Data Designer Configuration

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.

Generally, you should use the DataDesignerConfigBuilder to build your configuration, but you can also build it manually by instantiating the DataDesignerConfig class directly.

Classes:

Name	Description
`DataDesignerConfig`	Configuration for NeMo Data Designer.

`DataDesignerConfig`

Bases: ExportableConfigBase

Configuration for NeMo Data Designer.

This class defines the main configuration structure for NeMo Data Designer, which orchestrates the generation of synthetic data.

Attributes:

Name	Type	Description
`columns`	`list[Annotated[ColumnConfigT, Field(discriminator='column_type')]]`	Required list of column configurations defining how each column should be generated. Must contain at least one column.
`model_configs`	`list[ModelConfig] \| None`	Optional list of model configurations for LLM-based generation. Each model config defines the model, provider, and inference parameters.
`tool_configs`	`list[ToolConfig] \| None`	Optional list of tool configurations for MCP tool calling. Each tool config defines the provider, allowed tools, and execution limits.
`seed_config`	`SeedConfig \| None`	Optional seed dataset settings to use for generation.
`constraints`	`list[ColumnConstraintInputT] \| None`	Optional list of column constraints.
`profilers`	`list[ColumnProfilerConfigT] \| None`	Optional list of column profilers for analyzing generated data characteristics.

Methods:

Name	Description
`fingerprint`	Compute a deterministic content-addressable fingerprint of this config.

`fingerprint()`

Compute a deterministic content-addressable fingerprint of this config.

See data_designer.config.fingerprint.fingerprint_config for the full list of identity-relevant and excluded fields, and how custom column generators are identified.

Returns:

Type	Description
`dict[str, str \| int]`	A dict with `config_hash`, `config_hash_algo`, and
`dict[str, str \| int]`	`config_hash_version`.

Source code in packages/data-designer-config/src/data_designer/config/data_designer_config.py

def fingerprint(self) -> dict[str, str | int]:
    """Compute a deterministic content-addressable fingerprint of this config.

    See `data_designer.config.fingerprint.fingerprint_config` for the full
    list of identity-relevant and excluded fields, and how custom column
    generators are identified.

    Returns:
        A dict with `config_hash`, `config_hash_algo`, and
        `config_hash_version`.
    """
    return fingerprint_config(self)