openapi.yaml

openapi: 3.1.0
info:
  title: data-designer (plugin)
  version: 0.0.0
paths:
  /apis/data-designer/v2/workspaces/{workspace}/jobs/create:
    post:
      tags:
      - Data Designer
      summary: Create Job
      operationId: create_job_apis_data_designer_v2_workspaces__workspace__jobs_create_post
      parameters:
      - name: workspace
        in: path
        required: true
        schema:
          type: string
          title: Workspace
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/CreateJobRequest'
      responses:
        '201':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/CreateJob'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
    get:
      tags:
      - Data Designer
      summary: List Jobs
      operationId: list_jobs_apis_data_designer_v2_workspaces__workspace__jobs_create_get
      parameters:
      - name: workspace
        in: path
        required: true
        schema:
          type: string
          title: Workspace
      - name: page
        in: query
        required: false
        schema:
          type: integer
          exclusiveMinimum: 0
          description: Page number.
          default: 1
          title: Page
        description: Page number.
      - name: page_size
        in: query
        required: false
        schema:
          type: integer
          exclusiveMinimum: 0
          description: Page size.
          default: 10
          title: Page Size
        description: Page size.
      - name: sort
        in: query
        required: false
        schema:
          allOf:
          - $ref: '#/components/schemas/CreateJobsSortField'
          description: The field to sort by. To sort in decreasing order, use `-`
            in front of the field name.
          default: -created_at
        description: The field to sort by. To sort in decreasing order, use `-` in
          front of the field name.
      - in: query
        name: filter
        style: deepObject
        required: false
        explode: true
        schema:
          $ref: '#/components/schemas/CreateJobsListFilter'
        description: Filter jobs on various criteria.
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/CreateJobsPage'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
  /apis/data-designer/v2/workspaces/{workspace}/jobs/create/{job}/results/{name}:
    get:
      tags:
      - Data Designer
      summary: Get Job Result
      operationId: get_job_result_apis_data_designer_v2_workspaces__workspace__jobs_create__job__results__name__get
      parameters:
      - name: workspace
        in: path
        required: true
        schema:
          type: string
          title: Workspace
      - name: job
        in: path
        required: true
        schema:
          type: string
          title: Job
      - name: name
        in: path
        required: true
        schema:
          type: string
          title: Name
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/PlatformJobResultResponse'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
  /apis/data-designer/v2/workspaces/{workspace}/jobs/create/{job}/results/{name}/download:
    get:
      tags:
      - Data Designer
      summary: Download Job Result
      operationId: download_job_result_apis_data_designer_v2_workspaces__workspace__jobs_create__job__results__name__download_get
      parameters:
      - name: workspace
        in: path
        required: true
        schema:
          type: string
          title: Workspace
      - name: job
        in: path
        required: true
        schema:
          type: string
          title: Job
      - name: name
        in: path
        required: true
        schema:
          type: string
          title: Name
      responses:
        '200':
          description: Successful Response
          content:
            application/octet-stream:
              schema:
                type: string
                format: binary
        '404':
          description: Not Found
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
  /apis/data-designer/v2/workspaces/{workspace}/jobs/create/{name}:
    get:
      tags:
      - Data Designer
      summary: Get Job
      operationId: get_job_apis_data_designer_v2_workspaces__workspace__jobs_create__name__get
      parameters:
      - name: workspace
        in: path
        required: true
        schema:
          type: string
          title: Workspace
      - name: name
        in: path
        required: true
        schema:
          type: string
          title: Name
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/CreateJob'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
    delete:
      tags:
      - Data Designer
      summary: Delete Job
      operationId: delete_job_apis_data_designer_v2_workspaces__workspace__jobs_create__name__delete
      parameters:
      - name: workspace
        in: path
        required: true
        schema:
          type: string
          title: Workspace
      - name: name
        in: path
        required: true
        schema:
          type: string
          title: Name
      responses:
        '204':
          description: Successful Response
        '404':
          description: Job not Found
        '409':
          description: Job is not in a terminal state
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
  /apis/data-designer/v2/workspaces/{workspace}/jobs/create/{name}/cancel:
    post:
      tags:
      - Data Designer
      summary: Cancel Job
      operationId: cancel_job_apis_data_designer_v2_workspaces__workspace__jobs_create__name__cancel_post
      parameters:
      - name: workspace
        in: path
        required: true
        schema:
          type: string
          title: Workspace
      - name: name
        in: path
        required: true
        schema:
          type: string
          title: Name
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/CreateJob'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
  /apis/data-designer/v2/workspaces/{workspace}/jobs/create/{name}/logs:
    get:
      tags:
      - Data Designer
      summary: Get Job Logs
      operationId: get_job_logs_apis_data_designer_v2_workspaces__workspace__jobs_create__name__logs_get
      parameters:
      - name: workspace
        in: path
        required: true
        schema:
          type: string
          title: Workspace
      - name: name
        in: path
        required: true
        schema:
          type: string
          title: Name
      - name: limit
        in: query
        required: false
        schema:
          title: Limit
          type: integer
      - name: page_cursor
        in: query
        required: false
        schema:
          title: Page Cursor
          type: string
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/PlatformJobLogPage'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
  /apis/data-designer/v2/workspaces/{workspace}/jobs/create/{name}/results:
    get:
      tags:
      - Data Designer
      summary: List Job Results
      operationId: list_job_results_apis_data_designer_v2_workspaces__workspace__jobs_create__name__results_get
      parameters:
      - name: workspace
        in: path
        required: true
        schema:
          type: string
          title: Workspace
      - name: name
        in: path
        required: true
        schema:
          type: string
          title: Name
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/PlatformJobListResultResponse'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
  /apis/data-designer/v2/workspaces/{workspace}/jobs/create/{name}/status:
    get:
      tags:
      - Data Designer
      summary: Get Job Status
      operationId: get_job_status_apis_data_designer_v2_workspaces__workspace__jobs_create__name__status_get
      parameters:
      - name: workspace
        in: path
        required: true
        schema:
          type: string
          title: Workspace
      - name: name
        in: path
        required: true
        schema:
          type: string
          title: Name
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/PlatformJobStatusResponse'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
  /apis/data-designer/v2/workspaces/{workspace}/preview:
    post:
      tags:
      - Data Designer
      summary: Generate a small preview dataset by streaming NDJSON frames.
      description: Generate a small preview dataset by streaming NDJSON frames.
      operationId: PreviewFunction__route_apis_data_designer_v2_workspaces__workspace__preview_post
      parameters:
      - name: workspace
        in: path
        required: true
        schema:
          type: string
          title: Workspace
      - name: X-Request-ID
        in: header
        required: false
        schema:
          title: X-Request-Id
          type: string
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/PreviewSpec'
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
components:
  schemas:
    AgentRolloutFormat:
      type: string
      enum:
      - atif
      - claude_code
      - codex
      - hermes_agent
      - pi_coding_agent
      title: AgentRolloutFormat
    AgentRolloutSeedSource:
      properties:
        seed_type:
          type: string
          const: agent_rollout
          title: Seed Type
          default: agent_rollout
        path:
          title: Path
          description: Directory containing agent rollout artifacts. This field is
            required for ATIF trajectories. When omitted, built-in defaults are used
            for formats that define one. Claude Code defaults to ~/.claude/projects,
            Codex defaults to ~/.codex/sessions, Hermes Agent defaults to ~/.hermes/sessions,
            and Pi Coding Agent defaults to ~/.pi/agent/sessions. Relative local paths
            are resolved by the active filesystem provider when the seed is validated
            or read, not when the config object is constructed.
          type: string
        file_pattern:
          title: File Pattern
          description: Case-sensitive filename pattern used to match agent rollout
            files. When omitted, ATIF defaults to '*.json', Claude Code, Codex, and
            Pi Coding Agent default to '*.jsonl', and Hermes Agent defaults to '*.json*'.
          type: string
        recursive:
          type: boolean
          title: Recursive
          description: Whether to search nested subdirectories under the provided
            directory for matching files.
          default: true
        format:
          allOf:
          - $ref: '#/components/schemas/AgentRolloutFormat'
          description: Built-in agent rollout format to use for parsing trace files.
      type: object
      required:
      - format
      title: AgentRolloutSeedSource
    AudioContext:
      properties:
        modality:
          type: string
          const: audio
          title: Modality
          default: audio
        column_name:
          type: string
          title: Column Name
        data_type:
          $ref: '#/components/schemas/ModalityDataType'
        audio_format:
          $ref: '#/components/schemas/AudioFormat'
      type: object
      required:
      - column_name
      title: AudioContext
      description: 'Configuration for providing audio context to multimodal models.


        Audio context values are URL or base64 media values. Local paths may be

        passed through only in explicit URL mode so colocated model endpoints can

        read them directly. ``audio_format`` is consulted only for base64 sources.'
    AudioFormat:
      type: string
      enum:
      - mp3
      - wav
      title: AudioFormat
      description: Supported audio formats for audio context.
    BaseModel:
      properties: {}
      type: object
      title: BaseModel
    BernoulliMixtureSamplerParams:
      properties:
        p:
          type: number
          maximum: 1.0
          minimum: 0.0
          title: P
          description: Bernoulli distribution probability of success.
        dist_name:
          type: string
          title: Dist Name
          description: Mixture distribution name. Samples will be equal to the distribution
            sample with probability `p`, otherwise equal to 0. Must be a valid scipy.stats
            distribution name.
        dist_params:
          additionalProperties: true
          type: object
          title: Dist Params
          description: Parameters of the scipy.stats distribution given in `dist_name`.
        sampler_type:
          type: string
          const: bernoulli_mixture
          title: Sampler Type
          default: bernoulli_mixture
      additionalProperties: false
      type: object
      required:
      - p
      - dist_name
      - dist_params
      title: BernoulliMixtureSamplerParams
      description: "Parameters for sampling from a Bernoulli mixture distribution.\n\
        \nCombines a Bernoulli distribution with another continuous distribution,\
        \ creating a mixture\nwhere values are either 0 (with probability 1-p) or\
        \ sampled from the specified distribution\n(with probability p). This is useful\
        \ for modeling scenarios with many zero values mixed with\na continuous distribution\
        \ of non-zero values.\n\nCommon use cases include modeling sparse events,\
        \ zero-inflated data, or situations where\nan outcome either doesn't occur\
        \ (0) or follows a specific distribution when it does occur.\n\nAttributes:\n\
        \    p (required): Probability of sampling from the mixture distribution (non-zero\
        \ outcome).\n        Must be between 0.0 and 1.0 (inclusive). With probability\
        \ 1-p, the sample is 0.\n    dist_name (required): Name of the scipy.stats\
        \ distribution to sample from when outcome is non-zero.\n        Must be a\
        \ valid scipy.stats distribution name (e.g., \"norm\", \"gamma\", \"expon\"\
        ).\n    dist_params (required): Parameters for the specified scipy.stats distribution."
    BernoulliSamplerParams:
      properties:
        p:
          type: number
          maximum: 1.0
          minimum: 0.0
          title: P
          description: Probability of success.
        sampler_type:
          type: string
          const: bernoulli
          title: Sampler Type
          default: bernoulli
      additionalProperties: false
      type: object
      required:
      - p
      title: BernoulliSamplerParams
      description: "Parameters for sampling from a Bernoulli distribution.\n\nSamples\
        \ binary values (0 or 1) representing the outcome of a single trial with a\
        \ fixed\nprobability of success. This is the simplest discrete probability\
        \ distribution, useful for\nmodeling binary outcomes like success/failure,\
        \ yes/no, or true/false.\n\nAttributes:\n    p (required): Probability of\
        \ success (sampling 1). Must be between 0.0 and 1.0 (inclusive).\n       \
        \ The probability of failure (sampling 0) is automatically 1 - p."
    BinomialSamplerParams:
      properties:
        n:
          type: integer
          title: N
          description: Number of trials.
        p:
          type: number
          maximum: 1.0
          minimum: 0.0
          title: P
          description: Probability of success on each trial.
        sampler_type:
          type: string
          const: binomial
          title: Sampler Type
          default: binomial
      additionalProperties: false
      type: object
      required:
      - n
      - p
      title: BinomialSamplerParams
      description: "Parameters for sampling from a Binomial distribution.\n\nSamples\
        \ integer values representing the number of successes in a fixed number of\
        \ independent\nBernoulli trials, each with the same probability of success.\
        \ Commonly used to model the number\nof successful outcomes in repeated experiments.\n\
        \nAttributes:\n    n (required): Number of independent trials. Must be a positive\
        \ integer.\n    p (required): Probability of success on each trial. Must be\
        \ between 0.0 and 1.0 (inclusive)."
    CategorySamplerParams:
      properties:
        values:
          items:
            anyOf:
            - type: string
            - type: integer
            - type: number
          type: array
          minItems: 1
          title: Values
          description: List of possible categorical values that can be sampled from.
        weights:
          title: Weights
          description: List of unnormalized probability weights to assigned to each
            value, in order. Larger values will be sampled with higher probability.
          items:
            type: number
          type: array
        sampler_type:
          type: string
          const: category
          title: Sampler Type
          default: category
      additionalProperties: false
      type: object
      required:
      - values
      title: CategorySamplerParams
      description: "Parameters for categorical sampling with optional probability\
        \ weighting.\n\nSamples values from a discrete set of categories. When weights\
        \ are provided, values are\nsampled according to their assigned probabilities.\
        \ Without weights, uniform sampling is used.\n\nAttributes:\n    values (required):\
        \ List of possible categorical values to sample from. Can contain strings,\
        \ integers,\n        or floats. Must contain at least one value.\n    weights:\
        \ Optional unnormalized probability weights for each value. If provided, must\
        \ be\n        the same length as `values`. Weights are automatically normalized\
        \ to sum to 1.0.\n        Larger weights result in higher sampling probability\
        \ for the corresponding value."
