close()
Close httplib2 connections.
+delete(name, forceDelete=None, x__xgafv=None)
Deletes a RagFile.
@@ -104,11 +104,12 @@delete(name, x__xgafv=None)
+ delete(name, forceDelete=None, x__xgafv=None)
Deletes a RagFile.
Args:
name: string, Required. The name of the RagFile resource to be deleted. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}` (required)
+ forceDelete: boolean, Optional. If set to true, any errors generated by external vector database during the deletion will be ignored. The default value is false.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
diff --git a/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.html b/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.html
index 53fc25a8049..ad70427756b 100644
--- a/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.html
+++ b/docs/dyn/aiplatform_v1.projects.locations.reasoningEngines.html
@@ -145,8 +145,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -157,7 +157,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
@@ -284,8 +284,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -296,7 +296,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
@@ -365,8 +365,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -377,7 +377,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
@@ -451,8 +451,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -463,7 +463,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
diff --git a/docs/dyn/aiplatform_v1.publishers.models.html b/docs/dyn/aiplatform_v1.publishers.models.html
index 8cefb6ebf3f..1e15a8b18c9 100644
--- a/docs/dyn/aiplatform_v1.publishers.models.html
+++ b/docs/dyn/aiplatform_v1.publishers.models.html
@@ -477,35 +477,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -979,35 +950,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -2317,35 +2259,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
diff --git a/docs/dyn/aiplatform_v1.reasoningEngines.html b/docs/dyn/aiplatform_v1.reasoningEngines.html
index f15b920eeaf..7116864cab2 100644
--- a/docs/dyn/aiplatform_v1.reasoningEngines.html
+++ b/docs/dyn/aiplatform_v1.reasoningEngines.html
@@ -139,8 +139,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -151,7 +151,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
@@ -279,8 +279,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -291,7 +291,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
@@ -360,8 +360,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -372,7 +372,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
@@ -446,8 +446,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -458,7 +458,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
diff --git a/docs/dyn/aiplatform_v1beta1.endpoints.html b/docs/dyn/aiplatform_v1beta1.endpoints.html
index 032a2babc0b..d6931876260 100644
--- a/docs/dyn/aiplatform_v1beta1.endpoints.html
+++ b/docs/dyn/aiplatform_v1beta1.endpoints.html
@@ -488,35 +488,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -1004,35 +975,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -1788,35 +1730,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.cachedContents.html b/docs/dyn/aiplatform_v1beta1.projects.locations.cachedContents.html
index d078c4e3492..406c76c1560 100644
--- a/docs/dyn/aiplatform_v1beta1.projects.locations.cachedContents.html
+++ b/docs/dyn/aiplatform_v1beta1.projects.locations.cachedContents.html
@@ -326,35 +326,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -696,35 +667,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -1091,35 +1033,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -1472,35 +1385,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -1861,35 +1745,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -2232,35 +2087,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html b/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html
index 5f9cc1a9c39..21bd4c5b7bf 100644
--- a/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html
+++ b/docs/dyn/aiplatform_v1beta1.projects.locations.datasets.html
@@ -460,35 +460,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -956,35 +927,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.deploymentResourcePools.html b/docs/dyn/aiplatform_v1beta1.projects.locations.deploymentResourcePools.html
index dc05f3760e6..74b49219c77 100644
--- a/docs/dyn/aiplatform_v1beta1.projects.locations.deploymentResourcePools.html
+++ b/docs/dyn/aiplatform_v1beta1.projects.locations.deploymentResourcePools.html
@@ -128,6 +128,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -249,6 +252,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -309,6 +315,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -377,6 +386,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -478,6 +490,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html b/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html
index a78375ef11f..4a79d5015bb 100644
--- a/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html
+++ b/docs/dyn/aiplatform_v1beta1.projects.locations.endpoints.html
@@ -550,35 +550,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -735,6 +706,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -1061,6 +1035,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -2125,35 +2102,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -2544,6 +2492,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -2860,6 +2811,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -3139,6 +3093,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -3382,6 +3339,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -3650,6 +3610,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -4575,35 +4538,6 @@ Method Details
},
],
"googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
- "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
- "apiKeyConfig": { # The API secret. # The API secret.
- "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
- "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
- },
- },
- "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported.
- "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
- "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
- "httpElementLocation": "A String", # Optional. The location of the API key.
- "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
- },
- "authType": "A String", # Type of auth scheme.
- "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
- "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
- },
- "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
- "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
- },
- "oauthConfig": { # Config for user oauth. # Config for user oauth.
- "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
- },
- "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
- "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
- "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
- },
- },
},
"googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
"excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
@@ -5097,6 +5031,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.html b/docs/dyn/aiplatform_v1beta1.projects.locations.html
index f62c794953e..e9d0e05961d 100644
--- a/docs/dyn/aiplatform_v1beta1.projects.locations.html
+++ b/docs/dyn/aiplatform_v1beta1.projects.locations.html
@@ -325,6 +325,9 @@ Instance Methods
evaluateInstances(location, body=None, x__xgafv=None)
Evaluates instances based on a given metric.
+
+ generateSyntheticData(location, body=None, x__xgafv=None)
+Generates synthetic data based on the provided configuration.
Gets information about a location.
@@ -651,6 +654,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -868,6 +874,9 @@ Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.
{ # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
"metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count`
+ "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info
+ "a_key": "A String",
+ },
"target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
},
],
@@ -1780,6 +1789,153 @@ Method Details
}
generateSyntheticData(location, body=None, x__xgafv=None)
+ Generates synthetic data based on the provided configuration.
+
+Args:
+ location: string, Required. The resource name of the Location to run the job. Format: `projects/{project}/locations/{location}` (required)
+ body: object, The request body.
+ The object takes the form of:
+
+{ # Request message for DataFoundryService.GenerateSyntheticData.
+ "count": 42, # Required. The number of synthetic examples to generate. For this stateless API, the count is limited to a small number.
+ "examples": [ # Optional. A list of few-shot examples to guide the model's output style and format.
+ { # Represents a single synthetic example, composed of multiple fields. Used for providing few-shot examples in the request and for returning generated examples in the response.
