{"owner":"unslothai","repo":"unsloth","hasSpec":true,"specFile":"studio/frontend/data-designer.openapi (1).yaml","branch":"HEAD","format":"yaml","version":"3.x (YAML)","title":"unsloth","description":"","endpoints":[],"spec":"openapi: 3.1.0\ninfo:\n title: NeMo Data Designer Microservice\n description: Service for generating synthetic data.\n version: 1.5.0\npaths:\n /v1/data-designer/jobs:\n post:\n tags:\n - Data Designer\n summary: Create Job\n operationId: create_job_v1_data_designer_jobs_post\n requestBody:\n required: true\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/DataDesignerJobRequest'\n responses:\n '200':\n description: Successful Response\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/DataDesignerJob'\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n get:\n tags:\n - Data Designer\n summary: List Jobs\n operationId: list_jobs_v1_data_designer_jobs_get\n parameters:\n - name: page\n in: query\n required: false\n schema:\n type: integer\n exclusiveMinimum: 0\n description: Page number.\n default: 1\n title: Page\n description: Page number.\n - name: page_size\n in: query\n required: false\n schema:\n type: integer\n exclusiveMinimum: 0\n description: Page size.\n default: 10\n title: Page Size\n description: Page size.\n - name: sort\n in: query\n required: false\n schema:\n allOf:\n - $ref: '#/components/schemas/DataDesignerJobsSortField'\n description: The field to sort by. To sort in decreasing order, use `-`\n in front of the field name.\n default: -created_at\n description: The field to sort by. To sort in decreasing order, use `-` in\n front of the field name.\n - in: query\n name: filter\n style: deepObject\n required: false\n explode: true\n schema:\n $ref: '#/components/schemas/DataDesignerJobsListFilter'\n description: Filter jobs on various criteria.\n - in: query\n name: search\n style: deepObject\n required: false\n explode: true\n schema:\n $ref: '#/components/schemas/DataDesignerJobsSearch'\n description: \"\\nSearch jobs using substring matching.\\nYou can combine multiple\\\n \\ search fields and filters.\\n\\nFor example:\\n- `?search[name]=training`:\\\n \\ searches all jobs with 'training' in the name.\\n- `?search[project]=my-project`:\\\n \\ searches all jobs with 'my-project'\\n in the project field.\\n- `?search[name]=training&search[name]=eval`:\\\n \\ searches all jobs with\\n 'training' OR 'eval' in the name.\\n- `?search[name]=training&search[project]=my-project`:\\\n \\ searches all\\n jobs with 'training' in the name AND 'my-project' in the\\\n \\ project.\\n\"\n responses:\n '200':\n description: Successful Response\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/DataDesignerJobsPage'\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n /v1/data-designer/jobs/{job_id}:\n get:\n tags:\n - Data Designer\n summary: Get Job\n operationId: get_job_v1_data_designer_jobs__job_id__get\n parameters:\n - name: job_id\n in: path\n required: true\n schema:\n type: string\n title: Job Id\n responses:\n '200':\n description: Successful Response\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/DataDesignerJob'\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n delete:\n tags:\n - Data Designer\n summary: Delete Job\n operationId: delete_job_v1_data_designer_jobs__job_id__delete\n parameters:\n - name: job_id\n in: path\n required: true\n schema:\n type: string\n title: Job Id\n responses:\n '200':\n description: Successful Response\n content:\n application/json:\n schema: {}\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n /v1/data-designer/jobs/{job_id}/cancel:\n post:\n tags:\n - Data Designer\n summary: Cancel Job\n operationId: cancel_job_v1_data_designer_jobs__job_id__cancel_post\n parameters:\n - name: job_id\n in: path\n required: true\n schema:\n type: string\n title: Job Id\n responses:\n '200':\n description: Successful Response\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/DataDesignerJob'\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n /v1/data-designer/jobs/{job_id}/logs:\n get:\n tags:\n - Data Designer\n summary: Get Job Logs\n operationId: get_job_logs_v1_data_designer_jobs__job_id__logs_get\n parameters:\n - name: job_id\n in: path\n required: true\n schema:\n type: string\n title: Job Id\n - name: limit\n in: query\n required: false\n schema:\n anyOf:\n - type: integer\n - type: 'null'\n title: Limit\n - name: page_cursor\n in: query\n required: false\n schema:\n anyOf:\n - type: string\n - type: 'null'\n title: Page Cursor\n responses:\n '200':\n description: Successful Response\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/PlatformJobLogPage'\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n /v1/data-designer/jobs/{job_id}/results:\n get:\n tags:\n - Data Designer\n summary: List Job Results\n operationId: list_job_results_v1_data_designer_jobs__job_id__results_get\n parameters:\n - name: job_id\n in: path\n required: true\n schema:\n type: string\n title: Job Id\n responses:\n '200':\n description: Successful Response\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/PlatformJobListResultResponse'\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n /v1/data-designer/jobs/{job_id}/results/analysis/download:\n get:\n tags:\n - Data Designer\n summary: Download Job Result Analysis\n operationId: download_job_result_analysis_v1_data_designer_jobs__job_id__results_analysis_download_get\n parameters:\n - name: job_id\n in: path\n required: true\n schema:\n type: string\n title: Job Id\n responses:\n '200':\n description: Successful Response\n content:\n application/json:\n schema: {}\n '404':\n description: Not Found\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n /v1/data-designer/jobs/{job_id}/results/dataset/download:\n get:\n tags:\n - Data Designer\n summary: Download Job Result Dataset\n operationId: download_job_result_dataset_v1_data_designer_jobs__job_id__results_dataset_download_get\n parameters:\n - name: job_id\n in: path\n required: true\n schema:\n type: string\n title: Job Id\n responses:\n '200':\n description: Successful Response\n content:\n application/octet-stream:\n schema:\n type: string\n format: binary\n '404':\n description: Not Found\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n /v1/data-designer/jobs/{job_id}/results/{result_name}:\n get:\n tags:\n - Data Designer\n summary: Get Job Result\n operationId: get_job_result_v1_data_designer_jobs__job_id__results__result_name__get\n parameters:\n - name: job_id\n in: path\n required: true\n schema:\n type: string\n title: Job Id\n - name: result_name\n in: path\n required: true\n schema:\n type: string\n title: Result Name\n responses:\n '200':\n description: Successful Response\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/PlatformJobResultResponse'\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n /v1/data-designer/jobs/{job_id}/results/{result_name}/download:\n get:\n tags:\n - Data Designer\n summary: Download Job Result\n operationId: download_job_result_v1_data_designer_jobs__job_id__results__result_name__download_get\n parameters:\n - name: job_id\n in: path\n required: true\n schema:\n type: string\n title: Job Id\n - name: result_name\n in: path\n required: true\n schema:\n type: string\n title: Result Name\n responses:\n '200':\n description: Successful Response\n content:\n application/octet-stream:\n schema:\n type: string\n format: binary\n '404':\n description: Not Found\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n /v1/data-designer/jobs/{job_id}/status:\n get:\n tags:\n - Data Designer\n summary: Get Job Status\n operationId: get_job_status_v1_data_designer_jobs__job_id__status_get\n parameters:\n - name: job_id\n in: path\n required: true\n schema:\n type: string\n title: Job Id\n responses:\n '200':\n description: Successful Response\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/PlatformJobStatusResponse'\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n /v1/data-designer/preview:\n post:\n tags:\n - Data Designer\n summary: Generate preview Data Designer\n operationId: preview_v1_data_designer_preview_post\n requestBody:\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/PreviewRequest'\n required: true\n responses:\n '200':\n description: Successful Response\n content:\n application/jsonl:\n schema:\n $ref: '#/components/schemas/PreviewMessage'\n '422':\n description: Validation Error\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/HTTPValidationError'\n /v1/data-designer/settings:\n get:\n tags:\n - Data Designer\n summary: Get Data Designer settings\n description: Returns the settings available for Data Designer.\n operationId: get_settings_v1_data_designer_settings_get\n responses:\n '200':\n description: Successful Response\n content:\n application/json:\n schema:\n $ref: '#/components/schemas/SettingsResponse'\ncomponents:\n schemas:\n BernoulliMixtureSamplerParams:\n properties:\n p:\n type: number\n maximum: 1.0\n minimum: 0.0\n title: P\n description: Bernoulli distribution probability of success.\n dist_name:\n type: string\n title: Dist Name\n description: Mixture distribution name. Samples will be equal to the distribution\n sample with probability `p`, otherwise equal to 0. Must be a valid scipy.stats\n distribution name.\n dist_params:\n additionalProperties: true\n type: object\n title: Dist Params\n description: Parameters of the scipy.stats distribution given in `dist_name`.