    ChatCompletionInferenceParams:
      properties:
        generation_type:
          type: string
          const: chat-completion
          title: Generation Type
          default: chat-completion
        max_parallel_requests:
          type: integer
          minimum: 1.0
          title: Max Parallel Requests
          default: 4
        timeout:
          title: Timeout
          type: integer
          minimum: 1.0
        extra_body:
          title: Extra Body
          additionalProperties: true
          type: object
        temperature:
          anyOf:
          - type: number
          - $ref: '#/components/schemas/UniformDistribution'
          - $ref: '#/components/schemas/ManualDistribution'
          title: Temperature
        top_p:
          anyOf:
          - type: number
          - $ref: '#/components/schemas/UniformDistribution'
          - $ref: '#/components/schemas/ManualDistribution'
          title: Top P
        max_tokens:
          title: Max Tokens
          type: integer
          minimum: 1.0
      additionalProperties: false
      type: object
      title: ChatCompletionInferenceParams
      description: "Configuration for LLM inference parameters.\n\nAttributes:\n \
        \   generation_type: Type of generation, always \"chat-completion\" for this\
        \ class.\n    temperature: Sampling temperature (0.0-2.0). Can be a fixed\
        \ value or a distribution for dynamic sampling.\n    top_p: Nucleus sampling\
        \ probability (0.0-1.0). Can be a fixed value or a distribution for dynamic\
        \ sampling.\n    max_tokens: Maximum number of tokens to generate in the response."
    CodeLang:
      type: string
      enum:
      - bash
      - c
      - cobol
      - cpp
      - csharp
      - go
      - java
      - javascript
      - kotlin
      - python
      - ruby
      - rust
      - scala
      - swift
      - typescript
      - sql:sqlite
      - sql:tsql
      - sql:bigquery
      - sql:mysql
      - sql:postgres
      - sql:ansi
      title: CodeLang
    CodeValidatorParams:
      properties:
        validator_type:
          type: string
          const: code
          title: Validator Type
          description: Validator type discriminator, always 'code' for this validator
          default: code
        code_lang:
          allOf:
          - $ref: '#/components/schemas/CodeLang'
          description: The language of the code to validate
      additionalProperties: false
      type: object
      required:
      - code_lang
      title: CodeValidatorParams
      description: "Configuration for code validation. Supports Python and SQL code\
        \ validation.\n\nAttributes:\n    code_lang (required): The language of the\
        \ code to validate. Supported values include: `python`,\n        `sql:sqlite`,\
        \ `sql:postgres`, `sql:mysql`, `sql:tsql`, `sql:bigquery`, `sql:ansi`."
    ColumnInequalityConstraint:
      properties:
        target_column:
          type: string
          title: Target Column
          description: Name of the sampler column this constraint applies to
        constraint_type:
          type: string
          const: column_inequality
          title: Constraint Type
          description: Constraint type discriminator, always 'column_inequality' for
            this constraint
          default: column_inequality
        rhs:
          type: string
          title: Rhs
          description: Name of the other sampler column to compare against
        operator:
          allOf:
          - $ref: '#/components/schemas/InequalityOperator'
          description: Comparison operator
      additionalProperties: false
      type: object
      required:
      - target_column
      - rhs
      - operator
      title: ColumnInequalityConstraint
      description: "Constrain a sampler column to be less/greater than another sampler\
        \ column.\n\nOnly applies to sampler columns.\n\nAttributes:\n    rhs (required):\
        \ Name of the other sampler column to compare against.\n    operator (required):\
        \ Comparison operator (lt, le, gt, ge).\n\nInherited Attributes:\n    target_column\
        \ (required): Name of the sampler column this constraint applies to."
    CreateJob:
      properties:
        id:
          title: Id
          type: string
        name:
          type: string
          title: Name
        description:
          title: Description
          type: string
        project:
          title: Project
          type: string
        workspace:
          title: Workspace
          type: string
        created_at:
          title: Created At
          type: string
          format: date-time
        updated_at:
          title: Updated At
          type: string
          format: date-time
        spec:
          $ref: '#/components/schemas/DataDesignerStepConfig'
        status:
          $ref: '#/components/schemas/PlatformJobStatus'
        status_details:
          title: Status Details
          additionalProperties: true
          type: object
        error_details:
          title: Error Details
          additionalProperties: true
          type: object
        ownership:
          title: Ownership
          additionalProperties: true
          type: object
        custom_fields:
          title: Custom Fields
          additionalProperties: true
          type: object
      type: object
      required:
      - name
      - spec
      title: CreateJob
    CreateJobRequest:
      properties:
        name:
          title: Name
          type: string
        description:
          title: Description
          type: string
        project:
          title: Project
          type: string
        spec:
          $ref: '#/components/schemas/DataDesignerJobConfig'
        profile:
          title: Profile
          type: string
        options:
          title: Options
          additionalProperties: true
          type: object
        ownership:
          title: Ownership
          additionalProperties: true
          type: object
        custom_fields:
          title: Custom Fields
          additionalProperties: true
          type: object
        output_location:
          title: Output Location
          type: string
      type: object
      required:
      - spec
      title: CreateJobRequest
    CreateJobsListFilter:
      additionalProperties: false
      properties:
        created_at:
          allOf:
          - $ref: '#/components/schemas/DatetimeFilter'
          description: Jobs created at 'gte' datetime or 'lte' datetime.
        name:
          anyOf:
          - $ref: '#/components/schemas/StringFilter'
          - type: string
          description: Name of the job.
          title: Name
        workspace:
          description: Workspace of the job.
          title: Workspace
          type: string
        project:
          description: Project containing the job.
          title: Project
          type: string
        status:
          allOf:
          - $ref: '#/components/schemas/PlatformJobStatus'
          description: The current status.
        updated_at:
          allOf:
          - $ref: '#/components/schemas/DatetimeFilter'
          description: Jobs updated at 'gte' datetime or 'lte' datetime.
      title: CreateJobsListFilter
      type: object
    CreateJobsPage:
      properties:
        data:
          items:
            $ref: '#/components/schemas/CreateJob'
          type: array
          title: Data
        pagination:
          allOf:
          - $ref: '#/components/schemas/PaginationData'
          description: Pagination information.
        sort:
          title: Sort
          description: The field on which the results are sorted.
          type: string
        filter:
          title: Filter
          description: Filtering information.
          additionalProperties: true
          type: object
      type: object
      required:
      - data
      title: CreateJobsPage
    CreateJobsSortField:
      type: string
      enum:
      - created_at
      - -created_at
      - updated_at
      - -updated_at
      title: CreateJobsSortField
    CustomColumnConfig:
      properties:
        name:
          type: string
          title: Name
        drop:
          type: boolean
          title: Drop
          default: false
        column_type:
          type: string
          const: custom
          title: Column Type
          default: custom
        skip:
          $ref: '#/components/schemas/SkipConfig'
        propagate_skip:
          type: boolean
          title: Propagate Skip
          description: "If True (default), this column auto-skips when any of its\
            \ required_columns was skipped. Independent of skip \u2014 a column with\
            \ no SkipConfig still propagates upstream skips. Set to False for null-tolerant\
            \ columns."
          default: true
        generator_function:
          title: Generator Function
          description: Function decorated with @custom_column_generator
        generation_strategy:
          allOf:
          - $ref: '#/components/schemas/GenerationStrategy'
          description: 'Generation strategy: ''cell_by_cell'' for row-based or ''full_column''
            for batch-based'
          default: cell_by_cell
        generator_params:
          allOf:
          - $ref: '#/components/schemas/BaseModel'
          description: Optional typed configuration object passed as second argument
            to generator function
      additionalProperties: false
      type: object
      required:
      - name
      - generator_function
      title: CustomColumnConfig
      description: "Configuration for custom user-defined column generators.\n\nCustom\
        \ columns allow users to provide their own generation logic via a callable\
        \ function\ndecorated with `@custom_column_generator`. Two strategies are\
        \ supported: cell_by_cell\n(default, row-based) and full_column (batch-based\
        \ with DataFrame access).\n\nAttributes:\n    generator_function (required):\
        \ A callable decorated with @custom_column_generator.\n    generation_strategy:\
        \ \"cell_by_cell\" (row-based) or \"full_column\" (batch-based).\n    generator_params:\
        \ Optional typed configuration object (Pydantic BaseModel) passed\n      \
        \  as the second argument to the generator function.\n\nInherited Attributes:\n\
        \    name (required): Unique name of the column to be generated.\n    drop:\
        \ If True, generate this column but remove it from the final dataset."
    DataDesignerConfig:
      properties:
        columns:
          items:
            oneOf:
            - $ref: '#/components/schemas/CustomColumnConfig'
            - $ref: '#/components/schemas/ExpressionColumnConfig'
            - $ref: '#/components/schemas/LLMCodeColumnConfig'
            - $ref: '#/components/schemas/LLMJudgeColumnConfig'
            - $ref: '#/components/schemas/LLMStructuredColumnConfig'
            - $ref: '#/components/schemas/LLMTextColumnConfig'
            - $ref: '#/components/schemas/SamplerColumnConfig'
            - $ref: '#/components/schemas/SeedDatasetColumnConfig'
            - $ref: '#/components/schemas/ValidationColumnConfig'
            - $ref: '#/components/schemas/EmbeddingColumnConfig'
            - $ref: '#/components/schemas/ImageColumnConfig'
            discriminator:
              propertyName: column_type
              mapping:
                custom: '#/components/schemas/CustomColumnConfig'
                embedding: '#/components/schemas/EmbeddingColumnConfig'
                expression: '#/components/schemas/ExpressionColumnConfig'
                image: '#/components/schemas/ImageColumnConfig'
                llm-code: '#/components/schemas/LLMCodeColumnConfig'
                llm-judge: '#/components/schemas/LLMJudgeColumnConfig'
                llm-structured: '#/components/schemas/LLMStructuredColumnConfig'
                llm-text: '#/components/schemas/LLMTextColumnConfig'
                sampler: '#/components/schemas/SamplerColumnConfig'
                seed-dataset: '#/components/schemas/SeedDatasetColumnConfig'
                validation: '#/components/schemas/ValidationColumnConfig'
          type: array
          title: Columns
        model_configs:
          title: Model Configs
          items:
            $ref: '#/components/schemas/ModelConfig'
          type: array
        tool_configs:
          title: Tool Configs
          items:
            $ref: '#/components/schemas/ToolConfig'
          type: array
        seed_config:
          $ref: '#/components/schemas/SeedConfig'
        constraints:
          title: Constraints
          items:
            oneOf:
            - $ref: '#/components/schemas/ScalarInequalityConstraint'
            - $ref: '#/components/schemas/ColumnInequalityConstraint'
          type: array
        profilers:
          title: Profilers
          items:
            $ref: '#/components/schemas/JudgeScoreProfilerConfig'
          type: array
        processors:
          title: Processors
          items:
            oneOf:
            - $ref: '#/components/schemas/DropColumnsProcessorConfig'
            - $ref: '#/components/schemas/SchemaTransformProcessorConfig'
            discriminator:
              propertyName: processor_type
              mapping:
                drop_columns: '#/components/schemas/DropColumnsProcessorConfig'
                schema_transform: '#/components/schemas/SchemaTransformProcessorConfig'
          type: array
      additionalProperties: false
      type: object
      required:
      - columns
      title: DataDesignerConfig
      description: "Configuration for NeMo Data Designer.\n\nThis class defines the\
        \ main configuration structure for NeMo Data Designer,\nwhich the engine consumes\
        \ when generating synthetic data.\n\nAttributes:\n    columns: Required list\
        \ of column configurations defining how each column\n        should be generated.\
        \ May be empty for seeded processor-only configs.\n    model_configs: Optional\
        \ list of model configurations for LLM-based generation.\n        Each model\
        \ config defines the model, provider, and inference parameters.\n    tool_configs:\
        \ Optional list of tool configurations for MCP tool calling.\n        Each\
        \ tool config defines the provider, allowed tools, and execution limits.\n\
        \    seed_config: Optional seed dataset settings to use for generation.\n\
        \    constraints: Optional list of column constraints.\n    profilers: Optional\
        \ list of column profilers for analyzing generated data characteristics.\n\
        \    processors: Optional list of processor configurations for post-generation\
        \ transformations."
    DataDesignerJobConfig:
      properties:
        num_records:
          type: integer
          title: Num Records
        config:
          $ref: '#/components/schemas/DataDesignerConfig'
      type: object
      required:
      - num_records
      - config
      title: DataDesignerJobConfig
    DataDesignerStepConfig:
      properties:
        job_config:
          $ref: '#/components/schemas/DataDesignerJobConfig'
        model_providers:
          items:
            $ref: '#/components/schemas/ModelProvider'
          type: array
          title: Model Providers
        model_configs:
          items:
            $ref: '#/components/schemas/ModelConfig'
          type: array
          title: Model Configs
      type: object
      required:
      - job_config
      - model_providers
      - model_configs
      title: DataDesignerStepConfig
    DataFrameSeedSource:
      properties:
        seed_type:
          type: string
          const: df
          title: Seed Type
          default: df
      type: object
      title: DataFrameSeedSource
    DatetimeFilter:
      additionalProperties: false
      properties:
        $gte:
          description: Filter for results greater than or equal to this datetime.
          title: $Gte
          format: date-time
          type: string
        $lte:
          description: Filter for results less than or equal to this datetime.
          title: $Lte
          format: date-time
          type: string
      title: DatetimeFilter
      type: object
    DatetimeSamplerParams:
      properties:
        start:
          type: string
          title: Start
          description: Earliest possible datetime for sampling range, inclusive.
        end:
          type: string
          title: End
          description: Exclusive upper bound for datetime sampling range.
        unit:
          type: string
          enum:
          - Y
          - M
          - D
          - h
          - m
          - s
          title: Unit
          description: Sampling units, e.g. the smallest possible time interval between
            samples.
          default: D
        sampler_type:
          type: string
          const: datetime
          title: Sampler Type
          default: datetime
      additionalProperties: false
      type: object
      required:
      - start
      - end
      title: DatetimeSamplerParams
      description: "Parameters for uniform datetime sampling within a specified range.\n\
        \nSamples datetime values uniformly between a start and end date with a specified\
        \ granularity.\nThe sampling unit determines the smallest possible time interval\
        \ between consecutive samples.\n\nAttributes:\n    start (required): Earliest\
        \ possible datetime for the sampling range (inclusive). Must be a valid\n\
        \        datetime string parseable by pandas.to_datetime().\n    end (required):\
        \ Exclusive upper bound for the sampling range. Must be a valid\n        datetime\
        \ string parseable by pandas.to_datetime().\n    unit: Time unit for sampling\
        \ granularity. Options:\n        - \"Y\": Years\n        - \"M\": Months\n\
        \        - \"D\": Days (default)\n        - \"h\": Hours\n        - \"m\"\
        : Minutes\n        - \"s\": Seconds"
    DirectorySeedSource:
      properties:
        seed_type:
          type: string
          const: directory
          title: Seed Type
          default: directory
        path:
          type: string
          title: Path
          description: Directory containing seed artifacts. Relative local paths are
            resolved by the active filesystem provider when the seed is validated
            or read, not when the config object is constructed.
        file_pattern:
          type: string
          title: File Pattern
          description: Case-sensitive filename pattern used to match files under the
            provided directory. Patterns match basenames only, not relative paths.
          default: '*'
        recursive:
          type: boolean
          title: Recursive
          description: Whether to search nested subdirectories under the provided
            directory for matching files.
          default: true
      type: object
      required:
      - path
      title: DirectorySeedSource
    DistributionType:
      type: string
      enum:
      - uniform
      - manual
      title: DistributionType
      description: Types of distributions for sampling inference parameters.
