Data Designer's Config Builder

The config_builder module provides a high-level interface for constructing Data Designer configurations through the DataDesignerConfigBuilder class, enabling programmatic creation of DataDesignerConfig objects by incrementally adding column configurations, constraints, processors, and profilers.

You can use the builder to create Data Designer configurations from scratch or from existing configurations stored in YAML/JSON files via 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() to generate the final configuration object or write_config() to serialize it to disk.

Model configs are required

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, LLMCodeColumnConfig, LLMStructuredColumnConfig, and LLMJudgeColumnConfig). Each model configuration specifies the model alias, model provider, model ID, and inference parameters that will be used during data generation.

Classes:

Name	Description
BuilderConfig	Configuration container for Data Designer builder.
DataDesignerConfigBuilder	Config builder for Data Designer configurations.

BuilderConfig

Bases: ExportableConfigBase

Configuration container for Data Designer builder.

This class holds the main Data Designer configuration along with optional datastore settings needed for seed dataset operations.

Attributes:

Name	Type	Description
data_designer	DataDesignerConfig	The main Data Designer configuration containing columns, constraints, profilers, and other settings.
datastore_settings	Optional[DatastoreSettings]	Optional datastore settings for accessing external datasets.

DataDesignerConfigBuilder(model_configs=None)

Config builder for Data Designer configurations.

This class provides a high-level interface for building Data Designer configurations.

Initialize a new DataDesignerConfigBuilder instance.

Parameters:

Name	Type	Description	Default
model_configs	Optional[Union[list[ModelConfig], str, Path]]	Model configurations. Can be: - None to use default model configurations in local mode - A list of ModelConfig objects - A string or Path to a model configuration file	None

Methods:

Name	Description
add_column	Add a Data Designer column configuration to the current Data Designer configuration.
add_constraint	Add a constraint to the current Data Designer configuration.
add_model_config	Add a model configuration to the current Data Designer configuration.
add_processor	Add a processor to the current Data Designer configuration.
add_profiler	Add a profiler to the current Data Designer configuration.
build	Build a DataDesignerConfig instance based on the current builder configuration.
delete_column	Delete the column with the given name.
delete_constraints	Delete all constraints for the given target column.
delete_model_config	Delete a model configuration from the current Data Designer configuration by alias.
from_config	Create a DataDesignerConfigBuilder from an existing configuration.
get_builder_config	Get the builder config for the current Data Designer configuration.
get_column_config	Get a column configuration by name.
get_column_configs	Get all column configurations.
get_columns_excluding_type	Get all column configurations excluding the specified type.
get_columns_of_type	Get all column configurations of the specified type.
get_constraints	Get all constraints for the given target column.
get_llm_gen_columns	Get all LLM-generated column configurations.
get_processor_configs	Get processor configuration objects.
get_profilers	Get all profilers.
get_seed_config	Get the seed config for the current Data Designer configuration.
get_seed_datastore_settings	Get most recent datastore settings for the current Data Designer configuration.
num_columns_of_type	Get the count of columns of the specified type.
set_seed_datastore_settings	Set the datastore settings for the seed dataset.
validate	Validate the current Data Designer configuration.
with_seed_dataset	Add a seed dataset to the current Data Designer configuration.
write_config	Write the current configuration to a file.

Attributes:

Name	Type	Description
allowed_references	list[str]	Get all referenceable variables allowed in prompt templates and expressions.
info	ConfigBuilderInfo	Get the ConfigBuilderInfo object for this builder.
model_configs	list[ModelConfig]	Get the model configurations for this builder.

Source code in src/data_designer/config/config_builder.py

def __init__(self, model_configs: Optional[Union[list[ModelConfig], str, Path]] = None):
    """Initialize a new DataDesignerConfigBuilder instance.

