openapi-directory
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Building & bundling https://github.com/APIs-guru/openapi-directory for easy use from JS
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{"openapi":"3.0.0","servers":[{"url":"https://language.googleapis.com/"}],"info":{"contact":{"name":"Google","url":"https://google.com","x-twitter":"youtube"},"description":"Provides natural language understanding technologies, such as sentiment analysis, entity recognition, entity sentiment analysis, and other text annotations, to developers.","license":{"name":"Creative Commons Attribution 3.0","url":"http://creativecommons.org/licenses/by/3.0/"},"termsOfService":"https://developers.google.com/terms/","title":"Cloud Natural Language API","version":"v2","x-apiClientRegistration":{"url":"https://console.developers.google.com"},"x-apisguru-categories":["analytics","media"],"x-logo":{"url":"https://www.google.com/images/branding/googlelogo/2x/googlelogo_color_272x92dp.png"},"x-origin":[{"format":"google","url":"https://language.googleapis.com/$discovery/rest?version=v2","version":"v1"}],"x-preferred":true,"x-providerName":"googleapis.com","x-serviceName":"language"},"externalDocs":{"url":"https://cloud.google.com/natural-language/"},"tags":[{"name":"documents"}],"paths":{"/v2/documents:analyzeEntities":{"parameters":[{"$ref":"#/components/parameters/_.xgafv"},{"$ref":"#/components/parameters/access_token"},{"$ref":"#/components/parameters/alt"},{"$ref":"#/components/parameters/callback"},{"$ref":"#/components/parameters/fields"},{"$ref":"#/components/parameters/key"},{"$ref":"#/components/parameters/oauth_token"},{"$ref":"#/components/parameters/prettyPrint"},{"$ref":"#/components/parameters/quotaUser"},{"$ref":"#/components/parameters/upload_protocol"},{"$ref":"#/components/parameters/uploadType"}],"post":{"description":"Finds named entities (currently proper names and common nouns) in the text along with entity types, probability, mentions for each entity, and other properties.","operationId":"language.documents.analyzeEntities","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AnalyzeEntitiesRequest"}}}},"responses":{"200":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AnalyzeEntitiesResponse"}}},"description":"Successful 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text.","operationId":"language.documents.analyzeSentiment","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AnalyzeSentimentRequest"}}}},"responses":{"200":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AnalyzeSentimentResponse"}}},"description":"Successful 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Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.","in":"query","name":"key","schema":{"type":"string"}},"oauth_token":{"description":"OAuth 2.0 token for the current user.","in":"query","name":"oauth_token","schema":{"type":"string"}},"prettyPrint":{"description":"Returns response with indentations and line breaks.","in":"query","name":"prettyPrint","schema":{"type":"boolean"}},"quotaUser":{"description":"Available to use for quota purposes for server-side applications. 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Input document."},"encodingType":{"description":"The encoding type used by the API to calculate offsets.","enum":["NONE","UTF8","UTF16","UTF32"],"type":"string"}},"type":"object"},"AnalyzeEntitiesResponse":{"description":"The entity analysis response message.","properties":{"entities":{"description":"The recognized entities in the input document.","items":{"$ref":"#/components/schemas/Entity"},"type":"array"},"languageCode":{"description":"The language of the text, which will be the same as the language specified in the request or, if not specified, the automatically-detected language. See Document.language field for more details.","type":"string"},"languageSupported":{"description":"Whether the language is officially supported. The API may still return a response when the language is not supported, but it is on a best effort basis.","type":"boolean"}},"type":"object"},"AnalyzeSentimentRequest":{"description":"The sentiment analysis request message.","properties":{"document":{"$ref":"#/components/schemas/Document","description":"Required. Input document."},"encodingType":{"description":"The encoding type used by the API to calculate sentence offsets.","enum":["NONE","UTF8","UTF16","UTF32"],"type":"string"}},"type":"object"},"AnalyzeSentimentResponse":{"description":"The sentiment analysis response message.","properties":{"documentSentiment":{"$ref":"#/components/schemas/Sentiment","description":"The overall sentiment of the input document."