\n sampler_type:\n type: string\n const: bernoulli_mixture\n title: Sampler Type\n default: bernoulli_mixture\n additionalProperties: false\n type: object\n required:\n - p\n - dist_name\n - dist_params\n title: BernoulliMixtureSamplerParams\n description: \"Parameters for sampling from a Bernoulli mixture distribution.\\n\\\n \\nCombines a Bernoulli distribution with another continuous distribution,\\\n \\ creating a mixture\\nwhere values are either 0 (with probability 1-p) or\\\n \\ sampled from the specified distribution\\n(with probability p). This is useful\\\n \\ for modeling scenarios with many zero values mixed with\\na continuous distribution\\\n \\ of non-zero values.\\n\\nCommon use cases include modeling sparse events,\\\n \\ zero-inflated data, or situations where\\nan outcome either doesn't occur\\\n \\ (0) or follows a specific distribution when it does occur.\\n\\nAttributes:\\n\\\n \\ p: Probability of sampling from the mixture distribution (non-zero outcome).\\n\\\n \\ Must be between 0.0 and 1.0 (inclusive). With probability 1-p, the\\\n \\ sample is 0.\\n dist_name: Name of the scipy.stats distribution to sample\\\n \\ from when outcome is non-zero.\\n Must be a valid scipy.stats distribution\\\n \\ name (e.g., \\\"norm\\\", \\\"gamma\\\", \\\"expon\\\").\\n dist_params: Parameters\\\n \\ for the specified scipy.stats distribution.\"\n BernoulliSamplerParams:\n properties:\n p:\n type: number\n maximum: 1.0\n minimum: 0.0\n title: P\n description: Probability of success.\n sampler_type:\n type: string\n const: bernoulli\n title: Sampler Type\n default: bernoulli\n additionalProperties: false\n type: object\n required:\n - p\n title: BernoulliSamplerParams\n description: \"Parameters for sampling from a Bernoulli distribution.\\n\\nSamples\\\n \\ binary values (0 or 1) representing the outcome of a single trial with a\\\n \\ fixed\\nprobability of success. This is the simplest discrete probability\\\n \\ distribution, useful for\\nmodeling binary outcomes like success/failure,\\\n \\ yes/no, or true/false.\\n\\nAttributes:\\n p: Probability of success (sampling\\\n \\ 1). Must be between 0.0 and 1.0 (inclusive).\\n The probability of\\\n \\ failure (sampling 0) is automatically 1 - p.\"\n BinomialSamplerParams:\n properties:\n n:\n type: integer\n title: N\n description: Number of trials.\n p:\n type: number\n maximum: 1.0\n minimum: 0.0\n title: P\n description: Probability of success on each trial.\n sampler_type:\n type: string\n const: binomial\n title: Sampler Type\n default: binomial\n additionalProperties: false\n type: object\n required:\n - n\n - p\n title: BinomialSamplerParams\n description: \"Parameters for sampling from a Binomial distribution.\\n\\nSamples\\\n \\ integer values representing the number of successes in a fixed number of\\\n \\ independent\\nBernoulli trials, each with the same probability of success.\\\n \\ Commonly used to model the number\\nof successful outcomes in repeated experiments.\\n\\\n \\nAttributes:\\n n: Number of independent trials. Must be a positive integer.\\n\\\n \\ p: Probability of success on each trial. Must be between 0.0 and 1.0\\\n \\ (inclusive).\"\n BuildStage:\n type: string\n enum:\n - pre_batch\n - post_batch\n - pre_generation\n - post_generation\n title: BuildStage\n CategorySamplerParams:\n properties:\n values:\n items:\n anyOf:\n - type: string\n - type: integer\n - type: number\n type: array\n minItems: 1\n title: Values\n description: List of possible categorical values that can be sampled from.\n weights:\n type: array\n items:\n type: number\n title: Weights\n description: List of unnormalized probability weights to assigned to each\n value, in order. Larger values will be sampled with higher probability.\n sampler_type:\n type: string\n const: category\n title: Sampler Type\n default: category\n additionalProperties: false\n type: object\n required:\n - values\n title: CategorySamplerParams\n description: \"Parameters for categorical sampling with optional probability\\\n \\ weighting.\\n\\nSamples values from a discrete set of categories. When weights\\\n \\ are provided, values are\\nsampled according to their assigned probabilities.\\\n \\ Without weights, uniform sampling is used.\\n\\nAttributes:\\n values: List\\\n \\ of possible categorical values to sample from. Can contain strings, integers,\\n\\\n \\ or floats. Must contain at least one value.\\n weights: Optional\\\n \\ unnormalized probability weights for each value. If provided, must be\\n\\\n \\ the same length as `values`. Weights are automatically normalized\\\n \\ to sum to 1.0.\\n Larger weights result in higher sampling probability\\\n \\ for the corresponding value.\"\n CodeLang:\n type: string\n enum:\n - go\n - javascript\n - java\n - kotlin\n - python\n - ruby\n - rust\n - scala\n - swift\n - typescript\n - sql:sqlite\n - sql:tsql\n - sql:bigquery\n - sql:mysql\n - sql:postgres\n - sql:ansi\n title: CodeLang\n CodeValidatorParams:\n properties:\n code_lang:\n allOf:\n - $ref: '#/components/schemas/CodeLang'\n description: The language of the code to validate\n additionalProperties: false\n type: object\n required:\n - code_lang\n title: CodeValidatorParams\n description: \"Configuration for code validation. Supports Python and SQL code\\\n \\ validation.\\n\\nAttributes:\\n code_lang: The language of the code to validate.\\\n \\ Supported values include: `python`,\\n `sql:sqlite`, `sql:postgres`,\\\n \\ `sql:mysql`, `sql:tsql`, `sql:bigquery`, `sql:ansi`.\"\n ColumnInequalityConstraint:\n properties:\n target_column:\n type: string\n title: Target Column\n rhs:\n type: string\n title: Rhs\n operator:\n $ref: '#/components/schemas/InequalityOperator'\n additionalProperties: false\n type: object\n required:\n - target_column\n - rhs\n - operator\n title: ColumnInequalityConstraint\n DataDesignerConfig:\n properties:\n columns:\n items:\n oneOf:\n - $ref: '#/components/schemas/ExpressionColumnConfig'\n - $ref: '#/components/schemas/LLMCodeColumnConfig'\n - $ref: '#/components/schemas/LLMJudgeColumnConfig'\n - $ref: '#/components/schemas/LLMStructuredColumnConfig'\n - $ref: '#/components/schemas/LLMTextColumnConfig'\n - $ref: '#/components/schemas/SamplerColumnConfig'\n - $ref: '#/components/schemas/SeedDatasetColumnConfig'\n - $ref: '#/components/schemas/ValidationColumnConfig'\n discriminator:\n propertyName: column_type\n mapping:\n expression: '#/components/schemas/ExpressionColumnConfig'\n llm-code: '#/components/schemas/LLMCodeColumnConfig-Input'\n llm-judge: '#/components/schemas/LLMJudgeColumnConfig-Input'\n llm-structured: '#/components/schemas/LLMStructuredColumnConfig-Input'\n llm-text: '#/components/schemas/LLMTextColumnConfig-Input'\n sampler: '#/components/schemas/SamplerColumnConfig'\n seed-dataset: '#/components/schemas/SeedDatasetColumnConfig'\n validation: '#/components/schemas/ValidationColumnConfig-Input'\n type: array\n minItems: 1\n title: Columns\n model_configs:\n type: array\n items:\n $ref: '#/components/schemas/ModelConfigInput'\n title: Model Configs\n seed_config:\n $ref: '#/components/schemas/SeedConfig'\n constraints:\n type: array\n items:\n anyOf:\n - $ref: '#/components/schemas/ScalarInequalityConstraint'\n - $ref: '#/components/schemas/ColumnInequalityConstraint'\n title: Constraints\n profilers:\n type: array\n items:\n $ref: '#/components/schemas/JudgeScoreProfilerConfig'\n title: Profilers\n processors:\n type: array\n items:\n $ref: '#/components/schemas/ProcessorConfig'\n title: Processors\n additionalProperties: false\n type: object\n required:\n - columns\n title: DataDesignerConfig\n description: \"Configuration for NeMo Data Designer.\\n\\nThis class defines the\\\n \\ main configuration structure for NeMo Data Designer,\\nwhich orchestrates\\\n \\ the generation of synthetic data.\\n\\nAttributes:\\n columns: Required\\\n \\ list of column configurations defining how each column\\n should be\\\n \\ generated. Must contain at least one column.\\n model_configs: Optional\\\n \\ list of model configurations for LLM-based generation.\\n Each model\\\n \\ config defines the model, provider, and inference parameters.\\n seed_config:\\\n \\ Optional seed dataset settings to use for generation.\\n constraints:\\\n \\ Optional list of column constraints.\\n profilers: Optional list of column\\\n \\ profilers for analyzing generated data characteristics.\"\n DataDesignerJob:\n properties:\n id:\n type: string\n title: Id\n name:\n type: string\n title: Name\n description:\n type: string\n title: Description\n project:\n type: string\n title: Project\n namespace:\n type: string\n title: Namespace\n created_at:\n type: string\n title: Created At\n updated_at:\n type: string\n title: Updated At\n spec:\n $ref: '#/components/schemas/DataDesignerJobConfig'\n status:\n $ref: '#/components/schemas/PlatformJobStatus'\n status_details:\n type: object\n additionalProperties: true\n title: Status Details\n error_details:\n type: object\n additionalProperties: true\n title: Error Details\n ownership:\n type: object\n additionalProperties: true\n title: Ownership\n custom_fields:\n type: object\n additionalProperties: true\n title: Custom Fields\n type: object\n required:\n - name\n - spec\n title: DataDesignerJob\n DataDesignerJobConfig:\n properties:\n num_records:\n type: integer\n title: Num Records\n config:\n $ref: '#/components/schemas/DataDesignerConfig'\n type: object\n required:\n - num_records\n - config\n title: DataDesignerJobConfig\n DataDesignerJobRequest:\n properties:\n name:\n type: string\n title: Name\n description:\n type: string\n title: Description\n namespace:\n type: string\n title: Namespace\n project:\n type: string\n title: Project\n spec:\n $ref: '#/components/schemas/DataDesignerJobConfig'\n ownership:\n type: object\n additionalProperties: true\n title: Ownership\n custom_fields:\n type: object\n additionalProperties: true\n title: Custom Fields\n type: object\n required:\n - spec\n title: DataDesignerJobRequest\n DataDesignerJobsListFilter:\n properties:\n created_at:\n allOf:\n - $ref: '#/components/schemas/DatetimeFilter'\n description: Jobs created at 'gte' datetime or 'lte' datetime.