+ "fields": [ # Required. A list of fields that constitute an example.
+ { # Represents a single named field within a SyntheticExample.
+ "content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Required. The content of the field.
+ "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
+ { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
+ "codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
+ "outcome": "A String", # Required. Outcome of the code execution.
+ "output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
+ },
+ "executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
+ "code": "A String", # Required. The code to be executed.
+ "language": "A String", # Required. Programming language of the `code`.
+ },
+ "fileData": { # URI based data. # Optional. URI based data.
+ "displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "fileUri": "A String", # Required. URI.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
+ "args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
+ "a_key": "", # Properties of the object.
+ },
+ "id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
+ },
+ "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.
+ "id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
+ "response": { # Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.
+ "a_key": "", # Properties of the object.
+ },
+ },
+ "inlineData": { # Content blob. # Optional. Inlined bytes data.
+ "data": "A String", # Required. Raw bytes.
+ "displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "text": "A String", # Optional. Text part (can be code).
+ "thought": True or False, # Optional. Indicates if the part is thought from the model.
+ "thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
+ "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
+ "endOffset": "A String", # Optional. The end offset of the video.
+ "fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
+ "startOffset": "A String", # Optional. The start offset of the video.
+ },
+ },
+ ],
+ "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
+ },
+ "fieldName": "A String", # Optional. The name of the field.
+ },
+ ],
+ },
+ ],
+ "outputFieldSpecs": [ # Required. The schema of the desired output, defined by a list of fields.
+ { # Defines a specification for a single output field.
+ "fieldName": "A String", # Required. The name of the output field.
+ "fieldType": "A String", # Optional. The data type of the field. Defaults to CONTENT if not set.
+ "guidance": "A String", # Optional. Optional, but recommended. Additional guidance specific to this field to provide targeted instructions for the LLM to generate the content of a single output field. While the LLM can sometimes infer content from the field name, providing explicit guidance is preferred.
+ },
+ ],
+ "taskDescription": { # Defines a generation strategy based on a high-level task description. # Generate data from a high-level task description.
+ "taskDescription": "A String", # Required. A high-level description of the synthetic data to be generated.
+ },
+}
+
+ x__xgafv: string, V1 error format.
+ Allowed values
+ 1 - v1 error format
+ 2 - v2 error format
+
+Returns:
+ An object of the form:
+
+ { # The response containing the generated data.
+ "syntheticExamples": [ # A list of generated synthetic examples.
+ { # Represents a single synthetic example, composed of multiple fields. Used for providing few-shot examples in the request and for returning generated examples in the response.
+ "fields": [ # Required. A list of fields that constitute an example.
+ { # Represents a single named field within a SyntheticExample.
+ "content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Required. The content of the field.
+ "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
+ { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
+ "codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
+ "outcome": "A String", # Required. Outcome of the code execution.
+ "output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
+ },
+ "executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
+ "code": "A String", # Required. The code to be executed.
+ "language": "A String", # Required. Programming language of the `code`.
+ },
+ "fileData": { # URI based data. # Optional. URI based data.
+ "displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "fileUri": "A String", # Required. URI.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
+ "args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
+ "a_key": "", # Properties of the object.
+ },
+ "id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
+ },
+ "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.
+ "id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
+ "response": { # Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.
+ "a_key": "", # Properties of the object.
+ },
+ },
+ "inlineData": { # Content blob. # Optional. Inlined bytes data.
+ "data": "A String", # Required. Raw bytes.
+ "displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "text": "A String", # Optional. Text part (can be code).
+ "thought": True or False, # Optional. Indicates if the part is thought from the model.
+ "thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
+ "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
+ "endOffset": "A String", # Optional. The end offset of the video.
+ "fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
+ "startOffset": "A String", # Optional. The start offset of the video.
+ },
+ },
+ ],
+ "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
+ },
+ "fieldName": "A String", # Optional. The name of the field.
+ },
+ ],
+ },
+ ],
+}
+get(name, x__xgafv=None)
Gets information about a location. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.indexEndpoints.html b/docs/dyn/aiplatform_v1beta1.projects.locations.indexEndpoints.html index 80cd7572a80..85ab548e560 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.indexEndpoints.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.indexEndpoints.html @@ -143,6 +143,9 @@Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`. { # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count. "metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count` + "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info + "a_key": "A String", + }, "target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided. }, ], @@ -333,6 +336,9 @@Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`. { # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count. "metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count` + "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info + "a_key": "A String", + }, "target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided. }, ], @@ -587,6 +593,9 @@Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`. { # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count. "metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count` + "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info + "a_key": "A String", + }, "target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided. }, ], @@ -727,6 +736,9 @@Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`. { # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count. "metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count` + "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info + "a_key": "A String", + }, "target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided. }, ], @@ -870,6 +882,9 @@Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`. { # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count. "metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count` + "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info + "a_key": "A String", + }, "target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided. }, ], @@ -990,6 +1005,9 @@Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`. { # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count. "metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count` + "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info + "a_key": "A String", + }, "target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided. }, ], @@ -1118,6 +1136,9 @@Method Details
"autoscalingMetricSpecs": [ # Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`. { # The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count. "metricName": "A String", # Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count` + "monitoredResourceLabels": { # Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info + "a_key": "A String", + }, "target": 42, # The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided. }, ], diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.publishers.models.html b/docs/dyn/aiplatform_v1beta1.projects.locations.publishers.models.html index 08feb511f26..1de9ad88a44 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.publishers.models.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.publishers.models.html @@ -504,35 +504,6 @@Method Details
}, ], "googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model. - "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead. - "apiKeyConfig": { # The API secret. # The API secret. - "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version} - "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set. - }, - }, - "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported. - "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth. - "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource. - "apiKeyString": "A String", # Optional. The API key to be used in the request directly. - "httpElementLocation": "A String", # Optional. The location of the API key. - "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name. - }, - "authType": "A String", # Type of auth scheme. - "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth. - "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension. - }, - "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth. - "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource. - }, - "oauthConfig": { # Config for user oauth. # Config for user oauth. - "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time. - "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account. - }, - "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth. - "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time. - "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents). - }, - }, }, "googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. "excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"]. @@ -1092,35 +1063,6 @@Method Details
