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    Serve features from online store

    Vertex AI Feature Store lets you online serve feature values in real-time from a feature view within an online store. For example, you can serve feature values from a feature view for online predictions. A feature view must be synced at least once before you can online serve features from that feature.

    If the feature view is defined based on feature groups and features, Vertex AI Feature Store fetches the latest feature values corresponding to a specific entity ID. If there are multiple records with the same value in the ID column, Vertex AI Feature Store fetches the most recent non-null feature values, based on the feature_timestamp column.

    If the feature view is directly associated with a BigQuery data source without associating feature groups and features, then Vertex AI Feature Store fetches all the feature values from the data source. In this case, every row in the data source must contain a unique ID.

    Depending on the type of online serving configured for your online store, you can serve feature values in one of the following ways:

    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:

    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.

      Install the Google Cloud CLI.

      If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

      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.

      If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity .

    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.

      If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

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

    Fetch feature values using Bigtable online serving

    You can use Bigtable online serving to do the following:

    Fetch feature values for a single entity

    If your feature view has only one entity ID column, then you can serve features by specifying the value contained in that column. However, if the feature view has multiple entity ID columns, then you can locate a feature record by specifying the values in those columns to constitute the entity ID that's unique to the feature record.

    Fetch feature values from a feature view with only one entity ID column

    Use the following samples to fetch feature values for a specific entity ID, where the feature view has only one entity ID column.

    REST

    To fetch all the latest feature values for a specific entity ID from a FeatureView instance, send a POST request by using the featureViews.fetchFeatureValues method.

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

    • LOCATION_ID : Region where the online store is located, such as us-central1 .
    • PROJECT_ID : Your project ID.
    • FEATUREONLINESTORE_NAME : The name of the online store containing the feature view.
    • FEATUREVIEW_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.
    • FORMAT : Optional: The format in which you want to fetch the feature values. The following formats are supported:
      • KEY_VALUE
      • PROTO_STRUCT

    HTTP method and URL:

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

    Request JSON body:

    {
  data_key: { key: " ENTITY_ID 
" },
  data_format: " FORMAT 
"
}

    To send your request, choose one of these options:

    curl

    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/ FEATUREVIEW_NAME 
:fetchFeatureValues"

    PowerShell

    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/ FEATUREVIEW_NAME 
:fetchFeatureValues" | Select-Object -Expand Content

    You should receive a JSON response similar to the following:

    key_values {
  features {
    value {
      int64_value: 258348
    }
    name: "feature_0"
  }
  features {
    value {
      double_value: 0.96300036744534068
    }
    name: "feature_1"
  }
  features {
    value {
      double_value: 0.42787383695351083
    }
    name: "feature_2"
  }
  features {
    value {
      double_value: 0.12219888824743128
    }
    name: "feature_3"
  }
  features {
    value {
      double_value: 0.037523154697944622
    }
    name: "feature_4"
  }
  features {
    value {
      double_value: 0.1766952509448767
    }
    name: "feature_5"
  }
}

    Python

    Use the following sample to fetch feature values based on a specific entity ID using Bigtable online serving.

      from 
  
 google.cloud 
  
 import 
 aiplatform 
 from 
  
 vertexai.resources.preview.feature_store 
  
 import 
  FeatureOnlineStore 
 
 , 
  FeatureView 
 
 aiplatform 
 . 
  init 
 
 ( 
 project 
 = 
 " PROJECT_ID 
" 
 , 
 location 
 = 
 " LOCATION_ID 
" 
 ) 
 fos 
 = 
 FeatureOnlineStore 
 ( 
 " FEATUREONLINESTORE_NAME 
" 
 ) 
 fv 
 = 
 FeatureView 
 ( 
 " FEATUREVIEW_NAME 
" 
 , 
 feature_online_store_id 
 = 
 fos 
 . 
 name 
 ) 
 fv 
 . 
 read 
 ( 
 " ENTITY_ID 
" 
 )

    Replace the following:

    • LOCATION_ID : Region where the online store is located, such as us-central1 .

    • PROJECT_ID : Your project ID.

    • FEATUREONLINESTORE_NAME : The name of the online store containing the feature view.

    • FEATUREVIEW_NAME : The name of the feature view from which you want to serve feature values.

