MCP Tools Reference: us-central1-aiplatform.googleapis.com

Tool: list_endpoints

Lists Endpoints in a Location.

The following sample demonstrate how to use curl to invoke the list_endpoints MCP tool.

Curl Request
curl --location 'https://us-central1-aiplatform.googleapis.com/mcp' \ --header 'content-type: application/json' \ --header 'accept: application/json, text/event-stream' \ --data '{ "method": "tools/call", "params": { "name": "list_endpoints", "arguments": { // provide these details according to the tool's MCP specification } }, "jsonrpc": "2.0", "id": 1 }'

Input Schema

Request message for EndpointService.ListEndpoints.

ListEndpointsRequest

JSON representation
{ "parent": string, "filter": string, "pageSize": integer, "pageToken": string, "readMask": string, "gdcZone": string }

Fields
parent	string Required. The resource name of the Location from which to list the Endpoints. Format: projects/{project}/locations/{location}
filter	string Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. endpoint supports = and !=. endpoint represents the Endpoint ID, i.e. the last segment of the Endpoint's resource name. display_name supports = and !=. labels supports general map functions that is: labels.key=value - key:value equality labels.key:* or labels:key - key existence A key including a space must be quoted. labels."a key". base_model_name only supports =. Some examples: endpoint=1 displayName="myDisplayName" labels.myKey="myValue" baseModelName="text-bison"
pageSize	integer Optional. The standard list page size.
pageToken	string Optional. The standard list page token. Typically obtained via ListEndpointsResponse.next_page_token of the previous EndpointService.ListEndpoints call.
readMask	string (FieldMask format) Optional. Mask specifying which fields to read. This is a comma-separated list of fully qualified names of fields. Example: "user.displayName,photo".
gdcZone	string Optional. Configures the Google Distributed Cloud (GDC) environment for online prediction. Only set this field when the Endpoint is to be deployed in a GDC environment.

FieldMask

JSON representation
{ "paths": [ string ] }

Fields
paths[]	string The set of field mask paths.

Output Schema

Response message for EndpointService.ListEndpoints.

ListEndpointsResponse

JSON representation
{ "endpoints": [ { object (Endpoint) } ], "nextPageToken": string }

Fields
endpoints[]	object (Endpoint) List of Endpoints in the requested page.
nextPageToken	string A token to retrieve the next page of results. Pass to ListEndpointsRequest.page_token to obtain that page.

Endpoint

JSON representation

{ "name": string, "displayName": string, "description": string, "deployedModels": [ { object (DeployedModel) } ], "trafficSplit": { string: integer, ... }, "etag": string, "labels": { string: string, ... }, "createTime": string, "updateTime": string, "encryptionSpec": { object (EncryptionSpec) }, "network": string, "enablePrivateServiceConnect": boolean, "privateServiceConnectConfig": { object (PrivateServiceConnectConfig) }, "modelDeploymentMonitoringJob": string, "predictRequestResponseLoggingConfig": { object (PredictRequestResponseLoggingConfig) }, "dedicatedEndpointEnabled": boolean, "dedicatedEndpointDns": string, "clientConnectionConfig": { object (ClientConnectionConfig) }, "satisfiesPzs": boolean, "satisfiesPzi": boolean, "genAiAdvancedFeaturesConfig": { object (GenAiAdvancedFeaturesConfig) } }

Fields
name	string Identifier. The resource name of the Endpoint.
displayName	string Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
description	string The description of the Endpoint.
deployedModels[]	object (DeployedModel) Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
trafficSplit	map (key: string, value: integer) 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. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) 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. See https://goo.gl/xmQnxf for more information and examples of labels. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
createTime	string (Timestamp format) 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".
updateTime	string (Timestamp format) 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".
encryptionSpec	object (EncryptionSpec) 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.
network	string Optional. The full name of the Google Compute Engine network 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 or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.
enablePrivateServiceConnect (deprecated)	boolean This item is deprecated! Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
privateServiceConnectConfig	object (PrivateServiceConnectConfig) Optional. Configuration for private service connect. network and private_service_connect_config are mutually exclusive.
modelDeploymentMonitoringJob	string Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}
predictRequestResponseLoggingConfig	object (PredictRequestResponseLoggingConfig) Configures the request-response logging for online prediction.
dedicatedEndpointEnabled	boolean 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.
dedicatedEndpointDns	string Output only. DNS of the dedicated endpoint. Will only be populated if dedicated_endpoint_enabled 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://{endpoint_id}.{region}-{uid}.prediction.vertexai.goog.
clientConnectionConfig	object (ClientConnectionConfig) Configurations that are applied to the endpoint for online prediction.
satisfiesPzs	boolean Output only. Reserved for future use.
satisfiesPzi	boolean Output only. Reserved for future use.
genAiAdvancedFeaturesConfig	object (GenAiAdvancedFeaturesConfig) 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.

