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Code ReferenceConfigConfig API

data_designer.config.data_designer_config

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Module Contents

Classes

NameDescription
DataDesignerConfigConfiguration for NeMo Data Designer.

API

1class data_designer.config.data_designer_config.DataDesignerConfig(
2    /,
3    **data: typing.Any
4)

Bases: data_designer.config.exportable_config.ExportableConfigBase

Configuration for NeMo Data Designer.

This class defines the main configuration structure for NeMo Data Designer, which the engine consumes when generating synthetic data.

Parameters:

columns

Required list of column configurations defining how each column should be generated. Must contain at least one column.

model_configs

Optional list of model configurations for LLM-based generation. Each model config defines the model, provider, and inference parameters.

tool_configs

Optional list of tool configurations for MCP tool calling. Each tool config defines the provider, allowed tools, and execution limits.

seed_config

Optional seed dataset settings to use for generation.

constraints

Optional list of column constraints.

profilers

Optional list of column profilers for analyzing generated data characteristics.

processors

Optional list of processor configurations for post-generation transformations.

Attributes:

columns

Required list of column configurations defining how each column should be generated. Must contain at least one column.

model_configs

Optional list of model configurations for LLM-based generation. Each model config defines the model, provider, and inference parameters.

tool_configs

Optional list of tool configurations for MCP tool calling. Each tool config defines the provider, allowed tools, and execution limits.

seed_config

Optional seed dataset settings to use for generation.

constraints

Optional list of column constraints.

profilers

Optional list of column profilers for analyzing generated data characteristics.

processors

Optional list of processor configurations for post-generation transformations.

Initialization:

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

1columns: list[typing.Annotated[data_designer.config.column_types.ColumnConfigT, Field(discriminator='column_type')]] = Field(...)
1model_configs: list[data_designer.config.models.ModelConfig] | None
1tool_configs: list[data_designer.config.mcp.ToolConfig] | None
1seed_config: data_designer.config.seed.SeedConfig | None
1constraints: list[data_designer.config.sampler_constraints.ColumnConstraintInputT] | None
1profilers: list[data_designer.config.analysis.column_profilers.ColumnProfilerConfigT] | None
1processors: list[typing.Annotated[data_designer.config.processor_types.ProcessorConfigT, Field(discriminator='processor_type')]] | None
1_validate_subcategory_parents() -> typing_extensions.Self
1fingerprint() -> 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:

dict[str, str | int]

A dict with config_hash, config_hash_algo, and config_hash_version.