    DropColumnsProcessorConfig:
      properties:
        name:
          type: string
          title: Name
          description: The name of the processor, used to identify the processor in
            the results and to write the artifacts to disk.
        processor_type:
          type: string
          const: drop_columns
          title: Processor Type
          default: drop_columns
        column_names:
          items:
            type: string
          type: array
          title: Column Names
          description: List of column names to drop from the output dataset.
      additionalProperties: false
      type: object
      required:
      - name
      - column_names
      title: DropColumnsProcessorConfig
      description: "Drop columns from the output dataset (prefer ``drop=True`` in\
        \ the column config).\n\nThis processor removes specified columns from the\
        \ generated dataset. The dropped\ncolumns are saved separately in the `dropped-columns-parquet-files`\
        \ directory for reference.\nWhen this processor is added via the config builder,\
        \ the corresponding column\nconfigs are automatically marked with `drop =\
        \ True`.\n\nAttributes:\n    column_names (required): List of column names\
        \ to remove from the output dataset.\n\nInherited Attributes:\n    name (required):\
        \ Name of the processor."
    EmbeddingColumnConfig:
      properties:
        name:
          type: string
          title: Name
        drop:
          type: boolean
          title: Drop
          default: false
        column_type:
          type: string
          const: embedding
          title: Column Type
          default: embedding
        skip:
          $ref: '#/components/schemas/SkipConfig'
        propagate_skip:
          type: boolean
          title: Propagate Skip
          description: "If True (default), this column auto-skips when any of its\
            \ required_columns was skipped. Independent of skip \u2014 a column with\
            \ no SkipConfig still propagates upstream skips. Set to False for null-tolerant\
            \ columns."
          default: true
        target_column:
          type: string
          title: Target Column
          description: Name of the text column to generate embeddings for
        model_alias:
          type: string
          title: Model Alias
          description: Alias of the model to use for embedding generation
      additionalProperties: false
      type: object
      required:
      - name
      - target_column
      - model_alias
      title: EmbeddingColumnConfig
      description: "Configuration for embedding generation columns.\n\nEmbedding columns\
        \ generate embeddings for text input using a specified model.\n\nAttributes:\n\
        \    target_column (required): The column to generate embeddings for. The\
        \ column could be a single text string or a list of text strings in stringified\
        \ JSON format.\n        If it is a list of text strings in stringified JSON\
        \ format, the embeddings will be generated for each text string.\n    model_alias\
        \ (required): The model to use for embedding generation.\n\nInherited Attributes:\n\
        \    name (required): Unique name of the column to be generated.\n    drop:\
        \ If True, generate this column but remove it from the final dataset."
    EmbeddingInferenceParams:
      properties:
        generation_type:
          type: string
          const: embedding
          title: Generation Type
          default: embedding
        max_parallel_requests:
          type: integer
          minimum: 1.0
          title: Max Parallel Requests
          default: 4
        timeout:
          title: Timeout
          type: integer
          minimum: 1.0
        extra_body:
          title: Extra Body
          additionalProperties: true
          type: object
        encoding_format:
          type: string
          enum:
          - float
          - base64
          title: Encoding Format
          default: float
        dimensions:
          title: Dimensions
          type: integer
      additionalProperties: false
      type: object
      title: EmbeddingInferenceParams
      description: "Configuration for embedding generation parameters.\n\nAttributes:\n\
        \    generation_type: Type of generation, always \"embedding\" for this class.\n\
        \    encoding_format: Format of the embedding encoding (\"float\" or \"base64\"\
        ).\n    dimensions: Number of dimensions for the embedding."
    ExpressionColumnConfig:
      properties:
        name:
          type: string
          title: Name
        drop:
          type: boolean
          title: Drop
          default: false
        column_type:
          type: string
          const: expression
          title: Column Type
          default: expression
        skip:
          $ref: '#/components/schemas/SkipConfig'
        propagate_skip:
          type: boolean
          title: Propagate Skip
          description: "If True (default), this column auto-skips when any of its\
            \ required_columns was skipped. Independent of skip \u2014 a column with\
            \ no SkipConfig still propagates upstream skips. Set to False for null-tolerant\
            \ columns."
          default: true
        expr:
          type: string
          title: Expr
          description: Jinja2 expression to compute the column value from other columns
        dtype:
          type: string
          enum:
          - int
          - float
          - str
          - bool
          title: Dtype
          description: 'Data type for expression result: ''int'', ''float'', ''str'',
            or ''bool'''
          default: str
      additionalProperties: false
      type: object
      required:
      - name
      - expr
      title: ExpressionColumnConfig
      description: "Configuration for derived columns using Jinja2 expressions.\n\n\
        Expression columns compute values by evaluating Jinja2 templates that reference\
        \ other\ncolumns. Useful for transformations, concatenations, conditional\
        \ logic, and derived\nfeatures without requiring LLM generation. The expression\
        \ is evaluated row-by-row.\n\nAttributes:\n    expr (required): Jinja2 expression\
        \ to evaluate. Can reference other column values using\n        {{ column_name\
        \ }} syntax. Supports filters, conditionals, and arithmetic.\n        Must\
        \ be a valid, non-empty Jinja2 template.\n    dtype: Data type to cast the\
        \ result to. Must be one of \"int\", \"float\", \"str\", or \"bool\".\n  \
        \      Defaults to \"str\". Type conversion is applied after expression evaluation.\n\
        \nInherited Attributes:\n    name (required): Unique name of the column to\
        \ be generated.\n    drop: If True, generate this column but remove it from\
        \ the final dataset."
    FileContentsSeedSource:
      properties:
        seed_type:
          type: string
          const: file_contents
          title: Seed Type
          default: file_contents
        path:
          type: string
          title: Path
          description: Directory containing seed artifacts. Relative local paths are
            resolved by the active filesystem provider when the seed is validated
            or read, not when the config object is constructed.
        file_pattern:
          type: string
          title: File Pattern
          description: Case-sensitive filename pattern used to match files under the
            provided directory. Patterns match basenames only, not relative paths.
          default: '*'
        recursive:
          type: boolean
          title: Recursive
          description: Whether to search nested subdirectories under the provided
            directory for matching files.
          default: true
        encoding:
          type: string
          title: Encoding
          description: Text encoding used when reading matching files into the `content`
            column.
          default: utf-8
      type: object
      required:
      - path
      title: FileContentsSeedSource
    FileStorageType:
      type: string
      enum:
      - fileset
      title: FileStorageType
    FilesetFileSeedSource:
      properties:
        seed_type:
          type: string
          const: nmp
          title: Seed Type
          default: nmp
        path:
          type: string
          title: Path
      type: object
      required:
      - path
      title: FilesetFileSeedSource
    GaussianSamplerParams:
      properties:
        mean:
          type: number
          title: Mean
          description: Mean of the Gaussian distribution
        stddev:
          type: number
          title: Stddev
          description: Standard deviation of the Gaussian distribution
        decimal_places:
          title: Decimal Places
          description: Number of decimal places to round the sampled values to.
          type: integer
        sampler_type:
          type: string
          const: gaussian
          title: Sampler Type
          default: gaussian
      additionalProperties: false
      type: object
      required:
      - mean
      - stddev
      title: GaussianSamplerParams
      description: "Parameters for sampling from a Gaussian (Normal) distribution.\n\
        \nSamples continuous values from a normal distribution characterized by its\
        \ mean and standard\ndeviation. The Gaussian distribution is one of the most\
        \ commonly used probability distributions,\nappearing naturally in many real-world\
        \ phenomena due to the Central Limit Theorem.\n\nAttributes:\n    mean (required):\
        \ Mean (center) of the Gaussian distribution. This is the expected value and\
        \ the\n        location of the distribution's peak.\n    stddev (required):\
        \ Standard deviation of the Gaussian distribution. Controls the spread or\
        \ width\n        of the distribution. Must be positive.\n    decimal_places:\
        \ Optional number of decimal places to round sampled values to. If None,\n\
        \        values are not rounded."
    GenerationStrategy:
      type: string
      enum:
      - cell_by_cell
      - full_column
      title: GenerationStrategy
      description: Strategy for custom column generation.
    HTTPValidationError:
      properties:
        detail:
          items:
            $ref: '#/components/schemas/ValidationError'
          type: array
          title: Detail
      type: object
      title: HTTPValidationError
    HuggingFaceSeedSource:
      properties:
        seed_type:
          type: string
          const: hf
          title: Seed Type
          default: hf
        path:
          type: string
          title: Path
          description: Path to the seed data in HuggingFace. Wildcards are allowed.
            Examples include 'datasets/my-username/my-dataset/data/000_00000.parquet',
            'datasets/my-username/my-dataset/data/*.parquet', and 'datasets/my-username/my-dataset/**/*.parquet'
        token:
          title: Token
          type: string
        endpoint:
          type: string
          title: Endpoint
          default: https://huggingface.co
      type: object
      required:
      - path
      title: HuggingFaceSeedSource
    ImageColumnConfig:
      properties:
        name:
          type: string
          title: Name
        drop:
          type: boolean
          title: Drop
          default: false
        column_type:
          type: string
          const: image
          title: Column Type
          default: image
        skip:
          $ref: '#/components/schemas/SkipConfig'
        propagate_skip:
          type: boolean
          title: Propagate Skip
          description: "If True (default), this column auto-skips when any of its\
            \ required_columns was skipped. Independent of skip \u2014 a column with\
            \ no SkipConfig still propagates upstream skips. Set to False for null-tolerant\
            \ columns."
          default: true
        prompt:
          type: string
          title: Prompt
          description: Jinja2 template for the image generation prompt; can reference
            other columns via {{ column_name }}
        model_alias:
          type: string
          title: Model Alias
          description: Alias of the model to use for image generation
        multi_modal_context:
          title: Multi Modal Context
          description: Optional list of multimodal context inputs for image generation
          items:
            oneOf:
            - $ref: '#/components/schemas/ImageContext'
            - $ref: '#/components/schemas/AudioContext'
            - $ref: '#/components/schemas/VideoContext'
            discriminator:
              propertyName: modality
              mapping:
                audio: '#/components/schemas/AudioContext'
                image: '#/components/schemas/ImageContext'
                video: '#/components/schemas/VideoContext'
          type: array
      additionalProperties: false
      type: object
      required:
      - name
      - prompt
      - model_alias
      title: ImageColumnConfig
      description: "Configuration for image generation columns.\n\nImage columns generate\
        \ images using either autoregressive or diffusion models.\nThe API used is\
        \ automatically determined based on the model name:\n\nAttributes:\n    prompt\
        \ (required): Prompt template for image generation. Supports Jinja2 templating\
        \ to\n        reference other columns (e.g., \"Generate an image of a {{ character_name\
        \ }}\").\n        Must be a valid Jinja2 template.\n    model_alias (required):\
        \ The model to use for image generation.\n    multi_modal_context: Optional\
        \ list of multimodal contexts for generation.\n        Enables autoregressive\
        \ multimodal models to generate images based on image, audio, or video inputs.\n\
        \        Ignored by diffusion image-generation routes, which do not consume\
        \ multimodal context.\n\nInherited Attributes:\n    name (required): Unique\
        \ name of the column to be generated.\n    drop: If True, generate this column\
        \ but remove it from the final dataset."
    ImageContext:
      properties:
        modality:
          type: string
          const: image
          title: Modality
          default: image
        column_name:
          type: string
          title: Column Name
        data_type:
          $ref: '#/components/schemas/ModalityDataType'
        image_format:
          $ref: '#/components/schemas/ImageFormat'
      type: object
      required:
      - column_name
      title: ImageContext
      description: "Configuration for providing image context to multimodal models.\n\
        \nAttributes:\n    modality: The modality type (always \"image\").\n    column_name:\
        \ Name of the column containing image data.\n    data_type: Format of the\
        \ image data (\"url\", \"base64\", or None for auto-detection).\n        When\
        \ None, the format is auto-detected: URLs are passed through, file paths that\n\
        \        exist under base_path are loaded as base64, and other values are\
        \ assumed to be base64.\n    image_format: Image format (required when data_type\
        \ is explicitly \"base64\")."
    ImageFormat:
      type: string
      enum:
      - png
      - jpg
      - jpeg
      - gif
      - webp
      title: ImageFormat
      description: Supported image formats for image modality.
