arazzo: 1.0.1
info:
  title: Affinda Create Extractor with a Data Point
  summary: Create a custom extractor in an organization and add a custom data point to it.
  description: >-
    Builds a custom extraction model. A new extractor is created within an
    organization, then a custom data point is created and attached to that
    extractor so the model knows about a new field to extract. Finally the
    extractor is read back to confirm its configuration. Every step spells out its
    request inline so the flow can be read and executed without opening the
    underlying OpenAPI description.
    Note: the extractor and data point endpoints are marked deprecated in the v3
    specification but remain the documented way to author custom extraction models.
  version: 1.0.0
sourceDescriptions:
- name: affindaV3Api
  url: ../openapi/affinda-v3-openapi.yml
  type: openapi
workflows:
- workflowId: create-extractor-with-data-point
  summary: Create a custom extractor and attach a custom data point to it.
  description: >-
    Creates an extractor in the organization, creates a data point bound to that
    extractor, and reads the extractor back to confirm.
  inputs:
    type: object
    required:
    - organization
    - extractorName
    - dataPointName
    - dataPointSlug
    - annotationContentType
    properties:
      organization:
        type: string
        description: The organization identifier that will own the extractor and data point.
      extractorName:
        type: string
        description: The display name for the new extractor.
      dataPointName:
        type: string
        description: The display name for the new data point.
      dataPointSlug:
        type: string
        description: A unique slug for the data point.
      annotationContentType:
        type: string
        description: The content type of the data point (e.g. text, integer, float, date).
  steps:
  - stepId: createExtractor
    description: Create a custom extractor within the organization.
    operationId: createExtractor
    requestBody:
      contentType: application/json
      payload:
        name: $inputs.extractorName
        organization: $inputs.organization
    successCriteria:
    - condition: $statusCode == 201
    outputs:
      extractorIdentifier: $response.body#/identifier
  - stepId: createDataPoint
    description: >-
      Create a custom data point bound to the new extractor so it can extract an
      additional field.
    operationId: createDataPoint
    requestBody:
      contentType: application/json
      payload:
        name: $inputs.dataPointName
        slug: $inputs.dataPointSlug
        annotationContentType: $inputs.annotationContentType
        organization: $inputs.organization
        extractor: $steps.createExtractor.outputs.extractorIdentifier
    successCriteria:
    - condition: $statusCode == 201
    outputs:
      dataPointIdentifier: $response.body#/identifier
  - stepId: confirmExtractor
    description: Read the extractor back to confirm it was created with the data point.
    operationId: getExtractor
    parameters:
    - name: identifier
      in: path
      value: $steps.createExtractor.outputs.extractorIdentifier
    successCriteria:
    - condition: $statusCode == 200
    outputs:
      name: $response.body#/name
  outputs:
    extractorIdentifier: $steps.createExtractor.outputs.extractorIdentifier
    dataPointIdentifier: $steps.createDataPoint.outputs.dataPointIdentifier