\n name:\n type: string\n title: Name\n description: Name of the job.\n namespace:\n type: string\n title: Namespace\n description: Namespace of the job.\n project:\n type: string\n title: Project\n description: Project containing the job.\n status:\n allOf:\n - $ref: '#/components/schemas/PlatformJobStatus'\n description: The current status.\n updated_at:\n allOf:\n - $ref: '#/components/schemas/DatetimeFilter'\n description: Jobs updated at 'gte' datetime or 'lte' datetime.\n additionalProperties: false\n type: object\n title: DataDesignerJobsListFilter\n DataDesignerJobsPage:\n properties:\n object:\n type: string\n title: Object\n description: The type of object being returned.\n default: list\n data:\n items:\n $ref: '#/components/schemas/DataDesignerJob'\n type: array\n title: Data\n pagination:\n allOf:\n - $ref: '#/components/schemas/PaginationData'\n description: Pagination information.\n sort:\n type: string\n title: Sort\n description: The field on which the results are sorted.\n filter:\n allOf:\n - $ref: '#/components/schemas/DataDesignerJobsListFilter'\n description: Filtering information.\n search:\n allOf:\n - $ref: '#/components/schemas/DataDesignerJobsSearch'\n description: Search information.\n type: object\n required:\n - data\n title: DataDesignerJobsPage\n DataDesignerJobsSearch:\n properties:\n name:\n type: array\n items:\n type: string\n title: Name\n description: Search jobs where name contains any of these strings.\n project:\n type: array\n items:\n type: string\n title: Project\n description: Search jobs where project contains any of these strings.\n type: object\n title: DataDesignerJobsSearch\n DataDesignerJobsSortField:\n type: string\n enum:\n - created_at\n - -created_at\n - updated_at\n - -updated_at\n title: DataDesignerJobsSortField\n DatetimeFilter:\n properties:\n gte:\n type: string\n title: Gte\n description: Filter for results greater than or equal to this datetime.\n lte:\n type: string\n title: Lte\n description: Filter for results less than or equal to this datetime.\n additionalProperties: false\n type: object\n title: DatetimeFilter\n DatetimeSamplerParams:\n properties:\n start:\n type: string\n title: Start\n description: Earliest possible datetime for sampling range, inclusive.\n end:\n type: string\n title: End\n description: Latest possible datetime for sampling range, inclusive.\n unit:\n type: string\n enum:\n - Y\n - M\n - D\n - h\n - m\n - s\n title: Unit\n description: Sampling units, e.g. the smallest possible time interval between\n samples.\n default: D\n sampler_type:\n type: string\n const: datetime\n title: Sampler Type\n default: datetime\n additionalProperties: false\n type: object\n required:\n - start\n - end\n title: DatetimeSamplerParams\n description: \"Parameters for uniform datetime sampling within a specified range.\\n\\\n \\nSamples datetime values uniformly between a start and end date with a specified\\\n \\ granularity.\\nThe sampling unit determines the smallest possible time interval\\\n \\ between consecutive samples.\\n\\nAttributes:\\n start: Earliest possible\\\n \\ datetime for the sampling range (inclusive). Must be a valid\\n datetime\\\n \\ string parseable by pandas.to_datetime().\\n end: Latest possible datetime\\\n \\ for the sampling range (inclusive). Must be a valid\\n datetime string\\\n \\ parseable by pandas.to_datetime().\\n unit: Time unit for sampling granularity.\\\n \\ Options:\\n - \\\"Y\\\": Years\\n - \\\"M\\\": Months\\n - \\\"\\\n D\\\": Days (default)\\n - \\\"h\\\": Hours\\n - \\\"m\\\": Minutes\\n \\\n \\ - \\\"s\\\": Seconds\"\n DisplayModelProvider:\n properties:\n name:\n type: string\n title: Name\n provider_type:\n type: string\n title: Provider Type\n default: openai\n extra_body:\n type: object\n additionalProperties: true\n title: Extra Body\n allowed_models:\n type: array\n items:\n type: string\n title: Allowed Models\n additionalProperties: false\n type: object\n required:\n - name\n title: DisplayModelProvider\n DistributionType:\n type: string\n enum:\n - uniform\n - manual\n title: DistributionType\n ExpressionColumnConfig:\n properties:\n name:\n type: string\n title: Name\n drop:\n type: boolean\n title: Drop\n default: false\n column_type:\n type: string\n const: expression\n title: Column Type\n default: expression\n expr:\n type: string\n title: Expr\n dtype:\n type: string\n enum:\n - int\n - float\n - str\n - bool\n title: Dtype\n default: str\n additionalProperties: false\n type: object\n required:\n - name\n - expr\n title: ExpressionColumnConfig\n description: \"Configuration for derived columns using Jinja2 expressions.\\n\\n\\\n Expression columns compute values by evaluating Jinja2 templates that reference\\\n \\ other\\ncolumns. Useful for transformations, concatenations, conditional\\\n \\ logic, and derived\\nfeatures without requiring LLM generation. The expression\\\n \\ is evaluated row-by-row.\\n\\nAttributes:\\n expr: Jinja2 expression to\\\n \\ evaluate. Can reference other column values using\\n {{ column_name\\\n \\ }} syntax. Supports filters, conditionals, and arithmetic.\\n Must\\\n \\ be a valid, non-empty Jinja2 template.\\n dtype: Data type to cast the\\\n \\ result to. Must be one of \\\"int\\\", \\\"float\\\", \\\"str\\\", or \\\"bool\\\".\\n \\\n \\ Defaults to \\\"str\\\". Type conversion is applied after expression evaluation.\\n\\\n \\ column_type: Discriminator field, always \\\"expression\\\" for this configuration\\\n \\ type.\"\n FileStorageType:\n type: string\n enum:\n - nds\n title: FileStorageType\n GaussianSamplerParams:\n properties:\n mean:\n type: number\n title: Mean\n description: Mean of the Gaussian distribution\n stddev:\n type: number\n title: Stddev\n description: Standard deviation of the Gaussian distribution\n decimal_places:\n type: integer\n title: Decimal Places\n description: Number of decimal places to round the sampled values to.\n sampler_type:\n type: string\n const: gaussian\n title: Sampler Type\n default: gaussian\n additionalProperties: false\n type: object\n required:\n - mean\n - stddev\n title: GaussianSamplerParams\n description: \"Parameters for sampling from a Gaussian (Normal) distribution.\\n\\\n \\nSamples continuous values from a normal distribution characterized by its\\\n \\ mean and standard\\ndeviation. The Gaussian distribution is one of the most\\\n \\ commonly used probability distributions,\\nappearing naturally in many real-world\\\n \\ phenomena due to the Central Limit Theorem.\\n\\nAttributes:\\n mean: Mean\\\n \\ (center) of the Gaussian distribution. This is the expected value and the\\n\\\n \\ location of the distribution's peak.\\n stddev: Standard deviation\\\n \\ of the Gaussian distribution. Controls the spread or width\\n of the\\\n \\ distribution. Must be positive.\\n decimal_places: Optional number of\\\n \\ decimal places to round sampled values to. If None,\\n values are\\\n \\ not rounded.\"\n HTTPValidationError:\n properties:\n detail:\n items:\n $ref: '#/components/schemas/ValidationError'\n type: array\n title: Detail\n type: object\n title: HTTPValidationError\n ImageContext:\n properties:\n modality:\n allOf:\n - $ref: '#/components/schemas/Modality'\n default: image\n column_name:\n type: string\n title: Column Name\n data_type:\n $ref: '#/components/schemas/ModalityDataType'\n image_format:\n $ref: '#/components/schemas/ImageFormat'\n type: object\n required:\n - column_name\n - data_type\n title: ImageContext\n ImageFormat:\n type: string\n enum:\n - png\n - jpg\n - jpeg\n - gif\n - webp\n title: ImageFormat\n IndexRange:\n properties:\n start:\n type: integer\n minimum: 0.0\n title: Start\n description: The start index of the index range (inclusive)\n end:\n type: integer\n minimum: 0.0\n title: End\n description: The end index of the index range (inclusive)\n additionalProperties: false\n type: object\n required:\n - start\n - end\n title: IndexRange\n InequalityOperator:\n type: string\n enum:\n - lt\n - le\n - gt\n - ge\n title: InequalityOperator\n InferenceParametersInput:\n properties:\n temperature:\n anyOf:\n - type: number\n - $ref: '#/components/schemas/UniformDistribution'\n - $ref: '#/components/schemas/ManualDistribution'\n - type: 'null'\n title: Temperature\n top_p:\n anyOf:\n - type: number\n - $ref: '#/components/schemas/UniformDistribution'\n - $ref: '#/components/schemas/ManualDistribution'\n - type: 'null'\n title: Top P\n max_tokens:\n type: integer\n title: Max Tokens\n max_parallel_requests:\n type: integer\n minimum: 1.0\n title: Max Parallel Requests\n default: 4\n timeout:\n type: integer\n