sandbox/autopush-apigee-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-apigee-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-apikeys-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-apikeys-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-asia-east1-cloudbuild-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-asia-east1-cloudbuild-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-asia-east1-cloudbuild-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-auditrecording-pa-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-auditrecording-pa-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudaicompanionadmin-
dictionary_item_added
  • root['schemas']['DataTransformerRecommendation']
  • root['schemas']['DataTransformerRequest']
  • root['schemas']['DataTransformerResponse']
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudaicompanionadmin-v1
dictionary_item_added
  • root['schemas']['DataTransformerRecommendation']
  • root['schemas']['DataTransformerRequest']
  • root['schemas']['DataTransformerResponse']
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudaicompanionadmin-v1alpha
dictionary_item_added
  • root['schemas']['DataTransformerRecommendation']
  • root['schemas']['DataTransformerRequest']
  • root['schemas']['DataTransformerResponse']
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudaicompanionadmin-v1beta
dictionary_item_added
  • root['schemas']['DataTransformerRecommendation']
  • root['schemas']['DataTransformerRequest']
  • root['schemas']['DataTransformerResponse']
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudbuild-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudbuild-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudbuild-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudcrmeventbus-pa-
dictionary_item_added
  • root['schemas']['PrivacyDataGovernanceAttributesTargetedPolicySamplingCriteria']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['cmaVersionC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['contractingBusinessProfileIdC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['cpqSecOpsScheduleSignedDateC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['gcpAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['googleWorkspaceAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['legalEntityNameC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['lookerAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['mandiantLawFirmMsaSignedDateC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['psoAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['secOpsAgreementTypeC']
  • root['schemas']['PrivacyDataGovernanceAttributesAnnotationsStorageCustom']['properties']['targetedPolicySamplingCriteria']
values_changed
root['revision']
new_value20250705
old_value20250702
root['schemas']['EnterpriseCrmStorageVectorSite']['description']
new_valueSchema of Site__c table synced from Salesforce. Timestamps are stored in milliseconds. Next available tag: 52
old_valueSchema of Site__c table synced from Salesforce. Timestamps are stored in milliseconds. Next available tag: 42
sandbox/autopush-cloudcrmeventbus-pa-v1
dictionary_item_added
  • root['schemas']['PrivacyDataGovernanceAttributesTargetedPolicySamplingCriteria']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['cmaVersionC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['contractingBusinessProfileIdC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['cpqSecOpsScheduleSignedDateC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['gcpAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['googleWorkspaceAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['legalEntityNameC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['lookerAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['mandiantLawFirmMsaSignedDateC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['psoAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['secOpsAgreementTypeC']
  • root['schemas']['PrivacyDataGovernanceAttributesAnnotationsStorageCustom']['properties']['targetedPolicySamplingCriteria']
values_changed
root['revision']
new_value20250705
old_value20250702
root['schemas']['EnterpriseCrmStorageVectorSite']['description']
new_valueSchema of Site__c table synced from Salesforce. Timestamps are stored in milliseconds. Next available tag: 52
old_valueSchema of Site__c table synced from Salesforce. Timestamps are stored in milliseconds. Next available tag: 42
sandbox/autopush-cloudcrmeventbus-pa-v3
dictionary_item_added
  • root['schemas']['PrivacyDataGovernanceAttributesTargetedPolicySamplingCriteria']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['cmaVersionC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['contractingBusinessProfileIdC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['cpqSecOpsScheduleSignedDateC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['gcpAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['googleWorkspaceAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['legalEntityNameC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['lookerAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['mandiantLawFirmMsaSignedDateC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['psoAgreementTypeC']
  • root['schemas']['EnterpriseCrmStorageVectorSite']['properties']['secOpsAgreementTypeC']
  • root['schemas']['PrivacyDataGovernanceAttributesAnnotationsStorageCustom']['properties']['targetedPolicySamplingCriteria']
values_changed
root['revision']
new_value20250705
old_value20250702
root['schemas']['EnterpriseCrmStorageVectorSite']['description']
new_valueSchema of Site__c table synced from Salesforce. Timestamps are stored in milliseconds. Next available tag: 52
old_valueSchema of Site__c table synced from Salesforce. Timestamps are stored in milliseconds. Next available tag: 42
sandbox/autopush-cloudidentity-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudidentity-pa-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudidentity-pa-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudidentity-pa-v1beta1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudidentity-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-cloudidentity-v1beta1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-dlp-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-dlp-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-dlp-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-drivequal-drivemetadata-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-drivequal-drivemetadata-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-drivequal-drivemetadata-v1alpha
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-emmapplecodevice-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-emmapplecodevice-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-essentialcontacts-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-essentialcontacts-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-essentialcontacts-v1alpha1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-essentialcontacts-v1beta1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-firebasevertexai-
values_changed
root['revision']
new_value20250705
old_value20250702
iterable_item_added
root['schemas']['GoogleAiGenerativelanguageV1betaCandidate']['properties']['finishReason']['enum'][12]UNEXPECTED_TOOL_CALL
root['schemas']['GoogleAiGenerativelanguageV1betaCandidate']['properties']['finishReason']['enumDescriptions'][12]Model generated a tool call but no tools were enabled in the request.
sandbox/autopush-firebasevertexai-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-firebasevertexai-v1beta
values_changed
root['revision']
new_value20250705
old_value20250702
iterable_item_added
root['schemas']['GoogleAiGenerativelanguageV1betaCandidate']['properties']['finishReason']['enum'][12]UNEXPECTED_TOOL_CALL
root['schemas']['GoogleAiGenerativelanguageV1betaCandidate']['properties']['finishReason']['enumDescriptions'][12]Model generated a tool call but no tools were enabled in the request.
sandbox/autopush-gkeauth-
dictionary_item_added
  • root['schemas']['DefaultComputeClassConfig']
  • root['schemas']['ClusterAutoscaling']['properties']['defaultComputeClassConfig']
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-gkeauth-v1
dictionary_item_added
  • root['schemas']['DefaultComputeClassConfig']
  • root['schemas']['ClusterAutoscaling']['properties']['defaultComputeClassConfig']
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-gkeauth-v1beta1
dictionary_item_added
  • root['schemas']['DefaultComputeClassConfig']
  • root['schemas']['ClusterAutoscaling']['properties']['defaultComputeClassConfig']
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-myphonenumbers-pa-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-myphonenumbers-pa-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-networkconnectivity-
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/autopush-networkconnectivity-v1
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/autopush-networkconnectivity-v1alpha1
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/autopush-networkconnectivity-v1beta
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/autopush-notifications-pa-
dictionary_item_added
  • root['schemas']['GoogleInternalHomeFoyerV1Resources__CameraCustomizedNotificationPayload']['properties']['productCode']
values_changed
root['revision']
new_value20250705
old_value20250702
iterable_item_added
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enum'][35]WEEKEND_DUMP
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enumDescriptions'][35]A short video generated from multiple photos with skottie effect for the memories of Best of Month. See go/weekend-dump-prd-new
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['WalletTapandpayCommonApiTransit__TransitAgencyInfo']['properties']['transitHubName']['enum'][18]NAME_HID_CERTIFICATION
sandbox/autopush-notifications-pa-v1
dictionary_item_added
  • root['schemas']['GoogleInternalHomeFoyerV1Resources__CameraCustomizedNotificationPayload']['properties']['productCode']
values_changed
root['revision']
new_value20250705
old_value20250702
iterable_item_added
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enum'][35]WEEKEND_DUMP
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enumDescriptions'][35]A short video generated from multiple photos with skottie effect for the memories of Best of Month. See go/weekend-dump-prd-new
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['WalletTapandpayCommonApiTransit__TransitAgencyInfo']['properties']['transitHubName']['enum'][18]NAME_HID_CERTIFICATION
sandbox/autopush-ogads-pa-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-ogads-pa-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-people-pa-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-people-pa-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-people-pa-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-peoplestack-pa-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-peoplestack-pa-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-policytroubleshooter-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-policytroubleshooter-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-policytroubleshooter-v1beta
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-policytroubleshooter-v2alpha1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-policytroubleshooter-v3
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-policytroubleshooter-v3alpha
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-policytroubleshooter-v3beta
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-recaptchaenterprise-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-recaptchaenterprise-v1
dictionary_item_added
  • root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettingsActionSettings']
  • root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettingsChallengeSettings']
  • root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettings']['properties']['challengeSettings']
values_changed
root['revision']
new_value20250704
old_value20250702
root['schemas']['GoogleCloudRecaptchaenterpriseV1RiskAnalysis']['properties']['challenge']['description']
new_valueOutput only. Challenge information for POLICY_BASED_CHALLENGE and INVISIBLE keys
old_valueOutput only. Challenge information for SCORE_AND_CHALLENGE and INVISIBLE keys
root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettings']['properties']['challengeSecurityPreference']['description']
new_valueOptional. Settings for the frequency and difficulty at which this key triggers captcha challenges. This should only be specified for `IntegrationType` CHECKBOX, INVISIBLE or POLICY_BASED_CHALLENGE.
old_valueOptional. Settings for the frequency and difficulty at which this key triggers captcha challenges. This should only be specified for IntegrationTypes CHECKBOX and INVISIBLE and SCORE_AND_CHALLENGE.
iterable_item_added
root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettings']['properties']['integrationType']['enum'][4]POLICY_BASED_CHALLENGE
root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettings']['properties']['integrationType']['enumDescriptions'][4]Displays a visual challenge or not depending on the user risk analysis score.
sandbox/autopush-recaptchaenterprise-v1beta1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-serviceconsumermanagement-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-serviceconsumermanagement-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-servicemanagement-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-servicemanagement-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-serviceusage-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-serviceusage-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-translationhub-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-translationhub-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-translationhub-v1alpha
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-translationhub-v1beta
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-translationhub-v1main
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-us-central1-cloudbuild-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-us-central1-cloudbuild-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-us-central1-cloudbuild-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/autopush-userguard-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-userguard-v1
dictionary_item_added
  • root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettingsActionSettings']
  • root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettingsChallengeSettings']
  • root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettings']['properties']['challengeSettings']
values_changed
root['revision']
new_value20250704
old_value20250702
root['schemas']['GoogleCloudRecaptchaenterpriseV1RiskAnalysis']['properties']['challenge']['description']
new_valueOutput only. Challenge information for POLICY_BASED_CHALLENGE and INVISIBLE keys
old_valueOutput only. Challenge information for SCORE_AND_CHALLENGE and INVISIBLE keys
root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettings']['properties']['challengeSecurityPreference']['description']
new_valueOptional. Settings for the frequency and difficulty at which this key triggers captcha challenges. This should only be specified for `IntegrationType` CHECKBOX, INVISIBLE or POLICY_BASED_CHALLENGE.
old_valueOptional. Settings for the frequency and difficulty at which this key triggers captcha challenges. This should only be specified for IntegrationTypes CHECKBOX and INVISIBLE and SCORE_AND_CHALLENGE.
iterable_item_added
root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettings']['properties']['integrationType']['enum'][4]POLICY_BASED_CHALLENGE
root['schemas']['GoogleCloudRecaptchaenterpriseV1WebKeySettings']['properties']['integrationType']['enumDescriptions'][4]Displays a visual challenge or not depending on the user risk analysis score.
sandbox/autopush-userguard-v1beta1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/autopush-workstations-
dictionary_item_added
  • root['schemas']['WorkstationBoostConfig']['properties']['running']
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/autopush-workstations-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/autopush-workstations-v1alpha
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/autopush-workstations-v1beta
dictionary_item_added