DeployedModel

JSON representation

{ "id": string, "model": string, "gdcConnectedModel": string, "modelVersionId": string, "displayName": string, "createTime": string, "explanationSpec": { object (ExplanationSpec) }, "disableExplanations": boolean, "serviceAccount": string, "enableContainerLogging": boolean, "disableContainerLogging": boolean, "enableAccessLogging": boolean, "privateEndpoints": { object (PrivateEndpoints) }, "fasterDeploymentConfig": { object (FasterDeploymentConfig) }, "rolloutOptions": { object (RolloutOptions) }, "status": { object (Status) }, "systemLabels": { string: string, ... }, "checkpointId": string, "speculativeDecodingSpec": { object (SpeculativeDecodingSpec) }, // Union field prediction_resources can be only one of the following: "dedicatedResources": { object (DedicatedResources) }, "automaticResources": { object (AutomaticResources) }, "sharedResources": string, "fullFineTunedResources": { object (FullFineTunedResources) } // End of list of possible types for union field prediction_resources. }

Fields
id	string 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]/.
model	string 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 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.
gdcConnectedModel	string GDC pretrained / Gemini model name. The model name is a plain model name, e.g. gemini-1.5-flash-002.
modelVersionId	string Output only. The version ID of the model that is deployed.
displayName	string The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
createTime	string (Timestamp format) 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".
explanationSpec	object (ExplanationSpec) Explanation configuration for this DeployedModel. When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
disableExplanations	boolean If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec.
serviceAccount	string 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 the iam.serviceAccounts.actAs permission on this service account.
enableContainerLogging	boolean If true, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging. Only supported for custom-trained Models and AutoML Tabular Models.
disableContainerLogging	boolean For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
enableAccessLogging	boolean 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.
privateEndpoints	object (PrivateEndpoints) 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 if network is configured.
fasterDeploymentConfig	object (FasterDeploymentConfig) Configuration for faster model deployment.
rolloutOptions	object (RolloutOptions) Options for configuring rolling deployments.
status	object (Status) Output only. Runtime status of the deployed model.
systemLabels	map (key: string, value: string) System labels to apply to Model Garden deployments. System labels are managed by Google for internal use only. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
checkpointId	string The checkpoint id of the model.
speculativeDecodingSpec	object (SpeculativeDecodingSpec) Optional. Spec for configuring speculative decoding.
Union field prediction_resources. The prediction (for example, the machine) resources that the DeployedModel uses. The user is billed for the resources (at least their minimal amount) even if the DeployedModel receives no traffic. Not all Models support all resources types. See Model.supported_deployment_resources_types. Required except for Large Model Deploy use cases. prediction_resources can be only one of the following:
dedicatedResources	object (DedicatedResources) A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
automaticResources	object (AutomaticResources) A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
sharedResources	string The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
fullFineTunedResources	object (FullFineTunedResources) Optional. Resources for a full fine tuned model.

DedicatedResources

JSON representation
{ "machineSpec": { object (MachineSpec) }, "minReplicaCount": integer, "maxReplicaCount": integer, "requiredReplicaCount": integer, "initialReplicaCount": integer, "autoscalingMetricSpecs": [ { object (AutoscalingMetricSpec) } ], "spot": boolean, "flexStart": { object (FlexStart) }, "scaleToZeroSpec": { object (ScaleToZeroSpec) } }

Fields
machineSpec	object (MachineSpec) Required. Immutable. The specification of a single machine being used.
minReplicaCount	integer Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
maxReplicaCount	integer 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, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
requiredReplicaCount	integer Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
initialReplicaCount	integer Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count.
autoscalingMetricSpecs[]	object (AutoscalingMetricSpec) Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
spot	boolean Optional. If true, schedule the deployment workload on spot VMs.
flexStart	object (FlexStart) Optional. Immutable. If set, use DWS resource to schedule the deployment workload. reference: (https://cloud.google.com/blog/products/compute/introducing-dynamic-workload-scheduler)
scaleToZeroSpec	object (ScaleToZeroSpec) Optional. Specification for scale-to-zero feature.

