M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_V1_1_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_DOC_240M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_VIDEO_VIDEO_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M4W_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CULTURE_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_DETECTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_ALT_TEXT_NONEN_500M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SPATIAL_AWARE_PALI_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_3D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_NON_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_3D_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CC3M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_INFOGRAPHICS_LARGE_WEB_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_BIORXIV_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PLOT2TABLE_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TIKZ_DERENDERING_MERGED_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_2D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WIKIPEDIA_EQUATIONS_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_EQ2LATEX_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_ARXIV_EQUATIONS_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_HEAD_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_CLM_TRANSLATE_DATAV3_WEB_UNWMT_INCR_MIX", -"GEMINI_V2_NTL_NTLV4A_MONOLINGUAL_DEDUP_N5", -"GEMINI_V2_NTL_STT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_TRANSLIT_BILEX_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_BT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_FT_FIXED_TRANSLATE_DEDUP_N5", -"GEMINI_V2_CANARIES_SHUFFLED_COMPLIANT", -"CLOUD_GEMIT_CLOUD_FACTUALITY_GROUNDING_MAGI", -"CLOUD_GEMIT_MT_DIALGUE_LMSYS", -"CLOUD_GEMIT_MTS_DIALOGUE_V3", -"CLOUD_GEMIT_COMMIT_MSG_GEN_V3", -"CLOUD_GEMIT_CODE_IF_V1", -"CLOUD_GEMIT_CODE_SELF_REPAIR", -"CLOUD_GEMIT_IDENTITY", -"CLOUD_GEMIT_SEARCH_AUGMENTED_RESPONSE_GENERATION", -"CLOUD_GEMIT_AMPS", -"CLOUD_GEMIT_AQUA", -"CLOUD_GEMIT_COMMON_SENSE_REASONING_SCHEMA", -"CLOUD_GEMIT_GSM8K_SCHEMA", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_UN", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_EUROPARL", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_NEWSCOMMENTARY", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_2021_INCR", -"GEMINI_V1_TAIL_PATCH_GOALDMINE", -"GEMINI_V1_TAIL_PATCH_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION", -"GEMINI_V1_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V1_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V1_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V2_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V2_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_PMC", -"GEMINI_V2_TAIL_PATCH_VOXPOPULI", -"GEMINI_V2_TAIL_PATCH_FLEURS", -"GEMINI_V2_SSFS", -"GEMINI_V2_CODE_TRANSFORM_SYNTHETIC_ERROR_FIX", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_COMMITS", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_PR", -"GEMINI_V2_SQL_REPAIR_SFT", -"GEMINI_V2_JSON_MODE_SYS_INSTRUCTION", -"YT_CONTENT_INSPIRATION" -], -"enumDescriptions": [ -"", -"Wikipedia article Tensorflow datasets used by Tarzan and maintained by TFDS team.", -"Webdocs that have been filtered from the docjoins by the Tarzan team for use in the Tarzan training set.", -"", -"", -"'Full view' books dataset maintained by Oceanographers team, meaning 'ok to view the book in full in all localities'. Largely the same as 'public domain', but with potentially subtle distinction.", -"Filtered private books used by ULM: http://google3/learning/multipod/pax/lm/params/ulm/tasks.py;l=123;rcl=494241309. which corresponds with /cns/mf-d/home/multipod-language-data/private_books/books_filtered_en_resharded@50000", -"Google news dataset referenced in: http://google3/learning/brain/research/conversation/meena/t5/pretrain_tasks.py;l=922;rcl=496534668", -"The docjoins data for ULM /cns/yo-d/home/multipod-language-data/docjoins/rs=6.3/20220728/100B_docstructure_split/examples_en.tfrecord_lattice_05_score_01_HFV13@3929", -"", -"Meena full conversations. http://google3/learning/brain/research/conversation/meena/t5/pretrain_mixtures.py;l=675;rcl=496583228", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Academic dataset of math text. http://google3/learning/brain/research/conversation/meena/seqio/mixtures/experimental/bard.py;rcl=500222380", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Datasets managed by the Goodall team: deepmind-goodall@google.com", -"", -"", -"", -"", -"", -"", -"", -"Datasets used by Codepoet", -"Datasets used by Vertex", -"", -"", -"Datasets used by Gemini Public data", -"", -"", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"Github", -"", -"", -"", -"", -"", -"Arxiv", -"Others", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V1, order by precedence. Wikipedia", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Github dataset with license info. We prefer this to help cite proper licenses for code recitation.", -"", -"", -"", -"", -"", -"", -"ArXiv", -"Citable misc", -"", -"", -"Non-public books", -"", -"", -"Other", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Duet AI finetune datasets, order by precedence.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Bard ARCADE finetune dataset.", -"Mobile assistant finetune datasets.", -"", -"Genesis fine-tune datasets.", -"Cloud Security fine-tune datasets.", -"", -"", -"LABS AQA fine-tune datasets.", -"", -"", -"Gemini multimodal instruction tune(IT) and fine tune(FT) datasets datasets.", -"", -"", -"", -"", -"", -"", -"Gemini IT 1.2.7 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemit Bridge ULM FT dataset", -"Gemini Goose FT datasets.", -"", -"Gemini V2 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Cloud gemit pro FT datasets.", -"", -"", -"", -"", -"", -"", -"Cloud gemit ultra FT datasets.", -"", -"", -"", -"", -"Gemini V1 tail patch translation.", -"", -"", -"", -"Gemini V1 tail patch others.", -"", -"Gemini V1 and V2 shared tail patch.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V2 only tail patch.", -"", -"", -"Gemini V2 rev10", -"", -"", -"", -"", -"", -"Youtube Content Inpsiration." -], -"type": "string" -}, -"displayAttributionMessage": { -"description": "human-friendly string that contains information from doc_attribution which could be shown by clients", -"type": "string" -}, -"docAttribution": { -"$ref": "LanguageLabsAidaTrustRecitationProtoDocAttribution" -}, -"docOccurrences": { -"description": "number of documents that contained this segment", -"format": "int32", -"type": "integer" -}, -"endIndex": { -"format": "int32", -"type": "integer" -}, -"rawText": { -"description": "The raw text in the given input that is corresponding to the segment. It will be available only when 'return_segment_raw_text' is enabled in the request options.", -"type": "string" -}, -"segmentRecitationAction": { -"enum": [ -"ACTION_UNSPECIFIED", -"CITE", -"BLOCK", -"NO_ACTION", -"EXEMPT_FOUND_IN_PROMPT" -], -"enumDescriptions": [ -"", -"indicate that attribution must be shown for a Segment", -"indicate that a Segment should be blocked from being used", -"for tagging high-frequency code snippets", -"The recitation was found in prompt and is exempted from overall results" -], -"type": "string" -}, -"sourceCategory": { -"description": "The category of the source dataset where the segment came from. This is more stable than Dataset.", -"enum": [ -"SOURCE_CATEGORY_UNSPECIFIED", -"SOURCE_CATEGORY_WIKIPEDIA", -"SOURCE_CATEGORY_WEBDOCS", -"SOURCE_CATEGORY_GITHUB", -"SOURCE_CATEGORY_ARXIV", -"SOURCE_CATEGORY_PRIVATE_BOOKS", -"SOURCE_CATEGORY_OTHERS", -"SOURCE_CATEGORY_PUBLIC_BOOKS", -"SOURCE_CATEGORY_GNEWS" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"", -"", -"", -"" -], -"type": "string" -}, -"startIndex": { -"description": "The segment boundary start (inclusive) and end index (exclusive) in the given text. In the streaming RPC, the indexes always start from the beginning of the first text in the entire stream. The indexes are measured in UTF-16 code units.", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"LanguageLabsAidaTrustRecitationProtoStreamRecitationResult": { -"description": "The recitation result for one stream input", -"id": "LanguageLabsAidaTrustRecitationProtoStreamRecitationResult", -"properties": { -"dynamicSegmentResults": { -"description": "The recitation result against the given dynamic data source.", -"items": { -"$ref": "LanguageLabsAidaTrustRecitationProtoSegmentResult" -}, -"type": "array" -}, -"fullyCheckedTextIndex": { -"description": "Last index of input text fully checked for recitation in the entire streaming context. Would return `-1` if no Input was checked for recitation.", -"format": "int32", -"type": "integer" -}, -"recitationAction": { -"description": "The recitation action for one given input. When its segments contain different actions, the overall action will be returned in the precedence of BLOCK > CITE > NO_ACTION.", -"enum": [ -"ACTION_UNSPECIFIED", -"CITE", -"BLOCK", -"NO_ACTION", -"EXEMPT_FOUND_IN_PROMPT" -], -"enumDescriptions": [ -"", -"indicate that attribution must be shown for a Segment", -"indicate that a Segment should be blocked from being used", -"for tagging high-frequency code snippets", -"The recitation was found in prompt and is exempted from overall results" -], -"type": "string" -}, -"trainingSegmentResults": { -"description": "The recitation result against model training data.", -"items": { -"$ref": "LanguageLabsAidaTrustRecitationProtoSegmentResult" -}, -"type": "array" -} -}, -"type": "object" -}, -"LearningGenaiRecitationDocAttribution": { -"description": "The proto defines the attribution information for a document using whatever fields are most applicable for that document's datasource. For example, a Wikipedia article's attribution is in the form of its article title, a website is in the form of a URL, and a Github repo is in the form of a repo name. Next id: 30", -"id": "LearningGenaiRecitationDocAttribution", -"properties": { -"amarnaId": { -"type": "string" -}, -"arxivId": { -"type": "string" -}, -"author": { -"type": "string" -}, -"bibkey": { -"type": "string" -}, -"biorxivId": { -"description": "ID of the paper in bioarxiv like ddoi.org/{biorxiv_id} eg: https://doi.org/10.1101/343517", -"type": "string" -}, -"bookTitle": { -"type": "string" -}, -"bookVolumeId": { -"description": "The Oceanographers full-view books dataset uses a 'volume id' as the unique ID of a book. There is a deterministic function from a volume id to a URL under the books.google.com domain. Marked as 'optional' since a volume ID of zero is potentially possible and we want to distinguish that from the volume ID not being set.", -"format": "int64", -"type": "string" -}, -"conversationId": { -"type": "string" -}, -"dataset": { -"description": "The dataset this document comes from.", -"enum": [ -"DATASET_UNSPECIFIED", -"WIKIPEDIA", -"WEBDOCS", -"WEBDOCS_FINETUNE", -"GITHUB_MIRROR", -"BOOKS_FULL_VIEW", -"BOOKS_PRIVATE", -"GNEWS", -"ULM_DOCJOINS", -"ULM_DOCJOINS_DEDUPED", -"MEENA_FC", -"PODCAST", -"AQUA", -"WEB_ASR", -"BARD_GOLDEN", -"COMMON_SENSE_REASONING", -"MATH", -"MATH_REASONING", -"CLEAN_ARXIV", -"LAMDA_FACTUALITY_E2E_QUERY_GENERATION", -"LAMDA_FACTUALITY_E2E_RESPONSE_GENERATION", -"MASSIVE_FORUM_THREAD_SCORED_BARD", -"MASSIVE_FORUM_THREAD_SCORED_LONG_200", -"MASSIVE_FORUM_THREAD_SCORED_LONG_500", -"DOCUMENT_CHUNKS", -"MEENA_RESEARCH_PHASE_GOLDEN_MARKDOWN", -"MEENA_RESEARCH_PHASE_GOOGLERS", -"MEENA_RESPONSE_SAFETY_HUMAN_GEN", -"MEENA_RESPONSE_SAFETY_SCHEMA_NO_BROADCAST", -"MEENA_RESPONSE_SAFETY_V3_HUMAN_GEN2", -"MEENA_RESPONSE_SAFETY_V3_SCHEMA_NO_BROADCAST", -"LAMDA_FACTUALITY_TRIGGER", -"LAMDA_SAFETY_V2_SCHEMA_NO_BROADCAST", -"LAMDA_SSI_DISCRIMINATIVE", -"ASSISTANT_PERSONALITY_SAFETY", -"PODCAST_FINETUNE_DIALOG", -"WORLD_QUERY_GENERATOR", -"C4_JOINED_DOCJOINS", -"HOL4_THEORIES", -"HOL_LIGHT_THEORIES", -"HOLSTEPS", -"ISABELLE_STEP", -"ISABELLE_THEORIES", -"LEAN_MATHLIB_THEORIES", -"LEAN_STEP", -"MIZAR_THEORIES", -"COQ_STEP", -"COQ_THEORIES", -"AMPS_KHAN", -"AMPS_MATHEMATICA", -"CODEY_CODE", -"CODE_QA_SE", -"CODE_QA_SO", -"CODE_QA_FT_FORMAT", -"CODE_QA_FT_KNOWLEDGE", -"CODE_QA_GITHUB_FILTERED_CODE", -"BARD_PERSONALITY_GOLDEN", -"ULM_DOCJOINS_WITH_URLS_EN", -"ULM_DOCJOINS_WITH_URLS_I18N", -"GOODALL_MTV5_GITHUB", -"GOODALL_MTV5_BOOKS", -"GOODALL_MTV5_C4", -"GOODALL_MTV5_WIKIPEDIA", -"GOODALL_MW_TOP_100B", -"GOODALL_MW_STACK_EXCHANGE", -"GOODALL_MW_TOP_0_10B", -"GOODALL_MW_TOP_10B_20B", -"CODEY_NOTEBOOK_LM_PRETRAINING", -"VERTEX_SAFE_FLAN", -"GITHUB_MIRROR_V1_0_1", -"GITHUB_MIRROR_V2_1_0", -"CMS_WIKIPEDIA_LANG_FILTERED", -"CMS_STACKOVERFLOW_MULTILINGUAL", -"CMS_STACKEXCHANGE", -"PUBMED", -"GEMINI_DOCJOINS_EN_TOP10B_GCC", -"GEMINI_DOCJOINS_EN_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_EN_TOP20B_TOP100B_GCC", -"GEMINI_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_I18N_TOP20B_TOP100B_GCC", -"SIMPLIFIED_HTML_V1_GCC", -"GEMINI_DOCJOINS_TOXICITY_TAGGED_GCC", -"CMS_GITHUB_V4", -"GITHUB_HTML_V4", -"GITHUB_OTHER_V4", -"GITHUB_LONG_TAIL_V4", -"CMS_GITHUB_MULTIFILE_V4", -"GITHUB_DIFFS_WITH_COMMIT_MESSAGE", -"ULM_ARXIV", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_ENONLY", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_NONENONLY", -"QUORA", -"PODCASTS_ROBOTSTXT", -"COMBINED_REDDIT", -"CANARIES_SHUFFLED", -"CLM_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"TECHDOCS_DATA_SOURCE", -"SCIENCE_PDF_70M_DOCS_FILTERED", -"GEMINI_V1_CMS_WIKIPEDIA_LANG_FILTERED", -"GEMINI_V1_WIKIPEDIA_DIFFS", -"GEMINI_V1_DOCJOINS_EN_TOP10B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP10B_TOP20B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP20B_TOP100B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_TOP20B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP20B_TOP100B_GCC_050523", -"GEMINI_V1_SIMPLIFIED_HTML_V2_GCC", -"GEMINI_V1_CMS_STACKOVERFLOW_MULTILINGUAL_V2", -"GEMINI_V1_CMS_STACKEXCHANGE_DECONT", -"GEMINI_V1_QUORA", -"GEMINI_V1_COMBINED_REDDIT", -"GEMINI_V1_DOCJOIN_100B_EN_TOXICITY_TAGGED_GCC_FIXED_TAGS", -"GEMINI_V1_PUBMED", -"GEMINI_V1_WEB_MATH_V2", -"GEMINI_V1_CMS_GITHUB_V7", -"GEMINI_V1_CMS_GITHUB_DECONTAMINATED_V_7", -"GEMINI_V1_GITHUB_DIFF_WITH_COMMIT_MESSAGE_V2", -"GEMINI_V1_GITHUB_HTML_CSS_XML_V4", -"GEMINI_V1_GITHUB_OTHER_V4", -"GEMINI_V1_GITHUB_LONG_TAIL_V4", -"GEMINI_V1_GITHUB_JUPTYER_NOTEBOOKS_SSTABLE", -"GEMINI_V1_ULM_ARXIV_SSTABLE", -"GEMINI_V1_PODCASTS_ROBOTSTXT", -"GEMINI_V1_SCIENCE_PDF_68M_HQ_DOCS_