}, ], "googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model. - "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead. - "apiKeyConfig": { # The API secret. # The API secret. - "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version} - "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set. - }, - }, - "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported. - "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth. - "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource. - "apiKeyString": "A String", # Optional. The API key to be used in the request directly. - "httpElementLocation": "A String", # Optional. The location of the API key. - "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name. - }, - "authType": "A String", # Type of auth scheme. - "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth. - "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension. - }, - "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth. - "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource. - }, - "oauthConfig": { # Config for user oauth. # Config for user oauth. - "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time. - "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account. - }, - "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth. - "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time. - "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents). - }, - }, }, "googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. "excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"]. @@ -2191,35 +2133,6 @@Method Details
}, ], "googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model. - "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead. - "apiKeyConfig": { # The API secret. # The API secret. - "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version} - "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set. - }, - }, - "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API. Only API key is supported. - "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth. - "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource. - "apiKeyString": "A String", # Optional. The API key to be used in the request directly. - "httpElementLocation": "A String", # Optional. The location of the API key. - "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name. - }, - "authType": "A String", # Type of auth scheme. - "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth. - "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension. - }, - "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth. - "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource. - }, - "oauthConfig": { # Config for user oauth. # Config for user oauth. - "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time. - "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account. - }, - "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth. - "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time. - "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents). - }, - }, }, "googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. "excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"]. diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.ragCorpora.ragFiles.html b/docs/dyn/aiplatform_v1beta1.projects.locations.ragCorpora.ragFiles.html index 708927a6ea2..21f49d3f237 100644 --- a/docs/dyn/aiplatform_v1beta1.projects.locations.ragCorpora.ragFiles.html +++ b/docs/dyn/aiplatform_v1beta1.projects.locations.ragCorpora.ragFiles.html @@ -83,7 +83,7 @@Instance Methods
close()Close httplib2 connections.
+delete(name, forceDelete=None, x__xgafv=None)Deletes a RagFile.
@@ -104,11 +104,12 @@Method Details
delete(name, x__xgafv=None)
+ delete(name, forceDelete=None, x__xgafv=None)
Deletes a RagFile.
Args:
name: string, Required. The name of the RagFile resource to be deleted. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}` (required)
+ forceDelete: boolean, Optional. If set to true, any errors generated by external vector database during the deletion will be ignored. The default value is false.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.html b/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.html
index 01d1b35846f..f732bfa4966 100644
--- a/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.html
+++ b/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.html
@@ -144,12 +144,100 @@ Method Details
{ # ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.
"contextSpec": { # Configuration for how Agent Engine sub-resources should manage context. # Optional. Configuration for how Agent Engine sub-resources should manage context.
"memoryBankConfig": { # Specification for a Memory Bank. # Optional. Specification for a Memory Bank, which manages memories for the Agent Engine.
+ "customizationConfigs": [ # Optional. Configuration for how to customize Memory Bank behavior for a particular scope.
+ { # Configuration for organizing memories for a particular scope.
+ "generateMemoriesExamples": [ # Optional. Examples of how to generate memories for a particular scope.
+ { # An example of how to generate memories for a particular scope.
+ "conversationSource": { # A conversation source for the example. This is similar to `DirectContentsSource`. # A conversation source for the example.
+ "events": [ # Optional. The input conversation events for the example.
+ { # A single conversation event.
+ "content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Required. The content of the event.
+ "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
+ { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
+ "codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
+ "outcome": "A String", # Required. Outcome of the code execution.
+ "output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
+ },
+ "executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
+ "code": "A String", # Required. The code to be executed.
+ "language": "A String", # Required. Programming language of the `code`.
+ },
+ "fileData": { # URI based data. # Optional. URI based data.
+ "displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "fileUri": "A String", # Required. URI.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
+ "args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
+ "a_key": "", # Properties of the object.
+ },
+ "id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
+ },
+ "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.
+ "id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
+ "response": { # Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.
+ "a_key": "", # Properties of the object.
+ },
+ },
+ "inlineData": { # Content blob. # Optional. Inlined bytes data.
+ "data": "A String", # Required. Raw bytes.
+ "displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "text": "A String", # Optional. Text part (can be code).
+ "thought": True or False, # Optional. Indicates if the part is thought from the model.
+ "thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
+ "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
+ "endOffset": "A String", # Optional. The end offset of the video.
+ "fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
+ "startOffset": "A String", # Optional. The start offset of the video.
+ },
+ },
+ ],
+ "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
+ },
+ },
+ ],
+ },
+ "generatedMemories": [ # Optional. The memories that are expected to be generated from the input conversation. An empty list indicates that no memories are expected to be generated for the input conversation.
+ { # A memory generated by the operation.
+ "fact": "A String", # Required. The fact to generate a memory from.
+ },
+ ],
+ },
+ ],
+ "memoryTopics": [ # Optional. Topics of information that should be extracted from conversations and stored as memories. If not set, then Memory Bank's default topics will be used.
+ { # A topic of information that should be extracted from conversations and stored as memories.
+ "customMemoryTopic": { # A custom memory topic defined by the developer. # A custom memory topic defined by the developer.
+ "description": "A String", # Required. Description of the memory topic. This should explain what information should be extracted for this topic.
+ "label": "A String", # Required. The label of the topic.
+ },
+ "managedMemoryTopic": { # A managed memory topic defined by the system. # A managed memory topic defined by Memory Bank.
+ "managedTopicEnum": "A String", # Required. The managed topic.
+ },
+ },
+ ],
+ "scopeKeys": [ # Optional. The scope keys (i.e. 'user_id') for which to use this config. A request's scope must include all of the provided keys for the config to be used (order does not matter). If empty, then the config will be used for all requests that do not have a more specific config. Only one default config is allowed per Memory Bank.
+ "A String",
+ ],
+ },
+ ],
"generationConfig": { # Configuration for how to generate memories. # Optional. Configuration for how to generate memories for the Memory Bank.