MachineSpec

JSON representation
{ "machineType": string, "acceleratorType": enum (AcceleratorType), "acceleratorCount": integer, "gpuPartitionSize": string, "tpuTopology": string, "multihostGpuNodeCount": integer, "reservationAffinity": { object (ReservationAffinity) }, "minGpuDriverVersion": string }

Fields
machineType	string Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
acceleratorType	enum (AcceleratorType) Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
acceleratorCount	integer The number of accelerators to attach to the machine. For accelerator optimized machine types (https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Vertex will co-schedule the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Vertex will co-schedule 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
gpuPartitionSize	string Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to Nvidia GPU Partitioning for the available partition sizes. If set, the accelerator_count should be set to 1.
tpuTopology	string Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
multihostGpuNodeCount	integer Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
reservationAffinity	object (ReservationAffinity) Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
minGpuDriverVersion	string Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure.

ReservationAffinity

JSON representation
{ "reservationAffinityType": enum (Type), "key": string, "values": [ string ] }

Fields
reservationAffinityType	enum (Type) Required. Specifies the reservation affinity type.
key	string Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.
values[]	string Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.

AutoscalingMetricSpec

JSON representation
{ "metricName": string, "target": integer, "monitoredResourceLabels": { string: string, ... } }

Fields
metricName	string 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
target	integer 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.
monitoredResourceLabels	map (key: string, value: string) Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.

MonitoredResourceLabelsEntry

JSON representation
{ "key": string, "value": string }

Fields
key	string
value	string

FlexStart

JSON representation
{ "maxRuntimeDuration": string }

Fields
maxRuntimeDuration	string (Duration format) The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days. A duration in seconds with up to nine fractional digits, ending with 's'. Example: "3.5s".

Duration

JSON representation
{ "seconds": string, "nanos": integer }

Fields
seconds	string (int64 format) Signed seconds of the span of time. Must be from -315,576,000,000 to +315,576,000,000 inclusive. Note: these bounds are computed from: 60 sec/min * 60 min/hr * 24 hr/day * 365.25 days/year * 10000 years
nanos	integer Signed fractions of a second at nanosecond resolution of the span of time. Durations less than one second are represented with a 0 seconds field and a positive or negative nanos field. For durations of one second or more, a non-zero value for the nanos field must be of the same sign as the seconds field. Must be from -999,999,999 to +999,999,999 inclusive.

ScaleToZeroSpec

JSON representation
{ "minScaleupPeriod": string, "idleScaledownPeriod": string }

Fields
minScaleupPeriod	string (Duration format) Optional. Minimum duration that a deployment will be scaled up before traffic is evaluated for potential scale-down. [MinValue=300] (5 minutes) [MaxValue=28800] (8 hours) A duration in seconds with up to nine fractional digits, ending with 's'. Example: "3.5s".
idleScaledownPeriod	string (Duration format) Optional. Duration of no traffic before scaling to zero. [MinValue=300] (5 minutes) [MaxValue=28800] (8 hours) A duration in seconds with up to nine fractional digits, ending with 's'. Example: "3.5s".

AutomaticResources

JSON representation
{ "minReplicaCount": integer, "maxReplicaCount": integer }

Fields
minReplicaCount	integer 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.
maxReplicaCount	integer 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.

FullFineTunedResources

JSON representation
{ "deploymentType": enum (DeploymentType), "modelInferenceUnitCount": integer }

Fields
deploymentType	enum (DeploymentType) Required. The kind of deployment.
modelInferenceUnitCount	integer Optional. The number of model inference units to use for this deployment. This can only be specified for DEPLOYMENT_TYPE_PROD. The following table lists the number of model inference units for different model types: * Gemini 2.5 Flash * Foundation FMIU: 25 * Expansion FMIU: 4 * Gemini 2.5 Pro * Foundation FMIU: 32 * Expansion FMIU: 16 * Veo 3.0 (undistilled) * Foundation FMIU: 63 * Expansion FMIU: 7 * Veo 3.0 (distilled) * Foundation FMIU: 30 * Expansion FMIU: 10

Timestamp

JSON representation
{ "seconds": string, "nanos": integer }

Fields
seconds	string (int64 format) Represents seconds of UTC time since Unix epoch 1970-01-01T00:00:00Z. Must be between -62135596800 and 253402300799 inclusive (which corresponds to 0001-01-01T00:00:00Z to 9999-12-31T23:59:59Z).
nanos	integer Non-negative fractions of a second at nanosecond resolution. This field is the nanosecond portion of the duration, not an alternative to seconds. Negative second values with fractions must still have non-negative nanos values that count forward in time. Must be between 0 and 999,999,999 inclusive.

