feat: Update default models for extraction and normalization to GPT-5.6 Luna across configuration files and documentation. Remove deprecated code and improve pricing details in the UI components. Add tests to ensure correct model settings and pricing calculations.

services/extraction/worker/tasks.py

@@ -17,6 +17,7 @@ from infrastructure.serialization import flatten_extraction_result
 from api.desktop_ingest import (
     _finalize_radicado_if_ready,
     _paths_for_type,
+    _service_for_model,
     mark_task_completed_if_done,
 )
 from domain.profiles import get_profile
@@ -27,6 +28,27 @@ from domain.rate_limiter import get_rate_limiter
 logger = logging.getLogger(__name__)
 
 
+def _service_for_request(
+    ctx: dict[str, Any], request: dict[str, Any]
+) -> Any:
+    """Resuelve y reutiliza el servicio correspondiente al modelo solicitado."""
+    base_service = ctx["service"]
+    requested_model = str(request.get("model") or "").strip() or None
+    if not requested_model:
+        return base_service
+
+    current_model = getattr(getattr(base_service, "extractor", None), "model", None)
+    if current_model == requested_model:
+        return base_service
+
+    services_by_model = ctx.setdefault("services_by_model", {})
+    service = services_by_model.get(requested_model)
+    if service is None:
+        service = _service_for_model(base_service, requested_model)
+        services_by_model[requested_model] = service
+    return service
+
+
 async def process_document(
     ctx: dict[str, Any],
     *,
@@ -52,7 +74,6 @@ async def process_document(
         instruction: Instrucción de extracción personalizada.
         tiff_page_range: Rango de páginas TIFF [inicio, fin].
     """
-    service = ctx["service"]
     rate_limiter = get_rate_limiter()
     tiff_range = tuple(tiff_page_range) if tiff_page_range else None
 
@@ -72,6 +93,14 @@ async def process_document(
             logger.warning("Tarea %s no encontrada — saltando %s", task_id, filename)
             return {"skipped": True, "reason": "task_not_found"}
 
+        service = _service_for_request(ctx, request)
+        effective_model = getattr(getattr(service, "extractor", None), "model", "?")
+        logger.info(
+            "Modelo resuelto para task=%s: %s",
+            task_id,
+            effective_model,
+        )
+
         fields: list[str] = list(request.get("fields") or [])
         alias_map: dict[str, str] = dict(request.get("alias_map") or {})
         structure_mode: str = str(request.get("structure_mode") or "radicados")