Vertexai Connector

This section provides guides and references to use the VertexAI connector. Configure and schedule VertexAI metadata workflows from the Collate UI:

• Requirements
• Metadata Ingestion
• Troubleshooting

​

Requirements

​

VertexAI API Permissions

• Go to Cloud VertexAI Library enable API
• Select the GCP Project ID.
• Click on Enable API which will enable the data catalog api on the respective project.

​

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

​

Metadata Ingestion

To ingest metadata from Vertex AI, you need to create a service connection. The service connects Vertex AI with Collate. Once you create a service, Collate automatically starts ingesting metadata.

​

Step 1: Add New Service

1. In the left navigation, click Connections.
2. On the Connections page, click Add New Service.

​

Step 2: Select a Service and Connector

From the service type dropdown, select Mlmodel Services, then click the Vertex AI connector tile.

​

Step 3: Add Service Name and Description

• Enter a unique, descriptive Service Name. Collate identifies services by their service name. Enter a name that distinguishes this deployment from other Vertex AI services you are ingesting metadata from.
• Optional: Enter a Description for the service.

​

Step 4: Configure Connection Options

Specify where ingestion runs, provide your source credentials, and verify the connection.

​

Select Ingestion Runner

Select an Ingestion Runner: the runner where the ingestion pipeline will execute.

​

Enter Connection Details

Enter the connection details for Vertex AI. The right-hand panel in the UI displays inline help for each field.

• GCP 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.
  • GCP Credentials Values: Passing the raw credential values provided by VertexAI. This requires us to provide the following information, all provided by VertexAI:
    • Credentials type: Credentials Type is the type of the account, for a service account the value of this field is service_account. To fetch this key, look for the value associated with the type key in the service account key file.
    • Project ID: A project ID is a unique string used to differentiate your project from all others in Google Cloud. To fetch this key, look for the value associated with the project_id key in the service account key file. You can also pass multiple project id to ingest metadata from different VertexAI projects into one service.
    • Private Key ID: This is a unique identifier for the private key associated with the service account. To fetch this key, look for the value associated with the private_key_id key in the service account file.
    • Private Key: This is the private key associated with the service account that is used to authenticate and authorize access to VertexAI. To fetch this key, look for the value associated with the private_key key in the service account file.
    • Client Email: This is the email address associated with the service account. To fetch this key, look for the value associated with the client_email key in the service account key file.
    • Client ID: This is a unique identifier for the service account. To fetch this key, look for the value associated with the client_id key in the service account key file.
    • Auth URI: This is the URI for the authorization server. To fetch this key, look for the value associated with the auth_uri key in the service account key file. The default value to Auth URI is https://accounts.google.com/o/oauth2/auth.
    • Token URI: The Google Cloud Token URI is a specific endpoint used to obtain an OAuth 2.0 access token from the Google Cloud IAM service. This token allows you to authenticate and access various Google Cloud resources and APIs that require authorization. To fetch this key, look for the value associated with the token_uri key in the service account credentials file. Default Value to Token URI is https://oauth2.googleapis.com/token.
    • Authentication Provider X509 Certificate URL: This is the URL of the certificate that verifies the authenticity of the authorization server. To fetch this key, look for the value associated with the auth_provider_x509_cert_url key in the service account key file. The Default value for Auth Provider X509Cert URL is https://www.googleapis.com/oauth2/v1/certs
    • Client X509Cert URL: This is the URL of the certificate that verifies the authenticity of the service account. To fetch this key, look for the value associated with the client_x509_cert_url key in the service account key file.
  • GCP Credentials Path: Passing a local file path that contains the credentials.
• 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)

​

Test Connection

Once the credentials have been added, click on Test Connection and Save the changes.

​

Step 5: Configure Ingestion Options

In the What to Ingest step, use filter patterns to control which assets Collate ingests from your ML model service. Filter patterns use regular expressions applied to model names.