  • root['schemas']['WorkstationBoostConfig']['properties']['running']
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/content-autopush-apigee-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-autopush-apigee-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-autopush-dlp-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-autopush-dlp-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-autopush-dlp-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-autopush-notifications-pa-
dictionary_item_added
  • root['schemas']['GoogleInternalHomeFoyerV1Resources__CameraCustomizedNotificationPayload']['properties']['productCode']
values_changed
root['revision']
new_value20250705
old_value20250702
iterable_item_added
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enum'][35]WEEKEND_DUMP
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enumDescriptions'][35]A short video generated from multiple photos with skottie effect for the memories of Best of Month. See go/weekend-dump-prd-new
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['WalletTapandpayCommonApiTransit__TransitAgencyInfo']['properties']['transitHubName']['enum'][18]NAME_HID_CERTIFICATION
sandbox/content-autopush-notifications-pa-v1
dictionary_item_added
  • root['schemas']['GoogleInternalHomeFoyerV1Resources__CameraCustomizedNotificationPayload']['properties']['productCode']
values_changed
root['revision']
new_value20250705
old_value20250702
iterable_item_added
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enum'][35]WEEKEND_DUMP
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enumDescriptions'][35]A short video generated from multiple photos with skottie effect for the memories of Best of Month. See go/weekend-dump-prd-new
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['WalletTapandpayCommonApiTransit__TransitAgencyInfo']['properties']['transitHubName']['enum'][18]NAME_HID_CERTIFICATION
sandbox/content-autopush-people-pa-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-autopush-people-pa-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-autopush-people-pa-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-daily-cloudsearch-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-daily-cloudsearch-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-daily-dynamicmail-pa-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/content-daily-dynamicmail-pa-v2
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/content-hourly-dynamicmail-pa-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-hourly-dynamicmail-pa-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/content-staging-firebase-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/content-staging-firebase-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/content-staging-firebase-v1alpha
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/content-staging-firebase-v1beta1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/daily-cloudsearch-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/daily-cloudsearch-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/daily-dynamicmail-pa-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/daily-dynamicmail-pa-v2
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/daily3-emmapplecodevice-
values_changed
root['revision']
new_value20250702
old_value20250625
sandbox/daily3-emmapplecodevice-v1
values_changed
root['revision']
new_value20250702
old_value20250625
sandbox/daily4-emmapplecodevice-
values_changed
root['revision']
new_value20250703
old_value20250626
sandbox/daily4-emmapplecodevice-v1
values_changed
root['revision']
new_value20250703
old_value20250626
sandbox/dev-dialogflow-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-dialogflow-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-dialogflow-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-dialogflow-v2beta1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-dialogflow-v3
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-dialogflow-v3alpha1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-dialogflow-v3beta1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-us-central1-dialogflow-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-us-central1-dialogflow-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-us-central1-dialogflow-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-us-central1-dialogflow-v2beta1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-us-central1-dialogflow-v3
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-us-central1-dialogflow-v3alpha1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/dev-us-central1-dialogflow-v3beta1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/eu-staging-datacatalog-
values_changed
root['revision']
new_value20250704
old_value20250630
sandbox/eu-staging-datacatalog-v1
values_changed
root['revision']
new_value20250704
old_value20250630
sandbox/eu-staging-datacatalog-v1beta1
values_changed
root['revision']
new_value20250704
old_value20250630
sandbox/eu-staging-vision-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/eu-staging-vision-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/eu-staging-vision-v1p1beta1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/eu-staging-vision-v1p2beta1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/eu-staging-vision-v1p3beta1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/eu-staging-vision-v1p4beta1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/hourly-dynamicmail-pa-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/hourly-dynamicmail-pa-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/lite-staging-pubsub-
values_changed
root['revision']
new_value20250701
old_value20250624
sandbox/lite-staging-pubsub-v1
values_changed
root['revision']
new_value20250701
old_value20250624
sandbox/lite-staging-pubsub-v1beta2
values_changed
root['revision']
new_value20250701
old_value20250624
sandbox/ppissuer-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/ppissuer-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/preprod-automotivemaps-
values_changed
root['revision']
new_value20250702
old_value20250630
sandbox/preprod-automotivemaps-v1
values_changed
root['revision']
new_value20250702
old_value20250630
sandbox/preprod-cloudasset-
values_changed
root['revision']
new_value20250703
old_value20250630
sandbox/preprod-cloudasset-v1
values_changed
root['revision']
new_value20250703
old_value20250630
sandbox/preprod-cloudasset-v1beta1
values_changed
root['revision']
new_value20250703
old_value20250630
sandbox/preprod-cloudasset-v1p1beta1
values_changed
root['revision']
new_value20250703
old_value20250630
sandbox/preprod-cloudasset-v1p2alpha1
values_changed
root['revision']
new_value20250703
old_value20250630
sandbox/preprod-cloudasset-v1p2beta1
values_changed
root['revision']
new_value20250703
old_value20250630
sandbox/preprod-cloudasset-v1p5beta1
values_changed
root['revision']
new_value20250703
old_value20250630
sandbox/preprod-cloudasset-v1p7beta1
values_changed
root['revision']
new_value20250703
old_value20250630
sandbox/preprod-hangouts-
values_changed
root['revision']
new_value20250701
old_value20250629
sandbox/preprod-hangouts-v1
values_changed
root['revision']
new_value20250701
old_value20250630
sandbox/staging-accesscontextmanager-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-accesscontextmanager-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-accesscontextmanager-v1alpha
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-aerialview-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-aerialview-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-aerialview-v1beta
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-aida-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-aida-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-aiplatform-
dictionary_item_added
  • root['schemas']['GoogleCloudAiplatformV1beta1DeployedIndex']['properties']['enableDatapointUpsertLogging']
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-aiplatform-v1
dictionary_item_added
  • root['schemas']['GoogleCloudAiplatformV1DeployedIndex']['properties']['enableDatapointUpsertLogging']
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-aiplatform-v1alpha1
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-aiplatform-v1beta1
dictionary_item_added
  • root['schemas']['GoogleCloudAiplatformV1beta1DeployedIndex']['properties']['enableDatapointUpsertLogging']
