locations

Creates, updates, deletes, gets or lists a locations resource.

Overview

Name	locations
Type	Resource
Id	google.aiplatform.locations

Fields

The following fields are returned by SELECT queries:

get

Name	Datatype	Description
name	string	Resource name for the location, which may vary between implementations. For example: "projects/example-project/locations/us-east1"
displayName	string	The friendly name for this location, typically a nearby city name. For example, "Tokyo".
labels	object	Cross-service attributes for the location. For example {"cloud.googleapis.com/region": "us-east1"}
locationId	string	The canonical id for this location. For example: "us-east1".
metadata	object	Service-specific metadata. For example the available capacity at the given location.

list

Name	Datatype	Description
name	string	Resource name for the location, which may vary between implementations. For example: "projects/example-project/locations/us-east1"
displayName	string	The friendly name for this location, typically a nearby city name. For example, "Tokyo".
labels	object	Cross-service attributes for the location. For example {"cloud.googleapis.com/region": "us-east1"}
locationId	string	The canonical id for this location. For example: "us-east1".
metadata	object	Service-specific metadata. For example the available capacity at the given location.

Methods

The following methods are available for this resource:

Name	Accessible by	Required Params	Optional Params	Description
get	select	projectsId, locationsId		Gets information about a location.
list	select	projectsId	extraLocationTypes, filter, pageSize, pageToken	Lists information about the supported locations for this service. This method lists locations based on the resource scope provided in the ListLocationsRequest.name field: * Global locations: If name is empty, the method lists the public locations available to all projects. * Project-specific locations: If name follows the format projects/{project}, the method lists locations visible to that specific project. This includes public, private, or other project-specific locations enabled for the project. For gRPC and client library implementations, the resource name is passed as the name field. For direct service calls, the resource name is incorporated into the request path based on the specific service implementation and version.
ask_contexts	exec	projectsId, locationsId		Agentic Retrieval Ask API for RAG.
async_retrieve_contexts	exec	projectsId, locationsId		Asynchronous API to retrieves relevant contexts for a query.
augment_prompt	exec	projectsId, locationsId		Given an input prompt, it returns augmented prompt from vertex rag store to guide LLM towards generating grounded responses.
corroborate_content	exec	projectsId, locationsId		Given an input text, it returns a score that evaluates the factuality of the text. It also extracts and returns claims from the text and provides supporting facts.
deploy	exec	projectsId, locationsId		Deploys a model to a new endpoint.
evaluate_dataset	exec	projectsId, locationsId		Evaluates a dataset based on a set of given metrics.
evaluate_instances	exec	projectsId, locationsId		Evaluates instances based on a given metric.
generate_instance_rubrics	exec	projectsId, locationsId		Generates rubrics for a given prompt. A rubric represents a single testable criterion for evaluation. One input prompt could have multiple rubrics This RPC allows users to get suggested rubrics based on provided prompt, which can then be reviewed and used for subsequent evaluations.
generate_loss_clusters	exec	projectsId, locationsId		Generates loss clusters from evaluation results. This is a statelss API method that would not modify the EvaluationSet resource.
generate_synthetic_data	exec	projectsId, locationsId		Generates synthetic (artificial) data based on a description
generate_user_scenarios	exec	projectsId, locationsId		Generates user scenarios for agent evaluation.

Parameters

Parameters can be passed in the WHERE clause of a query. Check the Methods section to see which parameters are required or optional for each operation.

Name	Datatype	Description
locationsId	string
projectsId	string
extraLocationTypes	string
filter	string
pageSize	integer (int32)
pageToken	string

SELECT examples

get

Gets information about a location.

SELECT
name,
displayName,
labels,
locationId,
metadata
FROM google.aiplatform.locations
WHERE projectsId = '{{ projectsId }}' -- required
AND locationsId = '{{ locationsId }}' -- required
;

list

Lists information about the supported locations for this service. This method lists locations based on the resource scope provided in the ListLocationsRequest.name field: * Global locations: If name is empty, the method lists the public locations available to all projects. * Project-specific locations: If name follows the format projects/{project}, the method lists locations visible to that specific project. This includes public, private, or other project-specific locations enabled for the project. For gRPC and client library implementations, the resource name is passed as the name field. For direct service calls, the resource name is incorporated into the request path based on the specific service implementation and version.