​

How Filter Patterns Work

• Include: Add one or more comma-separated regular expressions. Collate ingests only assets whose names match at least one expression. Leave blank to include all assets.
• Exclude: Add one or more comma-separated regular expressions. Collate skips any asset whose name matches an expression. Leave blank to exclude nothing.

Rules match asset names using one of five expressions:

• contains: matches any name containing the value. For example, sales matches my_sales_data and sales_2024.
• starts with: matches names beginning with the value. For example, prod_ matches prod_db and prod_schema.
• ends with: matches names ending with the value. For example, _raw matches events_raw and logs_raw.
• is exactly: matches the exact name only. For example, analytics matches only analytics.
• matches regex: matches names using a regular expression. For example, ^prod_.*_v\d+$ matches prod_events_v1.

When both Include and Exclude are set, Exclude takes priority. Filter Options The ML Model section includes the following filter options:

• ML Model: Controls which ML models Collate ingests from the source.

Each section provides the following controls:

• Scan Mode: You can choose between the following scan modes:
  • Scan all: Ingests every asset of that type the connector can access. This is the default.
  • Only specific: Enables include rules so only assets matching at least one rule are ingested.
• Exclude system toggle: Use this toggle to automatically filter out system-reserved names defined by the connector — for example, Exclude system databases for the Databases section.
• Always exclude: Add permanent exclusion rules (shown in red). Assets matching these rules are never ingested, regardless of include rules.
• Preview: Shows a real-time summary of what will be in scope based on your current rules.
• Include rules (available only in Only specific mode): Click + Add to define a rule. Added rules appear as chips; an asset is included if it matches any rule.

​

Step 6: Create & Deploy

Click Create & Deploy to deploy the agent and start the first metadata ingestion run. Collate saves the service configuration and immediately begins pulling metadata from the source. To monitor ingestion progress or view the service you just added, go to Connections in the left navigation and select your service.

​

Configure Metadata Agent and Schedule Ingestion

The Metadata Agent extracts ML models, features, and other structural metadata from your source and keeps your Collate catalog in sync. It powers discovery, lineage, and governance across your data assets. When you click Create & Deploy, Collate automatically deploys a Metadata Agent for this service and triggers the first ingestion run. View its status and run history from the Agents tab on the service detail page. To configure the additional Metadata Agent and schedule ingestion, follow these steps:

1. In the left navigation, click Connections and select your service.
2. Click the Agents tab.
3. Click Add Agent and select Metadata from the dropdown. For some services, the dropdown is not available and clicking Add Agent takes you directly to the agent configuration page.
4. On the Configure Ingestion page, do the following and click Next.
   • Name this Ingestion: Enter a unique recognizable name for this ingestion pipeline.
   • Agent Setup: Configure the core parameters for this agent. The following fields are available:

     Field	Default	Description
     Db Service Prefixes	—	List of database service name prefixes used to resolve lineage between ML models and database assets. Accepted formats: DBService, DBService.Database, DBService.Database.Schema, DBService.Database.Schema.Table.
     Mark Deleted ML Models	On	Soft-delete ML models in Collate when they are removed from the source. Associated entities like lineage are also deleted.

     
   • Filter Patterns: Apply include or exclude rules to scope which ML models this agent ingests. These follow the same filter options described in Step 5.
   • Scope & Behaviour: Control what metadata to include and how to handle deletions. Toggle each option on or off based on your needs:

     Toggle	Default	Description
     Enable Debug Log	Off	Sets the ingestion log level to DEBUG. Useful for troubleshooting.
     Override Metadata	Off	When on, source values overwrite existing descriptions, tags, owners, and display names in Collate. When off, Collate only updates fields that have no existing value.
     Override Lineage	Off	When on, existing lineage is replaced with newly extracted lineage on each run.

     
5. On the Schedule Interval page, set when the agent runs:
   • Schedule: Choose a preset interval (Hourly, Daily, Weekly, Monthly) or enter a custom cron expression.
   • On-Demand: No automatic schedule; trigger the agent manually when needed.
   
6. Click Add to deploy the agent.

​

Troubleshooting