chore: Update discovery artifacts (#2373)

## Deleted keys were detected in the following stable discovery artifacts:
contentwarehouse v1 https://togithub.com/googleapis/google-api-python-client/commit/009c1d760563245a48632f3e2b9d27c3eb8bc36d
customsearch v1 https://togithub.com/googleapis/google-api-python-client/commit/44bffd356f2684e2e56c0a178f0925718ae93675

## Deleted keys were detected in the following pre-stable discovery artifacts:
aiplatform v1beta1 https://togithub.com/googleapis/google-api-python-client/commit/3fa443040429e8808266578e452f775912314352
alloydb v1alpha https://togithub.com/googleapis/google-api-python-client/commit/ef585cae2f98eabcfb13641859387283c4661661
alloydb v1beta https://togithub.com/googleapis/google-api-python-client/commit/ef585cae2f98eabcfb13641859387283c4661661
beyondcorp v1alpha https://togithub.com/googleapis/google-api-python-client/commit/0a1125d5f4780964404f765e68b600c20613bb92
dataform v1beta1 https://togithub.com/googleapis/google-api-python-client/commit/5fae742b0aa8176c401450be6f5887321ed47389

## Discovery Artifact Change Summary:
feat(aiplatform): update the api https://togithub.com/googleapis/google-api-python-client/commit/3fa443040429e8808266578e452f775912314352
feat(alertcenter): update the api https://togithub.com/googleapis/google-api-python-client/commit/05f7bba2e30d8c42707efa1c9a10d98d2bde54d7
feat(alloydb): update the api https://togithub.com/googleapis/google-api-python-client/commit/ef585cae2f98eabcfb13641859387283c4661661
feat(analyticsadmin): update the api https://togithub.com/googleapis/google-api-python-client/commit/a01ea2518b22a6f1d55a1e00f4046783cee81fff
feat(apphub): update the api https://togithub.com/googleapis/google-api-python-client/commit/1d8d9f693da8bf82e644255254a6cd018f54689d
feat(beyondcorp): update the api https://togithub.com/googleapis/google-api-python-client/commit/0a1125d5f4780964404f765e68b600c20613bb92
feat(bigquery): update the api https://togithub.com/googleapis/google-api-python-client/commit/090f47b2f09ea5e743d78632d1c8ec62ba20f0e2
feat(bigqueryconnection): update the api https://togithub.com/googleapis/google-api-python-client/commit/e131e87b0e584834a6c5518561112da7848fc7b3
feat(bigtableadmin): update the api https://togithub.com/googleapis/google-api-python-client/commit/af5a997473fc961704f4e38c525b776c8649ad7d
feat(chat): update the api https://togithub.com/googleapis/google-api-python-client/commit/492c171e6e096b8cb7ce1b2858ce801c67c2afae
feat(compute): update the api https://togithub.com/googleapis/google-api-python-client/commit/605a1e04cedccbd11b6ca5041231148d12bed7fa
feat(content): update the api https://togithub.com/googleapis/google-api-python-client/commit/ce3d8ce5f2d5f6a28a7e6813725df1833b2f94d9
feat(contentwarehouse): update the api https://togithub.com/googleapis/google-api-python-client/commit/009c1d760563245a48632f3e2b9d27c3eb8bc36d
feat(customsearch): update the api https://togithub.com/googleapis/google-api-python-client/commit/44bffd356f2684e2e56c0a178f0925718ae93675
feat(dataform): update the api https://togithub.com/googleapis/google-api-python-client/commit/5fae742b0aa8176c401450be6f5887321ed47389
feat(datamigration): update the api https://togithub.com/googleapis/google-api-python-client/commit/98b7c8c4e6f99e39afb443a815ac7463ab4931d9
feat(dataplex): update the api https://togithub.com/googleapis/google-api-python-client/commit/59e643d178fb925138e47949766a5e2b2d58968d
feat(dialogflow): update the api https://togithub.com/googleapis/google-api-python-client/commit/d546a689622e26425466afe28b40fe7667c5d34e
feat(discovery): update the api https://togithub.com/googleapis/google-api-python-client/commit/a212ce6b074bb64f664ef85ba90467576b482677
feat(discoveryengine): update the api https://togithub.com/googleapis/google-api-python-client/commit/0693eaef11a0aef49f6964636f3330857f1c0745
feat(domains): update the api https://togithub.com/googleapis/google-api-python-client/commit/22bd65689ef4b6bd0d2ec132b599b5fb9675f0a7
feat(factchecktools): update the api https://togithub.com/googleapis/google-api-python-client/commit/fa3431056ee2611a56bee2fc9c0b2fecf908d74f
feat(firestore): update the api https://togithub.com/googleapis/google-api-python-client/commit/3e1ba17c0a260014327e11c16058202521e717e2
feat(gkebackup): update the api https://togithub.com/googleapis/google-api-python-client/commit/4cd9a8e61b4bf70dc1564d28ee127000c61185f9
feat(gkehub): update the api https://togithub.com/googleapis/google-api-python-client/commit/40ce6ef08e67795a1eb6a5b9613cd2196583fc44
feat(metastore): update the api https://togithub.com/googleapis/google-api-python-client/commit/5961fb68340a3fc3ddfeef3dc97d4cc79c0450f1
feat(migrationcenter): update the api https://togithub.com/googleapis/google-api-python-client/commit/f0a44ad3d3983fcf3c23d46b7db024cdc15d3991
feat(orgpolicy): update the api https://togithub.com/googleapis/google-api-python-client/commit/ac7a596edc8bbf821aaeb84d2b8e0fd5e72b775f
feat(pubsub): update the api https://togithub.com/googleapis/google-api-python-client/commit/370d664904b3f7f69fbfeba696c27bbcccb16635
feat(recaptchaenterprise): update the api https://togithub.com/googleapis/google-api-python-client/commit/03ae14df0cba4b06750132cba199e1ed99ce7cc5
feat(redis): update the api https://togithub.com/googleapis/google-api-python-client/commit/b1058e876339125c50ad3b009e59d39b27c64069
feat(retail): update the api https://togithub.com/googleapis/google-api-python-client/commit/9e7975db06b57d14e8f7bedb27c80eb1f728bffc
feat(run): update the api https://togithub.com/googleapis/google-api-python-client/commit/f5fe0c7066cb16bdc98b3777bc5d9d0c9e7a2b31
feat(servicecontrol): update the api https://togithub.com/googleapis/google-api-python-client/commit/125f24d6a3c1b85763c937ac2bfcbba16712260b
feat(spanner): update the api https://togithub.com/googleapis/google-api-python-client/commit/a2968abfe65cd4cd00d1eebd1095eabf1533ec6a
feat(sqladmin): update the api https://togithub.com/googleapis/google-api-python-client/commit/6dde380674c579f090daeffc9ee1d1413648b104
feat(workstations): update the api https://togithub.com/googleapis/google-api-python-client/commit/ec2f3f7d89ca59c836afceb6c1ea36e11386e53a

docs/dyn/aiplatform_v1.projects.locations.deploymentResourcePools.html

@@ -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.

docs/dyn/aiplatform_v1.projects.locations.featureOnlineStores.featureViews.html

@@ -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;,
   },