DataDesigner/docs/concepts/custom_columns.md

Custom Columns

Custom columns let you implement your own generation logic using Python functions. Use them for multi-step LLM workflows, external API integration, or any scenario requiring full programmatic control. For reusable, distributable components, see Plugins instead.

Quick Start

import data_designer.config as dd

@dd.custom_column_generator(required_columns=["name"])
def create_greeting(row: dict) -> dict:
    row["greeting"] = f"Hello, {row['name']}!"
    return row

config_builder.add_column(
    dd.CustomColumnConfig(
        name="greeting",
        generator_function=create_greeting,
    )
)

Function Signatures

Three signatures are supported. Parameter names are validated:

Args	Signature	Use Case
1	fn(row) -> dict	Simple transforms
2	fn(row, generator_params) -> dict	With typed params
3	fn(row, generator_params, models) -> dict	LLM access via models dict

For full_column strategy, use df instead of row.

For LLM access without params, use generator_params: None:

@dd.custom_column_generator(required_columns=["name"], model_aliases=["my-model"])
def generate_message(row: dict, generator_params: None, models: dict) -> dict:
    response, _ = models["my-model"].generate(prompt=f"Greet {row['name']}")
    row["greeting"] = response
    return row

Model aliases are validated before generation starts. If an alias doesn't exist in your config, an error is raised during the health check.

Generation Strategies

Strategy	Input	Use Case
cell_by_cell (default)	row: dict	LLM calls, row-by-row logic
full_column	df: DataFrame	Vectorized DataFrame operations

Recommendation: Use cell_by_cell for LLM calls. The framework handles parallelization automatically. Use full_column only for vectorized operations that don't involve LLM calls.

For full_column, set generation_strategy=dd.GenerationStrategy.FULL_COLUMN.

The Decorator

@dd.custom_column_generator(
    required_columns=["col1"],        # DAG ordering
    side_effect_columns=["extra"],    # Additional columns created
    model_aliases=["model1"],         # Required for LLM access
)

Models Dict

The third argument is a dict of ModelFacade instances, keyed by alias. You must declare all models required in your custom column generator in model_aliases - this populates the models dict and enables health checks before generation starts.

@dd.custom_column_generator(model_aliases=["my-model"])
def my_generator(row: dict, generator_params: None, models: dict) -> dict:
    model = models["my-model"]
    response, trace = model.generate(
        prompt="...",
        parser=my_custom_parser,  # optional, defaults to identity
        system_prompt="...",
        max_correction_steps=3,
    )
    row["result"] = response
    return row

This gives you direct access to all ModelFacade capabilities: custom parsers, correction loops, structured output, tool use, etc.

Configuration

Parameter	Type	Required	Description
name	str	Yes	Column name
generator_function	Callable	Yes	Decorated function
generation_strategy	GenerationStrategy	No	CELL_BY_CELL or FULL_COLUMN
generator_params	BaseModel	No	Typed params passed to function
allow_resize	bool	No	Allow 1:N or N:1 generation

Resizing (1:N and N:1)

FULL_COLUMN: Set allow_resize=True and return a DataFrame with more or fewer rows than the input:

@dd.custom_column_generator(
    required_columns=["topic"],
    side_effect_columns=["variation_id"],
)
def expand_topics(df: pd.DataFrame, params: None, models: dict) -> pd.DataFrame:
    rows = []
    for _, row in df.iterrows():
        for i in range(3):  # Generate 3 variations per input
            rows.append({
                "topic": row["topic"],
                "question": f"Question {i+1} about {row['topic']}",
                "variation_id": i,
            })
    return pd.DataFrame(rows)

dd.CustomColumnConfig(
    name="question",
    generator_function=expand_topics,
    generation_strategy=dd.GenerationStrategy.FULL_COLUMN,
    allow_resize=True,
)

CELL_BY_CELL: With allow_resize=True, your function may return a single row (dict) or multiple rows (list[dict]). Return [] to drop that input row.

@dd.custom_column_generator(required_columns=["id"])
def expand_row(row: dict) -> list[dict]:
    return [
        {**row, "variant": "a"},
        {**row, "variant": "b"},
    ]

dd.CustomColumnConfig(
    name="variant",
    generator_function=expand_row,
    generation_strategy=dd.GenerationStrategy.CELL_BY_CELL,
    allow_resize=True,
)

Use cases:

• Expansion (1:N): Generate multiple variations per input
• Retraction (N:1): Filter, aggregate, or deduplicate records (FULL_COLUMN) or return [] per row (CELL_BY_CELL)

Multi-Turn Example

@dd.custom_column_generator(
    required_columns=["topic"],
    side_effect_columns=["draft", "critique"],
    model_aliases=["writer", "editor"],
)
def writer_editor(row: dict, generator_params: None, models: dict) -> dict:
    draft, _ = models["writer"].generate(prompt=f"Write about '{row['topic']}'")
    critique, _ = models["editor"].generate(prompt=f"Critique: {draft}")
    revised, _ = models["writer"].generate(prompt=f"Revise based on: {critique}\n\nOriginal: {draft}")

    row["final_text"] = revised
    row["draft"] = draft
    row["critique"] = critique
    return row

Development Testing

Test generators with real LLM calls without running the full pipeline:

data_designer = DataDesigner()
models = data_designer.get_models(["my-model"])
result = my_generator({"name": "Alice"}, None, models)

See Also

• Column Configs Reference
• Plugins Overview