Benjamin Admin
aa99111a87
Phase A½. The move from feature to product development: for every assessment, answer "how sure are we that this answer is COMPLETE?" — different from confidence. The product never claims full coverage; it makes its own knowledge state transparent and auditable. Shows what we do NOT know and why. - compliance/completeness/: assess_completeness(identified, corpus_status, uncertain, assumptions, assessed_obligations) -> CompletenessReport. Separates IDENTIFIED from ASSESSED (validated corpus AND determined applicability) and justifies every gap. Two kinds of open: corpus gap (future_corpus) and applicability uncertainty (query_required + deciding question, e.g. Data Act / generates_usage_data). - The metric is COUNTS, never a single percentage: "Identifiziert N · bewertet M · offen K · Unsicherheiten U · Begründung ja" + an honest audit statement. - ADR-007: auditable honesty; phase order A factory -> A½ Completeness -> B new domains; the transparency selling point. Deterministic, no LLM; corpus status + obligation count injected. - reference suite: "Regulatory Completeness" section runs an industrial-dishwasher assessment (assessed CRA/MaschinenVO; open EMV/Environmental=future_corpus, Data Act=query_required) and notes Environmental flips open->validated automatically once the corpus lands. 11 completeness tests (54 with adjacent modules), mypy --strict clean (15 files), check-loc 0. Product code with no app caller + ADR/reference = non-runtime -> no deploy (ADR-001). Freeze-safe. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
90 lines
4.6 KiB
Python
90 lines
4.6 KiB
Python
"""Regulatory Completeness Engine — measure auditable knowledge coverage for an assessment.
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Separates what we IDENTIFIED (triggered regulations) from what we ASSESSED (validated corpus AND
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determined applicability), and justifies every gap. Two kinds of „open":
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- corpus gap — no validated corpus yet (e.g. Environmental) -> future_corpus
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- applicability open — corpus exists but applicability is uncertain (Data Act) -> query_required
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The metric is COUNTS, never a single percentage. The audit statement says plainly „wir bewerteten M
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von N Domänen; K sind nicht im validierten Korpus und wurden bewusst nicht bewertet".
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Deterministic, computed-not-stored, no LLM, no new corpus/meta-model class (freeze v1.0). Python 3.9.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from .schemas import (
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Assumption, CompletenessReport, CorpusStatus, DomainCoverage, Exclusion,
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)
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_VALID = {s.value for s in CorpusStatus}
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def _status(corpus_status: Dict[str, str], reg: str) -> CorpusStatus:
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raw = corpus_status.get(reg, "unknown")
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return CorpusStatus(raw) if raw in _VALID else CorpusStatus.UNKNOWN
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def assess_completeness(
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identified_regulations: List[str],
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corpus_status: Dict[str, str],
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uncertain: Optional[List[Dict[str, Any]]] = None,
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assumptions: Optional[List[Dict[str, Any]]] = None,
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assessed_obligations: int = 0,
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) -> CompletenessReport:
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"""Build the auditable coverage report.
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`identified_regulations`: triggered/identified for this product. `corpus_status`: regulation ->
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one of validated/draft/unsupported/unknown (curated/injected corpus registry). `uncertain`:
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applicability-uncertain regulations [{regulation, deciding_question, reason}]. `assumptions`:
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[{key, value, note}]. `assessed_obligations`: count from Execution (injected, default 0).
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"""
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ids = sorted(set(identified_regulations))
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unc = uncertain or []
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unc_subjects = {str(u.get("regulation") or u.get("subject")) for u in unc if (u.get("regulation") or u.get("subject"))}
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coverage = [DomainCoverage(regulation=r, status=_status(corpus_status, r)) for r in ids]
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assessed = [r for r in ids if _status(corpus_status, r) == CorpusStatus.VALIDATED and r not in unc_subjects]
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open_regs = [r for r in ids if r not in assessed]
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open_corpora = [r for r in ids if _status(corpus_status, r) in (CorpusStatus.UNSUPPORTED, CorpusStatus.UNKNOWN)]
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exclusions: List[Exclusion] = []
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for u in unc:
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subj = str(u.get("regulation") or u.get("subject") or "")
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if not subj:
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continue
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exclusions.append(Exclusion(
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subject=subj, reason=str(u.get("reason", "Anwendbarkeit unsicher")),
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deciding_question=str(u.get("deciding_question", "")), resolution="query_required"))
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for r in open_regs:
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if r in unc_subjects:
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continue
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st = _status(corpus_status, r)
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if st == CorpusStatus.DRAFT:
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exclusions.append(Exclusion(subject=r, reason="Korpus in Bearbeitung (draft)", resolution="in_review"))
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else:
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exclusions.append(Exclusion(subject=r, reason="nicht im validierten Korpus", resolution="future_corpus"))
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covered_subjects = {e.subject for e in exclusions}
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justification = (not open_regs) or set(open_regs) <= covered_subjects
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assumptions_m = [Assumption(key=str(a.get("key", "")), value=str(a.get("value", "")), note=str(a.get("note", ""))) for a in (assumptions or [])]
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summary = "Identifiziert %d · bewertet %d · offen %d · Unsicherheiten %d · Begründung %s" % (
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len(ids), len(assessed), len(open_regs), len(unc), "ja" if justification else "nein")
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if open_regs:
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audit = (
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"Für dieses Produkt konnten wir %d von %d identifizierten regulatorischen Domänen vollständig "
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"bewerten. %d weitere %s noch nicht Bestandteil des validierten Korpus bzw. anwendungsunsicher "
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"und wurden deshalb bewusst nicht bewertet." % (
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len(assessed), len(ids), len(open_regs), "ist" if len(open_regs) == 1 else "sind"))
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else:
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audit = "Für dieses Produkt konnten wir alle %d identifizierten regulatorischen Domänen vollständig bewerten." % len(ids)
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return CompletenessReport(
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identified_regulations=ids, assessed_regulations=assessed, open_regulations=open_regs,
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open_corpora=open_corpora, coverage=coverage, assumptions=assumptions_m, exclusions=exclusions,
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uncertainties_count=len(unc), assessed_obligations=assessed_obligations,
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justification_present=justification, completeness_summary=summary, audit_statement=audit,
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)
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