feat(aiplatform): update the api

#### aiplatform:v1

The following keys were deleted:
- schemas.GoogleCloudAiplatformV1MachineSpec.properties.multihostGpuNodeCount (Total Keys: 2)

The following keys were added:
- schemas.CloudAiLargeModelsVisionGenerateVideoResponse.properties.videos (Total Keys: 2)
- schemas.CloudAiLargeModelsVisionGenerateVideoResponseVideo (Total Keys: 5)
- schemas.CloudAiLargeModelsVisionVideo.properties.text.type (Total Keys: 1)
- schemas.GoogleCloudAiplatformV1EnterpriseWebSearch (Total Keys: 2)
- schemas.GoogleCloudAiplatformV1GenerateContentResponseUsageMetadata.properties.toolUsePromptTokenCount (Total Keys: 3)
- schemas.GoogleCloudAiplatformV1GenerateContentResponseUsageMetadata.properties.toolUsePromptTokensDetails (Total Keys: 3)
- schemas.GoogleCloudAiplatformV1GenerateVideoResponse (Total Keys: 16)
- schemas.GoogleCloudAiplatformV1ReasoningEngineSpec.properties.deploymentSpec.$ref (Total Keys: 1)
- schemas.GoogleCloudAiplatformV1ReasoningEngineSpecDeploymentSpec (Total Keys: 6)
- schemas.GoogleCloudAiplatformV1SecretEnvVar (Total Keys: 4)
- schemas.GoogleCloudAiplatformV1SecretRef (Total Keys: 4)
- schemas.GoogleCloudAiplatformV1Tool.properties.enterpriseWebSearch.$ref (Total Keys: 1)

#### aiplatform:v1beta1

The following keys were added:
- schemas.CloudAiLargeModelsVisionGenerateVideoResponse.properties.videos (Total Keys: 2)
- schemas.CloudAiLargeModelsVisionGenerateVideoResponseVideo (Total Keys: 5)
- schemas.CloudAiLargeModelsVisionVideo.properties.text.type (Total Keys: 1)
- schemas.GoogleCloudAiplatformV1beta1DeployRequest (Total Keys: 22)
- schemas.GoogleCloudAiplatformV1beta1EnterpriseWebSearch (Total Keys: 2)
- schemas.GoogleCloudAiplatformV1beta1GenerateContentResponseUsageMetadata.properties.toolUsePromptTokenCount (Total Keys: 3)
- schemas.GoogleCloudAiplatformV1beta1GenerateContentResponseUsageMetadata.properties.toolUsePromptTokensDetails (Total Keys: 3)
- schemas.GoogleCloudAiplatformV1beta1GenerateVideoResponse.properties.generatedSamples.deprecated (Total Keys: 1)
- schemas.GoogleCloudAiplatformV1beta1GenerateVideoResponse.properties.videos (Total Keys: 2)
- schemas.GoogleCloudAiplatformV1beta1GenerateVideoResponseVideo (Total Keys: 5)
- schemas.GoogleCloudAiplatformV1beta1MachineSpec.properties.multihostGpuNodeCount (Total Keys: 2)
- schemas.GoogleCloudAiplatformV1beta1ReasoningEngineSpec.properties.deploymentSpec.$ref (Total Keys: 1)
- schemas.GoogleCloudAiplatformV1beta1ReasoningEngineSpecDeploymentSpec (Total Keys: 6)
- schemas.GoogleCloudAiplatformV1beta1SecretEnvVar (Total Keys: 4)
- schemas.GoogleCloudAiplatformV1beta1SecretRef (Total Keys: 4)
- schemas.GoogleCloudAiplatformV1beta1Tool.properties.enterpriseWebSearch.$ref (Total Keys: 1)

