fix(impressum): Findings aus 12 §5-TMG-Pattern-MCs statt verunreinigtem DB-Set

Der Agent lieferte "alles gruen": _load_controls gab auf macmini nur 3 von 75
doc_type='impressum'-MCs zurueck (Sidecar mc_classification.db hat nur 4/75 als
text-matchbar klassifiziert). Tiefere Ursache: die 75 doc_type='impressum'-MCs
sind fehl-klassifiziert (60/75 canonical_scope='other'; Prefixes TRD/SEC/GOV =
Geschaeftsbriefe/Marktplatz/Bestellung, NICHT §5 TMG Website-Impressum).

Fix: Der Impressum-Agent erzeugt Findings jetzt aus seinen 12 autoritativen
§5-TMG/DDG-Pattern-MCs (mcs.py) statt aus dem verunreinigten DB-Set —
deterministisch, scope-aware, field_id = semantisches Feld. Semantic-Validator-
Demote + Massnahmen + Rollup bleiben. Die 5-Impressum-GT-Tests laufen jetzt
echt durch: 0 Falsch-Positive.

DB-Master-Controls fuer Impressum deaktiviert bis zum MC-Re-Filtering (separate
Aufgabe: die doc_type-Klassifizierung der Vorgaenger-Session muss bereinigt
werden).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

backend-compliance/compliance/services/specialist_agents/impressum/agent.py

@@ -39,8 +39,7 @@ from .._base import (
 from .._pattern_library import record as record_pattern
 from .._rollup import rollup
 from .._semantic_validator import build_rename_action, validate_present
-from .mcs import MC_IDS, MCS, detect_automotive
-from .v3_engine import run_v3_pipeline
+from .mcs import MC_IDS, MCS, detect_automotive, scope_matches
 
 logger = logging.getLogger(__name__)
 
@@ -107,73 +106,53 @@ class ImpressumAgent(BaseSpecialistAgent):
                 notes="Impressum-Text zu kurz oder leer.",
             )
 