    ImageInferenceParams:
      properties:
        generation_type:
          type: string
          const: image
          title: Generation Type
          default: image
        max_parallel_requests:
          type: integer
          minimum: 1.0
          title: Max Parallel Requests
          default: 4
        timeout:
          title: Timeout
          type: integer
          minimum: 1.0
        extra_body:
          title: Extra Body
          additionalProperties: true
          type: object
      additionalProperties: false
      type: object
      title: ImageInferenceParams
      description: "Configuration for image generation models.\n\nWorks for both diffusion\
        \ and autoregressive image generation models. Pass all model-specific image\
        \ options via `extra_body`.\n\nAttributes:\n    generation_type: Type of generation,\
        \ always \"image\" for this class.\n\nExample:\n    ```python\n    # OpenAI-style\
        \ (DALL\xB7E): quality and size in extra_body or as top-level kwargs\n   \
        \ dd.ImageInferenceParams(\n        extra_body={\"size\": \"1024x1024\", \"\
        quality\": \"hd\"}\n    )\n\n    # Gemini-style: generationConfig.imageConfig\n\
        \    dd.ImageInferenceParams(\n        extra_body={\n            \"generationConfig\"\
        : {\n                \"imageConfig\": {\n                    \"aspectRatio\"\
        : \"1:1\",\n                    \"imageSize\": \"1024\"\n                }\n\
        \            }\n        }\n    )\n    ```"
    IndexRange:
      properties:
        start:
          type: integer
          minimum: 0.0
          title: Start
          description: The start index of the index range (inclusive)
        end:
          type: integer
          minimum: 0.0
          title: End
          description: The end index of the index range (inclusive)
      additionalProperties: false
      type: object
      required:
      - start
      - end
      title: IndexRange
    InequalityOperator:
      type: string
      enum:
      - lt
      - le
      - gt
      - ge
      title: InequalityOperator
    JudgeScoreProfilerConfig:
      properties:
        model_alias:
          type: string
          title: Model Alias
        summary_score_sample_size:
          title: Summary Score Sample Size
          default: 20
          type: integer
          minimum: 1.0
      additionalProperties: false
      type: object
      required:
      - model_alias
      title: JudgeScoreProfilerConfig
      description: "Configuration for the LLM-as-a-judge score profiler.\n\nAttributes:\n\
        \    model_alias: Alias of the LLM model to use for generating score distribution\
        \ summaries.\n        Must match a model alias defined in the Data Designer\
        \ configuration.\n    summary_score_sample_size: Number of score samples to\
        \ include when prompting the LLM\n        to generate summaries. Larger sample\
        \ sizes provide more context but increase\n        token usage. Must be at\
        \ least 1 when provided. Set to None to skip LLM-generated\n        summaries.\
        \ Defaults to 20."
    LLMCodeColumnConfig:
      properties:
        name:
          type: string
          title: Name
        drop:
          type: boolean
          title: Drop
          default: false
        column_type:
          type: string
          const: llm-code
          title: Column Type
          default: llm-code
        skip:
          $ref: '#/components/schemas/SkipConfig'
        propagate_skip:
          type: boolean
          title: Propagate Skip
          description: "If True (default), this column auto-skips when any of its\
            \ required_columns was skipped. Independent of skip \u2014 a column with\
            \ no SkipConfig still propagates upstream skips. Set to False for null-tolerant\
            \ columns."
          default: true
        prompt:
          type: string
          title: Prompt
          description: Jinja2 template for the LLM prompt; can reference other columns
            via {{ column_name }}
        model_alias:
          type: string
          title: Model Alias
          description: Alias of the model configuration to use for generation
        system_prompt:
          title: System Prompt
          description: Optional system prompt to set model behavior and constraints
          type: string
        multi_modal_context:
          title: Multi Modal Context
          description: Optional list of multimodal context inputs
          items:
            oneOf:
            - $ref: '#/components/schemas/ImageContext'
            - $ref: '#/components/schemas/AudioContext'
            - $ref: '#/components/schemas/VideoContext'
            discriminator:
              propertyName: modality
              mapping:
                audio: '#/components/schemas/AudioContext'
                image: '#/components/schemas/ImageContext'
                video: '#/components/schemas/VideoContext'
          type: array
        tool_alias:
          title: Tool Alias
          description: Optional alias of the tool configuration to use for MCP tool
            calls
          type: string
        with_trace:
          allOf:
          - $ref: '#/components/schemas/TraceType'
          description: 'Trace capture mode: NONE, LAST_MESSAGE, or ALL_MESSAGES'
          default: none
        extract_reasoning_content:
          type: boolean
          title: Extract Reasoning Content
          description: If True, capture chain-of-thought in {name}__reasoning_content
            column
          default: false
        code_lang:
          allOf:
          - $ref: '#/components/schemas/CodeLang'
          description: Target programming language or SQL dialect for code extraction
            from LLM response
      additionalProperties: false
      type: object
      required:
      - name
      - prompt
      - model_alias
      - code_lang
      title: LLMCodeColumnConfig
      description: "Configuration for code generation columns using Large Language\
        \ Models.\n\nExtends LLMTextColumnConfig to generate code snippets in specific\
        \ programming languages\nor SQL dialects. The generated code is automatically\
        \ extracted from markdown code blocks\nfor the specified language. Inherits\
        \ all prompt templating capabilities from LLMTextColumnConfig.\n\nAttributes:\n\
        \    code_lang (required): Programming language or SQL dialect for code generation.\
        \ Supported\n        values include: \"python\", \"javascript\", \"typescript\"\
        , \"java\", \"kotlin\", \"go\",\n        \"rust\", \"ruby\", \"scala\", \"\
        swift\", \"sql:sqlite\", \"sql:postgres\", \"sql:mysql\",\n        \"sql:tsql\"\
        , \"sql:bigquery\", \"sql:ansi\". See CodeLang enum for complete list.\n\n\
        Inherited Attributes:\n    name (required): Unique name of the column to be\
        \ generated.\n    prompt (required): Prompt template for code generation (supports\
        \ Jinja2).\n    model_alias (required): Alias of the model configuration to\
        \ use.\n    system_prompt: Optional system prompt (supports Jinja2).\n   \
        \ multi_modal_context: Optional multimodal contexts for generation.\n    tool_alias:\
        \ Optional tool configuration alias for MCP tool calls.\n    with_trace: Specifies\
        \ what trace information to capture in a `{column_name}__trace`\n        column.\
        \ Options are `TraceType.NONE` (default), `TraceType.LAST_MESSAGE`, or\n \
        \       `TraceType.ALL_MESSAGES`.\n    extract_reasoning_content: If True,\
        \ creates a `{column_name}__reasoning_content`\n        column containing\
        \ the reasoning content from the final assistant response.\n    drop: If True,\
        \ generate this column but remove it from the final dataset."
    LLMJudgeColumnConfig:
      properties:
        name:
          type: string
          title: Name
        drop:
          type: boolean
          title: Drop
          default: false
        column_type:
          type: string
          const: llm-judge
          title: Column Type
          default: llm-judge
        skip:
          $ref: '#/components/schemas/SkipConfig'
        propagate_skip:
          type: boolean
          title: Propagate Skip
          description: "If True (default), this column auto-skips when any of its\
            \ required_columns was skipped. Independent of skip \u2014 a column with\
            \ no SkipConfig still propagates upstream skips. Set to False for null-tolerant\
            \ columns."
          default: true
        prompt:
          type: string
          title: Prompt
          description: Jinja2 template for the LLM prompt; can reference other columns
            via {{ column_name }}
        model_alias:
          type: string
          title: Model Alias
          description: Alias of the model configuration to use for generation
        system_prompt:
          title: System Prompt
          description: Optional system prompt to set model behavior and constraints
          type: string
        multi_modal_context:
          title: Multi Modal Context
          description: Optional list of multimodal context inputs
          items:
            oneOf:
            - $ref: '#/components/schemas/ImageContext'
            - $ref: '#/components/schemas/AudioContext'
            - $ref: '#/components/schemas/VideoContext'
            discriminator:
              propertyName: modality
              mapping:
                audio: '#/components/schemas/AudioContext'
                image: '#/components/schemas/ImageContext'
                video: '#/components/schemas/VideoContext'
          type: array
        tool_alias:
          title: Tool Alias
          description: Optional alias of the tool configuration to use for MCP tool
            calls
          type: string
        with_trace:
          allOf:
          - $ref: '#/components/schemas/TraceType'
          description: 'Trace capture mode: NONE, LAST_MESSAGE, or ALL_MESSAGES'
          default: none
        extract_reasoning_content:
          type: boolean
          title: Extract Reasoning Content
          description: If True, capture chain-of-thought in {name}__reasoning_content
            column
          default: false
        scores:
          items:
            $ref: '#/components/schemas/Score'
          type: array
          minItems: 1
          title: Scores
          description: List of Score objects defining rubric criteria for LLM judge
            evaluation
      additionalProperties: false
      type: object
      required:
      - name
      - prompt
      - model_alias
      - scores
      title: LLMJudgeColumnConfig
      description: "Configuration for LLM-as-a-judge quality assessment and scoring\
        \ columns.\n\nExtends LLMTextColumnConfig to create judge columns that evaluate\
        \ and score other\ngenerated content based on the defined criteria. Useful\
        \ for quality assessment, preference\nranking, and multi-dimensional evaluation\
        \ of generated data. Inherits prompt templating\ncapabilities from LLMTextColumnConfig.\n\
        \nAttributes:\n    scores (required): List of Score objects defining the evaluation\
        \ dimensions. Each score\n        represents a different aspect to evaluate\
        \ (e.g., accuracy, relevance, fluency).\n        Must contain at least one\
        \ score.\n\nInherited Attributes:\n    name (required): Unique name of the\
        \ column to be generated.\n    prompt (required): Prompt template for the\
        \ judge evaluation (supports Jinja2).\n    model_alias (required): Alias of\
        \ the model configuration to use.\n    system_prompt: Optional system prompt\
        \ (supports Jinja2).\n    multi_modal_context: Optional multimodal contexts\
        \ for generation.\n    tool_alias: Optional tool configuration alias for MCP\
        \ tool calls.\n    with_trace: Specifies what trace information to capture\
        \ in a `{column_name}__trace`\n        column. Options are `TraceType.NONE`\
        \ (default), `TraceType.LAST_MESSAGE`, or\n        `TraceType.ALL_MESSAGES`.\n\
        \    extract_reasoning_content: If True, creates a `{column_name}__reasoning_content`\n\
        \        column containing the reasoning content from the final assistant\
        \ response.\n    drop: If True, generate this column but remove it from the\
        \ final dataset."
    LLMStructuredColumnConfig:
      properties:
        name:
          type: string
          title: Name
        drop:
          type: boolean
          title: Drop
          default: false
        column_type:
          type: string
          const: llm-structured
          title: Column Type
          default: llm-structured
        skip:
          $ref: '#/components/schemas/SkipConfig'
        propagate_skip:
          type: boolean
          title: Propagate Skip
          description: "If True (default), this column auto-skips when any of its\
            \ required_columns was skipped. Independent of skip \u2014 a column with\
            \ no SkipConfig still propagates upstream skips. Set to False for null-tolerant\
            \ columns."
          default: true
        prompt:
          type: string
          title: Prompt
          description: Jinja2 template for the LLM prompt; can reference other columns
            via {{ column_name }}
        model_alias:
          type: string
          title: Model Alias
          description: Alias of the model configuration to use for generation
        system_prompt:
          title: System Prompt
          description: Optional system prompt to set model behavior and constraints
          type: string
        multi_modal_context:
          title: Multi Modal Context
          description: Optional list of multimodal context inputs
          items:
            oneOf:
            - $ref: '#/components/schemas/ImageContext'
            - $ref: '#/components/schemas/AudioContext'
            - $ref: '#/components/schemas/VideoContext'
            discriminator:
              propertyName: modality
              mapping:
                audio: '#/components/schemas/AudioContext'
                image: '#/components/schemas/ImageContext'
                video: '#/components/schemas/VideoContext'
          type: array
        tool_alias:
          title: Tool Alias
          description: Optional alias of the tool configuration to use for MCP tool
            calls
          type: string
        with_trace:
          allOf:
          - $ref: '#/components/schemas/TraceType'
          description: 'Trace capture mode: NONE, LAST_MESSAGE, or ALL_MESSAGES'
          default: none
        extract_reasoning_content:
          type: boolean
          title: Extract Reasoning Content
          description: If True, capture chain-of-thought in {name}__reasoning_content
            column
          default: false
        output_format:
          anyOf:
          - additionalProperties: true
            type: object
          - {}
          title: Output Format
          description: Pydantic model or JSON schema dict defining the expected structured
            output shape
      additionalProperties: false
      type: object
      required:
      - name
      - prompt
      - model_alias
      - output_format
      title: LLMStructuredColumnConfig
      description: "Configuration for structured JSON generation columns using Large\
        \ Language Models.\n\nExtends LLMTextColumnConfig to generate structured data\
        \ conforming to a specified schema.\nUses JSON schema or Pydantic models to\
        \ define the expected output structure, enabling\ntype-safe and validated\
        \ structured output generation. Inherits prompt templating capabilities\n\
        from LLMTextColumnConfig.\n\nAttributes:\n    output_format (required): The\
        \ schema defining the expected output structure. Can be either:\n        -\
        \ A Pydantic BaseModel class (recommended)\n        - A JSON schema dictionary\n\
        \nInherited Attributes:\n    name (required): Unique name of the column to\
        \ be generated.\n    prompt (required): Prompt template for structured generation\
        \ (supports Jinja2).\n    model_alias (required): Alias of the model configuration\
        \ to use.\n    system_prompt: Optional system prompt (supports Jinja2).\n\
        \    multi_modal_context: Optional multimodal contexts for generation.\n \
        \   tool_alias: Optional tool configuration alias for MCP tool calls.\n  \
        \  with_trace: Specifies what trace information to capture in a `{column_name}__trace`\n\
        \        column. Options are `TraceType.NONE` (default), `TraceType.LAST_MESSAGE`,\
        \ or\n        `TraceType.ALL_MESSAGES`.\n    extract_reasoning_content: If\
        \ True, creates a `{column_name}__reasoning_content`\n        column containing\
        \ the reasoning content from the final assistant response.\n    drop: If True,\
        \ generate this column but remove it from the final dataset."