ExplanationSpec

JSON representation
{ "parameters": { object (ExplanationParameters) }, "metadata": { object (ExplanationMetadata) } }

Fields
parameters	object (ExplanationParameters) Required. Parameters that configure explaining of the Model's predictions.
metadata	object (ExplanationMetadata) Optional. Metadata describing the Model's input and output for explanation.

ExplanationParameters

JSON representation
{ "topK": integer, "outputIndices": array, // Union field method can be only one of the following: "sampledShapleyAttribution": { object (SampledShapleyAttribution) }, "integratedGradientsAttribution": { object (IntegratedGradientsAttribution) }, "xraiAttribution": { object (XraiAttribution) }, "examples": { object (Examples) } // End of list of possible types for union field method. }

Fields
topK	integer If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
outputIndices	array (ListValue format) If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
Union field method. method can be only one of the following:
sampledShapleyAttribution	object (SampledShapleyAttribution) An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
integratedGradientsAttribution	object (IntegratedGradientsAttribution) An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
xraiAttribution	object (XraiAttribution) An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples	object (Examples) Example-based explanations that returns the nearest neighbors from the provided dataset.

SampledShapleyAttribution

JSON representation
{ "pathCount": integer }

Fields
pathCount	integer Required. The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

IntegratedGradientsAttribution

JSON representation
{ "stepCount": integer, "smoothGradConfig": { object (SmoothGradConfig) }, "blurBaselineConfig": { object (BlurBaselineConfig) } }

Fields
stepCount	integer Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
smoothGradConfig	object (SmoothGradConfig) Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
blurBaselineConfig	object (BlurBaselineConfig) Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

SmoothGradConfig

JSON representation
{ "noisySampleCount": integer, // Union field GradientNoiseSigma can be only one of the following: "noiseSigma": number, "featureNoiseSigma": { object (FeatureNoiseSigma) } // End of list of possible types for union field GradientNoiseSigma. }

Fields
noisySampleCount	integer The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
Union field GradientNoiseSigma. Represents the standard deviation of the gaussian kernel that will be used to add noise to the interpolated inputs prior to computing gradients. GradientNoiseSigma can be only one of the following:
noiseSigma	number This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
featureNoiseSigma	object (FeatureNoiseSigma) This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.

FeatureNoiseSigma

JSON representation
{ "noiseSigma": [ { object (NoiseSigmaForFeature) } ] }

Fields
noiseSigma[]	object (NoiseSigmaForFeature) Noise sigma per feature. No noise is added to features that are not set.

NoiseSigmaForFeature

JSON representation
{ "name": string, "sigma": number }

Fields
name	string The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma	number This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.

BlurBaselineConfig

JSON representation
{ "maxBlurSigma": number }

Fields
maxBlurSigma	number The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.

XraiAttribution

JSON representation
{ "stepCount": integer, "smoothGradConfig": { object (SmoothGradConfig) }, "blurBaselineConfig": { object (BlurBaselineConfig) } }

Fields
stepCount	integer Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
smoothGradConfig	object (SmoothGradConfig) Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
blurBaselineConfig	object (BlurBaselineConfig) Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

Examples

JSON representation

{ "gcsSource": { object (GcsSource) }, "neighborCount": integer, // Union field source can be only one of the following: "exampleGcsSource": { object (ExampleGcsSource) } // End of list of possible types for union field source. // Union field config can be only one of the following: "nearestNeighborSearchConfig": value, "presets": { object (Presets) } // End of list of possible types for union field config. }

Fields
gcsSource	object (GcsSource) The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
neighborCount	integer The number of neighbors to return when querying for examples.
Union field source. source can be only one of the following:
exampleGcsSource	object (ExampleGcsSource) The Cloud Storage input instances.
Union field config. config can be only one of the following:
nearestNeighborSearchConfig	value (Value format) The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
presets	object (Presets) Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

ExampleGcsSource

JSON representation
{ "dataFormat": enum (DataFormat), "gcsSource": { object (GcsSource) } }

Fields
dataFormat	enum (DataFormat) The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource	object (GcsSource) The Cloud Storage location for the input instances.