title: Timeout\n extra_body:\n type: object\n additionalProperties: true\n title: Extra Body\n additionalProperties: false\n type: object\n title: InferenceParametersInput\n InferenceParametersOutput:\n properties:\n temperature:\n anyOf:\n - type: number\n - $ref: '#/components/schemas/UniformDistribution'\n - $ref: '#/components/schemas/ManualDistribution'\n - type: 'null'\n title: Temperature\n top_p:\n anyOf:\n - type: number\n - $ref: '#/components/schemas/UniformDistribution'\n - $ref: '#/components/schemas/ManualDistribution'\n - type: 'null'\n title: Top P\n max_tokens:\n type: integer\n title: Max Tokens\n max_parallel_requests:\n type: integer\n minimum: 1.0\n title: Max Parallel Requests\n default: 4\n timeout:\n type: integer\n title: Timeout\n extra_body:\n type: object\n additionalProperties: true\n title: Extra Body\n additionalProperties: false\n type: object\n title: InferenceParametersOutput\n JudgeScoreProfilerConfig:\n properties:\n model_alias:\n type: string\n title: Model Alias\n summary_score_sample_size:\n type: integer\n title: Summary Score Sample Size\n default: 20\n additionalProperties: false\n type: object\n required:\n - model_alias\n title: JudgeScoreProfilerConfig\n LLMCodeColumnConfig:\n properties:\n name:\n type: string\n title: Name\n drop:\n type: boolean\n title: Drop\n default: false\n column_type:\n type: string\n const: llm-code\n title: Column Type\n default: llm-code\n prompt:\n type: string\n title: Prompt\n model_alias:\n type: string\n title: Model Alias\n system_prompt:\n type: string\n title: System Prompt\n multi_modal_context:\n type: array\n items:\n $ref: '#/components/schemas/ImageContext'\n title: Multi Modal Context\n code_lang:\n $ref: '#/components/schemas/CodeLang'\n additionalProperties: false\n type: object\n required:\n - name\n - prompt\n - model_alias\n - code_lang\n title: LLMCodeColumnConfig\n description: \"Configuration for code generation columns using Large Language\\\n \\ Models.\\n\\nExtends LLMTextColumnConfig to generate code snippets in specific\\\n \\ programming languages\\nor SQL dialects. The generated code is automatically\\\n \\ extracted from markdown code blocks\\nfor the specified language. Inherits\\\n \\ all prompt templating capabilities.\\n\\nAttributes:\\n code_lang: Programming\\\n \\ language or SQL dialect for code generation. Supported\\n values include:\\\n \\ \\\"python\\\", \\\"javascript\\\", \\\"typescript\\\", \\\"java\\\", \\\"kotlin\\\", \\\"go\\\"\\\n ,\\n \\\"rust\\\", \\\"ruby\\\", \\\"scala\\\", \\\"swift\\\", \\\"sql:sqlite\\\", \\\"sql:postgres\\\"\\\n , \\\"sql:mysql\\\",\\n \\\"sql:tsql\\\", \\\"sql:bigquery\\\", \\\"sql:ansi\\\". See\\\n \\ CodeLang enum for complete list.\\n column_type: Discriminator field,\\\n \\ always \\\"llm-code\\\" for this configuration type.\"\n LLMJudgeColumnConfig:\n properties:\n name:\n type: string\n title: Name\n drop:\n type: boolean\n title: Drop\n default: false\n column_type:\n type: string\n const: llm-judge\n title: Column Type\n default: llm-judge\n prompt:\n type: string\n title: Prompt\n model_alias:\n type: string\n title: Model Alias\n system_prompt:\n type: string\n title: System Prompt\n multi_modal_context:\n type: array\n items:\n $ref: '#/components/schemas/ImageContext'\n title: Multi Modal Context\n scores:\n items:\n $ref: '#/components/schemas/Score'\n type: array\n minItems: 1\n title: Scores\n additionalProperties: false\n type: object\n required:\n - name\n - prompt\n - model_alias\n - scores\n title: LLMJudgeColumnConfig\n description: \"Configuration for LLM-as-a-judge quality assessment and scoring\\\n \\ columns.\\n\\nExtends LLMTextColumnConfig to create judge columns that evaluate\\\n \\ and score other\\ngenerated content based on the defined criteria. Useful\\\n \\ for quality assessment, preference\\nranking, and multi-dimensional evaluation\\\n \\ of generated data.\\n\\nAttributes:\\n scores: List of Score objects defining\\\n \\ the evaluation dimensions. Each score\\n represents a different aspect\\\n \\ to evaluate (e.g., accuracy, relevance, fluency).\\n Must contain\\\n \\ at least one score.\\n column_type: Discriminator field, always \\\"llm-judge\\\"\\\n \\ for this configuration type.\"\n LLMStructuredColumnConfig:\n properties:\n name:\n type: string\n title: Name\n drop:\n type: boolean\n title: Drop\n default: false\n column_type:\n type: string\n const: llm-structured\n title: Column Type\n default: llm-structured\n prompt:\n type: string\n title: Prompt\n model_alias:\n type: string\n title: Model Alias\n system_prompt:\n type: string\n title: System Prompt\n multi_modal_context:\n type: array\n items:\n $ref: '#/components/schemas/ImageContext'\n title: Multi Modal Context\n output_format:\n anyOf:\n - additionalProperties: true\n type: object\n - {}\n title: Output Format\n additionalProperties: false\n type: object\n required:\n - name\n - prompt\n - model_alias\n - output_format\n title: LLMStructuredColumnConfig\n description: \"Configuration for structured JSON generation columns using Large\\\n \\ Language Models.\\n\\nExtends LLMTextColumnConfig to generate structured data\\\n \\ conforming to a specified schema.\\nUses JSON schema or Pydantic models to\\\n \\ define the expected output structure, enabling\\ntype-safe and validated\\\n \\ structured output generation. Inherits prompt templating capabilities.\\n\\\n \\nAttributes:\\n output_format: The schema defining the expected output\\\n \\ structure. Can be either:\\n - A Pydantic BaseModel class (recommended)\\n\\\n \\ - A JSON schema dictionary\\n column_type: Discriminator field,\\\n \\ always \\\"llm-structured\\\" for this configuration type.\"\n LLMTextColumnConfig:\n properties:\n name:\n type: string\n title: Name\n drop:\n type: boolean\n title: Drop\n default: false\n column_type:\n type: string\n const: llm-text\n title: Column Type\n default: llm-text\n prompt:\n type: string\n title: Prompt\n model_alias:\n type: string\n title: Model Alias\n system_prompt:\n type: string\n title: System Prompt\n multi_modal_context:\n type: array\n items:\n $ref: '#/components/schemas/ImageContext'\n title: Multi Modal Context\n additionalProperties: false\n type: object\n required:\n - name\n - prompt\n - model_alias\n title: LLMTextColumnConfig\n description: \"Configuration for text generation columns using Large Language\\\n \\ Models.\\n\\nLLM text columns generate free-form text content using language\\\n \\ models via LiteLLM.\\nPrompts support Jinja2 templating to reference values\\\n \\ from other columns, enabling\\ncontext-aware generation. The generated text\\\n \\ can optionally include reasoning traces\\nwhen models support extended thinking.\\n\\\n \\nAttributes:\\n prompt: Prompt template for text generation. Supports Jinja2\\\n \\ syntax to\\n reference other columns (e.g., \\\"Write a story about\\\n \\ {{ character_name }}\\\").\\n Must be a valid Jinja2 template.\\n \\\n \\ model_alias: Alias of the model configuration to use for generation.\\n \\\n \\ Must match a model alias defined when initializing the DataDesignerConfigBuilder.\\n\\\n \\ system_prompt: Optional system prompt to set model behavior and constraints.\\n\\\n \\ Also supports Jinja2 templating. If provided, must be a valid Jinja2\\\n \\ template.\\n Do not put any output parsing instructions in the system\\\n \\ prompt. Instead,\\n use the appropriate column type for the output\\\n \\ you want to generate - e.g.,\\n `LLMStructuredColumnConfig` for structured\\\n \\ output, `LLMCodeColumnConfig` for code.\\n multi_modal_context: Optional\\\n \\ list of image contexts for multi-modal generation.\\n Enables vision-capable\\\n \\ models to generate text based on image inputs.\\n column_type: Discriminator\\\n \\ field, always \\\"llm-text\\\" for this configuration type.\"\n LocalCallableValidatorParams:\n properties:\n validation_function:\n title: Validation Function\n description: Function (Callable[[pd.DataFrame], pd.DataFrame]) to validate\n the data\n output_schema:\n type: object\n additionalProperties: true\n title: Output Schema\n description: Expected schema for local callable validator's output\n additionalProperties: false\n type: object\n required:\n - validation_function\n title: LocalCallableValidatorParams\n description: \"Configuration for local callable validation. Expects a function\\\n \\ to be passed that validates the data.\\n\\nAttributes:\\n validation_function:\\\n \\ Function (`Callable[[pd.DataFrame], pd.DataFrame]`) to validate the\\n \\\n \\ data. Output must contain a column `is_valid` of type `bool`.\\n \\\n \\ output_schema: The JSON schema for the local callable validator's output.\\\n \\ If not provided,\\n the output will not be validated.