feat: méthode TIM experte CPAM + moteur de règles étendu
CPAM — Méthode TIM (mémoire en défense) : - Réécriture CPAM_ARGUMENTATION avec raisonnement 5 passes TIM (contexte admin → motif réel → confrontation bio → hiérarchie → validation défensive) - _BIO_THRESHOLDS (19 entrées) + _build_bio_confrontation() pour confrontation biologie/diagnostic avec seuils chiffrés et verdicts - _format_response() dual format : nouveau TIM (moyens numérotés, tableau bio, codes non défendables, conclusion dispositive) + rétrocompat legacy - CPAM_ADVERSARIAL mis à jour pour vérifier honnêteté intellectuelle - Tests adaptés + 12 nouveaux tests (bio confrontation, format TIM) Moteur de règles : - Nouvelles règles YAML : demographic, diagnostic_conflicts, procedure_diagnosis, temporal, parcours - Bio extraction FAISS (synonymes vectoriels) - Veto engine enrichi (citations, Trackare skip, règles démographiques) - Decision engine : _apply_bio_rules_gen() + matchers analytiques Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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dom
@@ -38,8 +38,10 @@ from .cpam_context import ( # noqa: F401
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_get_code_label,
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_get_cim10_definitions,
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_BIO_INTERPRETATION,
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_BIO_THRESHOLDS,
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_assess_dossier_strength,
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_build_bio_summary,
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_build_bio_confrontation,
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_check_das_bio_coherence,
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)
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from .cpam_validation import _CIM10_CODE_RE, _validate_adversarial as _validate_adversarial, _assess_quality_tier as _assess_quality_tier, _fuzzy_match_ref as _fuzzy_match_ref, _sanitize_unauthorized_codes as _sanitize_unauthorized_codes # noqa: F401
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@@ -120,6 +122,11 @@ def generate_cpam_response(
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# 1. Passe 1 — Extraction structurée (compréhension avant argumentation)
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extraction = _extraction_pass(dossier, controle)
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degraded_pass1 = extraction is None
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if degraded_pass1:
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dossier.alertes_codage.append(
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"CPAM: passe 1 (extraction structurée) échouée → mode dégradé"
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)
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# 2. Recherche RAG ciblée
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sources = _search_rag_for_control(controle, dossier)
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@@ -153,6 +160,12 @@ def generate_cpam_response(
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logger.warning(" LLM non disponible — contre-argumentation non générée")
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return "", None, rag_sources
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# 5b. LOGIC-2 — Marquer le mode dégradé dans le résultat
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if degraded_pass1:
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result.setdefault("quality_flags", {})
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result["quality_flags"]["cpam_pass1_failed"] = True
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result["quality_flags"]["degraded_mode"] = True
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# 6. Sanitisation déterministe — supprime les codes CIM-10 hors périmètre
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sanitized = _sanitize_unauthorized_codes(result, dossier, controle)
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if sanitized:
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@@ -175,6 +188,16 @@ def generate_cpam_response(
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logger.warning(" CPAM : %d code(s) hors périmètre", len(code_warnings))
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# 8. Validation adversariale (cohérence factuelle)
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# LOGIC-3 : détecter si modèles identiques AVANT l'appel
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from ..config import check_adversarial_model_config
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same_model, model_msg = check_adversarial_model_config()
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if same_model:
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result.setdefault("quality_flags", {})
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result["quality_flags"]["adversarial_disabled_same_model"] = True
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dossier.alertes_codage.append(
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"Validation adversariale désactivée (modèles identiques)"
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)
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adversarial_warnings: list[str] = []
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validation = _validate_adversarial(result, tag_map, controle)
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if validation and not validation.get("coherent", True):
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@@ -186,48 +209,51 @@ def generate_cpam_response(
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if adversarial_warnings:
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adversarial_warnings.append(f"Score de confiance : {score}/10")
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# 8b. Boucle de correction (max 1 retry)
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if (validation
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and not validation.get("coherent", True)
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and validation.get("score_confiance", 10) <= 5
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and rule_enabled("RULE-CPAM-CORRECTION-LOOP")):
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# 8b. Boucle de correction (max 2 retries)
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max_corrections = 2
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for attempt in range(max_corrections):
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if not (validation
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and not validation.get("coherent", True)
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and validation.get("score_confiance", 10) <= 5
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and rule_enabled("RULE-CPAM-CORRECTION-LOOP")):
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break
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erreurs_v = validation.get("erreurs", [])
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logger.warning(" Score adversarial %s/10 — correction en cours (%d erreur(s))",
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validation.get("score_confiance"), len(erreurs_v))
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logger.warning(" Score adversarial %s/10 — correction %d/%d (%d erreur(s))",
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validation.get("score_confiance"), attempt + 1, max_corrections, len(erreurs_v))
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correction_prompt = _build_correction_prompt(prompt, result, validation)
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corrected = call_ollama(correction_prompt, temperature=0.0, max_tokens=16000, role="cpam")
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if corrected is None:
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corrected = call_anthropic(correction_prompt, temperature=0.0, max_tokens=16000)
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if corrected:
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# Re-valider la correction
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validation2 = _validate_adversarial(corrected, tag_map, controle)
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score2 = validation2.get("score_confiance", 0) if validation2 else 0
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score1 = validation.get("score_confiance", 0)
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if not corrected:
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break
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if score2 > score1:
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logger.info(" Correction acceptée (score %s → %s)", score1, score2)
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result = corrected
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validation = validation2
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# Sanitiser + recalculer les warnings
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_sanitize_unauthorized_codes(result, dossier, controle)
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ref_warnings = _validate_references(result, sources)
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grounding_warnings = _validate_grounding(result, tag_map)
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code_warnings = _validate_codes_in_response(result, dossier, controle)
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adversarial_warnings = []
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if validation and not validation.get("coherent", True):
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for e in validation.get("erreurs", []):
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if isinstance(e, str) and e.strip():
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adversarial_warnings.append(f"Incohérence détectée : {e}")
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if adversarial_warnings:
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adversarial_warnings.append(
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f"Score de confiance : {validation.get('score_confiance', '?')}/10"
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)
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else:
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logger.warning(" Correction rejetée (score %s → %s) — conserve l'original",
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score1, score2)
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validation2 = _validate_adversarial(corrected, tag_map, controle)
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score2 = validation2.get("score_confiance", 0) if validation2 else 0
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score1 = validation.get("score_confiance", 0)
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if score2 > score1:
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logger.info(" Correction %d acceptée (score %s → %s)", attempt + 1, score1, score2)
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result = corrected
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validation = validation2
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_sanitize_unauthorized_codes(result, dossier, controle)
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ref_warnings = _validate_references(result, sources)
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grounding_warnings = _validate_grounding(result, tag_map)
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code_warnings = _validate_codes_in_response(result, dossier, controle)
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adversarial_warnings = []
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if validation and not validation.get("coherent", True):
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for e in validation.get("erreurs", []):
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if isinstance(e, str) and e.strip():
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adversarial_warnings.append(f"Incohérence détectée : {e}")
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if adversarial_warnings:
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adversarial_warnings.append(
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f"Score de confiance : {validation.get('score_confiance', '?')}/10"
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)
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else:
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logger.warning(" Correction %d rejetée (score %s → %s)", attempt + 1, score1, score2)
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break
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all_warnings = ref_warnings + grounding_warnings + code_warnings + adversarial_warnings
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