build_manual_dq_rule_prompt_package

Public callable

Build copy/paste prompt package for manual DQ candidate generation.

Parameters:

Name	Type	Description	Default
sample_rows	Any	Optional small sample context serialized compactly for prompt ferry usage.	None
business_context	str	Static context embedded in prompt text.	''
dataset_name	str | None	Static dataset name embedded in prompt text.	None

Returns:

Type	Description
dict	Manual package containing reusable prompt text, schema expectations, and notes. Output is advisory and requires human review.

Source code in src/fabricops_kit/ai.py

def build_manual_dq_rule_prompt_package(sample_rows=None, business_context="", dataset_name=None, config: FrameworkConfig | None = None) -> dict:
    """Build copy/paste prompt package for manual DQ candidate generation.

    Parameters
    ----------
    sample_rows : Any, optional
        Optional small sample context serialized compactly for prompt ferry usage.
    business_context : str, optional
        Static context embedded in prompt text.
    dataset_name : str | None, optional
        Static dataset name embedded in prompt text.

    Returns
    -------
    dict
        Manual package containing reusable prompt text, schema expectations, and
        notes. Output is advisory and requires human review.
    """
    prompt = build_dq_rule_candidate_prompt(business_context=business_context, dataset_name=dataset_name, config=config)
    compact = _compact_sample_rows(sample_rows)
    return {
        "mode": "manual_prompt_ferry",
        "target_use": "dq_rule_candidates",
        "prompt": prompt,
        "full_prompt_text": f"{prompt}\n\nSample rows:\n{compact}" if compact else prompt,
        "expected_output_schema": ["rule_id", "table_name", "column_name", "rule_type", "severity", "reason", "evidence", "needs_human_review"],
        "notes": "Paste prompt into Copilot or another LLM, then review before storing approved deterministic rules.",
        "sample_rows": compact,
    }