\"\n ManualDistribution:\n properties:\n distribution_type:\n allOf:\n - $ref: '#/components/schemas/DistributionType'\n default: manual\n params:\n $ref: '#/components/schemas/ManualDistributionParams'\n additionalProperties: false\n type: object\n required:\n - params\n title: ManualDistribution\n ManualDistributionParams:\n properties:\n values:\n items:\n type: number\n type: array\n minItems: 1\n title: Values\n weights:\n type: array\n items:\n type: number\n title: Weights\n additionalProperties: false\n type: object\n required:\n - values\n title: ManualDistributionParams\n MessageType:\n type: string\n enum:\n - analysis\n - dataset\n - heartbeat\n - log\n title: MessageType\n Modality:\n type: string\n enum:\n - image\n title: Modality\n ModalityDataType:\n type: string\n enum:\n - url\n - base64\n title: ModalityDataType\n ModelConfigInput:\n properties:\n alias:\n type: string\n title: Alias\n model:\n type: string\n title: Model\n inference_parameters:\n $ref: '#/components/schemas/InferenceParametersInput'\n provider:\n type: string\n title: Provider\n additionalProperties: false\n type: object\n required:\n - alias\n - model\n title: ModelConfigInput\n ModelConfigOutput:\n properties:\n alias:\n type: string\n title: Alias\n model:\n type: string\n title: Model\n inference_parameters:\n $ref: '#/components/schemas/InferenceParametersOutput'\n provider:\n type: string\n title: Provider\n additionalProperties: false\n type: object\n required:\n - alias\n - model\n title: ModelConfigOutput\n PaginationData:\n properties:\n page:\n type: integer\n title: Page\n description: The current page number.\n page_size:\n type: integer\n title: Page Size\n description: The page size used for the query.\n current_page_size:\n type: integer\n title: Current Page Size\n description: The size for the current page.\n total_pages:\n type: integer\n title: Total Pages\n description: The total number of pages.\n total_results:\n type: integer\n title: Total Results\n description: The total number of results.\n type: object\n required:\n - page\n - page_size\n - current_page_size\n - total_pages\n - total_results\n title: PaginationData\n PartitionBlock:\n properties:\n index:\n type: integer\n minimum: 0.0\n title: Index\n description: The index of the partition to sample from\n default: 0\n num_partitions:\n type: integer\n minimum: 1.0\n title: Num Partitions\n description: The total number of partitions in the dataset\n default: 1\n additionalProperties: false\n type: object\n title: PartitionBlock\n PersonFromFakerSamplerParams:\n properties:\n locale:\n type: string\n title: Locale\n description: Locale string, determines the language and geographic locale\n that a synthetic person will be sampled from. E.g, en_US, en_GB, fr_FR,\n ...\n default: en_US\n sex:\n type: string\n title: Sex\n description: If specified, then only synthetic people of the specified sex\n will be sampled.\n city:\n anyOf:\n - type: string\n - items:\n type: string\n type: array\n title: City\n description: If specified, then only synthetic people from these cities\n will be sampled.\n age_range:\n items:\n type: integer\n type: array\n maxItems: 2\n minItems: 2\n title: Age Range\n description: If specified, then only synthetic people within this age range\n will be sampled.\n default:\n - 18\n - 114\n sampler_type:\n type: string\n const: person_from_faker\n title: Sampler Type\n default: person_from_faker\n additionalProperties: false\n type: object\n title: PersonFromFakerSamplerParams\n PersonSamplerParams:\n properties:\n locale:\n type: string\n title: Locale\n description: 'Locale that determines the language and geographic location\n that a synthetic person will be sampled from. Must be a locale supported\n by a managed Nemotron Personas dataset. Managed datasets exist for the\n following locales: en_US, ja_JP, en_IN, hi_IN.'\n default: en_US\n sex:\n type: string\n title: Sex\n description: If specified, then only synthetic people of the specified sex\n will be sampled.\n city:\n anyOf:\n - type: string\n - items:\n type: string\n type: array\n title: City\n description: If specified, then only synthetic people from these cities\n will be sampled.\n age_range:\n items:\n type: integer\n type: array\n maxItems: 2\n minItems: 2\n title: Age Range\n description: If specified, then only synthetic people within this age range\n will be sampled.\n default:\n - 18\n - 114\n select_field_values:\n type: object\n additionalProperties:\n items:\n type: string\n type: array\n title: Select Field Values\n description: Sample synthetic people with the specified field values. This\n is meant to be a flexible argument for selecting a subset of the population\n from the managed dataset. Note that this sampler does not support rare\n combinations of field values and will likely fail if your desired subset\n is not well-represented in the managed Nemotron Personas dataset. We generally\n recommend using the `sex`, `city`, and `age_range` arguments to filter\n the population when possible.\n examples:\n - education_level:\n - high_school\n - some_college\n - bachelors\n state:\n - NY\n - CA\n - OH\n - TX\n - NV\n with_synthetic_personas:\n type: boolean\n title: With Synthetic Personas\n description: If True, then append synthetic persona columns to each generated\n person.\n default: false\n sampler_type:\n type: string\n const: person\n title: Sampler Type\n default: person\n additionalProperties: false\n type: object\n title: PersonSamplerParams\n description: \"Parameters for sampling synthetic person data with demographic\\\n \\ attributes.\\n\\nGenerates realistic synthetic person data including names,\\\n \\ addresses, phone numbers, and other\\ndemographic information. Data can be\\\n \\ sampled from managed datasets (when available) or generated\\nusing Faker.\\\n \\ The sampler supports filtering by locale, sex, age, geographic location,\\\n \\ and can\\noptionally include synthetic persona descriptions.\\n\\nAttributes:\\n\\\n \\ locale: Locale string determining the language and geographic region\\\n \\ for synthetic people.\\n Format: language_COUNTRY (e.g., \\\"en_US\\\"\\\n , \\\"en_GB\\\", \\\"fr_FR\\\", \\\"de_DE\\\", \\\"es_ES\\\", \\\"ja_JP\\\").\\n Defaults\\\n \\ to \\\"en_US\\\".\\n sex: If specified, filters to only sample people of the\\\n \\ specified sex. Options: \\\"Male\\\" or\\n \\\"Female\\\". If None, samples\\\n \\ both sexes.\\n city: If specified, filters to only sample people from\\\n \\ the specified city or cities. Can be\\n a single city name (string)\\\n \\ or a list of city names.\\n age_range: Two-element list [min_age, max_age]\\\n \\ specifying the age range to sample from\\n (inclusive). Defaults to\\\n \\ a standard age range. Both values must be between minimum and\\n maximum\\\n \\ allowed ages.\\n with_synthetic_personas: If True, appends additional\\\n \\ synthetic persona columns including\\n personality traits, interests,\\\n \\ and background descriptions. Only supported for certain\\n locales\\\n \\ with managed datasets.\\n sample_dataset_when_available: If True, samples\\\n \\ from curated managed datasets when available\\n for the specified\\\n \\ locale. If False or unavailable, falls back to Faker-generated data.\\n \\\n \\ Managed datasets typically provide more realistic and diverse synthetic\\\n \\ people.\"\n PlatformJobListResultResponse:\n properties:\n object:\n type: string\n title: Object\n description: The type of object being returned.\n default: list\n data:\n items:\n $ref: '#/components/schemas/PlatformJobResultResponse'\n type: array\n title: Data\n type: object\n required:\n - data\n title: PlatformJobListResultResponse\n PlatformJobLog:\n properties:\n timestamp:\n type: string\n format: date-time\n title: Timestamp\n job_id:\n type: string\n title: Job Id\n job_step:\n type: string\n title: Job Step\n job_task:\n type: string\n title: Job Task\n message:\n type: string\n title: Message\n type: object\n required:\n - timestamp\n - job_id\n - job_step\n - job_task\n - message\n title: PlatformJobLog\n PlatformJobLogPage:\n properties:\n object:\n type: string\n title: Object\n description: The type of object being returned.\n default: list\n data:\n items:\n $ref: '#/components/schemas/PlatformJobLog'\n type: array\n title: Data\n total:\n type: integer\n title: Total\n next_page:\n type: string\n title: Next Page\n prev_page:\n type: string\n title: Prev Page\n type: object\n required:\n - data\n - total\n - next_page\n - prev_page\n title: PlatformJobLogPage\n PlatformJobResultResponse:\n properties:\n result_name:\n type: string\n title: Result Name\n job_id:\n type: string\n title: Job Id\n namespace:\n type: string\n title: Namespace\n project:\n type: string\n title: Project\n created_at:\n type: string\n format: date-time\n title: Created At\n updated_at:\n type: string\n format: date-time\n title: Updated At\n artifact_url:\n type: string\n title: Artifact Url\n artifact_storage_type:\n $ref: '#/components/schemas/FileStorageType'\n type: object\n required:\n - result_name\n - job_id\n - namespace\n - artifact_url\n - artifact_storage_type\n title: PlatformJobResultResponse\n PlatformJobStatus:\n type: string\n enum:\n - created\n - pending\n - active\n - cancelled\n - cancelling\n - error\n - completed\n - paused\n - pausing\n - resuming\n title: PlatformJobStatus\n description: 'Enumeration of possible job statuses.\n\n\n This enum represents the various states a job can be in during its lifecycle,\n\n from creation to a terminal state.'