IA : Claude substitut Qwen + auth rate limit prototype
- qualify_ai_service : ai_frame_async() avec Claude Haiku · round 1 → questions contextualisées si ANTHROPIC_API_KEY définie · round 2 → explication enrichie par Claude · fallback transparent sur ai_frame() si pas de clé (tests inchangés) - config : ANTHROPIC_API_KEY + ANTHROPIC_MODEL (claude-haiku-4-5-20251001) - requirements : anthropic>=0.97.0 - main : auth rate limit = RATE_LIMIT_DEFAULT partout (prototype mode) → supporte accès démo/test sans lockout en prod comme en dev Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Yvv
@@ -3,15 +3,23 @@
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Orchestrates a 2-round conversation that clarifies reversibility and urgency
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before producing a final QualificationResult.
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Currently a rule-based stub — will be replaced by Qwen3.6 (MacStudio) calls
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once the local LLM endpoint is available. The interface is stable: callers
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always receive AIFrameResponse; the underlying engine is swappable.
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Two entry points:
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ai_frame() — synchronous, rule-based only. Used by tests.
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ai_frame_async() — async, enriched by Claude API (or Qwen3.6 later).
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Falls back to ai_frame() if ANTHROPIC_API_KEY is unset.
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The interface (AIFrameRequest / AIFrameResponse) is stable; the underlying
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engine is swappable (Qwen3.6 MacStudio planned to replace Claude calls).
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"""
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from __future__ import annotations
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import json
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import logging
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from dataclasses import dataclass
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Schemas (dataclasses — no Pydantic dependency in the engine layer)
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@@ -66,8 +74,7 @@ class AIFrameResponse:
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# ---------------------------------------------------------------------------
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# Standard clarifying questions (stub — same regardless of context)
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# Real Qwen integration will generate context-aware questions
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# Standard clarifying questions (fallback when no context or API unavailable)
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# ---------------------------------------------------------------------------
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@@ -94,19 +101,18 @@ _CLARIFYING_QUESTIONS: list[AIQuestion] = [
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# ---------------------------------------------------------------------------
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# Core function
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# Synchronous rule-based engine (used directly by tests)
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# ---------------------------------------------------------------------------
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def ai_frame(request: AIFrameRequest) -> AIFrameResponse:
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"""Run one round of AI framing.
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"""Run one round of rule-based AI framing.
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Round 1 (messages=[]) → return 2 clarifying questions, done=False
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Round 2 (messages set) → parse answers, qualify, return result, done=True
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"""
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messages = request.messages or []
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# ── Round 1: no conversation yet ────────────────────────────────────────
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if not messages:
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return AIFrameResponse(
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done=False,
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@@ -115,7 +121,6 @@ def ai_frame(request: AIFrameRequest) -> AIFrameResponse:
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explanation=None,
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)
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# ── Round 2: answers present → qualify ──────────────────────────────────
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answers = _parse_answers(messages)
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result = _build_result(request, answers)
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explanation = _build_explanation(answers)
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@@ -129,7 +134,135 @@ def ai_frame(request: AIFrameRequest) -> AIFrameResponse:
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# ---------------------------------------------------------------------------
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# Helpers
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# Async Claude-enriched entry point (used by the router)
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# ---------------------------------------------------------------------------
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async def ai_frame_async(request: AIFrameRequest) -> AIFrameResponse:
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"""Async entry point: rule engine + Claude enrichment.
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Falls back to ai_frame() if ANTHROPIC_API_KEY is not configured.
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Qwen3.6 (MacStudio) will replace Claude calls when available.
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"""
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base = ai_frame(request)
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client = _get_claude_client()
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if client is None:
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return base
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messages = request.messages or []
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if not messages:
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# Round 1: context-aware questions
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enriched_questions = await _claude_questions(client, request.context, base.questions)
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return AIFrameResponse(done=False, questions=enriched_questions, result=None, explanation=None)
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else:
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# Round 2: enriched explanation
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enriched_explanation = await _claude_explanation(client, request, base)
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return AIFrameResponse(
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done=True,
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questions=[],
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result=base.result,
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explanation=enriched_explanation or base.explanation,
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)
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# ---------------------------------------------------------------------------
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# Claude helpers
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# ---------------------------------------------------------------------------
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def _get_claude_client():
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"""Return an AsyncAnthropic client if ANTHROPIC_API_KEY is set, else None."""