},"languageCode":{"description":"The language of the text, which will be the same as the language specified in the request or, if not specified, the automatically-detected language. See Document.language field for more details.","type":"string"},"languageSupported":{"description":"Whether the language is officially supported. The API may still return a response when the language is not supported, but it is on a best effort basis.","type":"boolean"},"sentences":{"description":"The sentiment for all the sentences in the document.","items":{"$ref":"#/components/schemas/Sentence"},"type":"array"}},"type":"object"},"AnnotateTextRequest":{"description":"The request message for the text annotation API, which can perform multiple analysis types in one call.","properties":{"document":{"$ref":"#/components/schemas/Document","description":"Required. Input document."},"encodingType":{"description":"The encoding type used by the API to calculate offsets.","enum":["NONE","UTF8","UTF16","UTF32"],"type":"string"},"features":{"$ref":"#/components/schemas/AnnotateTextRequestFeatures","description":"Required. The enabled features."}},"type":"object"},"AnnotateTextRequestFeatures":{"description":"All available features. Setting each one to true will enable that specific analysis for the input.","properties":{"classifyText":{"description":"Optional. Classify the full document into categories.","type":"boolean"},"extractDocumentSentiment":{"description":"Optional. Extract document-level sentiment.","type":"boolean"},"extractEntities":{"description":"Optional. Extract entities.","type":"boolean"},"moderateText":{"description":"Optional. Moderate the document for harmful and sensitive categories.","type":"boolean"}},"type":"object"},"AnnotateTextResponse":{"description":"The text annotations response message.","properties":{"categories":{"description":"Categories identified in the input document.","items":{"$ref":"#/components/schemas/ClassificationCategory"},"type":"array"},"documentSentiment":{"$ref":"#/components/schemas/Sentiment","description":"The overall sentiment for the document. Populated if the user enables AnnotateTextRequest.Features.extract_document_sentiment."},"entities":{"description":"Entities, along with their semantic information, in the input document. Populated if the user enables AnnotateTextRequest.Features.extract_entities or AnnotateTextRequest.Features.extract_entity_sentiment.","items":{"$ref":"#/components/schemas/Entity"},"type":"array"},"languageCode":{"description":"The language of the text, which will be the same as the language specified in the request or, if not specified, the automatically-detected language. See Document.language field for more details.","type":"string"},"languageSupported":{"description":"Whether the language is officially supported by all requested features. The API may still return a response when the language is not supported, but it is on a best effort basis.","type":"boolean"},"moderationCategories":{"description":"Harmful and sensitive categories identified in the input document.","items":{"$ref":"#/components/schemas/ClassificationCategory"},"type":"array"},"sentences":{"description":"Sentences in the input document. Populated if the user enables AnnotateTextRequest.Features.extract_document_sentiment.","items":{"$ref":"#/components/schemas/Sentence"},"type":"array"}},"type":"object"},"ClassificationCategory":{"description":"Represents a category returned from the text classifier.","properties":{"confidence":{"description":"The classifier's confidence of the category. Number represents how certain the classifier is that this category represents the given text.","format":"float","type":"number"},"name":{"description":"The name of the category representing the document.","type":"string"}},"type":"object"},"ClassifyTextRequest":{"description":"The document classification request message.","properties":{"document":{"$ref":"#/components/schemas/Document","description":"Required. Input document."}},"type":"object"},"ClassifyTextResponse":{"description":"The document classification response message.","properties":{"categories":{"description":"Categories representing the input document.","items":{"$ref":"#/components/schemas/ClassificationCategory"},"type":"array"},"languageCode":{"description":"The language of the text, which will be the same as the language specified in the request or, if not specified, the automatically-detected language. See Document.language field for more details.","type":"string"},"languageSupported":{"description":"Whether the language is officially supported. The API may still return a response when the language is not supported, but it is on a best effort basis.","type":"boolean"}},"type":"object"},"Color":{"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—for example, sRGB, Adobe RGB, DCI-P3, and BT.2020. By default, applications should assume the sRGB color space. When color equality needs to be decided, implementations, unless documented otherwise, treat two colors as equal if all their red, green, blue, and alpha values each differ by at most `1e-5`. Example (Java): import com.google.type.Color; // ... public static java.awt.Color fromProto(Color protocolor) { float alpha = protocolor.hasAlpha() ? protocolor.getAlpha().getValue() : 1.0; return new java.awt.Color( protocolor.getRed(), protocolor.getGreen(), protocolor.getBlue(), alpha); } public static Color toProto(java.awt.Color