\n PlatformJobStatusResponse:\n properties:\n job_id:\n type: string\n title: Job Id\n status:\n $ref: '#/components/schemas/PlatformJobStatus'\n status_details:\n additionalProperties: true\n type: object\n title: Status Details\n error_details:\n type: object\n additionalProperties: true\n title: Error Details\n steps:\n items:\n $ref: '#/components/schemas/PlatformJobStepStatusResponse'\n type: array\n title: Steps\n type: object\n required:\n - job_id\n - status\n - status_details\n - error_details\n - steps\n title: PlatformJobStatusResponse\n PlatformJobStepStatusResponse:\n properties:\n name:\n type: string\n title: Name\n status:\n $ref: '#/components/schemas/PlatformJobStatus'\n status_details:\n additionalProperties: true\n type: object\n title: Status Details\n error_details:\n type: object\n additionalProperties: true\n title: Error Details\n tasks:\n items:\n $ref: '#/components/schemas/PlatformJobTaskStatusResponse'\n type: array\n title: Tasks\n type: object\n required:\n - name\n - status\n - status_details\n - error_details\n - tasks\n title: PlatformJobStepStatusResponse\n PlatformJobTaskStatusResponse:\n properties:\n id:\n type: string\n title: Id\n status:\n $ref: '#/components/schemas/PlatformJobStatus'\n status_details:\n additionalProperties: true\n type: object\n title: Status Details\n error_details:\n type: object\n additionalProperties: true\n title: Error Details\n error_stack:\n type: string\n title: Error Stack\n type: object\n required:\n - id\n - status\n - status_details\n - error_details\n - error_stack\n title: PlatformJobTaskStatusResponse\n PoissonSamplerParams:\n properties:\n mean:\n type: number\n title: Mean\n description: Mean number of events in a fixed interval.\n sampler_type:\n type: string\n const: poisson\n title: Sampler Type\n default: poisson\n additionalProperties: false\n type: object\n required:\n - mean\n title: PoissonSamplerParams\n description: \"Parameters for sampling from a Poisson distribution.\\n\\nSamples\\\n \\ non-negative integer values representing the number of events occurring\\\n \\ in a fixed\\ninterval of time or space. The Poisson distribution is commonly\\\n \\ used to model count data\\nlike the number of arrivals, occurrences, or events\\\n \\ per time period.\\n\\nThe distribution is characterized by a single parameter\\\n \\ (mean/rate), and both the mean and\\nvariance equal this parameter value.\\n\\\n \\nAttributes:\\n mean: Mean number of events in the fixed interval (also\\\n \\ called rate parameter \\u03BB).\\n Must be positive. This represents\\\n \\ both the expected value and the variance of the\\n distribution.\"\n PreviewMessage:\n properties:\n message:\n type: string\n title: Message\n message_type:\n $ref: '#/components/schemas/MessageType'\n extra:\n type: object\n additionalProperties:\n type: string\n title: Extra\n additionalProperties: false\n type: object\n required:\n - message\n - message_type\n title: PreviewMessage\n PreviewRequest:\n properties:\n config:\n $ref: '#/components/schemas/DataDesignerConfig'\n num_records:\n type: integer\n title: Num Records\n type: object\n required:\n - config\n title: PreviewRequest\n ProcessorConfig:\n properties:\n build_stage:\n allOf:\n - $ref: '#/components/schemas/BuildStage'\n description: 'The stage at which the processor will run. Supported stages:\n post_batch'\n additionalProperties: false\n type: object\n required:\n - build_stage\n title: ProcessorConfig\n RemoteValidatorParams:\n properties:\n endpoint_url:\n type: string\n title: Endpoint Url\n description: URL of the remote endpoint\n output_schema:\n type: object\n additionalProperties: true\n title: Output Schema\n description: Expected schema for remote validator's output\n timeout:\n type: number\n exclusiveMinimum: 0.0\n title: Timeout\n description: The timeout for the HTTP request\n default: 30.0\n max_retries:\n type: integer\n minimum: 0.0\n title: Max Retries\n description: The maximum number of retry attempts\n default: 3\n retry_backoff:\n type: number\n exclusiveMinimum: 1.0\n title: Retry Backoff\n description: The backoff factor for the retry delay\n default: 2.0\n max_parallel_requests:\n type: integer\n minimum: 1.0\n title: Max Parallel Requests\n description: The maximum number of parallel requests to make\n default: 4\n additionalProperties: false\n type: object\n required:\n - endpoint_url\n title: RemoteValidatorParams\n description: \"Configuration for remote validation. Sends data to a remote endpoint\\\n \\ for validation.\\n\\nAttributes:\\n endpoint_url: The URL of the remote\\\n \\ endpoint.\\n output_schema: The JSON schema for the remote validator's\\\n \\ output. If not provided,\\n the output will not be validated.\\n \\\n \\ timeout: The timeout for the HTTP request in seconds. Defaults to 30.0.\\n\\\n \\ max_retries: The maximum number of retry attempts. Defaults to 3.\\n \\\n \\ retry_backoff: The backoff factor for the retry delay in seconds. Defaults\\\n \\ to 2.0.\\n max_parallel_requests: The maximum number of parallel requests\\\n \\ to make. Defaults to 4.\"\n SamplerColumnConfig:\n properties:\n name:\n type: string\n title: Name\n drop:\n type: boolean\n title: Drop\n default: false\n column_type:\n type: string\n const: sampler\n title: Column Type\n default: sampler\n sampler_type:\n $ref: '#/components/schemas/SamplerType'\n params:\n oneOf:\n - $ref: '#/components/schemas/SubcategorySamplerParams'\n - $ref: '#/components/schemas/CategorySamplerParams'\n - $ref: '#/components/schemas/DatetimeSamplerParams'\n - $ref: '#/components/schemas/PersonSamplerParams'\n - $ref: '#/components/schemas/PersonFromFakerSamplerParams'\n - $ref: '#/components/schemas/TimeDeltaSamplerParams'\n - $ref: '#/components/schemas/UUIDSamplerParams'\n - $ref: '#/components/schemas/BernoulliSamplerParams'\n - $ref: '#/components/schemas/BernoulliMixtureSamplerParams'\n - $ref: '#/components/schemas/BinomialSamplerParams'\n - $ref: '#/components/schemas/GaussianSamplerParams'\n - $ref: '#/components/schemas/PoissonSamplerParams'\n - $ref: '#/components/schemas/UniformSamplerParams'\n - $ref: '#/components/schemas/ScipySamplerParams'\n title: Params\n discriminator:\n propertyName: sampler_type\n mapping:\n bernoulli: '#/components/schemas/BernoulliSamplerParams'\n bernoulli_mixture: '#/components/schemas/BernoulliMixtureSamplerParams'\n binomial: '#/components/schemas/BinomialSamplerParams'\n category: '#/components/schemas/CategorySamplerParams'\n datetime: '#/components/schemas/DatetimeSamplerParams'\n gaussian: '#/components/schemas/GaussianSamplerParams'\n person: '#/components/schemas/PersonSamplerParams'\n person_from_faker: '#/components/schemas/PersonFromFakerSamplerParams'\n poisson: '#/components/schemas/PoissonSamplerParams'\n scipy: '#/components/schemas/ScipySamplerParams'\n subcategory: '#/components/schemas/SubcategorySamplerParams'\n timedelta: '#/components/schemas/TimeDeltaSamplerParams'\n uniform: '#/components/schemas/UniformSamplerParams'\n uuid: '#/components/schemas/UUIDSamplerParams'\n conditional_params:\n additionalProperties:\n oneOf:\n - $ref: '#/components/schemas/SubcategorySamplerParams'\n - $ref: '#/components/schemas/CategorySamplerParams'\n - $ref: '#/components/schemas/DatetimeSamplerParams'\n - $ref: '#/components/schemas/PersonSamplerParams'\n - $ref: '#/components/schemas/PersonFromFakerSamplerParams'\n - $ref: '#/components/schemas/TimeDeltaSamplerParams'\n - $ref: '#/components/schemas/UUIDSamplerParams'\n - $ref: '#/components/schemas/BernoulliSamplerParams'\n - $ref: '#/components/schemas/BernoulliMixtureSamplerParams'\n - $ref: '#/components/schemas/BinomialSamplerParams'\n - $ref: '#/components/schemas/GaussianSamplerParams'\n - $ref: '#/components/schemas/PoissonSamplerParams'\n - $ref: '#/components/schemas/UniformSamplerParams'\n - $ref: '#/components/schemas/ScipySamplerParams'\n discriminator:\n propertyName: sampler_type\n mapping:\n bernoulli: '#/components/schemas/BernoulliSamplerParams'\n bernoulli_mixture: '#/components/schemas/BernoulliMixtureSamplerParams'\n binomial: '#/components/schemas/BinomialSamplerParams'\n category: '#/components/schemas/CategorySamplerParams'\n datetime: '#/components/schemas/DatetimeSamplerParams'\n gaussian: '#/components/schemas/GaussianSamplerParams'\n person: '#/components/schemas/PersonSamplerParams'\n person_from_faker: '#/components/schemas/PersonFromFakerSamplerParams'\n poisson: '#/components/schemas/PoissonSamplerParams'\n scipy: '#/components/schemas/ScipySamplerParams'\n subcategory: '#/components/schemas/SubcategorySamplerParams'\n timedelta: '#/components/schemas/TimeDeltaSamplerParams'\n uniform: '#/components/schemas/UniformSamplerParams'\n uuid: '#/components/schemas/UUIDSamplerParams'\n type: object\n title: Conditional Params\n default: {}\n convert_to:\n type: string\n title: Convert To\n additionalProperties: false\n type: object\n required:\n - name\n - sampler_type\n - params\n title: SamplerColumnConfig\n description: \"Configuration for columns generated using numerical samplers.\\n\\\n \\nSampler columns provide efficient data generation using numerical samplers\\\n \\ for\\ncommon data types and distributions. Supported samplers include UUID\\\n \\ generation,\\ndatetime/timedelta sampling, person generation, category /\\\n \\ subcategory sampling,\\nand various statistical distributions (uniform, gaussian,\\\n \\ binomial, poisson, scipy).\\n\\nAttributes:\\n sampler_type: Type of sampler\\\n \\ to use. Available types include:\\n \\\"uuid\\\", \\\"category\\\", \\\"subcategory\\\"\\\n , \\\"uniform\\\", \\\"gaussian\\\", \\\"bernoulli\\\",\\n \\\"bernoulli_mixture\\\"\\\n , \\\"binomial\\\", \\\"poisson\\\", \\\"scipy\\\", \\\"person\\\", \\\"datetime\\\", \\\"timedelta\\\"\\\n .\\n params: Parameters specific to the chosen sampler type. Type varies\\\n \\ based on the `sampler_type`\\n (e.g., `CategorySamplerParams`, `UniformSamplerParams`,\\\n \\ `PersonSamplerParams`).