    • ENTITY_ID : The value of the ID column for which you want to serve the latest feature values.

    • FORMAT : Optional: The format in which you want to fetch the feature values. Supported formats include JSON KEY_VALUE pair and proto PROTO_STRUCT formats.

    Fetch feature values from a feature view with multiple entity ID columns

    Use the following samples to fetch feature values for a specific entity ID, where the feature view has multiple entity ID columns.

    REST

    To fetch all the latest feature values for a specific entity ID from a FeatureView instance, send a POST request by using the featureViews.fetchFeatureValues method.

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

    • LOCATION_ID : Region where the online store is located, such as us-central1 .
    • PROJECT_ID : Your project ID.
    • FEATUREONLINESTORE_NAME : The name of the online store containing the feature view.
    • FEATUREVIEW_NAME : The name of the feature view from which you want to serve feature values.
    • ENTITY_ID_PART_1 , ENTITY_ID_PART_2 , and ENTITY_ID_PART_3 : The parts of the entity ID in the entity ID columns configured for the feature view. These parts constitute the unique entity ID for the feature record.
    • FORMAT : Optional: The format in which you want to fetch the feature values. The following formats are supported:
      • KEY_VALUE
      • PROTO_STRUCT

    HTTP method and URL:

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

    Request JSON body:

    {
  data_key: { composite_key: { parts: [" ENTITY_ID_PART_1 
", " ENTITY_ID_PART_2 
", " ENTITY_ID_PART_3 
"] } },
  data_format: " FORMAT 
"
}

    To send your request, choose one of these options:

    curl

    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/ FEATUREVIEW_NAME 
:fetchFeatureValues"

    PowerShell

    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/ FEATUREVIEW_NAME 
:fetchFeatureValues" | Select-Object -Expand Content

    You should receive a JSON response similar to the following:

    key_values {
  features {
    value {
      int64_value: 258348
    }
    name: "feature_0"
  }
  features {
    value {
      double_value: 0.96300036744534068
    }
    name: "feature_1"
  }
  features {
    value {
      double_value: 0.42787383695351083
    }
    name: "feature_2"
  }
  features {
    value {
      double_value: 0.12219888824743128
    }
    name: "feature_3"
  }
  features {
    value {
      double_value: 0.037523154697944622
    }
    name: "feature_4"
  }
  features {
    value {
      double_value: 0.1766952509448767
    }
    name: "feature_5"
  }
}

    Python

    Use the following sample to fetch feature values based on a specific entity ID using Bigtable online serving.

      from 
  
 google.cloud 
  
 import 
 aiplatform 
 from 
  
 vertexai.resources.preview.feature_store 
  
 import 
  FeatureOnlineStore 
 
 , 
  FeatureView 
 
 aiplatform 
 . 
  init 
 
 ( 
 project 
 = 
 " PROJECT_ID 
" 
 , 
 location 
 = 
 " LOCATION_ID 
" 
 ) 
 fos 
 = 
 FeatureOnlineStore 
 ( 
 " FEATUREONLINESTORE_NAME 
" 
 ) 
 fv 
 = 
 FeatureView 
 ( 
 " FEATUREVIEW_NAME 
" 
 , 
 feature_online_store_id 
 = 
 fos 
 . 
 name 
 ) 
 fv 
 . 
 read 
 ([ 
 " ENTITY_ID_PART_1 
" 
 , 
 " ENTITY_ID_PART_2 
" 
 , 
 " ENTITY_ID_PART_3 
" 
 ])

    Replace the following:

    • LOCATION_ID : Region where the online store is located, such as us-central1 .

    • PROJECT_ID : Your project ID.

    • FEATUREONLINESTORE_NAME : The name of the online store containing the feature view.

    • FEATUREVIEW_NAME : The name of the feature view from which you want to serve feature values.

    • ENTITY_ID_PART_1 , ENTITY_ID_PART_2 , and ENTITY_ID_PART_3 : The parts of the entity ID in the entity ID columns configured for the feature view. These parts constitute the unique entity ID for the feature record.

    • FORMAT : Optional: The format in which you want to fetch the feature values. Supported formats include JSON KEY_VALUE pair and proto PROTO_STRUCT formats.

    Fetch feature values for a set of entities

    Use the following sample to fetch feature values by specifying a set of IDs from multiple entity ID columns using Bigtable online serving.

    REST

    To fetch all the latest feature values for a specific entity ID from a FeatureView instance, send a POST request by using the featureViews.streamingFetchFeatureValues method.

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

    • LOCATION_ID : Region where the online store is located, such as us-central1 .
    • PROJECT_ID : Your project ID.
    • FEATUREONLINESTORE_NAME : The name of the online store containing the feature view.
    • FEATUREVIEW_NAME : The name of the feature view from which you want to serve feature values.
    • ENTITY_ID_1 , ENTITY_ID_2 , ENTITY_ID_3 , and ENTITY_ID_4 : The set of values from the ID columns in the feature records from which you want to serve the latest feature values. Note that grouping the entity IDs as multiple nested lists in a request can help reduce latency, as Vertex AI Feature Store performs a separate read operation for each nested list of IDs.

    HTTP method and URL:

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

    Request JSON body:

    [
  {
    data_keys: [{key: " ENTITY_ID_1 
"}, {key: " ENTITY_ID_2 
"}],
    feature_view: "projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME 
/featureViews/ FEATUREVIEW_NAME 
"
  },
  {
    data_keys: [{key: " ENTITY_ID_3 
"}, {key: " ENTITY_ID_4 
"}],
    feature_view: "projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME 
/featureViews/ FEATUREVIEW_NAME 
"
  }
]

    To send your request, choose one of these options:

    curl

    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/ FEATUREVIEW_NAME 
:streamingFetchFeatureValues"

    PowerShell

    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/ FEATUREVIEW_NAME 
:streamingFetchFeatureValues" | Select-Object -Expand Content

    You should receive a JSON response similar to the following:

    [data {
  key_values {
    features {
      name: "movies"
      value {
        string_value: "movie_04"
      }
    }
    features {
      name: "feature_timestamp"
      value {
        int64_value: 1631694494000000
      }
    }
  }
  data_key {
    key: "eve"
  }
}
, data {
  key_values {
    features {
      name: "movies"
      value {
        string_value: "movie_03"
      }
    }
    features {
      name: "feature_timestamp"
      value {
        int64_value: 1631612115000000
      }
    }
  }
  data_key {
    key: "alice"
  }
}
data {
  key_values {
    features {
      name: "movies"
      value {
        string_value: "movie_02"
      }
    }
    features {
      name: "feature_timestamp"
      value {
        int64_value: 1631694494000000
      }
    }
  }
  data_key {
    key: "bob"
  }
}
]

    Python

    Use the following sample to fetch feature values from multiple entity ID columns using Bigtable online serving.

      from 
  
 google.cloud.aiplatform_v1beta1 
  
 import 
  FeatureOnlineStoreServiceClient 
 
 from 
  
 google.cloud.aiplatform_v1beta1.types 
  
 import 
  feature_online_store_service 
 
 as 
 feature_online_store_service_pb2 
 data_client 
 = 
 FeatureOnlineStoreServiceClient 
 ( 
 client_options 
 = 
 { 
 "api_endpoint" 
 : 
 f 
 " LOCATION_ID 
-aiplatform.googleapis.com" 
 } 
 ) 
 feature_view 
 = 
 "projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME 
/featureViews/ FEATUREVIEW_NAME 
" 
 keys_list 
 = 
 [ 
 [ 
 " ENTITY_ID_1 
" 
 , 
 " ENTITY_ID_2 
" 
 ], 
 [ 
 " ENTITY_ID_3 
" 
 , 
 " ENTITY_ID_4 
" 
 ] 
 ] 
 requests 
 = 
 [] 
 for 
 keys 
 in 
 keys_list 
 : 
 requests 
 . 
 append 
 ( 
 feature_online_store_service_pb2 
 . 
  StreamingFetchFeatureValuesRequest 
 
 ( 
 feature_view 
 = 
 feature_view 
 , 
 data_keys 
 = 
 [ 
 feature_online_store_service_pb2 
 . 
  FeatureViewDataKey 
 
 ( 
 key 
 = 
 key 
 ) 
 for 
 key 
 in 
 keys 
 ] 
 ) 
 ) 
 responses 
 = 
 data_client 
 . 
  streaming_fetch_feature_values 
 
 ( 
 requests 
 = 
 iter 
 ( 
 requests 
 ) 
 ) 
 responses 
 = 
 [ 
 response 
 for 
 response 
 in 
 responses 
 ]

    Replace the following:

    • LOCATION_ID : Region where the online store is located, such as us-central1 .

    • PROJECT_ID : Your project ID.

    • FEATUREONLINESTORE_NAME : The name of the online store containing the feature view.

    • FEATUREVIEW_NAME : The name of the feature view from which you want to serve feature values.

    • ENTITY_ID_1 , ENTITY_ID_2 , ENTITY_ID_3 , and ENTITY_ID_4 : The entity IDs from which you want to serve the latest feature values. Note that grouping the entity IDs as multiple nested lists in a request can help reduce latency, as Vertex AI Feature Store performs a separate read operation for each nested list of IDs.

    Fetch feature values using Optimized online serving from a public endpoint

    If you've configured your online store instance to serve feature values using Optimized online serving from a public endpoint, then you need to perform the following steps to fetch feature values from a feature view within the online store:

    1. Retrieve the public endpoint domain name for the FeatureOnlineStore instance.

    2. Fetch feature values from an entity ID using the public endpoint domain name.

    Retrieve the public endpoint domain name for the online store

    When you create and configure an online store instance for Optimized online serving with a public endpoint, Vertex AI Feature Store generates the public endpoint domain name for the online store. Before you can start serving feature values from a feature view in the online store, you need to retrieve the public endpoint domain name from the online store details.

    Use the following sample to retrieve the details of an online store instance.

    REST

    To retrieve the details of a FeatureOnlineStore resource in your project, send a GET request by using the featureOnlineStores.get method.

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

    • LOCATION_ID : Region where the online store is located, such as us-central1 .
    • PROJECT_ID : Your project ID.
    • FEATUREONLINESTORE_NAME : The name of the online store instance.

    HTTP method and URL:

    GET https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME

    To send your request, choose one of these options:

    curl

    Execute the following command:

    curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    "https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME 
"

    PowerShell

    Execute the following command:

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

    Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_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_1 
",
  "createTime": "2023-09-06T23:25:04.256314Z",
  "updateTime": "2023-09-06T23:25:04.256314Z",
  "etag": "AMEw9yMgoV0bAsYuKwVxz4Y7lOmxV7riNVHg217KaQAKORqvdqGCrQ1DIt8yHgoGXf8=",
  "state": "STABLE",
  "dedicatedServingEndpoint": {
    "publicEndpointDomainName": " PUBLIC_ENDPOINT_DOMAIN_NAME 
"
  },
  "optimized": {}
}

    You'll need the PUBLIC_ENDPOINT_DOMAIN_NAME from the response to fetch feature values in the following step.

    Fetch feature values from an entity ID

    After you retrieve the public endpoint domain name for the online store instance, use the following sample to fetch feature values for a specific entity ID using Optimized online serving.

    REST

    To fetch all the latest feature values for a specific entity ID from a FeatureView instance, send a POST request by using the featureViews.fetchFeatureValues method.

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

    • PUBLIC_ENDPOINT_DOMAIN_NAME : The public endpoint domain name for the online store instance that you retrieved using the featureOnlineStores.get method.
    • LOCATION_ID : Region where the online store is located, such as us-central1 .
    • PROJECT_ID : Your project ID.
    • FEATUREONLINESTORE_NAME : The name of the online store containing the feature view.
    • FEATUREVIEW_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.
    • FORMAT : Optional: The format used to fetch the feature values. The following formats are supported:
      • KEY_VALUE
      • PROTO_STRUCT

    HTTP method and URL:

    POST https:// PUBLIC_ENDPOINT_DOMAIN_NAME 
/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME 
/featureViews/ FEATUREVIEW_NAME 
:fetchFeatureValues

    Request JSON body:

    {
  data_key: { key: " ENTITY_ID 
" },
  data_format: " FORMAT 
"
}

    To send your request, choose one of these options:

    curl

    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:// PUBLIC_ENDPOINT_DOMAIN_NAME 
/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME 
/featureViews/ FEATUREVIEW_NAME 
:fetchFeatureValues"