"model": "A String", # Required. The model used to generate memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"similaritySearchConfig": { # Configuration for how to perform similarity search on memories. # Optional. Configuration for how to perform similarity search on memories. If not set, the Memory Bank will use the default embedding model `text-embedding-005`.
"embeddingModel": "A String", # Required. The model used to generate embeddings to lookup similar memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
+ "ttlConfig": { # Configuration for automatically setting the TTL ("time-to-live") of the memories in the Memory Bank. # Optional. Configuration for automatic TTL ("time-to-live") of the memories in the Memory Bank. If not set, TTL will not be applied automatically. The TTL can be explicitly set by modifying the `expire_time` of each Memory resource.
+ "defaultTtl": "A String", # Optional. The default TTL duration of the memories in the Memory Bank. This applies to all operations that create or update a memory.
+ "granularTtlConfig": { # Configuration for TTL of the memories in the Memory Bank based on the action that created or updated the memory. # Optional. The granular TTL configuration of the memories in the Memory Bank.
+ "createTtl": "A String", # Optional. The TTL duration for memories uploaded via CreateMemory.
+ "generateCreatedTtl": "A String", # Optional. The TTL duration for memories newly generated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED).
+ "generateUpdatedTtl": "A String", # Optional. The TTL duration for memories updated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED). In the case of an UPDATE action, the `expire_time` of the existing memory will be updated to the new value (now + TTL).
+ },
+ },
},
},
"createTime": "A String", # Output only. Timestamp when this ReasoningEngine was created.
@@ -175,8 +263,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -187,7 +275,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
@@ -293,12 +381,100 @@ Method Details
{ # ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.
"contextSpec": { # Configuration for how Agent Engine sub-resources should manage context. # Optional. Configuration for how Agent Engine sub-resources should manage context.
"memoryBankConfig": { # Specification for a Memory Bank. # Optional. Specification for a Memory Bank, which manages memories for the Agent Engine.
+ "customizationConfigs": [ # Optional. Configuration for how to customize Memory Bank behavior for a particular scope.
+ { # Configuration for organizing memories for a particular scope.
+ "generateMemoriesExamples": [ # Optional. Examples of how to generate memories for a particular scope.
+ { # An example of how to generate memories for a particular scope.
+ "conversationSource": { # A conversation source for the example. This is similar to `DirectContentsSource`. # A conversation source for the example.
+ "events": [ # Optional. The input conversation events for the example.
+ { # A single conversation event.
+ "content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Required. The content of the event.
+ "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
+ { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
+ "codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
+ "outcome": "A String", # Required. Outcome of the code execution.
+ "output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
+ },
+ "executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
+ "code": "A String", # Required. The code to be executed.
+ "language": "A String", # Required. Programming language of the `code`.
+ },
+ "fileData": { # URI based data. # Optional. URI based data.
+ "displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "fileUri": "A String", # Required. URI.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
+ "args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
+ "a_key": "", # Properties of the object.
+ },
+ "id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
+ },
+ "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.
+ "id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
+ "response": { # Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.
+ "a_key": "", # Properties of the object.
+ },
+ },
+ "inlineData": { # Content blob. # Optional. Inlined bytes data.
+ "data": "A String", # Required. Raw bytes.
+ "displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "text": "A String", # Optional. Text part (can be code).
+ "thought": True or False, # Optional. Indicates if the part is thought from the model.
+ "thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
+ "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
+ "endOffset": "A String", # Optional. The end offset of the video.
+ "fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
+ "startOffset": "A String", # Optional. The start offset of the video.
+ },
+ },
+ ],
+ "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
+ },
+ },
+ ],
+ },
+ "generatedMemories": [ # Optional. The memories that are expected to be generated from the input conversation. An empty list indicates that no memories are expected to be generated for the input conversation.
+ { # A memory generated by the operation.
+ "fact": "A String", # Required. The fact to generate a memory from.
+ },
+ ],
+ },
+ ],
+ "memoryTopics": [ # Optional. Topics of information that should be extracted from conversations and stored as memories. If not set, then Memory Bank's default topics will be used.
+ { # A topic of information that should be extracted from conversations and stored as memories.
+ "customMemoryTopic": { # A custom memory topic defined by the developer. # A custom memory topic defined by the developer.
+ "description": "A String", # Required. Description of the memory topic. This should explain what information should be extracted for this topic.
+ "label": "A String", # Required. The label of the topic.
+ },
+ "managedMemoryTopic": { # A managed memory topic defined by the system. # A managed memory topic defined by Memory Bank.
+ "managedTopicEnum": "A String", # Required. The managed topic.
+ },
+ },
+ ],
+ "scopeKeys": [ # Optional. The scope keys (i.e. 'user_id') for which to use this config. A request's scope must include all of the provided keys for the config to be used (order does not matter). If empty, then the config will be used for all requests that do not have a more specific config. Only one default config is allowed per Memory Bank.
+ "A String",
+ ],
+ },
+ ],
"generationConfig": { # Configuration for how to generate memories. # Optional. Configuration for how to generate memories for the Memory Bank.
"model": "A String", # Required. The model used to generate memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"similaritySearchConfig": { # Configuration for how to perform similarity search on memories. # Optional. Configuration for how to perform similarity search on memories. If not set, the Memory Bank will use the default embedding model `text-embedding-005`.
"embeddingModel": "A String", # Required. The model used to generate embeddings to lookup similar memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
+ "ttlConfig": { # Configuration for automatically setting the TTL ("time-to-live") of the memories in the Memory Bank. # Optional. Configuration for automatic TTL ("time-to-live") of the memories in the Memory Bank. If not set, TTL will not be applied automatically. The TTL can be explicitly set by modifying the `expire_time` of each Memory resource.
+ "defaultTtl": "A String", # Optional. The default TTL duration of the memories in the Memory Bank. This applies to all operations that create or update a memory.
+ "granularTtlConfig": { # Configuration for TTL of the memories in the Memory Bank based on the action that created or updated the memory. # Optional. The granular TTL configuration of the memories in the Memory Bank.
+ "createTtl": "A String", # Optional. The TTL duration for memories uploaded via CreateMemory.