-        # ── Layer 0 + 1 + 2 (Boost + Keyword + Embedding) ──────────
-        results, telemetry = await run_v3_pipeline(text, scope)
-        notes_parts.append(
-            f"v3-pipeline: {telemetry.get('total_mcs', 0)} DB-MCs · "
-            f"{telemetry.get('layer_0_field_hits', 0)} Pattern-Boosts · "
-            f"{telemetry.get('layer_0_boost_overrides', 0)} Boost-Overrides"
-        )
-        sec_drop = telemetry.get("sector_dropped", 0)
-        off_drop = telemetry.get("offtopic_dropped", 0)
-        if sec_drop or off_drop:
-            notes_parts.append(
-                f"Scope-Filter: {sec_drop} Branchen-MCs + "
-                f"{off_drop} themenfremde MCs entfernt"
-            )
-
-        # DB-MCs → Findings + Coverage
-        seen_db_mcs: set[str] = set()
-        for r in results:
-            mc_id = r.get("control_id") or ""
-            if not mc_id or mc_id in seen_db_mcs:
+        # ── Findings aus den 12 autoritativen §5-TMG/DDG-MCs (mcs.py) ──
+        # Die DB-Menge doc_type='impressum' ist verunreinigt (TRD/SEC/GOV-
+        # Controls statt §5 TMG, fehl-klassifiziert von einer Vorgaenger-
+        # Session), daher sind bis zum MC-Re-Filtering die 12 praezisen
+        # Pattern-MCs die Findings-Quelle. field_id = semantisches Feld
+        # (passt zum Semantic-Validator + den GT-Tests).
+        is_auto = "automotive" in scope
+        for mc in MCS:
+            if not scope_matches(mc, scope, is_auto):
+                coverage.append(McCoverage(
+                    mc_id=mc.mc_id, status="na",
+                    reason="nicht im Business-Scope",
+                ))
                 continue
-            seen_db_mcs.add(mc_id)
-            passed = bool(r.get("passed"))
-            sev = _SEV_TO_ENUM.get(
-                (r.get("severity") or "MEDIUM").upper(), Severity.MEDIUM,
-            )
-            coverage.append(McCoverage(
-                mc_id=mc_id,
-                status="ok" if passed else sev.value.lower(),
-                reason=str(r.get("matched_text") or r.get("hint") or "")[:120],
-            ))
-            if passed:
+            if any(p.search(text) for p in mc.patterns):
+                coverage.append(McCoverage(
+                    mc_id=mc.mc_id, status="ok", reason="Pattern-Treffer",
+                ))
                 continue
-            label = r.get("label") or r.get("hint") or ""
-            norm_str = str(r.get("regulation") or "")
-            if r.get("article"):
-                norm_str = (norm_str + f" Art. {r.get('article')}").strip()
+            sev = _SEV_TO_ENUM.get(mc.severity_if_missing, Severity.MEDIUM)
             findings.append(Finding(
-                check_id=f"DBMC-{mc_id}",
+                check_id=f"IMP-{mc.field_id}",
                 agent=self.agent_id,
                 agent_version=self.agent_version,
-                field_id=mc_id,
+                field_id=mc.field_id,
                 severity=sev,
-                severity_reason="db_mc_failed",
-                title=str(label)[:200] or f"DB-MC {mc_id} nicht erfüllt",
-                norm=norm_str,
+                severity_reason="pflichtangabe_missing",
+                title=f"Pflichtangabe fehlt: {mc.label}",
+                norm=mc.norm,
                 evidence="",
-                action=_build_measure(str(label), norm_str)[:400],
+                action=_build_measure(mc.label, mc.norm),
                 confidence=0.9,
                 sources=[EvidenceSource(
-                    source_type=SourceType.MC,
-                    source_id=mc_id,
-                    detail=str(r.get("source") or "keyword_match")[:120],
+                    source_type=SourceType.REGEX,
+                    source_id=mc.mc_id,
+                    detail="kein Pattern-Treffer im Text",
                     confidence=0.9,
                 )],
             ))
-
-        # Layer 0: eigene Pattern-IDs immer mit ins coverage (für UI)
-        boost_ids = set(telemetry.get("layer_0_field_ids") or [])
-        for mc in MCS:
-            cov_status = "ok" if mc.field_id in boost_ids else "na"
-            cov_reason = ("regex-boost hit"
-                           if mc.field_id in boost_ids
-                           else "kein Pattern-Treffer (kein Veto)")
             coverage.append(McCoverage(
-                mc_id=mc.mc_id, status=cov_status, reason=cov_reason,
+                mc_id=mc.mc_id, status=sev.value.lower(),
+                reason="kein Pattern-Treffer",
             ))
+        notes_parts.append(
+            f"{len(MCS)} §5-TMG-MCs geprüft · "
+            f"{len(findings)} Pflichtangabe(n) offen"
+        )
 
         # ── Layer 3: Semantic-Validator nur für HIGH/MEDIUM-Fails ──
         await self._semantic_demote(text, findings, coverage)
@@ -199,7 +178,7 @@ class ImpressumAgent(BaseSpecialistAgent):
         candidates = [
             f for f in findings
             if f.severity in (Severity.HIGH.value, Severity.MEDIUM.value)
-            and f.severity_reason == "db_mc_failed"
+            and f.severity_reason == "pflichtangabe_missing"
         ]
         if not candidates:
             return
@@ -232,9 +211,11 @@ class ImpressumAgent(BaseSpecialistAgent):
                 detail=f"LLM-confirmed: '{label_used}'",
                 confidence=conf,
             ))
-            # Coverage update + auto-learning
+            # Coverage update + auto-learning (mc_id steckt in der Quelle)
+            mc_id_for_cov = (finding.sources[0].source_id
+                             if finding.sources else "")
             for c in coverage:
-                if c.mc_id and c.mc_id == f"DBMC-{finding.field_id}":
+                if c.mc_id and c.mc_id == mc_id_for_cov:
                     c.status = "low"
                     c.reason = f"label_mismatch: '{label_used}'"
             try: