Use Vertex AI Feature Store

This document describes how to use Vertex AI Feature Store to perform the tasks such as synchronizing an online store, serving features from an online store, and search an online feature store using an entity or embeddings, by using the Vertex AI SDK for ABAP.

Vertex AI Feature Store is a managed, cloud-native feature store service. It streamlines your ML feature management and online serving processes by letting you manage your feature data in a BigQuery table or view.

In a typical workflow of a Vertex AI Feature Store setup, you first prepare your SAP data source in BigQuery, register your data sources by creating feature groups and features, and set up online store and feature view resources. You can then serve the latest feature values online from a feature view or perform a similarity search on the online feature store. For information about how to set up your Vertex AI Feature Store, see Vertex AI Feature Store.

Once your Vertex AI Feature Store is ready for online serving, you can use the Vertex AI SDK for ABAP for the following:

To provide the most up-to-date and accurate feature values for real-time serving during model prediction, sync your online store with the feature data source in BigQuery.
To provide feature values from an online store for real-time applications, serve features from the online store.
To retrieve a list of semantically similar or related entities from the online store, search using an entity or embedding.

Before you begin

Before using the Vertex AI SDK for ABAP with Vertex AI Feature Store, make sure that you or your administrators have completed the following prerequisites:

Enabled the Vertex AI API in your Google Cloud project.
Installed the Vertex AI SDK for ABAP in your SAP environment.
Set up authentication to access the Vertex AI API.
Set up online feature serving. To learn how to create and use an online store to host and serve data in BigQuery with Vertex AI Feature Store in an end-to-end workflow, see Jupyter Notebook tutorials for Vertex AI Feature Store.

Instantiate the ABAP class for Vertex AI Feature Store

To use the Vertex AI Feature Store in your ABAP application, you instantiate the class /GOOG/CL_FEATURE_STORE.

DATA(lo_client) = NEW /goog/cl_feature_store(
                          iv_key_name         = 'CLIENT_KEY'
                          iv_location         = 'LOCATION_ID'
                          iv_feature_store_id = 'FEATURE_ONLINE_STORE_NAME' ).

Replace the following:

CLIENT_KEY: The client key configured for authentication.
LOCATION_ID: Region where the online store is located, such as us-central1.
FEATURE_ONLINE_STORE_NAME: The name of the online store containing the feature view.

Sync online store

To ensure that your online store has the most up-to-date and accurate feature values for real-time serving during model prediction, you need to sync the feature view resources in your online store with the feature data source in BigQuery.

Before you begin

Because you access Vertex AI Feature Store through a regional endpoint, you need to create an RFC destination to establish connection to Vertex AI API. You then specify the RFC destination in the service mapping table.

Create an RFC destination for the Vertex AI API with the regional endpoint where your Vertex AI Feature Store resides. For example, if your Vertex AI Feature Store resides in us-east1, then the endpoint for the target host would be us-east1-aiplatform.googleapis.com. For information about regional endpoints, see Service endpoint. To create an RFC destination, perform the following steps:
1. In SAP GUI, execute the transaction code SM59.
2. Create a new RFC destination. For the RFC destination that you created, make sure the Connection Type is set as follows: G - HTTP connection to external server.
3. Go to the Technical Settings tab and enter the following details:
  - Target Host: Provide the regional endpoint where your Vertex AI Feature Store resides.
  - Service No.: Enter 443. This port number is used for secure communication.
4. Go to the Logon & Security tab and make sure that the SSL Certificate field is set with the option DFAULT SSL Client (Standard).
5. Save your changes.
Configure the service mapping table for the Vertex AI API:
1. In SAP GUI, execute the transaction code /GOOG/SDK_IMG.
  
  Alternatively, execute the transaction code SPRO, and then click SAP Reference IMG.
2. Click ABAP SDK for Google Cloud > Basic Settings > Configure Service Mapping.
3. Click New Entries.
4. Specify the RFC destination for the Vertex AI API:
  
  Name Service Name RFC Destination
  
  Client key for authentication. aiplatform:v1 Name of the RFC destination.
5. Save the new entry.

Name	Service Name	RFC Destination
Client key for authentication.	`aiplatform:v1`	Name of the RFC destination.

Sync Vertex AI Feature Store

To start a data sync in a feature view, you use the SYNC_FEATURE_DATA_TO_FS method.

DATA(lv_feature_view_sync_id) = lo_client->sync_feature_data_to_fs(
                                             iv_feature_view_id = 'FEATURE_VIEW_NAME'
                                        )->get_feature_sync_id( ).

Replace FEATURE_VIEW_NAME with the name of the feature view for which you want to start the data sync.

This method returns a feature view sync ID, which can be used to check the status of the sync.

Get the status of Vertex AI Feature Store sync

To get the status of a Vertex AI Feature Store sync, you can use the GET_FEATURE_SYNC_STATUS method.

DATA(ls_status) = lo_client->get_feature_sync_status(
                               iv_feature_view_id     = 'FEATURE_VIEW_NAME'
                               iv_feature_sync_id     = 'FEATURE_VIEW_SYNC_ID'
              )->get_feature_sync_status( ).

Replace the following:

FEATURE_VIEW_NAME: The name of the feature view for which you want to check the sync status.
FEATURE_VIEW_SYNC_ID: The feature view sync ID received from Sync Vertex AI Feature Store.

This method returns one of the following statuses along with the error message, if any: C: Complete, R: Running, or F: Failed

Sync Vertex AI Feature Store by using an SAP background job

To start a data sync in a feature view by using an SAP background job, you can use the SYNC_FEATURE_DATA_TO_FS_BGJOB method.

This method creates a background job in your SAP system, which starts the feature store sync and checks the status of the feature store sync every 30 seconds. If the feature store sync status is completed or failed, then the background job completes with a message accordingly. If the feature store sync is running, then the background job runs until the specified TIMEOUT value. Post timeout, the background job completes, however, the feature store sync continues to run.

DATA(ls_bgjob_details) = lo_client->sync_feature_data_to_fs_bgjob(
                                      iv_feature_view_id = 'FEATURE_VIEW_NAME'
                                      iv_timeout         = TIMEOUT
                                 )->get_background_job_details( ).

Replace the following:

FEATURE_VIEW_NAME: The name of the feature view for which you want to manually start the data sync.
TIMEOUT: Time in seconds after which the background job is timed out. This doesn't impact the Vertex AI Feature Store sync.

This method returns a background job name and the corresponding ID. This job can be monitored from within SAP to check status of the feature store sync.

Serve features from an online store

You can serve feature values from an online store for real-time applications, such as product recommendations. Depending on the type of online serving you've configured for your online store, you can serve feature values in one of the following ways:

Fetch feature values by using Bigtable online serving.
Fetch feature values by using Optimized online serving with a public endpoint.
Fetch feature values by using Optimized online serving with a Private Service Connect endpoint.

For more information about serving feature values, see Serve features from an online store.

Before you begin

Before you can start serving feature values from an online store, you need to retrieve the public endpoint domain name from the online store details, create an RFC destination for the endpoint, and then specify the RFC destination in the service mapping table.

Retrieve the public endpoint domain name for the online store:
1. In SAP GUI, execute the transaction code /GOOG/SDK_IMG.
  
  Alternatively, execute the transaction code SPRO, and then click SAP Reference IMG.
2. Click ABAP SDK for Google Cloud > Utilities > Vertex AI SDK: Get RFC information for Feature Store and Vector Search.
3. Select Get Feature Store Details.
4. Enter the following parameters:
  - Google Cloud Key Name: The client key for authentication to Google Cloud.
  - Location: The region where the online store is located.
  - Feature Store ID: The name of the online store containing the feature view.
5. Click Execute to view the details. Make a note of the public endpoint domain name against the Host label.
Create an RFC destination for the regional endpoint, public endpoint domain name, or the Private Service Connect endpoint of the online store. To create an RFC destination, perform the following steps:
1. In SAP GUI, execute the transaction code SM59.
2. Create a new RFC destination. For the RFC destination that you created, make sure the Connection Type is set as follows: G - HTTP connection to external server.
3. Go to the Technical Settings tab and enter the following details:
  - Target Host: Provide the public endpoint domain name where your Vertex AI Feature Store resides.
  - Service No.: Enter 443. This port number is used for secure communication.
4. Go to the Logon & Security tab and make sure that the SSL Certificate field is set with the option DFAULT SSL Client (Standard).
5. Save your changes.
Configure the service mapping table for the Vertex AI API:
1. In SAP GUI, execute the transaction code /GOOG/SDK_IMG.
  
  Alternatively, execute the transaction code SPRO, and then click SAP Reference IMG.
2. Click ABAP SDK for Google Cloud > Basic Settings > Configure Service Mapping.
3. Click New Entries.
4. Specify the RFC destination for the Vertex AI API:
  
  Name Service Name RFC Destination
  
  Client key for authentication. apiinvoker:v1 Name of the RFC destination.
5. Save the new entry.

Name	Service Name	RFC Destination
Client key for authentication.	`apiinvoker:v1`	Name of the RFC destination.

Fetch feature values

In Vertex AI Feature Store, an entity refers to an item that you want to track features for, such as a customer, a product, or any other data point. Each entity is associated with a set of features and their corresponding values.

To fetch feature values by specifying a single entity ID, use the method SERVE_FEATURE_VALUES.

DATA(lt_features) = lo_client->serve_feature_values(
                         iv_feature_view_id = 'FEATURE_VIEW_NAME'
                             it_entity_id       = 'ENTITY_ID'
                )->get_features( ).

Replace the following:

FEATURE_VIEW_NAME: The name of the feature view from which you want to serve feature values.
ENTITY_ID: The value of the ID column in the feature record from which you want to serve the latest feature values. Alternatively, you can provide an array of parts to construct the Entity ID. Ensure that the ID columns are in the same order as defined in the feature view.

Search for approximate nearest neighbors

For online stores created for optimized online serving, you can perform a vector similarity search to retrieve a list of semantically similar or related entities, also called approximate nearest neighbors. You can search for approximate nearest neighbors based on either an entity ID or an embedding.

Before you begin

When you create an online store for optimized online serving, Vertex AI Feature Store generates a public endpoint domain name for the online store. Before you can start searching for the nearest neighbors from a feature view in the online store, you need to retrieve the public endpoint domain name from the online store details, create an RFC destination for the endpoint, and then specify the RFC destination in the service mapping table.

Retrieve the public endpoint domain name for the online store:
1. In SAP GUI, execute the transaction code /GOOG/SDK_IMG.
  
  Alternatively, execute the transaction code SPRO, and then click SAP Reference IMG.
2. Click ABAP SDK for Google Cloud > Utilities > Vertex AI SDK: Get RFC information for Feature Store and Vector Search.
3. Select Get Feature Store Details.
4. Enter the following parameters:
  - Google Cloud Key Name: The client key for authentication to Google Cloud.
  - Location: The region where the online store is located.
  - Feature Store ID: The name of the online store containing the feature view.
5. Click Execute to view the details. Make a note of the public endpoint domain name against the Host label.
Create an RFC destination for the public endpoint domain name of the online store:
1. In SAP GUI, execute the transaction code SM59.
2. Create a new RFC destination. For the RFC destination that you created, make sure the Connection Type is set as follows: G - HTTP connection to external server.
3. Go to the Technical Settings tab and enter the following details:
  - Target Host: Provide the public endpoint domain name for the online store.
  - Service No.: Enter 443. This port number is used for secure communication.
4. Go to the Logon & Security tab and make sure that the SSL Certificate field is set with the option DFAULT SSL Client (Standard).
5. Save your changes.
Configure the service mapping table for the Vertex AI API:
1. In SAP GUI, execute the transaction code /GOOG/SDK_IMG.
  
  Alternatively, execute the transaction code SPRO, and then click SAP Reference IMG.
2. Click ABAP SDK for Google Cloud > Basic Settings > Configure Service Mapping.
3. Click New Entries.
4. Specify the RFC destination for the Vertex AI API:
  
  Name Service Name RFC Destination
  
  Client key for authentication. apiinvoker:v1 Name of the RFC destination.
5. Save the new entry.

Name	Service Name	RFC Destination
Client key for authentication.	`apiinvoker:v1`	Name of the RFC destination.

Retrieve nearest neighbor matches of an embedding

You can search for semantically related entities by specifying an embedding.

To search nearest neighbors for an embedding, send a request by using the SEARCH_USING_EMBEDDINGS method.

DATA(lt_embeddings) = VALUE /goog/cl_aiplatform_v1=>ty_t_/goog/num_float( EMBEDDING_DATA ).

DATA(lt_neighbours) = lo_client->search_using_embeddings(
  EXPORTING
    iv_feature_view_id = 'FEATURE_VIEW_NAME'
    it_embeddings      = lt_embeddings
    is_search_params   = VALUE #( neighbor_count     = 'NEIGHBOR_COUNT'
                                  return_full_entity = 'RETURN_FULL_ENTITY' )
)->get_neighbours( ).

Replace the following:

EMBEDDING_DATA: The embedding for which you want to retrieve the approximate nearest neighbor matches. An embedding is represented by an array of float values.
FEATURE_VIEW_NAME: The name of the feature view where you want to search for approximate nearest neighbor matches.
NEIGHBOR_COUNT: Number of approximate nearest neighbors you want to retrieve.
RETURN_FULL_ENTITY: Optional. Specify whether you want to include or exclude the features for the entities in the response. To include the features along with the entities in the response, enter ABAP_TRUE. The default value is ABAP_FALSE.

Retrieve nearest neighbor matches of an entity

You can search for semantically related entities by specifying an entity ID.

To search nearest neighbors for an entity ID, send a request by using the SEARCH_USING_ENTITY method.

DATA(lt_neighbours) = lo_client->search_using_entity(
  EXPORTING
    iv_feature_view_id = 'FEATURE_VIEW_NAME'
    iv_entity_id       = 'ENTITY_ID'
    is_search_params   = VALUE #( neighbor_count     = 'NEIGHBOR_COUNT'
                                  return_full_entity = 'RETURN_FULL_ENTITY' )
)->get_neighbours( ).

Replace the following:

FEATURE_VIEW_NAME: The name of the feature view where you want to search for approximate nearest neighbor matches.
ENTITY_ID:: Entity ID of the entity for which you want to retrieve approximate nearest neighbor matches.
NEIGHBOR_COUNT: Number of approximate nearest neighbors that you want to retrieve.
RETURN_FULL_ENTITY: Optional. Specify whether you want to include or exclude the features for the entities in the response. To include the features along with the entities in the response, enter ABAP_TRUE. The default value is ABAP_FALSE.

What's next

Learn about application development with the on-premises or any cloud edition of ABAP SDK for Google Cloud.
Ask your questions and discuss the Vertex AI SDK for ABAP with the community on Cloud Forums.