googleapis
Version:
Google APIs Client Library for Node.js
1,091 lines • 5.71 MB
TypeScript
import { OAuth2Client, JWT, Compute, UserRefreshClient, BaseExternalAccountClient, GaxiosResponseWithHTTP2, GoogleConfigurable, MethodOptions, StreamMethodOptions, GlobalOptions, GoogleAuth, BodyResponseCallback, APIRequestContext } from 'googleapis-common';
import { Readable } from 'stream';
export declare namespace aiplatform_v1 {
export interface Options extends GlobalOptions {
version: 'v1';
}
interface StandardParameters {
/**
* Auth client or API Key for the request
*/
auth?: string | OAuth2Client | JWT | Compute | UserRefreshClient | BaseExternalAccountClient | GoogleAuth;
/**
* V1 error format.
*/
'$.xgafv'?: string;
/**
* OAuth access token.
*/
access_token?: string;
/**
* Data format for response.
*/
alt?: string;
/**
* JSONP
*/
callback?: string;
/**
* Selector specifying which fields to include in a partial response.
*/
fields?: string;
/**
* API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
*/
key?: string;
/**
* OAuth 2.0 token for the current user.
*/
oauth_token?: string;
/**
* Returns response with indentations and line breaks.
*/
prettyPrint?: boolean;
/**
* Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
*/
quotaUser?: string;
/**
* Legacy upload protocol for media (e.g. "media", "multipart").
*/
uploadType?: string;
/**
* Upload protocol for media (e.g. "raw", "multipart").
*/
upload_protocol?: string;
}
/**
* Agent Platform API
*
* Build, scale, govern, and optimize sophisticated agents and models.
*
* @example
* ```js
* const {google} = require('googleapis');
* const aiplatform = google.aiplatform('v1');
* ```
*/
export class Aiplatform {
context: APIRequestContext;
batchPredictionJobs: Resource$Batchpredictionjobs;
customJobs: Resource$Customjobs;
dataLabelingJobs: Resource$Datalabelingjobs;
datasets: Resource$Datasets;
deploymentResourcePools: Resource$Deploymentresourcepools;
endpoints: Resource$Endpoints;
featureGroups: Resource$Featuregroups;
featureOnlineStores: Resource$Featureonlinestores;
featurestores: Resource$Featurestores;
hyperparameterTuningJobs: Resource$Hyperparametertuningjobs;
indexEndpoints: Resource$Indexendpoints;
indexes: Resource$Indexes;
media: Resource$Media;
metadataStores: Resource$Metadatastores;
migratableResources: Resource$Migratableresources;
modelDeploymentMonitoringJobs: Resource$Modeldeploymentmonitoringjobs;
models: Resource$Models;
notebookExecutionJobs: Resource$Notebookexecutionjobs;
notebookRuntimes: Resource$Notebookruntimes;
notebookRuntimeTemplates: Resource$Notebookruntimetemplates;
operations: Resource$Operations;
persistentResources: Resource$Persistentresources;
pipelineJobs: Resource$Pipelinejobs;
projects: Resource$Projects;
publishers: Resource$Publishers;
ragCorpora: Resource$Ragcorpora;
ragEngineConfig: Resource$Ragengineconfig;
reasoningEngines: Resource$Reasoningengines;
schedules: Resource$Schedules;
skills: Resource$Skills;
specialistPools: Resource$Specialistpools;
studies: Resource$Studies;
tensorboards: Resource$Tensorboards;
trainingPipelines: Resource$Trainingpipelines;
tuningJobs: Resource$Tuningjobs;
v1: Resource$V1;
constructor(options: GlobalOptions, google?: GoogleConfigurable);
}
/**
* Experimental parameters for video generation.
*/
export interface Schema$CloudAiLargeModelsVisionGenerateVideoExperiments {
/**
* Optional. Video codec to use for output.
*/
codec?: string | null;
/**
* Conditioning frames for veo experimental models ONLY, not to be confused with keyframes (ID:31) in GenerateVideoRequest.
