API Evangelist conversational phrasing for Amazon Neptune Neptune ML Model Transform API

# Generated by API Evangelist (build-phrasing.py). Our phrasing, not observed demand.
overlay: 1.0.0
info:
  title: API Evangelist conversational phrasing for Amazon Neptune Neptune ML Model Transform API
  version: 1.0.0
extends: openapi/amazon-neptune-model-transform-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: 4
- target: $.paths['/ml/modeltransform'].get
  update:
    x-apievangelist-phrasing:
      intent: List active model transform jobs
      effect: read
      questions:
      - Which Neptune ML model transform jobs are active?
      - Can I limit how many transform job IDs come back?
      instructions:
      - text: List my active model transform jobs.
      - text: Show up to {max} active transform job IDs.
        slots:
          max: query.maxItems
      method: generated
      generated: '2026-10-01'
- target: $.paths['/ml/modeltransform'].post
  update:
    x-apievangelist-phrasing:
      intent: Generate model artifacts for inference
      effect: write
      questions:
      - How do I create the embeddings needed to deploy my trained model to an endpoint?
      - Can I build a transform job from an existing training job?
      instructions:
      - text: Create a model transform job with output at {s3}.
        slots:
          s3: requestBody.modelTransformOutputS3Location
      - text: Create transform job {id} from training job {training_job}, writing to {s3}.
        slots:
          id: requestBody.id
          training_job: requestBody.mlModelTrainingJobId
          s3: requestBody.modelTransformOutputS3Location
      method: generated
      generated: '2026-10-01'
- target: $.paths['/ml/modeltransform/{id}'].get
  update:
    x-apievangelist-phrasing:
      intent: Check a model transform job's status
      effect: read
      questions:
      - Has my model transform job completed?
      - What state is a particular model transform job in?
      instructions:
      - text: Get the status of transform job {id}.
        slots:
          id: path.id
      - text: Tell me whether model transform job {id} has finished.
        slots:
          id: path.id
      method: generated
      generated: '2026-10-01'
- target: $.paths['/ml/modeltransform/{id}'].delete
  update:
    x-apievangelist-phrasing:
      intent: Stop a model transform job
      effect: destructive
      questions:
      - Can I stop a running model transform job?
      - Is there an option to clean up S3 artifacts when stopping a transform?
      instructions:
      - text: Stop model transform job {id}.
        slots:
          id: path.id
      - text: Stop transform job {id} and set clean to {clean}.
        slots:
          id: path.id
          clean: query.clean
      method: generated
      generated: '2026-10-01'