Author: yoshi-automation

docs/dyn/aiplatform_v1.endpoints.html

@@ -353,7 +353,9 @@ <h3>Method Details</h3>
   },
   &quot;tools&quot;: [ # Optional. A list of `Tools` the model may use to generate the next response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.
     { # Tool details that the model may use to generate response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. A Tool object should contain exactly one type of Tool (e.g FunctionDeclaration, Retrieval or GoogleSearchRetrieval).
-      &quot;codeExecution&quot;: { # Tool that executes code generated by the model, and automatically returns the result to the model. See also [ExecutableCode]and [CodeExecutionResult] which are input and output to this tool. # Optional. CodeExecution tool type. Enables the model to execute code as part of generation. This field is only used by the Gemini Developer API services.
+      &quot;codeExecution&quot;: { # Tool that executes code generated by the model, and automatically returns the result to the model. See also [ExecutableCode]and [CodeExecutionResult] which are input and output to this tool. # Optional. CodeExecution tool type. Enables the model to execute code as part of generation.
+      },
+      &quot;enterpriseWebSearch&quot;: { # Tool to search public web data, powered by Vertex AI Search and Sec4 compliance. # Optional. Tool to support searching public web data, powered by Vertex AI Search and Sec4 compliance.
       },
       &quot;functionDeclarations&quot;: [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 128 function declarations can be provided.
         { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
@@ -439,8 +441,8 @@ <h3>Method Details</h3>
       },
       &quot;retrieval&quot;: { # Defines a retrieval tool that model can call to access external knowledge. # Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation.
         &quot;disableAttribution&quot;: True or False, # Optional. Deprecated. This option is no longer supported.
-        &quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/products/agent-builder # Set to use data source powered by Vertex AI Search.
-          &quot;datastore&quot;: &quot;A String&quot;, # Required. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
+        &quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore or engine for grounding. datastore and engine are mutually exclusive. See https://cloud.google.com/products/agent-builder # Set to use data source powered by Vertex AI Search.
+          &quot;datastore&quot;: &quot;A String&quot;, # Optional. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
         },
         &quot;vertexRagStore&quot;: { # Retrieve from Vertex RAG Store for grounding. # Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.
           &quot;ragResources&quot;: [ # Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.
@@ -681,7 +683,9 @@ <h3>Method Details</h3>
   },
   &quot;tools&quot;: [ # Optional. A list of `Tools` the model may use to generate the next response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.
     { # Tool details that the model may use to generate response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. A Tool object should contain exactly one type of Tool (e.g FunctionDeclaration, Retrieval or GoogleSearchRetrieval).
-      &quot;codeExecution&quot;: { # Tool that executes code generated by the model, and automatically returns the result to the model. See also [ExecutableCode]and [CodeExecutionResult] which are input and output to this tool. # Optional. CodeExecution tool type. Enables the model to execute code as part of generation. This field is only used by the Gemini Developer API services.
+      &quot;codeExecution&quot;: { # Tool that executes code generated by the model, and automatically returns the result to the model. See also [ExecutableCode]and [CodeExecutionResult] which are input and output to this tool. # Optional. CodeExecution tool type. Enables the model to execute code as part of generation.
+      },
+      &quot;enterpriseWebSearch&quot;: { # Tool to search public web data, powered by Vertex AI Search and Sec4 compliance. # Optional. Tool to support searching public web data, powered by Vertex AI Search and Sec4 compliance.
       },
       &quot;functionDeclarations&quot;: [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 128 function declarations can be provided.
         { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
@@ -767,8 +771,8 @@ <h3>Method Details</h3>
       },
       &quot;retrieval&quot;: { # Defines a retrieval tool that model can call to access external knowledge. # Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation.
         &quot;disableAttribution&quot;: True or False, # Optional. Deprecated. This option is no longer supported.
-        &quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/products/agent-builder # Set to use data source powered by Vertex AI Search.
-          &quot;datastore&quot;: &quot;A String&quot;, # Required. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