GcsSource

JSON representation
{ "uris": [ string ] }

Fields
uris[]	string Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.

Value

JSON representation
{ // Union field kind can be only one of the following: "nullValue": null, "numberValue": number, "stringValue": string, "boolValue": boolean, "structValue": { object }, "listValue": array // End of list of possible types for union field kind. }

Fields
Union field kind. The kind of value. kind can be only one of the following:
nullValue	null Represents a null value.
numberValue	number Represents a double value.
stringValue	string Represents a string value.
boolValue	boolean Represents a boolean value.
structValue	object (Struct format) Represents a structured value.
listValue	array (ListValue format) Represents a repeated Value.

Struct

JSON representation
{ "fields": { string: value, ... } }

Fields
fields	map (key: string, value: value (Value format)) Unordered map of dynamically typed values. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.

FieldsEntry

JSON representation
{ "key": string, "value": value }

Fields
key	string
value	value (Value format)

ListValue

JSON representation
{ "values": [ value ] }

Fields
values[]	value (Value format) Repeated field of dynamically typed values.

Presets

JSON representation
{ "modality": enum (Modality), // Union field _query can be only one of the following: "query": enum (Query) // End of list of possible types for union field _query. }

Fields
modality	enum (Modality) The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
Union field _query. _query can be only one of the following:
query	enum (Query) Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

ExplanationMetadata

JSON representation
{ "inputs": { string: { object (InputMetadata) }, ... }, "outputs": { string: { object (OutputMetadata) }, ... }, "featureAttributionsSchemaUri": string, "latentSpaceSource": string }

Fields
inputs	map (key: string, value: object (InputMetadata)) Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
outputs	map (key: string, value: object (OutputMetadata)) Required. Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed. An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
featureAttributionsSchemaUri	string Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
latentSpaceSource	string Name of the source to generate embeddings for example based explanations.

InputsEntry

JSON representation
{ "key": string, "value": { object (InputMetadata) } }

Fields
key	string
value	object (InputMetadata)

InputMetadata

JSON representation
{ "inputBaselines": [ value ], "inputTensorName": string, "encoding": enum (Encoding), "modality": string, "featureValueDomain": { object (FeatureValueDomain) }, "indicesTensorName": string, "denseShapeTensorName": string, "indexFeatureMapping": [ string ], "encodedTensorName": string, "encodedBaselines": [ value ], "visualization": { object (Visualization) }, "groupName": string }

Fields
inputBaselines[]	value (Value format) Baseline inputs for this feature. If no baseline is specified, Vertex AI chooses the baseline for this feature. If multiple baselines are specified, Vertex AI returns the average attributions across them in Attribution.feature_attributions. For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor. For custom images, the element of the baselines must be in the same format as the feature's input in the instance[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.
inputTensorName	string Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
encoding	enum (Encoding) Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
modality	string Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
featureValueDomain	object (FeatureValueDomain) The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
indicesTensorName	string Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
denseShapeTensorName	string Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
indexFeatureMapping[]	string A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
encodedTensorName	string Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
encodedBaselines[]	value (Value format) A list of baselines for the encoded tensor. The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.
visualization	object (Visualization) Visualization configurations for image explanation.
groupName	string Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions, keyed by the group name.

FeatureValueDomain

JSON representation
{ "minValue": number, "maxValue": number, "originalMean": number, "originalStddev": number }

Fields
minValue	number The minimum permissible value for this feature.
maxValue	number The maximum permissible value for this feature.
originalMean	number If this input feature has been normalized to a mean value of 0, the original_mean specifies the mean value of the domain prior to normalization.
originalStddev	number If this input feature has been normalized to a standard deviation of 1.0, the original_stddev specifies the standard deviation of the domain prior to normalization.

Visualization

JSON representation
{ "type": enum (Type), "polarity": enum (Polarity), "colorMap": enum (ColorMap), "clipPercentUpperbound": number, "clipPercentLowerbound": number, "overlayType": enum (OverlayType) }

Fields
type	enum (Type) Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES.
polarity	enum (Polarity) Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.
colorMap	enum (ColorMap) The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue.
clipPercentUpperbound	number Excludes attributions above the specified percentile from the highlighted areas. Using the clip_percent_upperbound and clip_percent_lowerbound together can be useful for filtering out noise and making it easier to see areas of strong attribution. Defaults to 99.9.
clipPercentLowerbound	number Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.
overlayType	enum (OverlayType) How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE.

