Feat: Pulumi S3

back/app/backend/orquestrador.py

@@ -2,17 +2,16 @@ from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
 from langfuse.langchain import CallbackHandler
 
 from .config import REGION
-from .dynamo import langfuse, get_contexto
 from .agent_bedrock import create_agent
-from .tools import ReportTools
+from .tools import build_knowledge_base_tool
 
 
-def main(user_query, history, model, base):
+def main(user_query, history, model="anthropic.claude-sonnet-4-5-20250929-v1:0"):
     """Main execution function."""
-    report_tools = []
+    report_tools = [build_knowledge_base_tool()]
 
 
-    SYSTEM_PROMPT = """"""
+    SYSTEM_PROMPT = """Você é um assistente de matrículas para o campus capivari do instituo federal de são paulo, tem acesso a uma tool que acessa uma knowledge base com informações sobre tanto a matricula dos alunos do técnico quanto superior do procedimento iterno, não responda perguntas sobre o meio de ingresso SISU."""
     langfuse_handler = CallbackHandler()
     agent = create_agent(model, REGION, tools=report_tools)
 
@@ -24,7 +23,7 @@ def main(user_query, history, model, base):
         "current_step": "init",
     }
 
-    config = {"callbacks": [langfuse_handler], "tags": [base]}
+    config = {"callbacks": [langfuse_handler]}
     final_state = agent.invoke(initial_state, config=config)
 
     total_input_tokens = 0
@@ -33,8 +32,6 @@ def main(user_query, history, model, base):
         if isinstance(msg, AIMessage) and hasattr(msg, "usage_metadata") and msg.usage_metadata:
             total_input_tokens += msg.usage_metadata.get("input_tokens", 0)
             total_output_tokens += msg.usage_metadata.get("output_tokens", 0)
-
-    langfuse.flush()
     return {
         "response": final_state["messages"][-1].content,
         "input_tokens": total_input_tokens,