+        &quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore or engine for grounding. datastore and engine are mutually exclusive. See https://cloud.google.com/products/agent-builder # Set to use data source powered by Vertex AI Search.
+          &quot;datastore&quot;: &quot;A String&quot;, # Optional. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
         },
         &quot;vertexRagStore&quot;: { # Retrieve from Vertex RAG Store for grounding. # Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.
           &quot;ragResources&quot;: [ # Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.
@@ -980,7 +984,14 @@ <h3>Method Details</h3>
         &quot;tokenCount&quot;: 42, # Number of tokens.
       },
     ],
-    &quot;totalTokenCount&quot;: 42, # Total token count for prompt and response candidates.
+    &quot;toolUsePromptTokenCount&quot;: 42, # Output only. Number of tokens present in tool-use prompt(s).
+    &quot;toolUsePromptTokensDetails&quot;: [ # Output only. List of modalities that were processed for tool-use request inputs.
+      { # Represents token counting info for a single modality.
+        &quot;modality&quot;: &quot;A String&quot;, # The modality associated with this token count.
+        &quot;tokenCount&quot;: 42, # Number of tokens.
+      },
+    ],
+    &quot;totalTokenCount&quot;: 42, # Total token count for prompt, response candidates, and tool-use prompts (if present).
   },
 }</pre>
 </div>
@@ -1215,7 +1226,9 @@ <h3>Method Details</h3>
   },
   &quot;tools&quot;: [ # Optional. A list of `Tools` the model may use to generate the next response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.
     { # Tool details that the model may use to generate response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. A Tool object should contain exactly one type of Tool (e.g FunctionDeclaration, Retrieval or GoogleSearchRetrieval).
-      &quot;codeExecution&quot;: { # Tool that executes code generated by the model, and automatically returns the result to the model. See also [ExecutableCode]and [CodeExecutionResult] which are input and output to this tool. # Optional. CodeExecution tool type. Enables the model to execute code as part of generation. This field is only used by the Gemini Developer API services.
+      &quot;codeExecution&quot;: { # Tool that executes code generated by the model, and automatically returns the result to the model. See also [ExecutableCode]and [CodeExecutionResult] which are input and output to this tool. # Optional. CodeExecution tool type. Enables the model to execute code as part of generation.
+      },
+      &quot;enterpriseWebSearch&quot;: { # Tool to search public web data, powered by Vertex AI Search and Sec4 compliance. # Optional. Tool to support searching public web data, powered by Vertex AI Search and Sec4 compliance.
       },
       &quot;functionDeclarations&quot;: [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 128 function declarations can be provided.
         { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
@@ -1301,8 +1314,8 @@ <h3>Method Details</h3>
       },
       &quot;retrieval&quot;: { # Defines a retrieval tool that model can call to access external knowledge. # Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation.
         &quot;disableAttribution&quot;: True or False, # Optional. Deprecated. This option is no longer supported.
-        &quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/products/agent-builder # Set to use data source powered by Vertex AI Search.
-          &quot;datastore&quot;: &quot;A String&quot;, # Required. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
+        &quot;vertexAiSearch&quot;: { # Retrieve from Vertex AI Search datastore or engine for grounding. datastore and engine are mutually exclusive. See https://cloud.google.com/products/agent-builder # Set to use data source powered by Vertex AI Search.
+          &quot;datastore&quot;: &quot;A String&quot;, # Optional. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
         },
         &quot;vertexRagStore&quot;: { # Retrieve from Vertex RAG Store for grounding. # Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.
           &quot;ragResources&quot;: [ # Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.
@@ -1514,7 +1527,14 @@ <h3>Method Details</h3>
         &quot;tokenCount&quot;: 42, # Number of tokens.
       },
     ],
-    &quot;totalTokenCount&quot;: 42, # Total token count for prompt and response candidates.
+    &quot;toolUsePromptTokenCount&quot;: 42, # Output only. Number of tokens present in tool-use prompt(s).
+    &quot;toolUsePromptTokensDetails&quot;: [ # Output only. List of modalities that were processed for tool-use request inputs.
+      { # Represents token counting info for a single modality.
+        &quot;modality&quot;: &quot;A String&quot;, # The modality associated with this token count.
+        &quot;tokenCount&quot;: 42, # Number of tokens.
+      },
+    ],
+    &quot;totalTokenCount&quot;: 42, # Total token count for prompt, response candidates, and tool-use prompts (if present).
   },
 }</pre>
 </div>