OutputsEntry

JSON representation
{ "key": string, "value": { object (OutputMetadata) } }

Fields
key	string
value	object (OutputMetadata)

OutputMetadata

JSON representation
{ "outputTensorName": string, // Union field display_name_mapping can be only one of the following: "indexDisplayNameMapping": value, "displayNameMappingKey": string // End of list of possible types for union field display_name_mapping. }

Fields
outputTensorName	string Name of the output tensor. Required and is only applicable to Vertex AI provided images for Tensorflow.
Union field display_name_mapping. Defines how to map Attribution.output_index to Attribution.output_display_name. If neither of the fields are specified, Attribution.output_display_name will not be populated. display_name_mapping can be only one of the following:
indexDisplayNameMapping	value (Value format) Static mapping between the index and display name. Use this if the outputs are a deterministic n-dimensional array, e.g. a list of scores of all the classes in a pre-defined order for a multi-classification Model. It's not feasible if the outputs are non-deterministic, e.g. the Model produces top-k classes or sort the outputs by their values. The shape of the value must be an n-dimensional array of strings. The number of dimensions must match that of the outputs to be explained. The Attribution.output_display_name is populated by locating in the mapping with Attribution.output_index.
displayNameMappingKey	string Specify a field name in the prediction to look for the display name. Use this if the prediction contains the display names for the outputs. The display names in the prediction must have the same shape of the outputs, so that it can be located by Attribution.output_index for a specific output.

PrivateEndpoints

JSON representation
{ "predictHttpUri": string, "explainHttpUri": string, "healthHttpUri": string, "serviceAttachment": string }

Fields
predictHttpUri	string Output only. Http(s) path to send prediction requests.
explainHttpUri	string Output only. Http(s) path to send explain requests.
healthHttpUri	string Output only. Http(s) path to send health check requests.
serviceAttachment	string Output only. The name of the service attachment resource. Populated if private service connect is enabled.

FasterDeploymentConfig

JSON representation
{ "fastTryoutEnabled": boolean }

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

RolloutOptions

JSON representation

{ "previousDeployedModel": string, "revisionNumber": integer, // Union field max_unavailable can be only one of the following: "maxUnavailableReplicas": integer, "maxUnavailablePercentage": integer // End of list of possible types for union field max_unavailable. // Union field max_surge can be only one of the following: "maxSurgeReplicas": integer, "maxSurgePercentage": integer // End of list of possible types for union field max_surge. }

Fields
previousDeployedModel	string ID of the DeployedModel that this deployment should replace.
revisionNumber	integer Output only. Read-only. Revision number determines the relative priority of DeployedModels in the same rollout. The DeployedModel with the largest revision number specifies the intended state of the deployment.
Union field max_unavailable. Configures how many replicas are allowed to be unavailable during a rolling deployment. max_unavailable can be only one of the following:
maxUnavailableReplicas	integer Absolute count of replicas allowed to be unavailable.
maxUnavailablePercentage	integer Percentage of replicas allowed to be unavailable. For autoscaling deployments, this refers to the target replica count.
Union field max_surge. Configures how many additional replicas can be provisioned during a rolling deployment. max_surge can be only one of the following:
maxSurgeReplicas	integer Absolute count of allowed additional replicas.
maxSurgePercentage	integer Percentage of allowed additional replicas. For autoscaling deployments, this refers to the target replica count.

Status

JSON representation
{ "message": string, "lastUpdateTime": string, "availableReplicaCount": integer }

Fields
message	string Output only. The latest deployed model's status message (if any).
lastUpdateTime	string (Timestamp format) 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".
availableReplicaCount	integer Output only. The number of available replicas of the deployed model.

SystemLabelsEntry

JSON representation
{ "key": string, "value": string }

Fields
key	string
value	string

SpeculativeDecodingSpec

JSON representation
{ "speculativeTokenCount": integer, // Union field speculation can be only one of the following: "draftModelSpeculation": { object (DraftModelSpeculation) }, "ngramSpeculation": { object (NgramSpeculation) } // End of list of possible types for union field speculation. }

Fields
speculativeTokenCount	integer The number of speculative tokens to generate at each step.
Union field speculation. The type of speculation method to use. speculation can be only one of the following:
draftModelSpeculation	object (DraftModelSpeculation) draft model speculation.
ngramSpeculation	object (NgramSpeculation) N-Gram speculation.