SELECT
name,
displayName,
labels,
locationId,
metadata
FROM google.aiplatform.locations
WHERE projectsId = '{{ projectsId }}' -- required
AND extraLocationTypes = '{{ extraLocationTypes }}'
AND filter = '{{ filter }}'
AND pageSize = '{{ pageSize }}'
AND pageToken = '{{ pageToken }}'
;

Lifecycle Methods

ask_contexts

Agentic Retrieval Ask API for RAG.

EXEC google.aiplatform.locations.ask_contexts 
@projectsId='{{ projectsId }}' --required, 
@locationsId='{{ locationsId }}' --required 
@@json=
'{
"query": "{{ query }}", 
"tools": "{{ tools }}"
}'
;

async_retrieve_contexts

Asynchronous API to retrieves relevant contexts for a query.

EXEC google.aiplatform.locations.async_retrieve_contexts 
@projectsId='{{ projectsId }}' --required, 
@locationsId='{{ locationsId }}' --required 
@@json=
'{
"query": "{{ query }}", 
"tools": "{{ tools }}"
}'
;

augment_prompt

Given an input prompt, it returns augmented prompt from vertex rag store to guide LLM towards generating grounded responses.

EXEC google.aiplatform.locations.augment_prompt 
@projectsId='{{ projectsId }}' --required, 
@locationsId='{{ locationsId }}' --required 
@@json=
'{
"contents": "{{ contents }}", 
"model": "{{ model }}", 
"vertexRagStore": "{{ vertexRagStore }}"
}'
;

corroborate_content

Given an input text, it returns a score that evaluates the factuality of the text. It also extracts and returns claims from the text and provides supporting facts.

EXEC google.aiplatform.locations.corroborate_content 
@projectsId='{{ projectsId }}' --required, 
@locationsId='{{ locationsId }}' --required 
@@json=
'{
"content": "{{ content }}", 
"facts": "{{ facts }}", 
"parameters": "{{ parameters }}"
}'
;

deploy

Deploys a model to a new endpoint.

EXEC google.aiplatform.locations.deploy 
@projectsId='{{ projectsId }}' --required, 
@locationsId='{{ locationsId }}' --required 
@@json=
'{
"deployConfig": "{{ deployConfig }}", 
"endpointConfig": "{{ endpointConfig }}", 
"huggingFaceModelId": "{{ huggingFaceModelId }}", 
"modelConfig": "{{ modelConfig }}", 
"publisherModelName": "{{ publisherModelName }}"
}'
;

evaluate_dataset

Evaluates a dataset based on a set of given metrics.

EXEC google.aiplatform.locations.evaluate_dataset 
@projectsId='{{ projectsId }}' --required, 
@locationsId='{{ locationsId }}' --required 
@@json=
'{
"autoraterConfig": "{{ autoraterConfig }}", 
"dataset": "{{ dataset }}", 
"location": "{{ location }}", 
"metrics": "{{ metrics }}", 
"outputConfig": "{{ outputConfig }}"
}'
;

evaluate_instances

Evaluates instances based on a given metric.