\\n conditional_params: Optional dictionary for\\\n \\ conditional parameters. The dict keys\\n are the conditions that must\\\n \\ be met (e.g., \\\"age > 21\\\") for the conditional parameters\\n to be\\\n \\ used. The values of dict are the parameters to use when the condition is\\\n \\ met.\\n convert_to: Optional type conversion to apply after sampling.\\\n \\ Must be one of \\\"float\\\", \\\"int\\\", or \\\"str\\\".\\n Useful for converting\\\n \\ numerical samples to strings or other types.\\n column_type: Discriminator\\\n \\ field, always \\\"sampler\\\" for this configuration type.\\n\\n!!! tip \\\"Displaying\\\n \\ available samplers and their parameters\\\"\\n The config builder has an\\\n \\ `info` attribute that can be used to display the\\n available samplers\\\n \\ and their parameters:\\n ```python\\n config_builder.info.display(\\\"\\\n samplers\\\")\\n ```\"\n SamplerType:\n type: string\n enum:\n - bernoulli\n - bernoulli_mixture\n - binomial\n - category\n - datetime\n - gaussian\n - person\n - person_from_faker\n - poisson\n - scipy\n - subcategory\n - timedelta\n - uniform\n - uuid\n title: SamplerType\n SamplingStrategy:\n type: string\n enum:\n - ordered\n - shuffle\n title: SamplingStrategy\n ScalarInequalityConstraint:\n properties:\n target_column:\n type: string\n title: Target Column\n rhs:\n type: number\n title: Rhs\n operator:\n $ref: '#/components/schemas/InequalityOperator'\n additionalProperties: false\n type: object\n required:\n - target_column\n - rhs\n - operator\n title: ScalarInequalityConstraint\n ScipySamplerParams:\n properties:\n dist_name:\n type: string\n title: Dist Name\n description: Name of a scipy.stats distribution.\n dist_params:\n additionalProperties: true\n type: object\n title: Dist Params\n description: Parameters of the scipy.stats distribution given in `dist_name`.\n decimal_places:\n type: integer\n title: Decimal Places\n description: Number of decimal places to round the sampled values to.\n sampler_type:\n type: string\n const: scipy\n title: Sampler Type\n default: scipy\n additionalProperties: false\n type: object\n required:\n - dist_name\n - dist_params\n title: ScipySamplerParams\n description: \"Parameters for sampling from any scipy.stats continuous or discrete\\\n \\ distribution.\\n\\nProvides a flexible interface to sample from the wide range\\\n \\ of probability distributions\\navailable in scipy.stats. This enables advanced\\\n \\ statistical sampling beyond the built-in\\ndistribution types (Gaussian,\\\n \\ Uniform, etc.).\\n\\nSee: [scipy.stats documentation](https://docs.scipy.org/doc/scipy/reference/stats.html)\\n\\\n \\nAttributes:\\n dist_name: Name of the scipy.stats distribution to sample\\\n \\ from (e.g., \\\"beta\\\", \\\"gamma\\\",\\n \\\"lognorm\\\", \\\"expon\\\"). Must\\\n \\ be a valid distribution name from scipy.stats.\\n dist_params: Dictionary\\\n \\ of parameters for the specified distribution. Parameter names\\n and\\\n \\ values must match the scipy.stats distribution specification (e.g., {\\\"\\\n a\\\": 2, \\\"b\\\": 5}\\n for beta distribution, {\\\"scale\\\": 1.5} for exponential).\\n\\\n \\ decimal_places: Optional number of decimal places to round sampled values\\\n \\ to. If None,\\n values are not rounded.\"\n Score:\n properties:\n name:\n type: string\n title: Name\n description: A clear name for this score.\n description:\n type: string\n title: Description\n description: An informative and detailed assessment guide for using this\n score.\n options:\n additionalProperties:\n type: string\n type: object\n title: Options\n description: 'Score options in the format of {score: description}.'\n additionalProperties: false\n type: object\n required:\n - name\n - description\n - options\n title: Score\n description: \"Configuration for a \\\"score\\\" in an LLM judge evaluation.\\n\\n\\\n Defines a single scoring criterion with its possible values and descriptions.\\\n \\ Multiple\\nScore objects can be combined in an LLMJudgeColumnConfig to create\\\n \\ multi-dimensional\\nquality assessments.\\n\\nAttributes:\\n name: A clear,\\\n \\ concise name for this scoring dimension (e.g., \\\"Relevance\\\", \\\"Fluency\\\"\\\n ).\\n description: An informative and detailed assessment guide explaining\\\n \\ how to evaluate\\n this dimension. Should provide clear criteria for\\\n \\ scoring.\\n options: Dictionary mapping score values to their descriptions.\\\n \\ Keys can be integers\\n (e.g., 1-5 scale) or strings (e.g., \\\"Poor\\\"\\\n , \\\"Good\\\", \\\"Excellent\\\"). Values are\\n descriptions explaining what\\\n \\ each score level means.\"\n SeedConfig:\n properties:\n dataset:\n type: string\n title: Dataset\n sampling_strategy:\n allOf:\n - $ref: '#/components/schemas/SamplingStrategy'\n default: ordered\n selection_strategy:\n anyOf:\n - $ref: '#/components/schemas/IndexRange'\n - $ref: '#/components/schemas/PartitionBlock'\n title: Selection Strategy\n additionalProperties: false\n type: object\n required:\n - dataset\n title: SeedConfig\n description: \"Configuration for sampling data from a seed dataset.\\n\\nArgs:\\n\\\n \\ dataset: Path or identifier for the seed dataset.\\n sampling_strategy:\\\n \\ Strategy for how to sample rows from the dataset.\\n - ORDERED: Read\\\n \\ rows sequentially in their original order.\\n - SHUFFLE: Randomly\\\n \\ shuffle rows before sampling. When used with\\n selection_strategy,\\\n \\ shuffling occurs within the selected range/partition.\\n selection_strategy:\\\n \\ Optional strategy to select a subset of the dataset.\\n - IndexRange:\\\n \\ Select a specific range of indices (e.g., rows 100-200).\\n - PartitionBlock:\\\n \\ Select a partition by splitting the dataset into N equal parts.\\n \\\n \\ Partition indices are zero-based (index=0 is the first partition, index=1\\\n \\ is\\n the second, etc.).\\n\\nExamples:\\n Read rows sequentially\\\n \\ from start to end:\\n SeedConfig(dataset=\\\"my_data.parquet\\\", sampling_strategy=SamplingStrategy.ORDERED)\\n\\\n \\n Read rows in random order:\\n SeedConfig(dataset=\\\"my_data.parquet\\\"\\\n , sampling_strategy=SamplingStrategy.SHUFFLE)\\n\\n Read specific index range\\\n \\ (rows 100-199):\\n SeedConfig(\\n dataset=\\\"my_data.parquet\\\"\\\n ,\\n sampling_strategy=SamplingStrategy.ORDERED,\\n selection_strategy=IndexRange(start=100,\\\n \\ end=199)\\n )\\n\\n Read random rows from a specific index range\\\n \\ (shuffles within rows 100-199):\\n SeedConfig(\\n dataset=\\\"\\\n my_data.parquet\\\",\\n sampling_strategy=SamplingStrategy.SHUFFLE,\\n\\\n \\ selection_strategy=IndexRange(start=100, end=199)\\n )\\n\\\n \\n Read from partition 2 (3rd partition, zero-based) of 5 partitions (20%\\\n \\ of dataset):\\n SeedConfig(\\n dataset=\\\"my_data.parquet\\\"\\\n ,\\n sampling_strategy=SamplingStrategy.ORDERED,\\n selection_strategy=PartitionBlock(index=2,\\\n \\ num_partitions=5)\\n )\\n\\n Read shuffled rows from partition 0\\\n \\ of 10 partitions (shuffles within the partition):\\n SeedConfig(\\n\\\n \\ dataset=\\\"my_data.parquet\\\",\\n sampling_strategy=SamplingStrategy.SHUFFLE,\\n\\\n \\ selection_strategy=PartitionBlock(index=0, num_partitions=10)\\n\\\n \\ )\"\n SeedDatasetColumnConfig:\n properties:\n name:\n type: string\n title: Name\n drop:\n type: boolean\n title: Drop\n default: false\n column_type:\n type: string\n const: seed-dataset\n title: Column Type\n default: seed-dataset\n additionalProperties: false\n type: object\n required:\n - name\n title: SeedDatasetColumnConfig\n description: \"Configuration for columns sourced from seed datasets.\\n\\nThis\\\n \\ config marks columns that come from seed data. It is typically created\\n\\\n automatically when calling `with_seed_dataset()` on the builder, rather than\\n\\\n being instantiated directly by users.\\n\\nAttributes:\\n column_type: Discriminator\\\n \\ field, always \\\"seed-dataset\\\" for this configuration type.\"\n SettingsDefaults:\n properties:\n model_configs:\n items:\n $ref: '#/components/schemas/ModelConfigOutput'\n type: array\n title: Model Configs\n model_provider:\n type: string\n title: Model Provider\n type: object\n required:\n - model_configs\n - model_provider\n title: SettingsDefaults\n SettingsResponse:\n properties:\n defaults:\n $ref: '#/components/schemas/SettingsDefaults'\n model_providers:\n items:\n $ref: '#/components/schemas/DisplayModelProvider'\n type: array\n title: Model Providers\n type: object\n required:\n - defaults\n - model_providers\n title: SettingsResponse\n SubcategorySamplerParams:\n properties:\n category:\n type: string\n title: Category\n description: Name of parent category to this subcategory.\n values:\n additionalProperties:\n items:\n anyOf:\n - type: string\n - type: integer\n - type: number\n type: array\n type: object\n title: Values\n description: Mapping from each value of parent category to a list of subcategory\n values.\n sampler_type:\n type: string\n const: subcategory\n title: Sampler Type\n default: subcategory\n additionalProperties: false\n type: object\n required:\n - category\n - values\n title: SubcategorySamplerParams\n description: \"Parameters for subcategory sampling conditioned on a parent category\\\n \\ column.