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try:
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from app.config import settings
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if not settings.ANTHROPIC_API_KEY:
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return None
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import anthropic
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return anthropic.AsyncAnthropic(api_key=settings.ANTHROPIC_API_KEY)
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except Exception:
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return None
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async def _claude_questions(client, context: str | None, fallback: list[AIQuestion]) -> list[AIQuestion]:
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"""Generate context-aware clarifying questions. Falls back to standard questions on error."""
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if not context:
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return fallback
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from app.config import settings
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prompt = f"""Tu assistes à la qualification d'une décision collective dans la communauté Duniter/G1 (monnaie libre).
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Contexte de la décision : {context}
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Génère 2 questions de clarification courtes et précises pour mieux qualifier cette décision.
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Chaque question doit avoir exactement 3 options de réponse.
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Les questions doivent porter sur la réversibilité et l'urgence, adaptées au contexte.
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Réponds UNIQUEMENT en JSON valide, sans texte avant ni après :
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[
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{{"id": "reversibility", "text": "...", "options": ["option1", "option2", "option3"]}},
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{{"id": "urgency", "text": "...", "options": ["option1", "option2", "option3"]}}
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]"""
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try:
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message = await client.messages.create(
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model=settings.ANTHROPIC_MODEL,
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max_tokens=512,
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messages=[{"role": "user", "content": prompt}],
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)
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raw = message.content[0].text.strip()
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start = raw.find("[")
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end = raw.rfind("]") + 1
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data = json.loads(raw[start:end])
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return [AIQuestion(id=q["id"], text=q["text"], options=q["options"]) for q in data]
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except Exception as exc:
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logger.warning("Claude question generation failed: %s — using fallback", exc)
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return fallback
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async def _claude_explanation(client, request: AIFrameRequest, base: AIFrameResponse) -> str | None:
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"""Generate a contextual explanation for the qualification result."""
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if base.result is None:
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return None
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from app.config import settings
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answers = _parse_answers(request.messages or [])
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rev = answers.get("reversibility", "non précisé")
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urg = answers.get("urgency", "non précisé")
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context_line = f"\nContexte : {request.context}" if request.context else ""
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prompt = f"""Tu qualifies une décision collective pour la communauté Duniter/G1 (monnaie libre).{context_line}
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Paramètres :
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- Personnes concernées : {request.affected_count or 'non précisé'}
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- Dans le cadre d'un mandat : {'oui' if request.within_mandate else 'non'}
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- Décision structurante (on-chain) : {'oui' if request.is_structural else 'non'}
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- Réversibilité : {rev}
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- Urgence : {urg}
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Résultat du moteur de qualification :
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- Type : {base.result.decision_type}
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- Processus recommandé : {base.result.process}
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- Modalités : {', '.join(base.result.recommended_modalities) or 'aucune'}
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- Gravure on-chain : {'recommandée' if base.result.recommend_onchain else 'non nécessaire'}
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Rédige une explication courte (2-3 phrases) qui explique pourquoi ce processus est recommandé \
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pour cette décision spécifique. Sois direct et concis. Réponds en français."""
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try:
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message = await client.messages.create(
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model=settings.ANTHROPIC_MODEL,
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max_tokens=300,
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messages=[{"role": "user", "content": prompt}],
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)
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return message.content[0].text.strip()
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except Exception as exc:
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logger.warning("Claude explanation generation failed: %s — using fallback", exc)
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return None
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# ---------------------------------------------------------------------------
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# Rule-based helpers (shared by sync and async paths)
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# ---------------------------------------------------------------------------
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@@ -137,7 +270,6 @@ def _parse_answers(messages: list[AIMessage]) -> dict[str, str]:
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"""Extract question answers from the last user message.
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Expected format: "reversibility:<answer>|urgency:<answer>"
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Anything not matching is treated as free text for context.
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"""
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answers: dict[str, str] = {}
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for msg in reversed(messages):
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@@ -169,14 +301,10 @@ def _build_result(request: AIFrameRequest, answers: dict[str, str]) -> AIQualify
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reasons = list(base.reasons)
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# Reversibility adjustment
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reversibility = answers.get("reversibility", "")
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if "irréversible" in reversibility.lower():
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reasons.append("Décision irréversible : consensus élevé recommandé.")
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if not base.recommend_onchain and request.is_structural:
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pass # already handled by engine
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# Urgency note
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urgency = answers.get("urgency", "")
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if "urgente" in urgency.lower() or "< 1" in urgency:
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reasons.append("Urgence signalée : privilégier un protocole à délai court.")
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