    LLMTextColumnConfig:
      properties:
        name:
          type: string
          title: Name
        drop:
          type: boolean
          title: Drop
          default: false
        column_type:
          type: string
          const: llm-text
          title: Column Type
          default: llm-text
        skip:
          $ref: '#/components/schemas/SkipConfig'
        propagate_skip:
          type: boolean
          title: Propagate Skip
          description: "If True (default), this column auto-skips when any of its\
            \ required_columns was skipped. Independent of skip \u2014 a column with\
            \ no SkipConfig still propagates upstream skips. Set to False for null-tolerant\
            \ columns."
          default: true
        prompt:
          type: string
          title: Prompt
          description: Jinja2 template for the LLM prompt; can reference other columns
            via {{ column_name }}
        model_alias:
          type: string
          title: Model Alias
          description: Alias of the model configuration to use for generation
        system_prompt:
          title: System Prompt
          description: Optional system prompt to set model behavior and constraints
          type: string
        multi_modal_context:
          title: Multi Modal Context
          description: Optional list of multimodal context inputs
          items:
            oneOf:
            - $ref: '#/components/schemas/ImageContext'
            - $ref: '#/components/schemas/AudioContext'
            - $ref: '#/components/schemas/VideoContext'
            discriminator:
              propertyName: modality
              mapping:
                audio: '#/components/schemas/AudioContext'
                image: '#/components/schemas/ImageContext'
                video: '#/components/schemas/VideoContext'
          type: array
        tool_alias:
          title: Tool Alias
          description: Optional alias of the tool configuration to use for MCP tool
            calls
          type: string
        with_trace:
          allOf:
          - $ref: '#/components/schemas/TraceType'
          description: 'Trace capture mode: NONE, LAST_MESSAGE, or ALL_MESSAGES'
          default: none
        extract_reasoning_content:
          type: boolean
          title: Extract Reasoning Content
          description: If True, capture chain-of-thought in {name}__reasoning_content
            column
          default: false
      additionalProperties: false
      type: object
      required:
      - name
      - prompt
      - model_alias
      title: LLMTextColumnConfig
      description: "Configuration for text generation columns using Large Language\
        \ Models.\n\nLLM text columns generate free-form text content using language\
        \ models.\nPrompts support Jinja2 templating to reference values from other\
        \ columns, enabling\ncontext-aware generation. The generated text can optionally\
        \ include message traces\ncapturing the full conversation history.\n\nAttributes:\n\
        \    prompt (required): Prompt template for text generation. Supports Jinja2\
        \ syntax to\n        reference other columns (e.g., \"Write a story about\
        \ {{ character_name }}\").\n        Must be a valid Jinja2 template.\n   \
        \ model_alias (required): Alias of the model configuration to use for generation.\n\
        \        Must match a model alias defined when initializing the DataDesignerConfigBuilder.\n\
        \    system_prompt: Optional system prompt to set model behavior and constraints.\n\
        \        Also supports Jinja2 templating. If provided, must be a valid Jinja2\
        \ template.\n        Do not put any output parsing instructions in the system\
        \ prompt. Instead,\n        use the appropriate column type for the output\
        \ you want to generate - e.g.,\n        `LLMStructuredColumnConfig` for structured\
        \ output, `LLMCodeColumnConfig` for code.\n    multi_modal_context: Optional\
        \ list of multimodal contexts for generation.\n        Enables capable models\
        \ to generate text based on image, audio, or video inputs.\n    tool_alias:\
        \ Optional alias of the tool configuration to use for MCP tool calls.\n  \
        \      Must match a tool alias defined when initializing the DataDesignerConfigBuilder.\n\
        \        When provided, the model may call permitted tools during generation.\n\
        \    with_trace: Specifies what trace information to capture in a `{column_name}__trace`\n\
        \        column. Options are:\n        - `TraceType.NONE` (default): No trace\
        \ is captured.\n        - `TraceType.LAST_MESSAGE`: Only the final assistant\
        \ message is captured.\n        - `TraceType.ALL_MESSAGES`: Full conversation\
        \ history (system/user/assistant/tool).\n    extract_reasoning_content: If\
        \ True, creates a `{column_name}__reasoning_content` column\n        containing\
        \ only the reasoning_content from the final assistant response. This is\n\
        \        useful for models that expose chain-of-thought reasoning separately\
        \ from the main\n        response. Defaults to False.\n\nInherited Attributes:\n\
        \    name (required): Unique name of the column to be generated.\n    drop:\
        \ If True, generate this column but remove it from the final dataset."
    LocalCallableValidatorParams:
      properties:
        validator_type:
          type: string
          const: local_callable
          title: Validator Type
          description: Validator type discriminator, always 'local_callable' for this
            validator
          default: local_callable
        validation_function:
          title: Validation Function
          description: Function (Callable[[pd.DataFrame], pd.DataFrame]) to validate
            the data
        output_schema:
          title: Output Schema
          description: Expected schema for local callable validator's output
          additionalProperties: true
          type: object
      additionalProperties: false
      type: object
      required:
      - validation_function
      title: LocalCallableValidatorParams
      description: "Configuration for local callable validation. Expects a function\
        \ to be passed that validates the data.\n\nAttributes:\n    validation_function\
        \ (required): Function (`Callable[[pd.DataFrame], pd.DataFrame]`) to validate\
        \ the\n        data. Output must contain a column `is_valid` of type `bool`.\n\
        \    output_schema: The JSON schema for the local callable validator's output.\
        \ If not provided,\n        the output will not be validated."
    LocalFileSeedSource:
      properties:
        seed_type:
          type: string
          const: local
          title: Seed Type
          default: local
        path:
          type: string
          title: Path
          description: Path to a local seed dataset file or wildcard pattern. Relative
            paths are resolved from the current working directory when the config
            is loaded, not from the config file location.
      type: object
      required:
      - path
      title: LocalFileSeedSource
    ManualDistribution:
      properties:
        distribution_type:
          allOf:
          - $ref: '#/components/schemas/DistributionType'
          default: manual
        params:
          $ref: '#/components/schemas/ManualDistributionParams'
      additionalProperties: false
      type: object
      required:
      - params
      title: ManualDistribution
      description: "Manual (discrete) distribution for sampling inference parameters.\n\
        \nSamples from a discrete set of values with optional weights. Useful for\
        \ testing\nspecific values or creating custom probability distributions for\
        \ temperature or top_p.\n\nAttributes:\n    distribution_type: Type of distribution\
        \ (\"manual\").\n    params: Distribution parameters (values, weights)."
    ManualDistributionParams:
      properties:
        values:
          items:
            type: number
          type: array
          minItems: 1
          title: Values
        weights:
          title: Weights
          items:
            type: number
          type: array
      additionalProperties: false
      type: object
      required:
      - values
      title: ManualDistributionParams
      description: "Parameters for manual distribution sampling.\n\nAttributes:\n\
        \    values: List of possible values to sample from.\n    weights: Optional\
        \ list of weights for each value. If not provided, all values have equal probability."
    ModalityDataType:
      type: string
      enum:
      - url
      - base64
      title: ModalityDataType
      description: Data type formats for multimodal data.
    ModelConfig:
      properties:
        alias:
          type: string
          title: Alias
        model:
          type: string
          title: Model
        inference_parameters:
          oneOf:
          - $ref: '#/components/schemas/ChatCompletionInferenceParams'
          - $ref: '#/components/schemas/EmbeddingInferenceParams'
          - $ref: '#/components/schemas/ImageInferenceParams'
          title: Inference Parameters
          discriminator:
            propertyName: generation_type
            mapping:
              chat-completion: '#/components/schemas/ChatCompletionInferenceParams'
              embedding: '#/components/schemas/EmbeddingInferenceParams'
              image: '#/components/schemas/ImageInferenceParams'
        provider:
          type: string
          title: Provider
        skip_health_check:
          type: boolean
          title: Skip Health Check
          default: false
      additionalProperties: false
      type: object
      required:
      - alias
      - model
      - provider
      title: ModelConfig
      description: "Configuration for a model used for generation.\n\nAttributes:\n\
        \    alias: User-defined alias to reference in column configurations.\n  \
        \  model: Model identifier (e.g., from build.nvidia.com or other providers).\n\
        \    inference_parameters: Inference parameters for the model (temperature,\
        \ top_p, max_tokens, etc.).\n        The generation_type is determined by\
        \ the type of inference_parameters.\n    provider: Name of the model provider.\
        \ Must match the ``name`` field of a\n        ``ModelProvider`` registered\
        \ with the surrounding ``DataDesigner`` instance.\n    skip_health_check:\
        \ Whether to skip the health check for this model. Defaults to False."
    ModelProvider:
      properties:
        name:
          type: string
          title: Name
        endpoint:
          type: string
          title: Endpoint
        provider_type:
          type: string
          title: Provider Type
          default: openai
        api_key:
          title: Api Key
          type: string
        extra_body:
          title: Extra Body
          additionalProperties: true
          type: object
        extra_headers:
          title: Extra Headers
          additionalProperties:
            type: string
          type: object
      additionalProperties: false
      type: object
      required:
      - name
      - endpoint
      title: ModelProvider
      description: "Configuration for a custom model provider.\n\nAttributes:\n  \
        \  name: Name of the model provider.\n    endpoint: API endpoint URL for the\
        \ provider.\n    provider_type: Provider type (default: \"openai\"). Determines\
        \ the API format to use.\n    api_key: Optional API key for authentication.\n\
        \    extra_body: Additional parameters to pass in API requests.\n    extra_headers:\
        \ Additional headers to pass in API requests."
    PaginationData:
      properties:
        page:
          type: integer
          title: Page
          description: The current page number.
        page_size:
          type: integer
          title: Page Size
          description: The page size used for the query.
        current_page_size:
          type: integer
          title: Current Page Size
          description: The size for the current page.
        total_pages:
          type: integer
          title: Total Pages
          description: The total number of pages.
        total_results:
          type: integer
          title: Total Results
          description: The total number of results.
      type: object
      required:
      - page
      - page_size
      - current_page_size
      - total_pages
      - total_results
      title: PaginationData
    PartitionBlock:
      properties:
        index:
          type: integer
          minimum: 0.0
          title: Index
          description: The index of the partition to sample from
          default: 0
        num_partitions:
          type: integer
          minimum: 1.0
          title: Num Partitions
          description: The total number of partitions in the dataset
          default: 1
      additionalProperties: false
      type: object
      title: PartitionBlock
    PersonFromFakerSamplerParams:
      properties:
        locale:
          type: string
          title: Locale
          description: Locale string, determines the language and geographic locale
            that a synthetic person will be sampled from. E.g, en_US, en_GB, fr_FR,
            ...
          default: en_US
        sex:
          title: Sex
          description: If specified, then only synthetic people of the specified sex
            will be sampled.
          type: string
          enum:
          - Male
          - Female
        city:
          anyOf:
          - type: string
          - items:
              type: string
            type: array
          title: City
          description: If specified, then only synthetic people from these cities
            will be sampled.
        age_range:
          items:
            type: integer
          type: array
          maxItems: 2
          minItems: 2
          title: Age Range
          description: If specified, then only synthetic people within this age range
            will be sampled.
          default:
          - 18
          - 114
        sampler_type:
          type: string
          const: person_from_faker
          title: Sampler Type
          default: person_from_faker
      additionalProperties: false
      type: object
      title: PersonFromFakerSamplerParams
      description: "Parameters for sampling synthetic person data with demographic\
        \ attributes from Faker.\n\nUses the Faker library to generate random personal\
        \ information. The data is basic and not demographically\naccurate, but is\
        \ useful for quick testing, prototyping, or when realistic demographic distributions\
        \ are not\nrelevant for your use case. For demographically accurate person\
        \ data, use the `PersonSamplerParams` sampler.\n\nAttributes:\n    locale:\
        \ Locale string determining the language and geographic region for synthetic\
        \ people.\n        Can be any locale supported by Faker.\n    sex: If specified,\
        \ filters to only sample people of the specified sex. Options: \"Male\" or\n\
        \        \"Female\". If None, samples both sexes.\n    city: If specified,\
        \ filters to only sample people from the specified city or cities. Can be\n\
        \        a single city name (string) or a list of city names.\n    age_range:\
        \ Two-element list [min_age, max_age] specifying the age range to sample from\n\
        \        (inclusive). Defaults to a standard age range. Both values must be\
        \ between the minimum and\n        maximum allowed ages.\n    sampler_type:\
        \ Discriminator for the sampler type. Must be `SamplerType.PERSON_FROM_FAKER`."
    PersonSamplerParams:
      properties:
        locale:
          type: string
          title: Locale
          description: 'Locale that determines the language and geographic location
            that a synthetic person will be sampled from. Must be a locale supported
            by a managed Nemotron Personas dataset. Managed datasets exist for the
            following locales: en_US, en_IN, en_SG, fr_FR, hi_Deva_IN, hi_Latn_IN,
            ja_JP, ko_KR, pt_BR.'
          default: en_US
        sex:
          title: Sex
          description: If specified, then only synthetic people of the specified sex
            will be sampled.
          type: string
          enum:
          - Male
          - Female
        city:
          anyOf:
          - type: string
          - items:
              type: string
            type: array
          title: City
          description: If specified, then only synthetic people from these cities
            will be sampled.
        age_range:
          items:
            type: integer
          type: array
          maxItems: 2
          minItems: 2
          title: Age Range
          description: If specified, then only synthetic people within this age range
            will be sampled.
          default:
          - 18
          - 114
        select_field_values:
          title: Select Field Values
          description: Sample synthetic people with the specified field values. This
            is meant to be a flexible argument for selecting a subset of the population
            from the managed dataset. Note that this sampler does not support rare
            combinations of field values and will likely fail if your desired subset
            is not well-represented in the managed Nemotron Personas dataset. We generally
            recommend using the `sex`, `city`, and `age_range` arguments to filter
            the population when possible.
          examples:
          - education_level:
            - high_school
            - some_college
            - bachelors
            state:
            - NY
            - CA
            - OH
            - TX
            - NV
          additionalProperties:
            items:
              type: string
            type: array
          type: object
        with_synthetic_personas:
          type: boolean
          title: With Synthetic Personas
          description: If True, then append synthetic persona columns to each generated
            person.
          default: false
        sampler_type:
          type: string
          const: person
          title: Sampler Type
          default: person
      additionalProperties: false
      type: object
      title: PersonSamplerParams
      description: "Parameters for sampling synthetic person data with demographic\
        \ attributes.\n\nGenerates realistic synthetic person data including names,\
        \ addresses, phone numbers, and other\ndemographic information from managed\
        \ datasets. The sampler supports filtering by locale, sex, age,\ngeographic\
        \ location, and selected managed-dataset fields, and can optionally include\
        \ synthetic\npersona descriptions. For Faker-generated person data, use PersonFromFakerSamplerParams.\n\
        \nAttributes:\n    locale: Locale string determining the language and geographic\
        \ region for synthetic people.\n        Must be a locale supported by a managed\
        \ Nemotron Personas dataset. The dataset must\n        be downloaded and available\
        \ in the managed assets directory.\n    sex: If specified, filters to only\
        \ sample people of the specified sex. Options: \"Male\" or\n        \"Female\"\
        . If None, samples both sexes.\n    city: If specified, filters to only sample\
        \ people from the specified city or cities. Can be\n        a single city\
        \ name (string) or a list of city names.\n    age_range: Two-element list\
        \ [min_age, max_age] specifying the age range to sample from\n        (inclusive).\
        \ Defaults to a standard age range. Both values must be between minimum and\n\
        \        maximum allowed ages.\n    with_synthetic_personas: If True, appends\
        \ additional synthetic persona columns including\n        personality traits,\
        \ interests, and background descriptions. Only supported for certain\n   \
        \     locales with managed datasets.\n    select_field_values: Optional field-value\
        \ filters for managed datasets. Supported field\n        names are checked\
        \ against the managed person data fields."