GCC", -"GEMINI_V1_GITHUB_TECHDOCS_V2", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_EN", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_NONEN", -"GEMINI_V1_STEM_BOOKS_650K_TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_M3W_V2_FILTERED", -"GEMINI_V1_VQCOCA_1B_MULTIRES_WEBLI_EN_V4_350M_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_SCREENAI_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CULTURE_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_EN_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_I18N_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_NON_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_VTP_4F_VIDEO2TEXT_PREFIX", -"GEMINI_V1_FORMAL_MATH_WITHOUT_HOLSTEPS_AND_MIZAR", -"GEMINI_V1_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"GEMINI_V1_CANARIES_SHUFFLED_DOCJOIN_EN_NONEN_CODE_ARXIV_TRANSLATE", -"DUET_CLOUD_SECURITY_DOCS", -"DUET_GITHUB_CODE_SNIPPETS", -"DUET_GITHUB_FILES", -"DUET_GOBYEXAMPLE", -"DUET_GOLANG_DOCS", -"DUET_CLOUD_DOCS_TROUBLESHOOTING_TABLES", -"DUET_DEVSITE_DOCS", -"DUET_CLOUD_BLOG_POSTS", -"DUET_CLOUD_PODCAST_EPISODES", -"DUET_YOUTUBE_VIDEOS", -"DUET_CLOUD_SKILLS_BOOST", -"DUET_CLOUD_DOCS", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_GENERATED", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_HANDWRITTEN", -"DUET_GOOGLESQL_GENERATION", -"DUET_CLOUD_IX_PROMPTS", -"DUET_RAD", -"DUET_STACKOVERFLOW_ISSUES", -"DUET_STACKOVERFLOW_ANSWERS", -"BARD_ARCADE_GITHUB", -"MOBILE_ASSISTANT_MAGI_FILTERED_0825_373K", -"MOBILE_ASSISTANT_PALM24B_FILTERED_400K", -"GENESIS_NEWS_INSIGHTS", -"LABS_AQA_DSCOUT", -"LABS_AQA_TAILWIND", -"LABS_AQA_DELEWARE", -"GEMINI_MULTIMODAL_FT_URL", -"GEMINI_MULTIMODAL_FT_YT", -"GEMINI_MULTIMODAL_FT_SHUTTERSTOCK", -"GEMINI_MULTIMODAL_FT_NONE", -"GEMINI_MULTIMODAL_FT_OTHER", -"GEMINI_MULTIMODAL_FT_INK", -"GEMINI_MULTIMODAL_IT", -"GEMINI_IT_SHUTTERSTOCK", -"GEMINI_IT_M3W", -"GEMINI_IT_HEDGING", -"GEMINI_IT_DSCOUT_FACTUALITY", -"GEMINI_IT_AQUAMUSE", -"GEMINI_IT_SHOTGUN", -"GEMINI_IT_ACI_BENCH", -"GEMINI_IT_SPIDER_FILTERED", -"GEMINI_IT_TAB_SUM_BQ", -"GEMINI_IT_QA_WITH_URL", -"GEMINI_IT_CODE_INSTRUCT", -"GEMINI_IT_MED_PALM", -"GEMINI_IT_TASK_ORIENTED_DIALOG", -"GEMINI_IT_NIMBUS_GROUNDING_TO_PROMPT", -"GEMINI_IT_EITL_GEN", -"GEMINI_IT_HITL_GEN", -"GEMINI_IT_MECH", -"GEMINI_IT_TABLE_GEN", -"GEMINI_IT_NIMBUS_DECIBEL", -"GEMINI_IT_CLOUD_CODE_IF", -"GEMINI_IT_CLOUD_EUR_LEX_JSON", -"GEMINI_IT_CLOUD_OASST", -"GEMINI_IT_CLOUD_SELF_INSTRUCT", -"GEMINI_IT_CLOUD_UCS_AQUAMUSE", -"GEMIT_BRIDGE_SUFFIX_FT", -"CLOUD_SECURITY_PRETRAINING", -"CLOUD_SECURITY_FINETUNING", -"CLOUD_SECURITY_RAG_CISA", -"GEMINI_GOOSE_PUBLIC", -"GEMINI_GOOSE_SILOED", -"GEMINI_V2_CMS_WIKIPEDIA_LANG_FILTERED_GCC_PII", -"GEMINI_V2_WIKIPEDIA_DIFFS_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_QUORA_COMPLIANT", -"GEMINI_V2_FORUMS_V2_COMPLIANT", -"GEMINI_V2_CMS_STACKOVERFLOW_MULTILINGUAL_V2_COMPLIANT", -"GEMINI_V2_SIMPLIFIED_HTML_V2_CORRECT_FORMAT_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_TOXICITY_TAGGED_FIXED_TAGS_COMPLIANT", -"GEMINI_V2_CODEWEB_V1_COMPLIANT", -"GEMINI_V2_LEETCODE_GCC_PII", -"GEMINI_V2_CODE_CONTESTS_COMPLIANT", -"GEMINI_V2_CMS_GITHUB_MULTI_FILE_FOR_FIM_GEMBAGZ_FIXED_BYTES_LENGTHS", -"GEMINI_V2_GITHUB_EVALED_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_NON_EVAL_HIGH_PRI_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_LOW_PRI_LANGUAGES_AND_CONFIGS_COMPLIANT", -"GEMINI_V2_GITHUB_LONG_TAIL_AND_STRUCTURED_DATA_COMPLIANT", -"GEMINI_V2_GITHUB_PYTHON_NOTEBOOKS_COMPLIANT", -"GEMINI_V2_GITHUB_DIFFS_COMPLIANT", -"GEMINI_V2_GITHUB_TECHDOCS_COMPLIANT", -"GEMINI_V2_HIGH_QUALITY_CODE_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_SCIENCE_PDF_68M_HQ_DOCS_DEDUP_COMPLIANT_CLEAN_TEX", -"GEMINI_V2_ARXIV_2023_COMPLIANT", 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-"GEMINI_V2_NTL_SYN_FT_FIXED_TRANSLATE_DEDUP_N5", -"GEMINI_V2_CANARIES_SHUFFLED_COMPLIANT", -"CLOUD_GEMIT_CLOUD_FACTUALITY_GROUNDING_MAGI", -"CLOUD_GEMIT_MT_DIALGUE_LMSYS", -"CLOUD_GEMIT_MTS_DIALOGUE_V3", -"CLOUD_GEMIT_COMMIT_MSG_GEN_V3", -"CLOUD_GEMIT_CODE_IF_V1", -"CLOUD_GEMIT_CODE_SELF_REPAIR", -"CLOUD_GEMIT_IDENTITY", -"CLOUD_GEMIT_SEARCH_AUGMENTED_RESPONSE_GENERATION", -"CLOUD_GEMIT_AMPS", -"CLOUD_GEMIT_AQUA", -"CLOUD_GEMIT_COMMON_SENSE_REASONING_SCHEMA", -"CLOUD_GEMIT_GSM8K_SCHEMA", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_UN", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_EUROPARL", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_NEWSCOMMENTARY", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_2021_INCR", -"GEMINI_V1_TAIL_PATCH_GOALDMINE", -"GEMINI_V1_TAIL_PATCH_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION", -"GEMINI_V1_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V1_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V1_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V2_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V2_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_PMC", -"GEMINI_V2_TAIL_PATCH_VOXPOPULI", -"GEMINI_V2_TAIL_PATCH_FLEURS", -"GEMINI_V2_SSFS", -"GEMINI_V2_CODE_TRANSFORM_SYNTHETIC_ERROR_FIX", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_COMMITS", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_PR", -"GEMINI_V2_SQL_REPAIR_SFT", -"GEMINI_V2_JSON_MODE_SYS_INSTRUCTION", -"YT_CONTENT_INSPIRATION" -], -"enumDescriptions": [ -"", -"Wikipedia article Tensorflow datasets used by Tarzan and maintained by TFDS team.", -"Webdocs that have been filtered from the docjoins by the Tarzan team for use in the Tarzan training set.", -"", -"", -"'Full view' books dataset maintained by Oceanographers team, meaning 'ok to view the book in full in all localities'. Largely the same as 'public domain', but with potentially subtle distinction.", -"Filtered private books used by ULM: http://google3/learning/multipod/pax/lm/params/ulm/tasks.py;l=123;rcl=494241309. which corresponds with /cns/mf-d/home/multipod-language-data/private_books/books_filtered_en_resharded@50000", -"Google news dataset referenced in: http://google3/learning/brain/research/conversation/meena/t5/pretrain_tasks.py;l=922;rcl=496534668", -"The docjoins data for ULM /cns/yo-d/home/multipod-language-data/docjoins/rs=6.3/20220728/100B_docstructure_split/examples_en.tfrecord_lattice_05_score_01_HFV13@3929", -"", -"Meena full conversations. http://google3/learning/brain/research/conversation/meena/t5/pretrain_mixtures.py;l=675;rcl=496583228", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Academic dataset of math text. http://google3/learning/brain/research/conversation/meena/seqio/mixtures/experimental/bard.py;rcl=500222380", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Datasets managed by the Goodall team: deepmind-goodall@google.com", -"", -"", -"", -"", -"", -"", -"", -"Datasets used by Codepoet", -"Datasets used by Vertex", -"", -"", -"Datasets used by Gemini Public data", -"", -"", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"Github", -"", -"", -"", -"", -"", -"Arxiv", -"Others", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V1, order by precedence. Wikipedia", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"GitHub dataset with license info. We prefer this to help cite proper licenses for code recitation.", -"", -"", -"", -"", -"", -"", -"ArXiv", -"Citable misc", -"", -"", -"Non-public books", -"", -"", -"Other", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Duet AI finetune datasets, order by precedence.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Bard ARCADE finetune dataset", -"Mobile assistant finetune datasets.", -"", -"Genesis fine tuned datasets.", -"LABS AQA fine-tune datasets.", -"", -"", -"Gemini multimodal instruction tune(IT) and fine tune(FT) datasets datasets.", -"", -"", -"", -"", -"", -"", -"Gemini IT 1.2.7 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemit Bridge ULM FT dataset", -"Cloud Security fine tuned datasets.", -"", -"", -"Gemini Goose FT datasets.", -"", -"Gemini V2 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Cloud gemit pro FT datasets.", -"", -"", -"", -"", -"", -"", -"Cloud gemit ultra FT datasets.", -"", -"", -"", -"", -"Gemini V1 tail patch translation.", -"", -"", -"", -"Gemini V1 tail patch others.", -"", -"Gemini V1 and V2 shared tail patch.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V2 only tail patch.", -"", -"", -"Gemini V2 rev10", -"", -"", -"", -"", -"", -"Youtube Content Inspiration FT datasets." -], -"type": "string" -}, -"filepath": { -"type": "string" -}, -"geminiId": { -"type": "string" -}, -"gnewsArticleTitle": { -"type": "string" -}, -"goodallExampleId": { -"type": "string" -}, -"isOptOut": { -"description": "Whether the document is opted out.", -"type": "boolean" -}, -"isPrompt": { -"description": "When true, this attribution came from the user's prompt.", -"type": "boolean" -}, -"lamdaExampleId": { -"type": "string" -}, -"license": { -"type": "string" -}, -"meenaConversationId": { -"type": "string" -}, -"naturalLanguageCode": { -"description": "Natural (not programming) language of the document. Language code as defined by http://www.unicode.org/reports/tr35/#Identifiers and https://tools.ietf.org/html/bcp47. Currently applicable to full-view books. Use docinfo-util.h to set & read language fields. See go/iii.", -"type": "string" -}, -"noAttribution": { -"description": "True if this doc has no attribution information available. We use an explicit field for this instead of just implicitly leaving all the DocAttribution fields blank to distinguish a case where a bug/oversight has left the attribution information empty vs when we really have no attribution information available.", -"type": "boolean" -}, -"podcastUtteranceId": { -"type": "string" -}, -"publicationDate": { -"$ref": "GoogleTypeDate" -}, -"qualityScoreExperimentOnly": { -"description": "This field is for opt-out experiment only, MUST never be used during actual production/serving. ", -"format": "double", -"type": "number" -}, -"repo": { -"description": "Github repository", -"type": "string" -}, -"url": { -"description": "URL of a webdoc", -"type": "string" -}, -"volumeId": { -"type": "string" -}, -"wikipediaArticleTitle": { -"description": "Wikipedia article title. The Wikipedia TFDS dataset includes article titles but not URLs. While a URL is to the best of our knowledge a deterministic function of the title, we store the original title to reflect the information in the original dataset.", -"type": "string" -}, -"youtubeVideoId": { -"type": "string" -} -}, -"type": "object" -}, -"LearningGenaiRecitationRecitationResult": { -"description": "The recitation result for one input", -"id": "LearningGenaiRecitationRecitationResult", -"properties": { -"dynamicSegmentResults": { -"items": { -"$ref": "LearningGenaiRecitationSegmentResult" -}, -"type": "array" -}, -"recitationAction": { -"description": "The recitation action for one given input. When its segments contain different actions, the overall action will be returned in the precedence of BLOCK > CITE > NO_ACTION. When the given input is not found in any source, the recitation action will be NO_ACTION.", -"enum": [ -"ACTION_UNSPECIFIED", -"CITE", -"BLOCK", -"NO_ACTION", -"EXEMPT_FOUND_IN_PROMPT" -], -"enumDescriptions": [ -"", -"indicate that attribution must be shown for a Segment", -"indicate that a Segment should be blocked from being used", -"for tagging high-frequency code snippets", -"The recitation was found in prompt and is exempted from overall results" -], -"type": "string" -}, -"trainingSegmentResults": { -"items": { -"$ref": "LearningGenaiRecitationSegmentResult" -}, -"type": "array" -} -}, -"type": "object" -}, -"LearningGenaiRecitationSegmentResult": { -"description": "The recitation result for each segment in a given input.", -"id": "LearningGenaiRecitationSegmentResult", -"properties": { -"attributionDataset": { -"description": "The dataset the segment came from. Datasets change often as model evolves. Treat this field as informational only and avoid depending on it directly.", -"enum": [ -"DATASET_UNSPECIFIED", -"WIKIPEDIA", -"WEBDOCS", -"WEBDOCS_FINETUNE", -"GITHUB_MIRROR", -"BOOKS_FULL_VIEW", -"BOOKS_PRIVATE", -"GNEWS", -"ULM_DOCJOINS", -"ULM_DOCJOINS_DEDUPED", -"MEENA_FC", -"PODCAST", -"AQUA", -"WEB_ASR", -"BARD_GOLDEN", -"COMMON_SENSE_REASONING", -"MATH", -"MATH_REASONING", -"CLEAN_ARXIV", -"LAMDA_FACTUALITY_E2E_QUERY_GENERATION", -"LAMDA_FACTUALITY_E2E_RESPONSE_GENERATION", -"MASSIVE_FORUM_THREAD_SCORED_BARD", -"MASSIVE_FORUM_THREAD_SCORED_LONG_200", -"MASSIVE_FORUM_THREAD_SCORED_LONG_500", -"DOCUMENT_CHUNKS", -"MEENA_RESEARCH_PHASE_GOLDEN_MARKDOWN", -"MEENA_RESEARCH_PHASE_GOOGLERS", -"MEENA_RESPONSE_SAFETY_HUMAN_GEN", -"MEENA_RESPONSE_SAFETY_SCHEMA_NO_BROADCAST", -"MEENA_RESPONSE_SAFETY_V3_HUMAN_GEN2", -"MEENA_RESPONSE_SAFETY_V3_SCHEMA_NO_BROADCAST", -"LAMDA_FACTUALITY_TRIGGER", -"LAMDA_SAFETY_V2_SCHEMA_NO_BROADCAST", -"LAMDA_SSI_DISCRIMINATIVE", -"ASSISTANT_PERSONALITY_SAFETY", -"PODCAST_FINETUNE_DIALOG", -"WORLD_QUERY_GENERATOR", -"C4_JOINED_DOCJOINS", -"HOL4_THEORIES", -"HOL_LIGHT_THEORIES", -"HOLSTEPS", -"ISABELLE_STEP", -"ISABELLE_THEORIES", -"LEAN_MATHLIB_THEORIES", -"LEAN_STEP", -"MIZAR_THEORIES", -"COQ_STEP", -"COQ_THEORIES", -"AMPS_KHAN", -"AMPS_MATHEMATICA", -"CODEY_CODE", -"CODE_QA_SE", -"CODE_QA_SO", -"CODE_QA_FT_FORMAT", -"CODE_QA_FT_KNOWLEDGE", -"CODE_QA_GITHUB_FILTERED_CODE", -"BARD_PERSONALITY_GOLDEN", -"ULM_DOCJOINS_WITH_URLS_EN", -"ULM_DOCJOINS_WITH_URLS_I18N", -"GOODALL_MTV5_GITHUB", -"GOODALL_MTV5_BOOKS", -"GOODALL_MTV5_C4", -"GOODALL_MTV5_WIKIPEDIA", -"GOODALL_MW_TOP_100B", -"GOODALL_MW_STACK_EXCHANGE", -"GOODALL_MW_TOP_0_10B", -"GOODALL_MW_TOP_10B_20B", -"CODEY_NOTEBOOK_LM_PRETRAINING", -"VERTEX_SAFE_FLAN", -"GITHUB_MIRROR_V1_0_1", -"GITHUB_MIRROR_V2_1_0", -"CMS_WIKIPEDIA_LANG_FILTERED", -"CMS_STACKOVERFLOW_MULTILINGUAL", -"CMS_STACKEXCHANGE", -"PUBMED", -"GEMINI_DOCJOINS_EN_TOP10B_GCC", -"GEMINI_DOCJOINS_EN_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_EN_TOP20B_TOP100B_GCC", -"GEMINI_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_GCC", -"GEMINI_DOCJOINS_I18N_TOP10B_TOP20B_GCC", -"GEMINI_DOCJOINS_I18N_TOP20B_TOP100B_GCC", -"SIMPLIFIED_HTML_V1_GCC", -"GEMINI_DOCJOINS_TOXICITY_TAGGED_GCC", -"CMS_GITHUB_V4", -"GITHUB_HTML_V4", -"GITHUB_OTHER_V4", -"GITHUB_LONG_TAIL_V4", -"CMS_GITHUB_MULTIFILE_V4", -"GITHUB_DIFFS_WITH_COMMIT_MESSAGE", -"ULM_ARXIV", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_ENONLY", -"NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_040623_LONG_DEDUP_NONENONLY", -"QUORA", -"PODCASTS_ROBOTSTXT", -"COMBINED_REDDIT", -"CANARIES