+ "generateCreatedTtl": "A String", # Optional. The TTL duration for memories newly generated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED).
+ "generateUpdatedTtl": "A String", # Optional. The TTL duration for memories updated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED). In the case of an UPDATE action, the `expire_time` of the existing memory will be updated to the new value (now + TTL).
+ },
+ },
},
},
"createTime": "A String", # Output only. Timestamp when this ReasoningEngine was created.
@@ -324,8 +500,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -336,7 +512,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
@@ -384,12 +560,100 @@ Method Details
{ # ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.
"contextSpec": { # Configuration for how Agent Engine sub-resources should manage context. # Optional. Configuration for how Agent Engine sub-resources should manage context.
"memoryBankConfig": { # Specification for a Memory Bank. # Optional. Specification for a Memory Bank, which manages memories for the Agent Engine.
+ "customizationConfigs": [ # Optional. Configuration for how to customize Memory Bank behavior for a particular scope.
+ { # Configuration for organizing memories for a particular scope.
+ "generateMemoriesExamples": [ # Optional. Examples of how to generate memories for a particular scope.
+ { # An example of how to generate memories for a particular scope.
+ "conversationSource": { # A conversation source for the example. This is similar to `DirectContentsSource`. # A conversation source for the example.
+ "events": [ # Optional. The input conversation events for the example.
+ { # A single conversation event.
+ "content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Required. The content of the event.
+ "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
+ { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
+ "codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
+ "outcome": "A String", # Required. Outcome of the code execution.
+ "output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
+ },
+ "executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
+ "code": "A String", # Required. The code to be executed.
+ "language": "A String", # Required. Programming language of the `code`.
+ },
+ "fileData": { # URI based data. # Optional. URI based data.
+ "displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "fileUri": "A String", # Required. URI.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
+ "args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
+ "a_key": "", # Properties of the object.
+ },
+ "id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
+ },
+ "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.
+ "id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
+ "response": { # Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.
+ "a_key": "", # Properties of the object.
+ },
+ },
+ "inlineData": { # Content blob. # Optional. Inlined bytes data.
+ "data": "A String", # Required. Raw bytes.
+ "displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "text": "A String", # Optional. Text part (can be code).
+ "thought": True or False, # Optional. Indicates if the part is thought from the model.
+ "thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
+ "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
+ "endOffset": "A String", # Optional. The end offset of the video.
+ "fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
+ "startOffset": "A String", # Optional. The start offset of the video.
+ },
+ },
+ ],
+ "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
+ },
+ },
+ ],
+ },
+ "generatedMemories": [ # Optional. The memories that are expected to be generated from the input conversation. An empty list indicates that no memories are expected to be generated for the input conversation.
+ { # A memory generated by the operation.
+ "fact": "A String", # Required. The fact to generate a memory from.
+ },
+ ],
+ },
+ ],
+ "memoryTopics": [ # Optional. Topics of information that should be extracted from conversations and stored as memories. If not set, then Memory Bank's default topics will be used.
+ { # A topic of information that should be extracted from conversations and stored as memories.
+ "customMemoryTopic": { # A custom memory topic defined by the developer. # A custom memory topic defined by the developer.
+ "description": "A String", # Required. Description of the memory topic. This should explain what information should be extracted for this topic.
+ "label": "A String", # Required. The label of the topic.
+ },
+ "managedMemoryTopic": { # A managed memory topic defined by the system. # A managed memory topic defined by Memory Bank.
+ "managedTopicEnum": "A String", # Required. The managed topic.
+ },
+ },
+ ],
+ "scopeKeys": [ # Optional. The scope keys (i.e. 'user_id') for which to use this config. A request's scope must include all of the provided keys for the config to be used (order does not matter). If empty, then the config will be used for all requests that do not have a more specific config. Only one default config is allowed per Memory Bank.
+ "A String",
+ ],
+ },
+ ],
"generationConfig": { # Configuration for how to generate memories. # Optional. Configuration for how to generate memories for the Memory Bank.
"model": "A String", # Required. The model used to generate memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"similaritySearchConfig": { # Configuration for how to perform similarity search on memories. # Optional. Configuration for how to perform similarity search on memories. If not set, the Memory Bank will use the default embedding model `text-embedding-005`.
"embeddingModel": "A String", # Required. The model used to generate embeddings to lookup similar memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
+ "ttlConfig": { # Configuration for automatically setting the TTL ("time-to-live") of the memories in the Memory Bank. # Optional. Configuration for automatic TTL ("time-to-live") of the memories in the Memory Bank. If not set, TTL will not be applied automatically. The TTL can be explicitly set by modifying the `expire_time` of each Memory resource.
+ "defaultTtl": "A String", # Optional. The default TTL duration of the memories in the Memory Bank. This applies to all operations that create or update a memory.
+ "granularTtlConfig": { # Configuration for TTL of the memories in the Memory Bank based on the action that created or updated the memory. # Optional. The granular TTL configuration of the memories in the Memory Bank.
+ "createTtl": "A String", # Optional. The TTL duration for memories uploaded via CreateMemory.
+ "generateCreatedTtl": "A String", # Optional. The TTL duration for memories newly generated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED).
+ "generateUpdatedTtl": "A String", # Optional. The TTL duration for memories updated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED). In the case of an UPDATE action, the `expire_time` of the existing memory will be updated to the new value (now + TTL).
+ },
+ },
},
},
"createTime": "A String", # Output only. Timestamp when this ReasoningEngine was created.
@@ -415,8 +679,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -427,7 +691,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
@@ -480,12 +744,100 @@ Method Details
{ # ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.
"contextSpec": { # Configuration for how Agent Engine sub-resources should manage context. # Optional. Configuration for how Agent Engine sub-resources should manage context.
"memoryBankConfig": { # Specification for a Memory Bank. # Optional. Specification for a Memory Bank, which manages memories for the Agent Engine.
+ "customizationConfigs": [ # Optional. Configuration for how to customize Memory Bank behavior for a particular scope.
+ { # Configuration for organizing memories for a particular scope.
+ "generateMemoriesExamples": [ # Optional. Examples of how to generate memories for a particular scope.