    Args:
        model_configs: Model configurations. Can be:
            - None to use default model configurations in local mode
            - A list of ModelConfig objects
            - A string or Path to a model configuration file
    """
    if not can_run_data_designer_locally() and (model_configs is None or len(model_configs) == 0):
        raise BuilderConfigurationError("🛑 Model configurations are required!")

    self._column_configs = {}
    self._model_configs = load_model_configs(model_configs or get_default_model_configs())
    self._processor_configs: list[ProcessorConfig] = []
    self._seed_config: Optional[SeedConfig] = None
    self._constraints: list[ColumnConstraintT] = []
    self._profilers: list[ColumnProfilerConfigT] = []
    self._datastore_settings: Optional[DatastoreSettings] = None

allowed_references property

Get all referenceable variables allowed in prompt templates and expressions.

This includes all column names and their side effect columns that can be referenced in prompt templates and expressions within the configuration.

Returns:

Type	Description
list[str]	A list of variable names that can be referenced in templates and expressions.

info property

Get the ConfigBuilderInfo object for this builder.

Returns:

Type	Description
ConfigBuilderInfo	An object containing information about the configuration.

model_configs property

Get the model configurations for this builder.

Returns:

Type	Description
list[ModelConfig]	A list of ModelConfig objects used for data generation.

add_column(column_config=None, *, name=None, column_type=None, **kwargs)

Add a Data Designer column configuration to the current Data Designer configuration.

If no column config object is provided, you must provide the name, column_type, and any additional keyword arguments that are required by the column config constructor.

Parameters:

Name	Type	Description	Default
column_config	Optional[ColumnConfigT]	Data Designer column config object to add.	None
name	Optional[str]	Name of the column to add. This is only used if column_config is not provided.	None
column_type	Optional[DataDesignerColumnType]	Column type to add. This is only used if column_config is not provided.	None
**kwargs		Additional keyword arguments to pass to the column constructor.	{}

Returns:

Type	Description
Self	The current Data Designer config builder instance.

Source code in src/data_designer/config/config_builder.py

def add_column(
    self,
    column_config: Optional[ColumnConfigT] = None,
    *,
    name: Optional[str] = None,
    column_type: Optional[DataDesignerColumnType] = None,
    **kwargs,
) -> Self:
    """Add a Data Designer column configuration to the current Data Designer configuration.

    If no column config object is provided, you must provide the `name`, `column_type`, and any
    additional keyword arguments that are required by the column config constructor.

    Args:
        column_config: Data Designer column config object to add.
        name: Name of the column to add. This is only used if `column_config` is not provided.
        column_type: Column type to add. This is only used if `column_config` is not provided.
        **kwargs: Additional keyword arguments to pass to the column constructor.

    Returns:
        The current Data Designer config builder instance.
    """
    if column_config is None:
        if name is None or column_type is None:
            raise BuilderConfigurationError(
                "🛑 You must provide either a 'column_config' object or 'name' *and* 'column_type' "
                f"with additional keyword arguments. You provided {column_config=}, {name=}, and {column_type=}."
            )
        column_config = get_column_config_from_kwargs(name=name, column_type=column_type, **kwargs)

    allowed_column_configs = ColumnConfigT.__args__
    if not any(isinstance(column_config, t) for t in allowed_column_configs):
        raise InvalidColumnTypeError(
            f"🛑 Invalid column config object: '{column_config}'. Valid column config options are: "
            f"{', '.join([t.__name__ for t in allowed_column_configs])}"
        )

    self._column_configs[column_config.name] = column_config
    return self

add_constraint(constraint=None, *, constraint_type=None, **kwargs)

Add a constraint to the current Data Designer configuration.

Currently, constraints are only supported for numerical samplers.

You can either provide a constraint object directly, or provide a constraint type and additional keyword arguments to construct the constraint object. Valid constraint types are: - "scalar_inequality": Constraint between a column and a scalar value. - "column_inequality": Constraint between two columns.

Parameters:

Name	Type	Description	Default
constraint	Optional[ColumnConstraintT]	Constraint object to add.	None
constraint_type	Optional[ConstraintType]	Constraint type to add. Ignored when constraint is provided.	None
**kwargs		Additional keyword arguments to pass to the constraint constructor.	{}

Returns:

Type	Description
Self	The current Data Designer config builder instance.

Source code in src/data_designer/config/config_builder.py

def add_constraint(
    self,
    constraint: Optional[ColumnConstraintT] = None,
    *,
    constraint_type: Optional[ConstraintType] = None,
    **kwargs,
) -> Self:
    """Add a constraint to the current Data Designer configuration.

    Currently, constraints are only supported for numerical samplers.

    You can either provide a constraint object directly, or provide a constraint type and
    additional keyword arguments to construct the constraint object. Valid constraint types are:
        - "scalar_inequality": Constraint between a column and a scalar value.
        - "column_inequality": Constraint between two columns.

    Args:
        constraint: Constraint object to add.
        constraint_type: Constraint type to add. Ignored when `constraint` is provided.
        **kwargs: Additional keyword arguments to pass to the constraint constructor.

    Returns:
        The current Data Designer config builder instance.
    """
    if constraint is None:
        if constraint_type is None:
            raise BuilderConfigurationError(
                "🛑 You must provide either a 'constraint' object or 'constraint_type' "
                "with additional keyword arguments."
            )
        try:
            constraint_type = ConstraintType(constraint_type)
        except Exception:
            raise BuilderConfigurationError(
                f"🛑 Invalid constraint type: {constraint_type}. Valid options are: "
                f"{', '.join([t.value for t in ConstraintType])}"
            )
        if constraint_type == ConstraintType.SCALAR_INEQUALITY:
            constraint = ScalarInequalityConstraint(**kwargs)
        elif constraint_type == ConstraintType.COLUMN_INEQUALITY:
            constraint = ColumnInequalityConstraint(**kwargs)

    allowed_constraint_types = ColumnConstraintT.__args__
    if not any(isinstance(constraint, t) for t in allowed_constraint_types):
        raise BuilderConfigurationError(
            "🛑 Invalid constraint object. Valid constraint options are: "
            f"{', '.join([t.__name__ for t in allowed_constraint_types])}"
        )

    self._constraints.append(constraint)
    return self

add_model_config(model_config)

Add a model configuration to the current Data Designer configuration.

Parameters:

Name	Type	Description	Default
model_config	ModelConfig	The model configuration to add.	required

Source code in src/data_designer/config/config_builder.py

def add_model_config(self, model_config: ModelConfig) -> Self:
    """Add a model configuration to the current Data Designer configuration.

    Args:
        model_config: The model configuration to add.
    """
    if model_config.alias in [mc.alias for mc in self._model_configs]:
        raise BuilderConfigurationError(
            f"🛑 Model configuration with alias {model_config.alias} already exists. Please delete the existing model configuration or choose a different alias."
        )
    self._model_configs.append(model_config)
    return self

add_processor(processor_config=None, *, processor_type=None, **kwargs)

Add a processor to the current Data Designer configuration.

You can either provide a processor config object directly, or provide a processor type and additional keyword arguments to construct the processor config object.

Parameters:

Name	Type	Description	Default
processor_config	Optional[ProcessorConfig]	The processor configuration object to add.	None
processor_type	Optional[ProcessorType]	The type of processor to add.	None
**kwargs		Additional keyword arguments to pass to the processor constructor.	{}

Returns:

Type	Description
Self	The current Data Designer config builder instance.

Source code in src/data_designer/config/config_builder.py

def add_processor(
    self,
    processor_config: Optional[ProcessorConfig] = None,
    *,
    processor_type: Optional[ProcessorType] = None,
    **kwargs,
) -> Self:
    """Add a processor to the current Data Designer configuration.

    You can either provide a processor config object directly, or provide a processor type and
    additional keyword arguments to construct the processor config object.

    Args:
        processor_config: The processor configuration object to add.
        processor_type: The type of processor to add.
        **kwargs: Additional keyword arguments to pass to the processor constructor.

    Returns:
        The current Data Designer config builder instance.
    """
    if processor_config is None:
        if processor_type is None:
            raise BuilderConfigurationError(
                "🛑 You must provide either a 'processor_config' object or 'processor_type' "
                "with additional keyword arguments."
            )
        processor_config = get_processor_config_from_kwargs(processor_type=processor_type, **kwargs)

    # Checks elsewhere fail if DropColumnsProcessor drops a column but it is not marked for drop
    if processor_config.processor_type == ProcessorType.DROP_COLUMNS:
        for column in processor_config.column_names:
            if column in self._column_configs:
                self._column_configs[column].drop = True

    self._processor_configs.append(processor_config)
    return self

add_profiler(profiler_config)

Add a profiler to the current Data Designer configuration.

Parameters:

Name	Type	Description	Default
profiler_config	ColumnProfilerConfigT	The profiler configuration object to add.	required

Returns:

Type	Description
Self	The current Data Designer config builder instance.

Raises:

Type	Description
BuilderConfigurationError	If the profiler configuration is of an invalid type.

Source code in src/data_designer/config/config_builder.py

def add_profiler(self, profiler_config: ColumnProfilerConfigT) -> Self:
    """Add a profiler to the current Data Designer configuration.

    Args:
        profiler_config: The profiler configuration object to add.

    Returns:
        The current Data Designer config builder instance.

    Raises:
        BuilderConfigurationError: If the profiler configuration is of an invalid type.
    """
    if not isinstance(profiler_config, ColumnProfilerConfigT):
        if hasattr(ColumnProfilerConfigT, "__args__"):
            valid_options = ", ".join([t.__name__ for t in ColumnProfilerConfigT.__args__])
        else:
            valid_options = ColumnProfilerConfigT.__name__
        raise BuilderConfigurationError(f"🛑 Invalid profiler object. Valid profiler options are: {valid_options}")
    self._profilers.append(profiler_config)
    return self

build(*, skip_validation=False, raise_exceptions=False)

Build a DataDesignerConfig instance based on the current builder configuration.

Parameters:

Name	Type	Description	Default
skip_validation	bool	Whether to skip validation of the configuration.	False
raise_exceptions	bool	Whether to raise an exception if the configuration is invalid.	False

Returns:

Type	Description
DataDesignerConfig	The current Data Designer config object.

Source code in src/data_designer/config/config_builder.py

def build(self, *, skip_validation: bool = False, raise_exceptions: bool = False) -> DataDesignerConfig:
    """Build a DataDesignerConfig instance based on the current builder configuration.

    Args:
        skip_validation: Whether to skip validation of the configuration.
        raise_exceptions: Whether to raise an exception if the configuration is invalid.

    Returns:
        The current Data Designer config object.
    """
    if not skip_validation:
        self.validate(raise_exceptions=raise_exceptions)

    return DataDesignerConfig(
        model_configs=self._model_configs,
        seed_config=self._seed_config,
        columns=list(self._column_configs.values()),
        constraints=self._constraints or None,
        profilers=self._profilers or None,
        processors=self._processor_configs or None,
    )

delete_column(column_name)

Delete the column with the given name.

Parameters:

Name	Type	Description	Default
column_name	str	Name of the column to delete.	required

Returns:

Type	Description
Self	The current Data Designer config builder instance.

Raises:

Type	Description
BuilderConfigurationError	If trying to delete a seed dataset column.

Source code in src/data_designer/config/config_builder.py

def delete_column(self, column_name: str) -> Self:
    """Delete the column with the given name.

    Args:
        column_name: Name of the column to delete.

    Returns:
        The current Data Designer config builder instance.

    Raises:
        BuilderConfigurationError: If trying to delete a seed dataset column.
    """
    if isinstance(self._column_configs.get(column_name), SeedDatasetColumnConfig):
        raise BuilderConfigurationError("Seed columns cannot be deleted. Please update the seed dataset instead.")
    self._column_configs.pop(column_name, None)
    return self

delete_constraints(target_column)

Delete all constraints for the given target column.

Parameters:

Name	Type	Description	Default
target_column	str	Name of the column to remove constraints for.	required

Returns:

Type	Description
Self	The current Data Designer config builder instance.

Source code in src/data_designer/config/config_builder.py

def delete_constraints(self, target_column: str) -> Self:
    """Delete all constraints for the given target column.

    Args:
        target_column: Name of the column to remove constraints for.

    Returns:
        The current Data Designer config builder instance.
    """
    self._constraints = [c for c in self._constraints if c.target_column != target_column]
    return self

delete_model_config(alias)

Delete a model configuration from the current Data Designer configuration by alias.

Parameters:

Name	Type	Description	Default
alias	str	The alias of the model configuration to delete.	required

Source code in src/data_designer/config/config_builder.py

def delete_model_config(self, alias: str) -> Self:
    """Delete a model configuration from the current Data Designer configuration by alias.

    Args:
        alias: The alias of the model configuration to delete.
    """
    self._model_configs = [mc for mc in self._model_configs if mc.alias != alias]
    if len(self._model_configs) == 0:
        logger.warning(
            f"⚠️ No model configurations found after deleting model configuration with alias {alias}. Please add a model configuration before building the configuration."
        )
    return self

from_config(config) classmethod

Create a DataDesignerConfigBuilder from an existing configuration.

Parameters:

Name	Type	Description	Default
config	Union[dict, str, Path, BuilderConfig]	Configuration source. Can be: - A dictionary containing the configuration - A string or Path to a YAML/JSON configuration file - A BuilderConfig object	required

Returns:

Type	Description
Self	A new instance populated with the configuration from the provided source.

Raises:

Type	Description
ValueError	If the config format is invalid.
ValidationError	If the builder config loaded from the config is invalid.

Source code in src/data_designer/config/config_builder.py

@classmethod
def from_config(cls, config: Union[dict, str, Path, BuilderConfig]) -> Self:
    """Create a DataDesignerConfigBuilder from an existing configuration.

    Args:
        config: Configuration source. Can be:
            - A dictionary containing the configuration
            - A string or Path to a YAML/JSON configuration file
            - A BuilderConfig object

    Returns:
        A new instance populated with the configuration from the provided source.

    Raises:
        ValueError: If the config format is invalid.
        ValidationError: If the builder config loaded from the config is invalid.
    """
    if isinstance(config, BuilderConfig):
        builder_config = config
    else:
        json_config = json.loads(serialize_data(smart_load_yaml(config)))
        builder_config = BuilderConfig.model_validate(json_config)

    builder = cls(model_configs=builder_config.data_designer.model_configs)
    config = builder_config.data_designer

    for col in config.columns:
        builder.add_column(col)

    for constraint in config.constraints or []:
        builder.add_constraint(constraint=constraint)

    if config.seed_config:
        if builder_config.datastore_settings is None:
            if can_run_data_designer_locally():
                seed_dataset_reference = LocalSeedDatasetReference(dataset=config.seed_config.dataset)
            else:
                raise BuilderConfigurationError("🛑 Datastore settings are required.")
        else:
            seed_dataset_reference = DatastoreSeedDatasetReference(
                dataset=config.seed_config.dataset,
                datastore_settings=builder_config.datastore_settings,
            )
            builder.set_seed_datastore_settings(builder_config.datastore_settings)
        builder.with_seed_dataset(
            seed_dataset_reference,
            sampling_strategy=config.seed_config.sampling_strategy,
            selection_strategy=config.seed_config.selection_strategy,
        )

    return builder

get_builder_config()

Get the builder config for the current Data Designer configuration.

Returns:

Type	Description
BuilderConfig	The builder config.

Source code in src/data_designer/config/config_builder.py

def get_builder_config(self) -> BuilderConfig:
    """Get the builder config for the current Data Designer configuration.

    Returns:
        The builder config.
    """
    return BuilderConfig(data_designer=self.build(), datastore_settings=self._datastore_settings)

get_column_config(name)

Get a column configuration by name.

Parameters:

Name	Type	Description	Default
name	str	Name of the column to retrieve the config for.	required

Returns:

Type	Description
ColumnConfigT	The column configuration object.

Raises:

Type	Description
KeyError	If no column with the given name exists.

Source code in src/data_designer/config/config_builder.py

def get_column_config(self, name: str) -> ColumnConfigT:
    """Get a column configuration by name.

    Args:
        name: Name of the column to retrieve the config for.

    Returns:
        The column configuration object.

    Raises:
        KeyError: If no column with the given name exists.
    """
    return self._column_configs[name]

get_column_configs()

Get all column configurations.

Returns:

Type	Description
list[ColumnConfigT]	A list of all column configuration objects.

Source code in src/data_designer/config/config_builder.py

def get_column_configs(self) -> list[ColumnConfigT]:
    """Get all column configurations.

    Returns:
        A list of all column configuration objects.
    """
    return list(self._column_configs.values())

get_columns_excluding_type(column_type)

Get all column configurations excluding the specified type.

Parameters:

Name	Type	Description	Default
column_type	DataDesignerColumnType	The type of columns to exclude.	required

Returns:

Type	Description
list[ColumnConfigT]	A list of column configurations that do not match the specified type.

Source code in src/data_designer/config/config_builder.py

def get_columns_excluding_type(self, column_type: DataDesignerColumnType) -> list[ColumnConfigT]:
    """Get all column configurations excluding the specified type.

    Args:
        column_type: The type of columns to exclude.

    Returns:
        A list of column configurations that do not match the specified type.
    """
    column_type = resolve_string_enum(column_type, DataDesignerColumnType)
    return [c for c in self._column_configs.values() if c.column_type != column_type]

get_columns_of_type(column_type)

Get all column configurations of the specified type.

Parameters:

Name	Type	Description	Default
column_type	DataDesignerColumnType	The type of columns to filter by.	required

Returns:

Type	Description
list[ColumnConfigT]	A list of column configurations matching the specified type.

Source code in src/data_designer/config/config_builder.py

def get_columns_of_type(self, column_type: DataDesignerColumnType) -> list[ColumnConfigT]:
    """Get all column configurations of the specified type.

    Args:
        column_type: The type of columns to filter by.

    Returns:
        A list of column configurations matching the specified type.
    """
    column_type = resolve_string_enum(column_type, DataDesignerColumnType)
    return [c for c in self._column_configs.values() if c.column_type == column_type]

get_constraints(target_column)

Get all constraints for the given target column.

Parameters:

Name	Type	Description	Default
target_column	str	Name of the column to get constraints for.	required

Returns:

Type	Description
list[ColumnConstraintT]	A list of constraint objects targeting the specified column.

Source code in src/data_designer/config/config_builder.py

def get_constraints(self, target_column: str) -> list[ColumnConstraintT]:
    """Get all constraints for the given target column.

    Args:
        target_column: Name of the column to get constraints for.

    Returns:
        A list of constraint objects targeting the specified column.
    """
    return [c for c in self._constraints if c.target_column == target_column]

get_llm_gen_columns()

Get all LLM-generated column configurations.

Returns:

Type	Description
list[ColumnConfigT]	A list of column configurations that use LLM generation.

Source code in src/data_designer/config/config_builder.py

def get_llm_gen_columns(self) -> list[ColumnConfigT]:
    """Get all LLM-generated column configurations.

    Returns:
        A list of column configurations that use LLM generation.
    """
    return [c for c in self._column_configs.values() if column_type_is_llm_generated(c.column_type)]

get_processor_configs()

Get processor configuration objects.

Returns:

Type	Description
dict[BuildStage, list[ProcessorConfig]]	A dictionary of processor configuration objects by dataset builder stage.

Source code in src/data_designer/config/config_builder.py

def get_processor_configs(self) -> dict[BuildStage, list[ProcessorConfig]]:
    """Get processor configuration objects.

    Returns:
        A dictionary of processor configuration objects by dataset builder stage.
    """
    return self._processor_configs

get_profilers()

Get all profilers.

Returns:

Type	Description
list[ColumnProfilerConfigT]	A list of profiler configuration objects.

Source code in src/data_designer/config/config_builder.py

def get_profilers(self) -> list[ColumnProfilerConfigT]:
    """Get all profilers.

    Returns:
        A list of profiler configuration objects.
    """
    return self._profilers

get_seed_config()

Get the seed config for the current Data Designer configuration.

Returns:

Type	Description
Optional[SeedConfig]	The seed config if configured, None otherwise.

Source code in src/data_designer/config/config_builder.py

def get_seed_config(self) -> Optional[SeedConfig]:
    """Get the seed config for the current Data Designer configuration.

    Returns:
        The seed config if configured, None otherwise.
    """
    return self._seed_config

get_seed_datastore_settings()

Get most recent datastore settings for the current Data Designer configuration.

Returns:

Type	Description
Optional[DatastoreSettings]	The datastore settings if configured, None otherwise.

Source code in src/data_designer/config/config_builder.py

def get_seed_datastore_settings(self) -> Optional[DatastoreSettings]:
    """Get most recent datastore settings for the current Data Designer configuration.

    Returns:
        The datastore settings if configured, None otherwise.
    """
    return None if not self._datastore_settings else DatastoreSettings.model_validate(self._datastore_settings)

num_columns_of_type(column_type)

Get the count of columns of the specified type.

Parameters:

Name	Type	Description	Default
column_type	DataDesignerColumnType	The type of columns to count.	required

Returns:

Type	Description
int	The number of columns matching the specified type.

Source code in src/data_designer/config/config_builder.py

def num_columns_of_type(self, column_type: DataDesignerColumnType) -> int:
    """Get the count of columns of the specified type.

    Args:
        column_type: The type of columns to count.

    Returns:
        The number of columns matching the specified type.
    """
    return len(self.get_columns_of_type(column_type))

set_seed_datastore_settings(datastore_settings)

Set the datastore settings for the seed dataset.

Parameters:

Name	Type	Description	Default
datastore_settings	Optional[DatastoreSettings]	The datastore settings to use for the seed dataset.	required

Source code in src/data_designer/config/config_builder.py

def set_seed_datastore_settings(self, datastore_settings: Optional[DatastoreSettings]) -> Self:
    """Set the datastore settings for the seed dataset.

    Args:
        datastore_settings: The datastore settings to use for the seed dataset.
    """
    self._datastore_settings = datastore_settings
    return self

validate(*, raise_exceptions=False)

Validate the current Data Designer configuration.

Parameters:

Name	Type	Description	Default
raise_exceptions	bool	Whether to raise an exception if the configuration is invalid.	False

Returns:

Type	Description
Self	The current Data Designer config builder instance.

Raises:

Type	Description
InvalidConfigError	If the configuration is invalid and raise_exceptions is True.

Source code in src/data_designer/config/config_builder.py

def validate(self, *, raise_exceptions: bool = False) -> Self:
    """Validate the current Data Designer configuration.

    Args:
        raise_exceptions: Whether to raise an exception if the configuration is invalid.

    Returns:
        The current Data Designer config builder instance.

    Raises:
        InvalidConfigError: If the configuration is invalid and raise_exceptions is True.
    """

    violations = validate_data_designer_config(
        columns=list(self._column_configs.values()),
        processor_configs=self._processor_configs,
        allowed_references=self.allowed_references,
    )
    rich_print_violations(violations)
    if raise_exceptions and len([v for v in violations if v.level == ViolationLevel.ERROR]) > 0:
        raise InvalidConfigError(
            "🛑 Your configuration contains validation errors. Please address the indicated issues and try again."
        )
    if len(violations) == 0:
        logger.info("✅ Validation passed")
    return self

with_seed_dataset(dataset_reference, *, sampling_strategy=SamplingStrategy.ORDERED, selection_strategy=None)

Add a seed dataset to the current Data Designer configuration.

This method sets the seed dataset for the configuration and automatically creates SeedDatasetColumnConfig objects for each column found in the dataset. The column names are fetched from the dataset source (Hugging Face Hub or NeMo Microservices Datastore).

Parameters:

Name	Type	Description	Default
dataset_reference	SeedDatasetReference	Seed dataset reference for fetching from the datastore.	required
sampling_strategy	SamplingStrategy	The sampling strategy to use when generating data from the seed dataset. Defaults to ORDERED sampling.	ORDERED

Returns:

Type	Description
Self	The current Data Designer config builder instance.

Source code in src/data_designer/config/config_builder.py

def with_seed_dataset(
    self,
    dataset_reference: SeedDatasetReference,
    *,
    sampling_strategy: SamplingStrategy = SamplingStrategy.ORDERED,
    selection_strategy: Optional[Union[IndexRange, PartitionBlock]] = None,
) -> Self:
    """Add a seed dataset to the current Data Designer configuration.

    This method sets the seed dataset for the configuration and automatically creates
    SeedDatasetColumnConfig objects for each column found in the dataset. The column
    names are fetched from the dataset source (Hugging Face Hub or NeMo Microservices Datastore).

    Args:
        dataset_reference: Seed dataset reference for fetching from the datastore.
        sampling_strategy: The sampling strategy to use when generating data from the seed dataset.
            Defaults to ORDERED sampling.

    Returns:
        The current Data Designer config builder instance.
    """
    self._seed_config = SeedConfig(
        dataset=dataset_reference.dataset,
        sampling_strategy=sampling_strategy,
        selection_strategy=selection_strategy,
    )
    self.set_seed_datastore_settings(
        dataset_reference.datastore_settings if hasattr(dataset_reference, "datastore_settings") else None
    )
    for column_name in fetch_seed_dataset_column_names(dataset_reference):
        self._column_configs[column_name] = SeedDatasetColumnConfig(name=column_name)
    return self

write_config(path, indent=2, **kwargs)

Write the current configuration to a file.

Parameters:

Name	Type	Description	Default
path	Union[str, Path]	Path to the file to write the configuration to.	required
indent	Optional[int]	Indentation level for the output file (default: 2).	2
**kwargs		Additional keyword arguments passed to the serialization methods used.	{}

Raises:

Type	Description
BuilderConfigurationError	If the file format is unsupported.

Source code in src/data_designer/config/config_builder.py

def write_config(self, path: Union[str, Path], indent: Optional[int] = 2, **kwargs) -> None:
    """Write the current configuration to a file.

    Args:
        path: Path to the file to write the configuration to.
        indent: Indentation level for the output file (default: 2).
        **kwargs: Additional keyword arguments passed to the serialization methods used.

    Raises:
        BuilderConfigurationError: If the file format is unsupported.
    """
    cfg = self.get_builder_config()
    suffix = Path(path).suffix
    if suffix in {".yaml", ".yml"}:
        cfg.to_yaml(path, indent=indent, **kwargs)
    elif suffix == ".json":
        cfg.to_json(path, indent=indent, **kwargs)
    else:
        raise BuilderConfigurationError(f"🛑 Unsupported file type: {suffix}. Must be `.yaml`, `.yml` or `.json`.")