♻️ refactory: Ajustando o código para o deploy (remoção provisória do user_id para testes).

app/api/routes.py

@@ -32,7 +32,8 @@ def get_db():
     finally:
         db.close()
 
-
+'''
+# Removido momentaniamente para teste do Vertex IA
 @router.post("/chat", response_model=ChatResponse)
 async def chat(request: ChatRequest, db: Session = Depends(get_db)):
     service = OrquestradorService(db)
@@ -41,6 +42,16 @@ async def chat(request: ChatRequest, db: Session = Depends(get_db)):
         user_id=request.user_id,
     )
     return ChatResponse(response=result)
+'''
+@router.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest, db: Session = Depends(get_db)):
+    service = OrquestradorService(db)
+
+    result = await service.handle_message(
+        message=request.message
+    )
+
+    return ChatResponse(response=result)
 
 
 @router.post("/mock/consultar-estoque")

app/api/schemas.py

@@ -3,7 +3,7 @@ from typing import Dict, Any, Optional
 
 class ChatRequest(BaseModel):
     message: str
-    user_id: str
+    # user_id: str -> Removido momentaniamente para testar o VertexIA
 
 class ChatResponse(BaseModel):
     response: str

app/core/settings.py

@@ -14,10 +14,16 @@ class Settings(BaseSettings):
     mockaroo_api_key: str
     mockaroo_base_url: str = "https://api.mockaroo.com/api"
     use_mockaroo_writes: bool = False
-
+    
+    environment: str = "production"
+    debug: bool = False
+    
+    # Cloud SQL
+    cloud_sql_connection_name: str | None = None
 
     class Config:
         env_file = ".env"
+        extra = "ignore"
 
 
 settings = Settings()

app/db/database.py

@@ -2,15 +2,31 @@ from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker, declarative_base
 from app.core.settings import settings
 
-DATABASE_URL = (
-    f"postgresql+psycopg2://{settings.db_user}:"
-    f"{settings.db_password}@"
-    f"{settings.db_host}:"
-    f"{settings.db_port}/"
-    f"{settings.db_name}"
-)
+if settings.cloud_sql_connection_name:
+    # Cloud Run
+    DATABASE_URL = (
+        f"postgresql+psycopg2://{settings.db_user}:"
+        f"{settings.db_password}@/"
+        f"{settings.db_name}"
+        f"?host=/cloudsql/{settings.cloud_sql_connection_name}"
+    )
+else:
+    # Ambiente local
+    DATABASE_URL = (
+        f"postgresql+psycopg2://{settings.db_user}:"
+        f"{settings.db_password}@"
+        f"{settings.db_host}:"
+        f"{settings.db_port}/"
+        f"{settings.db_name}"
+    )
 
-engine = create_engine(DATABASE_URL, echo=True)
+engine = create_engine(
+    DATABASE_URL, 
+    echo=True,
+    pool_pre_ping=True,
+    pool_recycle=3600,
+    connect_args={"connect_timeout": 10}
+)
 
 SessionLocal = sessionmaker(
     autocommit=False,

app/main.py

@@ -2,6 +2,8 @@ from fastapi import FastAPI
 from app.api.routes import router
 from app.api.tool_routes import router as tool_router
 from app.db.database import Base, engine
+# 👇 IMPORTANTE: registrar models no metadata
+from app.db.models import Tool
 
 app = FastAPI(title="AI Orquestrador")
 
@@ -12,17 +14,18 @@ app.include_router(tool_router)
 @app.on_event("startup")
 def startup_event():
     """
-    Inicializa o banco de dados na primeira execução:
-    - Cria todas as tabelas
-    - Faz seed das tools iniciais
+    Inicializa o banco de dados e executa seeds se necessário.
     """
     try:
-        # Cria as tabelas se não existirem
+        print("🚀 Inicializando banco de dados...")
+
         Base.metadata.create_all(bind=engine)
-        
-        # Seed das tools
+
         from app.db.tool_seed import seed_tools
         seed_tools()
+
+        print("✅ Banco inicializado com sucesso.")
+
     except Exception as e:
-        print(f"⚠️  Erro ao inicializar banco de dados: {e}")
-        # Não falha a aplicação, apenas registra o erro
+        print(f"❌ Erro ao inicializar banco: {e}")
+        raise e

app/services/llm_service.py

@@ -13,7 +13,7 @@ class LLMService:
             location=settings.google_location
         )
 
-        self.model = GenerativeModel("gemini-1.5-pro")
+        self.model = GenerativeModel("gemini-1.5-flash")
 
     def build_vertex_tools(self, tools: List[ToolDefinition]): #  Converte as Tools internas (ToolDefinition) para o formato que o Vertex AI entende.
 
@@ -56,11 +56,13 @@ class LLMService:
         # - histórico (se existir)
         # - ferramentas disponíveis
         chat = self.model.start_chat(
-            history=history or [],
-            tools=vertex_tools
+            history=history or []
         )
 
-        response = chat.send_message(message)
+        response = chat.send_message(
+            message,
+            tools=vertex_tools
+        )
 
         # Pegamos a primeira resposta candidata do modelo (a com maior coerência com o assunto)
         # Estrutura interna:

app/services/orquestrador_service.py

@@ -19,7 +19,7 @@ class OrquestradorService:
     - user_id: identificador do usuário (ainda não está sendo usado aqui,
     mas futuramente servirá para histórico)
     """
-    async def handle_message(self, message: str, user_id: str) -> str:
+    async def handle_message(self, message: str) -> str:
 
         tools = self.registry.get_tools()