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feat(all): auto-regenerate discovery clients (#2369)
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‎aiplatform/v1/aiplatform-api.json‎

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‎aiplatform/v1/aiplatform-gen.go‎

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‎aiplatform/v1beta1/aiplatform-api.json‎

Lines changed: 139 additions & 29 deletions
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@@ -5072,7 +5072,7 @@
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"type": "string"
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},
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"parent": {
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"description": "Required. The resource name of the Location to create FeatureOnlineStores. Format: `projects/{project}/locations/{location}'`",
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"description": "Required. The resource name of the Location to create FeatureOnlineStores. Format: `projects/{project}/locations/{location}`",
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"location": "path",
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"pattern": "^projects/[^/]+/locations/[^/]+$",
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"required": true,
@@ -17279,7 +17279,7 @@
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}
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}
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},
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"revision": "20240105",
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"revision": "20240117",
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"rootUrl": "https://aiplatform.googleapis.com/",
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"schemas": {
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"CloudAiLargeModelsVisionEmbedVideoResponse": {
@@ -17989,7 +17989,7 @@
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"type": "boolean"
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},
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"languageFilterResult": {
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"$ref": "LearningServingLlmLanguageFilterResult",
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"$ref": "LearningGenaiRootLanguageFilterResult",
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"description": "Language filter result from SAFT LangId."
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},
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"raiSignals": {
@@ -20050,6 +20050,10 @@
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"genericMetadata": {
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"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata",
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"description": "Operation metadata for PersistentResource."
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},
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"progressMessage": {
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"description": "Progress Message for Create LRO",
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"type": "string"
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}
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},
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"type": "object"
@@ -26210,7 +26214,7 @@
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},
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"modelSourceInfo": {
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"$ref": "GoogleCloudAiplatformV1beta1ModelSourceInfo",
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"description": "Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.",
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"description": "Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.",
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"readOnly": true
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},
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"name": {
@@ -27772,7 +27776,12 @@
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"INVALID_AVRO_SYNTAX",
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"INVALID_EMBEDDING_ID",
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"EMBEDDING_SIZE_MISMATCH",
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"NAMESPACE_MISSING"
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"NAMESPACE_MISSING",
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"PARSING_ERROR",
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"DUPLICATE_NAMESPACE",
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"OP_IN_DATAPOINT",
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"MULTIPLE_VALUES",
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"INVALID_NUMERIC_VALUE"
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],
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"enumDescriptions": [
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"Default, shall not be used.",
@@ -27782,7 +27791,12 @@
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"Invalid avro format.",
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"The embedding id is not valid.",
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"The size of the embedding vectors does not match with the specified dimension.",
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"The `namespace` field is missing."
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"The `namespace` field is missing.",
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"Generic catch-all error. Only used for validation failure where the root cause cannot be easily retrieved programmatically.",
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"There are multiple restricts with the same `namespace` value.",
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"Numeric restrict has operator specified in datapoint.",
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"Numeric restrict has multiple values specified.",
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"Numeric restrict has invalid numeric value specified."
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],
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"type": "string"
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},
@@ -28514,6 +28528,10 @@
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"description": "The runtime config of a PipelineJob.",
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"id": "GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfig",
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"properties": {
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"defaultRuntime": {
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"$ref": "GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfigDefaultRuntime",
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"description": "Optional. The default runtime for the PipelineJob. If not provided, Vertex Custom Job is used as the runtime. For Vertex Custom Job, please refer to https://cloud.google.com/vertex-ai/docs/training/overview"
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},
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"failurePolicy": {
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"description": "Represents the failure policy of a pipeline. Currently, the default of a pipeline is that the pipeline will continue to run until no more tasks can be executed, also known as PIPELINE_FAILURE_POLICY_FAIL_SLOW. However, if a pipeline is set to PIPELINE_FAILURE_POLICY_FAIL_FAST, it will stop scheduling any new tasks when a task has failed. Any scheduled tasks will continue to completion.",
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"enum": [
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},
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfigDefaultRuntime": {
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"description": "The default runtime for the PipelineJob.",
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"id": "GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfigDefaultRuntime",
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"properties": {
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"persistentResourceRuntimeDetail": {
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"$ref": "GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfigPersistentResourceRuntimeDetail",
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"description": "Persistent resource based runtime detail."
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}
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},
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfigInputArtifact": {
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"description": "The type of an input artifact.",
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"id": "GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfigInputArtifact",
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},
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfigPersistentResourceRuntimeDetail": {
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"description": "Persistent resource based runtime detail. For more information, refer to https://cloud.google.com/vertex-ai/docs/training/persistent-resource-overview",
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"id": "GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfigPersistentResourceRuntimeDetail",
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"properties": {
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"persistentResourceName": {
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"description": "Persistent resource name. Format: `projects/{project}/locations/{location}/persistentResources/{persistent_resource}`",
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"type": "string"
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}
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},
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1PipelineTaskDetail": {
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"description": "The runtime detail of a task execution.",
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"id": "GoogleCloudAiplatformV1beta1PipelineTaskDetail",
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1ReportRuntimeEventRequest": {
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"description": "LINT.IfChange(report_event_message_types) Request message for NotebookInternalService.ReportRuntimeEvent.",
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"description": "Request message for NotebookInternalService.ReportRuntimeEvent.",
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"id": "GoogleCloudAiplatformV1beta1ReportRuntimeEventRequest",
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"properties": {
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"eventDetails": {
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"genericMetadata": {
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"$ref": "GoogleCloudAiplatformV1beta1GenericOperationMetadata",
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"description": "Operation metadata for PersistentResource."
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},
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"progressMessage": {
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"description": "Progress Message for Update LRO",
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"type": "string"
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}
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},
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"type": "object"
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"type": "object"
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},
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"LanguageLabsAidaTrustRecitationProtoDocAttribution": {
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"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:28",
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"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",
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"id": "LanguageLabsAidaTrustRecitationProtoDocAttribution",
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"properties": {
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"amarnaId": {
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"bibkey": {
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"type": "string"
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},
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"biorxivId": {
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"description": "ID of the paper in bioarxiv like ddoi.org/{biorxiv_id} eg: https://doi.org/10.1101/343517",
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"type": "string"
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},
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"bookTitle": {
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"type": "string"
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},
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"wikipediaArticleTitle": {
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"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.",
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"type": "string"
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},
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"youtubeVideoId": {
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"description": "The unique video id from Youtube. Example: AkoGsW52Ir0",
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"type": "string"
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}
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},
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"type": "object"
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"type": "object"
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},
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"LearningGenaiRecitationDocAttribution": {
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"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: 28",
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"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",
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"id": "LearningGenaiRecitationDocAttribution",
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"properties": {
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"amarnaId": {
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"bibkey": {
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"type": "string"
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},
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"biorxivId": {
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"description": "ID of the paper in bioarxiv like ddoi.org/{biorxiv_id} eg: https://doi.org/10.1101/343517",
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"type": "string"
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},
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"bookTitle": {
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"type": "string"
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},
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"wikipediaArticleTitle": {
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"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.",
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"type": "string"
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},
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"youtubeVideoId": {
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"type": "string"
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}
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},
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"type": "object"
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"type": "string"
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},
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"languageFilterResult": {
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"$ref": "LearningServingLlmLanguageFilterResult"
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"$ref": "LearningGenaiRootLanguageFilterResult"
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},
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"raiOutput": {
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"$ref": "LearningGenaiRootRAIOutput",
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},
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"type": "object"
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},
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"LearningGenaiRootGroundingMetadata": {
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"id": "LearningGenaiRootGroundingMetadata",
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"properties": {
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"citations": {
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"items": {
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"$ref": "LearningGenaiRootGroundingMetadataCitation"
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},
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"type": "array"
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},
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"groundingCancelled": {
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"description": "True if grounding is cancelled, for example, no facts being retrieved.",
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"type": "boolean"
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},
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"searchQueries": {
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"items": {
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"type": "string"
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},
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"type": "array"
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}
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},
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"type": "object"
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},
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"LearningGenaiRootGroundingMetadataCitation": {
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"id": "LearningGenaiRootGroundingMetadataCitation",
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"properties": {
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"endIndex": {
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"description": "Index in the prediction output where the citation ends (exclusive). Must be \u003e start_index and \u003c len(output).",
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"format": "int32",
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"type": "integer"
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},
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"factIndex": {
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"description": "Index of the fact supporting this claim. Should be within the range of the `world_facts` in the GenerateResponse.",
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"format": "int32",
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"type": "integer"
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},
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"score": {
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"description": "Confidence score of this entailment. Value is [0,1] with 1 is the most confidence.",
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"format": "double",
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"type": "number"
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},
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"startIndex": {
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"description": "Index in the prediction output where the citation starts (inclusive). Must be \u003e= 0 and \u003c end_index.",
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"format": "int32",
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"type": "integer"
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}
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},
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"type": "object"
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},
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"LearningGenaiRootHarm": {
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"id": "LearningGenaiRootHarm",
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"properties": {
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},
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"type": "object"
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},
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"LearningGenaiRootLanguageFilterResult": {
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"id": "LearningGenaiRootLanguageFilterResult",
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"properties": {
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"allowed": {
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"description": "False when query or response should be filtered out due to unsupported language.",
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"type": "boolean"
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},
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"detectedLanguage": {
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"description": "Language of the query or response.",
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"type": "string"
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},
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"detectedLanguageProbability": {
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"description": "Probability of the language predicted as returned by LangID.",
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"format": "float",
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"type": "number"
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}
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},
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"type": "object"
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},
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"LearningGenaiRootMetricOutput": {
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"id": "LearningGenaiRootMetricOutput",
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"properties": {
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},
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"type": "object"
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},
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"LearningServingLlmLanguageFilterResult": {
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"id": "LearningServingLlmLanguageFilterResult",
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"properties": {
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"allowed": {
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"description": "False when query or response should be filtered out due to unsupported language.",
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"type": "boolean"
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},
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"detectedLanguage": {
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"description": "Language of the query or response.",
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"type": "string"
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},
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"detectedLanguageProbability": {
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"description": "Probability of the language predicted as returned by LangID.",
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"format": "float",
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"type": "number"
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}
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},
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"type": "object"
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},
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"LearningServingLlmMessageMetadata": {
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"description": "LINT.IfChange This metadata contains additional information required for debugging.",
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"id": "LearningServingLlmMessageMetadata",
@@ -40875,6 +40982,9 @@
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],
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"type": "string"
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},
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"groundingMetadata": {
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"$ref": "LearningGenaiRootGroundingMetadata"
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},
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"isFallback": {
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"description": "Applies to Response message only. Indicates whether the message is a fallback and the response would have otherwise been empty.",
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"type": "boolean"

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