# 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 Data Processing API
  version: 1.0.0
extends: openapi/amazon-neptune-data-processing-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/dataprocessing'].get
  update:
    x-apievangelist-phrasing:
      intent: List active ML data processing jobs
      effect: read
      questions:
      - Which Neptune ML data processing jobs are running right now?
      - Can I cap how many data processing job IDs come back?
      - What graph data export jobs for model training are active in my cluster?
      instructions:
      - text: List my active Neptune ML data processing jobs.
      - text: Show up to {max} active data processing job IDs.
        slots:
          max: query.maxItems
      method: generated
      generated: '2026-10-01'
- target: $.paths['/ml/dataprocessing'].post
  update:
    x-apievangelist-phrasing:
      intent: Start an ML data processing job
      effect: write
      questions:
      - How do I export and prepare my graph data for Neptune ML model training?
      - Can I reuse an earlier data processing job when my graph data changes?
      - What S3 locations does a Neptune ML data processing job need for input and output?
      instructions:
      - text: Create a data processing job reading from {input} and writing processed data to {output}.
        slots:
          input: requestBody.inputDataS3Location
          output: requestBody.processedDataS3Location
      - text: Start data processing job {id} with input {input} and output {output}.
        slots:
          id: requestBody.id
          input: requestBody.inputDataS3Location
          output: requestBody.processedDataS3Location
      - text: Process graph data from {input} into {output} on a {instance} instance.
        slots:
          input: requestBody.inputDataS3Location
          output: requestBody.processedDataS3Location
          instance: requestBody.processingInstanceType
      method: generated
      generated: '2026-10-01'
- target: $.paths['/ml/dataprocessing/{id}'].get
  update:
    x-apievangelist-phrasing:
      intent: Check an ML data processing job's status
      effect: read
      questions:
      - Has my Neptune ML data processing job finished yet?
      - What is the current status of a specific data processing job?
      instructions:
      - text: Get the status of data processing job {id}.
        slots:
          id: path.id
      - text: Check whether data processing job {id} has completed.
        slots:
          id: path.id
      method: generated
      generated: '2026-10-01'
- target: $.paths['/ml/dataprocessing/{id}'].delete
  update:
    x-apievangelist-phrasing:
      intent: Stop an ML data processing job
      effect: destructive
      questions:
      - Can I stop a data processing job that is still running?
      - Is there a way to also delete the S3 artifacts when I stop a data processing job?
      instructions:
      - text: Stop data processing job {id}.
        slots:
          id: path.id
      - text: Stop data processing job {id} and clean up its S3 artifacts.
        slots:
          id: path.id
      method: generated
      generated: '2026-10-01'