*/
conditioningFrames?: Schema$CloudAiLargeModelsVisionGenerateVideoExperimentsConditioningFrame[];
/**
* Human pose parameters for Pose Control
*/
humanPose?: Schema$CloudAiLargeModelsVisionHumanPose;
/**
* Model names, as defined in: xyz
*/
modelName?: string | null;
/**
* Number of diffusion steps
*/
numDiffusionSteps?: number | null;
/**
* Prompt chunks for "ProModel" prompting. If set, the prompt will not be rewritten, and top-level prompt ignored.
*/
promptInputs?: Schema$CloudAiLargeModelsVisionPromptInputs;
/**
* Optional tag for tracking the source of this request. Allowed values: "colab", "comfyui", "curl", "flowresearch", "vertexstudio". Unrecognized tags are recorded as "unknown" in metrics. Tags do not affect video generation behavior. Up to 16 characters, ASCII alphanumeric, hyphens, and underscores only.
*/
requestOriginTag?: string | null;
/**
* If true (default), truncate input videos that exceed the model's maximum frame count by applying a frame_selection_config to __video_file__ inputs. Set to false to preserve the existing fail-fast behavior.
*/
truncateInputVideo?: boolean | null;
/**
* GCS URI of the grayscale video mask for Differential Diffusion. Maps to sdedit_video_tmax_scale_map
*/
videoTransformMaskGcsUri?: string | null;
/**
* SDEdit: Scalar noise level (0.0 to 1.0) Maps to sdedit_tmax
*/
videoTransformStrength?: number | null;
}
export interface Schema$CloudAiLargeModelsVisionGenerateVideoExperimentsConditioningFrame {
/**
* The index of the frame, starts with zero and must be a multiple of 8.
*/
frameNum?: number | null;
/**
* The image data for this conditioning frame.
*/
image?: Schema$CloudAiLargeModelsVisionGenerateVideoRequestImage;
}
/**
* The image bytes or Cloud Storage URI to make the prediction on.
*/
export interface Schema$CloudAiLargeModelsVisionGenerateVideoRequestImage {
/**
* Blob ID of the image. This is used for storing the large images in the request.
*/
blobId?: string | null;
/**
* Base64 encoded bytes string representing the image.
*/
bytesBase64Encoded?: string | null;
gcsUri?: string | null;
/**
* The MIME type of the content of the image. Only the images in below listed MIME types are supported. - image/jpeg - image/png
*/
mimeType?: string | null;
}
/**
* Generate video response.
*/
export interface Schema$CloudAiLargeModelsVisionGenerateVideoResponse {
/**
* The generates samples.
*/
generatedSamples?: Schema$CloudAiLargeModelsVisionMedia[];
/**
* Returns if any videos were filtered due to RAI policies.
*/
raiMediaFilteredCount?: number | null;
/**
* Returns rai failure reasons if any.
*/
raiMediaFilteredReasons?: string[] | null;
/**
* List of videos, used to align naming with the external response.
*/
videos?: Schema$CloudAiLargeModelsVisionGenerateVideoResponseVideo[];
}
export interface Schema$CloudAiLargeModelsVisionGenerateVideoResponseVideo {
/**
* Base64 encoded bytes string representing the video.
*/
bytesBase64Encoded?: string | null;
/**
* Optional metadata returned from experimental requests. Likely only includes the rewritten prompt chunks.
*/
experimentsMetadata?: Schema$CloudAiLargeModelsVisionGenerateVideoExperiments;
/**
* Cloud Storage URI where the generated video is written.
*/
gcsUri?: string | null;
/**
* The MIME type of the content of the video. - video/mp4
*/
mimeType?: string | null;
}
export interface Schema$CloudAiLargeModelsVisionHumanPose {
/**
* GCS URI of the human pose video to condition video generation.
*/
bodyLandmarksGcsUri?: string | null;
/**
* GCS URI of the face landmarks video to condition video generation.
*/
faceLandmarksGcsUri?: string | null;
/**
* GCS URI of the performance mesh to condition video generation.
*/
perfMeshGcsUri?: string | null;
}
/**
* Image.
