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feat(all): auto-regenerate discovery clients (#2382)
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yoshi-automation committed Jan 28, 2024
1 parent 8f26f51 commit 5534cf2
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97 changes: 94 additions & 3 deletions aiplatform/v1/aiplatform-api.json
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"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-/`.",
"location": "query",
"type": "string"
},
"preflightValidations": {
"description": "Optional. Whether to do component level validations before job creation. Currently we only support Google First Party Component/Pipelines.",
"location": "query",
"type": "boolean"
}
},
"path": "v1/{+parent}/pipelineJobs",
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}
}
},
"revision": "20240117",
"revision": "20240125",
"rootUrl": "https://aiplatform.googleapis.com/",
"schemas": {
"CloudAiLargeModelsVisionEmbedVideoResponse": {
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],
"type": "string"
},
"groundingMetadata": {
"$ref": "LearningGenaiRootGroundingMetadata",
"description": "Grounding metadata. Combine with the facts list from response to generate grounding citations for this choice."
},
"index": {
"description": "Index of the candidate.",
"format": "int32",
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},
"type": "object"
},
"CloudAiNlLlmProtoServiceFact": {
"description": "A condense version of WorldFact (assistant/boq/lamda/factuality/proto/factuality.proto) to propagate the essential information about the fact used in factuality to the upstream caller.",
"id": "CloudAiNlLlmProtoServiceFact",
"properties": {
"query": {
"description": "Query that is used to retrieve this fact.",
"type": "string"
},
"summary": {
"description": "If present, the summary/snippet of the fact.",
"type": "string"
},
"title": {
"description": "If present, it refers to the title of this fact.",
"type": "string"
},
"url": {
"description": "If present, this URL links to the webpage of the fact.",
"type": "string"
}
},
"type": "object"
},
"CloudAiNlLlmProtoServiceFunctionCall": {
"description": "Function call details.",
"id": "CloudAiNlLlmProtoServiceFunctionCall",
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"$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.",
"items": {
"$ref": "CloudAiNlLlmProtoServiceFact"
},
"type": "array"
},
"promptFeedback": {
"$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."
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"format": "google-duration",
"type": "string"
},
"modelLevelMetaData": {
"$ref": "CloudAiNlLlmProtoServicePartVideoMetadataModelLevelMetadata",
"description": "Internal only model level metadata."
},
"startOffset": {
"description": "The start offset of the video.",
"format": "google-duration",
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},
"type": "object"
},
"CloudAiNlLlmProtoServicePartVideoMetadataModelLevelMetadata": {
"description": "Internal only fields",
"id": "CloudAiNlLlmProtoServicePartVideoMetadataModelLevelMetadata",
"properties": {
"fps": {
"description": "Frame rate to decode from this video.",
"format": "float",
"type": "number"
},
"numFrames": {
"description": "Number of frames to decode from this video.",
"format": "int32",
"type": "integer"
}
},
"type": "object"
},
"CloudAiNlLlmProtoServicePromptFeedback": {
"description": "Content filter results for a prompt sent in the request.",
"id": "CloudAiNlLlmProtoServicePromptFeedback",
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"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"
},
"preflightValidations": {
"description": "Optional. Whether to do component level validations before job creation. Currently we only support Google First Party Component/Pipelines.",
"type": "boolean"
}
},
"type": "object"
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"id": "GoogleCloudAiplatformV1Explanation",
"properties": {
"attributions": {
"description": "Output only. Feature attributions grouped by predicted outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. If users set ExplanationParameters.top_k, the attributions are sorted by instance_output_value in descending order. If ExplanationParameters.output_indices is specified, the attributions are stored by Attribution.output_index in the same order as they appear in the output_indices.",
"description": "Output only. Feature attributions grouped by predicted outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. By default, we provide Shapley values for the predicted class. However, you can configure the explanation request to generate Shapley values for any other classes too. For example, if a model predicts a probability of `0.4` for approving a loan application, the model's decision is to reject the application since `p(reject) = 0.6 \u003e p(approve) = 0.4`, and the default Shapley values would be computed for rejection decision and not approval, even though the latter might be the positive class. If users set ExplanationParameters.top_k, the attributions are sorted by instance_output_value in descending order. If ExplanationParameters.output_indices is specified, the attributions are stored by Attribution.output_index in the same order as they appear in the output_indices.",
"items": {
"$ref": "GoogleCloudAiplatformV1Attribution"
},
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"readOnly": true,
"type": "string"
},
"pscAutomatedEndpoints": {
"description": "Output only. PscAutomatedEndpoints is populated if private service connect is enabled if PscAutomatedConfig is set.",
"items": {
"$ref": "GoogleCloudAiplatformV1PscAutomatedEndpoints"
},
"readOnly": true,
"type": "array"
},
"serviceAttachment": {
"description": "Output only. The name of the service attachment resource. Populated if private service connect is enabled.",
"readOnly": true,
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},
"type": "object"
},
"GoogleCloudAiplatformV1PscAutomatedEndpoints": {
"description": "PscAutomatedEndpoints defines the output of the forwarding rule automatically created by each PscAutomationConfig.",
"id": "GoogleCloudAiplatformV1PscAutomatedEndpoints",
"properties": {
"matchAddress": {
"description": "Ip Address created by the automated forwarding rule.",
"type": "string"
},
"network": {
"description": "Corresponding network in pscAutomationConfigs.",
"type": "string"
},
"projectId": {
"description": "Corresponding project_id in pscAutomationConfigs",
"type": "string"
}
},
"type": "object"
},
"GoogleCloudAiplatformV1PublisherModel": {
"description": "A Model Garden Publisher Model.",
"id": "GoogleCloudAiplatformV1PublisherModel",
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"type": "array"
},
"role": {
"description": "Role that is assigned to the list of `members`, or principals. For example, `roles/viewer`, `roles/editor`, or `roles/owner`.",
"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"
}
},
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