_SHUFFLED", -"CLM_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"TECHDOCS_DATA_SOURCE", -"SCIENCE_PDF_70M_DOCS_FILTERED", -"GEMINI_V1_CMS_WIKIPEDIA_LANG_FILTERED", -"GEMINI_V1_WIKIPEDIA_DIFFS", -"GEMINI_V1_DOCJOINS_EN_TOP10B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP10B_TOP20B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP20B_TOP100B_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_EN_TOP100B_ALL_INDEXED_GCC_NODEDUP_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP10B_TOP20B_GCC_050523", -"GEMINI_V1_DOCJOINS_I18N_TOP20B_TOP100B_GCC_050523", -"GEMINI_V1_SIMPLIFIED_HTML_V2_GCC", -"GEMINI_V1_CMS_STACKOVERFLOW_MULTILINGUAL_V2", -"GEMINI_V1_CMS_STACKEXCHANGE_DECONT", -"GEMINI_V1_QUORA", -"GEMINI_V1_COMBINED_REDDIT", -"GEMINI_V1_DOCJOIN_100B_EN_TOXICITY_TAGGED_GCC_FIXED_TAGS", -"GEMINI_V1_PUBMED", -"GEMINI_V1_WEB_MATH_V2", -"GEMINI_V1_CMS_GITHUB_V7", -"GEMINI_V1_CMS_GITHUB_DECONTAMINATED_V_7", -"GEMINI_V1_GITHUB_DIFF_WITH_COMMIT_MESSAGE_V2", -"GEMINI_V1_GITHUB_HTML_CSS_XML_V4", -"GEMINI_V1_GITHUB_OTHER_V4", -"GEMINI_V1_GITHUB_LONG_TAIL_V4", -"GEMINI_V1_GITHUB_JUPTYER_NOTEBOOKS_SSTABLE", -"GEMINI_V1_ULM_ARXIV_SSTABLE", -"GEMINI_V1_PODCASTS_ROBOTSTXT", -"GEMINI_V1_SCIENCE_PDF_68M_HQ_DOCS_GCC", -"GEMINI_V1_GITHUB_TECHDOCS_V2", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_EN", -"GEMINI_V1_NONPUB_COPYRIGHT_BOOKS_V2_70_CONF_LONG_DEDUP_NONEN", -"GEMINI_V1_STEM_BOOKS_650K_TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_M3W_V2_FILTERED", -"GEMINI_V1_VQCOCA_1B_MULTIRES_WEBLI_EN_V4_350M_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_SCREENAI_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CULTURE_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_EN_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_CC3M_I18N_PREFIXED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_OCR_NON_EN_PREFIXED_FILTERED_IMAGE2TEXT", -"GEMINI_V1_VQCOCA_1B_MULTIRES_VTP_4F_VIDEO2TEXT_PREFIX", -"GEMINI_V1_FORMAL_MATH_WITHOUT_HOLSTEPS_AND_MIZAR", -"GEMINI_V1_TRANSLATE_DATAV2_ALLTIERS_GCC_MIX", -"GEMINI_V1_CANARIES_SHUFFLED_DOCJOIN_EN_NONEN_CODE_ARXIV_TRANSLATE", -"DUET_CLOUD_SECURITY_DOCS", -"DUET_GITHUB_CODE_SNIPPETS", -"DUET_GITHUB_FILES", -"DUET_GOBYEXAMPLE", -"DUET_GOLANG_DOCS", -"DUET_CLOUD_DOCS_TROUBLESHOOTING_TABLES", -"DUET_DEVSITE_DOCS", -"DUET_CLOUD_BLOG_POSTS", -"DUET_CLOUD_PODCAST_EPISODES", -"DUET_YOUTUBE_VIDEOS", -"DUET_CLOUD_SKILLS_BOOST", -"DUET_CLOUD_DOCS", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_GENERATED", -"DUET_CLOUD_GITHUB_CODE_SNIPPETS_HANDWRITTEN", -"DUET_GOOGLESQL_GENERATION", -"DUET_CLOUD_IX_PROMPTS", -"DUET_RAD", -"DUET_STACKOVERFLOW_ISSUES", -"DUET_STACKOVERFLOW_ANSWERS", -"BARD_ARCADE_GITHUB", -"MOBILE_ASSISTANT_MAGI_FILTERED_0825_373K", -"MOBILE_ASSISTANT_PALM24B_FILTERED_400K", -"GENESIS_NEWS_INSIGHTS", -"LABS_AQA_DSCOUT", -"LABS_AQA_TAILWIND", -"LABS_AQA_DELEWARE", -"GEMINI_MULTIMODAL_FT_URL", -"GEMINI_MULTIMODAL_FT_YT", -"GEMINI_MULTIMODAL_FT_SHUTTERSTOCK", -"GEMINI_MULTIMODAL_FT_NONE", -"GEMINI_MULTIMODAL_FT_OTHER", -"GEMINI_MULTIMODAL_FT_INK", -"GEMINI_MULTIMODAL_IT", -"GEMINI_IT_SHUTTERSTOCK", -"GEMINI_IT_M3W", -"GEMINI_IT_HEDGING", -"GEMINI_IT_DSCOUT_FACTUALITY", -"GEMINI_IT_AQUAMUSE", -"GEMINI_IT_SHOTGUN", -"GEMINI_IT_ACI_BENCH", -"GEMINI_IT_SPIDER_FILTERED", -"GEMINI_IT_TAB_SUM_BQ", -"GEMINI_IT_QA_WITH_URL", -"GEMINI_IT_CODE_INSTRUCT", -"GEMINI_IT_MED_PALM", -"GEMINI_IT_TASK_ORIENTED_DIALOG", -"GEMINI_IT_NIMBUS_GROUNDING_TO_PROMPT", -"GEMINI_IT_EITL_GEN", -"GEMINI_IT_HITL_GEN", -"GEMINI_IT_MECH", -"GEMINI_IT_TABLE_GEN", -"GEMINI_IT_NIMBUS_DECIBEL", -"GEMINI_IT_CLOUD_CODE_IF", -"GEMINI_IT_CLOUD_EUR_LEX_JSON", -"GEMINI_IT_CLOUD_OASST", -"GEMINI_IT_CLOUD_SELF_INSTRUCT", -"GEMINI_IT_CLOUD_UCS_AQUAMUSE", -"GEMIT_BRIDGE_SUFFIX_FT", -"CLOUD_SECURITY_PRETRAINING", -"CLOUD_SECURITY_FINETUNING", -"CLOUD_SECURITY_RAG_CISA", -"GEMINI_GOOSE_PUBLIC", -"GEMINI_GOOSE_SILOED", -"GEMINI_V2_CMS_WIKIPEDIA_LANG_FILTERED_GCC_PII", -"GEMINI_V2_WIKIPEDIA_DIFFS_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP10B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP10B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP20B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP20B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP100B_211123_PII_FILTERED", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_NONARTICLES_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_NONEN_TOP100B_111323_WITHOUT_CJKT_STOP_ARTICLES_COMPLIANT", -"GEMINI_V2_ENGLISH_ARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_ENGLISH_NONARTICLES_TOP500B_211123_PII_FILTERED", -"GEMINI_V2_QUORA_COMPLIANT", -"GEMINI_V2_FORUMS_V2_COMPLIANT", -"GEMINI_V2_CMS_STACKOVERFLOW_MULTILINGUAL_V2_COMPLIANT", -"GEMINI_V2_SIMPLIFIED_HTML_V2_CORRECT_FORMAT_COMPLIANT", -"GEMINI_V2_GEMINI_DOCJOINS_TOXICITY_TAGGED_FIXED_TAGS_COMPLIANT", -"GEMINI_V2_CODEWEB_V1_COMPLIANT", -"GEMINI_V2_LEETCODE_GCC_PII", -"GEMINI_V2_CODE_CONTESTS_COMPLIANT", -"GEMINI_V2_CMS_GITHUB_MULTI_FILE_FOR_FIM_GEMBAGZ_FIXED_BYTES_LENGTHS", -"GEMINI_V2_GITHUB_EVALED_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_NON_EVAL_HIGH_PRI_LANGUAGES_COMPLIANT", -"GEMINI_V2_GITHUB_LOW_PRI_LANGUAGES_AND_CONFIGS_COMPLIANT", -"GEMINI_V2_GITHUB_LONG_TAIL_AND_STRUCTURED_DATA_COMPLIANT", -"GEMINI_V2_GITHUB_PYTHON_NOTEBOOKS_COMPLIANT", -"GEMINI_V2_GITHUB_DIFFS_COMPLIANT", -"GEMINI_V2_GITHUB_TECHDOCS_COMPLIANT", -"GEMINI_V2_HIGH_QUALITY_CODE_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_SCIENCE_PDF_68M_HQ_DOCS_DEDUP_COMPLIANT_CLEAN_TEX", -"GEMINI_V2_ARXIV_2023_COMPLIANT", -"GEMINI_V2_FORMAL_COMPLIANT", -"GEMINI_V2_CMS_STACKEXCHANGE_COMPLIANT", -"GEMINI_V2_PUBMED_COMPLIANT", -"GEMINI_V2_WEB_MATH_V3_COMPLIANT", -"GEMINI_V2_SCIENCEWEB_V0_GCC_PII", -"GEMINI_V2_WEB_POLYMATH_V1_COMPLIANT", -"GEMINI_V2_MATH_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_BIOLOGY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_PHYSICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHEMISTRY_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MACHINE_LEARNING_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_QA_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_ECONOMICS_V2_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_MEDICAL_TARGETED_DATA_COMPLIANT2", -"GEMINI_V2_CHESS_COMPLIANT", -"GEMINI_V2_YOUTUBE_SCIENCE_V4_FILTERED_COMPLIANT", -"GEMINI_V2_GOALDMINE_XL_GENERATED_PLUS_GT_NO_DM_MATH_COMPLIANT", -"GEMINI_V2_FIRSTTIMES_SCIENCE_PDF_DEDUP_HQ_LENGTH_FILTERED_COMPLIANT", -"GEMINI_V2_PODCASTS_COMPLIANT", -"GEMINI_V2_EN_NONSCIENCE_PDF_DEDUP_46M_DOCS_COMPLIANT", -"GEMINI_V2_NONPUB_COPYRIGHT_BOOKS_V3_70_CONF_082323_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_COPYRIGHT_BOOKS_V3_111823_LONG_DEDUP_ENONLY_COMPLIANT", -"GEMINI_V2_STEM_BOOKS_318K_TEXT_COMPLIANT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M3W_WITH_IMAGE_TOKENS_INSERTED_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_T2I_TEXT_TO_IMAGE_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_EN_V4_350M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_I18N_680M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_FULL_HTML_75M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SCREENAI_V1_1_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_OCR_DOC_240M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SHUTTERSTOCK_VIDEO_VIDEO_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_M4W_INTERLEAVED_COMPLIANT_PII_FILTERED_SOFT", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CULTURE_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_DETECTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WEBLI_ALT_TEXT_NONEN_500M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_SPATIAL_AWARE_PALI_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_3D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TABLE2MD_V2_NON_EN_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_3D_DOC_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CC3M_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_INFOGRAPHICS_LARGE_WEB_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_BIORXIV_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PLOT2TABLE_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_TIKZ_DERENDERING_MERGED_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_CLOUDAI_TABLE2HTML_2D_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_WIKIPEDIA_EQUATIONS_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_PHOTOMATH_EQ2LATEX_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_CACHED_VQCOCA_MMFT_17T_ARXIV_EQUATIONS_V2_IMAGE_TO_TEXT_COMPLIANT_PII_FILTERED", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_ASR_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_SUP_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_TTS_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_PODIOSET_INTERLEAVE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_ENUS_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_SCIENCE_I18N_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_1P5M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_USM2B_MLPV5_YT_INTERLEAVE_HEAD_4M_GEMBAGZ_V2_COMPLIANT", -"GEMINI_V2_CLM_TRANSLATE_DATAV3_WEB_UNWMT_INCR_MIX", -"GEMINI_V2_NTL_NTLV4A_MONOLINGUAL_DEDUP_N5", -"GEMINI_V2_NTL_STT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_TRANSLIT_BILEX_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_BT_TRANSLATE_DEDUP_N5", -"GEMINI_V2_NTL_SYN_FT_FIXED_TRANSLATE_DEDUP_N5", -"GEMINI_V2_CANARIES_SHUFFLED_COMPLIANT", -"CLOUD_GEMIT_CLOUD_FACTUALITY_GROUNDING_MAGI", -"CLOUD_GEMIT_MT_DIALGUE_LMSYS", -"CLOUD_GEMIT_MTS_DIALOGUE_V3", -"CLOUD_GEMIT_COMMIT_MSG_GEN_V3", -"CLOUD_GEMIT_CODE_IF_V1", -"CLOUD_GEMIT_CODE_SELF_REPAIR", -"CLOUD_GEMIT_IDENTITY", -"CLOUD_GEMIT_SEARCH_AUGMENTED_RESPONSE_GENERATION", -"CLOUD_GEMIT_AMPS", -"CLOUD_GEMIT_AQUA", -"CLOUD_GEMIT_COMMON_SENSE_REASONING_SCHEMA", -"CLOUD_GEMIT_GSM8K_SCHEMA", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_UN", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_EUROPARL", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_WMT_NEWSCOMMENTARY", -"GEMINI_V1_TAIL_PATCH_TRANSLATION_2021_INCR", -"GEMINI_V1_TAIL_PATCH_GOALDMINE", -"GEMINI_V1_TAIL_PATCH_PHOTOMATH_IM2SOL_PROBLEM_AND_SOLUTION", -"GEMINI_V1_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V1_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V1_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V1_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V1_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_CCAI_DIALOG_SUM_HUMAN", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_PUNTING", -"GEMINI_V2_TAIL_PATCH_MATH_REASONING_NON_PUNTING", -"GEMINI_V2_TAIL_PATCH_JSON_TABLE_EXTRACTION", -"GEMINI_V2_TAIL_PATCH_BIRD_SQL_LITE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_ANSWERABLE", -"GEMINI_V2_TAIL_PATCH_OPEN_BOOKS_QA_UNANSWERABLE", -"GEMINI_V2_TAIL_PATCH_PMC", -"GEMINI_V2_TAIL_PATCH_VOXPOPULI", -"GEMINI_V2_TAIL_PATCH_FLEURS", -"GEMINI_V2_SSFS", -"GEMINI_V2_CODE_TRANSFORM_SYNTHETIC_ERROR_FIX", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_COMMITS", -"GEMINI_V2_CODE_TRANSFORM_GITHUB_PR", -"GEMINI_V2_SQL_REPAIR_SFT", -"GEMINI_V2_JSON_MODE_SYS_INSTRUCTION", -"YT_CONTENT_INSPIRATION" -], -"enumDescriptions": [ -"", -"Wikipedia article Tensorflow datasets used by Tarzan and maintained by TFDS team.", -"Webdocs that have been filtered from the docjoins by the Tarzan team for use in the Tarzan training set.", -"", -"", -"'Full view' books dataset maintained by Oceanographers team, meaning 'ok to view the book in full in all localities'. Largely the same as 'public domain', but with potentially subtle distinction.", -"Filtered private books used by ULM: http://google3/learning/multipod/pax/lm/params/ulm/tasks.py;l=123;rcl=494241309. which corresponds with /cns/mf-d/home/multipod-language-data/private_books/books_filtered_en_resharded@50000", -"Google news dataset referenced in: http://google3/learning/brain/research/conversation/meena/t5/pretrain_tasks.py;l=922;rcl=496534668", -"The docjoins data for ULM /cns/yo-d/home/multipod-language-data/docjoins/rs=6.3/20220728/100B_docstructure_split/examples_en.tfrecord_lattice_05_score_01_HFV13@3929", -"", -"Meena full conversations. http://google3/learning/brain/research/conversation/meena/t5/pretrain_mixtures.py;l=675;rcl=496583228", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Academic dataset of math text. http://google3/learning/brain/research/conversation/meena/seqio/mixtures/experimental/bard.py;rcl=500222380", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Datasets managed by the Goodall team: deepmind-goodall@google.com", -"", -"", -"", -"", -"", -"", -"", -"Datasets used by Codepoet", -"Datasets used by Vertex", -"", -"", -"Datasets used by Gemini Public data", -"", -"", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"Github", -"", -"", -"", -"", -"", -"Arxiv", -"Others", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V1, order by precedence. Wikipedia", -"", -"Public webdocs", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"GitHub dataset with license info. We prefer this to help cite proper licenses for code recitation.", -"", -"", -"", -"", -"", -"", -"ArXiv", -"Citable misc", -"", -"", -"Non-public books", -"", -"", -"Other", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Duet AI finetune datasets, order by precedence.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Bard ARCADE finetune dataset", -"Mobile assistant finetune datasets.", -"", -"Genesis fine tuned datasets.", -"LABS AQA fine-tune datasets.", -"", -"", -"Gemini multimodal instruction tune(IT) and fine tune(FT) datasets datasets.", -"", -"", -"", -"", -"", -"", -"Gemini IT 1.2.7 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemit Bridge ULM FT dataset", -"Cloud Security fine tuned datasets.", -"", -"", -"Gemini Goose FT datasets.", -"", -"Gemini V2 datasets", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Cloud gemit pro FT datasets.", -"", -"", -"", -"", -"", -"", -"Cloud gemit ultra FT datasets.", -"", -"", -"", -"", -"Gemini V1 tail patch translation.", -"", -"", -"", -"Gemini V1 tail patch others.", -"", -"Gemini V1 and V2 shared tail patch.