Create a feature view instance

You can create a feature view within an existing online store instance. While creating a feature view, you can associate features with it in the following ways:

• Add feature groups and features from Feature Registry: Associate with existing feature groups and features from the Feature Registry. A feature group specifies the location of the BigQuery data source. A feature within the feature group points to a specific feature column within that data source. You can associate a feature view with multiple feature groups.

• Add features from a BigQuery source: Directly associate a BigQuery data source, such as a BigQuery table or view, and specify at least one entity ID column.

After you create a feature view, Vertex AI Feature Store syncs the latest feature values from the BigQuery data source. If you set the query parameter run_sync_immediately=true, then Vertex AI Feature Store syncs the feature values when you create the feature view. Otherwise, Vertex AI Feature Store syncs the feature values according to the sync schedule specified for the feature view.

Before you begin

Authenticate to Vertex AI, unless you've done so already.

Select the tab for how you plan to use the samples on this page:

Console

When you use the Google Cloud console to access Google Cloud services and APIs, you don't need to set up authentication.

Python

To use the Python samples on this page in a local development environment, install and initialize the gcloud CLI, and then set up Application Default Credentials with your user credentials.

1. Install the Google Cloud CLI.

2. To initialize the gcloud CLI, run the following command:

   gcloud init

3. If you're using a local shell, then create local authentication credentials for your user account:

   gcloud auth application-default login

   You don't need to do this if you're using Cloud Shell.

For more information, see Set up authentication for a local development environment.

REST

To use the REST API samples on this page in a local development environment, you use the credentials you provide to the gcloud CLI.

Install the Google Cloud CLI, then initialize it by running the following command:

gcloud init

For more information, see Authenticate for using REST in the Google Cloud authentication documentation.

Sync feature data in a feature view

Vertex AI Feature Store can refresh or sync the feature values from the BigQuery data source to the feature view. You can specify the type of data sync for a feature view by using the FeatureView.sync_config parameter.

Vertex AI Feature Store supports the following types of data sync:

• Scheduled data sync: You can specify the schedule or frequency for the data sync. You can choose this scheduled data sync for a feature view, regardless of the online serving type specified for the online store instance.

  If your feature view is configured to use scheduled data sync, you can optionally skip the wait until the next scheduled sync operation, by manually initiating the data sync. For more information about manually triggering a data sync, see Sync feature data to online store.

• Continuous data sync: The feature data is refreshed whenever the feature data in the BigQuery data source is updated. You can choose this type of data sync for a feature view only if all of the following conditions are fulfilled:

  • The online store instance is configured for Bigtable online serving.

  • The feature view is associated with feature groups and feature resources.

  • The BigQuery data source is located in any of the following regions:

    • eu

    • us

    • us-central1

  Continuous data sync has the following limitations:

  • You can't update a feature view if you select continuous data sync for it.

  • Only new feature records are synced from the BigQuery data source. Continuous data sync doesn't sync feature records that you update or delete in BigQuery.

During online serving, if you want to serve only the latest feature values, including null values, you must use the following setup:

1. Register your feature data source by creating a feature group with the dense parameter set to true.

2. Choose Bigtable online serving when you create the online store instance.

3. Use the cron parameter to set up scheduled data sync when you create your feature views.

If you use any other configuration while setting up your feature data source and online serving, Vertex AI Feature Store serves only the latest non-null feature values. If the latest value of a feature is null, then Vertex AI Feature Store serves the most recent non-null historical value for the feature. If a non-null historical value isn't available, then Vertex AI Feature Store serves null as the feature value.

Optimize costs during scheduled data sync

A data sync operation might involve costs for BigQuery resource usage. Follow these guidelines to optimize these costs and improve performance during a data sync:

• Don't configure the sync schedule to run more frequently than the frequency at which the data is expected to change in the BigQuery source.

• Optimize the size of the feature data source in BigQuery. While creating the feature view, only include the data that you need for online serving.

• Avoid running complex aggregations in BigQuery. Run a SELECT * query on the table or view to estimate the volume and duration of data processing.

• While setting the scaling options for the online store, set max_node_count to a value that's high enough to cover high loads during a data sync.

• Schedule the sync for different feature views at different times within the same online store.

• If your BigQuery table contains extensive historical data, consider partitioning the table using timestamps and specify a time range for retrieving the feature data. This minimizes the retrieval of obsolete feature data during sync.

• Bigtable utilization increases during data syncs. For feature views created within online stores for Bigtable online serving, schedule sync jobs during off-peak times for best performance.