    PlatformJobListResultResponse:
      properties:
        data:
          items:
            $ref: '#/components/schemas/PlatformJobResultResponse'
          type: array
          title: Data
      type: object
      required:
      - data
      title: PlatformJobListResultResponse
    PlatformJobLog:
      properties:
        timestamp:
          type: string
          format: date-time
          title: Timestamp
        job:
          type: string
          title: Job
        job_step:
          type: string
          title: Job Step
        job_task:
          type: string
          title: Job Task
        message:
          type: string
          title: Message
      type: object
      required:
      - timestamp
      - job
      - job_step
      - job_task
      - message
      title: PlatformJobLog
    PlatformJobLogPage:
      properties:
        data:
          items:
            $ref: '#/components/schemas/PlatformJobLog'
          type: array
          title: Data
        total:
          type: integer
          title: Total
        next_page:
          title: Next Page
          type: string
        prev_page:
          title: Prev Page
          type: string
      type: object
      required:
      - data
      - total
      - next_page
      - prev_page
      title: PlatformJobLogPage
    PlatformJobResultResponse:
      properties:
        name:
          type: string
          title: Name
        job:
          type: string
          title: Job
        workspace:
          type: string
          title: Workspace
        project:
          title: Project
          type: string
        created_at:
          type: string
          format: date-time
          title: Created At
        updated_at:
          type: string
          format: date-time
          title: Updated At
        artifact_url:
          type: string
          title: Artifact Url
        artifact_storage_type:
          $ref: '#/components/schemas/FileStorageType'
        download_url:
          title: Download Url
          type: string
      type: object
      required:
      - name
      - job
      - workspace
      - artifact_url
      - artifact_storage_type
      title: PlatformJobResultResponse
    PlatformJobStatus:
      type: string
      enum:
      - created
      - pending
      - active
      - cancelled
      - cancelling
      - error
      - completed
      - paused
      - pausing
      - resuming
      title: PlatformJobStatus
      description: 'Enumeration of possible job statuses.


        This enum represents the various states a job can be in during its lifecycle,

        from creation to a terminal state.'
    PlatformJobStatusResponse:
      properties:
        id:
          type: string
          title: Id
        name:
          type: string
          title: Name
        status:
          $ref: '#/components/schemas/PlatformJobStatus'
        status_details:
          additionalProperties: true
          type: object
          title: Status Details
        error_details:
          title: Error Details
          additionalProperties: true
          type: object
        steps:
          items:
            $ref: '#/components/schemas/PlatformJobStepStatusResponse'
          type: array
          title: Steps
        created_at:
          type: string
          format: date-time
          title: Created At
        updated_at:
          type: string
          format: date-time
          title: Updated At
      type: object
      required:
      - id
      - name
      - status
      - status_details
      - error_details
      - steps
      - created_at
      - updated_at
      title: PlatformJobStatusResponse
    PlatformJobStepStatusResponse:
      properties:
        id:
          type: string
          title: Id
        name:
          type: string
          title: Name
        status:
          $ref: '#/components/schemas/PlatformJobStatus'
        status_details:
          additionalProperties: true
          type: object
          title: Status Details
        error_details:
          title: Error Details
          additionalProperties: true
          type: object
        tasks:
          items:
            $ref: '#/components/schemas/PlatformJobTaskStatusResponse'
          type: array
          title: Tasks
        created_at:
          type: string
          format: date-time
          title: Created At
        updated_at:
          type: string
          format: date-time
          title: Updated At
      type: object
      required:
      - id
      - name
      - status
      - status_details
      - error_details
      - tasks
      - created_at
      - updated_at
      title: PlatformJobStepStatusResponse
    PlatformJobTaskStatusResponse:
      properties:
        id:
          type: string
          title: Id
        name:
          type: string
          title: Name
        status:
          $ref: '#/components/schemas/PlatformJobStatus'
        status_details:
          additionalProperties: true
          type: object
          title: Status Details
        error_details:
          title: Error Details
          additionalProperties: true
          type: object
        error_stack:
          title: Error Stack
          type: string
        created_at:
          type: string
          format: date-time
          title: Created At
        updated_at:
          type: string
          format: date-time
          title: Updated At
      type: object
      required:
      - id
      - name
      - status
      - status_details
      - error_details
      - error_stack
      - created_at
      - updated_at
      title: PlatformJobTaskStatusResponse
    PoissonSamplerParams:
      properties:
        mean:
          type: number
          title: Mean
          description: Mean number of events in a fixed interval.
        sampler_type:
          type: string
          const: poisson
          title: Sampler Type
          default: poisson
      additionalProperties: false
      type: object
      required:
      - mean
      title: PoissonSamplerParams
      description: "Parameters for sampling from a Poisson distribution.\n\nSamples\
        \ non-negative integer values representing the number of events occurring\
        \ in a fixed\ninterval of time or space. The Poisson distribution is commonly\
        \ used to model count data\nlike the number of arrivals, occurrences, or events\
        \ per time period.\n\nThe distribution is characterized by a single parameter\
        \ (mean/rate), and both the mean and\nvariance equal this parameter value.\n\
        \nAttributes:\n    mean (required): Mean number of events in the fixed interval\
        \ (also called rate parameter \u03BB).\n        Must be positive. This represents\
        \ both the expected value and the variance of the\n        distribution."
    PreviewSpec:
      properties:
        config:
          $ref: '#/components/schemas/DataDesignerConfig'
        num_records:
          title: Num Records
          type: integer
      type: object
      required:
      - config
      title: PreviewSpec
    RemoteValidatorParams:
      properties:
        validator_type:
          type: string
          const: remote
          title: Validator Type
          description: Validator type discriminator, always 'remote' for this validator
          default: remote
        endpoint_url:
          type: string
          title: Endpoint Url
          description: URL of the remote endpoint
        output_schema:
          title: Output Schema
          description: Expected schema for remote validator's output
          additionalProperties: true
          type: object
        timeout:
          type: number
          exclusiveMinimum: 0.0
          title: Timeout
          description: The timeout for the HTTP request
          default: 30.0
        max_retries:
          type: integer
          minimum: 0.0
          title: Max Retries
          description: The maximum number of retry attempts
          default: 3
        retry_backoff:
          type: number
          exclusiveMinimum: 1.0
          title: Retry Backoff
          description: The backoff factor for the retry delay
          default: 2.0
        max_parallel_requests:
          type: integer
          minimum: 1.0
          title: Max Parallel Requests
          description: The maximum number of parallel requests to make
          default: 4
      additionalProperties: false
      type: object
      required:
      - endpoint_url
      title: RemoteValidatorParams
      description: "Configuration for remote validation. Sends data to a remote endpoint\
        \ for validation.\n\nAttributes:\n    endpoint_url (required): The URL of\
        \ the remote endpoint.\n    output_schema: The JSON schema for the remote\
        \ validator's output. If not provided,\n        the output will not be validated.\n\
        \    timeout: The timeout for the HTTP request in seconds. Defaults to 30.0.\n\
        \    max_retries: The maximum number of retry attempts. Defaults to 3.\n \
        \   retry_backoff: The backoff factor for the retry delay in seconds. Defaults\
        \ to 2.0.\n    max_parallel_requests: The maximum number of parallel requests\
        \ to make. Defaults to 4."
    SamplerColumnConfig:
      properties:
        name:
          type: string
          title: Name
        drop:
          type: boolean
          title: Drop
          default: false
        column_type:
          type: string
          const: sampler
          title: Column Type
          default: sampler
        skip:
          $ref: '#/components/schemas/SkipConfig'
        propagate_skip:
          type: boolean
          title: Propagate Skip
          description: "If True (default), this column auto-skips when any of its\
            \ required_columns was skipped. Independent of skip \u2014 a column with\
            \ no SkipConfig still propagates upstream skips. Set to False for null-tolerant\
            \ columns."
          default: true
        sampler_type:
          allOf:
          - $ref: '#/components/schemas/SamplerType'
          description: Type of sampler to use (e.g., uuid, category, uniform, gaussian,
            person, datetime)
        params:
          oneOf:
          - $ref: '#/components/schemas/SubcategorySamplerParams'
          - $ref: '#/components/schemas/CategorySamplerParams'
          - $ref: '#/components/schemas/DatetimeSamplerParams'
          - $ref: '#/components/schemas/PersonSamplerParams'
          - $ref: '#/components/schemas/PersonFromFakerSamplerParams'
          - $ref: '#/components/schemas/TimeDeltaSamplerParams'
          - $ref: '#/components/schemas/UUIDSamplerParams'
          - $ref: '#/components/schemas/BernoulliSamplerParams'
          - $ref: '#/components/schemas/BernoulliMixtureSamplerParams'
          - $ref: '#/components/schemas/BinomialSamplerParams'
          - $ref: '#/components/schemas/GaussianSamplerParams'
          - $ref: '#/components/schemas/PoissonSamplerParams'
          - $ref: '#/components/schemas/UniformSamplerParams'
          - $ref: '#/components/schemas/ScipySamplerParams'
          title: Params
          description: Parameters specific to the chosen sampler type
          discriminator:
            propertyName: sampler_type
            mapping:
              bernoulli: '#/components/schemas/BernoulliSamplerParams'
              bernoulli_mixture: '#/components/schemas/BernoulliMixtureSamplerParams'
              binomial: '#/components/schemas/BinomialSamplerParams'
              category: '#/components/schemas/CategorySamplerParams'
              datetime: '#/components/schemas/DatetimeSamplerParams'
              gaussian: '#/components/schemas/GaussianSamplerParams'
              person: '#/components/schemas/PersonSamplerParams'
              person_from_faker: '#/components/schemas/PersonFromFakerSamplerParams'
              poisson: '#/components/schemas/PoissonSamplerParams'
              scipy: '#/components/schemas/ScipySamplerParams'
              subcategory: '#/components/schemas/SubcategorySamplerParams'
              timedelta: '#/components/schemas/TimeDeltaSamplerParams'
              uniform: '#/components/schemas/UniformSamplerParams'
              uuid: '#/components/schemas/UUIDSamplerParams'
        conditional_params:
          additionalProperties:
            oneOf:
            - $ref: '#/components/schemas/SubcategorySamplerParams'
            - $ref: '#/components/schemas/CategorySamplerParams'
            - $ref: '#/components/schemas/DatetimeSamplerParams'
            - $ref: '#/components/schemas/PersonSamplerParams'
            - $ref: '#/components/schemas/PersonFromFakerSamplerParams'
            - $ref: '#/components/schemas/TimeDeltaSamplerParams'
            - $ref: '#/components/schemas/UUIDSamplerParams'
            - $ref: '#/components/schemas/BernoulliSamplerParams'
            - $ref: '#/components/schemas/BernoulliMixtureSamplerParams'
            - $ref: '#/components/schemas/BinomialSamplerParams'
            - $ref: '#/components/schemas/GaussianSamplerParams'
            - $ref: '#/components/schemas/PoissonSamplerParams'
            - $ref: '#/components/schemas/UniformSamplerParams'
            - $ref: '#/components/schemas/ScipySamplerParams'
            discriminator:
              propertyName: sampler_type
              mapping:
                bernoulli: '#/components/schemas/BernoulliSamplerParams'
                bernoulli_mixture: '#/components/schemas/BernoulliMixtureSamplerParams'
                binomial: '#/components/schemas/BinomialSamplerParams'
                category: '#/components/schemas/CategorySamplerParams'
                datetime: '#/components/schemas/DatetimeSamplerParams'
                gaussian: '#/components/schemas/GaussianSamplerParams'
                person: '#/components/schemas/PersonSamplerParams'
                person_from_faker: '#/components/schemas/PersonFromFakerSamplerParams'
                poisson: '#/components/schemas/PoissonSamplerParams'
                scipy: '#/components/schemas/ScipySamplerParams'
                subcategory: '#/components/schemas/SubcategorySamplerParams'
                timedelta: '#/components/schemas/TimeDeltaSamplerParams'
                uniform: '#/components/schemas/UniformSamplerParams'
                uuid: '#/components/schemas/UUIDSamplerParams'
          type: object
          title: Conditional Params
          description: Optional dictionary for conditional parameters; keys are conditions,
            values are params to use when met
        convert_to:
          title: Convert To
          description: 'Optional type conversion after sampling: ''float'', ''int'',
            or ''str'' for numerical samplers; a strftime format string (e.g., ''%Y-%m-%d'')
            for datetime/timedelta samplers. Datetime/timedelta columns default to
            ISO-8601 (e.g., 2024-01-15T09:30:00) when omitted.'
