### 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.
#### 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 check out the [service account keys documentation](https://cloud.google.com/iam/docs/keys-create-delete#iam-service-account-keys-create-console) to learn how to create and download service account keys.
* **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](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 (for example, `us-central1`, `europe-west4`).
### 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.
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