color) { float red = (float) color.getRed(); float green = (float) color.getGreen(); float blue = (float) color.getBlue(); float denominator = 255.0; Color.Builder resultBuilder = Color .newBuilder() .setRed(red / denominator) .setGreen(green / denominator) .setBlue(blue / denominator); int alpha = color.getAlpha(); if (alpha != 255) { result.setAlpha( FloatValue .newBuilder() .setValue(((float) alpha) / denominator) .build()); } return resultBuilder.build(); } // ... Example (iOS / Obj-C): // ... static UIColor* fromProto(Color* protocolor) { float red = [protocolor red]; float green = [protocolor green]; float blue = [protocolor blue]; FloatValue* alpha_wrapper = [protocolor alpha]; float alpha = 1.0; if (alpha_wrapper != nil) { alpha = [alpha_wrapper value]; } return [UIColor colorWithRed:red green:green blue:blue alpha:alpha]; } static Color* toProto(UIColor* color) { CGFloat red, green, blue, alpha; if (![color getRed:&red green:&green blue:&blue alpha:&alpha]) { return nil; } Color* result = [[Color alloc] init]; [result setRed:red]; [result setGreen:green]; [result setBlue:blue]; if (alpha <= 0.9999) { [result setAlpha:floatWrapperWithValue(alpha)]; } [result autorelease]; return result; } // ... Example (JavaScript): // ... var protoToCssColor = function(rgb_color) { var redFrac = rgb_color.red || 0.0; var greenFrac = rgb_color.green || 0.0; var blueFrac = rgb_color.blue || 0.0; var red = Math.floor(redFrac * 255); var green = Math.floor(greenFrac * 255); var blue = Math.floor(blueFrac * 255); if (!('alpha' in rgb_color)) { return rgbToCssColor(red, green, blue); } var alphaFrac = rgb_color.alpha.value || 0.0; var rgbParams = [red, green, blue].join(','); return ['rgba(', rgbParams, ',', alphaFrac, ')'].join(''); }; var rgbToCssColor = function(red, green, blue) { var rgbNumber = new Number((red << 16) | (green << 8) | blue); var hexString = rgbNumber.toString(16); var missingZeros = 6 - hexString.length; var resultBuilder = ['#']; for (var i = 0; i < missingZeros; i++) { resultBuilder.push('0'); } resultBuilder.push(hexString); return resultBuilder.join(''); }; // ...","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"},"CpuMetric":{"description":"Metric for billing reports.","properties":{"coreNumber":{"description":"Required. Number of CPU cores.","format":"int64","type":"string"},"coreSec":{"description":"Required. Total seconds of core usage, e.g. 4.","format":"int64","type":"string"},"cpuType":{"description":"Required. Type of cpu, e.g. N2.","enum":["UNKNOWN_CPU_TYPE","A2","A3","C2","C2D","CUSTOM","E2","G2","C3","M2","M1","N1","N2_CUSTOM","N2","N2D"],"type":"string"},"machineSpec":{"description":"Required. Machine spec, e.g. N1_STANDARD_4.","enum":["UNKNOWN_MACHINE_SPEC","N1_STANDARD_2","N1_STANDARD_4","N1_STANDARD_8","N1_STANDARD_16","N1_STANDARD_32","N1_STANDARD_64","N1_STANDARD_96","N1_HIGHMEM_2","N1_HIGHMEM_4","N1_HIGHMEM_8","N1_HIGHMEM_16","N1_HIGHMEM_32","N1_HIGHMEM_64","N1_HIGHMEM_96","N1_HIGHCPU_2","N1_HIGHCPU_4","N1_HIGHCPU_8","N1_HIGHCPU_16","N1_HIGHCPU_32","N1_HIGHCPU_64","N1_HIGHCPU_96","A2_HIGHGPU_1G","A2_HIGHGPU_2G","A2_HIGHGPU_4G","A2_HIGHGPU_8G","A2_MEGAGPU_16G","A2_ULTRAGPU_1G","A2_ULTRAGPU_2G","A2_ULTRAGPU_4G","A2_ULTRAGPU_8G","A3_HIGHGPU_8G","E2_STANDARD_2","E2_STANDARD_4","E2_STANDARD_8","E2_STANDARD_16","E2_STANDARD_32","E2_HIGHMEM_2","E2_HIGHMEM_4","E2_HIGHMEM_8","E2_HIGHMEM_16","E2_HIGHCPU_2","E2_HIGHCPU_4","E2_HIGHCPU_8","E2_HIGHCPU_16","E2_HIGHCPU_32","N2_STANDARD_2","N2_STANDARD_4","N2_STANDARD_8","N2_STANDARD_16","N2_STANDARD_32","N2_STANDARD_48","N2_STANDARD_64","N2_STANDARD_80","N2_STANDARD_96","N2_STANDARD_128","N2_HIGHMEM_2","N2_HIGHMEM_4","N2_HIGHMEM_8","N2_HIGHMEM_16","N2_HIGHMEM_32","N2_HIGHMEM_48","N2_HIGHMEM_64","N2_HIGHMEM_80","N2_HIGHMEM_96","N2_HIGHMEM_128","N2_HIGHCPU_2","N2_HIGHCPU_4","N2_HIGHCPU_8","N2_HIGHCPU_16","N2_HIGHCPU_32","N2_HIGHCPU_48","N2_HIGHCPU_64","N2_HIGHCPU_80","N2_HIGHCPU_96","N2D_STANDARD_2","N2D_STANDARD_4","N2D_STANDARD_8","N2D_STANDARD_16","N2D_STANDARD_32","N2D_STANDARD_48","N2D_STANDARD_64","N2D_STANDARD_80","N2D_STANDARD_96","N2D_STANDARD_128","N2D_STANDARD_224","N2D_HIGHMEM_2","N2D_HIGHMEM_4","N2D_HIGHMEM_8","N2D_HIGHMEM_16","N2D_HIGHMEM_32","N2D_HIGHMEM_48","N2D_HIGHMEM_64","N2D_HIGHMEM_80","N2D_HIGHMEM_96","N2D_HIGHCPU_2","N2D_HIGHCPU_4","N2D_HIGHCPU_8","N2D_HIGHCPU_16","N2D_HIGHCPU_32","N2D_HIGHCPU_48","N2D_HIGHCPU_64","N2D_HIGHCPU_80","N2D_HIGHCPU_96","N2D_HIGHCPU_128","N2D_HIGHCPU_224","C2_STANDARD_4","C2_STANDARD_8","C2_STANDARD_16","C2_STANDARD_30","C2_STANDARD_60","C2D_STANDARD_2","C2D_STANDARD_4","C2D_STANDARD_8","C2D_STANDARD_16","C2D_STANDARD_32","C2D_STANDARD_56","C2D_STANDARD_112","C2D_HIGHCPU_2","C2D_HIGHCPU_4","C2D_HIGHCPU_8","C2D_HIGHCPU_16","C2D_HIGHCPU_32","C2D_HIGHCPU_56","C2D_HIGHCPU_112","C2D_HIGHMEM_2","C2D_HIGHMEM_4","C2D_HIGHMEM_8","C2D_HIGHMEM_16","C2D_HIGHMEM_32","C2D_HIGHMEM_56","C2D_HIGHMEM_112","G2_STANDARD_4","G2_STANDARD_8","G2_STANDARD_12","G2_STANDARD_16","G2_STANDARD_24","G2_STANDARD_32","G2_STANDARD_48","G2_STANDARD_96","C3_STANDARD_4","C3_STANDARD_8","C3_STANDARD_22","C3_STANDARD_44","C3_STANDARD_88","C3_STANDARD_176","C3_HIGHCPU_4","C3_HIGHCPU_8","C3_HIGHCPU_22","C3_HIGHCPU_44","C3_HIGHCPU_88","C3_HIGHCPU_176","C3_HIGHMEM_4","C3_HIGHMEM_8","C3_HIGHMEM_22","C3_HIGHMEM_44","C3_HIGHMEM_88","C3_HIGHMEM_176"],"type":"string"},"trackingLabels":{"additionalProperties":{"type":"string"},"description":"Billing