🛡️ fix(orchestration): endurecer extracao estruturada e fallbacks de llm

app/core/settings.py

@@ -11,6 +11,7 @@ class Settings(BaseSettings):
     google_project_id: str
     google_location: str = "us-central1"
     vertex_model_name: str = "gemini-2.5-pro"
+    vertex_bundle_model_name: str = "gemini-2.5-pro"
 
     # Tools database (MySQL)
     db_host: str = "127.0.0.1"

app/services/ai/llm_service.py

@@ -39,7 +39,11 @@ class LLMService:
 
         configured = settings.vertex_model_name.strip()
         fallback_models = ["gemini-2.5-pro", "gemini-2.5-flash", "gemini-2.0-flash-001"]
-        self.model_names = [configured] + [m for m in fallback_models if m != configured]
+        self.model_names = self._build_model_sequence(configured, *fallback_models)
+        self.bundle_model_names = self._build_model_sequence(
+            settings.vertex_bundle_model_name.strip(),
+            *self.model_names,
+        )
 
     def _log_llm_event(self, event: str, **payload) -> None:
         logger.info("llm_service_event=%s payload=%s", event, payload)
@@ -206,6 +210,14 @@ class LLMService:
             LLMService._models[model_name] = model
         return model
 
+    def _build_model_sequence(self, *model_names: str | None) -> list[str]:
+        sequence: list[str] = []
+        for item in model_names:
+            candidate = str(item or "").strip()
+            if candidate and candidate not in sequence:
+                sequence.append(candidate)
+        return sequence
+
     def _extract_response_payload(self, response) -> Dict[str, Any]:
         candidate = response.candidates[0] if getattr(response, "candidates", None) else None
         content = getattr(candidate, "content", None)
@@ -224,7 +236,22 @@ class LLMService:
             if isinstance(text_value, str) and text_value.strip():
                 text_parts.append(text_value)
 
-        response_text = "\n".join(text_parts).strip() or None
+        response_text = "\n".join(text_parts).strip()
+        if not response_text:
+            fallback_text = None
+            for carrier in (response, candidate, content):
+                if carrier is None:
+                    continue
+                try:
+                    text_value = getattr(carrier, "text", None)
+                except (AttributeError, ValueError):
+                    text_value = None
+                if isinstance(text_value, str) and text_value.strip():
+                    fallback_text = text_value.strip()
+                    break
+            response_text = fallback_text or None
+        else:
+            response_text = response_text or None
         return {
             "response": response_text,
             "tool_call": tool_call,
@@ -235,9 +262,12 @@ class LLMService:
         message: str,
         tools: List[ToolDefinition],
         history: List[Dict[str, Any]] = None,
+        preferred_models: List[str] | None = None,
+        generation_config: Dict[str, Any] | None = None,
     ) -> Dict[str, Any]:
         """Gera resposta textual ou chamada de tool a partir da mensagem do usuario."""
         vertex_tools = self.build_vertex_tools(tools)
+        candidate_models = self._build_model_sequence(*(preferred_models or []), *self.model_names)
 
         response = None
         last_error = None
@@ -247,13 +277,18 @@ class LLMService:
 
         # Tenta o modelo configurado e cai para nomes alternativos
         # quando o principal nao estiver disponivel no projeto/regiao.
-        for model_name in self.model_names:
+        for model_name in candidate_models:
             attempts += 1
             try:
                 model = self._get_model(model_name)
-                chat = model.start_chat(history=history or [])
                 send_kwargs = {"tools": vertex_tools} if vertex_tools else {}
-                response = await asyncio.to_thread(chat.send_message, message, **send_kwargs)
+                if generation_config:
+                    send_kwargs["generation_config"] = generation_config
+                if history:
+                    chat = model.start_chat(history=history)
+                    response = await asyncio.to_thread(chat.send_message, message, **send_kwargs)
+                else:
+                    response = await asyncio.to_thread(model.generate_content, message, **send_kwargs)
                 selected_model_name = model_name
                 break
             except NotFound as err:
@@ -297,3 +332,5 @@ class LLMService:
         except Exception:
             # Warmup e melhor esforco; falhas nao devem bloquear inicializacao.
             return
+
+

