# 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 v1.1-preview.3…
  version: 1.0.0
extends: openapi/microsoft-azure-configurationsv1dot1preview1-api-openapi.yml
actions:
- target: $.info
  update:
    x-apievangelist-phrasing:
      method: generated
      generated: '2026-10-01'
      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.
      method: generated
      generated: '2026-10-01'
- 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 export models every {modelExportFrequency}.
        slots:
          logRetentionDays: requestBody.logRetentionDays
          modelExportFrequency: requestBody.modelExportFrequency
      - text: Switch the Personalizer learning mode to {learningMode}.
        slots:
          learningMode: requestBody.learningMode
      method: generated
      generated: '2026-10-01'
- target: $.paths['/configurations/policy'].get
  update:
    x-apievangelist-phrasing:
      intent: Get Personalizer learning settings
      effect: read
      questions:
      - Which learning policy is Personalizer currently training with?
      - How can I see the learning settings arguments in use right now?
      instructions:
      - text: Get the learning settings Personalizer is currently using.
      - text: Show me the active Personalizer learning policy.
      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 arguments for the Personalizer learning policy?
      instructions:
      - text: Set the Personalizer learning policy to {name} with arguments {arguments}.
        slots:
          name: requestBody.name
          arguments: requestBody.arguments
      - text: Update Personalizer training to use policy {name}.
        slots:
          name: requestBody.name
      method: generated
      generated: '2026-09-26'
- target: $.paths['/configurations/policy'].delete
  update:
    x-apievangelist-phrasing:
      intent: Reset Personalizer learning settings
      effect: destructive
      questions:
      - How do I put Personalizer's learning settings back to the defaults?
      - Can I undo my custom learning policy in Personalizer?
      instructions:
      - text: Reset Personalizer learning settings to default.
      - text: Discard my custom Personalizer learning policy.
      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:
      - Can I promote the learning settings and model from an offline evaluation to production?
      - How do I make an evaluation's policy the live Personalizer model?
      instructions:
      - text: Apply policy {policyName} from evaluation {evaluationId} as the online learning settings.
        slots:
          policyName: requestBody.policyName
          evaluationId: requestBody.evaluationId
      - text: Make evaluation {evaluationId}'s policy {policyName} the current Personalizer model.
        slots:
          evaluationId: requestBody.evaluationId
          policyName: requestBody.policyName
      method: generated
      generated: '2026-09-26'