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@@ -346,6 +346,7 @@ <h3>Method Details</h3>
         &quot;minReplicaCount&quot;: 42, # Required. Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
       },
       &quot;disableContainerLogging&quot;: True or False, # For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
+      &quot;disableExplanations&quot;: True or False, # If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec.
       &quot;displayName&quot;: &quot;A String&quot;, # The display name of the DeployedModel. If not provided upon creation, the Model&#x27;s display_name is used.
       &quot;enableAccessLogging&quot;: True or False, # If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.
       &quot;explanationSpec&quot;: { # Specification of Model explanation. # Explanation configuration for this DeployedModel. When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
 
@@ -149,6 +149,20 @@ <h3>Method Details</h3>
     ],
     &quot;projectNumber&quot;: &quot;A String&quot;, # Optional. The project number of the parent project of the Feature Groups.
   },
+  &quot;indexConfig&quot;: { # Configuration for vector indexing. # Optional. Configuration for index preparation for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
+    &quot;bruteForceConfig&quot;: { # Configuration options for using brute force search. # Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
+    },
+    &quot;crowdingColumn&quot;: &quot;A String&quot;, # Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it&#x27;s guaranteed that no more than K entities of the same crowding attribute are returned in the response.
+    &quot;distanceMeasureType&quot;: &quot;A String&quot;, # Optional. The distance measure used in nearest neighbor search.
+    &quot;embeddingColumn&quot;: &quot;A String&quot;, # Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
+    &quot;embeddingDimension&quot;: 42, # Optional. The number of dimensions of the input embedding.
+    &quot;filterColumns&quot;: [ # Optional. Columns of features that&#x27;re used to filter vector search results.
+      &quot;A String&quot;,
+    ],
+    &quot;treeAhConfig&quot;: { # Configuration options for the tree-AH algorithm. # Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
+      &quot;leafNodeEmbeddingCount&quot;: &quot;A String&quot;, # Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
+    },
+  },
   &quot;labels&quot;: { # Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded).&quot; System reserved label keys are prefixed with &quot;aiplatform.googleapis.com/&quot; and are immutable.
     &quot;a_key&quot;: &quot;A String&quot;,
   },
@@ -340,6 +354,20 @@ <h3>Method Details</h3>
     ],
     &quot;projectNumber&quot;: &quot;A String&quot;, # Optional. The project number of the parent project of the Feature Groups.
   },
+  &quot;indexConfig&quot;: { # Configuration for vector indexing. # Optional. Configuration for index preparation for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
+    &quot;bruteForceConfig&quot;: { # Configuration options for using brute force search. # Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
+    },
+    &quot;crowdingColumn&quot;: &quot;A String&quot;, # Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it&#x27;s guaranteed that no more than K entities of the same crowding attribute are returned in the response.
+    &quot;distanceMeasureType&quot;: &quot;A String&quot;, # Optional. The distance measure used in nearest neighbor search.
+    &quot;embeddingColumn&quot;: &quot;A String&quot;, # Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
+    &quot;embeddingDimension&quot;: 42, # Optional. The number of dimensions of the input embedding.
+    &quot;filterColumns&quot;: [ # Optional. Columns of features that&#x27;re used to filter vector search results.
+      &quot;A String&quot;,
+    ],
+    &quot;treeAhConfig&quot;: { # Configuration options for the tree-AH algorithm. # Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
+      &quot;leafNodeEmbeddingCount&quot;: &quot;A String&quot;, # Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
+    },
+  },
   &quot;labels&quot;: { # Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded).&quot; System reserved label keys are prefixed with &quot;aiplatform.googleapis.com/&quot; and are immutable.
     &quot;a_key&quot;: &quot;A String&quot;,
   },
@@ -391,6 +419,20 @@ <h3>Method Details</h3>
         ],
         &quot;projectNumber&quot;: &quot;A String&quot;, # Optional. The project number of the parent project of the Feature Groups.
       },
+      &quot;indexConfig&quot;: { # Configuration for vector indexing. # Optional. Configuration for index preparation for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
+        &quot;bruteForceConfig&quot;: { # Configuration options for using brute force search. # Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
+        },
+        &quot;crowdingColumn&quot;: &quot;A String&quot;, # Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it&#x27;s guaranteed that no more than K entities of the same crowding attribute are returned in the response.
+        &quot;distanceMeasureType&quot;: &quot;A String&quot;, # Optional. The distance measure used in nearest neighbor search.
+        &quot;embeddingColumn&quot;: &quot;A String&quot;, # Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
+        &quot;embeddingDimension&quot;: 42, # Optional. The number of dimensions of the input embedding.
+        &quot;filterColumns&quot;: [ # Optional. Columns of features that&#x27;re used to filter vector search results.
+          &quot;A String&quot;,
+        ],
+        &quot;treeAhConfig&quot;: { # Configuration options for the tree-AH algorithm. # Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
+          &quot;leafNodeEmbeddingCount&quot;: &quot;A String&quot;, # Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
+        },
+      },
       &quot;labels&quot;: { # Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded).&quot; System reserved label keys are prefixed with &quot;aiplatform.googleapis.com/&quot; and are immutable.
         &quot;a_key&quot;: &quot;A String&quot;,
       },
@@ -448,6 +490,20 @@ <h3>Method Details</h3>
     ],
     &quot;projectNumber&quot;: &quot;A String&quot;, # Optional. The project number of the parent project of the Feature Groups.
   },
+  &quot;indexConfig&quot;: { # Configuration for vector indexing. # Optional. Configuration for index preparation for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
+    &quot;bruteForceConfig&quot;: { # Configuration options for using brute force search. # Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
+    },
+    &quot;crowdingColumn&quot;: &quot;A String&quot;, # Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it&#x27;s guaranteed that no more than K entities of the same crowding attribute are returned in the response.
+    &quot;distanceMeasureType&quot;: &quot;A String&quot;, # Optional. The distance measure used in nearest neighbor search.
+    &quot;embeddingColumn&quot;: &quot;A String&quot;, # Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
+    &quot;embeddingDimension&quot;: 42, # Optional. The number of dimensions of the input embedding.
+    &quot;filterColumns&quot;: [ # Optional. Columns of features that&#x27;re used to filter vector search results.
+      &quot;A String&quot;,
+    ],
+    &quot;treeAhConfig&quot;: { # Configuration options for the tree-AH algorithm. # Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
+      &quot;leafNodeEmbeddingCount&quot;: &quot;A String&quot;, # Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.
+    },
+  },
   &quot;labels&quot;: { # Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded).&quot; System reserved label keys are prefixed with &quot;aiplatform.googleapis.com/&quot; and are immutable.
     &quot;a_key&quot;: &quot;A String&quot;,
   },