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-analyticsdata-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-analyticsdata-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-analyticsdata-v1alpha
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-analyticsdata-v1beta
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-analyticssuitefrontend-pa-
values_changed
root['revision']
new_value20250703
old_value20250702
sandbox/staging-analyticssuitefrontend-pa-v1
values_changed
root['revision']
new_value20250703
old_value20250702
sandbox/staging-apigee-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-apigee-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-apikeys-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-apikeys-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-arcorecloudanchor-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-arcorecloudanchor-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-arcorecloudanchor-v1beta2
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-auditrecording-pa-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-auditrecording-pa-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-automotivemaps-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-automotivemaps-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-blobcomments-pa-
dictionary_item_added
  • root['schemas']['CheckBox']['properties']['onValue']
values_changed
root['revision']
new_value20250703
old_value20250626
sandbox/staging-blobcomments-pa-v1
dictionary_item_added
  • root['schemas']['CheckBox']['properties']['onValue']
values_changed
root['revision']
new_value20250703
old_value20250626
sandbox/staging-capacityplanner-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-capacityplanner-v1alpha
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-cloudaicompanionadmin-
dictionary_item_added
  • root['schemas']['DataTransformerRecommendation']
  • root['schemas']['DataTransformerRequest']
  • root['schemas']['DataTransformerResponse']
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-cloudaicompanionadmin-v1
dictionary_item_added
  • root['schemas']['DataTransformerRecommendation']
  • root['schemas']['DataTransformerRequest']
  • root['schemas']['DataTransformerResponse']
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-cloudaicompanionadmin-v1alpha
dictionary_item_added
  • root['schemas']['DataTransformerRecommendation']
  • root['schemas']['DataTransformerRequest']
  • root['schemas']['DataTransformerResponse']
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-cloudaicompanionadmin-v1beta
dictionary_item_added
  • root['schemas']['DataTransformerRecommendation']
  • root['schemas']['DataTransformerRequest']
  • root['schemas']['DataTransformerResponse']
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-cloudasset-
values_changed
root['revision']
new_value20250705
old_value20250701
sandbox/staging-cloudasset-v1
dictionary_item_added
  • root['resources']['v1']['methods']['analyzeIamPolicy']['parameters']['ciemDiscoverySessionId']
values_changed
root['revision']
new_value20250705
old_value20250701
sandbox/staging-cloudasset-v1beta1
values_changed
root['revision']
new_value20250705
old_value20250701
sandbox/staging-cloudasset-v1p1beta1
values_changed
root['revision']
new_value20250705
old_value20250701
sandbox/staging-cloudasset-v1p2alpha1
values_changed
root['revision']
new_value20250705
old_value20250701
sandbox/staging-cloudasset-v1p2beta1
values_changed
root['revision']
new_value20250705
old_value20250701
sandbox/staging-cloudasset-v1p5alpha1
values_changed
root['revision']
new_value20250705
old_value20250701
sandbox/staging-cloudasset-v1p5beta1
values_changed
root['revision']
new_value20250705
old_value20250701
sandbox/staging-cloudasset-v1p7beta1
values_changed
root['revision']
new_value20250705
old_value20250701
sandbox/staging-cloudbilling-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-cloudbilling-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-cloudbilling-v1beta
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-cloudbilling-v2beta
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-cloudkms-
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-cloudkms-v1
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-cloudresourcemanager-
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-cloudresourcemanager-v1
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-cloudresourcemanager-v1beta1
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-cloudresourcemanager-v2
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-cloudresourcemanager-v2alpha1
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-cloudresourcemanager-v2beta1
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-cloudresourcemanager-v3
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-cloudsearch-
values_changed
root['revision']
new_value20250702
old_value20250625
sandbox/staging-cloudsearch-v1
values_changed
root['revision']
new_value20250702
old_value20250625
sandbox/staging-cloudtrace-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-cloudtrace-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-cloudtrace-v2
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-cloudtrace-v2beta1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-container-
dictionary_item_added
  • root['schemas']['BootDisk']
  • root['schemas']['EvictionGracePeriod']
  • root['schemas']['EvictionMinimumReclaim']
  • root['schemas']['EvictionSignals']
  • root['schemas']['LustreCsiDriverConfig']
  • root['schemas']['AddonsConfig']['properties']['lustreCsiDriverConfig']
  • root['schemas']['LinuxNodeConfig']['properties']['transparentHugepageDefrag']
  • root['schemas']['LinuxNodeConfig']['properties']['transparentHugepageEnabled']
  • root['schemas']['NodeConfig']['properties']['bootDisk']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionMaxPodGracePeriodSeconds']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionMinimumReclaim']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionSoft']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionSoftGracePeriod']
  • root['schemas']['NodeKubeletConfig']['properties']['maxParallelImagePulls']
  • root['schemas']['UpdateNodePoolRequest']['properties']['bootDisk']
values_changed
root['revision']
new_value20250701
old_value20250624
root['schemas']['LinuxNodeConfig']['properties']['sysctls']['description']
new_valueThe Linux kernel parameters to be applied to the nodes and all pods running on the nodes. The following parameters are supported. net.core.busy_poll net.core.busy_read net.core.netdev_max_backlog net.core.rmem_max net.core.rmem_default net.core.wmem_default net.core.wmem_max net.core.optmem_max net.core.somaxconn net.ipv4.tcp_rmem net.ipv4.tcp_wmem net.ipv4.tcp_tw_reuse net.ipv4.tcp_max_orphans net.netfilter.nf_conntrack_max net.netfilter.nf_conntrack_buckets net.netfilter.nf_conntrack_tcp_timeout_close_wait net.netfilter.nf_conntrack_tcp_timeout_time_wait net.netfilter.nf_conntrack_tcp_timeout_established net.netfilter.nf_conntrack_acct kernel.shmmni kernel.shmmax kernel.shmall fs.aio-max-nr fs.file-max fs.inotify.max_user_instances fs.inotify.max_user_watches fs.nr_open vm.dirty_background_ratio vm.dirty_expire_centisecs vm.dirty_ratio vm.dirty_writeback_centisecs vm.max_map_count vm.overcommit_memory vm.overcommit_ratio vm.vfs_cache_pressure vm.swappiness vm.watermark_scale_factor vm.min_free_kbytes
old_valueThe Linux kernel parameters to be applied to the nodes and all pods running on the nodes. The following parameters are supported. net.core.busy_poll net.core.busy_read net.core.netdev_max_backlog net.core.rmem_max net.core.rmem_default net.core.wmem_default net.core.wmem_max net.core.optmem_max net.core.somaxconn net.ipv4.tcp_rmem net.ipv4.tcp_wmem net.ipv4.tcp_tw_reuse net.netfilter.nf_conntrack_max net.netfilter.nf_conntrack_buckets net.netfilter.nf_conntrack_tcp_timeout_close_wait net.netfilter.nf_conntrack_tcp_timeout_time_wait net.netfilter.nf_conntrack_tcp_timeout_established net.netfilter.nf_conntrack_acct kernel.shmmni kernel.shmmax kernel.shmall vm.max_map_count
root['schemas']['NodeConfig']['properties']['diskSizeGb']['description']
new_valueSize of the disk attached to each node, specified in GB. The smallest allowed disk size is 10GB. If unspecified, the default disk size is 100GB.
old_valueSize of the disk attached to each node, specified in GB. The smallest allowed disk size is 10GB. TODO(b/395671893) - Deprecate disk_size_gb and disk_type fields. If unspecified, the default disk size is 100GB.