app/services/orchestration/message_planner.py

@@ -58,6 +58,7 @@ class MessagePlanner:
         except Exception:
             logger.exception("Falha ao extrair plano da mensagem com LLM. user_id=%s", user_id)
             return default
+
     async def extract_turn_bundle(self, message: str, user_id: int | None) -> dict:
         user_context = f"user_id={user_id}" if user_id is not None else "user_id=anonimo"
         default_turn_decision = self.normalizer.empty_turn_decision()
@@ -100,32 +101,40 @@ class MessagePlanner:
         )
         prompt = (
             "Analise a mensagem do usuario e retorne APENAS JSON valido com duas secoes: turn_decision e message_plan.\n"
-            "Nao use markdown. Nao escreva texto fora do JSON. Nao invente dados ausentes.\n\n"
+            "Sem markdown, sem texto fora do JSON, sem inventar dados ausentes.\n\n"
-            "Formato obrigatorio:\n"
+            "Contrato:\n"
             f"{schema_example}\n\n"
-            "Regras gerais:\n"
+            "Regras:\n"
-            "- turn_decision resume a intencao principal do turno completo.\n"
+            "- turn_decision resume a intencao principal do turno; domain deve ser review, sales ou general.\n"
-            "- message_plan.orders separa pedidos operacionais em ordem de aparicao; se nao houver pedido operacional, use um unico item general com a mensagem inteira.\n"
+            "- message_plan.orders separa pedidos operacionais em ordem; se nao houver pedido operacional, use um unico item general com a mensagem inteira.\n"
-            "- Cada item de orders deve conter domain, message e entities. Mantenha message curta e fiel ao texto do usuario.\n"
+            "- Cada order deve ter domain, message e entities; mantenha message curta e fiel ao texto do usuario.\n"
-            "- domain deve ser review, sales ou general.\n"
+            "- Preencha apenas dados claros. Use entities.generic_memory.orcamento_max para teto/faixa de preco e perfil_veiculo para suv/sedan/hatch/pickup.\n"
-            "- Em pedidos de compra com faixa de preco ou orcamento (ex.: 70 mil, ate 50 mil, R$ 45000), preencha entities.generic_memory.orcamento_max nas secoes relevantes.\n"
+            "- Se faltar dado para continuar um fluxo, use action=ask_missing_fields e preencha missing_fields e response_to_user. Se nao houver acao operacional, use action=answer_user.\n"
-            "- Em pedidos com tipo de carro (ex.: suv, sedan, hatch, pickup), preencha entities.generic_memory.perfil_veiculo nas secoes relevantes.\n"
+            "- Compra efetiva: intent=order_create, domain=sales, prefira tool_name=realizar_pedido.\n"
-            "- Se faltar dado para continuar um fluxo, use action=ask_missing_fields e preencha missing_fields e response_to_user.\n"
+            "- Listar pedidos: intent=order_list, domain=sales, action=call_tool, tool_name=listar_pedidos.\n"
-            "- Se nao houver acao operacional, use action=answer_user.\n"
+            "- Consultar/listar/buscar/ver estoque para compra: intent=inventory_search, domain=sales, action=call_tool, tool_name=consultar_estoque; tool_arguments so com filtros explicitamente pedidos, como preco_max, categoria e opcionalmente limite.\n"
-            "- Se o usuario quiser efetivar a compra de um veiculo, use intent=order_create, domain=sales e prefira tool_name=realizar_pedido.\n"
+            "- Listar revisoes: intent=review_list, domain=review, action=call_tool, tool_name=listar_agendamentos_revisao.\n"
-            "- Se o usuario quiser listar pedidos, use intent=order_list, domain=sales, action=call_tool e tool_name=listar_pedidos.\n"
+            "- Cancelar revisao: intent=review_cancel, domain=review, prefira tool_name=cancelar_agendamento_revisao.\n"
-            "- Se o usuario quiser consultar estoque com filtros de compra, use intent=inventory_search e domain=sales.\n"
+            "- Remarcar revisao: intent=review_reschedule, domain=review, prefira tool_name=editar_data_revisao.\n"
-            "- Se o usuario quiser listar revisoes, use intent=review_list, domain=review, action=call_tool e tool_name=listar_agendamentos_revisao.\n"
+            "- Avaliar troca com modelo, ano e km: domain=sales, action=call_tool, tool_name=avaliar_veiculo_troca e informe esses campos em tool_arguments.\n\n"
-            "- Se o usuario quiser cancelar revisao, use intent=review_cancel, domain=review e prefira tool_name=cancelar_agendamento_revisao.\n"
-            "- Se o usuario quiser remarcar revisao, use intent=review_reschedule, domain=review e prefira tool_name=editar_data_revisao.\n"
-            "- Se o usuario quiser avaliar um veiculo na troca e houver modelo, ano e km, use domain=sales, action=call_tool, tool_name=avaliar_veiculo_troca e informe esses campos em tool_arguments.\n\n"
             f"Contexto: {user_context}\n"
             f"Mensagem do usuario: {message}"
         )
+        preferred_models = getattr(self.llm, "bundle_model_names", None)
+        bundle_generation_config = {
+            "candidate_count": 1,
+            "temperature": 0,
+            "max_output_tokens": 768,
+        }
 