Configure the service account for a feature view

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Each feature view uses a service account to access the source data in BigQuery during sync. Vertex AI Feature Store assigns the BigQuery Data Viewer Identity and Access Management (IAM) role to this service account.

By default, a feature view uses the service account configured for your project. With this configuration, any user with permission to create a feature view in your project can access the feature data in BigQuery.

Alternatively, you can configure the feature view to use its own service account. Vertex AI Feature Store then sets up a dedicated service account for the feature view. With this configuration, you can restrict access to feature data in BigQuery or grant access to additional users. You can specify the service account configuration by using the FeatureView.service_agent_type parameter.

Configure vector retrieval for a feature view

You can configure vector retrieval for a feature view within an online store created for Optimized online serving by using the FeatureView.index_config parameter. For information about how to prepare or update the BigQuery data source to support embeddings by including the embedding column, see Data source preparation guidelines.

Note that you can configure vector retrieval and manage embeddings only if the feature view is created by specifying a BigQuery source URI and not from feature groups and features from Feature Registry.

For more information about how to search for approximate nearest neighbors using embeddings in Vertex AI Feature Store, see Search using embeddings.

Create a feature view from feature groups

You can create a feature view based on feature data registered using feature groups and features. To associate multiple BigQuery data sources with the same feature view, you can specify multiple feature groups.

If you create a feature view by specifying feature groups and features:

• Your data source must have a feature_timestamp column and can contain historical data.

• Vertex AI Feature Store serves only the latest feature values based on the feature timestamp.

• You can't configure embedding management for the feature view.

Create a feature view with the default service account configuration

Use the following sample to create a feature view by associating multiple feature groups without specifying a service account configuration.

REST

To create a FeatureView resource, send a POST request by using the featureViews.create method.

Before using any of the request data, make the following replacements:

• LOCATION_ID: Region where you want to create the feature view, such as us-central1.
• PROJECT_ID: Your project ID.
• FEATUREONLINESTORE_NAME: The name of the online store instance where you want to create the feature view.
• FEATUREVIEW_NAME: The name of the new feature view instance that you want to create.
• FEATUREGROUP_NAME_A and FEATUREGROUP_NAME_B: The names of the feature groups from which you want to add features to the feature view.
• FEATURE_ID_A1 and FEATURE_ID_A2: Feature IDs from the feature group FEATUREGROUP_NAME_A that you want to add to the feature view.
• FEATURE_ID_B1 and FEATURE_ID_B2: Feature IDs from the feature group FEATUREGROUP_NAME_B that you want to add to the feature view.
• SYNC_CONFIG: Enter one of the following sync configurations for the feature view:
  • To use scheduled data sync, provide the sync schedule in the following format:
    "cron": "cron_schedule_expression"
    Replace `cron_schedule_expression` with the cron schedule expression representing the frequency for syncing data to the feature view. For more information, see cron.
  • To use continuous data sync, enter the following:
    "continuous": true You can use continuous data sync only if the online store instance containing the feature view is configured for Bigtable online serving.

HTTP method and URL:

POST https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME

Request JSON body:

{
  "feature_registry_source": {
    "feature_groups": [
      {
        "feature_group_id": "FEATUREGROUP_NAME_A",
        "feature_ids": [ "FEATURE_ID_A1", "FEATURE_ID_A2" ]
      },
      {
        "feature_group_id": "FEATUREGROUP_NAME_B",
        "feature_ids": [ "FEATURE_ID_B1", "FEATURE_ID_B2" ]
      }
    ]
  },
  "sync_config": {
    SYNC_CONFIG
  }
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews/FEATUREVIEW_NAME/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateFeatureViewOperationMetadata",
    "genericMetadata": {
      "createTime": "2023-09-15T02:11:29.458820Z",
      "updateTime": "2023-09-15T02:11:29.458820Z"
    }
  }
}

Create a feature view by specifying a service account configuration

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Use the following sample to create a feature view from feature groups by specifying a service account configuration.

REST

To create a FeatureView resource, send a POST request by using the featureViews.create method.