*/
export interface Schema$CloudAiLargeModelsVisionImage {
/**
* Image encoding, encoded as "image/png" or "image/jpg".
*/
encoding?: string | null;
/**
* Generation seed for the sampled image. This parameter is exposed to the user only if one of the following is true: 1. The user specified per-example seeds in the request. 2. The user doesn't specify the generation seed in the request.
*/
generationSeed?: number | null;
/**
* Raw bytes.
*/
image?: string | null;
/**
* RAI scores for generated image.
*/
imageRaiScores?: Schema$CloudAiLargeModelsVisionImageRAIScores;
/**
* Image size. The size of the image. Can be self reported, or computed from the image bytes.
*/
imageSize?: Schema$CloudAiLargeModelsVisionImageImageSize;
/**
* RAI info for image.
*/
raiInfo?: Schema$CloudAiLargeModelsVisionRaiInfo;
/**
* Semantic filter info for image.
*/
semanticFilterResponse?: Schema$CloudAiLargeModelsVisionSemanticFilterResponse;
/**
* Text/Expanded text input for imagen.
*/
text?: string | null;
/**
* Path to another storage (typically Google Cloud Storage).
*/
uri?: string | null;
}
/**
* Image size.
*/
export interface Schema$CloudAiLargeModelsVisionImageImageSize {
channels?: number | null;
height?: number | null;
width?: number | null;
}
/**
* RAI scores for generated image returned.
*/
export interface Schema$CloudAiLargeModelsVisionImageRAIScores {
/**
* Agile watermark score for image.
*/
agileWatermarkDetectionScore?: number | null;
}
/**
* Media.
*/
export interface Schema$CloudAiLargeModelsVisionMedia {
/**
* Image.
*/
image?: Schema$CloudAiLargeModelsVisionImage;
/**
* Video
*/
video?: Schema$CloudAiLargeModelsVisionVideo;
}
export interface Schema$CloudAiLargeModelsVisionNamedBoundingBox {
classes?: string[] | null;
entities?: string[] | null;
scores?: number[] | null;
x1?: number | null;
x2?: number | null;
y1?: number | null;
y2?: number | null;
}
/**
* "Direct" prompting for Experimental Video Generation. These will be sent directly to the LDM without being rewritten.
*/
export interface Schema$CloudAiLargeModelsVisionPromptInputs {
/**
* Description of audio content in the video, without speech.
*/
audioPrompt?: string | null;
/**
* Negative description of audio content in the video.
*/
negativeAudioPrompt?: string | null;
/**
* Single negative prompt for what not to generate.
*/
negativePrompt?: string | null;
/**
* 2s, 256 tokens per chunk, 4 total chunks. Required.
*/
promptChunks?: string[] | null;
/**
* Spoken transcript of the video for characters.
*/
transcript?: string | null;
}
/**
* Next ID: 6
*/
export interface Schema$CloudAiLargeModelsVisionRaiInfo {
/**
* List of blocked entities from the blocklist if it is detected.
*/
blockedEntities?: string[] | null;
/**
* The list of detected labels for different rai categories.
*/
detectedLabels?: Schema$CloudAiLargeModelsVisionRaiInfoDetectedLabels[];
/**
* The model name used to indexing into the RaiFilterConfig map. Would either be one of imagegeneration@002-006, imagen-3.0-... api endpoint names, or internal names used for mapping to different filter configs (genselfie, ai_watermark) than its api endpoint.
*/
modelName?: string | null;
/**
* List of rai categories' information to return
*/
raiCategories?: string[] | null;
/**
* List of rai scores mapping to the rai categories. Rounded to 1 decimal place.
*/
scores?: number[] | null;
}
/**
* Filters returning list of deteceted labels, scores, and bounding boxes.
*/
export interface Schema$CloudAiLargeModelsVisionRaiInfoDetectedLabels {
/**
* The list of detected entities for the rai signal.
*/
entities?: Schema$CloudAiLargeModelsVisionRaiInfoDetectedLabelsEntity[];
/**
* The RAI category for the deteceted labels.
*/
raiCategory?: string | null;
}
/**
* An integer bounding box of original pixels of the image for the detected labels.