         for attempt in range(2):
             try:
-                result = await self.llm.generate_response(message=prompt, tools=[])
+                result = await self.llm.generate_response(
+                    message=prompt,
+                    tools=[],
+                    preferred_models=preferred_models if attempt == 0 else None,
+                    generation_config=bundle_generation_config,
+                )
                 text = (result.get("response") or "").strip()
                 payload = self.normalizer.parse_json_object(text)
                 if not isinstance(payload, dict):
@@ -178,6 +187,7 @@ class MessagePlanner:
             "has_turn_decision": False,
             "has_message_plan": False,
         }
+
     async def extract_routing(self, message: str, user_id: int | None) -> dict:
         plan = await self.extract_message_plan(message=message, user_id=user_id)
         return {
@@ -189,7 +199,6 @@ class MessagePlanner:
                 for item in plan.get("orders", [])
             ]
         }
-
     async def extract_entities(self, message: str, user_id: int | None) -> dict:
         user_context = f"user_id={user_id}" if user_id is not None else "user_id=anonimo"
         prompt = (
@@ -312,6 +321,8 @@ class MessagePlanner:
             "- Em pedidos com tipo de carro (ex.: suv, sedan, hatch, pickup), preencha entities.generic_memory.perfil_veiculo.\n"
             "- Se o usuario quiser efetivar a compra de um veiculo, use intent='order_create', domain='sales' e prefira tool_name='realizar_pedido'.\n"
             "- Se o usuario quiser listar os pedidos dele, use intent='order_list', domain='sales', action='call_tool' e tool_name='listar_pedidos'.\n"
+            "- Se o usuario quiser consultar, listar, buscar ou ver veiculos/estoque para compra, use intent='inventory_search', domain='sales', action='call_tool' e tool_name='consultar_estoque'.\n"
+            "- Em consultar_estoque, preencha tool_arguments apenas com filtros claramente expressos pelo usuario, como preco_max, categoria e opcionalmente limite.\n"
             "- Se o usuario quiser listar agendamentos de revisao, use intent='review_list', domain='review', action='call_tool' e tool_name='listar_agendamentos_revisao'.\n"
             "- Se o usuario quiser cancelar um agendamento de revisao, use intent='review_cancel', domain='review' e prefira tool_name='cancelar_agendamento_revisao'.\n"
             "- Se o usuario quiser remarcar um agendamento de revisao, use intent='review_reschedule', domain='review' e prefira tool_name='editar_data_revisao'.\n"
@@ -370,3 +381,5 @@ class MessagePlanner:
             "cancel_order_fields": self.normalizer.normalize_cancel_order_fields(coerced.get("cancel_order_fields")),
             "intents": self.normalizer.normalize_intents(coerced.get("intents")),
         }
+
+

app/services/orchestration/technical_normalizer.py

@@ -92,7 +92,7 @@ def extract_budget_from_text(text: str) -> float | None:
 
     normalized = normalize_text(candidate)
     keyword_match = re.search(
-        r"(?:ate|até|de|por|orcamento|orçamento)\s+(\d[\d\.\,\s]{1,12})(?!\d)",
+        r"(?:ate|até|de|por|orcamento|orçamento)\s+(\d[\d\.\,\s]{1,12})(?!\d)",
         normalized,
         flags=re.IGNORECASE,
     )
@@ -128,8 +128,12 @@ def normalize_bool(value) -> bool | None:
 
 def normalize_datetime_connector(text: str) -> str:
     compact = " ".join(str(text or "").strip().split())
-    return re.sub(r"\s+[aàáâã]s\s+", " ", compact, flags=re.IGNORECASE).strip()
+    return re.sub(
-
+        r"((?:\d{1,2}[/-]\d{1,2}[/-]\d{4})|(?:\d{4}[/-]\d{1,2}[/-]\d{1,2}))\s+(?:[^\d\s]{1,6}\s+){1,2}(\d{1,2}:\d{2}(?::\d{2})?(?:\s*(?:Z|[+-]\d{2}:\d{2}))?)",
+        r"\1 \2",
+        compact,
+        flags=re.IGNORECASE,
+    ).strip()
 
 def try_parse_iso_datetime(text: str) -> datetime | None:
     candidate = str(text or "").strip()

tests/test_llm_service.py

@@ -62,6 +62,25 @@ class LLMServiceResponseParsingTests(unittest.TestCase):
 
         self.assertEqual(payload, {"response": "Resposta simples", "tool_call": None})
 
