Output only. The resource name of the Endpoint.

Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

The description of the Endpoint.

Output only. The models deployed in this Endpoint. To add or remove DeployedModels useEndpointService.DeployModel andEndpointService.UndeployModel respectively.

A map from a DeployedModel's id to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel.

If a DeployedModel's id is not listed in this map, then it receives no traffic.

The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment.

Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

The labels with user-defined metadata to organize your endpoints.

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.

Seehttps://goo.gl/xmQnxf for more information and examples of labels.

Output only. timestamp when this Endpoint was created.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples:"2014-10-02T15:01:23Z","2014-10-02T15:01:23.045123456Z" or"2014-10-02T15:01:23+05:30".

Output only. timestamp when this Endpoint was last updated.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples:"2014-10-02T15:01:23Z","2014-10-02T15:01:23.045123456Z" or"2014-10-02T15:01:23+05:30".

Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key.

Optional. The full name of the Google Compute Enginenetwork to which the Endpoint should be peered.

Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network.

Only one of the fields,network orenablePrivateServiceConnect, can be set.

Format:projects/{project}/global/networks/{network}. Where{project} is a project number, as in12345, and{network} is network name.

Only one of the fields,network orenablePrivateServiceConnect, can be set.

Optional. Configuration for private service connect.

network andprivateServiceConnectConfig are mutually exclusive.

Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled byJobService.CreateModelDeploymentMonitoringJob. Format:projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{modelDeploymentMonitoringJob}

Configures the request-response logging for online prediction.

If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon.

Output only. DNS of the dedicated endpoint. Will only be populated if dedicatedEndpointEnabled is true. Depending on the features enabled, uid might be a random number or a string. For example, if fast_tryout is enabled, uid will be fasttryout. Format:https://{endpointId}.{region}-{uid}.prediction.vertexai.goog.

Configurations that are applied to the endpoint for online prediction.

Output only. reserved for future use.

Output only. reserved for future use.

Optional. Configuration for GenAiAdvancedFeatures. If the endpoint is serving GenAI models, advanced features like native RAG integration can be configured. Currently, only Model Garden models are supported.

Immutable. The id of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this id.

This value should be 1-10 characters, and valid characters are/[0-9]/.

The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.

The resource name may contain version id or version alias to specify the version. Example:projects/{project}/locations/{location}/models/{model}@2 orprojects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.

GDC pretrained / Gemini model name. The model name is a plain model name, e.g. gemini-1.5-flash-002.

Output only. The version id of the model that is deployed.

The display name of the DeployedModel. If not provided upon creation, the Model's displayName is used.

Output only. timestamp when the DeployedModel was created.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples:"2014-10-02T15:01:23Z","2014-10-02T15:01:23.045123456Z" or"2014-10-02T15:01:23+05:30".

Explanation configuration for this DeployedModel.

When deploying a Model usingEndpointService.DeployModel, this value overrides the value ofModel.explanation_spec. All fields ofexplanationSpec are optional in the request. If a field ofexplanationSpec is not populated, the value of the same field ofModel.explanation_spec is inherited. If the correspondingModel.explanation_spec is not populated, all fields of theexplanationSpec will be used for the explanation configuration.

If true, deploy the model without explainable feature, regardless the existence ofModel.explanation_spec orexplanationSpec.

The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project.

Users deploying the Model must have theiam.serviceAccounts.actAs permission on this service account.

For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will sendstderr andstdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject toCloud Logging pricing.

user can disable container logging by setting this flag to true.

If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request.

Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated ifnetwork is configured.

Configuration for faster model deployment.

Output only. Runtime status of the deployed model.

System labels to apply to Model Garden deployments. System labels are managed by Google for internal use only.

The checkpoint id of the model.

Optional. Spec for configuring speculative decoding.

Output only. Http(s) path to send prediction requests.

Output only. Http(s) path to send explain requests.

Output only. Http(s) path to send health check requests.

Output only. The name of the service attachment resource. Populated if private service connect is enabled.

If true, enable fast tryout feature for this deployed model.

Output only. The latest deployed model's status message (if any).

Output only. The time at which the status was last updated.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples:"2014-10-02T15:01:23Z","2014-10-02T15:01:23.045123456Z" or"2014-10-02T15:01:23+05:30".

Output only. The number of available replicas of the deployed model.

The number of speculative tokens to generate at each step.

Required. The resource name of the draft model.

The number of last N input tokens used as ngram to search/match against the previous prompt sequence. This is equal to the N in N-Gram. The default value is 3 if not specified.

If logging is enabled or not.

Percentage of requests to be logged, expressed as a fraction in range(0,1].

BigQuery table for logging. If only given a project, a new dataset will be created with namelogging_<endpoint-display-name>_<endpoint-id> where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores). If no table name is given, a new table will be created with namerequest_response_logging

Customizable online prediction request timeout.

A duration in seconds with up to nine fractional digits, ending with 's'. Example:"3.5s".

Configuration for Retrieval Augmented Generation feature.

If true, enable Retrieval Augmented Generation in ChatCompletion request. Once enabled, the endpoint will be identified as GenAI endpoint and Arthedain router will be used.