chore: Update discovery artifacts

docs/dyn/admin_reports_v1.activities.html

@@ -320,7 +320,25 @@ <h3>Method Details</h3>
               &quot;title&quot;: &quot;A String&quot;, # Title of the label
             },
           ],
-          &quot;id&quot;: &quot;A String&quot;, # Identifier of the resource.
+          &quot;id&quot;: &quot;A String&quot;, # Identifier of the resource, such as a doc_id for a Drive document, a conference_id for a Meet conference, or a &quot;gaia_id/rfc2822_message_id&quot; for an email.
+          &quot;ownerDetails&quot;: { # Details of the owner of the resource. # Owner details of the resource.
+            &quot;ownerIdentity&quot;: [ # Identity details of the owner(s) of the resource.
+              { # Identity details of the owner of the resource.
+                &quot;customerIdentity&quot;: { # Identity of the Google Workspace customer who owns the resource. # Identity of the Google Workspace customer who owns the resource.
+                  &quot;id&quot;: &quot;A String&quot;, # Customer id.
+                },
+                &quot;groupIdentity&quot;: { # Identity of the group who owns the resource. # Identity of the group who owns the resource.
+                  &quot;groupEmail&quot;: &quot;A String&quot;, # Group email.
+                  &quot;id&quot;: &quot;A String&quot;, # Group gaia id.
+                },
+                &quot;userIdentity&quot;: { # Identity of the user who owns the resource. # Identity of the user who owns the resource.
+                  &quot;id&quot;: &quot;A String&quot;, # User gaia id.
+                  &quot;userEmail&quot;: &quot;A String&quot;, # User email.
+                },
+              },
+            ],
+            &quot;ownerType&quot;: &quot;A String&quot;, # Type of the owner of the resource.
+          },
           &quot;relation&quot;: &quot;A String&quot;, # Defines relationship of the resource to the events
           &quot;title&quot;: &quot;A String&quot;, # Title of the resource. For instance, in case of a drive document, this would be the title of the document. In case of an email, this would be the subject.
           &quot;type&quot;: &quot;A String&quot;, # Type of the resource - document, email, chat message

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

@@ -281,6 +281,11 @@ <h3>Method Details</h3>
       },
       &quot;sampleCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
     },
+    &quot;datasetCustomMetrics&quot;: [ # Optional. Specifications for custom dataset-level aggregations.
+      { # Defines a custom dataset-level aggregation.
+        &quot;displayName&quot;: &quot;A String&quot;, # Optional. A display name for this custom summary metric. Used to prefix keys in the output summaryMetrics map. If not provided, a default name like &quot;dataset_custom_metric_1&quot;, &quot;dataset_custom_metric_2&quot;, etc., will be generated based on the order in the repeated field.
+      },
+    ],
     &quot;metrics&quot;: [ # Required. The metrics to be calculated in the evaluation run.
       { # The metric used for evaluation runs.
         &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
@@ -1389,7 +1394,7 @@ <h3>Method Details</h3>
         &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
         &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
       },
-      &quot;model&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
+      &quot;model&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or endpoint to use. Anthropic and Llama third-party models are also supported through Model Garden. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Third-party model format: `projects/{project}/locations/{location}/publishers/anthropic/models/{model}` `projects/{project}/locations/{location}/publishers/llama/models/{model}` Endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
     },
   },
   &quot;labels&quot;: { # Optional. Labels for the evaluation run.
@@ -1555,6 +1560,11 @@ <h3>Method Details</h3>
       },
       &quot;sampleCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
     },
+    &quot;datasetCustomMetrics&quot;: [ # Optional. Specifications for custom dataset-level aggregations.
+      { # Defines a custom dataset-level aggregation.
+        &quot;displayName&quot;: &quot;A String&quot;, # Optional. A display name for this custom summary metric. Used to prefix keys in the output summaryMetrics map. If not provided, a default name like &quot;dataset_custom_metric_1&quot;, &quot;dataset_custom_metric_2&quot;, etc., will be generated based on the order in the repeated field.
+      },
+    ],
     &quot;metrics&quot;: [ # Required. The metrics to be calculated in the evaluation run.
       { # The metric used for evaluation runs.
         &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
@@ -2663,7 +2673,7 @@ <h3>Method Details</h3>
         &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
         &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
       },
-      &quot;model&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
+      &quot;model&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or endpoint to use. Anthropic and Llama third-party models are also supported through Model Garden. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Third-party model format: `projects/{project}/locations/{location}/publishers/anthropic/models/{model}` `projects/{project}/locations/{location}/publishers/llama/models/{model}` Endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
     },
   },
   &quot;labels&quot;: { # Optional. Labels for the evaluation run.
@@ -2871,6 +2881,11 @@ <h3>Method Details</h3>
       },
       &quot;sampleCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
     },
+    &quot;datasetCustomMetrics&quot;: [ # Optional. Specifications for custom dataset-level aggregations.
+      { # Defines a custom dataset-level aggregation.
+        &quot;displayName&quot;: &quot;A String&quot;, # Optional. A display name for this custom summary metric. Used to prefix keys in the output summaryMetrics map. If not provided, a default name like &quot;dataset_custom_metric_1&quot;, &quot;dataset_custom_metric_2&quot;, etc., will be generated based on the order in the repeated field.
+      },
+    ],
     &quot;metrics&quot;: [ # Required. The metrics to be calculated in the evaluation run.
       { # The metric used for evaluation runs.
         &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
@@ -3979,7 +3994,7 @@ <h3>Method Details</h3>
         &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
         &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
       },
-      &quot;model&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
+      &quot;model&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or endpoint to use. Anthropic and Llama third-party models are also supported through Model Garden. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Third-party model format: `projects/{project}/locations/{location}/publishers/anthropic/models/{model}` `projects/{project}/locations/{location}/publishers/llama/models/{model}` Endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
     },
   },
   &quot;labels&quot;: { # Optional. Labels for the evaluation run.
@@ -4158,6 +4173,11 @@ <h3>Method Details</h3>
           },
           &quot;sampleCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
         },
+        &quot;datasetCustomMetrics&quot;: [ # Optional. Specifications for custom dataset-level aggregations.
+          { # Defines a custom dataset-level aggregation.
+            &quot;displayName&quot;: &quot;A String&quot;, # Optional. A display name for this custom summary metric. Used to prefix keys in the output summaryMetrics map. If not provided, a default name like &quot;dataset_custom_metric_1&quot;, &quot;dataset_custom_metric_2&quot;, etc., will be generated based on the order in the repeated field.
+          },
+        ],
         &quot;metrics&quot;: [ # Required. The metrics to be calculated in the evaluation run.
           { # The metric used for evaluation runs.
             &quot;computationBasedMetricSpec&quot;: { # Specification for a computation based metric. # Spec for a computation based metric.
@@ -5266,7 +5286,7 @@ <h3>Method Details</h3>
             &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
             &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
           },
-          &quot;model&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
+          &quot;model&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or endpoint to use. Anthropic and Llama third-party models are also supported through Model Garden. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Third-party model format: `projects/{project}/locations/{location}/publishers/anthropic/models/{model}` `projects/{project}/locations/{location}/publishers/llama/models/{model}` Endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
         },
       },
       &quot;labels&quot;: { # Optional. Labels for the evaluation run.