          type: string
      additionalProperties: false
      type: object
      required:
      - name
      - sampler_type
      - params
      title: SamplerColumnConfig
      description: "Configuration for columns generated using built-in samplers.\n\
        \nSampler columns provide efficient data generation for common data types\
        \ and\ndistributions. Supported samplers include UUID generation,\ndatetime/timedelta\
        \ sampling, person generation, category / subcategory sampling,\nand various\
        \ statistical distributions (uniform, gaussian, binomial, poisson, scipy).\n\
        \nAttributes:\n    sampler_type (required): Type of sampler to use. Available\
        \ types include:\n        \"uuid\", \"category\", \"subcategory\", \"uniform\"\
        , \"gaussian\", \"bernoulli\",\n        \"bernoulli_mixture\", \"binomial\"\
        , \"poisson\", \"scipy\", \"person\",\n        \"person_from_faker\", \"datetime\"\
        , \"timedelta\".\n    params (required): Parameters specific to the chosen\
        \ sampler type. Type varies based on the `sampler_type`\n        (e.g., `CategorySamplerParams`,\
        \ `UniformSamplerParams`, `PersonSamplerParams`).\n    conditional_params:\
        \ Optional dictionary for conditional parameters. The dict keys\n        are\
        \ the conditions that must be met (e.g., \"age > 21\") for the conditional\
        \ parameters\n        to be used. The values of dict are the parameters to\
        \ use when the condition is met.\n    convert_to: Optional type conversion\
        \ to apply after sampling. For numerical samplers,\n        must be one of\
        \ \"float\", \"int\", or \"str\". For datetime and timedelta samplers, accepts\n\
        \        a strftime format string (e.g., ``\"%Y-%m-%d\"``, ``\"%m/%d/%Y %H:%M\"\
        ``). When omitted,\n        datetime/timedelta columns default to ISO-8601\
        \ format (e.g., ``2024-01-15T09:30:00``).\n\nInherited Attributes:\n    name\
        \ (required): Unique name of the column to be generated.\n    drop: If True,\
        \ generate this column but remove it from the final dataset.\n\n!!! tip \"\
        Displaying available samplers and their parameters\"\n    The config builder\
        \ has an `info` attribute that can be used to display the\n    available samplers\
        \ and their parameters:\n    ```python\n    config_builder.info.display(\"\
        samplers\")\n    ```"
    SamplerType:
      type: string
      enum:
      - bernoulli
      - bernoulli_mixture
      - binomial
      - category
      - datetime
      - gaussian
      - person
      - person_from_faker
      - poisson
      - scipy
      - subcategory
      - timedelta
      - uniform
      - uuid
      title: SamplerType
    SamplingStrategy:
      type: string
      enum:
      - ordered
      - shuffle
      title: SamplingStrategy
    ScalarInequalityConstraint:
      properties:
        target_column:
          type: string
          title: Target Column
          description: Name of the sampler column this constraint applies to
        constraint_type:
          type: string
          const: scalar_inequality
          title: Constraint Type
          description: Constraint type discriminator, always 'scalar_inequality' for
            this constraint
          default: scalar_inequality
        rhs:
          type: number
          title: Rhs
          description: Scalar value to compare against
        operator:
          allOf:
          - $ref: '#/components/schemas/InequalityOperator'
          description: Comparison operator
      additionalProperties: false
      type: object
      required:
      - target_column
      - rhs
      - operator
      title: ScalarInequalityConstraint
      description: "Constrain a sampler column to be less/greater than a scalar value.\n\
        \nOnly applies to sampler columns.\n\nAttributes:\n    rhs (required): Scalar\
        \ value to compare against.\n    operator (required): Comparison operator\
        \ (lt, le, gt, ge).\n\nInherited Attributes:\n    target_column (required):\
        \ Name of the sampler column this constraint applies to."
    SchemaTransformProcessorConfig:
      properties:
        name:
          type: string
          title: Name
          description: The name of the processor, used to identify the processor in
            the results and to write the artifacts to disk.
        processor_type:
          type: string
          const: schema_transform
          title: Processor Type
          default: schema_transform
        template:
          additionalProperties: true
          type: object
          title: Template
          description: "\n        Dictionary specifying columns and templates to use\
            \ in the new dataset with transformed schema.\n\n        Each key is a\
            \ new column name, and each value is an object containing Jinja2 templates\
            \ - for instance, a string or a list of strings.\n        Values must\
            \ be JSON-serializable.\n\n        Example:\n\n        ```python\n   \
            \     template = {\n            \"list_of_strings\": [\"{{ col1 }}\",\
            \ \"{{ col2 }}\"],\n            \"uppercase_string\": \"{{ col1 | upper\
            \ }}\",\n            \"lowercase_string\": \"{{ col2 | lower }}\",\n \
            \       }\n        ```\n\n        The above templates will create an new\
            \ dataset with three columns: \"list_of_strings\", \"uppercase_string\"\
            , and \"lowercase_string\".\n        References to columns \"col1\" and\
            \ \"col2\" in the templates will be replaced with the actual values of\
            \ the columns in the dataset.\n        "
      additionalProperties: false
      type: object
      required:
      - name
      - template
      title: SchemaTransformProcessorConfig
      description: "Configuration for transforming the dataset schema using Jinja2\
        \ templates.\n\nThis processor creates a new dataset with a transformed schema.\
        \ Each key in the\ntemplate becomes a column in the output, and values are\
        \ Jinja2 templates that\ncan reference any column in the batch. The transformed\
        \ dataset is written to\na `processors-files/{processor_name}/` directory\
        \ alongside the main dataset.\n\nAttributes:\n    template (required): Dictionary\
        \ defining the output schema. Keys are new column names,\n        values are\
        \ Jinja2 templates (strings, lists, or nested structures).\n        Must be\
        \ JSON-serializable.\n\nInherited Attributes:\n    name (required): Name of\
        \ the processor."
    ScipySamplerParams:
      properties:
        dist_name:
          type: string
          title: Dist Name
          description: Name of a scipy.stats distribution.
        dist_params:
          additionalProperties: true
          type: object
          title: Dist Params
          description: Parameters of the scipy.stats distribution given in `dist_name`.
        decimal_places:
          title: Decimal Places
          description: Number of decimal places to round the sampled values to.
          type: integer
        sampler_type:
          type: string
          const: scipy
          title: Sampler Type
          default: scipy
      additionalProperties: false
      type: object
      required:
      - dist_name
      - dist_params
      title: ScipySamplerParams
      description: "Parameters for sampling from any scipy.stats continuous or discrete\
        \ distribution.\n\nProvides a flexible interface to sample from the wide range\
        \ of probability distributions\navailable in scipy.stats. This enables advanced\
        \ statistical sampling beyond the built-in\ndistribution types (Gaussian,\
        \ Uniform, etc.).\n\nSee: [scipy.stats documentation](https://docs.scipy.org/doc/scipy/reference/stats.html)\n\
        \nAttributes:\n    dist_name (required): Name of the scipy.stats distribution\
        \ to sample from (e.g., \"beta\", \"gamma\",\n        \"lognorm\", \"expon\"\
        ). Must be a valid distribution name from scipy.stats.\n    dist_params (required):\
        \ Dictionary of parameters for the specified distribution. Parameter names\n\
        \        and values must match the scipy.stats distribution specification\
        \ (e.g., {\"a\": 2, \"b\": 5}\n        for beta distribution, {\"scale\":\
        \ 1.5} for exponential).\n    decimal_places: Optional number of decimal places\
        \ to round sampled values to. If None,\n        values are not rounded."
    Score:
      properties:
        name:
          type: string
          title: Name
          description: A clear name for this score.
        description:
          type: string
          title: Description
          description: An informative and detailed assessment guide for using this
            score.
        options:
          additionalProperties:
            type: string
          type: object
          title: Options
          description: 'Score options in the format of {score: description}.'
      additionalProperties: false
      type: object
      required:
      - name
      - description
      - options
      title: Score
      description: "Configuration for a \"score\" in an LLM judge evaluation.\n\n\
        Defines a single scoring criterion with its possible values and descriptions.\
        \ Multiple\nScore objects can be combined in an LLMJudgeColumnConfig to create\
        \ multi-dimensional\nquality assessments.\n\nAttributes:\n    name (required):\
        \ A clear, concise name for this scoring dimension (e.g., \"Relevance\", \"\
        Fluency\").\n    description (required): An informative and detailed assessment\
        \ guide explaining how to evaluate\n        this dimension. Should provide\
        \ clear criteria for scoring.\n    options (required): Dictionary mapping\
        \ score values to their descriptions. Keys can be integers\n        (e.g.,\
        \ 1-5 scale) or strings (e.g., \"Poor\", \"Good\", \"Excellent\"). Values\
        \ are\n        descriptions explaining what each score level means."
    SeedConfig:
      properties:
        source:
          oneOf:
          - $ref: '#/components/schemas/LocalFileSeedSource'
          - $ref: '#/components/schemas/HuggingFaceSeedSource'
          - $ref: '#/components/schemas/DataFrameSeedSource'
          - $ref: '#/components/schemas/DirectorySeedSource'
          - $ref: '#/components/schemas/FileContentsSeedSource'
          - $ref: '#/components/schemas/AgentRolloutSeedSource'
          - $ref: '#/components/schemas/FilesetFileSeedSource'
          title: Source
          discriminator:
            propertyName: seed_type
            mapping:
              agent_rollout: '#/components/schemas/AgentRolloutSeedSource'
              df: '#/components/schemas/DataFrameSeedSource'
              directory: '#/components/schemas/DirectorySeedSource'
              file_contents: '#/components/schemas/FileContentsSeedSource'
              hf: '#/components/schemas/HuggingFaceSeedSource'
              local: '#/components/schemas/LocalFileSeedSource'
              nmp: '#/components/schemas/FilesetFileSeedSource'
        sampling_strategy:
          allOf:
          - $ref: '#/components/schemas/SamplingStrategy'
          default: ordered
        selection_strategy:
          anyOf:
          - $ref: '#/components/schemas/IndexRange'
          - $ref: '#/components/schemas/PartitionBlock'
          title: Selection Strategy
      additionalProperties: false
      type: object
      required:
      - source
      title: SeedConfig
      description: "Configuration for sampling data from a seed dataset.\n\nAttributes:\n\
        \    source: A SeedSource defining where the seed data exists\n    sampling_strategy:\
        \ Strategy for how to sample rows from the dataset.\n        - ORDERED: Read\
        \ rows sequentially in their original order.\n        - SHUFFLE: Randomly\
        \ shuffle rows before sampling. When used with\n          selection_strategy,\
        \ shuffling occurs within the selected range/partition.\n    selection_strategy:\
        \ Optional strategy to select a subset of the dataset.\n        - IndexRange:\
        \ Select a specific range of indices (e.g., rows 100-200).\n        - PartitionBlock:\
        \ Select a partition by splitting the dataset into N equal parts.\n      \
        \    Partition indices are zero-based (index=0 is the first partition, index=1\
        \ is\n          the second, etc.).\n\nExamples:\n    Read rows sequentially\
        \ from start to end:\n        SeedConfig(\n            source=LocalFileSeedSource(path=\"\
        my_data.parquet\"),\n            sampling_strategy=SamplingStrategy.ORDERED\n\
        \        )\n\n    Read rows in random order:\n        SeedConfig(\n      \
        \      source=LocalFileSeedSource(path=\"my_data.parquet\"),\n           \
        \ sampling_strategy=SamplingStrategy.SHUFFLE\n        )\n\n    Read specific\
        \ index range (rows 100-199):\n        SeedConfig(\n            source=LocalFileSeedSource(path=\"\
        my_data.parquet\"),\n            sampling_strategy=SamplingStrategy.ORDERED,\n\
        \            selection_strategy=IndexRange(start=100, end=199)\n        )\n\
        \n    Read random rows from a specific index range (shuffles within rows 100-199):\n\
        \        SeedConfig(\n            source=LocalFileSeedSource(path=\"my_data.parquet\"\
        ),\n            sampling_strategy=SamplingStrategy.SHUFFLE,\n            selection_strategy=IndexRange(start=100,\
        \ end=199)\n        )\n\n    Read from partition 2 (3rd partition, zero-based)\
        \ of 5 partitions (20% of dataset):\n        SeedConfig(\n            source=LocalFileSeedSource(path=\"\
        my_data.parquet\"),\n            sampling_strategy=SamplingStrategy.ORDERED,\n\
        \            selection_strategy=PartitionBlock(index=2, num_partitions=5)\n\
        \        )\n\n    Read shuffled rows from partition 0 of 10 partitions (shuffles\
        \ within the partition):\n        SeedConfig(\n            source=LocalFileSeedSource(path=\"\
        my_data.parquet\"),\n            sampling_strategy=SamplingStrategy.SHUFFLE,\n\
        \            selection_strategy=PartitionBlock(index=0, num_partitions=10)\n\
        \        )"
    SeedDatasetColumnConfig:
      properties:
        name:
          type: string
          title: Name
        drop:
          type: boolean
          title: Drop
          default: false
        column_type:
          type: string
          const: seed-dataset
          title: Column Type
          default: seed-dataset
        skip:
          $ref: '#/components/schemas/SkipConfig'
        propagate_skip:
          type: boolean
          title: Propagate Skip
          description: "If True (default), this column auto-skips when any of its\
            \ required_columns was skipped. Independent of skip \u2014 a column with\
            \ no SkipConfig still propagates upstream skips. Set to False for null-tolerant\
            \ columns."
          default: true
      additionalProperties: false
      type: object
      required:
      - name
      title: SeedDatasetColumnConfig
      description: "Configuration for columns sourced from seed datasets.\n\nThis\
        \ config marks columns that come from seed data. It is typically created\n\
        automatically when calling `with_seed_dataset()` on the builder, rather than\n\
        being instantiated directly by users.\n\nInherited Attributes:\n    name (required):\
        \ Unique name of the column to be generated.\n    drop: If True, generate\
        \ this column but remove it from the final dataset."
    SkipConfig:
      properties:
        when:
          type: string
          title: When
          description: Jinja2 expression (including {{ }} delimiters); when truthy,
            skip generation for this row.
        value:
          anyOf:
          - type: boolean
          - type: integer
          - type: number
          - type: string
          title: Value
          description: Value to write for skipped cells. Defaults to None (becomes
            NaN/pd.NA in DataFrame).
      additionalProperties: false
      type: object
      required:
      - when
      title: SkipConfig
      description: "Expression gate for conditional column generation.\n\nAttach to\
        \ a ``SingleColumnConfig`` via ``skip=SkipConfig(...)`` to gate\ngeneration\
        \ on a Jinja2 expression.  Controls *when* to skip; propagation\nof upstream\
        \ skips is controlled separately by ``propagate_skip`` on\n``SingleColumnConfig``.\n\
        \nAttributes:\n    when: Jinja2 expression (including ``{{ }}`` delimiters);\
        \ when truthy,\n        skip generation for this row.\n    value: Value to\
        \ write for skipped cells.  Defaults to ``None``\n        (becomes ``NaN``/``pd.NA``\
        \ in the DataFrame)."