+ { # An example of how to generate memories for a particular scope.
+ "conversationSource": { # A conversation source for the example. This is similar to `DirectContentsSource`. # A conversation source for the example.
+ "events": [ # Optional. The input conversation events for the example.
+ { # A single conversation event.
+ "content": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Required. The content of the event.
+ "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
+ { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
+ "codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
+ "outcome": "A String", # Required. Outcome of the code execution.
+ "output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
+ },
+ "executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
+ "code": "A String", # Required. The code to be executed.
+ "language": "A String", # Required. Programming language of the `code`.
+ },
+ "fileData": { # URI based data. # Optional. URI based data.
+ "displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "fileUri": "A String", # Required. URI.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
+ "args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
+ "a_key": "", # Properties of the object.
+ },
+ "id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
+ },
+ "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.
+ "id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
+ "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
+ "response": { # Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.
+ "a_key": "", # Properties of the object.
+ },
+ },
+ "inlineData": { # Content blob. # Optional. Inlined bytes data.
+ "data": "A String", # Required. Raw bytes.
+ "displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
+ "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
+ },
+ "text": "A String", # Optional. Text part (can be code).
+ "thought": True or False, # Optional. Indicates if the part is thought from the model.
+ "thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
+ "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
+ "endOffset": "A String", # Optional. The end offset of the video.
+ "fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
+ "startOffset": "A String", # Optional. The start offset of the video.
+ },
+ },
+ ],
+ "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
+ },
+ },
+ ],
+ },
+ "generatedMemories": [ # Optional. The memories that are expected to be generated from the input conversation. An empty list indicates that no memories are expected to be generated for the input conversation.
+ { # A memory generated by the operation.
+ "fact": "A String", # Required. The fact to generate a memory from.
+ },
+ ],
+ },
+ ],
+ "memoryTopics": [ # Optional. Topics of information that should be extracted from conversations and stored as memories. If not set, then Memory Bank's default topics will be used.
+ { # A topic of information that should be extracted from conversations and stored as memories.
+ "customMemoryTopic": { # A custom memory topic defined by the developer. # A custom memory topic defined by the developer.
+ "description": "A String", # Required. Description of the memory topic. This should explain what information should be extracted for this topic.
+ "label": "A String", # Required. The label of the topic.
+ },
+ "managedMemoryTopic": { # A managed memory topic defined by the system. # A managed memory topic defined by Memory Bank.
+ "managedTopicEnum": "A String", # Required. The managed topic.
+ },
+ },
+ ],
+ "scopeKeys": [ # Optional. The scope keys (i.e. 'user_id') for which to use this config. A request's scope must include all of the provided keys for the config to be used (order does not matter). If empty, then the config will be used for all requests that do not have a more specific config. Only one default config is allowed per Memory Bank.
+ "A String",
+ ],
+ },
+ ],
"generationConfig": { # Configuration for how to generate memories. # Optional. Configuration for how to generate memories for the Memory Bank.
"model": "A String", # Required. The model used to generate memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
"similaritySearchConfig": { # Configuration for how to perform similarity search on memories. # Optional. Configuration for how to perform similarity search on memories. If not set, the Memory Bank will use the default embedding model `text-embedding-005`.
"embeddingModel": "A String", # Required. The model used to generate embeddings to lookup similar memories. Format: `projects/{project}/locations/{location}/publishers/google/models/{model}`.
},
+ "ttlConfig": { # Configuration for automatically setting the TTL ("time-to-live") of the memories in the Memory Bank. # Optional. Configuration for automatic TTL ("time-to-live") of the memories in the Memory Bank. If not set, TTL will not be applied automatically. The TTL can be explicitly set by modifying the `expire_time` of each Memory resource.
+ "defaultTtl": "A String", # Optional. The default TTL duration of the memories in the Memory Bank. This applies to all operations that create or update a memory.
+ "granularTtlConfig": { # Configuration for TTL of the memories in the Memory Bank based on the action that created or updated the memory. # Optional. The granular TTL configuration of the memories in the Memory Bank.
+ "createTtl": "A String", # Optional. The TTL duration for memories uploaded via CreateMemory.
+ "generateCreatedTtl": "A String", # Optional. The TTL duration for memories newly generated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED).
+ "generateUpdatedTtl": "A String", # Optional. The TTL duration for memories updated via GenerateMemories (GenerateMemoriesResponse.GeneratedMemory.Action.CREATED). In the case of an UPDATE action, the `expire_time` of the existing memory will be updated to the new value (now + TTL).
+ },
+ },
},
},
"createTime": "A String", # Output only. Timestamp when this ReasoningEngine was created.
@@ -511,8 +863,8 @@ Method Details
"value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
},
],
- "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100.
- "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1.
+ "maxInstances": 42, # Optional. The maximum number of application instances that can be launched to handle increased traffic. Defaults to 100. Range: [1, 1000]. If VPC-SC or PSC-I is enabled, the acceptable range is [1, 100].
+ "minInstances": 42, # Optional. The minimum number of application instances that will be kept running at all times. Defaults to 1. Range: [0, 10].
"pscInterfaceConfig": { # Configuration for PSC-I. # Optional. Configuration for PSC-I.
"dnsPeeringConfigs": [ # Optional. DNS peering configurations. When specified, Vertex AI will attempt to configure DNS peering zones in the tenant project VPC to resolve the specified domains using the target network's Cloud DNS. The user must grant the dns.peer role to the Vertex AI Service Agent on the target project.
{ # DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.
@@ -523,7 +875,7 @@ Method Details
],
"networkAttachment": "A String", # Optional. The name of the Compute Engine [network attachment](https://cloud.google.com/vpc/docs/about-network-attachments) to attach to the resource within the region and user project. To specify this field, you must have already [created a network attachment] (https://cloud.google.com/vpc/docs/create-manage-network-attachments#create-network-attachments). This field is only used for resources using PSC-I.
