OpenMetadata/openmetadata-docs/content/v1.8.x-SNAPSHOT/connectors/ml-model/vertexai/yaml.md

title	slug	collate
Run the VertexAI Connector Externally	/connectors/ml-model/vertexai/yaml	true

{% connectorDetailsHeader name="VertexAI" stage="BETA" platform="Collate" availableFeatures=["ML Store", "ML Features", "Hyper parameters"] unavailableFeatures=[] / %}

In this section, we provide guides and references to use the VertexAI connector.

Configure and schedule VertexAI metadata from the OpenMetadata UI:

• Requirements
• Metadata Ingestion

{% partial file="/v1.8/connectors/external-ingestion-deployment.md" /%}

Requirements

Python Requirements

{% partial file="/v1.8/connectors/python-requirements.md" /%}

To run the VertexAI ingestion, you will need to install:

pip3 install "openmetadata-ingestion[vertexai]"

GCP Permissions

To execute metadata extraction workflow successfully the user or the service account should have enough access to fetch required data. Following table describes the minimum required permissions

{% multiTablesWrapper %}

#	GCP Permission	Required For
1	aiplatform.models.get	Metadata Ingestion
2	aiplatform.models.list	Metadata Ingestion

{% /multiTablesWrapper %}

Metadata Ingestion

1. Define the YAML Config

This is a sample config for VertexAI:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

credentials: You can authenticate with your vertexai instance using either GCP Credentials Path where you can specify the file path of the service account key or you can pass the values directly by choosing the GCP Credentials Values from the service account key file.

You can checkout this documentation on how to create the service account keys and download it.

gcpConfig:

1. Passing the raw credential values provided by VertexAI. This requires us to provide the following information, all provided by VertexAI:

{% /codeInfo %}

{% partial file="/v1.8/connectors/yaml/common/gcp-config.md" /%}

{% codeInfo srNumber=4 %}

2. Passing a local file path that contains the credentials:

• gcpCredentialsPath

Location: Location refers to the geographical region where your resources, such as datasets, models, and endpoints, are physically hosted.(e.g. us-central1, europe-west4)

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

source:
  type: vertexai
  serviceName: localvx
  serviceConnection:
    config:
        type: VertexAI

        credentials:
            gcpConfig:

{% partial file="/v1.8/connectors/yaml/common/gcp-config.md" /%}

        location: PROJECT LOCATION/REGION (us-central1)

        # connectionOptions:
        #   key: value

        # connectionArguments:
        #   key: value

{% partial file="/v1.8/connectors/yaml/database/source-config.md" /%}

{% partial file="/v1.8/connectors/yaml/ingestion-sink.md" /%}

{% partial file="/v1.8/connectors/yaml/workflow-config.md" /%}

{% /codeBlock %}

{% /codePreview %}

{% partial file="/v1.8/connectors/yaml/ingestion-cli.md" /%}