    StringFilter:
      additionalProperties: false
      properties:
        $eq:
          description: Filter for results equal to this value.
          title: $Eq
          type: string
        $like:
          description: Filter for results matching this pattern.
          title: $Like
          type: string
        $in:
          description: Filter for results in this list of values.
          title: $In
          items:
            type: string
          type: array
        $nin:
          description: Filter for results not in this list of values.
          title: $Nin
          items:
            type: string
          type: array
      title: StringFilter
      type: object
    SubcategorySamplerParams:
      properties:
        category:
          type: string
          title: Category
          description: Name of parent category to this subcategory.
        values:
          additionalProperties:
            items:
              anyOf:
              - type: string
              - type: integer
              - type: number
            type: array
          type: object
          title: Values
          description: Mapping from each value of parent category to a list of subcategory
            values.
        sampler_type:
          type: string
          const: subcategory
          title: Sampler Type
          default: subcategory
      additionalProperties: false
      type: object
      required:
      - category
      - values
      title: SubcategorySamplerParams
      description: "Parameters for subcategory sampling conditioned on a parent category\
        \ column.\n\nSamples subcategory values based on the value of a parent category\
        \ column. Each parent\ncategory value maps to its own list of possible subcategory\
        \ values, enabling hierarchical\nor conditional sampling patterns.\n\nAttributes:\n\
        \    category (required): Name of the parent category column that this subcategory\
        \ depends on.\n        The parent column must be generated before this subcategory\
        \ column.\n    values (required): Mapping from each parent category value\
        \ to a list of possible subcategory values.\n        Each key must correspond\
        \ to a value that appears in the parent category column."
    TimeDeltaSamplerParams:
      properties:
        dt_min:
          type: integer
          minimum: 0.0
          title: Dt Min
          description: Minimum possible time-delta for sampling range, inclusive.
            Must be less than `dt_max`.
        dt_max:
          type: integer
          exclusiveMinimum: 0.0
          title: Dt Max
          description: Maximum possible time-delta for sampling range, exclusive.
            Must be greater than `dt_min`.
        reference_column_name:
          type: string
          title: Reference Column Name
          description: Name of an existing datetime column to condition time-delta
            sampling on.
        unit:
          type: string
          enum:
          - D
          - h
          - m
          - s
          title: Unit
          description: Sampling units, e.g. the smallest possible time interval between
            samples.
          default: D
        sampler_type:
          type: string
          const: timedelta
          title: Sampler Type
          default: timedelta
      additionalProperties: false
      type: object
      required:
      - dt_min
      - dt_max
      - reference_column_name
      title: TimeDeltaSamplerParams
      description: "Parameters for sampling time deltas relative to a reference datetime\
        \ column.\n\nSamples time offsets within a specified range and adds them to\
        \ values from a reference\ndatetime column. This is useful for generating\
        \ related datetime columns like order dates\nand delivery dates, or event\
        \ start times and end times.\n\nNote:\n    Years and months are not supported\
        \ as timedelta units because they have variable lengths.\n    See: [pandas\
        \ timedelta documentation](https://pandas.pydata.org/docs/user_guide/timedeltas.html)\n\
        \nAttributes:\n    dt_min (required): Minimum time-delta value (inclusive).\
        \ Must be non-negative and less than `dt_max`.\n        Specified in units\
        \ defined by the `unit` parameter.\n    dt_max (required): Maximum time-delta\
        \ value (exclusive). Must be positive and greater than `dt_min`.\n       \
        \ Specified in units defined by the `unit` parameter.\n    reference_column_name\
        \ (required): Name of an existing datetime column to add the time-delta to.\n\
        \        This column must be generated before the timedelta column.\n    unit:\
        \ Time unit for the delta values. Options:\n        - \"D\": Days (default)\n\
        \        - \"h\": Hours\n        - \"m\": Minutes\n        - \"s\": Seconds"
    ToolConfig:
      properties:
        tool_alias:
          type: string
          title: Tool Alias
        providers:
          items:
            type: string
          type: array
          title: Providers
        allow_tools:
          title: Allow Tools
          items:
            type: string
          type: array
        max_tool_call_turns:
          type: integer
          minimum: 1.0
          title: Max Tool Call Turns
          default: 5
        timeout_sec:
          title: Timeout Sec
          type: number
          exclusiveMinimum: 0.0
      additionalProperties: false
      type: object
      required:
      - tool_alias
      - providers
      title: ToolConfig
      description: "Configuration for permitting MCP tools on an LLM column.\n\nToolConfig\
        \ defines which tools are available for use during LLM generation.\nIt references\
        \ one or more MCP providers by name and can optionally restrict\nwhich tools\
        \ from those providers are permitted.\n\nAttributes:\n    tool_alias (str):\
        \ User-defined alias to reference this tool configuration in column configs.\n\
        \    providers (list[str]): Names of the MCP providers to use for tool calls.\
        \ Tools can be\n        drawn from multiple providers.\n    allow_tools (list[str]\
        \ | None): Optional allowlist of tool names that restricts which\n       \
        \ tools are permitted. If None, all tools from the specified providers are\
        \ allowed.\n        Defaults to None.\n    max_tool_call_turns (int): Maximum\
        \ number of tool-calling turns permitted in a single\n        generation.\
        \ A turn is one iteration where the LLM requests tool calls. With parallel\n\
        \        tool calling, a single turn may execute multiple tools simultaneously.\
        \ Defaults to 5.\n    timeout_sec (float | None): Timeout in seconds for MCP\
        \ tool calls. Defaults to None (no timeout).\n\nExamples:\n    >>> ToolConfig(\n\
        \    ...     tool_alias=\"search-tools\",\n    ...     providers=[\"doc-search-mcp\"\
        , \"web-search-mcp\"],\n    ...     allow_tools=[\"search_docs\", \"list_docs\"\
        ],\n    ...     max_tool_call_turns=10,\n    ...     timeout_sec=30.0,\n \
        \   ... )"
    TraceType:
      type: string
      enum:
      - none
      - last_message
      - all_messages
      title: TraceType
      description: "Specifies the type of reasoning trace to capture for LLM columns.\n\
        \nTraces capture the conversation history during LLM generation, which is\n\
        useful for debugging, analysis, and understanding model behavior.\n\nAttributes:\n\
        \    NONE: No trace is captured. This is the default.\n    LAST_MESSAGE: Only\
        \ the final assistant message is captured.\n    ALL_MESSAGES: The full conversation\
        \ history (system/user/assistant/tool)\n        is captured."
    UUIDSamplerParams:
      properties:
        prefix:
          title: Prefix
          description: String prepended to the front of the UUID.
          type: string
        short_form:
          type: boolean
          title: Short Form
          description: If true, all UUIDs sampled will be truncated at 8 characters.
          default: false
        uppercase:
          type: boolean
          title: Uppercase
          description: If true, all letters in the UUID will be capitalized.
          default: false
        sampler_type:
          type: string
          const: uuid
          title: Sampler Type
          default: uuid
      additionalProperties: false
      type: object
      title: UUIDSamplerParams
      description: "Parameters for generating UUID (Universally Unique Identifier)\
        \ values.\n\nGenerates UUID4 (random) identifiers with optional formatting\
        \ options. UUIDs are useful\nfor creating unique identifiers for records,\
        \ entities, or transactions.\n\nAttributes:\n    prefix: Optional string to\
        \ prepend to each UUID. Useful for creating namespaced or\n        typed identifiers\
        \ (e.g., \"user-\", \"order-\", \"txn-\").\n    short_form: If True, truncates\
        \ UUIDs to 8 characters (first segment only). Default is False\n        for\
        \ full 32-character UUIDs (excluding hyphens).\n    uppercase: If True, converts\
        \ all hexadecimal letters to uppercase. Default is False for\n        lowercase\
        \ UUIDs."
    UniformDistribution:
      properties:
        distribution_type:
          allOf:
          - $ref: '#/components/schemas/DistributionType'
          default: uniform
        params:
          $ref: '#/components/schemas/UniformDistributionParams'
      additionalProperties: false
      type: object
      required:
      - params
      title: UniformDistribution
      description: "Uniform distribution for sampling inference parameters.\n\nSamples\
        \ values uniformly between low and high bounds. Useful for exploring\na continuous\
        \ range of values for temperature or top_p.\n\nAttributes:\n    distribution_type:\
        \ Type of distribution (\"uniform\").\n    params: Distribution parameters\
        \ (low, high)."
    UniformDistributionParams:
      properties:
        low:
          type: number
          title: Low
        high:
          type: number
          title: High
      additionalProperties: false
      type: object
      required:
      - low
      - high
      title: UniformDistributionParams
      description: "Parameters for uniform distribution sampling.\n\nAttributes:\n\
        \    low: Lower bound (inclusive).\n    high: Upper bound (exclusive)."
    UniformSamplerParams:
      properties:
        low:
          type: number
          title: Low
          description: Lower bound of the uniform distribution, inclusive.
        high:
          type: number
          title: High
          description: Upper bound of the uniform distribution.
        decimal_places:
          title: Decimal Places
          description: Number of decimal places to round the sampled values to.
          type: integer
        sampler_type:
          type: string
          const: uniform
          title: Sampler Type
          default: uniform
      additionalProperties: false
      type: object
      required:
      - low
      - high
      title: UniformSamplerParams
      description: "Parameters for sampling from a continuous Uniform distribution.\n\
        \nSamples continuous values uniformly from a specified range, where every\
        \ value in the range\nhas equal probability of being sampled. This is useful\
        \ when all values within a range are\nequally likely, such as random percentages,\
        \ proportions, or unbiased measurements.\n\nAttributes:\n    low (required):\
        \ Lower bound of the uniform distribution (inclusive). Can be any real number.\n\
        \    high (required): Upper bound of the uniform distribution. Must be greater\
        \ than `low`.\n    decimal_places: Optional number of decimal places to round\
        \ sampled values to. If None,\n        values are not rounded and may have\
        \ many decimal places."
    ValidationColumnConfig:
      properties:
        name:
          type: string
          title: Name
        drop:
          type: boolean
          title: Drop
          default: false
        column_type:
          type: string
          const: validation
          title: Column Type
          default: validation
        skip:
          $ref: '#/components/schemas/SkipConfig'
        propagate_skip:
          type: boolean
          title: Propagate Skip
          description: "If True (default), this column auto-skips when any of its\
            \ required_columns was skipped. Independent of skip \u2014 a column with\
            \ no SkipConfig still propagates upstream skips. Set to False for null-tolerant\
            \ columns."
          default: true
        target_columns:
          items:
            type: string
          type: array
          title: Target Columns
          description: List of column names to validate
        validator_type:
          allOf:
          - $ref: '#/components/schemas/ValidatorType'
          description: 'Validation method: ''code'', ''local_callable'', or ''remote'''
        validator_params:
          oneOf:
          - $ref: '#/components/schemas/CodeValidatorParams'
          - $ref: '#/components/schemas/LocalCallableValidatorParams'
          - $ref: '#/components/schemas/RemoteValidatorParams'
          title: Validator Params
          description: Validator-specific parameters (e.g., CodeValidatorParams)
          discriminator:
            propertyName: validator_type
            mapping:
              code: '#/components/schemas/CodeValidatorParams'
              local_callable: '#/components/schemas/LocalCallableValidatorParams'
              remote: '#/components/schemas/RemoteValidatorParams'
        batch_size:
          type: integer
          minimum: 1.0
          title: Batch Size
          description: Number of records to process in each batch
          default: 10
      additionalProperties: false
      type: object
      required:
      - name
      - target_columns
      - validator_type
      - validator_params
      title: ValidationColumnConfig
      description: "Configuration for validation columns that validate existing columns.\n\
        \nValidation columns execute validation logic against specified target columns\
        \ and return\nstructured results indicating pass/fail status with validation\
        \ details. Supports multiple\nvalidation strategies: code execution (Python/SQL),\
        \ local callable functions (library only),\nand remote HTTP endpoints.\n\n\
        Attributes:\n    target_columns (required): List of column names to validate.\
        \ These columns are passed to the\n        validator for validation. All target\
        \ columns must exist in the dataset\n        before validation runs.\n   \
        \ validator_type (required): The type of validator to use. Options:\n    \
        \    - \"code\": Execute code (Python or SQL) for validation. The code receives\
        \ a\n          DataFrame with target columns and must return a DataFrame with\
        \ validation results.\n        - \"local_callable\": Call a local Python function\
        \ with the data. Only supported\n          when running DataDesigner locally.\n\
        \        - \"remote\": Send data to a remote HTTP endpoint for validation.\n\
        \    validator_params (required): Parameters specific to the validator type.\
        \ Type varies by validator:\n        - CodeValidatorParams: Specifies code\
        \ language (python or SQL dialect like\n          \"sql:postgres\", \"sql:mysql\"\
        ).\n        - LocalCallableValidatorParams: Provides validation function (Callable[[pd.DataFrame],\n\
        \          pd.DataFrame]) and optional output schema for validation results.\n\
        \        - RemoteValidatorParams: Configures endpoint URL, HTTP timeout, retry\
        \ behavior\n          (max_retries, retry_backoff), and parallel request limits\
        \ (max_parallel_requests).\n    batch_size: Number of records to process in\
        \ each validation batch. Defaults to 10.\n        Larger batches are more\
        \ efficient but use more memory. Adjust based on validator\n        complexity\
        \ and available resources.\n\nInherited Attributes:\n    name (required):\
        \ Unique name of the column to be generated.\n    drop: If True, generate\
        \ this column but remove it from the final dataset."
    ValidationError:
      properties:
        loc:
          items:
            anyOf:
            - type: string
            - type: integer
          type: array
          title: Location
        msg:
          type: string
          title: Message
        type:
          type: string
          title: Error Type
        input:
          title: Input
        ctx:
          type: object
          title: Context
          additionalProperties: true
      type: object
      required:
      - loc
      - msg
      - type
      title: ValidationError
    ValidatorType:
      type: string
      enum:
      - code
      - local_callable
      - remote
      title: ValidatorType
    VideoContext:
      properties:
        modality:
          type: string
          const: video
          title: Modality
          default: video
        column_name:
          type: string
          title: Column Name
        data_type:
          $ref: '#/components/schemas/ModalityDataType'
        video_format:
          $ref: '#/components/schemas/VideoFormat'
      type: object
      required:
      - column_name
      title: VideoContext
      description: 'Configuration for providing video context to multimodal models.


        Video context values are URL or base64 media values. Local paths may be

        passed through only in explicit URL mode so colocated model endpoints can

        read them directly. ``video_format`` is consulted only for base64 sources.'
    VideoFormat:
      type: string
      enum:
      - mp4
      - mov
      - webm
      title: VideoFormat
      description: Supported video formats for video context.