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Gemini V2 only tail patch.", -"", -"", -"Gemini V2 rev10", -"", -"", -"", -"", -"", -"Youtube Content Inspiration FT datasets." -], -"type": "string" -}, -"displayAttributionMessage": { -"description": "human-friendly string that contains information from doc_attribution which could be shown by clients", -"type": "string" -}, -"docAttribution": { -"$ref": "LearningGenaiRecitationDocAttribution" -}, -"docOccurrences": { -"description": "number of documents that contained this segment", -"format": "int32", -"type": "integer" -}, -"endIndex": { -"format": "int32", -"type": "integer" -}, -"rawText": { -"description": "The raw text in the given input that is corresponding to the segment. It will be available only when 'return_segment_raw_text' is enabled in the request options.", -"type": "string" -}, -"segmentRecitationAction": { -"enum": [ -"ACTION_UNSPECIFIED", -"CITE", -"BLOCK", -"NO_ACTION", -"EXEMPT_FOUND_IN_PROMPT" -], -"enumDescriptions": [ -"", -"indicate that attribution must be shown for a Segment", -"indicate that a Segment should be blocked from being used", -"for tagging high-frequency code snippets", -"The recitation was found in prompt and is exempted from overall results" -], -"type": "string" -}, -"sourceCategory": { -"description": "The category of the source dataset where the segment came from. This is more stable than Dataset.", -"enum": [ -"SOURCE_CATEGORY_UNSPECIFIED", -"SOURCE_CATEGORY_WIKIPEDIA", -"SOURCE_CATEGORY_WEBDOCS", -"SOURCE_CATEGORY_GITHUB", -"SOURCE_CATEGORY_ARXIV", -"SOURCE_CATEGORY_PRIVATE_BOOKS", -"SOURCE_CATEGORY_OTHERS", -"SOURCE_CATEGORY_PUBLIC_BOOKS", -"SOURCE_CATEGORY_GNEWS" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"", -"", -"", -"" -], -"type": "string" -}, -"startIndex": { -"description": "The segment boundary start (inclusive) and end index (exclusive) in the given text. In the streaming RPC, the indexes always start from the beginning of the first text in the entire stream. The indexes are measured in UTF-16 code units.", -"format": "int32", -"type": "integer" -} -}, -"type": "object" -}, -"LearningGenaiRootCalculationType": { -"description": "The type used for final weights calculation.", -"id": "LearningGenaiRootCalculationType", -"properties": { -"scoreType": { -"enum": [ -"TYPE_UNKNOWN", -"TYPE_SAFE", -"TYPE_POLICY", -"TYPE_GENERATION" -], -"enumDescriptions": [ -"Unknown scorer type.", -"Safety scorer.", -"Policy scorer.", -"Generation scorer." -], -"type": "string" -}, -"weights": { -"format": "double", -"type": "number" -} -}, -"type": "object" -}, -"LearningGenaiRootClassifierOutput": { -"id": "LearningGenaiRootClassifierOutput", -"properties": { -"ruleOutput": { -"$ref": "LearningGenaiRootRuleOutput", -"deprecated": true, -"description": "If set, this is the output of the first matching rule." -}, -"ruleOutputs": { -"description": "outputs of all matching rule.", -"items": { -"$ref": "LearningGenaiRootRuleOutput" -}, -"type": "array" -}, -"state": { -"$ref": "LearningGenaiRootClassifierState", -"description": "The results of data_providers and metrics." -} -}, -"type": "object" -}, -"LearningGenaiRootClassifierOutputSummary": { -"id": "LearningGenaiRootClassifierOutputSummary", -"properties": { -"metrics": { -"items": { -"$ref": "LearningGenaiRootMetricOutput" -}, -"type": "array" -}, -"ruleOutput": { -"$ref": "LearningGenaiRootRuleOutput", -"deprecated": true, -"description": "Output of the first matching rule." -}, -"ruleOutputs": { -"description": "outputs of all matching rule.", -"items": { -"$ref": "LearningGenaiRootRuleOutput" -}, -"type": "array" +"GoogleCloudAiplatformV1beta1UpdateFeatureGroupOperationMetadata": { +"description": "Details of operations that perform update FeatureGroup.", +"id": "GoogleCloudAiplatformV1beta1UpdateFeatureGroupOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for FeatureGroup." } }, "type": "object" }, -"LearningGenaiRootClassifierState": { -"description": "DataProviderOutput and MetricOutput can be saved between calls to the Classifier framework. For instance, you can run the query classifier, get outputs from those metrics, then use them in a result classifier as well. Example rule based on this idea: and_rules { rule { metric_name: 'query_safesearch_v2' ... } rule { metric_name: 'response_safesearch_v2' ... } }", -"id": "LearningGenaiRootClassifierState", +"GoogleCloudAiplatformV1beta1UpdateFeatureOnlineStoreOperationMetadata": { +"description": "Details of operations that perform update FeatureOnlineStore.", +"id": "GoogleCloudAiplatformV1beta1UpdateFeatureOnlineStoreOperationMetadata", "properties": { -"dataProviderOutput": { -"items": { -"$ref": "LearningGenaiRootDataProviderOutput" -}, -"type": "array" -}, -"metricOutput": { -"items": { -"$ref": "LearningGenaiRootMetricOutput" -}, -"type": "array" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for FeatureOnlineStore." } }, "type": "object" }, -"LearningGenaiRootCodeyChatMetadata": { -"description": "Stores all metadata relating to AIDA DoConversation.", -"id": "LearningGenaiRootCodeyChatMetadata", +"GoogleCloudAiplatformV1beta1UpdateFeatureOperationMetadata": { +"description": "Details of operations that perform update Feature.", +"id": "GoogleCloudAiplatformV1beta1UpdateFeatureOperationMetadata", "properties": { -"codeLanguage": { -"description": "Indicates the programming language of the code if the message is a code chunk.", -"enum": [ -"UNSPECIFIED", -"ALL", -"TEXT", -"CPP", -"PYTHON", -"KOTLIN", -"JAVA", -"JAVASCRIPT", -"GO", -"R", -"JUPYTER_NOTEBOOK", -"TYPESCRIPT", -"HTML", -"SQL", -"BASH", -"C", -"DART", -"GRADLE", -"GROOVY", -"JAVADOC", -"JSON", -"MAKEFILE", -"MARKDOWN", -"PROTO", -"XML", -"YAML" -], -"enumDescriptions": [ -"Unspecified Language.", -"All languages.", -"Not code.", -"The most common, well-supported languages. C++ code.", -"Python code.", -"Kotlin code.", -"Java code.", -"JavaScript code.", -"Go code.", -"R code.", -"Jupyter notebook.", -"TypeScript code.", -"HTML code.", -"SQL code.", -"Other languages in alphabetical order. BASH code.", -"C code.", -"Dart code.", -"Gradle code.", -"Groovy code.", -"API documentation.", -"JSON code.", -"Makefile code.", -"Markdown code.", -"Protocol buffer.", -"XML code.", -"YAML code." -], -"type": "string" -} -}, -"type": "object" -}, -"LearningGenaiRootCodeyCheckpoint": { -"description": "Describes a sample at a checkpoint for post-processing.", -"id": "LearningGenaiRootCodeyCheckpoint", -"properties": { -"codeyTruncatorMetadata": { -"$ref": "LearningGenaiRootCodeyTruncatorMetadata", -"description": "Metadata that describes what was truncated at this checkpoint." -}, -"currentSample": { -"description": "Current state of the sample after truncator.", -"type": "string" -}, -"postInferenceStep": { -"description": "Postprocessor run that yielded this checkpoint.", -"enum": [ -"STEP_POST_PROCESSING_STEP_UNSPECIFIED", -"STEP_ORIGINAL_MODEL_OUTPUT", -"STEP_MODEL_OUTPUT_DEDUPLICATION", -"STEP_STOP_SEQUENCE_TRUNCATION", -"STEP_HEURISTIC_TRUNCATION", -"STEP_WALD_TRUNCATION", -"STEP_WHITESPACE_TRUNCATION", -"STEP_FINAL_DEDUPLICATION", -"STEP_TOXICITY_CHECK", -"STEP_RECITATION_CHECK", -"STEP_RETURNED", -"STEP_WALKBACK_CORRECTION", -"STEP_SCORE_THRESHOLDING", -"STEP_MODEL_CONFIG_STOP_SEQUENCE_TRUNCATION", -"STEP_CUSTOM_STOP_SEQUENCE_TRUNCATION", -"STEP_EXPECTED_SAMPLE_SIZE", -"STEP_TREE_TRIM_TRUNCATION" -], -"enumDeprecated": [ -false, -false, -false, -true, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false -], -"enumDescriptions": [ -"", -"Original model outputs as-is.", -"Original model outputs after deduplication.", -"StopSequencePostProcessor.", -"Heuristic SuffixTruncator step.", -"Go service post-processor.", -"Truncate trailing whitespace and filter whitespace-only completions.", -"Deduplicate after all truncations.", -"Toxicity returns true.", -"Recitation causes BLOCK.", -"Return the response to the API.", -"Correcting walkback constraint (samples are dropped if they don't match the prefix constraint).", -"Thresholding samples based on a minimum score.", -"StopSequencePostProcessor.", -"StopSequencePostProcessor.", -"Drop extra number of samples that exceeds expected_samples.", -"Truncated by highest end token score." -], -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for Feature Update." } }, "type": "object" }, -"LearningGenaiRootCodeyCompletionMetadata": { -"description": "Stores all metadata relating to Completion.", -"id": "LearningGenaiRootCodeyCompletionMetadata", +"GoogleCloudAiplatformV1beta1UpdateFeatureViewOperationMetadata": { +"description": "Details of operations that perform update FeatureView.", +"id": "GoogleCloudAiplatformV1beta1UpdateFeatureViewOperationMetadata", "properties": { -"checkpoints": { -"items": { -"$ref": "LearningGenaiRootCodeyCheckpoint" -}, -"type": "array" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for FeatureView Update." } }, "type": "object" }, -"LearningGenaiRootCodeyGenerationMetadata": { -"description": "Stores all metadata relating to GenerateCode.", -"id": "LearningGenaiRootCodeyGenerationMetadata", +"GoogleCloudAiplatformV1beta1UpdateFeaturestoreOperationMetadata": { +"description": "Details of operations that perform update Featurestore.", +"id": "GoogleCloudAiplatformV1beta1UpdateFeaturestoreOperationMetadata", "properties": { -"output": { -"description": "Last state of the sample before getting dropped/returned.", -"type": "string" -}, -"postInferenceStep": { -"description": "Last Codey postprocessing step for this sample before getting dropped/returned.", -"enum": [ -"STEP_POST_PROCESSING_STEP_UNSPECIFIED", -"STEP_ORIGINAL_MODEL_OUTPUT", -"STEP_MODEL_OUTPUT_DEDUPLICATION", -"STEP_STOP_SEQUENCE_TRUNCATION", -"STEP_HEURISTIC_TRUNCATION", -"STEP_WALD_TRUNCATION", -"STEP_WHITESPACE_TRUNCATION", -"STEP_FINAL_DEDUPLICATION", -"STEP_TOXICITY_CHECK", -"STEP_RECITATION_CHECK", -"STEP_RETURNED", -"STEP_WALKBACK_CORRECTION", -"STEP_SCORE_THRESHOLDING", -"STEP_MODEL_CONFIG_STOP_SEQUENCE_TRUNCATION", -"STEP_CUSTOM_STOP_SEQUENCE_TRUNCATION", -"STEP_EXPECTED_SAMPLE_SIZE", -"STEP_TREE_TRIM_TRUNCATION" -], -"enumDeprecated": [ -false, -false, -false, -true, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false, -false -], -"enumDescriptions": [ -"", -"Original model outputs as-is.", -"Original model outputs after deduplication.", -"StopSequencePostProcessor.", -"Heuristic SuffixTruncator step.", -"Go service post-processor.", -"Truncate trailing whitespace and filter whitespace-only completions.", -"Deduplicate after all truncations.", -"Toxicity returns true.", -"Recitation causes BLOCK.", -"Return the response to the API.", -"Correcting walkback constraint (samples are dropped if they don't match the prefix constraint).", -"Thresholding samples based on a minimum score.", -"StopSequencePostProcessor.", -"StopSequencePostProcessor.", -"Drop extra number of samples that exceeds expected_samples.", -"Truncated by highest end token score." -], -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for Featurestore." } }, "type": "object" }, -"LearningGenaiRootCodeyOutput": { -"description": "Top-level wrapper used to store all things codey-related.", -"id": "LearningGenaiRootCodeyOutput", +"GoogleCloudAiplatformV1beta1UpdateIndexOperationMetadata": { +"description": "Runtime operation information for IndexService.UpdateIndex.", +"id": "GoogleCloudAiplatformV1beta1UpdateIndexOperationMetadata", "properties": { -"codeyChatMetadata": { -"$ref": "LearningGenaiRootCodeyChatMetadata" -}, -"codeyCompletionMetadata": { -"$ref": "LearningGenaiRootCodeyCompletionMetadata" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." }, -"codeyGenerationMetadata": { -"$ref": "LearningGenaiRootCodeyGenerationMetadata" +"nearestNeighborSearchOperationMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadata", +"description": "The operation metadata with regard to Matching Engine Index operation." } }, "type": "object" }, -"LearningGenaiRootCodeyTruncatorMetadata": { -"description": "Metadata describing what was truncated at each checkpoint.", -"id": "LearningGenaiRootCodeyTruncatorMetadata", +"GoogleCloudAiplatformV1beta1UpdateModelDeploymentMonitoringJobOperationMetadata": { +"description": "Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob.", +"id": "GoogleCloudAiplatformV1beta1UpdateModelDeploymentMonitoringJobOperationMetadata", "properties": { -"cutoffIndex": { -"description": "Index of the current sample that trims off truncated text.", -"format": "int32", -"type": "integer" -}, -"truncatedText": { -"description": "Text that was truncated at a specific checkpoint.", -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." } }, "type": "object" }, -"LearningGenaiRootControlDecodingConfigThreshold": { -"description": "Score threshold for a category.", -"id": "LearningGenaiRootControlDecodingConfigThreshold", +"GoogleCloudAiplatformV1beta1UpdateModelMonitorOperationMetadata": { +"description": "Runtime operation information for ModelMonitoringService.UpdateModelMonitor.", +"id": "GoogleCloudAiplatformV1beta1UpdateModelMonitorOperationMetadata", "properties": { -"policy": { -"enum": [ -"UNSPECIFIED", -"DANGEROUS_CONTENT", -"HARASSMENT", -"HATE_SPEECH", -"SEXUALLY_EXPLICIT" -], -"enumDescriptions": [ -"", -"", -"", -"", -"" -], -"type": "string" -}, -"scoreMax": { -"format": "float", -"type": "number" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." } }, "type": "object" }, -"LearningGenaiRootControlDecodingRecord": { -"description": "Holds one control decoding record.", -"id": "LearningGenaiRootControlDecodingRecord", +"GoogleCloudAiplatformV1beta1UpdatePersistentResourceOperationMetadata": { +"description": "Details of operations that perform update PersistentResource.", +"id": "GoogleCloudAiplatformV1beta1UpdatePersistentResourceOperationMetadata", "properties": { -"prefixes": { -"description": "Prefixes feeded into scorer.", -"type": "string" -}, -"scores": { -"description": "Per policy scores returned from Scorer. Expect to have the same number of scores as in `thresholds`.", -"items": { -"$ref": "LearningGenaiRootControlDecodingRecordPolicyScore" -}, -"type": "array" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for PersistentResource." }, -"suffiexes": { -"description": "Suffixes feeded into scorer.", +"progressMessage": { +"description": "Progress Message for Update LRO", "type": "string" -}, -"thresholds": { -"description": "Per policy thresholds from user config.", -"items": { -"$ref": "LearningGenaiRootControlDecodingConfigThreshold" -}, -"type": "array" } }, "type": "object" }, -"LearningGenaiRootControlDecodingRecordPolicyScore": { -"id": "LearningGenaiRootControlDecodingRecordPolicyScore", +"GoogleCloudAiplatformV1beta1UpdateSpecialistPoolOperationMetadata": { +"description": "Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.", +"id": "GoogleCloudAiplatformV1beta1UpdateSpecialistPoolOperationMetadata", "properties": { -"policy": { -"enum": [ -"UNSPECIFIED", -"DANGEROUS_CONTENT", -"HARASSMENT", -"HATE_SPEECH", -"SEXUALLY_EXPLICIT" -], -"enumDescriptions": [ -"", -"", -"", -"", -"" -], -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." }, -"score": { -"format": "float", -"type": "number" +"specialistPool": { +"description": "Output only. The name of the SpecialistPool to which the specialists are being added. Format: `projects/{project_id}/locations/{location_id}/specialistPools/{specialist_pool}`", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"LearningGenaiRootControlDecodingRecords": { -"id": "LearningGenaiRootControlDecodingRecords", +"GoogleCloudAiplatformV1beta1UpdateTensorboardOperationMetadata": { +"description": "Details of operations that perform update Tensorboard.", +"id": "GoogleCloudAiplatformV1beta1UpdateTensorboardOperationMetadata", "properties": { -"records": { -"description": "One ControlDecodingRecord record maps to one rewind.", -"items": { -"$ref": "LearningGenaiRootControlDecodingRecord" -}, -"type": "array" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "Operation metadata for Tensorboard." } }, "type": "object" }, -"LearningGenaiRootDataProviderOutput": { -"id": "LearningGenaiRootDataProviderOutput", +"GoogleCloudAiplatformV1beta1UpgradeNotebookRuntimeOperationMetadata": { +"description": "Metadata information for NotebookService.UpgradeNotebookRuntime.", +"id": "GoogleCloudAiplatformV1beta1UpgradeNotebookRuntimeOperationMetadata", "properties": { -"name": { -"type": "string" +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The operation generic information." }, -"status": { -"$ref": "UtilStatusProto", -"description": "If set, this DataProvider failed and this is the error message." +"progressMessage": { +"description": "A human-readable message that shows the intermediate progress details of NotebookRuntime.", +"type": "string" } }, "type": "object" }, -"LearningGenaiRootFilterMetadata": { -"id": "LearningGenaiRootFilterMetadata", -"properties": { -"confidence": { -"description": "Filter confidence.", -"enum": [ -"FILTER_CONFIDENCE_UNKNOWN", -"FILTER_CONFIDENCE_VERY_LOW", -"FILTER_CONFIDENCE_LOW", -"FILTER_CONFIDENCE_MEDIUM", -"FILTER_CONFIDENCE_HIGH", -"FILTER_CONFIDENCE_VERY_HIGH" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"" -], -"type": "string" +"GoogleCloudAiplatformV1beta1UpgradeNotebookRuntimeRequest": { +"description": "Request message for NotebookService.UpgradeNotebookRuntime.", +"id": "GoogleCloudAiplatformV1beta1UpgradeNotebookRuntimeRequest", +"properties": {}, +"type": "object" }, -"debugInfo": { -"$ref": "LearningGenaiRootFilterMetadataFilterDebugInfo", -"description": "Debug info for the message." +"GoogleCloudAiplatformV1beta1UploadModelOperationMetadata": { +"description": "Details of ModelService.UploadModel operation.", +"id": "GoogleCloudAiplatformV1beta1UploadModelOperationMetadata", +"properties": { +"genericMetadata": { +"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata", +"description": "The common part of the operation metadata." +} }, -"fallback": { -"description": "A fallback message chosen by the applied filter.", -"type": "string" +"type": "object" }, -"info": { -"description": "Additional info for the filter.", -"type": "string" +"GoogleCloudAiplatformV1beta1UploadModelRequest": { +"description": "Request message for ModelService.UploadModel.", +"id": "GoogleCloudAiplatformV1beta1UploadModelRequest", +"properties": { +"model": { +"$ref": "GoogleCloudAiplatformV1beta1Model", +"description": "Required. The Model to create." }, -"name": { -"description": "Name of the filter that triggered.", +"modelId": { +"description": "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.", "type": "string" }, -"reason": { -"description": "Filter reason.", -"enum": [ -"FILTER_REASON_UNKNOWN", -"FILTER_REASON_NOT_FILTERED", -"FILTER_REASON_SENSITIVE", -"FILTER_REASON_RECITATION", -"FILTER_REASON_LANGUAGE", -"FILTER_REASON_TAKEDOWN", -"FILTER_REASON_CLASSIFIER", -"FILTER_REASON_EMPTY_RESPONSE", -"FILTER_REASON_SIMILARITY_TAKEDOWN", -"FILTER_REASON_UNSAFE", -"FILTER_REASON_PAIRWISE_CLASSIFIER", -"FILTER_REASON_CODEY", -"FILTER_REASON_URL", -"FILTER_REASON_EMAIL", -"FILTER_REASON_SAFETY_CAT", -"FILTER_REASON_REQUEST_RESPONSE_TAKEDOWN", -"FILTER_REASON_RAI_PQC", -"FILTER_REASON_ATLAS", -"FILTER_REASON_RAI_CSAM", -"FILTER_REASON_RAI_FRINGE", -"FILTER_REASON_RAI_SPII", -"FILTER_REASON_RAI_IMAGE_VIOLENCE", -"FILTER_REASON_RAI_IMAGE_PORN", -"FILTER_REASON_RAI_IMAGE_CSAM", -"FILTER_REASON_RAI_IMAGE_PEDO", -"FILTER_REASON_RAI_IMAGE_CHILD", -"FILTER_REASON_RAI_VIDEO_FRAME_VIOLENCE", -"FILTER_REASON_RAI_VIDEO_FRAME_PORN", -"FILTER_REASON_RAI_VIDEO_FRAME_CSAM", -"FILTER_REASON_RAI_VIDEO_FRAME_PEDO", -"FILTER_REASON_RAI_VIDEO_FRAME_CHILD", -"FILTER_REASON_RAI_CONTEXTUAL_DANGEROUS", -"FILTER_REASON_RAI_GRAIL_TEXT", -"FILTER_REASON_RAI_GRAIL_IMAGE", -"FILTER_REASON_RAI_SAFETYCAT", -"FILTER_REASON_TOXICITY", -"FILTER_REASON_ATLAS_PRICING", -"FILTER_REASON_ATLAS_BILLING", -"FILTER_REASON_ATLAS_NON_ENGLISH_QUESTION", -"FILTER_REASON_ATLAS_NOT_RELATED_TO_GCP", -"FILTER_REASON_ATLAS_AWS_AZURE_RELATED", -"FILTER_REASON_XAI", -"FILTER_CONTROL_DECODING" -], -"enumDescriptions": [ -"Unknown filter reason.", -"Input not filtered.", -"Sensitive content.", -"Recited content.", -"Language filtering", -"Takedown policy", -"Classifier Module", -"Empty response message.", -"Similarity takedown.", -"Unsafe responses from scorers.", -"Pairwise classifier.", -"Codey Filter.", -"URLs Filter.", -"Emails Filter.", -"SafetyCat filter.", -"Request Response takedown.", -"RAI Filter.", -"Atlas specific topic filter", -"RAI Filter.", -"RAI Filter.", -"RAI Filter.", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"RAI Filter", -"Grail Text", -"Grail Image", -"SafetyCat.", -"Toxic content.", -"Atlas specific topic filter for pricing questions.", -"Atlas specific topic filter for billing questions.", -"Atlas specific topic filter for non english questions.", -"Atlas specific topic filter for non GCP questions.", -"Atlas specific topic filter aws/azure related questions.", -"Right now we don't do any filtering for XAI. Adding this just want to differentiatiat the XAI output metadata from other SafetyCat RAI output metadata", -"The response are filtered because it could not pass the control decoding thresholds and the maximum rewind attempts is reached." -], +"parentModel": { +"description": "Optional. The resource name of the model into which to upload the version. Only specify this field when uploading a new version.", "type": "string" }, -"text": { -"description": "The input query or generated response that is getting filtered.", +"serviceAccount": { +"description": "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.).", "type": "string" } }, "type": "object" }, -"LearningGenaiRootFilterMetadataFilterDebugInfo": { -"id": "LearningGenaiRootFilterMetadataFilterDebugInfo", +"GoogleCloudAiplatformV1beta1UploadModelResponse": { +"description": "Response message of ModelService.UploadModel operation.", +"id": "GoogleCloudAiplatformV1beta1UploadModelResponse", "properties": { -"classifierOutput": { -"$ref": "LearningGenaiRootClassifierOutput" -}, -"defaultMetadata": { +"model": { +"description": "The name of the uploaded Model resource. Format: `projects/{project}/locations/{location}/models/{model}`", "type": "string" }, -"languageFilterResult": { -"$ref": "LearningGenaiRootLanguageFilterResult" -}, -"raiOutput": { -"$ref": "LearningGenaiRootRAIOutput", -"description": "Safety filter output information for LLM Root RAI harm check." -}, -"raiResult": { -"$ref": "CloudAiNlLlmProtoServiceRaiResult" -}, -"raiSignal": { -"$ref": "CloudAiNlLlmProtoServiceRaiSignal", -"deprecated": true -}, -"records": { -"$ref": "LearningGenaiRootControlDecodingRecords", -"description": "Number of rewinds by controlled decoding." -}, -"streamRecitationResult": { -"$ref": "LanguageLabsAidaTrustRecitationProtoStreamRecitationResult", -"deprecated": true -}, -"takedownResult": { -"$ref": "LearningGenaiRootTakedownResult" -}, -"toxicityResult": { -"$ref": "LearningGenaiRootToxicityResult" +"modelVersionId": { +"description": "Output only. The version ID of the model that is uploaded.", +"readOnly": true, +"type": "string" } }, "type": "object" }, -"LearningGenaiRootGroundingMetadata": { -"id": "LearningGenaiRootGroundingMetadata", +"GoogleCloudAiplatformV1beta1UploadRagFileConfig": { +"description": "Config for uploading RagFile.", +"id": "GoogleCloudAiplatformV1beta1UploadRagFileConfig", "properties": { -"citations": { -"items": { -"$ref": "LearningGenaiRootGroundingMetadataCitation" -}, -"type": "array" -}, -"groundingCancelled": { -"description": "True if grounding is cancelled, for example, no facts being retrieved.", -"type": "boolean" -}, -"searchQueries": { -"items": { -"type": "string" -}, -"type": "array" +"ragFileChunkingConfig": { +"$ref": "GoogleCloudAiplatformV1beta1RagFileChunkingConfig", +"description": "Specifies the size and overlap of chunks after uploading RagFile." } }, "type": "object" }, -"LearningGenaiRootGroundingMetadataCitation": { -"id": "LearningGenaiRootGroundingMetadataCitation", +"GoogleCloudAiplatformV1beta1UploadRagFileRequest": { +"description": "Request message for VertexRagDataService.UploadRagFile.", +"id": "GoogleCloudAiplatformV1beta1UploadRagFileRequest", "properties": { -"endIndex": { -"description": "Index in the prediction output where the citation ends (exclusive). Must be > start_index and <= len(output).", -"format": "int32", -"type": "integer" -}, -"factIndex": { -"description": "Index of the fact supporting this claim. Should be within the range of the `world_facts` in the GenerateResponse.", -"format": "int32", -"type": "integer" -}, -"score": { -"description": "Confidence score of this entailment. Value is [0,1] with 1 is the most confidence.", -"format": "double", -"type": "number" +"ragFile": { +"$ref": "GoogleCloudAiplatformV1beta1RagFile", +"description": "Required. The RagFile to upload." }, -"startIndex": { -"description": "Index in the prediction output where the citation starts (inclusive). Must be >= 0 and < end_index.", -"format": "int32", -"type": "integer" +"uploadRagFileConfig": { +"$ref": "GoogleCloudAiplatformV1beta1UploadRagFileConfig", +"description": "Required. The config for the RagFiles to be uploaded into the RagCorpus. VertexRagDataService.UploadRagFile." } }, "type": "object" }, -"LearningGenaiRootHarm": { -"id": "LearningGenaiRootHarm", +"GoogleCloudAiplatformV1beta1UploadRagFileResponse": { +"description": "Response message for VertexRagDataService.UploadRagFile.", +"id": "GoogleCloudAiplatformV1beta1UploadRagFileResponse", "properties": { -"contextualDangerous": { -"description": "Please do not use, this is still under development.", -"type": "boolean" -}, -"csam": { -"type": "boolean" -}, -"fringe": { -"type": "boolean" -}, -"grailImageHarmType": { -"$ref": "LearningGenaiRootHarmGrailImageHarmType" -}, -"grailTextHarmType": { -"$ref": "LearningGenaiRootHarmGrailTextHarmType" -}, -"imageChild": { -"type": "boolean" -}, -"imageCsam": { -"type": "boolean" -}, -"imagePedo": { -"type": "boolean" -}, -"imagePorn": { -"description": "Image signals", -"type": "boolean" -}, -"imageViolence": { -"type": "boolean" -}, -"pqc": { -"type": "boolean" -}, -"safetycat": { -"$ref": "LearningGenaiRootHarmSafetyCatCategories" -}, -"spii": { -"$ref": "LearningGenaiRootHarmSpiiFilter", -"description": "Spii Filter uses buckets http://google3/google/privacy/dlp/v2/storage.proto;l=77;rcl=584719820 to classify the input. LMRoot converts the bucket into double score. For example the score for \"POSSIBLE\" is 3 / 5 = 0.6 ." -}, -"threshold": { -"format": "double", -"type": "number" -}, -"videoFrameChild": { -"type": "boolean" -}, -"videoFrameCsam": { -"type": "boolean" -}, -"videoFramePedo": { -"type": "boolean" -}, -"videoFramePorn": { -"description": "Video frame signals", -"type": "boolean" +"error": { +"$ref": "GoogleRpcStatus", +"description": "The error that occurred while processing the RagFile." }, -"videoFrameViolence": { -"type": "boolean" +"ragFile": { +"$ref": "GoogleCloudAiplatformV1beta1RagFile", +"description": "The RagFile that had been uploaded into the RagCorpus." } }, "type": "object" }, -"LearningGenaiRootHarmGrailImageHarmType": { -"description": "Harm type for images", -"id": "LearningGenaiRootHarmGrailImageHarmType", +"GoogleCloudAiplatformV1beta1UpsertDatapointsRequest": { +"description": "Request message for IndexService.UpsertDatapoints", +"id": "GoogleCloudAiplatformV1beta1UpsertDatapointsRequest", "properties": { -"imageHarmType": { +"datapoints": { +"description": "A list of datapoints to be created/updated.", "items": { -"enum": [ -"IMAGE_HARM_TYPE_UNSPECIFIED", -"IMAGE_HARM_TYPE_PORN", -"IMAGE_HARM_TYPE_VIOLENCE", -"IMAGE_HARM_TYPE_CSAI", -"IMAGE_HARM_TYPE_PEDO", -"IMAGE_HARM_TYPE_MINORS", -"IMAGE_HARM_TYPE_DANGEROUS", -"IMAGE_HARM_TYPE_MEDICAL", -"IMAGE_HARM_TYPE_RACY", -"IMAGE_HARM_TYPE_OBSCENE", -"IMAGE_HARM_TYPE_MINOR_PRESENCE", -"IMAGE_HARM_TYPE_GENERATIVE_MINOR_PRESENCE", -"IMAGE_HARM_TYPE_GENERATIVE_REALISTIC_VISIBLE_FACE" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"" -], -"type": "string" +"$ref": "GoogleCloudAiplatformV1beta1IndexDatapoint" }, "type": "array" +}, +"updateMask": { +"description": "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.", +"format": "google-fieldmask", +"type": "string" } }, "type": "object" }, -"LearningGenaiRootHarmGrailTextHarmType": { -"description": "Harm type for text", -"id": "LearningGenaiRootHarmGrailTextHarmType", +"GoogleCloudAiplatformV1beta1UpsertDatapointsResponse": { +"description": "Response message for IndexService.UpsertDatapoints", +"id": "GoogleCloudAiplatformV1beta1UpsertDatapointsResponse", +"properties": {}, +"type": "object" +}, +"GoogleCloudAiplatformV1beta1UserActionReference": { +"description": "References an API call. It contains more information about long running operation and Jobs that are triggered by the API call.", +"id": "GoogleCloudAiplatformV1beta1UserActionReference", "properties": { -"harmType": { -"items": { -"enum": [ -"HARM_TYPE_UNSPECIFIED", -"HARM_TYPE_HATE", -"HARM_TYPE_TOXICITY", -"HARM_TYPE_VIOLENCE", -"HARM_TYPE_CSAI", -"HARM_TYPE_SEXUAL", -"HARM_TYPE_FRINGE", -"HARM_TYPE_POLITICAL", -"HARM_TYPE_MEMORIZATION", -"HARM_TYPE_SPII", -"HARM_TYPE_NEW_DANGEROUS", -"HARM_TYPE_MEDICAL", -"HARM_TYPE_HARASSMENT" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"New definition of dangerous.", -"", -"" -], +"dataLabelingJob": { +"description": "For API calls that start a LabelingJob. Resource name of the LabelingJob. Format: `projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}`", "type": "string" }, -"type": "array" +"method": { +"description": "The method name of the API RPC call. For example, \"/google.cloud.aiplatform.