},
- "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * For supported 'memory' values and syntax, go to https://cloud.google.com/run/docs/configuring/memory-limits
+ "resourceLimits": { # Optional. Resource limits for each container. Only 'cpu' and 'memory' keys are supported. Defaults to {"cpu": "4", "memory": "4Gi"}. * The only supported values for CPU are '1', '2', '4', '6' and '8'. For more information, go to https://cloud.google.com/run/docs/configuring/cpu. * The only supported values for memory are '1Gi', '2Gi', ... '32 Gi'. * For required cpu on different memory values, go to https://cloud.google.com/run/docs/configuring/memory-limits
"a_key": "A String",
},
"secretEnv": [ # Optional. Environment variables where the value is a secret in Cloud Secret Manager. To use this feature, add 'Secret Manager Secret Accessor' role (roles/secretmanager.secretAccessor) to AI Platform Reasoning Engine Service Agent.
diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.memories.html b/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.memories.html
index f69f23d7175..b3300c825e0 100644
--- a/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.memories.html
+++ b/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.memories.html
@@ -208,7 +208,7 @@ Method Details
body: object, The request body.
The object takes the form of:
-{ # Request message for MemoryBankService.GenerateMemories.
+{ # Request message for MemoryBankService.GenerateMemories. Maximum size is 8 MB.
"directContentsSource": { # Defines a direct source of content from which to generate the memories. # Defines a direct source of content as the source content from which to generate memories.
"events": [ # Required. The source content (i.e. chat history) to generate memories from.
{ # A single piece of conversation from which to generate memories.
diff --git a/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.sandboxEnvironments.html b/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.sandboxEnvironments.html
index d1b0b979a79..bfb55a75d79 100644
--- a/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.sandboxEnvironments.html
+++ b/docs/dyn/aiplatform_v1beta1.projects.locations.reasoningEngines.sandboxEnvironments.html
@@ -82,10 +82,271 @@ Instance Methods
Close httplib2 connections.
+
+ create(parent, body=None, x__xgafv=None)
+Creates a SandboxEnvironment in a given reasoning engine.
+
+Deletes the specific SandboxEnvironment.
+
+ execute(name, body=None, x__xgafv=None)
+Executes using a sandbox environment.
+
+Gets details of the specific SandboxEnvironment.
+
+ list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)
+Lists SandboxEnvironments in a given reasoning engine.
+
+Retrieves the next page of results.
Method Details
close()
Close httplib2 connections.
+
+ create(parent, body=None, x__xgafv=None)
+ Creates a SandboxEnvironment in a given reasoning engine.
+
+Args:
+ parent: string, Required. The resource name of the reasoning engine to create the SandboxEnvironment in. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}`. (required)
+ body: object, The request body.
+ The object takes the form of:
+
+{ # SandboxEnvironment is a containerized environment that provides a customizable secure execution runtime for AI agents.
+ "createTime": "A String", # Output only. The timestamp when this SandboxEnvironment was created.
+ "displayName": "A String", # Required. The display name of the SandboxEnvironment.
+ "metadata": "", # Output only. Additional information about the SandboxEnvironment.
+ "name": "A String", # Identifier. The name of the SandboxEnvironment.
+ "spec": { # The specification of a SandboxEnvironment. # Optional. The configuration of the SandboxEnvironment.
+ "codeExecutionEnvironment": { # The code execution environment with customized settings. # Optional. The code execution environment.
+ "codeLanguage": "A String", # The coding language supported in this environment.
+ "dependencies": [ # Optional. The additional dependencies to install in the code execution environment. For example, "pandas==2.2.3".
+ "A String",
+ ],
+ "env": [ # Optional. The environment variables to set in the code execution environment.
+ { # Represents an environment variable present in a Container or Python Module.
+ "name": "A String", # Required. Name of the environment variable. Must be a valid C identifier.
+ "value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
+ },
+ ],
+ "machineConfig": "A String", # The machine config of the code execution environment.
+ },
+ },
+ "state": "A String", # Output only. The runtime state of the SandboxEnvironment.
+ "updateTime": "A String", # Output only. The timestamp when this SandboxEnvironment was most recently updated.
+}
+
+ x__xgafv: string, V1 error format.
+ Allowed values
+ 1 - v1 error format
+ 2 - v2 error format
+
+Returns:
+ An object of the form:
+
+ { # This resource represents a long-running operation that is the result of a network API call.
+ "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
+ "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
+ "code": 42, # The status code, which should be an enum value of google.rpc.Code.
+ "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
+ {
+ "a_key": "", # Properties of the object. Contains field @type with type URL.
+ },
+ ],
+ "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
+ },
+ "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
+ "a_key": "", # Properties of the object. Contains field @type with type URL.
+ },
+ "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
+ "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
+ "a_key": "", # Properties of the object. Contains field @type with type URL.
+ },
+}
+
+
+
+ delete(name, x__xgafv=None)
+ Deletes the specific SandboxEnvironment.
+
+Args:
+ name: string, Required. The resource name of the SandboxEnvironment to delete. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/sandboxEnvironments/{sandbox_environment}` (required)
+ x__xgafv: string, V1 error format.
+ Allowed values
+ 1 - v1 error format
+ 2 - v2 error format
+
+Returns:
+ An object of the form:
+
+ { # This resource represents a long-running operation that is the result of a network API call.
+ "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
+ "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
+ "code": 42, # The status code, which should be an enum value of google.rpc.Code.
+ "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
+ {
+ "a_key": "", # Properties of the object. Contains field @type with type URL.
+ },
+ ],
+ "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
+ },
+ "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
+ "a_key": "", # Properties of the object. Contains field @type with type URL.
+ },
+ "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
+ "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
+ "a_key": "", # Properties of the object. Contains field @type with type URL.
+ },
+}
+
+
+
+ execute(name, body=None, x__xgafv=None)
+ Executes using a sandbox environment.
+
+Args:
+ name: string, Required. The resource name of the sandbox environment to execute. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/sandboxEnvironments/{sandbox_environment}` (required)
+ body: object, The request body.
+ The object takes the form of:
+
+{ # Request message for SandboxEnvironmentExecutionService.Execute.
+ "inputs": [ # Required. The inputs to the sandbox environment.
+ { # Container for bytes-encoded data such as video frame, audio sample, or a complete binary/text data.
+ "data": "A String", # Required. The data in the chunk.