*/
export interface Schema$CloudAiLargeModelsVisionRaiInfoDetectedLabelsBoundingBox {
/**
* The X coordinate of the top-left corner, in pixels.
*/
x1?: number | null;
/**
* The X coordinate of the bottom-right corner, in pixels.
*/
x2?: number | null;
/**
* The Y coordinate of the top-left corner, in pixels.
*/
y1?: number | null;
/**
* The Y coordinate of the bottom-right corner, in pixels.
*/
y2?: number | null;
}
/**
* The properties for a detected entity from the rai signal.
*/
export interface Schema$CloudAiLargeModelsVisionRaiInfoDetectedLabelsEntity {
/**
* Bounding box of the label
*/
boundingBox?: Schema$CloudAiLargeModelsVisionRaiInfoDetectedLabelsBoundingBox;
/**
* Description of the label
*/
description?: string | null;
/**
* The intersection ratio between the detection bounding box and the mask.
*/
iouScore?: number | null;
/**
* MID of the label
*/
mid?: string | null;
/**
* Confidence score of the label
*/
score?: number | null;
}
export interface Schema$CloudAiLargeModelsVisionSemanticFilterResponse {
/**
* Class labels of the bounding boxes that failed the semantic filtering. Bounding box coordinates.
*/
namedBoundingBoxes?: Schema$CloudAiLargeModelsVisionNamedBoundingBox[];
/**
* This response is added when semantic filter config is turned on in EditConfig. It reports if this image is passed semantic filter response. If passed_semantic_filter is false, the bounding box information will be populated for user to check what caused the semantic filter to fail.
*/
passedSemanticFilter?: boolean | null;
}
/**
* Video
*/
export interface Schema$CloudAiLargeModelsVisionVideo {
/**
* Base 64 encoded video bytes.
*/
encodedVideo?: string | null;
/**
* Video encoding, for example "video/mp4".
*/
encoding?: string | null;
/**
* Text/Expanded text input for Help Me Write.
*/
text?: string | null;
/**
* Path to another storage (typically Google Cloud Storage).
*/
uri?: string | null;
/**
* Raw bytes.
*/
video?: string | null;
}
/**
* Create API error message for Vertex Pipeline.
*/
export interface Schema$CloudAiPlatformCommonCreatePipelineJobApiErrorDetail {
/**
* The error root cause returned by CreatePipelineJob API.
*/
errorCause?: string | null;
/**
* Public messages contains actionable items for the error cause.
*/
publicMessage?: string | null;
}
/**
* Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; \} service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); \} Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); \} Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged.
*/
export interface Schema$GoogleApiHttpBody {
/**
* The HTTP Content-Type header value specifying the content type of the body.
*/
contentType?: string | null;
/**
* The HTTP request/response body as raw binary.
*/
data?: string | null;
/**
* Application specific response metadata. Must be set in the first response for streaming APIs.
*/
extensions?: Array<{
[key: string]: any;
}> | null;
}
/**
* 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.
*/
export interface Schema$GoogleCloudAiplatformV1ActiveLearningConfig {
/**
* Max number of human labeled DataItems.
*/
maxDataItemCount?: string | null;
/**
* Max percent of total DataItems for human labeling.
*/
maxDataItemPercentage?: number | null;
/**
* Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
*/
sampleConfig?: Schema$GoogleCloudAiplatformV1SampleConfig;
/**
* 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.
*/
trainingConfig?: Schema$GoogleCloudAiplatformV1TrainingConfig;
}
/**
* Request message for MetadataService.AddContextArtifactsAndExecutions.
*/
export interface Schema$GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsRequest {
/**
* The resource names of the Artifacts to attribute to the Context. Format: `projects/{project\}/locations/{location\}/metadataStores/{metadatastore\}/artifacts/{artifact\}`
*/
artifacts?: string[] | null;
/**
* The resource names of the Executions to associate with the Context. Format: `projects/{project\}/locations/{location\}/metadataStores/{metadatastore\}/executions/{execution\}`
*/
executions?: string[] | null;
}
/**
* Response message for MetadataService.AddContextArtifactsAndExecutions.
*/
export interface Schema$GoogleCloudAiplatformV1AddContextArtifactsAndExecutionsResponse {
}
/**
* Request message for MetadataService.AddContextChildren.
*/
export interface Schema$GoogleCloudAiplatformV1AddContextChildrenRequest {
/**
* The resource names of the child Contexts.
*/
childContexts?: string[] | null;
}
/**
* Response message for MetadataService.AddContextChildren.
*/
export interface Schema$GoogleCloudAiplatformV1AddContextChildrenResponse {
}
/**
* Request message for MetadataService.AddExecutionEvents.
*/
export interface Schema$GoogleCloudAiplatformV1AddExecutionEventsRequest {
/**
* The Events to create and add.
*/
events?: Schema$GoogleCloudAiplatformV1Event[];
}
/**
* Response message for MetadataService.AddExecutionEvents.
*/
export interface Schema$GoogleCloudAiplatformV1AddExecutionEventsResponse {
}
/**
* Request message for VizierService.AddTrialMeasurement.
*/
export interface Schema$GoogleCloudAiplatformV1AddTrialMeasurementRequest {
/**
* Required. The measurement to be added to a Trial.
*/
measurement?: Schema$GoogleCloudAiplatformV1Measurement;
}
/**
* The aggregation result for the entire dataset and all metrics.
*/
export interface Schema$GoogleCloudAiplatformV1AggregationOutput {
/**
* One AggregationResult per metric.
*/
aggregationResults?: Schema$GoogleCloudAiplatformV1AggregationResult[];
/**
* The dataset used for evaluation & aggregation.
*/
dataset?: Schema$GoogleCloudAiplatformV1EvaluationDataset;
}
/**
* The aggregation result for a single metric.
*/
export interface Schema$GoogleCloudAiplatformV1AggregationResult {
/**
* Aggregation metric.
*/
aggregationMetric?: string | null;
/**
* Results for bleu metric.
*/
bleuMetricValue?: Schema$GoogleCloudAiplatformV1BleuMetricValue;
/**
* Result for code execution metric.
*/
customCodeExecutionResult?: Schema$GoogleCloudAiplatformV1CustomCodeExecutionResult;
/**
* Results for exact match metric.
*/
exactMatchMetricValue?: Schema$GoogleCloudAiplatformV1ExactMatchMetricValue;
/**
* Result for pairwise metric.
*/
pairwiseMetricResult?: Schema$GoogleCloudAiplatformV1PairwiseMetricResult;
/**
* Result for pointwise metric.
*/
pointwiseMetricResult?: Schema$GoogleCloudAiplatformV1PointwiseMetricResult;
/**
* Results for rouge metric.
*/
rougeMetricValue?: Schema$GoogleCloudAiplatformV1RougeMetricValue;
}
/**
* Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.
*/
export interface Schema$GoogleCloudAiplatformV1Annotation {
/**
* Output only. The source of the Annotation.
*/
annotationSource?: Schema$GoogleCloudAiplatformV1UserActionReference;
/**
* Output only. Timestamp when this Annotation was created.
*/
createTime?: string | null;
/**
* Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
*/
etag?: string | null;
/**
* 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.
*/
labels?: {
[key: string]: string;
} | null;
/**
* Output only. Resource name of the Annotation.
*/
name?: string | null;
/**
* Required. The schema of the payload can be found in payload_schema.
*/
payload?: any | null;
/**
* 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.
*/
payloadSchemaUri?: string | null;
/**
* Output only. Timestamp when this Annotation was last updated.
*/
updateTime?: string | null;
}
/**
* Identifies a concept with which DataItems may be annotated with.
*/
export interface Schema$GoogleCloudAiplatformV1AnnotationSpec {
/**
* Output only. Timestamp when this AnnotationSpec was created.
*/
createTime?: string | null;
/**
* Required. The user-defined name of the AnnotationSpec. The name can be up to 128 characters long and can consist of any UTF-8 characters.
*/
displayName?: string | null;
/**
* Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
*/
etag?: string | null;
/**
* Output only. Resource name of the AnnotationSpec.
