# Generated by API Evangelist (build-phrasing.py). Our phrasing, not observed demand. overlay: 1.0.0 info: title: API Evangelist conversational phrasing for Microsoft Azure Personalizer Client 2022-09-01-preview… version: 1.0.0 extends: openapi/microsoft-azure-configurations20220901preview-api-openapi.yml actions: - target: $.info update: x-apievangelist-phrasing: method: generated generated: '2026-09-26' generator: build-phrasing.py label: Generated by API Evangelist operations: 6 - target: $.paths['/configurations/service'].get update: x-apievangelist-phrasing: intent: Get the Personalizer service configuration effect: read questions: - What is my Personalizer service's current reward wait time and exploration percentage? - Can I see how often Personalizer exports models and how long it keeps logs? instructions: - text: Get the Personalizer service configuration. - text: Show my current Personalizer reward, exploration and retention settings. - text: Get the Personalizer service configuration using API version {apiVersion}. slots: apiVersion: query.api-version method: generated generated: '2026-09-26' - target: $.paths['/configurations/service'].put update: x-apievangelist-phrasing: intent: Update the Personalizer service configuration effect: write questions: - How do I change the exploration percentage for my Personalizer service? - Can I adjust the reward wait time and default reward Personalizer uses? - Is it possible to turn on auto-optimization or switch Personalizer's learning mode? instructions: - text: Set Personalizer exploration to {explorationPercentage} percent and reward wait time to {rewardWaitTime}. slots: explorationPercentage: requestBody.explorationPercentage rewardWaitTime: requestBody.rewardWaitTime - text: Update Personalizer to keep logs for {logRetentionDays} days and retrain models every {modelRetrainDays} days. slots: logRetentionDays: requestBody.logRetentionDays modelRetrainDays: requestBody.modelRetrainDays - text: Switch the Personalizer learning mode to {learningMode}. slots: learningMode: requestBody.learningMode method: generated generated: '2026-09-26' - target: $.paths['/configurations/policy'].get update: x-apievangelist-phrasing: intent: Get Personalizer learning settings effect: read questions: - Which learning settings is Personalizer currently using to train models? - Can I read the active learning policy arguments of my Personalizer resource? instructions: - text: Get the current Personalizer learning settings. - text: Show the learning policy Personalizer trains with. - text: Read the Personalizer learning policy with API version {apiVersion}. slots: apiVersion: query.api-version method: generated generated: '2026-09-26' - target: $.paths['/configurations/policy'].put update: x-apievangelist-phrasing: intent: Update Personalizer learning settings effect: write questions: - How do I change the learning settings Personalizer uses to train models? - Can I supply custom learning policy arguments to Personalizer? instructions: - text: Set the Personalizer learning policy {name} with arguments {arguments}. slots: name: requestBody.name arguments: requestBody.arguments - text: Update Personalizer's training arguments to {arguments}. slots: arguments: requestBody.arguments method: generated generated: '2026-09-26' - target: $.paths['/configurations/policy'].delete update: x-apievangelist-phrasing: intent: Reset Personalizer learning settings to default effect: destructive questions: - Can I undo my custom learning settings and go back to Personalizer's defaults? - What resets the Personalizer learning policy? instructions: - text: Reset the Personalizer learning settings to default. - text: Discard my custom Personalizer learning policy. - text: Reset Personalizer learning settings to default using API version {apiVersion}. slots: apiVersion: query.api-version method: generated generated: '2026-09-26' - target: $.paths['/configurations/applyFromEvaluation'].post update: x-apievangelist-phrasing: intent: Apply settings from an offline evaluation effect: write questions: - How do I make the learning settings from an offline evaluation my live Personalizer settings? - Can I promote a policy found in an evaluation to the online model? instructions: - text: Apply policy {policyName} from offline evaluation {evaluationId} as the current Personalizer settings. slots: policyName: requestBody.policyName evaluationId: requestBody.evaluationId - text: Promote the learning settings and model from evaluation {evaluationId} using policy {policyName}. slots: evaluationId: requestBody.evaluationId policyName: requestBody.policyName method: generated generated: '2026-09-26'