Adds initial files]

code/app.py

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+from fastapi import FastAPI,Header
+import uvicorn
+"""
+Simple LangGraph Agent using @tool decorator
+
+Clean implementation with decorator-based tool definitions.
+"""
+
+from typing import Annotated, TypedDict
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode, tools_condition
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.tools import tool
+from langchain_aws import ChatBedrock
+app = FastAPI()
+
+# Define tools using @tool decorator
+rules="""- Mulheres acima de 45 anos ou menopausada
+- Homens com mais de 70 anos;
+- Osteogênese imperfeita (para esta patologia, poderá haver a liberação de (02) dois 
+exames ao ano - cada 180 dias);
+- RX com osteopenia ou fratura patológica;
+- Antecedente pessoal de fratura após os 40 anos: punho, ombros, vértebras, quadril;
+- Parente de primeiro grau com osteoporose.
+- Mulheres com massa corporal <20kg/m2 ou peso < 57,8kg;
+- Menopausa antes dos 45 anos ou hipogonasismo crônico (falência ovariana 
+precoce);
+- Uso de glicocorticóides (>=7,5 prednizona/ dia equivalente por mais três meses, ou 
+presença de síndrome de cushing;
+- Hiperparatireoidismo primário;
+- Uso prolongado de anticonvulsivantes (< 10 anos);
+- Síndrome de má absorção crônica ou desnutrição doenças inflamatória intestinal 
+(independente da causa: bariatrica, celiacos, intolerancia a lactose).
+- Quimioterapia, se sobrevida esperada for longa (< 5 anos);
+- Diminuição documentada de altura;
+- Presença de cifose após menopausa.
+- Imobilização prolongada"""
+SYSTEM_PROMPT="""
+You are a assisant, your job is to aprove or not a procedure based on the following rules:"""
++rules+"""
+Your input will be a json, evaluate it based on the rules and return:
+Aproved: If one of the criteira is met
+Reproved: If not a single one of the criterias are met."""
+@tool
+def add(a: int, b: int) -> int:
+    """Add two numbers together.
+    
+    Args:
+        a: First number
+        b: Second number
+    """
+    return a + b
+
+
+@tool
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers.
+    
+    Args:
+        a: First number
+        b: Second number
+    """
+    return a * b
+
+
+@tool
+def get_word_length(word: str) -> int:
+    """Get the length of a word.
+    
+    Args:
+        word: The word to measure
+    """
+    return len(word)
+
+
+@tool
+def search_info(topic: str) -> str:
+    """Search for information about a topic (mock implementation).
+    
+    Args:
+        topic: The topic to search for
+    """
+    # Mock response - replace with actual search/API
+    return f"Information about {topic}: This is a mock response. In production, this would return real data."
+
+
+# Define agent state
+class AgentState(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+# Define tools list
+tools = [add, multiply, get_word_length, search_info]
+
+
+# Agent node
+def call_model(state: AgentState):
+    """Call the LLM with current state and tools."""
+    
+    model = ChatBedrock(
+        model_id="arn:aws:bedrock:us-east-2:232048051668:application-inference-profile/uy4xskop19zn",
+        region_name="us-east-2",
+        provider="anthropic"
+    )
+    
+    model_with_tools = model.bind_tools(tools)
+    
+    messages = [
+        SystemMessage(content=SYSTEM_PROMPT)
+    ] + state["messages"]
+    
+    response = model_with_tools.invoke(messages)
+    
+    return {"messages": [response]}
+
+
+# Build the graph
+def create_agent():
+    """Create and compile the agent graph."""
+    
+    workflow = StateGraph(AgentState)
+    
+    # Add nodes
+    workflow.add_node("agent", call_model)
+    workflow.add_node("tools", ToolNode(tools))
+    
+    # Add edges
+    workflow.add_edge(START, "agent")
+    workflow.add_conditional_edges("agent", tools_condition)
+    workflow.add_edge("tools", "agent")
+    
+    return workflow.compile()
+
+
+# Main execution
+
+
+@app.post("/")
+async def root(json:str= Header(...)):
+    agent = create_agent()
+    query=json
+    result = agent.invoke(
+            {"messages": [HumanMessage(content=query)]},
+            config={"recursion_limit": 10}
+        )
+        
+    final_message = result["messages"][-1]
+    return {"status": "success", "message": final_message.content}
+
+@app.get("/health")
+async def health():
+    return {"status": "healthy"}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)