{apiVersion}.DatasetService.CreateDataset\"", +"type": "string" +}, +"operation": { +"description": "For API calls that return a long running operation. Resource name of the long running operation. Format: `projects/{project}/locations/{location}/operations/{operation}`", +"type": "string" } }, "type": "object" }, -"LearningGenaiRootHarmSafetyCatCategories": { -"id": "LearningGenaiRootHarmSafetyCatCategories", +"GoogleCloudAiplatformV1beta1Value": { +"description": "Value is the value of the field.", +"id": "GoogleCloudAiplatformV1beta1Value", "properties": { -"categories": { -"items": { -"enum": [ -"SAFETYCAT_CATEGORY_UNSPECIFIED", -"TOXICITY", -"OBSCENE", -"SEXUAL", -"INSULT", -"IDENTITY_HATE", -"DEATH_HARM_TRAGEDY", -"VIOLENCE_ABUSE", -"FIREARMS_WEAPONS", -"PUBLIC_SAFETY", -"HEALTH", -"RELIGION_BELIEF", -"DRUGS", -"WAR_CONFLICT", -"POLITICS", -"FINANCE", -"LEGAL", -"DANGEROUS", -"DANGEROUS_SEVERITY", -"HARASSMENT_SEVERITY", -"HATE_SEVERITY", -"SEXUAL_SEVERITY" -], -"enumDescriptions": [ -"", -"SafetyCat categories.", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"", -"Following categories are only supported in SAFETY_CAT_TEXT_V3_PAX model", -"", -"", -"", -"" -], +"doubleValue": { +"description": "A double value.", +"format": "double", +"type": "number" +}, +"intValue": { +"description": "An integer value.", +"format": "int64", "type": "string" }, -"type": "array" +"stringValue": { +"description": "A string value.", +"type": "string" } }, "type": "object" }, -"LearningGenaiRootHarmSpiiFilter": { -"id": "LearningGenaiRootHarmSpiiFilter", +"GoogleCloudAiplatformV1beta1VertexAISearch": { +"description": "Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation", +"id": "GoogleCloudAiplatformV1beta1VertexAISearch", "properties": { -"usBankRoutingMicr": { -"type": "boolean" -}, -"usEmployerIdentificationNumber": { -"type": "boolean" -}, -"usSocialSecurityNumber": { -"type": "boolean" +"datastore": { +"description": "Required. Fully-qualified Vertex AI Search's datastore resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`", +"type": "string" } }, "type": "object" }, -"LearningGenaiRootInternalMetadata": { -"id": "LearningGenaiRootInternalMetadata", +"GoogleCloudAiplatformV1beta1VertexRagStore": { +"description": "Retrieve from Vertex RAG Store for grounding.", +"id": "GoogleCloudAiplatformV1beta1VertexRagStore", "properties": { -"scoredTokens": { +"ragCorpora": { +"deprecated": true, +"description": "Optional. Deprecated. Please use rag_resources instead.", "items": { -"$ref": "LearningGenaiRootScoredToken" +"type": "string" }, "type": "array" -} }, -"type": "object" +"ragResources": { +"description": "Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1VertexRagStoreRagResource" }, -"LearningGenaiRootLanguageFilterResult": { -"id": "LearningGenaiRootLanguageFilterResult", -"properties": { -"allowed": { -"description": "False when query or response should be filtered out due to unsupported language.", -"type": "boolean" +"type": "array" }, -"detectedLanguage": { -"description": "Language of the query or response.", -"type": "string" +"similarityTopK": { +"description": "Optional. Number of top k results to return from the selected corpora.", +"format": "int32", +"type": "integer" }, -"detectedLanguageProbability": { -"description": "Probability of the language predicted as returned by LangID.", -"format": "float", +"vectorDistanceThreshold": { +"description": "Optional. Only return results with vector distance smaller than the threshold.", +"format": "double", "type": "number" } }, "type": "object" }, -"LearningGenaiRootMetricOutput": { -"id": "LearningGenaiRootMetricOutput", +"GoogleCloudAiplatformV1beta1VertexRagStoreRagResource": { +"description": "The definition of the Rag resource.", +"id": "GoogleCloudAiplatformV1beta1VertexRagStoreRagResource", "properties": { -"debug": { +"ragCorpus": { +"description": "Optional. RagCorpora resource name. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}`", "type": "string" }, -"name": { -"description": "Name of the metric.", +"ragFileIds": { +"description": "Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field.", +"items": { "type": "string" }, -"numericValue": { -"format": "double", -"type": "number" -}, -"status": { -"$ref": "UtilStatusProto" -}, -"stringValue": { -"type": "string" +"type": "array" } }, "type": "object" }, -"LearningGenaiRootPerRequestProcessorDebugMetadataFactualityDebugMetadata": { -"id": "LearningGenaiRootPerRequestProcessorDebugMetadataFactualityDebugMetadata", +"GoogleCloudAiplatformV1beta1VideoMetadata": { +"description": "Metadata describes the input video content.", +"id": "GoogleCloudAiplatformV1beta1VideoMetadata", "properties": { -"factRetrievalMillisecondsByProvider": { -"additionalProperties": { -"format": "int64", +"endOffset": { +"description": "Optional. The end offset of the video.", +"format": "google-duration", "type": "string" }, -"description": "Latency spent on fact retrievals. There might be multiple retrievals from different fact providers.", -"type": "object" -}, -"prompt2queryMilliseconds": { -"description": "Latency spent on prompt2query. The procedure generates a search-friendly query given the original prompt.", -"format": "int64", +"startOffset": { +"description": "Optional. The start offset of the video.", +"format": "google-duration", "type": "string" } }, "type": "object" }, -"LearningGenaiRootRAIOutput": { -"description": "This is per harm.", -"id": "LearningGenaiRootRAIOutput", +"GoogleCloudAiplatformV1beta1WorkerPoolSpec": { +"description": "Represents the spec of a worker pool in a job.", +"id": "GoogleCloudAiplatformV1beta1WorkerPoolSpec", "properties": { -"allowed": { -"type": "boolean" +"containerSpec": { +"$ref": "GoogleCloudAiplatformV1beta1ContainerSpec", +"description": "The custom container task." }, -"harm": { -"$ref": "LearningGenaiRootHarm" +"diskSpec": { +"$ref": "GoogleCloudAiplatformV1beta1DiskSpec", +"description": "Disk spec." }, -"name": { -"type": "string" +"machineSpec": { +"$ref": "GoogleCloudAiplatformV1beta1MachineSpec", +"description": "Optional. Immutable. The specification of a single machine." }, -"score": { -"format": "double", -"type": "number" -} +"nfsMounts": { +"description": "Optional. List of NFS mount spec.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1NfsMount" }, -"type": "object" +"type": "array" }, -"LearningGenaiRootRegexTakedownResult": { -"id": "LearningGenaiRootRegexTakedownResult", -"properties": { -"allowed": { -"description": "False when query or response should be taken down due to match with a blocked regex, true otherwise.", -"type": "boolean" +"pythonPackageSpec": { +"$ref": "GoogleCloudAiplatformV1beta1PythonPackageSpec", +"description": "The Python packaged task." }, -"takedownRegex": { -"description": "Regex used to decide that query or response should be taken down. Empty when query or response is kept.", +"replicaCount": { +"description": "Optional. The number of worker replicas to use for this worker pool.", +"format": "int64", "type": "string" } }, "type": "object" }, -"LearningGenaiRootRequestResponseTakedownResult": { -"id": "LearningGenaiRootRequestResponseTakedownResult", +"GoogleCloudAiplatformV1beta1WriteFeatureValuesPayload": { +"description": "Contains Feature values to be written for a specific entity.", +"id": "GoogleCloudAiplatformV1beta1WriteFeatureValuesPayload", "properties": { -"allowed": { -"description": "False when response has to be taken down per above config.", -"type": "boolean" -}, -"requestTakedownRegex": { -"description": "Regex used to match the request.", +"entityId": { +"description": "Required. The ID of the entity.", "type": "string" }, -"responseTakedownRegex": { -"description": "Regex used to decide that response should be taken down. Empty when response is kept.", -"type": "string" -} +"featureValues": { +"additionalProperties": { +"$ref": "GoogleCloudAiplatformV1beta1FeatureValue" }, +"description": "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.", "type": "object" -}, -"LearningGenaiRootRoutingDecision": { -"description": "Holds the final routing decision, by storing the model_config_id. And individual scores each model got.", -"id": "LearningGenaiRootRoutingDecision", -"properties": { -"metadata": { -"$ref": "LearningGenaiRootRoutingDecisionMetadata" -}, -"modelConfigId": { -"description": "The selected model to route traffic to.", -"type": "string" } }, "type": "object" }, -"LearningGenaiRootRoutingDecisionMetadata": { -"description": "Debug metadata about the routing decision.", -"id": "LearningGenaiRootRoutingDecisionMetadata", +"GoogleCloudAiplatformV1beta1WriteFeatureValuesRequest": { +"description": "Request message for FeaturestoreOnlineServingService.WriteFeatureValues.", +"id": "GoogleCloudAiplatformV1beta1WriteFeatureValuesRequest", "properties": { -"scoreBasedRoutingMetadata": { -"$ref": "LearningGenaiRootRoutingDecisionMetadataScoreBased" +"payloads": { +"description": "Required. The entities to be written. Up to 100,000 feature values can be written across all `payloads`.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1WriteFeatureValuesPayload" }, -"tokenLengthBasedRoutingMetadata": { -"$ref": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBased" +"type": "array" } }, "type": "object" }, -"LearningGenaiRootRoutingDecisionMetadataScoreBased": { -"description": "If we are routing using scored based configuration, then the metadata about that is available in this proto.", -"id": "LearningGenaiRootRoutingDecisionMetadataScoreBased", -"properties": { -"matchedRule": { -"$ref": "LearningGenaiRootScoreBasedRoutingConfigRule", -"description": "The rule that was matched." -}, -"score": { -"$ref": "LearningGenaiRootScore", -"description": "The score that was generated by the router i.e. the model." -}, -"usedDefaultFallback": { -"description": "No rules were matched & therefore used the default fallback.", -"type": "boolean" -} -}, +"GoogleCloudAiplatformV1beta1WriteFeatureValuesResponse": { +"description": "Response message for FeaturestoreOnlineServingService.WriteFeatureValues.", +"id": "GoogleCloudAiplatformV1beta1WriteFeatureValuesResponse", +"properties": {}, "type": "object" }, -"LearningGenaiRootRoutingDecisionMetadataTokenLengthBased": { -"id": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBased", +"GoogleCloudAiplatformV1beta1WriteTensorboardExperimentDataRequest": { +"description": "Request message for TensorboardService.WriteTensorboardExperimentData.", +"id": "GoogleCloudAiplatformV1beta1WriteTensorboardExperimentDataRequest", "properties": { -"modelInputTokenMetadata": { -"items": { -"$ref": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelInputTokenMetadata" -}, -"type": "array" -}, -"modelMaxTokenMetadata": { +"writeRunDataRequests": { +"description": "Required. Requests containing per-run TensorboardTimeSeries data to write.", "items": { -"$ref": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelMaxTokenMetadata" +"$ref": "GoogleCloudAiplatformV1beta1WriteTensorboardRunDataRequest" }, "type": "array" } }, "type": "object" }, -"LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelInputTokenMetadata": { -"id": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelInputTokenMetadata", -"properties": { -"computedInputTokenLength": { -"description": "The length computed by backends using the formatter & tokenizer specific to the model", -"format": "int32", -"type": "integer" -}, -"modelId": { -"type": "string" -}, -"pickedAsFallback": { -"description": "If true, the model was selected as a fallback, since no model met requirements.", -"type": "boolean" -}, -"selected": { -"description": "If true, the model was selected since it met the requriements.", -"type": "boolean" -} -}, +"GoogleCloudAiplatformV1beta1WriteTensorboardExperimentDataResponse": { +"description": "Response message for TensorboardService.WriteTensorboardExperimentData.", +"id": "GoogleCloudAiplatformV1beta1WriteTensorboardExperimentDataResponse", +"properties": {}, "type": "object" }, -"LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelMaxTokenMetadata": { -"id": "LearningGenaiRootRoutingDecisionMetadataTokenLengthBasedModelMaxTokenMetadata", +"GoogleCloudAiplatformV1beta1WriteTensorboardRunDataRequest": { +"description": "Request message for TensorboardService.WriteTensorboardRunData.", +"id": "GoogleCloudAiplatformV1beta1WriteTensorboardRunDataRequest", "properties": { -"maxNumInputTokens": { -"format": "int32", -"type": "integer" +"tensorboardRun": { +"description": "Required. The resource name of the TensorboardRun to write data to. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}`", +"type": "string" }, -"maxNumOutputTokens": { -"format": "int32", -"type": "integer" +"timeSeriesData": { +"description": "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.", +"items": { +"$ref": "GoogleCloudAiplatformV1beta1TimeSeriesData" }, -"modelId": { -"type": "string" +"type": "array" } }, "type": "object" }, -"LearningGenaiRootRuleOutput": { -"id": "LearningGenaiRootRuleOutput", -"properties": { -"decision": { -"enum": [ -"NO_MATCH", -"MATCH" -], -"enumDescriptions": [ -"This rule was not matched. When used in a ClassifierOutput, this means that no rules were matched.", -"This is a generic \"match\" message, indicating that a rule was triggered. Usually you would use this for a categorization classifier." -], -"type": "string" -}, -"name": { -"type": "string" -} -}, +"GoogleCloudAiplatformV1beta1WriteTensorboardRunDataResponse": { +"description": "Response message for TensorboardService.WriteTensorboardRunData.", +"id": "GoogleCloudAiplatformV1beta1WriteTensorboardRunDataResponse", +"properties": {}, "type": "object" }, -"LearningGenaiRootScore": { -"id": "LearningGenaiRootScore", +"GoogleCloudAiplatformV1beta1XraiAttribution": { +"description": "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.", +"id": "GoogleCloudAiplatformV1beta1XraiAttribution", "properties": { -"calculationType": { -"$ref": "LearningGenaiRootCalculationType" -}, -"internalMetadata": { -"$ref": "LearningGenaiRootInternalMetadata", -"description": "The internal_metadata is intended to be used by internal processors and will be cleared before returns." -}, -"thresholdType": { -"$ref": "LearningGenaiRootThresholdType" +"blurBaselineConfig": { +"$ref": "GoogleCloudAiplatformV1beta1BlurBaselineConfig", +"description": "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" }, -"tokensAndLogprobPerDecodingStep": { -"$ref": "LearningGenaiRootTokensAndLogProbPerDecodingStep", -"description": "Top candidate tokens and log probabilities at each decoding step." +"smoothGradConfig": { +"$ref": "GoogleCloudAiplatformV1beta1SmoothGradConfig", +"description": "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" }, -"value": { -"format": "double", -"type": "number" +"stepCount": { +"description": "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.", +"format": "int32", +"type": "integer" } }, "type": "object" }, -"LearningGenaiRootScoreBasedRoutingConfigRule": { -"id": "LearningGenaiRootScoreBasedRoutingConfigRule", +"GoogleCloudLocationListLocationsResponse": { +"description": "The response message for Locations.ListLocations.", +"id": "GoogleCloudLocationListLocationsResponse", "properties": { -"equalOrGreaterThan": { -"$ref": "LearningGenaiRootScore", -"description": "NOTE: Hardest examples have smaller values in their routing scores." +"locations": { +"description": "A list of locations that matches the specified filter in the request.", +"items": { +"$ref": "GoogleCloudLocationLocation" }, -"lessThan": { -"$ref": "LearningGenaiRootScore" +"type": "array" }, -"modelConfigId": { -"description": "This model_config_id points to ModelConfig::id which allows us to find the ModelConfig to route to. This is part of the banks specified in the ModelBankConfig.", +"nextPageToken": { +"description": "The standard List next-page token.", "type": "string" } }, "type": "object" }, -"LearningGenaiRootScoredSimilarityTakedownPhrase": { -"description": "Proto containing the results from the Universal Sentence Encoder / Other models", -"id": "LearningGenaiRootScoredSimilarityTakedownPhrase", +"GoogleCloudLocationLocation": { +"description": "A resource that represents a Google Cloud location.", +"id": "GoogleCloudLocationLocation", "properties": { -"phrase": { -"$ref": "LearningGenaiRootSimilarityTakedownPhrase" +"displayName": { +"description": "The friendly name for this location, typically a nearby city name. For example, \"Tokyo\".", +"type": "string" }, -"similarityScore": { -"format": "float", -"type": "number" -} +"labels": { +"additionalProperties": { +"type": "string" }, +"description": "Cross-service attributes for the location. For example {\"cloud.googleapis.com/region\": \"us-east1\"}", "type": "object" }, -"LearningGenaiRootScoredToken": { -"description": "A token with its own score.", -"id": "LearningGenaiRootScoredToken", -"properties": { -"endTokenScore": { -"description": "Each end_token_score is a logprob for how well the completion would end at a particular token. See http://google3/labs/language/aida/config/proto/model_config.proto;l=376;rcl=573039459", -"format": "float", -"type": "number" -}, -"score": { -"description": "Each score is the logprob for the token in model response.", -"format": "float", -"type": "number" -}, -"token": { +"locationId": { +"description": "The canonical id for this location. For example: `\"us-east1\"`.", "type": "string" -} }, +"metadata": { +"additionalProperties": { +"description": "Properties of the object. Contains field @type with type URL.", +"type": "any" +}, +"description": "Service-specific metadata. For example the available capacity at the given location.", "type": "object" }, -"LearningGenaiRootSimilarityTakedownPhrase": { -"description": "Each SimilarityTakedownPhrase treats a logical group of blocked and allowed phrases together along with a corresponding punt If the closest matching response is of the allowed type, we allow the response If the closest matching response is of the blocked type, we block the response. eg: Blocked phrase - \"All lives matter\"", -"id": "LearningGenaiRootSimilarityTakedownPhrase", -"properties": { -"blockedPhrase": { +"name": { +"description": "Resource name for the location, which may vary between implementations. For example: `\"projects/example-project/locations/us-east1\"`", "type": "string" } }, "type": "object" }, -"LearningGenaiRootSimilarityTakedownResult": { -"id": "LearningGenaiRootSimilarityTakedownResult", +"GoogleIamV1Binding": { +"description": "Associates `members`, or principals, with a `role`.", +"id": "GoogleIamV1Binding", "properties": { -"allowed": { -"description": "False when query or response should be taken down by any of the takedown rules, true otherwise.", -"type": "boolean" +"condition": { +"$ref": "GoogleTypeExpr", +"description": "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)." }, -"scoredPhrases": { -"description": "List of similar phrases with score. Set only if allowed=false.", +"members": { +"description": "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/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workforce identity pool. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}`: All workforce identities in a group. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All workforce identities with a specific attribute value. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*`: All identities in a workforce identity pool. * `principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workload identity pool. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}`: A workload identity pool group. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{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/global/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.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: Deleted single identity in a workforce identity pool. For example, `deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value`.", "items": { -"$ref": "LearningGenaiRootScoredSimilarityTakedownPhrase" +"type": "string" }, "type": "array" +}, +"role": { +"description": "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).", +"type": "string" } }, "type": "object" }, -"LearningGenaiRootTakedownResult": { -"id": "LearningGenaiRootTakedownResult", +"GoogleIamV1GetIamPolicyRequest": { +"description": "Request message for `GetIamPolicy` method.", +"id": "GoogleIamV1GetIamPolicyRequest", "properties": { -"allowed": { -"description": "False when query or response should be taken down by any of the takedown rules, true otherwise.", -"type": "boolean" -}, -"regexTakedownResult": { -"$ref": "LearningGenaiRootRegexTakedownResult" -}, -"requestResponseTakedownResult": { -"$ref": "LearningGenaiRootRequestResponseTakedownResult" -}, -"similarityTakedownResult": { -"$ref": "LearningGenaiRootSimilarityTakedownResult" +"options": { +"$ref": "GoogleIamV1GetPolicyOptions", +"description": "OPTIONAL: A `GetPolicyOptions` object for specifying options to `GetIamPolicy`." } }, "type": "object" }, -"LearningGenaiRootThresholdType": { -"description": "The type of score that bundled with a threshold, and will not be attending the final score calculation. How each score type uses the threshold can be implementation details.", -"id": "LearningGenaiRootThresholdType", +"GoogleIamV1GetPolicyOptions": { +"description": "Encapsulates settings provided to GetIamPolicy.", +"id": "GoogleIamV1GetPolicyOptions", "properties": { -"scoreType": { -"enum": [ -"TYPE_UNKNOWN", -"TYPE_SAFE", -"TYPE_POLICY", -"TYPE_GENERATION" -], -"enumDescriptions": [ -"Unknown scorer type.", -"Safety scorer.", -"Policy scorer.", -"Generation scorer." -], -"type": "string" -}, -"threshold": { -"format": "double", -"type": "number" +"requestedPolicyVersion": { +"description": "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).", +"format": "int32", +"type": "integer" } }, "type": "object" }, -"LearningGenaiRootTokensAndLogProbPerDecodingStep": { -"description": "Results of RandomSamplingParams::top_k_logprob_per_decoding_step.", -"id": "LearningGenaiRootTokensAndLogProbPerDecodingStep", +"GoogleIamV1Policy": { +"description": "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/).", +"id": "GoogleIamV1Policy", "properties": { -"chosenCandidates": { -"description": "Length = total number of decoding steps. The chosen candidates may or may not be in top_candidates.", +"bindings": { +"description": "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`.", "items": { -"$ref": "LearningGenaiRootTokensAndLogProbPerDecodingStepCandidate" +"$ref": "GoogleIamV1Binding" }, "type": "array" }, -"topCandidates": { -"description": "Length = total number of decoding steps.", -"items": { -"$ref": "LearningGenaiRootTokensAndLogProbPerDecodingStepTopCandidates" +"etag": { +"description": "`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.", +"format": "byte", +"type": "string" }, -"type": "array" +"version": { +"description": "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).", +"format": "int32", +"type": "integer" } }, "type": "object" }, -"LearningGenaiRootTokensAndLogProbPerDecodingStepCandidate": { -"description": "A candidate at a decoding step.", -"id": "LearningGenaiRootTokensAndLogProbPerDecodingStepCandidate", +"GoogleIamV1SetIamPolicyRequest": { +"description": "Request message for `SetIamPolicy` method.", +"id": "GoogleIamV1SetIamPolicyRequest", "properties": { -"logProbability": { -"description": "The candidate's log probability.", -"format": "float", -"type": "number" -}, -"token": { -"description": "The candidate\u2019s token value.", -"type": "string" +"policy": { +"$ref": "GoogleIamV1Policy", +"description": "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." } }, "type": "object" }, -"LearningGenaiRootTokensAndLogProbPerDecodingStepTopCandidates": { -"description": "Candidates with top log probabilities at each decoding step.", -"id": "LearningGenaiRootTokensAndLogProbPerDecodingStepTopCandidates", +"GoogleIamV1TestIamPermissionsRequest": { +"description": "Request message for `TestIamPermissions` method.", +"id": "GoogleIamV1TestIamPermissionsRequest", "properties": { -"candidates": { -"description": "Sorted by log probability in descending order.", +"permissions": { +"description": "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).", "items": { -"$ref": "LearningGenaiRootTokensAndLogProbPerDecodingStepCandidate" +"type": "string" }, "type": "array" } }, "type": "object" }, -"LearningGenaiRootToxicityResult": { -"description": "A model can generate multiple signals and this captures all the generated signals for a single message.", -"id": "LearningGenaiRootToxicityResult", +"GoogleIamV1TestIamPermissionsResponse": { +"description": "Response message for `TestIamPermissions` method.", +"id": "GoogleIamV1TestIamPermissionsResponse", "properties": { -"signals": { +"permissions": { +"description": "A subset of `TestPermissionsRequest.permissions` that the caller is allowed.", "items": { -"$ref": "LearningGenaiRootToxicitySignal" +"type": "string" }, "type": "array" } }, "type": "object" }, -"LearningGenaiRootToxicitySignal": { -"description": "Proto to capture a signal generated by the toxicity model.", -"id": "LearningGenaiRootToxicitySignal", +"GoogleLongrunningListOperationsResponse": { +"description": "The response message for Operations.ListOperations.", +"id": "GoogleLongrunningListOperationsResponse", "properties": { -"allowed": { -"type": "boolean" -}, -"label": { -"enum": [ -"LABEL_UNSPECIFIED", -"NOT_SENSITIVE", -"SENSITIVE", -"ACCIDENTS_DISASTERS", -"ADULT", -"COMPUTER_SECURITY", -"CONTROVERSIAL_SOCIAL_ISSUES", -"DEATH_TRAGEDY", -"DRUGS", -"IDENTITY_ETHNICITY", -"FINANCIAL_HARDSHIP", -"FIREARMS_WEAPONS", -"HEALTH", -"INSULT", -"LEGAL", -"MENTAL_HEALTH", -"POLITICS", -"RELIGION_BELIEFS", -"SAFETY", -"SELF_HARM", -"SPECIAL_NEEDS", -"TERRORISM", -"TOXIC", -"TROUBLED_RELATIONSHIP", -"VIOLENCE_ABUSE", -"VULGAR", -"WAR_CONFLICT" -], -"enumDescriptions": [ -"Default label.", -"Input is not sensitive.", -"Input is sensitive.", -"Input is related to accidents or disasters.", -"Input contains adult content.", -"Input is related to computer security.", -"Input contains controversial social issues.", -"Input is related to death tragedy.", -"Input is related to drugs.", -"Input is related to identity or ethnicity.", -"Input is related to financial hardship.", -"Input is related to firearms or weapons.", -"Input contains health related information.", -"Input may be an insult.", -"Input is related to legal content.", -"Input contains mental health related information.", -"Input is related to politics.", -"Input is related to religions or beliefs.", -"Input is related to safety.", -"Input is related to self-harm.", -"Input is related to special needs.", -"Input is related to terrorism.", -"Input is toxic.", -"Input is related to troubled relationships.", -"Input contains content about violence or abuse.", -"Input is vulgar.", -"Input is related to war and conflict." -], +"nextPageToken": { +"description": "The standard List next-page token.", "type": "string" }, -"score": { -"format": "float", -"type": "number" -} -}, -"type": "object" -}, -"LearningGenaiRootTranslationRequestInfo": { -"description": "Each TranslationRequestInfo corresponds to a request sent to the translation server.", -"id": "LearningGenaiRootTranslationRequestInfo", -"properties": { -"detectedLanguageCodes": { -"description": "The ISO-639 language code of source text in the initial request, detected automatically, if no source language was passed within the initial request. If the source language was passed, auto-detection of the language does not occur and this field is empty.", +"operations": { +"description": "A list of operations that matches the specified filter in the request.", "items": { -"type": "string" +"$ref": "GoogleLongrunningOperation" }, "type": "array" -}, -"totalContentSize": { -"description": "The sum of the size of all the contents in the request.", -"format": "int64", -"type": "string" -} -}, -"type": "object" -}, -"LearningServingLlmAtlasOutputMetadata": { -"id": "LearningServingLlmAtlasOutputMetadata", -"properties": { -"requestTopic": { -"type": "string" -}, -"source": { -"enum": [ -"UNKNOWN", -"FACTUALITY", -"INFOBOT", -"LLM" -], -"enumDescriptions": [ -"", -"", -"", -"" -], -"type": "string" } }, "type": "object" }, -"LearningServingLlmMessageMetadata": { -"description": "LINT.IfChange This metadata contains additional information required for debugging. Next ID: 28", -"id": "LearningServingLlmMessageMetadata", +"GoogleLongrunningOperation": { +"description": "This resource represents a long-running operation that is the result of a network API call.", +"id": "GoogleLongrunningOperation", "properties": { -"atlasMetadata": { -"$ref": "LearningServingLlmAtlasOutputMetadata" -}, -"classifierSummary": { -"$ref": "LearningGenaiRootClassifierOutputSummary", -"description": "Summary of classifier