+ "metadata": { # Metadata for a chunk. # Optional. Metadata that is associated with the data in the payload.
+ "attributes": { # Optional. Attributes attached to the data. The keys have semantic conventions and the consumers of the attributes should know how to deserialize the value bytes based on the keys.
+ "a_key": "A String",
+ },
+ },
+ "mimeType": "A String", # Required. Mime type of the chunk data. See https://www.iana.org/assignments/media-types/media-types.xhtml for the full list.
+ },
+ ],
+}
+
+ x__xgafv: string, V1 error format.
+ Allowed values
+ 1 - v1 error format
+ 2 - v2 error format
+
+Returns:
+ An object of the form:
+
+ { # Response message for SandboxEnvironmentExecutionService.Execute.
+ "outputs": [ # The outputs from the sandbox environment.
+ { # Container for bytes-encoded data such as video frame, audio sample, or a complete binary/text data.
+ "data": "A String", # Required. The data in the chunk.
+ "metadata": { # Metadata for a chunk. # Optional. Metadata that is associated with the data in the payload.
+ "attributes": { # Optional. Attributes attached to the data. The keys have semantic conventions and the consumers of the attributes should know how to deserialize the value bytes based on the keys.
+ "a_key": "A String",
+ },
+ },
+ "mimeType": "A String", # Required. Mime type of the chunk data. See https://www.iana.org/assignments/media-types/media-types.xhtml for the full list.
+ },
+ ],
+}
+
+
+
+ get(name, x__xgafv=None)
+ Gets details of the specific SandboxEnvironment.
+
+Args:
+ name: string, Required. The resource name of the sandbox environment. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/sandboxEnvironments/{sandbox_environment}` (required)
+ x__xgafv: string, V1 error format.
+ Allowed values
+ 1 - v1 error format
+ 2 - v2 error format
+
+Returns:
+ An object of the form:
+
+ { # SandboxEnvironment is a containerized environment that provides a customizable secure execution runtime for AI agents.
+ "createTime": "A String", # Output only. The timestamp when this SandboxEnvironment was created.
+ "displayName": "A String", # Required. The display name of the SandboxEnvironment.
+ "metadata": "", # Output only. Additional information about the SandboxEnvironment.
+ "name": "A String", # Identifier. The name of the SandboxEnvironment.
+ "spec": { # The specification of a SandboxEnvironment. # Optional. The configuration of the SandboxEnvironment.
+ "codeExecutionEnvironment": { # The code execution environment with customized settings. # Optional. The code execution environment.
+ "codeLanguage": "A String", # The coding language supported in this environment.
+ "dependencies": [ # Optional. The additional dependencies to install in the code execution environment. For example, "pandas==2.2.3".
+ "A String",
+ ],
+ "env": [ # Optional. The environment variables to set in the code execution environment.
+ { # Represents an environment variable present in a Container or Python Module.
+ "name": "A String", # Required. Name of the environment variable. Must be a valid C identifier.
+ "value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
+ },
+ ],
+ "machineConfig": "A String", # The machine config of the code execution environment.
+ },
+ },
+ "state": "A String", # Output only. The runtime state of the SandboxEnvironment.
+ "updateTime": "A String", # Output only. The timestamp when this SandboxEnvironment was most recently updated.
+}
+
+
+
+ list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)
+ Lists SandboxEnvironments in a given reasoning engine.
+
+Args:
+ parent: string, Required. The resource name of the reasoning engine to list sandbox environments from. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}` (required)
+ filter: string, Optional. The standard list filter. More detail in [AIP-160](https://google.aip.dev/160).
+ pageSize: integer, Optional. The maximum number of SandboxEnvironments to return. The service may return fewer than this value. If unspecified, at most 100 SandboxEnvironments will be returned.
+ pageToken: string, Optional. The standard list page token, received from a previous `ListSandboxEnvironments` call. Provide this to retrieve the subsequent page.
+ x__xgafv: string, V1 error format.
+ Allowed values
+ 1 - v1 error format
+ 2 - v2 error format
+
+Returns:
+ An object of the form:
+
+ { # Response message for SandboxEnvironmentService.ListSandboxEnvironments.
+ "nextPageToken": "A String", # A token, which can be sent as ListSandboxEnvironmentsRequest.page_token to retrieve the next page. Absence of this field indicates there are no subsequent pages.
+ "sandboxEnvironments": [ # The SandboxEnvironments matching the request.
+ { # SandboxEnvironment is a containerized environment that provides a customizable secure execution runtime for AI agents.
+ "createTime": "A String", # Output only. The timestamp when this SandboxEnvironment was created.
+ "displayName": "A String", # Required. The display name of the SandboxEnvironment.
+ "metadata": "", # Output only. Additional information about the SandboxEnvironment.
+ "name": "A String", # Identifier. The name of the SandboxEnvironment.
+ "spec": { # The specification of a SandboxEnvironment. # Optional. The configuration of the SandboxEnvironment.
+ "codeExecutionEnvironment": { # The code execution environment with customized settings. # Optional. The code execution environment.
+ "codeLanguage": "A String", # The coding language supported in this environment.
+ "dependencies": [ # Optional. The additional dependencies to install in the code execution environment. For example, "pandas==2.2.3".
+ "A String",
+ ],
+ "env": [ # Optional. The environment variables to set in the code execution environment.
+ { # Represents an environment variable present in a Container or Python Module.
+ "name": "A String", # Required. Name of the environment variable. Must be a valid C identifier.
+ "value": "A String", # Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
+ },
+ ],
+ "machineConfig": "A String", # The machine config of the code execution environment.
+ },
+ },
+ "state": "A String", # Output only. The runtime state of the SandboxEnvironment.
+ "updateTime": "A String", # Output only. The timestamp when this SandboxEnvironment was most recently updated.
+ },
+ ],
+}
+
+
+
+ list_next()
+ Retrieves the next page of results.
+
+ Args:
+ previous_request: The request for the previous page. (required)
+ previous_response: The response from the request for the previous page. (required)
+
+ Returns:
+ A request object that you can call 'execute()' on to request the next
+ page. Returns None if there are no more items in the collection.
+
+
+