*/
name?: string | null;
/**
* Output only. Timestamp when AnnotationSpec was last updated.
*/
updateTime?: string | null;
}
/**
* The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead.
*/
export interface Schema$GoogleCloudAiplatformV1ApiAuth {
/**
* The API secret.
*/
apiKeyConfig?: Schema$GoogleCloudAiplatformV1ApiAuthApiKeyConfig;
}
/**
* The API secret.
*/
export interface Schema$GoogleCloudAiplatformV1ApiAuthApiKeyConfig {
/**
* Required. The SecretManager secret version resource name storing API key. e.g. projects/{project\}/secrets/{secret\}/versions/{version\}
*/
apiKeySecretVersion?: string | null;
/**
* The API key string. Either this or `api_key_secret_version` must be set.
*/
apiKeyString?: string | null;
}
/**
* Response message for SessionService.AppendEvent.
*/
export interface Schema$GoogleCloudAiplatformV1AppendEventResponse {
}
/**
* Instance of a general artifact.
*/
export interface Schema$GoogleCloudAiplatformV1Artifact {
/**
* Output only. Timestamp when this Artifact was created.
*/
createTime?: string | null;
/**
* Description of the Artifact
*/
description?: string | null;
/**
* User provided display name of the Artifact. May be up to 128 Unicode characters.
*/
displayName?: string | null;
/**
* An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
*/
etag?: string | null;
/**
* 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).
*/
labels?: {
[key: string]: string;
} | null;
/**
* Properties of the Artifact. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.
*/
metadata?: {
[key: string]: any;
} | null;
/**
* Output only. The resource name of the Artifact.
*/
name?: string | null;
/**
* 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.
*/
schemaTitle?: string | null;
/**
* 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.
*/
schemaVersion?: string | null;
/**
* 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.
*/
state?: string | null;
/**
* Output only. Timestamp when this Artifact was last updated.
*/
updateTime?: string | null;
/**
* The uniform resource identifier of the artifact file. May be empty if there is no actual artifact file.
*/
uri?: string | null;
}
/**
* Agentic Retrieval Ask API for RAG. Request message for VertexRagService.AskContexts.
*/
export interface Schema$GoogleCloudAiplatformV1AskContextsRequest {
/**
* Required. Single RAG retrieve query.
*/
query?: Schema$GoogleCloudAiplatformV1RagQuery;
/**
* Optional. The tools to use for AskContexts.
*/
tools?: Schema$GoogleCloudAiplatformV1Tool[];
}
/**
* Response message for VertexRagService.AskContexts.
*/
export interface Schema$GoogleCloudAiplatformV1AskContextsResponse {
/**
* The contexts of the query.
*/
contexts?: Schema$GoogleCloudAiplatformV1RagContexts;
/**
* The Retrieval Response.
*/
response?: string | null;
}
/**
* Metadata information for NotebookService.AssignNotebookRuntime.
*/
export interface Schema$GoogleCloudAiplatformV1AssignNotebookRuntimeOperationMetadata {
/**
* The operation generic information.
*/
genericMetadata?: Schema$GoogleCloudAiplatformV1GenericOperationMetadata;
/**
* A human-readable message that shows the intermediate progress details of NotebookRuntime.
*/
progressMessage?: string | null;
}
/**
* Request message for NotebookService.AssignNotebookRuntime.
*/
export interface Schema$GoogleCloudAiplatformV1AssignNotebookRuntimeRequest {
/**
* Required. Provide runtime specific information (e.g. runtime owner, notebook id) used for NotebookRuntime assignment.
*/
notebookRuntime?: Schema$GoogleCloudAiplatformV1NotebookRuntime;
/**
* Optional. User specified ID for the notebook runtime.
*/
notebookRuntimeId?: string | null;
/**
* Required. The resource name of the NotebookRuntimeTemplate based on which a NotebookRuntime will be assigned (reuse or create a new one).
*/
notebookRuntimeTemplate?: string | null;
}
/**
* Request message for ReasoningEngineExecutionService.AsyncQueryReasoningEngine.
