Benjamin Admin
9d79cf1576
Know-how-Freeze der Website-Compliance-Runde (DSE/Cookie/Impressum/AGB). docs: platform_evidence_v1 (Evidenz-/Qualitaetsnachweis, echte Zahlen), nutzungsbedingungen_mapping (neues Modul = Mapping, empirisch belegt), platform_checker_matrix (Meta-Modell verification_method x decision_method), verification_method, platform_validation_v1. code: checkers/ (reusable Pruefer-Library base+reference+embedding+llm, im Container validiert), agb/ (decision_method-Routing + Checker-Prototypen, 71% FP -> ~0 validiert). Dev-only, kein Prod-Push; Benchmark-GTs/Korpora im internen Archiv (data-retention). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
"""Pruefer-Library — gemeinsames Interface. Siehe docs platform_checker_matrix.md.
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Ein Checker prueft EINEN Control gegen EIN Dokument und liefert: vorhanden / fehlt
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/ unklar (+ Evidence). Module (DSE/Impressum/AGB/...) liefern nur Control-Metadaten
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ueber `ControlSpec` (verification_method + decision_method + checker-spezifische
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Config); die Engine routet method-agnostisch zum passenden Checker.
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Ziel der Plattform: 14k Controls -> 7 Pruefertypen -> wenige Pruefer. Ein neues
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Modul wird damit ein Klassifizierungs-, kein Forschungsproblem.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Optional, Protocol, runtime_checkable
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class VerificationMethod:
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"""Achse 1 — WELCHER Pruefer-Typ (Kategorie)."""
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FIELD = "FIELD"
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REFERENCE = "REFERENCE"
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BEHAVIOR = "BEHAVIOR"
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PRESENTATION = "PRESENTATION"
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CONTENT = "CONTENT"
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PROCESS = "PROCESS"
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TECHNICAL = "TECHNICAL"
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CONTRACTUAL = "CONTRACTUAL"
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class DecisionMethod:
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"""Achse 2 — WIE entschieden wird (konkreter Mechanismus)."""
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REGEX = "REGEX"
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EMBEDDING = "EMBEDDING"
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LLM = "LLM"
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LINK_RESOLVER = "LINK_RESOLVER"
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PLAYWRIGHT = "PLAYWRIGHT"
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AUDIT = "AUDIT"
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SCANNER = "SCANNER"
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@dataclass
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class ControlSpec:
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"""Routing-Metadaten + checker-spezifische Config eines Controls. Module fuellen
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nur die fuer ihren decision_method relevanten Felder."""
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control_id: str
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verification_method: str
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decision_method: str
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label: str = ""
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severity: str = "MEDIUM"
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patterns: list[str] = field(default_factory=list) # FIELD/REGEX, REFERENCE
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paraphrases: list[str] = field(default_factory=list) # CONTENT (EMBEDDING/LLM)
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embed_threshold: Optional[float] = None # EMBEDDING (per-Control)
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topic_regex: str = "" # LLM: Section-Retrieval
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question: str = "" # LLM: Pruef-Frage
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extra: dict[str, Any] = field(default_factory=dict)
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@dataclass
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class DocContext:
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"""Das zu pruefende Artefakt. `text` = Volltext; `url`/`rendered` fuer
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PRESENTATION/BEHAVIOR (Playwright) — spaeter."""
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text: str = ""
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url: str = ""
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rendered: Any = None
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@dataclass
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class CheckResult:
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present: Optional[bool] # True=erfuellt, False=fehlt, None=unklar (fail-safe)
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evidence: str = ""
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confidence: float = 0.0
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source: str = "" # welcher Pruefer/Tier geantwortet hat
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detail: dict[str, Any] = field(default_factory=dict)
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@runtime_checkable
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class Checker(Protocol):
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"""Alle Pruefer haben dieselbe Signatur -> die Engine ist method-agnostisch und
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routet nur ueber ctrl.verification_method / ctrl.decision_method."""
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verification_method: str
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async def check(self, ctrl: ControlSpec, doc: DocContext) -> CheckResult:
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...
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