Before using any of the request data, make the following replacements:

• LOCATION_ID: Region where you want to create the feature view, such as us-central1.
• PROJECT_ID: Your project ID.
• FEATUREONLINESTORE_NAME: The name of the online store instance where you want to create the feature view.
• FEATUREVIEW_NAME: The name of the new feature view instance that you want to create.
• FEATUREGROUP_NAME_A and FEATUREGROUP_NAME_B: The names of the feature groups from which you want to add features to the feature view.
• FEATURE_ID_A1 and FEATURE_ID_A2: Feature IDs from the feature group FEATUREGROUP_NAME_A that you want to add to the feature view.
• FEATURE_ID_B1 and FEATURE_ID_B2: Feature IDs from the feature group FEATUREGROUP_NAME_B that you want to add to the feature view.
• SYNC_CONFIG: Enter one of the following sync configurations for the feature view:
  • To use scheduled data sync, provide the sync schedule in the following format:
    "cron": "cron_schedule_expression"
    Replace `cron_schedule_expression` with the cron schedule expression representing the frequency for syncing data to the feature view. For more information, see cron.
  • To use continuous data sync, enter the following:
    "continuous": true You can use continuous data sync only if the online store instance containing the feature view is configured for Bigtable online serving.
• SERVICE_AGENT_TYPE: Optional: Service account configuration for the feature view. Supported service agent types include the following:
  • SERVICE_AGENT_TYPE_PROJECT: Use the project-level service account for the feature view. This is the default configuration.
  • SERVICE_AGENT_TYPE_FEATURE_VIEW: Set up and use a dedicated service account for the feature view.

HTTP method and URL:

POST https://LOCATION_ID-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME

Request JSON body:

{
  "feature_registry_source": {
    "feature_groups": [
      {
        "feature_group_id": "FEATUREGROUP_NAME_A",
        "feature_ids": [ "FEATURE_ID_A1", "FEATURE_ID_A2" ]
      },
      {
        "feature_group_id": "FEATUREGROUP_NAME_B",
        "feature_ids": [ "FEATURE_ID_B1", "FEATURE_ID_B2" ]
      }
    ]
  },
  "sync_config": {
    SYNC_CONFIG
  },
  "service_agent_type": "SERVICE_AGENT_TYPE"
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews/FEATUREVIEW_NAME/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1beta1.CreateFeatureViewOperationMetadata",
    "genericMetadata": {
      "createTime": "2023-09-15T02:11:29.458820Z",
      "updateTime": "2023-09-15T02:11:29.458820Z"
    }
  }
}

Create a feature view from a BigQuery source

If you want to serve features online without registering your BigQuery data source using feature groups and features, you can create a feature view by specifying the URI of the BigQuery data source.

If you create a feature view by specifying the data source:

• You can't include a feature_timestamp column in the BigQuery table or view.

• You can't include historical feature values in the data source. Every row must contain a unique entity ID.

Create a feature view that uses the default service account and doesn't support embeddings

Use the following samples to create a feature view that doesn't support embeddings, by directly associating a BigQuery data source and without specifying a service account configuration.

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

from google.cloud import aiplatform
from vertexai.resources.preview import feature_store
from typing import List


def create_feature_view_from_bq_source(
    project: str,
    location: str,
    existing_feature_online_store_id: str,
    feature_view_id: str,
    bq_table_uri: str,
    entity_id_columns: List[str],
):
    aiplatform.init(project=project, location=location)
    fos = feature_store.FeatureOnlineStore(existing_feature_online_store_id)
    fv = fos.create_feature_view(
        name=feature_view_id,
        source=feature_store.utils.FeatureViewBigQuerySource(
            uri=bq_table_uri, entity_id_columns=entity_id_columns
        ),
    )
    return fv

• project: Your project ID.
• location: Region where you want to create the feature view, such as us-central1.
• existing_feature_online_store_id: The name of the online store instance where you want to create the feature view.
• feature_view_id: The name of the new feature view instance that you want to create.
• bq_table_uri: URI of the BigQuery source table or view.
• entity_id_columns: The names of the columns containing the entity IDs. You can specify either one column or multiple columns.
  • To specify only one entity ID column, specify the column name in the following format:
    "entity_id_column_name".
  • To specify multiple entity ID columns, specify the column names in the following format:
    ["entity_id_column_1_name", "entity_id_column_2_name", ...].

REST

To create a FeatureView resource, send a POST request by using the featureViews.create method.

Before using any of the request data, make the following replacements:

• LOCATION_ID: Region where you want to create the feature view, such as us-central1.
• PROJECT_ID: Your project ID.
• FEATUREONLINESTORE_NAME: The name of the online store instance where you want to create the feature view.
• FEATUREVIEW_NAME: The name of the new feature view that you want to create.
• PROJECT_NAME: Your project name.
• DATASET_NAME: Your BigQuery dataset name.
• TABLE_NAME: The name of the table from your BigQuery dataset.
• ENTITY_ID_COLUMNS: The names of the column(s) containing the entity IDs. You can specify either one column or multiple columns.
  • To specify only one entity ID column, specify the column name in the following format:
    "entity_id_column_name"
  • To specify multiple entity ID columns, specify the column names in the following format:
    ["entity_id_column_1_name", "entity_id_column_2_name", ...]
• CRON: Cron schedule expression representing the frequency for syncing data to the feature view. For more information, see cron.

HTTP method and URL:

POST https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME

Request JSON body:

{
  "big_query_source": {
    "uri": "bq://PROJECT_NAME.DATASET_NAME.TABLE_NAME",
    "entity_id_columns": "ENTITY_ID_COLUMNS"
  },
  "sync_config": {
    "cron": "CRON"
  }
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews/FEATUREVIEW_NAME/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateFeatureViewOperationMetadata",
    "genericMetadata": {
      "createTime": "2023-09-15T02:11:29.458820Z",
      "updateTime": "2023-09-15T02:11:29.458820Z"
    }
  }
}

Create a feature view that uses the default service account and supports embeddings

Use the following samples to create a feature view with embedding support by directly associating a BigQuery data source and without specifying a service account configuration.

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

from google.cloud import aiplatform
from vertexai.resources.preview import feature_store
from typing import List


def create_feature_view_from_bq_source_with_embedding_management(
    project: str,
    location: str,
    existing_feature_online_store_id: str,
    feature_view_id: str,
    bq_table_uri: str,
    entity_id_columns: List[str],
    embedding_column: str,
    embedding_dimensions: int,
):
    aiplatform.init(project=project, location=location)

    fos = feature_store.FeatureOnlineStore(existing_feature_online_store_id)

    bigquery_source = feature_store.utils.FeatureViewBigQuerySource(
        uri=bq_table_uri,
        entity_id_columns=entity_id_columns,
    )
    index_config = feature_store.utils.IndexConfig(
        embedding_column=embedding_column,
        dimensions=embedding_dimensions,
        algorithm_config=feature_store.utils.TreeAhConfig(),
    )
    fv = fos.create_feature_view(
        name=feature_view_id,
        source=bigquery_source,
        index_config=index_config,
    )
    return fv

• project: Your project ID.
• location: Region where you want to create the feature view, such as us-central1.
• existing_feature_online_store_id: The name of the online store instance where you want to create the feature view.
• feature_view_id: The name of the new feature view instance that you want to create.
• bq_table_uri: URI of the BigQuery source table or view.
• entity_id_columns: The names of the columns containing the entity IDs. You can specify either one column or multiple columns.
  • To specify only one entity ID column, specify the column name in the following format:
    "entity_id_column_name".
  • To specify multiple entity ID columns, specify the column names in the following format:
    ["entity_id_column_1_name", "entity_id_column_2_name", ...].
• embedding_column: The name of the column containing the source data to create the index for vector search. This is required only if you want to manage embeddings with the feature view.
• embedding_dimensions: Optional. The size, expressed as number of dimensions, of an embedding in the embedding column.

REST

To create a FeatureView resource, send a POST request by using the featureViews.create method.

Before using any of the request data, make the following replacements:

• LOCATION_ID: Region where you want to create the feature view, such as us-central1.
• PROJECT_ID: Your project ID.
• FEATUREONLINESTORE_NAME: The name of the online store instance where you want to create the feature view.
• FEATUREVIEW_NAME: The name of the new feature view that you want to create.
• PROJECT_NAME: Your project name.
• DATASET_NAME: Your BigQuery dataset name.
• TABLE_NAME: The name of the table from your BigQuery dataset.
• ENTITY_ID_COLUMNS: The names of the column(s) containing the entity IDs. You can specify either one column or multiple columns.
  • To specify only one entity ID column, specify the column name in the following format:
    "entity_id_column_name"
  • To specify multiple entity ID columns, specify the column names in the following format:
    ["entity_id_column_1_name", "entity_id_column_2_name", ...]
• CRON: Cron schedule expression representing the frequency for syncing data to the feature view. For more information, see cron.
• EMBEDDING_COLUMN: The name of the column containing the source data to create the index for vector search. This is required only if you want to manage embeddings with the feature view.
• FILTER_COLUMN_1 and FILTER_COLUMN_2: Optional: The names of the columns used to filter the vector search results.
• CROWDING_COLUMN: Optional: The name of the column containing the crowding attributes.
• EMBEDDING_DIMENSION: Optional: The size, expressed as number of dimensions, of an embedding in the embedding column.

HTTP method and URL:

POST https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME

Request JSON body:

{
  "big_query_source": {
    "uri": "bq://PROJECT_NAME.DATASET_NAME.TABLE_NAME",
    "entity_id_columns": "ENTITY_ID_COLUMNS"
  },
  "sync_config": {
    "cron": "CRON"
  },
  "index_config": {
    "embedding_column": "EMBEDDING_COLUMN",
    "filter_columns": ["FILTER_COLUMN_1", "FILTER_COLUMN_2"],
    "crowding_column": "CROWDING_COLUMN",
    "embedding_dimension": EMBEDDING_DIMENSION
    "tree_ah_config": {}
  }
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews/FEATUREVIEW_NAME/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateFeatureViewOperationMetadata",
    "genericMetadata": {
      "createTime": "2023-09-15T02:11:29.458820Z",
      "updateTime": "2023-09-15T02:11:29.458820Z"
    }
  }
}

Create a feature view with embedding management by specifying a service account configuration

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Use the following sample to create a feature view with embedding support by directly associating a BigQuery data source and specifying a service account configuration.

REST

To create a FeatureView resource with embeddings support, send a POST request by using the featureViews.create method and specifying the FeatureView.index_config parameter. Note that you can use embedding management only if the online store is configured for Optimized online serving.

Before using any of the request data, make the following replacements:

• LOCATION_ID: Region where you want to create the feature view, such as us-central1.
• PROJECT_ID: Your project ID.
• FEATUREONLINESTORE_NAME: The name of the online store instance where you want to create the feature view.
• FEATUREVIEW_NAME: The name of the new feature view that you want to create.
• PROJECT_NAME: Your project name.
• DATASET_NAME: Your BigQuery dataset name.
• TABLE_NAME: The name of the table from your BigQuery dataset.
• ENTITY_ID_COLUMNS: The names of the column(s) containing the entity IDs. You can specify either one column or multiple columns.
  • To specify only one entity ID column, specify the column name in the following format:
    "entity_id_column_name"
  • To specify multiple entity ID columns, specify the column names in the following format:
    ["entity_id_column_1_name", "entity_id_column_2_name", ...]
• CRON: Cron schedule expression representing the frequency for syncing data to the feature view. For more information, see cron.
• SERVICE_AGENT_TYPE: Service account configuration for the feature view. Supported service agent types include the following:
  • SERVICE_AGENT_TYPE_PROJECT: Use the project-level service account for the feature view. This is the default configuration.
  • SERVICE_AGENT_TYPE_FEATURE_VIEW: Set up and use a dedicated service account for the feature view.
• EMBEDDING_COLUMN: The name of the column containing the source data to create the index for vector search. This is required only if you want to manage embeddings with the feature view.
• FILTER_COLUMN_1 and FILTER_COLUMN_2: Optional: The names of the columns used to filter the vector search results.
• CROWDING_COLUMN: Optional: The name of the column containing the crowding attributes.
• EMBEDDING_DIMENSION: Optional: The size, expressed as number of dimensions, of an embedding in the embedding column.

HTTP method and URL:

POST https://LOCATION_ID-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME

Request JSON body:

{
  "big_query_source": {
    "uri": "bq://PROJECT_NAME.DATASET_NAME.TABLE_NAME",
    "entity_id_columns": "ENTITY_ID_COLUMNS"
  },
  "sync_config": {
    "cron": "CRON"
  },
  "service_agent_type": "SERVICE_AGENT_TYPE",
  "index_config": {
    "embedding_column": "EMBEDDING_COLUMN",
    "filter_columns": ["FILTER_COLUMN_1", "FILTER_COLUMN_2"],
    "crowding_column": "CROWDING_COLUMN",
    "embedding_dimension": EMBEDDING_DIMENSION
    "tree_ah_config": {}
  }
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews?feature_view_id=FEATUREVIEW_NAME" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews/FEATUREVIEW_NAME/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1beta1.CreateFeatureViewOperationMetadata",
    "genericMetadata": {
      "createTime": "2023-09-15T02:11:29.458820Z",
      "updateTime": "2023-09-15T02:11:29.458820Z"
    }
  }
}

What's next

• Learn how to serve features online.

• Learn how to update a feature view.

• Learn how to delete a feature view.