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Copy file name to clipboardExpand all lines: fern/AGENTS.md
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@@ -12,7 +12,7 @@ This folder contains the Fern docs site for NeMo Data Designer. Use `fern/README
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## Generated Artifacts
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-`make generate-fern-api-reference` creates gitignored API reference files in `fern/code-reference/`.
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-`make generate-fern-api-reference` creates gitignored API reference files in `fern/code-reference/` for `data_designer.config`, `data_designer.interface`, and curated engine extension modules.
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-`py2fern` only descends into Python packages. Add `__init__.py` to any new subdirectory whose modules should appear in the API reference.
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-`make generate-fern-notebooks` creates gitignored notebook files in `fern/components/notebooks/`.
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-`docs/notebook_source/*.py` is the notebook source of truth.
Copy file name to clipboardExpand all lines: fern/README.md
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### 1. Python API reference (gitignored - must regenerate)
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`make generate-fern-api-reference` uses `py2fern` to extract API docs from the local Python source (`packages/data-designer-config/src/data_designer/config`). The output lands in `fern/code-reference/data-designer/` (gitignored).
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`make generate-fern-api-reference` uses `py2fern` to extract API docs from local Python source. The output lands in `fern/code-reference/` (gitignored), preserving the existing Config API folder and adding Interface and curated Engine extension API folders.
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```bash
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make generate-fern-api-reference
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```
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`py2fern` only descends into Python packages. Add `__init__.py` to any new subdirectory whose modules should appear in the API reference.
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The `libraries:` block in [`docs.yml`](docs.yml) still documents the equivalent Fern-native generator. Run `make generate-fern-api-reference-native` only when you want the Fern CLI output and have Fern auth.
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The `libraries:` block in [`docs.yml`](docs.yml) still documents the Fern-native config generator. Run `make generate-fern-api-reference-native` only when you want the Fern CLI output and have Fern auth.
Copy file name to clipboardExpand all lines: fern/versions/latest/pages/code_reference/config/column_configs.mdx
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<Note>
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`column_type` is a discriminator field
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The `column_type` argument is used to identify column types when deserializing the [Data Designer Configuration](/code-reference/topic-overviews/data-designer-config) from JSON/YAML. It acts as the discriminator in a [discriminated union](https://docs.pydantic.dev/latest/concepts/unions/#discriminated-unions), allowing Pydantic to automatically determine which column configuration class to instantiate.
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The `column_type` argument is used to identify column types when deserializing the [Data Designer Configuration](/code-reference/config/data-designer-config) from JSON/YAML. It acts as the discriminator in a [discriminated union](https://docs.pydantic.dev/latest/concepts/unions/#discriminated-unions), allowing Pydantic to automatically determine which column configuration class to instantiate.
The `config_builder` module provides a high-level interface for constructing Data Designer configurations through the [DataDesignerConfigBuilder](#data_designer.config.config_builder.DataDesignerConfigBuilder) class, enabling programmatic creation of [DataDesignerConfig](/code-reference/topic-overviews/data-designer-config#data_designer.config.data_designer_config.DataDesignerConfig) objects by incrementally adding column configurations, constraints, processors, and profilers.
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The `config_builder` module provides a high-level interface for constructing Data Designer configurations through the [DataDesignerConfigBuilder](#data_designer.config.config_builder.DataDesignerConfigBuilder) class, enabling programmatic creation of [DataDesignerConfig](/code-reference/config/data-designer-config#data_designer.config.data_designer_config.DataDesignerConfig) objects by incrementally adding column configurations, constraints, processors, and profilers.
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You can use the builder to create Data Designer configurations from scratch or from existing configurations stored in YAML/JSON files via [`from_config()`](#data_designer.config.config_builder.DataDesignerConfigBuilder.from_config). The builder includes validation capabilities to catch configuration errors early and can work with seed datasets from local sources or external datastores. Once configured, use [`build()`](#data_designer.config.config_builder.DataDesignerConfigBuilder.build) to generate the final configuration object or [`write_config()`](#data_designer.config.config_builder.DataDesignerConfigBuilder.write_config) to serialize it to disk.
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<Note>
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Model configs are required
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[DataDesignerConfigBuilder](#data_designer.config.config_builder.DataDesignerConfigBuilder) requires a list of model configurations at initialization. This tells the builder which model aliases can be referenced by LLM-generated columns (such as [`LLMTextColumnConfig`](/code-reference/topic-overviews/column-configs#data_designer.config.column_configs.LLMTextColumnConfig), [`LLMCodeColumnConfig`](/code-reference/topic-overviews/column-configs#data_designer.config.column_configs.LLMCodeColumnConfig), [`LLMStructuredColumnConfig`](/code-reference/topic-overviews/column-configs#data_designer.config.column_configs.LLMStructuredColumnConfig), and [`LLMJudgeColumnConfig`](/code-reference/topic-overviews/column-configs#data_designer.config.column_configs.LLMJudgeColumnConfig)). Each model configuration specifies the model alias, model provider, model ID, and inference parameters that will be used during data generation.
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[DataDesignerConfigBuilder](#data_designer.config.config_builder.DataDesignerConfigBuilder) requires a list of model configurations at initialization. This tells the builder which model aliases can be referenced by LLM-generated columns (such as [`LLMTextColumnConfig`](/code-reference/config/column-configs#data_designer.config.column_configs.LLMTextColumnConfig), [`LLMCodeColumnConfig`](/code-reference/config/column-configs#data_designer.config.column_configs.LLMCodeColumnConfig), [`LLMStructuredColumnConfig`](/code-reference/config/column-configs#data_designer.config.column_configs.LLMStructuredColumnConfig), and [`LLMJudgeColumnConfig`](/code-reference/config/column-configs#data_designer.config.column_configs.LLMJudgeColumnConfig)). Each model configuration specifies the model alias, model provider, model ID, and inference parameters that will be used during data generation.
Copy file name to clipboardExpand all lines: fern/versions/latest/pages/code_reference/config/data_designer_config.mdx
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[DataDesignerConfig](#data_designer.config.data_designer_config.DataDesignerConfig) is the main configuration object for builder datasets with Data Designer. It is a declarative configuration for defining the dataset you want to generate column-by-column, including options for dataset post-processing, validation, and profiling.
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Generally, you should use the [DataDesignerConfigBuilder](/code-reference/topic-overviews/config-builder#data_designer.config.config_builder.DataDesignerConfigBuilder) to build your configuration, but you can also build it manually by instantiating the [DataDesignerConfig](#data_designer.config.data_designer_config.DataDesignerConfig) class directly.
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Generally, you should use the [DataDesignerConfigBuilder](/code-reference/config/config-builder#data_designer.config.config_builder.DataDesignerConfigBuilder) to build your configuration, but you can also build it manually by instantiating the [DataDesignerConfig](#data_designer.config.data_designer_config.DataDesignerConfig) class directly.
The `data-designer-config` package provides `data_designer.config`, the configuration layer of Data Designer. It contains the objects used to describe dataset structure, model access, tool access, seed data, sampler parameters, validators, processors, run settings, plugin registrations, and analysis results.
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This package is the base of the dependency chain. Engine and interface code consume these config objects, but config objects do not execute generation directly.
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For programmatic configuration work, start with [config_builder](config-builder) and [data_designer_config](data-designer-config). Use the narrower pages for exact constructor fields for columns, models, MCP tools, seeds, processors, samplers, validators, or profiling results.
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