    PowerShell

    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:// PUBLIC_ENDPOINT_DOMAIN_NAME 
/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME 
/featureViews/ FEATUREVIEW_NAME 
:fetchFeatureValues" | Select-Object -Expand Content

    You should receive a JSON response similar to the following:

    key_values {
  features {
    value {
      int64_value: 258348
    }
    name: "feature_0"
  }
  features {
    value {
      double_value: 0.96300036744534068
    }
    name: "feature_1"
  }
  features {
    value {
      double_value: 0.42787383695351083
    }
    name: "feature_2"
  }
  features {
    value {
      double_value: 0.12219888824743128
    }
    name: "feature_3"
  }
  features {
    value {
      double_value: 0.037523154697944622
    }
    name: "feature_4"
  }
  features {
    value {
      double_value: 0.1766952509448767
    }
    name: "feature_5"
  }
}

    Python

    Use the following sample to fetch feature values based on a specific entity ID using Optimized online serving.

      from 
  
 google.cloud.aiplatform_v1 
  
 import 
  FeatureOnlineStoreServiceClient 
 
 from 
  
 google.cloud.aiplatform_v1.types 
  
 import 
  feature_online_store_service 
 
 as 
 feature_online_store_service_pb2 
 data_client 
 = 
 FeatureOnlineStoreServiceClient 
 ( 
 client_options 
 = 
 { 
 "api_endpoint" 
 : 
 f 
 " PUBLIC_ENDPOINT_DOMAIN_NAME 
" 
 } 
 ) 
 data_client 
 . 
  fetch_feature_values 
 
 ( 
 request 
 = 
 feature_online_store_service_pb2 
 . 
  FetchFeatureValuesRequest 
 
 ( 
 feature_view 
 = 
 f 
 "projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME 
/featureViews/ FEATUREVIEW_NAME 
" 
 , 
 data_key 
 = 
 feature_online_store_service_pb2 
 . 
  FeatureViewDataKey 
 
 ( 
 key 
 = 
 f 
 " ENTITY_ID 
" 
 ), 
 data_format 
 = 
 feature_online_store_service_pb2 
 . 
  FeatureViewDataFormat 
 
 . 
  FORMAT 
 
 , 
 ) 
 )

    Replace the following:

    • PUBLIC_ENDPOINT_DOMAIN_NAME : The public endpoint domain name for the online store instance that you retrieved using the featureOnlineStores.get method.

    • LOCATION_ID : Region where the online store is located, such as us-central1 .

    • PROJECT_ID : Your project ID.

    • FEATUREONLINESTORE_NAME : The name of the online store containing the feature view.

    • FEATUREVIEW_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.

    • FORMAT : Optional: The format in which you want to fetch the feature values. Supported formats include JSON KEY_VALUE pair and proto PROTO_STRUCT formats.

    Fetch feature values using Optimized online serving from a Private Service Connect endpoint

    If you've configured your online store instance to serve feature values using Optimized online serving from a Private Service Connect endpoint, then you need to perform the following steps to fetch feature values from a feature view within the online store:

    1. Retrieve the Private Service Connect configuration for the FeatureOnlineStore instance.

    2. Add an endpoint for Private Service Connect to your network configuration.

    3. Connect to the Private Service Connect endpoint over gRPC.

    4. Fetch feature values from an entity ID.

    Retrieve the service attachment string for the online store

    When you create and configure an online store instance for Optimized online serving with a Private Service Connect endpoint, Vertex AI Feature Store generates a service attachment string that you can use to set up the Private Service Connect endpoint. You can retrieve the service attachment string from the online store details.

    Use the following sample to retrieve the details of an online store instance.

    REST

    To retrieve the details of a FeatureOnlineStore resource in your project, send a GET request by using the featureOnlineStores.get method.

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

    • LOCATION_ID : Region where the online store is located, such as us-central1 .
    • PROJECT_ID : Your project ID.
    • FEATUREONLINESTORE_NAME : The name of the online store instance.