*/
export interface Schema$GoogleCloudAiplatformV1AsyncQueryReasoningEngineRequest {
/**
* Optional. Input Cloud Storage URI for the Async query. If you are not bringing your own container (BYOC), the content of the file should be a JSON object with an `input` field matching the `input` field of `QueryReasoningEngineRequest` (e.g. `{ "input": { "user_id": "hello", "message":"$QUERY"\} \}`). For BYOC, the content of the file depends on the the agent application.
*/
inputGcsUri?: string | null;
/**
* Optional. Output Cloud Storage URI for the Async query. This contains the final response of the query.
*/
outputGcsUri?: string | null;
}
/**
* Request message for VertexRagService.AsyncRetrieveContexts.
*/
export interface Schema$GoogleCloudAiplatformV1AsyncRetrieveContextsRequest {
/**
* Required. Single RAG retrieve query.
*/
query?: Schema$GoogleCloudAiplatformV1RagQuery;
/**
* Optional. The tools to use for AskContexts.
*/
tools?: Schema$GoogleCloudAiplatformV1Tool[];
}
/**
* Attribution that explains a particular prediction output.
*/
export interface Schema$GoogleCloudAiplatformV1Attribution {
/**
* 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.
*/
approximationError?: number | null;
/**
* 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.
*/
baselineOutputValue?: number | null;
/**
* 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).
*/
featureAttributions?: any | null;
/**
* 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.
*/
instanceOutputValue?: number | null;
/**
* 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.
*/
outputDisplayName?: string | null;
/**
* 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.
*/
outputIndex?: number[] | null;
/**
* Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs.
*/
outputName?: string | null;
}
/**
* Request message for AugmentPrompt.
*/
export interface Schema$GoogleCloudAiplatformV1AugmentPromptRequest {
/**
* Optional. Input content to augment, only text format is supported for now.
*/
contents?: Schema$GoogleCloudAiplatformV1Content[];
/**
* Optional. Metadata of the backend deployed model.
*/
model?: Schema$GoogleCloudAiplatformV1AugmentPromptRequestModel;
/**
* Optional. Retrieves contexts from the Vertex RagStore.
*/
vertexRagStore?: Schema$GoogleCloudAiplatformV1VertexRagStore;
}
/**
* Metadata of the backend deployed model.
*/
export interface Schema$GoogleCloudAiplatformV1AugmentPromptRequestModel {
/**
* Optional. The model that the user will send the augmented prompt for content generation.
*/
model?: string | null;
/**
* Optional. The model version of the backend deployed model.
*/
modelVersion?: string | null;
}
/**
* Response message for AugmentPrompt.
*/
export interface Schema$GoogleCloudAiplatformV1AugmentPromptResponse {
/**
* Augmented prompt, only text format is supported for now.
*/
augmentedPrompt?: Schema$GoogleCloudAiplatformV1Content[];
/**
* Retrieved facts from RAG data sources.
*/
facts?: Schema$GoogleCloudAiplatformV1Fact[];
}
/**
* Auth configuration to run the extension.
*/
export interface Schema$GoogleCloudAiplatformV1AuthConfig {
/**
* Config for API key auth.
*/
apiKeyConfig?: Schema$GoogleCloudAiplatformV1AuthConfigApiKeyConfig;
/**
* Type of auth scheme.
*/
authType?: string | null;
/**
* Config for Google Service Account auth.
*/
googleServiceAccountConfig?: Schema$GoogleCloudAiplatformV1AuthConfigGoogleServiceAccountConfig;
/**
* Config for HTTP Basic auth.
*/
httpBasicAuthConfig?: Schema$GoogleCloudAiplatformV1AuthConfigHttpBasicAuthConfig;
/**
* Config for user oauth.
*/
oauthConfig?: Schema$GoogleCloudAiplatformV1AuthConfigOauthConfig;
/**
* Config for user OIDC auth.
*/
oidcConfig?: Schema$GoogleCloudAiplatformV1AuthConfigOidcConfig;
}
/**
* Config for authentication with API key.
