feat(navigator): Product Regulatory Navigator as a thin missing-facts layer

Step 2 of the convergence sequence. The Navigator sits over the
CanonicalProductRegulatoryProfile (prefilled from company-profile / ProductWizard)
and reports ONLY which facts are still missing + prioritized questions to collect
them. It decides which facts are needed, NEVER what applies — that stays with the
Scope Engine (step 3). No regulation logic, no UI, no Go, no RAG.

- NavigatorQuestion (interaction type, NOT a compliance-meta-model class — freeze
  v1.0 untouched): question_id, target_field, label, why_needed,
  regulatory_domains_unblocked (static metadata), answer_type, options, priority.
- QUESTION_CATALOG: 12 questions over canonical gaps — P0 (markets, role,
  lifecycle, machine/component), P1 (radio, usage-data, security-function,
  environmental wastewater/air/chemicals triggers), P2 (structured BOM).
- engine: navigate() -> missing_facts + suggested_questions (priority-sorted) +
  completeness_summary (ready_for_scope = no P0 missing); apply_answers() ->
  updated profile. Pure field-presence; no scope import.
- 8 tests: <=10 questions for a filled company-profile, known facts not re-asked,
  environmental = trigger questions only (no law evaluation), apply round-trip,
  P0 ordering, ready_for_scope. 41 tests green, mypy clean, LOC ok.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

backend-compliance/compliance/navigator/engine.py

@@ -0,0 +1,116 @@
+"""Product Regulatory Navigator engine — missing-facts only.
+
+`navigate(profile)` reports which canonical fields are still unknown and the
+prioritized questions to fill them. `apply_answers(profile, answers)` returns the
+updated profile. It NEVER decides what applies — that is the Scope Engine (step 3).
+Pure field-presence checking; no scope-engine import, no regulation evaluation.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Type
+
+from pydantic import BaseModel, Field
+
+from compliance.profile.canonical import (
+    CanonicalLifecyclePhase,
+    CanonicalProductRegulatoryProfile,
+    EconomicOperatorRole,
+    ProductComponent,
+)
+
+from .questions import QUESTION_CATALOG, NavigatorQuestion, QuestionPriority
+
+_ENUM_FIELDS: Dict[str, Type[Any]] = {
+    "economic_operator_role": EconomicOperatorRole,
+    "lifecycle_phase": CanonicalLifecyclePhase,
+}
+
+
+class CompletenessSummary(BaseModel):
+    total_relevant: int
+    answered: int
+    missing: int
+    missing_by_priority: Dict[str, int] = Field(default_factory=dict)
+    ready_for_scope: bool  # True once no P0 fact is missing
+    note: str = ""
+
+
+class NavigatorResult(BaseModel):
+    missing_facts: List[str] = Field(default_factory=list)  # canonical target fields
+    suggested_questions: List[NavigatorQuestion] = Field(default_factory=list)
+    completeness_summary: CompletenessSummary
+
+
+def _value(profile: CanonicalProductRegulatoryProfile, dotted: str) -> Any:
+    if "." in dotted:
+        head, tail = dotted.split(".", 1)
+        return getattr(getattr(profile, head), tail, None)
+    return getattr(profile, dotted, None)
+
+
+def _is_unknown(profile: CanonicalProductRegulatoryProfile, q: NavigatorQuestion) -> bool:
+    value = _value(profile, q.target_field)
+    if value is None:
+        return True
+    if isinstance(value, list) and not value:
+        return True
+    return False
+
+
+def navigate(profile: CanonicalProductRegulatoryProfile) -> NavigatorResult:
+    missing = [q for q in QUESTION_CATALOG if _is_unknown(profile, q)]
+    missing.sort(key=lambda q: q.order())
+
+    by_priority: Dict[str, int] = {}
+    for q in missing:
+        by_priority[q.priority.value] = by_priority.get(q.priority.value, 0) + 1
+    ready = QuestionPriority.P0.value not in by_priority
+
+    total = len(QUESTION_CATALOG)
+    summary = CompletenessSummary(
+        total_relevant=total,
+        answered=total - len(missing),
+        missing=len(missing),
+        missing_by_priority=by_priority,
+        ready_for_scope=ready,
+        note=(
+            "%d von %d Fakten vorhanden; %d offen. Scope-Engine startklar: %s."
+            % (total - len(missing), total, len(missing), "ja" if ready else "nein (P0 fehlt)")
+        ),
+    )
+    return NavigatorResult(
+        missing_facts=[q.target_field for q in missing],
+        suggested_questions=missing,
+        completeness_summary=summary,
+    )
+
+
+def _coerce(q: NavigatorQuestion, value: Any) -> Any:
+    if q.target_field in _ENUM_FIELDS:
+        return _ENUM_FIELDS[q.target_field](value)
+    if q.target_field == "components":
+        return [c if isinstance(c, ProductComponent) else ProductComponent(**c) for c in (value or [])]
+    if q.answer_type.value in {"country_list", "multiselect"}:
+        return list(value or [])
+    if q.answer_type.value == "bool":
+        return bool(value)
+    return value
+
+
+def apply_answers(
+    profile: CanonicalProductRegulatoryProfile, answers: Dict[str, Any]
+) -> CanonicalProductRegulatoryProfile:
+    updated = profile.model_copy(deep=True)
+    by_id = {q.question_id: q for q in QUESTION_CATALOG}
+    for question_id, raw in answers.items():
+        q = by_id.get(question_id)
+        if q is None or raw is None:
+            continue
+        value = _coerce(q, raw)
+        if "." in q.target_field:
+            head, tail = q.target_field.split(".", 1)
+            setattr(getattr(updated, head), tail, value)
+        else:
+            setattr(updated, q.target_field, value)
+    return updated