tracking labels. They do not contain any user data but only the labels set by Vertex Core Infra itself. Tracking labels' keys are defined with special format: goog-[\\p{Ll}\\p{N}]+ E.g. \"key\": \"goog-k8s-cluster-name\",\"value\": \"us-east1-b4rk\"","type":"object"}},"type":"object"},"DiskMetric":{"properties":{"diskType":{"description":"Required. Type of Disk, e.g. REGIONAL_SSD.","enum":["UNKNOWN_DISK_TYPE","REGIONAL_SSD","REGIONAL_STORAGE","PD_SSD","PD_STANDARD","STORAGE_SNAPSHOT"],"type":"string"},"gibSec":{"description":"Required. Seconds of physical disk usage, e.g. 3600.","format":"int64","type":"string"}},"type":"object"},"Document":{"description":"Represents the input to API methods.","properties":{"content":{"description":"The content of the input in string format. Cloud audit logging exempt since it is based on user data.","type":"string"},"gcsContentUri":{"description":"The Google Cloud Storage URI where the file content is located. This URI must be of the form: gs://bucket_name/object_name. For more details, see https://cloud.google.com/storage/docs/reference-uris. NOTE: Cloud Storage object versioning is not supported.","type":"string"},"languageCode":{"description":"Optional. The language of the document (if not specified, the language is automatically detected). Both ISO and BCP-47 language codes are accepted. [Language Support](https://cloud.google.com/natural-language/docs/languages) lists currently supported languages for each API method. If the language (either specified by the caller or automatically detected) is not supported by the called API method, an `INVALID_ARGUMENT` error is returned.","type":"string"},"type":{"description":"Required. If the type is not set or is `TYPE_UNSPECIFIED`, returns an `INVALID_ARGUMENT` error.","enum":["TYPE_UNSPECIFIED","PLAIN_TEXT","HTML"],"type":"string"}},"type":"object"},"Entity":{"description":"Represents a phrase in the text that is a known entity, such as a person, an organization, or location. The API associates information, such as probability and mentions, with entities.","properties":{"mentions":{"description":"The mentions of this entity in the input document. The API currently supports proper noun mentions.","items":{"$ref":"#/components/schemas/EntityMention"},"type":"array"},"metadata":{"additionalProperties":{"type":"string"},"description":"Metadata associated with the entity. For the metadata associated with other entity types, see the Type table below.","type":"object"},"name":{"description":"The representative name for the entity.","type":"string"},"sentiment":{"$ref":"#/components/schemas/Sentiment","description":"For calls to AnalyzeEntitySentiment or if AnnotateTextRequest.Features.extract_entity_sentiment is set to true, this field will contain the aggregate sentiment expressed for this entity in the provided document."},"type":{"description":"The entity type.","enum":["UNKNOWN","PERSON","LOCATION","ORGANIZATION","EVENT","WORK_OF_ART","CONSUMER_GOOD","OTHER","PHONE_NUMBER","ADDRESS","DATE","NUMBER","PRICE"],"type":"string"}},"type":"object"},"EntityMention":{"description":"Represents a mention for an entity in the text. Currently, proper noun mentions are supported.","properties":{"probability":{"description":"Probability score associated with the entity. The score shows the probability of the entity mention being the entity type. The score is in (0, 1] range.","format":"float","type":"number"},"sentiment":{"$ref":"#/components/schemas/Sentiment","description":"For calls to AnalyzeEntitySentiment or if AnnotateTextRequest.Features.extract_entity_sentiment is set to true, this field will contain the sentiment expressed for this mention of the entity in the provided document."},"text":{"$ref":"#/components/schemas/TextSpan","description":"The mention text."},"type":{"description":"The type of the entity mention.","enum":["TYPE_UNKNOWN","PROPER","COMMON"],"type":"string"}},"type":"object"},"GpuMetric":{"properties":{"gpuSec":{"description":"Required. Seconds of GPU usage, e.g. 3600.","format":"int64","type":"string"},"gpuType":{"description":"Required. Type of GPU, e.g. NVIDIA_TESLA_V100.","enum":["UNKNOWN_GPU_TYPE","NVIDIA_TESLA_A100","NVIDIA_A100_80GB","NVIDIA_TESLA_K80","NVIDIA_L4","NVIDIA_TESLA_P100","NVIDIA_TESLA_P4","NVIDIA_TESLA_T4","NVIDIA_TESLA_V100","NVIDIA_H100_80GB"],"type":"string"},"machineSpec":{"description":"Required. Machine