Data Designer

Usage Tips and Common Pitfalls

• Sampler and validation columns need both a type and params. E.g., sampler_type="category" with params=dd.CategorySamplerParams(...).
• Jinja2 templates in prompt, system_prompt, and expr fields: reference columns with {{ column_name }}, nested fields with {{ column_name.field }}.
• SamplerColumnConfig: Takes params, not sampler_params.
• LLM judge score access: LLMJudgeColumnConfig produces a nested dict where each score name maps to {reasoning: str, score: int}. To get the numeric score, use the .score attribute. For example, for a judge column named quality with a score named correctness, use {{ quality.correctness.score }}. Using {{ quality.correctness }} returns the full dict, not the numeric score.

Troubleshooting

• data-designer CLI not found: Tell the user that data-designer is not installed in this environment (requires Python >= 3.10). Ask if they would like you to create a virtual environment and install it, or if they prefer to do it themselves. Do not install anything without the user's permission.
• Network errors during preview: A sandbox environment may be blocking outbound requests. Ask the user for permission to retry the command with the sandbox disabled. Only as a last resort, if retrying outside the sandbox also fails, tell the user to run the command themselves.

Output Template

Write a Python file to the current directory with a load_config_builder() function returning a DataDesignerConfigBuilder. Name the file descriptively (e.g., customer_reviews.py). Use PEP 723 inline metadata for dependencies.

# /// script
# dependencies = [
#   "data-designer", # always required
#   "pydantic", # only if this script imports from pydantic
#   # add additional dependencies here
# ]
# ///
import data_designer.config as dd
from pydantic import BaseModel, Field


# Use Pydantic models when the output needs to conform to a specific schema
class MyStructuredOutput(BaseModel):
    field_one: str = Field(description="...")
    field_two: int = Field(description="...")


# Use custom generators when built-in column types aren't enough
@dd.custom_column_generator(
    required_columns=["col_a"],
    side_effect_columns=["extra_col"],
)
def generator_function(row: dict) -> dict:
    # add custom logic here that depends on "col_a" and update row in place
    row["name_in_custom_column_config"] = "custom value"
    row["extra_col"] = "extra value"
    return row


def load_config_builder() -> dd.DataDesignerConfigBuilder:
    config_builder = dd.DataDesignerConfigBuilder()

    # Seed dataset (only if the user explicitly mentions a seed dataset path)
    # config_builder.with_seed_dataset(dd.LocalFileSeedSource(path="path/to/seed.parquet"))

    # config_builder.add_column(...)
    # config_builder.add_processor(...)

    return config_builder

Only include Pydantic models, custom generators, seed datasets, and extra dependencies when the task requires them.