+    def test_extract_response_payload_falls_back_to_response_text_accessor(self):
+        service = LLMService.__new__(LLMService)
+        response = SimpleNamespace(
+            text='{"ok": true}',
+            candidates=[
+                SimpleNamespace(
+                    content=SimpleNamespace(
+                        parts=[
+                            SimpleNamespace(function_call=None),
+                        ]
+                    )
+                )
+            ]
+        )
+
+        payload = service._extract_response_payload(response)
+
+        self.assertEqual(payload, {"response": '{"ok": true}', "tool_call": None})
+
 
 class LLMServiceImageWorkflowPromptTests(unittest.TestCase):
     def test_build_image_workflow_prompt_preserves_visible_payment_time(self):
@@ -102,3 +121,87 @@ class LLMServiceImageWorkflowPromptTests(unittest.TestCase):
             response,
             "Registrar pagamento de aluguel: contrato LOC-20260319-33CD6567; valor R$ 379,80.",
         )
+
+class LLMServiceDispatchTests(unittest.IsolatedAsyncioTestCase):
+    async def test_generate_response_uses_generate_content_when_history_is_empty(self):
+        service = LLMService.__new__(LLMService)
+        service.model_names = ["gemini-2.5-pro"]
+        service._log_llm_event = lambda *args, **kwargs: None
+        service.build_vertex_tools = lambda tools: None
+
+        class DummyChat:
+            def __init__(self):
+                self.calls = []
+
+            def send_message(self, message, **kwargs):
+                self.calls.append((message, kwargs))
+                return SimpleNamespace(candidates=[])
+
+        class DummyModel:
+            def __init__(self):
+                self.generate_calls = []
+                self.chat = DummyChat()
+
+            def generate_content(self, message, **kwargs):
+                self.generate_calls.append((message, kwargs))
+                return SimpleNamespace(candidates=[])
+
+            def start_chat(self, history):
+                raise AssertionError("nao deveria abrir chat quando nao ha historico")
+
+        model = DummyModel()
+        service._get_model = lambda model_name: model
+        service._extract_response_payload = lambda response: {"response": "ok", "tool_call": None}
+
+        generation_config = {"temperature": 0, "max_output_tokens": 128}
+
+        payload = await service.generate_response(
+            message="teste",
+            tools=[],
+            history=[],
+            generation_config=generation_config,
+        )
+
+        self.assertEqual(payload, {"response": "ok", "tool_call": None})
+        self.assertEqual(
+            model.generate_calls,
+            [("teste", {"generation_config": generation_config})],
+        )
+
+    async def test_generate_response_uses_chat_when_history_is_present(self):
+        service = LLMService.__new__(LLMService)
+        service.model_names = ["gemini-2.5-pro"]
+        service._log_llm_event = lambda *args, **kwargs: None
+        service.build_vertex_tools = lambda tools: None
+
+        class DummyChat:
+            def __init__(self):
+                self.calls = []
+
+            def send_message(self, message, **kwargs):
+                self.calls.append((message, kwargs))
+                return SimpleNamespace(candidates=[])
+
+        class DummyModel:
+            def __init__(self):
+                self.chat = DummyChat()
+                self.histories = []
+
+            def generate_content(self, message, **kwargs):
+                raise AssertionError("nao deveria usar generate_content quando ha historico")
+
+            def start_chat(self, history):
+                self.histories.append(history)
+                return self.chat
+
+        model = DummyModel()
+        service._get_model = lambda model_name: model
+        service._extract_response_payload = lambda response: {"response": "ok", "tool_call": None}
+        history = [{"role": "user", "parts": ["oi"]}]
+
+        payload = await service.generate_response(message="teste", tools=[], history=history)
+
+        self.assertEqual(payload, {"response": "ok", "tool_call": None})
+        self.assertEqual(model.histories, [history])
+        self.assertEqual(model.chat.calls, [("teste", {})])
+