spec, e.g. N1_STANDARD_4.","enum":["UNKNOWN_MACHINE_SPEC","N1_STANDARD_2","N1_STANDARD_4","N1_STANDARD_8","N1_STANDARD_16","N1_STANDARD_32","N1_STANDARD_64","N1_STANDARD_96","N1_HIGHMEM_2","N1_HIGHMEM_4","N1_HIGHMEM_8","N1_HIGHMEM_16","N1_HIGHMEM_32","N1_HIGHMEM_64","N1_HIGHMEM_96","N1_HIGHCPU_2","N1_HIGHCPU_4","N1_HIGHCPU_8","N1_HIGHCPU_16","N1_HIGHCPU_32","N1_HIGHCPU_64","N1_HIGHCPU_96","A2_HIGHGPU_1G","A2_HIGHGPU_2G","A2_HIGHGPU_4G","A2_HIGHGPU_8G","A2_MEGAGPU_16G","A2_ULTRAGPU_1G","A2_ULTRAGPU_2G","A2_ULTRAGPU_4G","A2_ULTRAGPU_8G","A3_HIGHGPU_8G","E2_STANDARD_2","E2_STANDARD_4","E2_STANDARD_8","E2_STANDARD_16","E2_STANDARD_32","E2_HIGHMEM_2","E2_HIGHMEM_4","E2_HIGHMEM_8","E2_HIGHMEM_16","E2_HIGHCPU_2","E2_HIGHCPU_4","E2_HIGHCPU_8","E2_HIGHCPU_16","E2_HIGHCPU_32","N2_STANDARD_2","N2_STANDARD_4","N2_STANDARD_8","N2_STANDARD_16","N2_STANDARD_32","N2_STANDARD_48","N2_STANDARD_64","N2_STANDARD_80","N2_STANDARD_96","N2_STANDARD_128","N2_HIGHMEM_2","N2_HIGHMEM_4","N2_HIGHMEM_8","N2_HIGHMEM_16","N2_HIGHMEM_32","N2_HIGHMEM_48","N2_HIGHMEM_64","N2_HIGHMEM_80","N2_HIGHMEM_96","N2_HIGHMEM_128","N2_HIGHCPU_2","N2_HIGHCPU_4","N2_HIGHCPU_8","N2_HIGHCPU_16","N2_HIGHCPU_32","N2_HIGHCPU_48","N2_HIGHCPU_64","N2_HIGHCPU_80","N2_HIGHCPU_96","N2D_STANDARD_2","N2D_STANDARD_4","N2D_STANDARD_8","N2D_STANDARD_16","N2D_STANDARD_32","N2D_STANDARD_48","N2D_STANDARD_64","N2D_STANDARD_80","N2D_STANDARD_96","N2D_STANDARD_128","N2D_STANDARD_224","N2D_HIGHMEM_2","N2D_HIGHMEM_4","N2D_HIGHMEM_8","N2D_HIGHMEM_16","N2D_HIGHMEM_32","N2D_HIGHMEM_48","N2D_HIGHMEM_64","N2D_HIGHMEM_80","N2D_HIGHMEM_96","N2D_HIGHCPU_2","N2D_HIGHCPU_4","N2D_HIGHCPU_8","N2D_HIGHCPU_16","N2D_HIGHCPU_32","N2D_HIGHCPU_48","N2D_HIGHCPU_64","N2D_HIGHCPU_80","N2D_HIGHCPU_96","N2D_HIGHCPU_128","N2D_HIGHCPU_224","C2_STANDARD_4","C2_STANDARD_8","C2_STANDARD_16","C2_STANDARD_30","C2_STANDARD_60","C2D_STANDARD_2","C2D_STANDARD_4","C2D_STANDARD_8","C2D_STANDARD_16","C2D_STANDARD_32","C2D_STANDARD_56","C2D_STANDARD_112","C2D_HIGHCPU_2","C2D_HIGHCPU_4","C2D_HIGHCPU_8","C2D_HIGHCPU_16","C2D_HIGHCPU_32","C2D_HIGHCPU_56","C2D_HIGHCPU_112","C2D_HIGHMEM_2","C2D_HIGHMEM_4","C2D_HIGHMEM_8","C2D_HIGHMEM_16","C2D_HIGHMEM_32","C2D_HIGHMEM_56","C2D_HIGHMEM_112","G2_STANDARD_4","G2_STANDARD_8","G2_STANDARD_12","G2_STANDARD_16","G2_STANDARD_24","G2_STANDARD_32","G2_STANDARD_48","G2_STANDARD_96","C3_STANDARD_4","C3_STANDARD_8","C3_STANDARD_22","C3_STANDARD_44","C3_STANDARD_88","C3_STANDARD_176","C3_HIGHCPU_4","C3_HIGHCPU_8","C3_HIGHCPU_22","C3_HIGHCPU_44","C3_HIGHCPU_88","C3_HIGHCPU_176","C3_HIGHMEM_4","C3_HIGHMEM_8","C3_HIGHMEM_22","C3_HIGHMEM_44","C3_HIGHMEM_88","C3_HIGHMEM_176"],"type":"string"},"trackingLabels":{"additionalProperties":{"type":"string"},"description":"Billing tracking labels. They do not contain any user data but only the labels set by Vertex Core Infra itself. Tracking labels' keys are defined with special format: goog-[\\p{Ll}\\p{N}]+ E.g. \"key\": \"goog-k8s-cluster-name\",\"value\": \"us-east1-b4rk\"","type":"object"}},"type":"object"},"InfraUsage":{"description":"Infra Usage of billing metrics. Next ID: 6","properties":{"cpuMetrics":{"description":"Aggregated core metrics since requested start_time.","items":{"$ref":"#/components/schemas/CpuMetric"},"type":"array"},"diskMetrics":{"description":"Aggregated persistent disk metrics since requested start_time.","items":{"$ref":"#/components/schemas/DiskMetric"},"type":"array"},"gpuMetrics":{"description":"Aggregated gpu metrics since requested start_time.","items":{"$ref":"#/components/schemas/GpuMetric"},"type":"array"},"ramMetrics":{"description":"Aggregated ram metrics since requested start_time.","items":{"$ref":"#/components/schemas/RamMetric"},"type":"array"},"tpuMetrics":{"description":"Aggregated tpu metrics since requested start_time.","items":{"$ref":"#/components/schemas/TpuMetric"},"type":"array"}},"type":"object"},"ModerateTextRequest":{"description":"The document moderation request message.","properties":{"document":{"$ref":"#/components/schemas/Document","description":"Required. Input document."}},"type":"object"},"ModerateTextResponse":{"description":"The document moderation response message.","properties":{"languageCode":{"description":"The language of the text, which will be the same as the language specified in the request or, if not specified, the automatically-detected language. See Document.language field for more details.","type":"string"},"languageSupported":{"description":"Whether the language is officially supported. The API may still return a response when the language is not supported, but it is on a best effort basis.","type":"boolean"},"moderationCategories":{"description":"Harmful and sensitive categories representing the input document.","items":{"$ref":"#/components/schemas/ClassificationCategory"},"type":"array"}},"type":"object"},"RamMetric":{"properties":{"gibSec":{"description":"Required. VM memory in Gigabyte second, e.g. 3600. Using int64 type to match billing metrics definition.","format":"int64","type":"string"},"machineSpec":{"description":"Required. Machine