Get Endpoint

vertex_ai.get_endpoint
ActionGoogle Cloudconnection required

Calls aiplatform v1 GET /v1/projects/{projectsId}/locations/{locationsId}/endpoints/{endpointsId}.

Gets an Endpoint.

Connection

Required
Yes — the node will not save without one
Auth type
OAuth 2.0
Providers
Google Vertex AI
Secret shape
{"client_id", "client_secret", "refresh_token"}
Config it needs
project_id, region
Resolved by
config.connection_id, else node.connection_id

Configuration

2 fields, 1 required. A dotted path is a nested object and name[] an array of objects — the editor renders the same tree.

FieldTypeRequiredDefaultConstraintsExample
location
Location or region; taken from the connection when unset.
stringNo———
endpoint
Endpoint name or id.
stringYes———

Output

One shape.

result

Extra keys beyond the ones listed can appear.

FieldTypeExample
clientConnectionConfig
object—
createTime
string—
dedicatedEndpointDns
string—
dedicatedEndpointEnabled
boolean—
deployedModels
array—
description
string—
displayName
string—
enablePrivateServiceConnect
boolean—
encryptionSpec
object—
etag
string—
gdcConfig
object—
genAiAdvancedFeaturesConfig
object—
labels
object—
modelDeploymentMonitoringJob
string—
name
string—
network
string—
predictRequestResponseLoggingConfig
object—
privateServiceConnectConfig
object—
satisfiesPzi
boolean—
satisfiesPzs
boolean—
trafficSplit
object—
updateTime
string—
{
  "clientConnectionConfig": {},
  "createTime": "",
  "dedicatedEndpointDns": "",
  "dedicatedEndpointEnabled": false,
  "deployedModels": [],
  "description": "",
  "displayName": "",
  "enablePrivateServiceConnect": false,
  "encryptionSpec": {},
  "etag": "",
  "gdcConfig": {},
  "genAiAdvancedFeaturesConfig": {},
  "labels": {},
  "modelDeploymentMonitoringJob": "",
  "name": "",
  "network": "",
  "predictRequestResponseLoggingConfig": {},
  "privateServiceConnectConfig": {},
  "satisfiesPzi": false,
  "satisfiesPzs": false,
  "trafficSplit": {},
  "updateTime": ""
}

See Get Endpoint run in a workflow.

In a technical demo we add the Google Vertex AI connection, drop the node on the canvas and let Workflow Health pre-flight it before the first real run.