generate_governance_candidates_with_fabric_ai

Public callable

Execute Fabric AI Functions to append governance suggestions to a DataFrame.

Parameters:

Name	Type	Description	Default
profile_df	Any	Fabric PySpark DataFrame with an .ai.generate_response extension. Expected row columns include table_name, column_name, data_type, and profile_summary for prompt placeholders.	required
business_context	str	Static context embedded in prompt text.	''
dataset_name	str | None	Static dataset name embedded in prompt text.	None
output_col		Fabric AI execution options.	'ai_governance_candidate'
error_col		Fabric AI execution options.	'ai_governance_candidate'
response_format		Fabric AI execution options.	'ai_governance_candidate'
concurrency		Fabric AI execution options.	'ai_governance_candidate'

Returns:

Type	Description
Any	Enriched DataFrame containing AI suggestion output columns.

Source code in src/fabricops_kit/ai.py

def generate_governance_candidates_with_fabric_ai(profile_df, business_context="", dataset_name=None, output_col="ai_governance_candidate", error_col="ai_governance_error", response_format="json_object", concurrency=20, config: FrameworkConfig | None = None):
    """Execute Fabric AI Functions to append governance suggestions to a DataFrame.

    Parameters
    ----------
    profile_df : Any
        Fabric PySpark DataFrame with an ``.ai.generate_response`` extension.
        Expected row columns include ``table_name``, ``column_name``, ``data_type``,
        and ``profile_summary`` for prompt placeholders.
    business_context : str, optional
        Static context embedded in prompt text.
    dataset_name : str | None, optional
        Static dataset name embedded in prompt text.
    output_col, error_col, response_format, concurrency
        Fabric AI execution options.

    Returns
    -------
    Any
        Enriched DataFrame containing AI suggestion output columns.
    """
    _require_fabric_ai_dataframe(profile_df, "generate_governance_candidates_with_fabric_ai")
    prompt = build_governance_candidate_prompt(business_context=business_context, dataset_name=dataset_name, config=config)
    return profile_df.ai.generate_response(prompt=prompt, is_prompt_template=True, output_col=output_col, error_col=error_col, response_format=response_format, concurrency=concurrency)