spec, e.g. N1_STANDARD_4.","enum":["UNKNOWN_MACHINE_SPEC","N1_STANDARD_2","N1_STANDARD_4","N1_STANDARD_8","N1_STANDARD_16","N1_STANDARD_32","N1_STANDARD_64","N1_STANDARD_96","N1_HIGHMEM_2","N1_HIGHMEM_4","N1_HIGHMEM_8","N1_HIGHMEM_16","N1_HIGHMEM_32","N1_HIGHMEM_64","N1_HIGHMEM_96","N1_HIGHCPU_2","N1_HIGHCPU_4","N1_HIGHCPU_8","N1_HIGHCPU_16","N1_HIGHCPU_32","N1_HIGHCPU_64","N1_HIGHCPU_96","A2_HIGHGPU_1G","A2_HIGHGPU_2G","A2_HIGHGPU_4G","A2_HIGHGPU_8G","A2_MEGAGPU_16G","A2_ULTRAGPU_1G","A2_ULTRAGPU_2G","A2_ULTRAGPU_4G","A2_ULTRAGPU_8G","A3_HIGHGPU_8G","E2_STANDARD_2","E2_STANDARD_4","E2_STANDARD_8","E2_STANDARD_16","E2_STANDARD_32","E2_HIGHMEM_2","E2_HIGHMEM_4","E2_HIGHMEM_8","E2_HIGHMEM_16","E2_HIGHCPU_2","E2_HIGHCPU_4","E2_HIGHCPU_8","E2_HIGHCPU_16","E2_HIGHCPU_32","N2_STANDARD_2","N2_STANDARD_4","N2_STANDARD_8","N2_STANDARD_16","N2_STANDARD_32","N2_STANDARD_48","N2_STANDARD_64","N2_STANDARD_80","N2_STANDARD_96","N2_STANDARD_128","N2_HIGHMEM_2","N2_HIGHMEM_4","N2_HIGHMEM_8","N2_HIGHMEM_16","N2_HIGHMEM_32","N2_HIGHMEM_48","N2_HIGHMEM_64","N2_HIGHMEM_80","N2_HIGHMEM_96","N2_HIGHMEM_128","N2_HIGHCPU_2","N2_HIGHCPU_4","N2_HIGHCPU_8","N2_HIGHCPU_16","N2_HIGHCPU_32","N2_HIGHCPU_48","N2_HIGHCPU_64","N2_HIGHCPU_80","N2_HIGHCPU_96","N2D_STANDARD_2","N2D_STANDARD_4","N2D_STANDARD_8","N2D_STANDARD_16","N2D_STANDARD_32","N2D_STANDARD_48","N2D_STANDARD_64","N2D_STANDARD_80","N2D_STANDARD_96","N2D_STANDARD_128","N2D_STANDARD_224","N2D_HIGHMEM_2","N2D_HIGHMEM_4","N2D_HIGHMEM_8","N2D_HIGHMEM_16","N2D_HIGHMEM_32","N2D_HIGHMEM_48","N2D_HIGHMEM_64","N2D_HIGHMEM_80","N2D_HIGHMEM_96","N2D_HIGHCPU_2","N2D_HIGHCPU_4","N2D_HIGHCPU_8","N2D_HIGHCPU_16","N2D_HIGHCPU_32","N2D_HIGHCPU_48","N2D_HIGHCPU_64","N2D_HIGHCPU_80","N2D_HIGHCPU_96","N2D_HIGHCPU_128","N2D_HIGHCPU_224","C2_STANDARD_4","C2_STANDARD_8","C2_STANDARD_16","C2_STANDARD_30","C2_STANDARD_60","C2D_STANDARD_2","C2D_STANDARD_4","C2D_STANDARD_8","C2D_STANDARD_16","C2D_STANDARD_32","C2D_STANDARD_56","C2D_STANDARD_112","C2D_HIGHCPU_2","C2D_HIGHCPU_4","C2D_HIGHCPU_8","C2D_HIGHCPU_16","C2D_HIGHCPU_32","C2D_HIGHCPU_56","C2D_HIGHCPU_112","C2D_HIGHMEM_2","C2D_HIGHMEM_4","C2D_HIGHMEM_8","C2D_HIGHMEM_16","C2D_HIGHMEM_32","C2D_HIGHMEM_56","C2D_HIGHMEM_112","G2_STANDARD_4","G2_STANDARD_8","G2_STANDARD_12","G2_STANDARD_16","G2_STANDARD_24","G2_STANDARD_32","G2_STANDARD_48","G2_STANDARD_96","C3_STANDARD_4","C3_STANDARD_8","C3_STANDARD_22","C3_STANDARD_44","C3_STANDARD_88","C3_STANDARD_176","C3_HIGHCPU_4","C3_HIGHCPU_8","C3_HIGHCPU_22","C3_HIGHCPU_44","C3_HIGHCPU_88","C3_HIGHCPU_176","C3_HIGHMEM_4","C3_HIGHMEM_8","C3_HIGHMEM_22","C3_HIGHMEM_44","C3_HIGHMEM_88","C3_HIGHMEM_176"],"type":"string"},"memories":{"description":"Required. VM memory in gb.","format":"double","type":"number"},"ramType":{"description":"Required. Type of ram.","enum":["UNKNOWN_RAM_TYPE","A2","A3","C2","C2D","CUSTOM","E2","G2","C3","M2","M1","N1","N2_CUSTOM","N2","N2D"],"type":"string"},"trackingLabels":{"additionalProperties":{"type":"string"},"description":"Billing tracking labels. They do not contain any user data but only the labels set by Vertex Core Infra itself. Tracking labels' keys are defined with special format: goog-[\\p{Ll}\\p{N}]+ E.g. \"key\": \"goog-k8s-cluster-name\",\"value\": \"us-east1-b4rk\"","type":"object"}},"type":"object"},"Sentence":{"description":"Represents a sentence in the input document.","properties":{"sentiment":{"$ref":"#/components/schemas/Sentiment","description":"For calls to AnalyzeSentiment or if AnnotateTextRequest.Features.extract_document_sentiment is set to true, this field will contain the sentiment for the sentence."},"text":{"$ref":"#/components/schemas/TextSpan","description":"The sentence text."}},"type":"object"},"Sentiment":{"description":"Represents the feeling associated with the entire text or entities in the text.","properties":{"magnitude":{"description":"A non-negative number in the [0, +inf) range, which represents the absolute magnitude of sentiment regardless of score (positive or negative).","format":"float","type":"number"},"score":{"description":"Sentiment score between -1.0 (negative sentiment) and 1.0 (positive sentiment).","format":"float","type":"number"}},"type":"object"},"Status":{"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).","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":"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"},"TextSpan":{"description":"Represents a text span in the input document.","properties":{"beginOffset":{"description":"The API calculates the beginning offset of the content in the original document according to the EncodingType specified in the API request.","format":"int32","type":"integer"},"content":{"description":"The content of the text span, which is a substring of the document.","type":"string"}},"type":"object"},"TpuMetric":{"properties":{"tpuSec":{"description":"Required. Seconds of TPU usage, e.g. 3600.","format":"int64","type":"string"},"tpuType":{"description":"Required. Type of TPU, e.g. TPU_V2, TPU_V3_POD.","enum":["UNKNOWN_TPU_TYPE","TPU_V2_POD","TPU_V2","TPU_V3_POD","TPU_V3","TPU_V5_LITEPOD"],"type":"string"}},"type":"object"},"XPSArrayStats":{"description":"The data statistics of a series of ARRAY values.","properties":{"commonStats":{"$ref":"#/components/schemas/XPSCommonStats"},"memberStats":{"$ref":"#/components/schemas/XPSDataStats","description":"Stats of all the values of all arrays, as if they were a single long series of data. The type depends on the element type of the array."