"""Regulatory Completeness — auditable knowledge coverage, not confidence.

An internal quality machine: for an assessment it reports identified vs assessed regulations and
justifies every open or excluded domain (corpus gap -> future_corpus; applicability uncertain ->
query_required). The metric is counts, never a single percentage. The product never claims full
coverage — it makes its own knowledge state transparent and auditable. Deterministic, no LLM, no
new corpus/meta-model class (freeze v1.0).
"""

from __future__ import annotations

from .engine import assess_completeness
from .schemas import (
    Assumption, CompletenessReport, CorpusStatus, DomainCoverage, Exclusion,
)

__all__ = [
    "assess_completeness",
    "CompletenessReport",
    "CorpusStatus",
    "DomainCoverage",
    "Exclusion",
    "Assumption",
]
