MCP Tools Reference: aiplatform.googleapis.com

string

Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.

string

User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}) . The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

string ( Timestamp format)

Output only. Timestamp when this version 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" .

string ( Timestamp format)

Output only. Timestamp when this version was most recently 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" .

string

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

string

The description of the Model.

string

The description of this version.

string

The default checkpoint id of a model version.

object ( PredictSchemata )

The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain .

string

Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object . AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably 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.

value ( Value format)

Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema . Unset if the Model does not have any additional information.

object ( ExportFormat )

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

string

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

string

Optional. This field is populated if the model is produced by a pipeline job.

object ( ModelContainerSpec )

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel , and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

string

Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.

enum ( DeploymentResourcesType )

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions ( PredictionService.Predict or PredictionService.Explain ). Such a Model can serve predictions by using a BatchPredictionJob , if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats .

string

Output only. The formats this Model supports in BatchPredictionJob.input_config . If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema.

The possible formats are:

• jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource .

• csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource .

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource .

• tf-record-gzip Similar to tf-record , but the file is gzipped. Uses GcsSource .

• bigquery Each instance is a single row in BigQuery. Uses BigQuerySource .

• file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object.

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob . However, if it has supported_deployment_resources_types , it could serve online predictions by using PredictionService.Predict or PredictionService.Explain .

string

Output only. The formats this Model supports in BatchPredictionJob.output_config . If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema).

The possible formats are:

• jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination .

• csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination .

• bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination .

If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob . However, if it has supported_deployment_resources_types , it could serve online predictions by using PredictionService.Predict or PredictionService.Explain .

string ( Timestamp format)

Output only. Timestamp when this Model was uploaded into Vertex AI.

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" .

string ( Timestamp format)

Output only. Timestamp when this Model was most recently 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" .

object ( DeployedModelRef )

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

object ( ExplanationSpec )

The default explanation specification for this Model.

The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated.

All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model , or explanation_spec of BatchPredictionJob .

If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob .

string

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

map (key: string, value: string)

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

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" } .

object ( DataStats )

Stats of data used for training or evaluating the Model.

Only populated when the Model is trained by a TrainingPipeline with data_input_config .

object ( EncryptionSpec )

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

object ( ModelSourceInfo )

Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.

object ( OriginalModelInfo )

Output only. If this Model is a copy of another Model, this contains info about the original.

string

Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact} .

object ( BaseModelSource )

Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

boolean

Output only. Reserved for future use.

boolean

Output only. Reserved for future use.

object ( Checkpoint )

Optional. Output only. The checkpoints of the model.

number

Represents a JSON number. Must not be NaN , Infinity or -Infinity , since those are not supported in JSON. This also cannot represent large Int64 values, since JSON format generally does not support them in its number type.

string

Represents a JSON string.

boolean

Represents a JSON boolean ( true or false literal in JSON).

object ( Struct format)

Represents a JSON object.

array ( ListValue format)

Represents a JSON array.

enum ( ExportableContent )

Output only. The content of this Model that may be exported.

string

Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT . Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT 's "exec" form, not its "shell" form.

If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD , if either exists. If this field is not specified and the container does not have an ENTRYPOINT , then refer to the Docker documentation about how CMD and ENTRYPOINT interact .

If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD .

In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax:

$( VARIABLE_NAME )

Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$ ; for example:

$$( VARIABLE_NAME )

This field corresponds to the command field of the Kubernetes Containers v1 core API .

string

Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD . Specify this field as an array of executable and arguments, similar to a Docker CMD 's "default parameters" form.

If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD .

If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact .

In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax:

$( VARIABLE_NAME )

Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$ ; for example:

$$( VARIABLE_NAME )

This field corresponds to the args field of the Kubernetes Containers v1 core API .

object ( EnvVar )

Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables.

Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar :

 [
  {
    "name": "VAR_1",
    "value": "foo"
  },
  {
    "name": "VAR_2",
    "value": "$(VAR_1) bar"
  }
] 

If you switch the order of the variables in the example, then the expansion does not occur.

This field corresponds to the env field of the Kubernetes Containers v1 core API .

object ( Port )

Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port.

If you do not specify this field, it defaults to following value:

 [
  {
    "containerPort": 8080
  }
] 

Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API .

string

Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response.

For example, if you set this field to /foo , then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec 's ports field.

If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint :

/v1/endpoints/ ENDPOINT /deployedModels/ DEPLOYED_MODEL :predict

The placeholders in this value are replaced as follows:

• ENDPOINT : The last segment (following endpoints/ )of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable .)

• DEPLOYED_MODEL : DeployedModel.id of the DeployedModel . (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable .)

string

Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks .

For example, if you set this field to /bar , then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec 's ports field.

If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint :

/v1/endpoints/ ENDPOINT /deployedModels/ DEPLOYED_MODEL :predict

The placeholders in this value are replaced as follows:

• ENDPOINT : The last segment (following endpoints/ )of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable .)

• DEPLOYED_MODEL : DeployedModel.id of the DeployedModel . (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable .)

string

Immutable. Invoke route prefix for the custom container. "/*" is the only supported value right now. By setting this field, any non-root route on this model will be accessible with invoke http call eg: "/invoke/foo/bar", however the [PredictionService.Invoke] RPC is not supported yet.

Only one of predict_route or invoke_route_prefix can be set, and we default to using predict_route if this field is not set. If this field is set, the Model can only be deployed to dedicated endpoint.

object ( Port )

Immutable. List of ports to expose from the container. Vertex AI sends gRPC prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port.

If you do not specify this field, gRPC requests to the container will be disabled.

Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.

string ( Duration format)

Immutable. Deployment timeout. Limit for deployment timeout is 2 hours.

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

string ( int64 format)

Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.

object ( Probe )

Immutable. Specification for Kubernetes startup probe.

object ( Probe )

Immutable. Specification for Kubernetes readiness probe.

object ( Probe )

Immutable. Specification for Kubernetes liveness probe.

integer

Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds.

Maps to Kubernetes probe argument 'timeoutSeconds'.

integer

Number of consecutive failures before the probe is considered failed. Defaults to 3. Minimum value is 1.

Maps to Kubernetes probe argument 'failureThreshold'.

integer

Number of consecutive successes before the probe is considered successful. Defaults to 1. Minimum value is 1.

Maps to Kubernetes probe argument 'successThreshold'.

integer

Number of seconds to wait before starting the probe. Defaults to 0. Minimum value is 0.

Maps to Kubernetes probe argument 'initialDelaySeconds'.

object ( ExecAction )

ExecAction probes the health of a container by executing a command.

object ( HttpGetAction )

HttpGetAction probes the health of a container by sending an HTTP GET request.

object ( GrpcAction )

GrpcAction probes the health of a container by sending a gRPC request.

object ( TcpSocketAction )

TcpSocketAction probes the health of a container by opening a TCP socket connection.

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).

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 .

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

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.

object ( Examples )

Example-based explanations that returns the nearest neighbors from the provided dataset.

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.

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.

object ( ExampleGcsSource )

The Cloud Storage input instances.

value ( Value format)

The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig .

object ( Presets )

Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

enum ( Query )

Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE .

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 .

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.

object ( GenieSource )

Information about the base model of Genie models.