DraftModelSpeculation

JSON representation
{ "draftModel": string }

Fields
draftModel	string Required. The resource name of the draft model.

NgramSpeculation

JSON representation
{ "ngramSize": integer }

Fields
ngramSize	integer 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.

TrafficSplitEntry

JSON representation
{ "key": string, "value": integer }

Fields
key	string
value	integer

LabelsEntry

JSON representation
{ "key": string, "value": string }

Fields
key	string
value	string

EncryptionSpec

JSON representation
{ "kmsKeyName": string }

Fields
kmsKeyName	string Required. Resource name of the Cloud KMS key used to protect the resource. The Cloud KMS key must be in the same region as the resource. It must have the format projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}.

PrivateServiceConnectConfig

JSON representation
{ "enablePrivateServiceConnect": boolean, "projectAllowlist": [ string ], "pscAutomationConfigs": [ { object (PSCAutomationConfig) } ], "enableSecurePrivateServiceConnect": boolean, "serviceAttachment": string }

Fields
enablePrivateServiceConnect	boolean Required. If true, expose the IndexEndpoint via private service connect.
projectAllowlist[]	string A list of Projects from which the forwarding rule will target the service attachment.
pscAutomationConfigs[]	object (PSCAutomationConfig) Optional. List of projects and networks where the PSC endpoints will be created. This field is used by Online Inference(Prediction) only.
enableSecurePrivateServiceConnect	boolean Optional. If set to true, enable secure private service connect with IAM authorization. Otherwise, private service connect will be done without authorization. Note latency will be slightly increased if authorization is enabled.
serviceAttachment	string Output only. The name of the generated service attachment resource. This is only populated if the endpoint is deployed with PrivateServiceConnect.

PSCAutomationConfig

JSON representation
{ "projectId": string, "network": string, "ipAddress": string, "forwardingRule": string, "state": enum (PSCAutomationState), "errorMessage": string }

Fields
projectId	string Required. Project id used to create forwarding rule.
network	string Required. The full name of the Google Compute Engine network. Format: projects/{project}/global/networks/{network}.
ipAddress	string Output only. IP address rule created by the PSC service automation.
forwardingRule	string Output only. Forwarding rule created by the PSC service automation.
state	enum (PSCAutomationState) Output only. The state of the PSC service automation.
errorMessage	string Output only. Error message if the PSC service automation failed.

PredictRequestResponseLoggingConfig

JSON representation
{ "enabled": boolean, "samplingRate": number, "errorSamplingRate": number, "bigqueryDestination": { object (BigQueryDestination) }, "requestResponseLoggingSchemaVersion": string, "enableOtelLogging": boolean }

Fields
enabled	boolean If logging is enabled or not.
samplingRate	number Percentage of requests to be logged, expressed as a fraction in range(0,1].
errorSamplingRate	number Optional. Percentage of failed requests to be logged, expressed as a fraction in range [0,1]. Only non-transient errors will be logged (currently 500/Internal errors).
bigqueryDestination	object (BigQueryDestination) BigQuery table for logging. If only given a project, a new dataset will be created with name logging_<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 name request_response_logging
requestResponseLoggingSchemaVersion	string Output only. The schema version used in creating the BigQuery table for the request response logging. The versions are "v1" and "v2". The current default version is "v1".
enableOtelLogging	boolean This field is used for large models. If true, in addition to the original large model logs, logs will be converted in OTel schema format, and saved in otel_log column. Default value is false.

BigQueryDestination

JSON representation
{ "outputUri": string }

Fields
outputUri	string Required. BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.

ClientConnectionConfig

JSON representation
{ "inferenceTimeout": string }

Fields
inferenceTimeout	string (Duration format) Customizable online prediction request timeout. A duration in seconds with up to nine fractional digits, ending with 's'. Example: "3.5s".

GenAiAdvancedFeaturesConfig

JSON representation
{ "ragConfig": { object (RagConfig) } }

Fields
ragConfig	object (RagConfig) Configuration for Retrieval Augmented Generation feature.

RagConfig

JSON representation
{ "enableRag": boolean }

Fields
enableRag	boolean 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.

Tool Annotations

Destructive Hint: ❌ | Idempotent Hint: ❌ | Read Only Hint: ✅ | Open World Hint: ❌