sandbox/staging-container-v1
dictionary_item_added
  • root['schemas']['BootDisk']
  • root['schemas']['EvictionGracePeriod']
  • root['schemas']['EvictionMinimumReclaim']
  • root['schemas']['EvictionSignals']
  • root['schemas']['LustreCsiDriverConfig']
  • root['schemas']['AddonsConfig']['properties']['lustreCsiDriverConfig']
  • root['schemas']['LinuxNodeConfig']['properties']['transparentHugepageDefrag']
  • root['schemas']['LinuxNodeConfig']['properties']['transparentHugepageEnabled']
  • root['schemas']['NodeConfig']['properties']['bootDisk']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionMaxPodGracePeriodSeconds']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionMinimumReclaim']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionSoft']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionSoftGracePeriod']
  • root['schemas']['NodeKubeletConfig']['properties']['maxParallelImagePulls']
  • root['schemas']['UpdateNodePoolRequest']['properties']['bootDisk']
values_changed
root['revision']
new_value20250701
old_value20250624
root['schemas']['LinuxNodeConfig']['properties']['sysctls']['description']
new_valueThe Linux kernel parameters to be applied to the nodes and all pods running on the nodes. The following parameters are supported. net.core.busy_poll net.core.busy_read net.core.netdev_max_backlog net.core.rmem_max net.core.rmem_default net.core.wmem_default net.core.wmem_max net.core.optmem_max net.core.somaxconn net.ipv4.tcp_rmem net.ipv4.tcp_wmem net.ipv4.tcp_tw_reuse net.ipv4.tcp_max_orphans net.netfilter.nf_conntrack_max net.netfilter.nf_conntrack_buckets net.netfilter.nf_conntrack_tcp_timeout_close_wait net.netfilter.nf_conntrack_tcp_timeout_time_wait net.netfilter.nf_conntrack_tcp_timeout_established net.netfilter.nf_conntrack_acct kernel.shmmni kernel.shmmax kernel.shmall fs.aio-max-nr fs.file-max fs.inotify.max_user_instances fs.inotify.max_user_watches fs.nr_open vm.dirty_background_ratio vm.dirty_expire_centisecs vm.dirty_ratio vm.dirty_writeback_centisecs vm.max_map_count vm.overcommit_memory vm.overcommit_ratio vm.vfs_cache_pressure vm.swappiness vm.watermark_scale_factor vm.min_free_kbytes
old_valueThe Linux kernel parameters to be applied to the nodes and all pods running on the nodes. The following parameters are supported. net.core.busy_poll net.core.busy_read net.core.netdev_max_backlog net.core.rmem_max net.core.rmem_default net.core.wmem_default net.core.wmem_max net.core.optmem_max net.core.somaxconn net.ipv4.tcp_rmem net.ipv4.tcp_wmem net.ipv4.tcp_tw_reuse net.netfilter.nf_conntrack_max net.netfilter.nf_conntrack_buckets net.netfilter.nf_conntrack_tcp_timeout_close_wait net.netfilter.nf_conntrack_tcp_timeout_time_wait net.netfilter.nf_conntrack_tcp_timeout_established net.netfilter.nf_conntrack_acct kernel.shmmni kernel.shmmax kernel.shmall vm.max_map_count
sandbox/staging-container-v1beta1
dictionary_item_added
  • root['schemas']['BootDisk']
  • root['schemas']['EvictionGracePeriod']
  • root['schemas']['EvictionMinimumReclaim']
  • root['schemas']['EvictionSignals']
  • root['schemas']['LustreCsiDriverConfig']
  • root['schemas']['AddonsConfig']['properties']['lustreCsiDriverConfig']
  • root['schemas']['LinuxNodeConfig']['properties']['transparentHugepageDefrag']
  • root['schemas']['LinuxNodeConfig']['properties']['transparentHugepageEnabled']
  • root['schemas']['NodeConfig']['properties']['bootDisk']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionMaxPodGracePeriodSeconds']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionMinimumReclaim']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionSoft']
  • root['schemas']['NodeKubeletConfig']['properties']['evictionSoftGracePeriod']
  • root['schemas']['NodeKubeletConfig']['properties']['maxParallelImagePulls']
  • root['schemas']['UpdateNodePoolRequest']['properties']['bootDisk']
values_changed
root['revision']
new_value20250701
old_value20250624
root['schemas']['LinuxNodeConfig']['properties']['sysctls']['description']
new_valueThe Linux kernel parameters to be applied to the nodes and all pods running on the nodes. The following parameters are supported. net.core.busy_poll net.core.busy_read net.core.netdev_max_backlog net.core.rmem_max net.core.rmem_default net.core.wmem_default net.core.wmem_max net.core.optmem_max net.core.somaxconn net.ipv4.tcp_rmem net.ipv4.tcp_wmem net.ipv4.tcp_tw_reuse net.ipv4.tcp_max_orphans net.netfilter.nf_conntrack_max net.netfilter.nf_conntrack_buckets net.netfilter.nf_conntrack_tcp_timeout_close_wait net.netfilter.nf_conntrack_tcp_timeout_time_wait net.netfilter.nf_conntrack_tcp_timeout_established net.netfilter.nf_conntrack_acct kernel.shmmni kernel.shmmax kernel.shmall fs.aio-max-nr fs.file-max fs.inotify.max_user_instances fs.inotify.max_user_watches fs.nr_open vm.dirty_background_ratio vm.dirty_expire_centisecs vm.dirty_ratio vm.dirty_writeback_centisecs vm.max_map_count vm.overcommit_memory vm.overcommit_ratio vm.vfs_cache_pressure vm.swappiness vm.watermark_scale_factor vm.min_free_kbytes
old_valueThe Linux kernel parameters to be applied to the nodes and all pods running on the nodes. The following parameters are supported. net.core.busy_poll net.core.busy_read net.core.netdev_max_backlog net.core.rmem_max net.core.rmem_default net.core.wmem_default net.core.wmem_max net.core.optmem_max net.core.somaxconn net.ipv4.tcp_rmem net.ipv4.tcp_wmem net.ipv4.tcp_tw_reuse net.netfilter.nf_conntrack_max net.netfilter.nf_conntrack_buckets net.netfilter.nf_conntrack_tcp_timeout_close_wait net.netfilter.nf_conntrack_tcp_timeout_time_wait net.netfilter.nf_conntrack_tcp_timeout_established net.netfilter.nf_conntrack_acct kernel.shmmni kernel.shmmax kernel.shmall vm.max_map_count
root['schemas']['NodeConfig']['properties']['diskSizeGb']['description']
new_valueSize of the disk attached to each node, specified in GB. The smallest allowed disk size is 10GB. If unspecified, the default disk size is 100GB.
old_valueSize of the disk attached to each node, specified in GB. The smallest allowed disk size is 10GB. TODO(b/395671893) - Deprecate disk_size_gb and disk_type fields. If unspecified, the default disk size is 100GB.
sandbox/staging-databaseinsights-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-databaseinsights-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-datamanager-
values_changed
root['revision']
new_value20250702
old_value20250701
sandbox/staging-datamanager-v1
values_changed
root['revision']
new_value20250702
old_value20250701
sandbox/staging-essentialcontacts-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-essentialcontacts-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-essentialcontacts-v1alpha1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-essentialcontacts-v1beta1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-firebase-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-firebase-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-firebase-v1alpha
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-firebase-v1beta1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-firebaseextensions-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-firebaseextensions-v1beta
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-firebaseextensionspublisher-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-firebaseextensionspublisher-v1beta
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-firebaseremoteconfigrealtime-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-firebaseremoteconfigrealtime-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-generativelanguage-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-generativelanguage-v1
values_changed
root['revision']
new_value20250705
old_value20250702
iterable_item_added
root['schemas']['Candidate']['properties']['finishReason']['enum'][12]UNEXPECTED_TOOL_CALL
root['schemas']['Candidate']['properties']['finishReason']['enumDescriptions'][12]Model generated a tool call but no tools were enabled in the request.
sandbox/staging-generativelanguage-v1alpha
values_changed
root['revision']
new_value20250705
old_value20250702
iterable_item_added
root['schemas']['Candidate']['properties']['finishReason']['enum'][12]UNEXPECTED_TOOL_CALL
root['schemas']['Candidate']['properties']['finishReason']['enumDescriptions'][12]Model generated a tool call but no tools were enabled in the request.
sandbox/staging-generativelanguage-v1beta
values_changed
root['revision']
new_value20250705
old_value20250702
iterable_item_added
root['schemas']['Candidate']['properties']['finishReason']['enum'][12]UNEXPECTED_TOOL_CALL
root['schemas']['Candidate']['properties']['finishReason']['enumDescriptions'][12]Model generated a tool call but no tools were enabled in the request.
sandbox/staging-generativelanguage-v1beta1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-generativelanguage-v1beta2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-generativelanguage-v1beta3
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-geoar-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-geoar-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-geoar-v1beta2
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-geofeedtaskrouting-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-geofeedtaskrouting-v1alpha
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-growth-pa-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-growth-pa-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-guidedhelp-pa-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-guidedhelp-pa-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-iam-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-iam-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-iam-v1beta
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-iam-v2
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-iam-v2alpha
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-iam-v2beta
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-iam-v3
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-iam-v3alpha
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-iam-v3beta
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-iamcredentials-
values_changed
root['revision']
new_value20250703
old_value20250702
sandbox/staging-iamcredentials-v1
values_changed
root['revision']
new_value20250703
old_value20250702
sandbox/staging-iap-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-iap-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-iap-v1beta1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-instantmessaging-pa-
dictionary_item_added
  • root['schemas']['InstantmessagingStagingPaSandboxGoogleInternalCommunicationsInstantmessagingV1GroupContextExtensions']
  • root['schemas']['InstantmessagingStagingPaSandboxGoogleInternalCommunicationsInstantmessagingV1GetMlsGroupInfoResponse']['properties']['groupContextExtensions']
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-instantmessaging-pa-v1
dictionary_item_added
  • root['schemas']['InstantmessagingStagingPaSandboxGoogleInternalCommunicationsInstantmessagingV1GroupContextExtensions']
  • root['schemas']['InstantmessagingStagingPaSandboxGoogleInternalCommunicationsInstantmessagingV1GetMlsGroupInfoResponse']['properties']['groupContextExtensions']
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-loasproject-pa-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-loasproject-pa-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-logging-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-logging-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-logging-v1beta3
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-logging-v2
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-logging-v2beta1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-mapsplatformdatasets-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-mapsplatformdatasets-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-mapsplatformdatasets-v1alpha
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-monitoring-
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-monitoring-v1
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-monitoring-v3
values_changed
root['revision']
new_value20250704
old_value20250702
sandbox/staging-myphonenumbers-pa-
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-myphonenumbers-pa-v1
values_changed
root['revision']
new_value20250704
old_value20250701
sandbox/staging-networkconnectivity-
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-networkconnectivity-v1
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-networkconnectivity-v1alpha1
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-networkconnectivity-v1beta
values_changed
root['revision']
new_value20250703
old_value20250701
sandbox/staging-notifications-pa-
dictionary_item_added
  • root['schemas']['GoogleInternalHomeFoyerV1Resources__CameraCustomizedNotificationPayload']['properties']['productCode']
  • root['schemas']['GoogleInternalTapandpayV1PassesNotifications_NotificationStyleProgressInfo_ProgressIcon']['properties']['progressIconLocalIcon']
values_changed
root['revision']
new_value20250705
old_value20250701
root['schemas']['GoogleInternalTapandpayV1PassesNotifications_NotificationStyleProgressInfo_ProgressIcon']['properties']['progressIconUrl']['description']
new_valueThe url for the progress tracker icon for the progress bar
old_valueThe url for the progress tracker icon for the progress bar.
iterable_item_added
root['schemas']['GoogleInternalTapandpayV1PassesTemplates__LocalResource']['properties']['iconType']['enum'][16]WALLET_FLIGHT_PROGRESS_ICON
root['schemas']['GoogleLogsTapandpayAndroid__BulletinEvent']['properties']['contentType']['enum'][146]GRU_FINISH_SETUP_WALLET_TIP
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enum'][35]WEEKEND_DUMP
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enumDescriptions'][35]A short video generated from multiple photos with skottie effect for the memories of Best of Month. See go/weekend-dump-prd-new
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['Sidekick__ClusterMetadata']['properties']['needBundleType']['enum'][528]ASTRIA_OMNI
root['schemas']['Sidekick__ClusterMetadata']['properties']['needBundleType']['enumDescriptions'][528]Need bundle type for Astria Omni personalized content.
root['schemas']['Sidekick__SemanticProperties']['properties']['cardCategory']['enum'][980]ASTRIA_OMNI
root['schemas']['WalletTapandpayCommonApiTransit__TransitAgencyInfo']['properties']['transitHubName']['enum'][18]NAME_HID_CERTIFICATION
sandbox/staging-notifications-pa-v1
dictionary_item_added
  • root['schemas']['GoogleInternalHomeFoyerV1Resources__CameraCustomizedNotificationPayload']['properties']['productCode']
  • root['schemas']['GoogleInternalTapandpayV1PassesNotifications_NotificationStyleProgressInfo_ProgressIcon']['properties']['progressIconLocalIcon']
values_changed
root['revision']
new_value20250705
old_value20250701
root['schemas']['GoogleInternalTapandpayV1PassesNotifications_NotificationStyleProgressInfo_ProgressIcon']['properties']['progressIconUrl']['description']
new_valueThe url for the progress tracker icon for the progress bar
old_valueThe url for the progress tracker icon for the progress bar.
iterable_item_added
root['schemas']['GoogleInternalTapandpayV1PassesTemplates__LocalResource']['properties']['iconType']['enum'][16]WALLET_FLIGHT_PROGRESS_ICON
root['schemas']['GoogleLogsTapandpayAndroid__BulletinEvent']['properties']['contentType']['enum'][146]GRU_FINISH_SETUP_WALLET_TIP
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaEmoneyApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__PayFelicaPostpaidApiInvocationEvent']['properties']['secureElementUnifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enum'][48]UNIFIED_ERROR_PASMO_CHARGE_BLOCKED_ERROR
root['schemas']['GoogleLogsTapandpayAndroid__SePrepaidCardMonetOperationError']['properties']['unifiedError']['enumDescriptions'][48]PasmoSdkError.PasmoSdkErrorCode.RESULT_ERROR_8H024.
