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>
63 lines
2.7 KiB
Python
63 lines
2.7 KiB
Python
"""Schemas for the Regulatory Completeness Engine — auditable knowledge-coverage, not confidence.
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For an assessment it answers „wie sicher sind wir, dass diese Antwort VOLLSTÄNDIG ist?" by separating
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IDENTIFIED regulations from ASSESSED ones (those in the validated corpus) and listing every open or
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excluded domain WITH a reason. The metric is counts, never a single „87%". This is an internal quality
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machine: the product never claims full coverage — it makes its own knowledge state transparent.
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Deterministic, computed-not-stored, no new meta-model class (freeze v1.0). Python 3.9 compatible.
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"""
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from __future__ import annotations
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from enum import Enum
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from typing import List
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from pydantic import BaseModel, Field
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class CorpusStatus(str, Enum):
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"""The maturity of our knowledge corpus for a regulation/domain."""
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VALIDATED = "validated" # we can fully assess this
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DRAFT = "draft" # partial / under review
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UNSUPPORTED = "unsupported" # triggered but no corpus yet
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UNKNOWN = "unknown" # not in our registry at all
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class DomainCoverage(BaseModel):
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regulation: str
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status: CorpusStatus = CorpusStatus.UNKNOWN
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note: str = ""
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class Exclusion(BaseModel):
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"""A domain/regulation DELIBERATELY not assessed — always with a reason (the heart of the engine)."""
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subject: str
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reason: str
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deciding_question: str = "" # what would resolve it (if a query)
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resolution: str = "future_corpus" # query_required | future_corpus | not_applicable
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class Assumption(BaseModel):
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key: str
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value: str = ""
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note: str = ""
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class CompletenessReport(BaseModel):
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"""The auditable coverage report for one assessment — counts + justification, NO single percentage."""
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identified_regulations: List[str] = Field(default_factory=list)
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assessed_regulations: List[str] = Field(default_factory=list) # in the validated corpus
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open_regulations: List[str] = Field(default_factory=list) # identified but not validated
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open_corpora: List[str] = Field(default_factory=list) # missing domains worth building
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coverage: List[DomainCoverage] = Field(default_factory=list)
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assumptions: List[Assumption] = Field(default_factory=list)
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exclusions: List[Exclusion] = Field(default_factory=list)
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uncertainties_count: int = 0
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assessed_obligations: int = 0 # injected (Execution-owned)
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justification_present: bool = False
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completeness_summary: str = "" # "Identifiziert N · bewertet M · offen K · ..."
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audit_statement: str = "" # the honest narrative sentence
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