feat(ner-first): integrate NER-first flow into pipeline (steps 5-6)

Step 5: anonymise_document_regex now accepts optional NER managers,
runs NER on the original (unmasked) text, and cross-validates
regex-extracted names against NER detections + INSEE gazetteers.
NER-only detections (names found by NER but missed by regex) are
also added. Falls back to original behavior when no NER is available.

Step 6: process_pdf passes NER managers into anonymise_document_regex
for NER-first cross-validation. The existing NER safety net pass on
masked text is preserved (double-pass: original + masked text).

Quality score: 100.0/100 (A+), zero regression.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

anonymizer_core_refactored_onnx.py

@@ -2446,7 +2446,8 @@ def _apply_trackare_hits_to_text(text: str, audit: List[PiiHit]) -> str:
 
 # ----------------- Anonymisation (regex) -----------------
 
-def anonymise_document_regex(pages_text: List[str], tables_lines: List[List[str]], cfg: Dict[str, Any]) -> AnonResult:
+def anonymise_document_regex(pages_text: List[str], tables_lines: List[List[str]], cfg: Dict[str, Any],
+                              eds_pseudo_mgr=None, gliner_mgr=None, camembert_mgr=None) -> AnonResult:
     audit: List[PiiHit] = []
 
     # Phase 0 : extraction globale des noms depuis les champs structurés
@@ -2464,8 +2465,39 @@ def anonymise_document_regex(pages_text: List[str], tables_lines: List[List[str]
         extracted_names.update(trackare_names)
         audit.extend(trackare_hits)
         all_candidates.extend(trackare_candidates)
-    # Fusionner les force_names des deux sources
-    all_force_names = doc_force_names | trackare_force_names
+
+    # --- NER-first : validation croisée des noms extraits par regex ---
+    # Exécuter NER sur le texte original (non masqué) si un moteur NER est disponible
+    ner_detections: List[NerDetection] = []
+    if eds_pseudo_mgr or gliner_mgr or camembert_mgr:
+        ner_detections = _run_ner_on_original_text(
+            pages_text, eds_pseudo_mgr, gliner_mgr, camembert_mgr, cfg
+        )
+
+    # Valider les candidats par croisement NER + INSEE
+    if ner_detections or _INSEE_NOMS_FAMILLE:
+        validated_names, validated_force = _cross_validate_name_candidates(
+            all_candidates, ner_detections, _INSEE_NOMS_FAMILLE, _INSEE_PRENOMS_SET,
+            _MEDICAL_STOP_WORDS_SET
+        )
+        # Utiliser les noms validés
+        all_names = validated_names
+        all_force_names = validated_force
+
+        # Ajouter les détections NER-only (noms trouvés par NER mais pas par regex)
+        for det in ner_detections:
+            if det.label in ("NOM", "PRENOM") and len(det.token) >= 4:
+                if det.token.lower() not in _MEDICAL_STOP_WORDS_SET:
+                    all_names.add(det.token)
+
+        log.info("NER-first cross-validation: %d noms validés (dont %d force), "
+                 "%d NER-only ajoutés (sur %d détections NER)",
+                 len(validated_names), len(validated_force),
+                 len(all_names) - len(validated_names), len(ner_detections))
+    else:
+        # Pas de NER, pas d'INSEE → fallback comportement original
+        all_names = extracted_names
+        all_force_names = doc_force_names | trackare_force_names
 
     # Phase 0c-url : détection et masquage des URLs (y compris coupées par saut de ligne)
     # Ex: "https://courrier\n.avenir-numerique.fr/owa/#path=/mail/inbox"
@@ -2555,8 +2587,9 @@ def anonymise_document_regex(pages_text: List[str], tables_lines: List[List[str]
     # Les PII détectés dans les tables sont toujours dans l'audit (Phase 1 regex).
 
     # Phase 2 : application globale des noms extraits (rattrapage)
-    if extracted_names:
-        text_out = _apply_extracted_names(text_out, extracted_names, audit, force_names=all_force_names)
+    # Utilise all_names (validé par NER-first si disponible, sinon extracted_names original)
+    if all_names:
+        text_out = _apply_extracted_names(text_out, all_names, audit, force_names=all_force_names)
 
     # Phase 2b : application globale des PiiHit (EPISODE, RPPS, FINESS)
     text_out = _apply_trackare_hits_to_text(text_out, audit)
@@ -4102,8 +4135,16 @@ def process_pdf(
     cfg = load_dictionaries(config_path)
     pages_text, tables_lines, ocr_used, ocr_word_map = extract_text_with_fallback_ocr(pdf_path)
 
-    # 1) Regex rules
-    anon = anonymise_document_regex(pages_text, tables_lines, cfg)
+    # 1) Regex rules + NER-first cross-validation
+    # Passer les NER managers pour que anonymise_document_regex exécute le NER
+    # sur le texte original (non masqué) et valide les noms extraits par regex
+    _eds_mgr_for_regex = ner_manager if (EdsPseudoManager is not None and isinstance(ner_manager, EdsPseudoManager)) else None
+    anon = anonymise_document_regex(
+        pages_text, tables_lines, cfg,
+        eds_pseudo_mgr=_eds_mgr_for_regex,
+        gliner_mgr=gliner_manager,
+        camembert_mgr=camembert_manager,
+    )
 
     # 1b) VLM (optionnel) — sur les PDFs scannés uniquement
     if ocr_used and vlm_manager is not None and VlmManager is not None: