nemo_microservices.data_designer.config.column_configs

Module Contents

Classes

ExpressionColumnConfig	Configuration for derived columns using Jinja2 expressions.
LLMCodeColumnConfig	Configuration for code generation columns using Large Language Models.
LLMJudgeColumnConfig	Configuration for LLM-as-a-judge quality assessment and scoring columns.
LLMStructuredColumnConfig	Configuration for structured JSON generation columns using Large Language Models.
LLMTextColumnConfig	Configuration for text generation columns using Large Language Models.
SamplerColumnConfig	Configuration for columns generated using numerical samplers.
Score	Configuration for a “score” in an LLM judge evaluation.
SeedDatasetColumnConfig	Configuration for columns sourced from seed datasets.
SingleColumnConfig	Abstract base class for all single-column configuration types.
ValidationColumnConfig	Configuration for validation columns that validate existing columns.

API

class nemo_microservices.data_designer.config.column_configs.ExpressionColumnConfig(/, **data: typing.Any)

Bases: nemo_microservices.data_designer.config.column_configs.SingleColumnConfig

Configuration for derived columns using Jinja2 expressions.

Expression columns compute values by evaluating Jinja2 templates that reference other columns. Useful for transformations, concatenations, conditional logic, and derived features without requiring LLM generation. The expression is evaluated row-by-row.

Attributes: expr: Jinja2 expression to evaluate. Can reference other column values using syntax. Supports filters, conditionals, and arithmetic. Must be a valid, non-empty Jinja2 template. dtype: Data type to cast the result to. Must be one of “int”, “float”, “str”, or “bool”. Defaults to “str”. Type conversion is applied after expression evaluation. column_type: Discriminator field, always “expression” for this configuration type.

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.

assert_expression_valid_jinja() → typing_extensions.Self

Validate that the expression is a valid, non-empty Jinja2 template.

Returns: The validated instance.

Raises: InvalidConfigError: If expression is empty or contains invalid Jinja2 syntax.

column_type: Literal[expression]

‘expression’

dtype: Literal[int, float, str, bool]

‘str’

expr: str

None

name: str

None

property required_columns: list[str]

Returns the columns referenced in the expression template.

class nemo_microservices.data_designer.config.column_configs.LLMCodeColumnConfig(/, **data: typing.Any)

Bases: nemo_microservices.data_designer.config.column_configs.LLMTextColumnConfig

Configuration for code generation columns using Large Language Models.

Extends LLMTextColumnConfig to generate code snippets in specific programming languages or SQL dialects. The generated code is automatically extracted from markdown code blocks for the specified language. Inherits all prompt templating capabilities.

Attributes: code_lang: Programming language or SQL dialect for code generation. Supported values include: “python”, “javascript”, “typescript”, “java”, “kotlin”, “go”, “rust”, “ruby”, “scala”, “swift”, “sql:sqlite”, “sql:postgres”, “sql:mysql”, “sql:tsql”, “sql:bigquery”, “sql:ansi”. See CodeLang enum for complete list. column_type: Discriminator field, always “llm-code” for this configuration type.

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.

code_lang: nemo_microservices.data_designer.config.utils.code_lang.CodeLang

None

column_type: Literal[llm-code]

‘llm-code’

class nemo_microservices.data_designer.config.column_configs.LLMJudgeColumnConfig(/, **data: typing.Any)

Bases: nemo_microservices.data_designer.config.column_configs.LLMTextColumnConfig

Configuration for LLM-as-a-judge quality assessment and scoring columns.

Extends LLMTextColumnConfig to create judge columns that evaluate and score other generated content based on the defined criteria. Useful for quality assessment, preference ranking, and multi-dimensional evaluation of generated data.

Attributes: scores: List of Score objects defining the evaluation dimensions. Each score represents a different aspect to evaluate (e.g., accuracy, relevance, fluency). Must contain at least one score. column_type: Discriminator field, always “llm-judge” for this configuration type.

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.

column_type: Literal[llm-judge]

‘llm-judge’

scores: list[nemo_microservices.data_designer.config.column_configs.Score]

‘Field(…)’

class nemo_microservices.data_designer.config.column_configs.LLMStructuredColumnConfig(/, **data: typing.Any)

Bases: nemo_microservices.data_designer.config.column_configs.LLMTextColumnConfig

Configuration for structured JSON generation columns using Large Language Models.

Extends LLMTextColumnConfig to generate structured data conforming to a specified schema. Uses JSON schema or Pydantic models to define the expected output structure, enabling type-safe and validated structured output generation. Inherits prompt templating capabilities.

Attributes: output_format: The schema defining the expected output structure. Can be either: - A Pydantic BaseModel class (recommended) - A JSON schema dictionary column_type: Discriminator field, always “llm-structured” for this configuration type.

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.

column_type: Literal[llm-structured]

‘llm-structured’

output_format: Union[dict, Type[pydantic.BaseModel]]

None

validate_output_format() → typing_extensions.Self

Convert Pydantic model to JSON schema if needed.

Returns: The validated instance with output_format as a JSON schema dict.

class nemo_microservices.data_designer.config.column_configs.LLMTextColumnConfig(/, **data: typing.Any)

Bases: nemo_microservices.data_designer.config.column_configs.SingleColumnConfig

Configuration for text generation columns using Large Language Models.

LLM text columns generate free-form text content using language models via LiteLLM. Prompts support Jinja2 templating to reference values from other columns, enabling context-aware generation. The generated text can optionally include reasoning traces when models support extended thinking.

Attributes: prompt: Prompt template for text generation. Supports Jinja2 syntax to reference other columns (e.g., “Write a story about “). Must be a valid Jinja2 template. model_alias: Alias of the model configuration to use for generation. Must match a model alias defined when initializing the DataDesignerConfigBuilder. system_prompt: Optional system prompt to set model behavior and constraints. Also supports Jinja2 templating. If provided, must be a valid Jinja2 template. Do not put any output parsing instructions in the system prompt. Instead, use the appropriate column type for the output you want to generate - e.g., LLMStructuredColumnConfig for structured output, LLMCodeColumnConfig for code. multi_modal_context: Optional list of image contexts for multi-modal generation. Enables vision-capable models to generate text based on image inputs. column_type: Discriminator field, always “llm-text” for this configuration type.

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.

assert_prompt_valid_jinja() → typing_extensions.Self

Validate that prompt and system_prompt are valid Jinja2 templates.

Returns: The validated instance.

Raises: InvalidConfigError: If prompt or system_prompt contains invalid Jinja2 syntax.

column_type: Literal[llm-text]

‘llm-text’

model_alias: str

None

multi_modal_context: Optional[list[nemo_microservices.data_designer.config.models.ImageContext]]

None

prompt: str

None

property required_columns: list[str]

Get columns referenced in the prompt and system_prompt templates.

Returns: List of unique column names referenced in Jinja2 templates.

property side_effect_columns: list[str]

Returns the reasoning trace column, which may be generated alongside the main column.

Reasoning traces are only returned if the served model parses and returns reasoning content.

Returns: List containing the reasoning trace column name.

system_prompt: Optional[str]

None

class nemo_microservices.data_designer.config.column_configs.SamplerColumnConfig(/, **data: typing.Any)

Bases: nemo_microservices.data_designer.config.column_configs.SingleColumnConfig

Configuration for columns generated using numerical samplers.

Sampler columns provide efficient data generation using numerical samplers for common data types and distributions. Supported samplers include UUID generation, datetime/timedelta sampling, person generation, category / subcategory sampling, and various statistical distributions (uniform, gaussian, binomial, poisson, scipy).

Attributes: sampler_type: Type of sampler to use. Available types include: “uuid”, “category”, “subcategory”, “uniform”, “gaussian”, “bernoulli”, “bernoulli_mixture”, “binomial”, “poisson”, “scipy”, “person”, “datetime”, “timedelta”. params: Parameters specific to the chosen sampler type. Type varies based on the sampler_type (e.g., CategorySamplerParams, UniformSamplerParams, PersonSamplerParams). conditional_params: Optional dictionary for conditional parameters. The dict keys are the conditions that must be met (e.g., “age > 21”) for the conditional parameters to be used. The values of dict are the parameters to use when the condition is met. convert_to: Optional type conversion to apply after sampling. Must be one of “float”, “int”, or “str”. Useful for converting numerical samples to strings or other types. column_type: Discriminator field, always “sampler” for this configuration type.

!!! tip “Displaying available samplers and their parameters” The config builder has an info attribute that can be used to display the available samplers and their parameters: python     config_builder.info.display("samplers")    

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.

column_type: Literal[sampler]

‘sampler’

conditional_params: dict[str, Annotated[nemo_microservices.data_designer.config.sampler_params.SamplerParamsT, Discriminator('sampler_type')]]

None

convert_to: Optional[str]

None

classmethod inject_sampler_type_into_params(data: dict) → dict

Inject sampler_type into params dict to enable discriminated union resolution.

