Lytics · OpenAPI Overlay 1.0.0

API Evangelist conversational phrasing for Lytics ML Models API

7 actions 7 updates phrasing extends openapi/lytics-ml-models-api-openapi.yml
Generated by API Evangelist Written by API Evangelist tooling for Lytics's API. It is a proposal applied on top of the contract, not a document Lytics publishes.
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What the actions change

x-apievangelist-phrasing

Targets 7

$.info
$.paths['/ml'].get
$.paths['/ml'].post
$.paths['/ml/{id}'].get
$.paths['/ml/{id}'].put
$.paths['/ml/{id}'].delete
$.paths['/ml/{id}/summary'].get

OpenAPI Overlay

Raw ↑
# Generated by API Evangelist (build-phrasing.py). Our phrasing, not observed demand.
overlay: 1.0.0
info:
  title: API Evangelist conversational phrasing for Lytics ML Models API
  version: 1.0.0
extends: openapi/lytics-ml-models-api-openapi.yml
actions:
- target: $.info
  update:
    x-apievangelist-phrasing:
      method: generated
      generated: '2026-10-02'
      generator: build-phrasing.py
      label: Generated by API Evangelist
      operations: 6
- target: $.paths['/ml'].get
  update:
    x-apievangelist-phrasing:
      intent: List ML models
      effect: read
      questions:
      - Which machine learning models have been built on my account?
      - Can I see all my ML models in one list?
      instructions:
      - text: List all my ML models.
      - text: Show the ML models on account {account_id}.
        slots:
          account_id: query.account_id
      method: generated
      generated: '2026-10-02'
- target: $.paths['/ml'].post
  update:
    x-apievangelist-phrasing:
      intent: Create an ML model
      effect: write
      questions:
      - How do I train a model that predicts a target audience from a source audience?
      - What do I need to set to create a new ML model?
      instructions:
      - text: Create ML model {name} predicting {target} from {source}.
        slots:
          name: requestBody.name
          target: requestBody.target
          source: requestBody.source
      - text: Build a {type} ML model labelled {label} with config {config}.
        slots:
          type: requestBody.type
          label: requestBody.label
          config: requestBody.config
      method: generated
      generated: '2026-10-02'
- target: $.paths['/ml/{id}'].get
  update:
    x-apievangelist-phrasing:
      intent: Get one ML model
      effect: read
      questions:
      - Is a particular ML model active and healthy?
      - Can I fetch the full record of one ML model by its id?
      instructions:
      - text: Show ML model {id}.
        slots:
          id: path.id
      - text: Fetch the full definition of ML model {id}.
        slots:
          id: path.id
      method: generated
      generated: '2026-10-02'
- target: $.paths['/ml/{id}'].put
  update:
    x-apievangelist-phrasing:
      intent: Activate, hide or relabel an ML model
      effect: write
      questions:
      - Can I deactivate an ML model without deleting it?
      - Is it possible to hide or rename an existing ML model?
      instructions:
      - text: Set ML model {id} active to {is_active}.
        slots:
          id: path.id
          is_active: query.is_active
      - text: Relabel ML model {id} as {label} and set hidden to {hidden}.
        slots:
          id: path.id
          label: query.label
          hidden: query.hidden
      method: generated
      generated: '2026-10-02'
- target: $.paths['/ml/{id}'].delete
  update:
    x-apievangelist-phrasing:
      intent: Delete an ML model
      effect: destructive
      questions:
      - How do I permanently remove an ML model?
      - Can I delete a model I trained by mistake?
      instructions:
      - text: Delete ML model {id}.
        slots:
          id: path.id
      - text: Permanently remove the ML model {id}.
        slots:
          id: path.id
      method: generated
      generated: '2026-10-02'
- target: $.paths['/ml/{id}/summary'].get
  update:
    x-apievangelist-phrasing:
      intent: Get an ML model's performance summary
      effect: read
      questions:
      - How accurate is my ML model, according to its summary?
      - Which features drive one model's predictions?
      instructions:
      - text: Show the summary of ML model {id}.
        slots:
          id: path.id
      - text: Summarize how ML model {id} is performing.
        slots:
          id: path.id
      method: generated
      generated: '2026-10-02'

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