EXEC google.aiplatform.locations.evaluate_instances 
@projectsId='{{ projectsId }}' --required, 
@locationsId='{{ locationsId }}' --required 
@@json=
'{
"autoraterConfig": "{{ autoraterConfig }}", 
"bleuInput": "{{ bleuInput }}", 
"coherenceInput": "{{ coherenceInput }}", 
"cometInput": "{{ cometInput }}", 
"exactMatchInput": "{{ exactMatchInput }}", 
"fluencyInput": "{{ fluencyInput }}", 
"fulfillmentInput": "{{ fulfillmentInput }}", 
"groundednessInput": "{{ groundednessInput }}", 
"instance": "{{ instance }}", 
"location": "{{ location }}", 
"metricSources": "{{ metricSources }}", 
"metrics": "{{ metrics }}", 
"metricxInput": "{{ metricxInput }}", 
"pairwiseMetricInput": "{{ pairwiseMetricInput }}", 
"pairwiseQuestionAnsweringQualityInput": "{{ pairwiseQuestionAnsweringQualityInput }}", 
"pairwiseSummarizationQualityInput": "{{ pairwiseSummarizationQualityInput }}", 
"pointwiseMetricInput": "{{ pointwiseMetricInput }}", 
"questionAnsweringCorrectnessInput": "{{ questionAnsweringCorrectnessInput }}", 
"questionAnsweringHelpfulnessInput": "{{ questionAnsweringHelpfulnessInput }}", 
"questionAnsweringQualityInput": "{{ questionAnsweringQualityInput }}", 
"questionAnsweringRelevanceInput": "{{ questionAnsweringRelevanceInput }}", 
"rougeInput": "{{ rougeInput }}", 
"rubricBasedInstructionFollowingInput": "{{ rubricBasedInstructionFollowingInput }}", 
"safetyInput": "{{ safetyInput }}", 
"summarizationHelpfulnessInput": "{{ summarizationHelpfulnessInput }}", 
"summarizationQualityInput": "{{ summarizationQualityInput }}", 
"summarizationVerbosityInput": "{{ summarizationVerbosityInput }}", 
"toolCallValidInput": "{{ toolCallValidInput }}", 
"toolNameMatchInput": "{{ toolNameMatchInput }}", 
"toolParameterKeyMatchInput": "{{ toolParameterKeyMatchInput }}", 
"toolParameterKvMatchInput": "{{ toolParameterKvMatchInput }}", 
"trajectoryAnyOrderMatchInput": "{{ trajectoryAnyOrderMatchInput }}", 
"trajectoryExactMatchInput": "{{ trajectoryExactMatchInput }}", 
"trajectoryInOrderMatchInput": "{{ trajectoryInOrderMatchInput }}", 
"trajectoryPrecisionInput": "{{ trajectoryPrecisionInput }}", 
"trajectoryRecallInput": "{{ trajectoryRecallInput }}", 
"trajectorySingleToolUseInput": "{{ trajectorySingleToolUseInput }}"
}'
;

generate_instance_rubrics

Generates rubrics for a given prompt. A rubric represents a single testable criterion for evaluation. One input prompt could have multiple rubrics This RPC allows users to get suggested rubrics based on provided prompt, which can then be reviewed and used for subsequent evaluations.

EXEC google.aiplatform.locations.generate_instance_rubrics 
@projectsId='{{ projectsId }}' --required, 
@locationsId='{{ locationsId }}' --required 
@@json=
'{
"agentConfig": "{{ agentConfig }}", 
"contents": "{{ contents }}", 
"location": "{{ location }}", 
"metricResourceName": "{{ metricResourceName }}", 
"predefinedRubricGenerationSpec": "{{ predefinedRubricGenerationSpec }}", 
"rubricGenerationSpec": "{{ rubricGenerationSpec }}"
}'
;

generate_loss_clusters

Generates loss clusters from evaluation results. This is a statelss API method that would not modify the EvaluationSet resource.

EXEC google.aiplatform.locations.generate_loss_clusters 
@projectsId='{{ projectsId }}' --required, 
@locationsId='{{ locationsId }}' --required 
@@json=
'{
"configs": "{{ configs }}", 
"evaluationSet": "{{ evaluationSet }}", 
"inlineResults": "{{ inlineResults }}"
}'
;

generate_synthetic_data

Generates synthetic (artificial) data based on a description

EXEC google.aiplatform.locations.generate_synthetic_data 
@projectsId='{{ projectsId }}' --required, 
@locationsId='{{ locationsId }}' --required 
@@json=
'{
"count": {{ count }}, 
"examples": "{{ examples }}", 
"outputFieldSpecs": "{{ outputFieldSpecs }}", 
"taskDescription": "{{ taskDescription }}"
}'
;

generate_user_scenarios

Generates user scenarios for agent evaluation.

EXEC google.aiplatform.locations.generate_user_scenarios 
@projectsId='{{ projectsId }}' --required, 
@locationsId='{{ locationsId }}' --required 
@@json=
'{
"agents": "{{ agents }}", 
"allowCrossRegionModel": {{ allowCrossRegionModel }}, 
"geminiAgentConfig": "{{ geminiAgentConfig }}", 
"rootAgentId": "{{ rootAgentId }}", 
"userScenarioGenerationConfig": "{{ userScenarioGenerationConfig }}"
}'
;