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient_ItemPhotosViewInfo_AssistantViewInfo']['properties']['hideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enum'][35]WEEKEND_DUMP
root['schemas']['PhotosMediaClient__ItemCompositionInfo']['properties']['type']['enumDescriptions'][35]A short video generated from multiple photos with skottie effect for the memories of Best of Month. See go/weekend-dump-prd-new
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enum'][4]STICKER
root['schemas']['PhotosMediaClient__ItemPhotosViewInfo']['properties']['assistantHideReason']['items']['enumDescriptions'][4]The photo is a sticker which should be hidden from standard views and only shown in the sticker view.
root['schemas']['Sidekick__ClusterMetadata']['properties']['needBundleType']['enum'][528]ASTRIA_OMNI
root['schemas']['Sidekick__ClusterMetadata']['properties']['needBundleType']['enumDescriptions'][528]Need bundle type for Astria Omni personalized content.
root['schemas']['Sidekick__SemanticProperties']['properties']['cardCategory']['enum'][980]ASTRIA_OMNI
root['schemas']['WalletTapandpayCommonApiTransit__TransitAgencyInfo']['properties']['transitHubName']['enum'][18]NAME_HID_CERTIFICATION
sandbox/staging-ogads-pa-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/staging-ogads-pa-v1
values_changed
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sandbox/staging-orgpolicy-
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sandbox/staging-orgpolicy-v2
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sandbox/staging-orgpolicy-v2alpha1
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sandbox/staging-people-pa-
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sandbox/staging-people-pa-v1
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sandbox/staging-people-pa-v2
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sandbox/staging-peoplestack-pa-
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sandbox/staging-peoplestack-pa-v1
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sandbox/staging-policyremediator-
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sandbox/staging-policyremediator-v1alpha
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sandbox/staging-policytroubleshooter-
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sandbox/staging-policytroubleshooter-v1
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sandbox/staging-policytroubleshooter-v1beta
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sandbox/staging-policytroubleshooter-v2alpha1
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sandbox/staging-policytroubleshooter-v3
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sandbox/staging-policytroubleshooter-v3alpha
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sandbox/staging-policytroubleshooter-v3beta
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sandbox/staging-privacysandboxmaven-
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sandbox/staging-privacysandboxmaven-v1
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sandbox/staging-pubsub-
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sandbox/staging-pubsub-v1
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sandbox/staging-pubsub-v1beta2
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sandbox/staging-retail-
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root['schemas']['GoogleCloudRetailV2betaSearchRequest']['properties']['userAttributes']['description']
new_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplicate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
old_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplcate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
sandbox/staging-retail-v1
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sandbox/staging-retail-v2
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root['schemas']['GoogleCloudRetailV2SearchRequest']['properties']['userAttributes']['description']
new_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplicate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
old_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplcate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
sandbox/staging-retail-v2alpha
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root['schemas']['GoogleCloudRetailV2alphaSearchRequest']['properties']['userAttributes']['description']
new_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplicate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
old_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplcate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
sandbox/staging-retail-v2beta
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root['schemas']['GoogleCloudRetailV2betaSearchRequest']['properties']['userAttributes']['description']
new_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplicate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
old_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplcate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
sandbox/staging-salesforceshopping-
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sandbox/staging-salesforceshopping-v1
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sandbox/staging-searchresearcherresults-
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sandbox/staging-searchresearcherresults-v1
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sandbox/staging-serviceaccountcert-
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sandbox/staging-serviceaccountcert-v1
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sandbox/staging-servicekeys-
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sandbox/staging-servicekeys-v1
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sandbox/staging-servicemanagement-
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sandbox/staging-servicemanagement-v1
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sandbox/staging-shoppingdataintegration-
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sandbox/staging-shoppingdataintegration-v1
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sandbox/staging-taskassist-pa-
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sandbox/staging-taskassist-pa-v1
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sandbox/staging-taskassist-pa-v2
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sandbox/staging-tasks-pa-
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sandbox/staging-tasks-pa-v1
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sandbox/staging-toolresults-
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sandbox/staging-toolresults-v1
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sandbox/staging-toolresults-v1beta3
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sandbox/staging-tos-pa-
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sandbox/staging-tos-pa-v1
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sandbox/staging-travelpartnerprices-
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sandbox/staging-travelpartnerprices-v1
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sandbox/staging-vision-
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sandbox/staging-vision-v1
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sandbox/staging-vision-v1p1beta1
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sandbox/staging-vision-v1p2beta1
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sandbox/staging-vision-v1p3beta1
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sandbox/staging-vision-v1p4beta1
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sandbox/staging-visualpositioning-
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sandbox/staging-visualpositioning-v1
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sandbox/staging-visualpositioning-v1beta2
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sandbox/staging-wrenchworks-
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sandbox/staging-wrenchworks-v1
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sandbox/tasks-pa-
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sandbox/tasks-pa-v1
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sandbox/test-bigqueryconnection-
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sandbox/test-bigqueryconnection-v1
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sandbox/test-bigqueryconnection-v1beta1
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sandbox/test-bigquerydatatransfer-
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sandbox/test-bigquerydatatransfer-v1
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sandbox/test-bigqueryreservation-
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sandbox/test-bigqueryreservation-v1
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sandbox/test-cloudasset-
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sandbox/test-cloudasset-v1
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sandbox/test-cloudasset-v1beta1
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sandbox/test-cloudasset-v1p1beta1
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sandbox/test-cloudasset-v1p2alpha1
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sandbox/test-cloudasset-v1p2beta1
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sandbox/test-cloudasset-v1p5alpha1
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sandbox/test-cloudasset-v1p5beta1
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sandbox/test-cloudasset-v1p7beta1
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sandbox/test-container-
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sandbox/test-container-v1
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  • root['schemas']['ClusterAutoscaling']['properties']['defaultComputeClassConfig']
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sandbox/test-container-v1beta1
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  • root['schemas']['ClusterAutoscaling']['properties']['defaultComputeClassConfig']
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sandbox/test-dataaccessauditlogging-pa-
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sandbox/test-dataaccessauditlogging-pa-v1
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sandbox/test-dialogflow-
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sandbox/test-dialogflow-v1
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sandbox/test-dialogflow-v2
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sandbox/test-dialogflow-v2beta1
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sandbox/test-dialogflow-v3
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sandbox/test-dialogflow-v3alpha1
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sandbox/test-dialogflow-v3beta1
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sandbox/test-discoveryengine-
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  • root['schemas']['GoogleCloudDiscoveryengineV1alphaDataConnectorRealtimeSyncConfigStreamingError']
  • root['schemas']['GoogleCloudDiscoveryengineV1alphaDataConnectorRealtimeSyncConfig']['properties']['streamingError']