\\n\\nSamples subcategory values based on the value of a parent category\\\n \\ column. Each parent\\ncategory value maps to its own list of possible subcategory\\\n \\ values, enabling hierarchical\\nor conditional sampling patterns.\\n\\nAttributes:\\n\\\n \\ category: Name of the parent category column that this subcategory depends\\\n \\ on.\\n The parent column must be generated before this subcategory\\\n \\ column.\\n values: Mapping from each parent category value to a list of\\\n \\ possible subcategory values.\\n Each key must correspond to a value\\\n \\ that appears in the parent category column.\"\n TimeDeltaSamplerParams:\n properties:\n dt_min:\n type: integer\n minimum: 0.0\n title: Dt Min\n description: Minimum possible time-delta for sampling range, inclusive.\n Must be less than `dt_max`.\n dt_max:\n type: integer\n exclusiveMinimum: 0.0\n title: Dt Max\n description: Maximum possible time-delta for sampling range, exclusive.\n Must be greater than `dt_min`.\n reference_column_name:\n type: string\n title: Reference Column Name\n description: Name of an existing datetime column to condition time-delta\n sampling on.\n unit:\n type: string\n enum:\n - D\n - h\n - m\n - s\n title: Unit\n description: Sampling units, e.g. the smallest possible time interval between\n samples.\n default: D\n sampler_type:\n type: string\n const: timedelta\n title: Sampler Type\n default: timedelta\n additionalProperties: false\n type: object\n required:\n - dt_min\n - dt_max\n - reference_column_name\n title: TimeDeltaSamplerParams\n description: \"Parameters for sampling time deltas relative to a reference datetime\\\n \\ column.\\n\\nSamples time offsets within a specified range and adds them to\\\n \\ values from a reference\\ndatetime column. This is useful for generating\\\n \\ related datetime columns like order dates\\nand delivery dates, or event\\\n \\ start times and end times.\\n\\nNote:\\n Years and months are not supported\\\n \\ as timedelta units because they have variable lengths.\\n See: [pandas\\\n \\ timedelta documentation](https://pandas.pydata.org/docs/user_guide/timedeltas.html)\\n\\\n \\nAttributes:\\n dt_min: Minimum time-delta value (inclusive). Must be non-negative\\\n \\ and less than `dt_max`.\\n Specified in units defined by the `unit`\\\n \\ parameter.\\n dt_max: Maximum time-delta value (exclusive). Must be positive\\\n \\ and greater than `dt_min`.\\n Specified in units defined by the `unit`\\\n \\ parameter.\\n reference_column_name: Name of an existing datetime column\\\n \\ to add the time-delta to.\\n This column must be generated before\\\n \\ the timedelta column.\\n unit: Time unit for the delta values. Options:\\n\\\n \\ - \\\"D\\\": Days (default)\\n - \\\"h\\\": Hours\\n - \\\"m\\\"\\\n : Minutes\\n - \\\"s\\\": Seconds\"\n UUIDSamplerParams:\n properties:\n prefix:\n type: string\n title: Prefix\n description: String prepended to the front of the UUID.\n short_form:\n type: boolean\n title: Short Form\n description: If true, all UUIDs sampled will be truncated at 8 characters.\n default: false\n uppercase:\n type: boolean\n title: Uppercase\n description: If true, all letters in the UUID will be capitalized.\n default: false\n sampler_type:\n type: string\n const: uuid\n title: Sampler Type\n default: uuid\n additionalProperties: false\n type: object\n title: UUIDSamplerParams\n description: \"Parameters for generating UUID (Universally Unique Identifier)\\\n \\ values.\\n\\nGenerates UUID4 (random) identifiers with optional formatting\\\n \\ options. UUIDs are useful\\nfor creating unique identifiers for records,\\\n \\ entities, or transactions.\\n\\nAttributes:\\n prefix: Optional string to\\\n \\ prepend to each UUID. Useful for creating namespaced or\\n typed identifiers\\\n \\ (e.g., \\\"user-\\\", \\\"order-\\\", \\\"txn-\\\").\\n short_form: If True, truncates\\\n \\ UUIDs to 8 characters (first segment only). Default is False\\n for\\\n \\ full 32-character UUIDs (excluding hyphens).\\n uppercase: If True, converts\\\n \\ all hexadecimal letters to uppercase. Default is False for\\n lowercase\\\n \\ UUIDs.\"\n UniformDistribution:\n properties:\n distribution_type:\n allOf:\n - $ref: '#/components/schemas/DistributionType'\n default: uniform\n params:\n $ref: '#/components/schemas/UniformDistributionParams'\n additionalProperties: false\n type: object\n required:\n - params\n title: UniformDistribution\n UniformDistributionParams:\n properties:\n low:\n type: number\n title: Low\n high:\n type: number\n title: High\n additionalProperties: false\n type: object\n required:\n - low\n - high\n title: UniformDistributionParams\n UniformSamplerParams:\n properties:\n low:\n type: number\n title: Low\n description: Lower bound of the uniform distribution, inclusive.\n high:\n type: number\n title: High\n description: Upper bound of the uniform distribution, inclusive.\n decimal_places:\n type: integer\n title: Decimal Places\n description: Number of decimal places to round the sampled values to.\n sampler_type:\n type: string\n const: uniform\n title: Sampler Type\n default: uniform\n additionalProperties: false\n type: object\n required:\n - low\n - high\n title: UniformSamplerParams\n description: \"Parameters for sampling from a continuous Uniform distribution.\\n\\\n \\nSamples continuous values uniformly from a specified range, where every\\\n \\ value in the range\\nhas equal probability of being sampled. This is useful\\\n \\ when all values within a range are\\nequally likely, such as random percentages,\\\n \\ proportions, or unbiased measurements.\\n\\nAttributes:\\n low: Lower bound\\\n \\ of the uniform distribution (inclusive). Can be any real number.\\n high:\\\n \\ Upper bound of the uniform distribution (inclusive). Must be greater than\\\n \\ `low`.\\n decimal_places: Optional number of decimal places to round sampled\\\n \\ values to. If None,\\n values are not rounded and may have many decimal\\\n \\ places.\"\n ValidationColumnConfig:\n properties:\n name:\n type: string\n title: Name\n drop:\n type: boolean\n title: Drop\n default: false\n column_type:\n type: string\n const: validation\n title: Column Type\n default: validation\n target_columns:\n items:\n type: string\n type: array\n title: Target Columns\n validator_type:\n $ref: '#/components/schemas/ValidatorType'\n validator_params:\n anyOf:\n - $ref: '#/components/schemas/CodeValidatorParams'\n - $ref: '#/components/schemas/LocalCallableValidatorParams'\n - $ref: '#/components/schemas/RemoteValidatorParams'\n title: Validator Params\n batch_size:\n type: integer\n minimum: 1.0\n title: Batch Size\n description: Number of records to process in each batch\n default: 10\n additionalProperties: false\n type: object\n required:\n - name\n - target_columns\n - validator_type\n - validator_params\n title: ValidationColumnConfig\n description: \"Configuration for validation columns that validate existing columns.\\n\\\n \\nValidation columns execute validation logic against specified target columns\\\n \\ and return\\nstructured results indicating pass/fail status with validation\\\n \\ details. Supports multiple\\nvalidation strategies: code execution (Python/SQL),\\\n \\ local callable functions (library only),\\nand remote HTTP endpoints.\\n\\n\\\n Attributes:\\n target_columns: List of column names to validate. These columns\\\n \\ are passed to the\\n validator for validation. All target columns\\\n \\ must exist in the dataset\\n before validation runs.\\n validator_type:\\\n \\ The type of validator to use. Options:\\n - \\\"code\\\": Execute code\\\n \\ (Python or SQL) for validation. The code receives a\\n DataFrame\\\n \\ with target columns and must return a DataFrame with validation results.\\n\\\n \\ - \\\"local_callable\\\": Call a local Python function with the data.\\\n \\ Only supported\\n when running DataDesigner locally.\\n -\\\n \\ \\\"remote\\\": Send data to a remote HTTP endpoint for validation. Useful for\\n\\\n \\ validator_params: Parameters specific to the validator type. Type varies\\\n \\ by validator:\\n - CodeValidatorParams: Specifies code language (python\\\n \\ or SQL dialect like\\n \\\"sql:postgres\\\", \\\"sql:mysql\\\").\\n \\\n \\ - LocalCallableValidatorParams: Provides validation function (Callable[[pd.DataFrame],\\n\\\n \\ pd.DataFrame]) and optional output schema for validation results.\\n\\\n \\ - RemoteValidatorParams: Configures endpoint URL, HTTP timeout, retry\\\n \\ behavior\\n (max_retries, retry_backoff), and parallel request limits\\\n \\ (max_parallel_requests).\\n batch_size: Number of records to process in\\\n \\ each validation batch. Defaults to 10.\\n Larger batches are more\\\n \\ efficient but use more memory. Adjust based on validator\\n complexity\\\n \\ and available resources.\\n column_type: Discriminator field, always \\\"\\\n validation\\\" for this configuration type.\"\n ValidationError:\n properties:\n loc:\n items:\n anyOf:\n - type: string\n - type: integer\n type: array\n title: Location\n msg:\n type: string\n title: Message\n type:\n type: string\n title: Error Type\n type: object\n required:\n - loc\n - msg\n - type\n title: ValidationError\n ValidatorType:\n type: string\n enum:\n - code\n - local_callable\n - remote\n title: ValidatorType\ntags:\n- name: Data Designer\n description: Operations related to synthetic data generation.\n- name: Health Checks\n description: Operations related to NeMo Microservices platform health.\n"}