output. We attach this to all messages regardless of whether classification rules triggered or not." -}, -"codeyOutput": { -"$ref": "LearningGenaiRootCodeyOutput", -"description": "Contains metadata related to Codey Processors." -}, -"currentStreamTextLength": { -"format": "uint32", -"type": "integer" -}, -"deleted": { -"description": "Whether the corresponding message has been deleted.", +"done": { +"description": "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.", "type": "boolean" }, -"filterMeta": { -"description": "Metadata for filters that triggered.", -"items": { -"$ref": "LearningGenaiRootFilterMetadata" +"error": { +"$ref": "GoogleRpcStatus", +"description": "The error result of the operation in case of failure or cancellation." }, -"type": "array" +"metadata": { +"additionalProperties": { +"description": "Properties of the object. Contains field @type with type URL.", +"type": "any" }, -"finalMessageScore": { -"$ref": "LearningGenaiRootScore", -"description": "This score is finally used for ranking the message. This will be same as the score present in `Message.score` field." +"description": "Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.", +"type": "object" }, -"finishReason": { -"description": "NOT YET IMPLEMENTED.", -"enum": [ -"UNSPECIFIED", -"RETURN", -"STOP", -"MAX_TOKENS", -"FILTER", -"TOP_N_FILTERED" -], -"enumDescriptions": [ -"", -"Return all the tokens back. This typically implies no filtering or stop sequence was triggered.", -"Finished due to provided stop sequence.", -"Model has emitted the maximum number of tokens as specified by max_decoding_steps.", -"Finished due to triggering some post-processing filter.", -"Filtered out due to Top_N < Response_Candidates.Size()" -], +"name": { +"description": "The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.", "type": "string" }, -"groundingMetadata": { -"$ref": "LearningGenaiRootGroundingMetadata" -}, -"isCode": { -"description": "Applies to streaming response message only. Whether the message is a code.", -"type": "boolean" -}, -"isFallback": { -"description": "Applies to Response message only. Indicates whether the message is a fallback and the response would have otherwise been empty.", -"type": "boolean" -}, -"langidResult": { -"$ref": "NlpSaftLangIdResult", -"description": "Result from nlp_saft DetectLanguage method. Currently the predicted language code and language probability is used." -}, -"language": { -"description": "Detected language.", -"type": "string" +"response": { +"additionalProperties": { +"description": "Properties of the object. Contains field @type with type URL.", +"type": "any" }, -"lmPrefix": { -"description": "The LM prefix used to generate this response.", -"type": "string" +"description": "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`.", +"type": "object" +} }, -"originalText": { -"description": "The original text generated by LLM. This is the raw output for debugging purposes.", -"type": "string" +"type": "object" }, -"perStreamDecodedTokenCount": { -"description": "Number of tokens decoded by the model as part of a stream. This count may be different from `per_stream_returned_token_count` which, is counted after any response rewriting or truncation. Applies to streaming response only.", -"format": "int32", -"type": "integer" +"GoogleProtobufEmpty": { +"description": "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); }", +"id": "GoogleProtobufEmpty", +"properties": {}, +"type": "object" }, -"perStreamReturnedTokenCount": { -"description": "Number of tokens returned per stream in a response candidate after any response rewriting or truncation. Applies to streaming response only. Applies to Gemini models only.", +"GoogleRpcStatus": { +"description": "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).", +"id": "GoogleRpcStatus", +"properties": { +"code": { +"description": "The status code, which should be an enum value of google.rpc.Code.", "format": "int32", "type": "integer" }, -"raiOutputs": { -"description": "Results of running RAI on the query or this response candidate. One output per rai_config. It will be populated regardless of whether the threshold is exceeded or not.", +"details": { +"description": "A list of messages that carry the error details. There is a common set of message types for APIs to use.", "items": { -"$ref": "LearningGenaiRootRAIOutput" -}, -"type": "array" -}, -"recitationResult": { -"$ref": "LearningGenaiRecitationRecitationResult", -"description": "Recitation Results. It will be populated as long as Recitation processing is enabled, regardless of recitation outcome." -}, -"returnTokenCount": { -"description": "NOT IMPLEMENTED TODO (b/334187574) Remove this field after Labs migrates to per_stream_returned_token_count and total_returned_token_count.", -"format": "int32", -"type": "integer" +"additionalProperties": { +"description": "Properties of the object. Contains field @type with type URL.", +"type": "any" }, -"scores": { -"description": "All the different scores for a message are logged here.", -"items": { -"$ref": "LearningGenaiRootScore" +"type": "object" }, "type": "array" }, -"streamTerminated": { -"description": "Whether the response is terminated during streaming return. Only used for streaming requests.", -"type": "boolean" -}, -"totalDecodedTokenCount": { -"description": "Total tokens decoded so far per response_candidate. For streaming: Count of all the tokens decoded so far (aggregated count). For unary: Count of all the tokens decoded per response_candidate.", -"format": "int32", -"type": "integer" -}, -"totalReturnedTokenCount": { -"description": "Total number of tokens returned in a response candidate. For streaming, it is the aggregated count (i.e. total so far) Applies to Gemini models only.", -"format": "int32", -"type": "integer" -}, -"translatedUserPrompts": { -"description": "Translated user-prompt used for RAI post processing. This is for internal processing only. We will translate in pre-processor and pass the translated text to the post processor using this field. It will be empty if non of the signals requested need translation.", -"items": { +"message": { +"description": "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.", "type": "string" -}, -"type": "array" -}, -"vertexRaiResult": { -"$ref": "CloudAiNlLlmProtoServiceRaiResult", -"description": "The metadata from Vertex SafetyCat processors" } }, "type": "object" }, -"NlpSaftLangIdLocalesResult": { -"id": "NlpSaftLangIdLocalesResult", +"GoogleTypeColor": { +"description": "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\u2014for 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(''); }; // ...", +"id": "GoogleTypeColor", "properties": { -"predictions": { -"description": "List of locales in which the text would be considered acceptable. Sorted in descending order according to each locale's respective likelihood. For example, if a Portuguese text is acceptable in both Brazil and Portugal, but is more strongly associated with Brazil, then the predictions would be [\"pt-BR\", \"pt-PT\"], in that order. May be empty, indicating that the model did not predict any acceptable locales.", -"items": { -"$ref": "NlpSaftLangIdLocalesResultLocale" +"alpha": { +"description": "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).", +"format": "float", +"type": "number" }, -"type": "array" -} +"blue": { +"description": "The amount of blue in the color as a value in the interval [0, 1].", +"format": "float", +"type": "number" }, -"type": "object" +"green": { +"description": "The amount of green in the color as a value in the interval [0, 1].", +"format": "float", +"type": "number" }, -"NlpSaftLangIdLocalesResultLocale": { -"id": "NlpSaftLangIdLocalesResultLocale", -"properties": { -"languageCode": { -"description": "A BCP 47 language code that includes region information. For example, \"pt-BR\" or \"pt-PT\". This field will always be populated.", -"type": "string" +"red": { +"description": "The amount of red in the color as a value in the interval [0, 1].", +"format": "float", +"type": "number" } }, "type": "object" }, -"NlpSaftLangIdResult": { -"id": "NlpSaftLangIdResult", +"GoogleTypeDate": { +"description": "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", +"id": "GoogleTypeDate", "properties": { -"modelVersion": { -"description": "The version of the model used to create these annotations.", -"enum": [ -"VERSION_UNSPECIFIED", -"INDEXING_20181017", -"INDEXING_20191206", -"INDEXING_20200313", -"INDEXING_20210618", -"STANDARD_20220516" -], -"enumDescriptions": [ -"", -"", -"", -"", -"", -"" -], -"type": "string" -}, -"predictions": { -"description": "This field stores the n-best list of possible BCP 47 language code strings for a given input sorted in descending order according to each code's respective probability.", -"items": { -"$ref": "NlpSaftLanguageSpan" -}, -"type": "array" +"day": { +"description": "Day of a month. Must be from 1 to 31 and valid for the year and month, or 0 to specify a year by itself or a year and month where the day isn't significant.", +"format": "int32", +"type": "integer" }, -"spanPredictions": { -"description": "This field stores language predictions of subspans of the input, when available. Each LanguageSpanSequence is a sequence of LanguageSpans. A particular sequence of LanguageSpans has an associated probability, and need not necessarily cover the entire input. If no language could be predicted for any span, then this field may be empty.", -"items": { -"$ref": "NlpSaftLanguageSpanSequence" +"month": { +"description": "Month of a year. Must be from 1 to 12, or 0 to specify a year without a month and day.", +"format": "int32", +"type": "integer" }, -"type": "array" +"year": { +"description": "Year of the date. Must be from 1 to 9999, or 0 to specify a date without a year.", +"format": "int32", +"type": "integer" } }, "type": "object" }, -"NlpSaftLanguageSpan": { -"id": "NlpSaftLanguageSpan", +"GoogleTypeExpr": { +"description": "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.", +"id": "GoogleTypeExpr", "properties": { -"end": { -"format": "int32", -"type": "integer" -}, -"languageCode": { -"description": "A BCP 47 language code for this span.", +"description": { +"description": "Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.", "type": "string" }, -"locales": { -"$ref": "NlpSaftLangIdLocalesResult", -"description": "Optional field containing any information that was predicted about the specific locale(s) of the span." +"expression": { +"description": "Textual representation of an expression in Common Expression Language syntax.", +"type": "string" }, -"probability": { -"description": "A probability associated with this prediction.", -"format": "float", -"type": "number" +"location": { +"description": "Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.", +"type": "string" }, -"start": { -"description": "Start and end byte offsets, inclusive, within the given input string. A value of -1 implies that this field is not set. Both fields must either be set with a nonnegative value or both are unset. If both are unset then this LanguageSpan applies to the entire input.", -"format": "int32", -"type": "integer" +"title": { +"description": "Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.", +"type": "string" } }, "type": "object" }, -"NlpSaftLanguageSpanSequence": { -"id": "NlpSaftLanguageSpanSequence", +"GoogleTypeInterval": { +"description": "Represents a time interval, encoded as a Timestamp start (inclusive) and a Timestamp end (exclusive). The start must be less than or equal to the end. When the start equals the end, the interval is empty (matches no time). When both start and end are unspecified, the interval matches any time.", +"id": "GoogleTypeInterval", "properties": { -"languageSpans": { -"description": "A sequence of LanguageSpan objects, each assigning a language to a subspan of the input.", -"items": { -"$ref": "NlpSaftLanguageSpan" -}, -"type": "array" +"endTime": { +"description": "Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end.", +"format": "google-datetime", +"type": "string" }, -"probability": { -"description": "The probability of this sequence of LanguageSpans.", -"format": "float", -"type": "number" +"startTime": { +"description": "Optional. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start.", +"format": "google-datetime", +"type": "string" } }, "type": "object" }, -"Proto2BridgeMessageSet": { -"description": "This is proto2's version of MessageSet.", -"id": "Proto2BridgeMessageSet", -"properties": {}, -"type": "object" -}, -"UtilStatusProto": { -"description": "Wire-format for a Status object", -"id": "UtilStatusProto", +"GoogleTypeMoney": { +"description": "Represents an amount of money with its currency type.", +"id": "GoogleTypeMoney", "properties": { -"canonicalCode": { -"description": "The canonical error code (see codes.proto) that most closely corresponds to this status. This may be missing, and in the common case of the generic space, it definitely will be.", -"format": "int32", -"type": "integer" +"currencyCode": { +"description": "The three-letter currency code defined in ISO 4217.", +"type": "string" }, -"code": { -"description": "Numeric code drawn from the space specified below. Often, this is the canonical error space, and code is drawn from google3/util/task/codes.proto", +"nanos": { +"description": "Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If `units` is positive, `nanos` must be positive or zero. If `units` is zero, `nanos` can be positive, zero, or negative. If `units` is negative, `nanos` must be negative or zero. For example $-1.75 is represented as `units`=-1 and `nanos`=-750,000,000.", "format": "int32", "type": "integer" }, -"message": { -"description": "Detail message", -"type": "string" -}, -"messageSet": { -"$ref": "Proto2BridgeMessageSet", -"description": "message_set associates an arbitrary proto message with the status." -}, -"space": { -"description": "The following are usually only present when code != 0 Space to which this status belongs", +"units": { +"description": "The whole units of the amount. For example if `currencyCode` is `\"USD\"`, then 1 unit is one US dollar.", +"format": "int64", "type": "string" } },list_next()+Retrieves the next page of results. + + Args: + previous_request: The request for the previous page. (required) + previous_response: The response from the request for the previous page. (required) + + Returns: + A request object that you can call 'execute()' on to request the next + page. Returns None if there are no more items in the collection. ++