This allows users to pass params as a simple dict without the sampler_type field, which will be automatically added based on the outer sampler_type field.

params: Annotated[nemo_microservices.data_designer.config.sampler_params.SamplerParamsT, Discriminator('sampler_type')]

None

sampler_type: nemo_microservices.data_designer.config.sampler_params.SamplerType

None

class nemo_microservices.data_designer.config.column_configs.Score(/, **data: typing.Any)

Bases: nemo_microservices.data_designer.config.base.ConfigBase

Configuration for a “score” in an LLM judge evaluation.

Defines a single scoring criterion with its possible values and descriptions. Multiple Score objects can be combined in an LLMJudgeColumnConfig to create multi-dimensional quality assessments.

Attributes: name: A clear, concise name for this scoring dimension (e.g., “Relevance”, “Fluency”). description: An informative and detailed assessment guide explaining how to evaluate this dimension. Should provide clear criteria for scoring. options: Dictionary mapping score values to their descriptions. Keys can be integers (e.g., 1-5 scale) or strings (e.g., “Poor”, “Good”, “Excellent”). Values are descriptions explaining what each score level means.

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.

description: str

‘Field(…)’

name: str

‘Field(…)’

options: dict[Union[int, str], str]

‘Field(…)’

class nemo_microservices.data_designer.config.column_configs.SeedDatasetColumnConfig(/, **data: typing.Any)

Bases: nemo_microservices.data_designer.config.column_configs.SingleColumnConfig

Configuration for columns sourced from seed datasets.

This config marks columns that come from seed data. It is typically created automatically when calling with_seed_dataset() on the builder, rather than being instantiated directly by users.

Attributes: column_type: Discriminator field, always “seed-dataset” for this configuration type.

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.

column_type: Literal[seed-dataset]

‘seed-dataset’

class nemo_microservices.data_designer.config.column_configs.SingleColumnConfig(/, **data: typing.Any)

Bases: nemo_microservices.data_designer.config.base.ConfigBase, abc.ABC

Abstract base class for all single-column configuration types.

This class serves as the foundation for all column configurations in DataDesigner, defining shared fields and properties across all column types.

Attributes: name: Unique name of the column to be generated. drop: If True, the column will be generated but removed from the final dataset. Useful for intermediate columns that are dependencies for other columns. column_type: Discriminator field that identifies the specific column type. Subclasses must override this field to specify the column type with a Literal value.

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.

column_type: str

None

drop: bool

False

name: str

None

property required_columns: list[str]

Returns a list of column names that must exist before this column can be generated.

Returns: List of column names that this column depends on. Empty list indicates no dependencies. Override in subclasses to specify dependencies.

property side_effect_columns: list[str]

Returns a list of additional columns that this column will create as a side effect.

Some column types generate additional metadata or auxiliary columns alongside the primary column (e.g., reasoning traces for LLM columns).

Returns: List of column names that this column will create as a side effect. Empty list indicates no side effect columns. Override in subclasses to specify side effects.

class nemo_microservices.data_designer.config.column_configs.ValidationColumnConfig(/, **data: typing.Any)

Bases: nemo_microservices.data_designer.config.column_configs.SingleColumnConfig

Configuration for validation columns that validate existing columns.

Validation columns execute validation logic against specified target columns and return structured results indicating pass/fail status with validation details. Supports multiple validation strategies: code execution (Python/SQL), local callable functions (library only), and remote HTTP endpoints.

Attributes: target_columns: List of column names to validate. These columns are passed to the validator for validation. All target columns must exist in the dataset before validation runs. validator_type: The type of validator to use. Options: - “code”: Execute code (Python or SQL) for validation. The code receives a DataFrame with target columns and must return a DataFrame with validation results. - “local_callable”: Call a local Python function with the data. Only supported when running DataDesigner locally. - “remote”: Send data to a remote HTTP endpoint for validation. Useful for validator_params: Parameters specific to the validator type. Type varies by validator: - CodeValidatorParams: Specifies code language (python or SQL dialect like “sql:postgres”, “sql:mysql”). - LocalCallableValidatorParams: Provides validation function (Callable[[pd.DataFrame], pd.DataFrame]) and optional output schema for validation results. - RemoteValidatorParams: Configures endpoint URL, HTTP timeout, retry behavior (max_retries, retry_backoff), and parallel request limits (max_parallel_requests). batch_size: Number of records to process in each validation batch. Defaults to 10. Larger batches are more efficient but use more memory. Adjust based on validator complexity and available resources. column_type: Discriminator field, always “validation” for this configuration type.

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.

batch_size: int

‘Field(…)’

column_type: Literal[validation]

‘validation’

property required_columns: list[str]

Returns the columns that need to be validated.

target_columns: list[str]

None

validator_params: nemo_microservices.data_designer.config.validator_params.ValidatorParamsT

None

validator_type: nemo_microservices.data_designer.config.validator_params.ValidatorType

None