}},"type":"object"},"XPSBatchPredictResponse":{"properties":{"exampleSet":{"$ref":"#/components/schemas/XPSExampleSet","description":"Examples for batch prediction result. Under full API implementation, results are stored in shared RecordIO of AnnotatedExample protobufs, the annotations field of which is populated by XPS backend."}},"type":"object"},"XPSBoundingBoxMetricsEntry":{"description":"Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.","properties":{"confidenceMetricsEntries":{"description":"Metrics for each label-match confidence_threshold from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99.","items":{"$ref":"#/components/schemas/XPSBoundingBoxMetricsEntryConfidenceMetricsEntry"},"type":"array"},"iouThreshold":{"description":"The intersection-over-union threshold value used to compute this metrics entry.","format":"float","type":"number"},"meanAveragePrecision":{"description":"The mean average precision.","format":"float","type":"number"}},"type":"object"},"XPSBoundingBoxMetricsEntryConfidenceMetricsEntry":{"description":"Metrics for a single confidence threshold.","properties":{"confidenceThreshold":{"description":"The confidence threshold value used to compute the metrics.","format":"float","type":"number"},"f1Score":{"description":"The harmonic mean of recall and precision.","format":"float","type":"number"},"precision":{"description":"Precision for the given confidence threshold.","format":"float","type":"number"},"recall":{"description":"Recall for the given confidence threshold.","format":"float","type":"number"}},"type":"object"},"XPSCategoryStats":{"description":"The data statistics of a series of CATEGORY values.","properties":{"commonStats":{"$ref":"#/components/schemas/XPSCommonStats"},"topCategoryStats":{"description":"The statistics of the top 20 CATEGORY values, ordered by CategoryStats.SingleCategoryStats.count.","items":{"$ref":"#/components/schemas/XPSCategoryStatsSingleCategoryStats"},"type":"array"}},"type":"object"},"XPSCategoryStatsSingleCategoryStats":{"description":"The statistics of a single CATEGORY value.","properties":{"count":{"description":"The number of occurrences of this value in the series.","format":"int64","type":"string"},"value":{"description":"The CATEGORY value.","type":"string"}},"type":"object"},"XPSClassificationEvaluationMetrics":{"description":"Model evaluation metrics for classification problems. It can be used for image and video classification. Next tag: 9.","properties":{"auPrc":{"description":"The Area under precision recall curve metric.","format":"float","type":"number"},"auRoc":{"description":"The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.","format":"float","type":"number"},"baseAuPrc":{"description":"The Area under precision recall curve metric based on priors.","format":"float","type":"number"},"confidenceMetricsEntries":{"description":"Metrics that have confidence thresholds. Precision-recall curve can be derived from it.","items":{"$ref":"#/components/schemas/XPSConfidenceMetricsEntry"},"type":"array"},"confusionMatrix":{"$ref":"#/components/schemas/XPSConfusionMatrix","description":"Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of annotation specs is no more than 10. Only set for model level evaluation, not for evaluation per label."},"evaluatedExamplesCount":{"description":"The number of examples used for model evaluation.","format":"int32","type":"integer"},"logLoss":{"description":"The Log Loss metric.","format":"float","type":"number"}},"type":"object"},"XPSColorMap":{"description":"Map from color to display name. Will only be used by Image Segmentation for uCAIP.","properties":{"annotationSpecIdToken":{"description":"Should be used during training.","type":"string"},"color":{"$ref":"#/components/schemas/Color","deprecated":true,"description":"This type is deprecated in favor of the IntColor below. This is because google.type.Color represent color has a float which semantically does not reflect discrete classes/categories concept. Moreover, to handle it well we need to have some tolerance when converting to a discretized color. As such, the recommendation is to have API surface still use google.type.Color while internally IntColor is used."},"displayName":{"description":"Should be used during preprocessing.","type":"string"},"intColor":{"$ref":"#/components/schemas/XPSColorMapIntColor"}},"type":"object"},"XPSColorMapIntColor":{"description":"RGB color and each channel is represented by an integer.","properties":{"blue":{"description":"The value should be in range of [0, 255].","format":"int32","type":"integer"},"green":{"description":"The value should be in range of [0, 255].","format":"int32","type":"integer"},"red":{"description":"The value should be in range of [0, 255].","format":"int32","type":"integer"}},"type":"object"},"XPSColumnSpec":{"properties":{"columnId":{"description":"The unique id of the column. When Preprocess, the Tables BE will popuate the order id of the column, which reflects the order of the column inside the table, i.e. 0 means the first column in the table, N-1 means the last column. 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It's outputed in Preprocess and a required input for RefreshTablesStats and Train.","type":"string"},"forecastingMetadata":{"$ref":"#/components/schemas/XPSColumnSpecForecastingMetadata"},"topCorrelatedColumns":{"description":"It's outputed in RefreshTablesStats, and a required input in Train.","items":{"$ref":"#/components/schemas/XPSColumnSpecCorrelatedColumn"},"type":"array"}},"type":"object"},"XPSColumnSpecCorrelatedColumn":{"description":"Identifies a table's column, and its correlation with the column this ColumnSpec describes.","properties":{"columnId":{"format":"int32","type":"integer"},"correlationStats":{"$ref":"#/components/schemas/XPSCorrelationStats"}},"type":"object"},"XPSColumnSpecForecastingMetadata":{"description":"=========================================================================== # The fields below are used exclusively for Forecasting.","properties":{"columnType":{"description":"The type of the column for FORECASTING model training purposes.","enum":["COLUMN_TYPE_UNSPECIFIED","KEY","KEY_METADATA","TIME_SERIES_AVAILABLE_PAST_ONLY","TIME_SERIES_AVAILABLE_PAST_AND_FUTURE"],"type":"string"}},"type":"object"},"XPSCommonStats":{"description":"Common statistics for a column with a specified data type.","properties":{"distinctValueCount":{"format":"int64","type":"string"},"nullValueCount":{"format":"int64","type":"string"},"validValueCount":{"format":"int64","type":"string"}},"type":"object"},"XPSConfidenceMetricsEntry":{"description":"ConfidenceMetricsEntry includes generic precision, recall, f1 score etc. Next tag: 16.","properties":{"confidenceThreshold":{"description":"Metrics are computed with an assumption that the model never return predictions with score lower than this value.","format":"float","type":"number"},"f1Score":{"description":"The harmonic mean of recall and precision.","format":"float","type":"number"},"f1ScoreAt1":{"description":"The harmonic mean of recall_at1 and precision_at1.","format":"float","type":"number"},"falseNegativeCount":{"description":"The number of ground truth labels that are not matched by a model created label.","format":"int64","type":"string"},"falsePositiveCount":{"description":"The number of model created labels that do not match a ground truth label.","format":"int64","type":"string"},"falsePositiveRate":{"description":"False Positive Rate for the given confidence threshold.","format":"float","type":"number"},"falsePositiveRateAt1":{"description":"The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example.","format":"float","type":"number"},"positionThreshold":{"description":"Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold.","format":"int32","type":"integer"},"precision":{"description":"Precision for the given confidence threshold.","format":"float","type":"number"},"precisionAt1":{"description":"The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example.","format":"float","type":"number"},"recall":{"description":"Recall (true positive rate) for the given confidence threshold.","format":"float","type":"number"},"recallAt1":{"description":"The recall (true positive rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example.","format":"float","type":"number"},"trueNegativeCount":{"description":"The number of labels that were not created by the model, but if they would, they would not match a ground truth label.","format":"int64","type":"string"},"truePositiveCount":{"description":"The number of model created labels that match a ground truth label.","format":"int64","type":"string"}},"type":"object"},"XPSConfusionMatrix":{"description":"Confusion matrix of the model running the classification.","properties":{"annotationSpecIdToken":{"description":"For the following three repeated fields, only one is intended to be set. annotation_spec_id_token is preferable to be set. ID tokens of the annotation specs used in the confusion matrix.","items":{"type":"string"},"type":"array"},"category":{"description":"Category (mainly for segmentation). Set only for image segmentation models. Note: uCAIP Image Segmentation should use annotation_spec_id_token.","items":{"format":"int32","type":"integer"},"type":"array"},"row":{"description":"Rows in the confusion matrix. The number of rows is equal to the size of `annotation_spec_id_token`. `row[i].value[j]` is the number of examples that have ground truth of the `annotation_spec_id_token[i]` and are predicted as `annotation_spec_id_token[j]` by the model being evaluated.","items":{"$ref":"#/components/schemas/XPSConfusionMatrixRow"},"type":"array"},"sentimentLabel":{"description":"Sentiment labels used in the confusion matrix. Set only for text sentiment models. For AutoML Text Revamp, use `annotation_spec_id_token` instead and leave this field empty.","