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root['schemas']['GoogleCloudDiscoveryengineV1alphaSearchRequest']['properties']['rankingExpression']['description']
new_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `document_age`: The time in hours elapsed since the document was last updated, a floating-point number (e.g., 0.25 means 15 minutes). * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
old_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
root['schemas']['GoogleCloudDiscoveryengineV1betaSearchRequest']['properties']['rankingExpression']['description']
new_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `document_age`: The time in hours elapsed since the document was last updated, a floating-point number (e.g., 0.25 means 15 minutes). * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
old_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
sandbox/test-discoveryengine-v1
dictionary_item_added
  • root['schemas']['GoogleCloudDiscoveryengineV1alphaDataConnectorRealtimeSyncConfigStreamingError']
  • root['schemas']['GoogleCloudDiscoveryengineV1alphaDataConnectorRealtimeSyncConfig']['properties']['streamingError']
values_changed
root['revision']
new_value20250705
old_value20250702
root['schemas']['GoogleCloudDiscoveryengineV1alphaSearchRequest']['properties']['rankingExpression']['description']
new_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `document_age`: The time in hours elapsed since the document was last updated, a floating-point number (e.g., 0.25 means 15 minutes). * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
old_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
root['schemas']['GoogleCloudDiscoveryengineV1betaSearchRequest']['properties']['rankingExpression']['description']
new_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `document_age`: The time in hours elapsed since the document was last updated, a floating-point number (e.g., 0.25 means 15 minutes). * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
old_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
sandbox/test-discoveryengine-v1alpha
dictionary_item_added
  • root['resources']['projects']['resources']['locations']['resources']['collections']['resources']['dataConnector']['methods']['acquireAccessToken']
  • root['resources']['projects']['resources']['locations']['resources']['collections']['resources']['dataStores']['resources']['widgetConfigs']['methods']['get']['parameters']['getWidgetConfigRequestOption.turnOffCollectionComponents']
  • root['resources']['projects']['resources']['locations']['resources']['collections']['resources']['engines']['resources']['widgetConfigs']['methods']['get']['parameters']['getWidgetConfigRequestOption.turnOffCollectionComponents']
  • root['resources']['projects']['resources']['locations']['resources']['dataStores']['resources']['widgetConfigs']['methods']['get']['parameters']['getWidgetConfigRequestOption.turnOffCollectionComponents']
  • root['schemas']['GoogleCloudDiscoveryengineV1alphaAcquireAccessTokenRequest']
  • root['schemas']['GoogleCloudDiscoveryengineV1alphaAcquireAccessTokenResponse']
  • root['schemas']['GoogleCloudDiscoveryengineV1alphaDataConnectorRealtimeSyncConfigStreamingError']
  • root['schemas']['GoogleCloudDiscoveryengineV1alphaRefreshTokenInfo']
  • root['schemas']['GoogleCloudDiscoveryengineV1alphaDataConnectorRealtimeSyncConfig']['properties']['streamingError']
values_changed
root['revision']
new_value20250705
old_value20250702
root['schemas']['GoogleCloudDiscoveryengineV1alphaSearchRequest']['properties']['rankingExpression']['description']
new_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `document_age`: The time in hours elapsed since the document was last updated, a floating-point number (e.g., 0.25 means 15 minutes). * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
old_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
root['schemas']['GoogleCloudDiscoveryengineV1betaSearchRequest']['properties']['rankingExpression']['description']
new_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `document_age`: The time in hours elapsed since the document was last updated, a floating-point number (e.g., 0.25 means 15 minutes). * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
old_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
sandbox/test-discoveryengine-v1beta
dictionary_item_added
  • root['schemas']['GoogleCloudDiscoveryengineV1alphaDataConnectorRealtimeSyncConfigStreamingError']
  • root['schemas']['GoogleCloudDiscoveryengineV1alphaDataConnectorRealtimeSyncConfig']['properties']['streamingError']
values_changed
root['revision']
new_value20250705
old_value20250702
root['schemas']['GoogleCloudDiscoveryengineV1alphaSearchRequest']['properties']['rankingExpression']['description']
new_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `document_age`: The time in hours elapsed since the document was last updated, a floating-point number (e.g., 0.25 means 15 minutes). * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
old_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
root['schemas']['GoogleCloudDiscoveryengineV1betaSearchRequest']['properties']['rankingExpression']['description']
new_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `document_age`: The time in hours elapsed since the document was last updated, a floating-point number (e.g., 0.25 means 15 minutes). * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
old_valueOptional. The ranking expression controls the customized ranking on retrieval documents. This overrides ServingConfig.ranking_expression. The syntax and supported features depend on the `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by "+". * ranking_expression = function, { " + ", function }; Supported functions: * double * relevance_score * double * dotProduct(embedding_field_path) Function variables: * `relevance_score`: pre-defined keywords, used for measure relevance between query and document. * `embedding_field_path`: the document embedding field used with query embedding vector. * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector. Example ranking expression: If document has an embedding field doc_embedding, the ranking expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and combinations of those chained using + or * operators) are supported: * `double` * `signal` * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN, 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 | double, else returns signal1. Here are a few examples of ranking formulas that use the supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score` with slight `semantic_smilarity_score` adjustment. - `0.2 * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`: semantic similarity adjustment that is calculated using the embeddings generated by a proprietary Google model. This score determines how semantically similar a search query is to a document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25) ranking function. This score is calculated using a probabilistic model to estimate the probability that a document is relevant to a given query. * `relevance_score`: semantic relevance adjustment that uses a proprietary Google model to determine the meaning and intent behind a user's query in context with the content in the documents. * `pctr_rank`: predicted conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the relevance and attractiveness of a search result from a user's perspective. A higher pCTR suggests that the result is more likely to satisfy the user's query and intent, making it a valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank * `topicality_rank`: topicality adjustment as a rank. Uses proprietary Google model to determine the keyword-based overlap between the query and the document. * `base_rank`: the default rank of the result
sandbox/test-logging-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-logging-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-logging-v1beta3
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-logging-v2
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-logging-v2beta1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-monitoring-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-monitoring-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-monitoring-v3
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-recommendationengine-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-recommendationengine-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-recommendationengine-v1alpha
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-recommendationengine-v1beta1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-retail-
values_changed
root['revision']
new_value20250705
old_value20250702
root['schemas']['GoogleCloudRetailV2betaSearchRequest']['properties']['userAttributes']['description']
new_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplicate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
old_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplcate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
sandbox/test-retail-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-retail-v2
values_changed
root['revision']
new_value20250705
old_value20250702
root['schemas']['GoogleCloudRetailV2SearchRequest']['properties']['userAttributes']['description']
new_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplicate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
old_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplcate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
sandbox/test-retail-v2alpha
values_changed
root['revision']
new_value20250705
old_value20250702
root['schemas']['GoogleCloudRetailV2alphaSearchRequest']['properties']['userAttributes']['description']
new_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplicate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
old_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplcate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
sandbox/test-retail-v2beta
values_changed
root['revision']
new_value20250705
old_value20250702
root['schemas']['GoogleCloudRetailV2betaSearchRequest']['properties']['userAttributes']['description']
new_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplicate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
old_valueOptional. The user attributes that could be used for personalization of search results. * Populate at most 100 key-value pairs per query. * Only supports string keys and repeated string values. * Duplcate keys are not allowed within a single query. Example: user_attributes: [ { key: "pets" value { values: "dog" values: "cat" } }, { key: "state" value { values: "CA" } } ]
sandbox/test-storagetransfer-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-storagetransfer-v1
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-vision-
values_changed
root['revision']
new_value20250705
old_value20250702
sandbox/test-vision-v1
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root['revision']
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sandbox/us-staging-vision-v1p4beta1
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