    HTTP method and URL:

    GET https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME

    To send your request, choose one of these options:

    curl

    Execute the following command:

    curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    "https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME 
"

    PowerShell

    Execute the following command:

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

    Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https:// LOCATION_ID 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_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_1 
",
  "createTime": "2023-09-06T23:25:04.256314Z",
  "updateTime": "2023-09-06T23:25:04.256314Z",
  "etag": "AMEw9yMgoV0bAsYuKwVxz4Y7lOmxV7riNVHg217KaQAKORqvdqGCrQ1DIt8yHgoGXf8=",
  "state": "STABLE",
  "dedicatedServingEndpoint": {
    "privateServiceConnectConfig": {
      "enablePrivateServiceConnect": "true",
      "projectAllowlist": [
        " PROJECT_NAME 
"
      ]
    },
    serviceAttachment: " SERVICE_ATTACHMENT_STRING 
"
  },
  "optimized": {}
}

    You'll need the SERVICE_ATTACHMENT_STRING from the response to fetch feature values in the following step.

    Add an endpoint for Private Service Connect

    To add a Private Service Connect endpoint for Optimized online serving to your network configuration, perform the following steps:

    1. On the Google Cloud console, select the project containing the online store instance.

    2. Create an endpoint for Private Service Connect by specifying the SERVICE_ATTACHMENT_STRING as the Target service. For information about how to create an endpoint for Private Service Connect, see Create an endpoint .

    After you create the endpoint, it appears in the Connected endpointstab on the Private Service Connectpage. The IP address of the endpoint appears in the IP addressescolumn.

    Go to the Connected endpoints tab

    You'll need to use this IP address to connect to the endpoint for your online store instance to the Private Service Connect endpoint over gRPC in the following step.

    Connect to the Private Service Connect endpoint over gRPC

    Use the following code sample to connect to the Private Service Connect endpoint created for your online store over gRPC.

    Python

      from 
  
 google.cloud.aiplatform_v1 
  
 import 
  FeatureOnlineStoreServiceClient 
 
 from 
  
 google.cloud.aiplatform_v1.services.feature_online_store_service.transports.grpc 
  
 import 
 FeatureOnlineStoreServiceGrpcTransport 
 import 
  
 grpc 
 data_client 
 = 
 FeatureOnlineStoreServiceClient 
 ( 
 transport 
 = 
 FeatureOnlineStoreServiceGrpcTransport 
 ( 
 # Add the IP address of the Endpoint you just created. 
 channel 
 = 
 grpc 
 . 
 insecure_channel 
 ( 
 " ENDPOINT_IP 
:10002" 
 ) 
 ) 
 )

    Replace the following:

    • ENDPOINT_IP : The IP address of the endpoint in the IP addressescolumn on the Private Service Connectpage.

    Fetch feature values from an entity ID

    After you've connected to the Private Service Connect endpoint over gRPC, use the following sample to fetch feature values for a specific entity ID using Optimized online serving.

    Python

      data_client 
 . 
 fetch_feature_values 
 ( 
 request 
 = 
 feature_online_store_service_pb2 
 . 
 FetchFeatureValuesRequest 
 ( 
 feature_view 
 = 
 f 
 "projects/ PROJECT_ID 
/locations/ LOCATION_ID 
/featureOnlineStores/ FEATUREONLINESTORE_NAME 
/featureViews/ FEATUREVIEW_NAME 
" 
 , 
 data_key 
 = 
 feature_online_store_service_pb2 
 . 
 FeatureViewDataKey 
 ( 
  ENTITY_ID 
 
 ), 
 data_format 
 = 
 feature_online_store_service_pb2 
 . 
 FeatureViewDataFormat 
 . 
 PROTO_STRUCT 
 , 
 ) 
 )

    Replace the following:

    • LOCATION_ID : Region where the online store is located, such as us-central1 .

    • PROJECT_ID : Your project ID.

    • FEATUREONLINESTORE_NAME : The name of the online store containing the feature view.

    • FEATUREVIEW_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.

    • FORMAT : Optional: The format in which you want to fetch the feature values. Supported formats include JSON key-value pair and proto Struct formats. Note that the proto Struct format doesn't support the bytes feature value type. If you want to fetch feature values that are formatted as bytes, use JSON as the response format.

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