*/
export interface Schema$GoogleCloudAiplatformV1AuthConfigApiKeyConfig {
/**
* Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project\}/secrets/{secrete\}/versions/{version\}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - 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.
*/
apiKeySecret?: string | null;
/**
* Optional. The API key to be used in the request directly.
*/
apiKeyString?: string | null;
/**
* Optional. The location of the API key.
*/
httpElementLocation?: string | null;
/**
* Optional. 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.
*/
name?: string | null;
}
/**
* Config for Google Service Account Authentication.
*/
export interface Schema$GoogleCloudAiplatformV1AuthConfigGoogleServiceAccountConfig {
/**
* Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
*/
serviceAccount?: string | null;
}
/**
* Config for HTTP Basic Authentication.
*/
export interface Schema$GoogleCloudAiplatformV1AuthConfigHttpBasicAuthConfig {
/**
* 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.
*/
credentialSecret?: string | null;
}
/**
* Config for user oauth.
*/
export interface Schema$GoogleCloudAiplatformV1AuthConfigOauthConfig {
/**
* Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
*/
accessToken?: string | null;
/**
* The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
*/
serviceAccount?: string | null;
}
/**
* Config for user OIDC auth.
*/
export interface Schema$GoogleCloudAiplatformV1AuthConfigOidcConfig {
/**
* OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
*/
idToken?: string | null;
/**
* 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).
*/
serviceAccount?: string | null;
}
/**
* 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.
*/
export interface Schema$GoogleCloudAiplatformV1AutomaticResources {
/**
* Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
*/
maxReplicaCount?: number | null;
/**
* Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
*/
minReplicaCount?: number | null;
}
/**
* The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset.
*/
export interface Schema$GoogleCloudAiplatformV1AutoraterConfig {
/**
* Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project\}/locations/{location\}/publishers/x/models/x` Tuned model endpoint format: `projects/{project\}/locations/{location\}/endpoints/{endpoint\}`
*/
autoraterModel?: string | null;
/**
* Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
*/
flipEnabled?: boolean | null;
/**
* Optional. Configuration options for model generation and outputs.
*/
generationConfig?: Schema$GoogleCloudAiplatformV1GenerationConfig;
/**
* Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
*/
samplingCount?: number | null;
}
/**
* The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
*/
export interface Schema$GoogleCloudAiplatformV1AutoscalingMetricSpec {
/**
* Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count` * `pubsub.googleapis.com/subscription/num_undelivered_messages` * `prometheus.googleapis.com/vertex_dcgm_fi_dev_gpu_util` * `prometheus.googleapis.com/vertex_vllm_gpu_cache_usage_perc` * `prometheus.googleapis.com/vertex_vllm_num_requests_waiting`
*/
metricName?: string | null;
/**
* 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.
*/
target?: number | null;
}
/**
* The storage details for Avro input content.
*/
export interface Schema$GoogleCloudAiplatformV1AvroSource {
/**
* Required. Google Cloud Storage location.
*/
gcsSource?: Schema$GoogleCloudAiplatformV1GcsSource;
}
/**
* Request message for PipelineService.BatchCancelPipelineJobs.
*/
export interface Schema$GoogleCloudAiplatformV1BatchCancelPipelineJobsRequest {
/**
* Required. The names of the PipelineJobs to cancel. A maximum of 32 PipelineJobs can be cancelled in a batch. Format: `projects/{project\}/locations/{location\}/pipelineJobs/{pipelineJob\}`
*/
names?: string[] | null;
}
/**
* Details of operations that perform batch create Features.
*/
export interface Schema$GoogleCloudAiplatformV1BatchCreateFeaturesOperationMetadata {
/**
* Operation metadata for Feature.
*/
genericMetadata?: Schema$GoogleCloudAiplatformV1GenericOperationMetadata;
}
/**
* Request message for FeaturestoreService.BatchCreateFeatures. Request message for FeatureRegistryService.BatchCreateFeatures.
*/
export interface Schema$GoogleCloudAiplatformV1BatchCreateFeaturesRequest {
/**
* Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType / FeatureGroup. The `parent` field in each child request message can be omitted. If `parent` is set in a child request, then the value must match the `par