"""
Use the custom BaseChatModel-like class ChatQwenOpenAICompat with LangGraph.

Prereqs:
  pip install -U langgraph langchain httpx

Env:
  export QWEN_API_KEY=sk-...
  export QWEN_BASE_URL=https://dashscope-intl.aliyuncs.com/compatible-mode/v1
  export QWEN_MODEL=qwen3-coder-flash
"""

from langgraph.prebuilt import create_react_agent
from langchain_core.messages import HumanMessage

try:
    from langchain.tools import tool
except Exception:
    from langchain_core.tools import tool  # type: ignore

from langgraph_qwen import ChatQwenOpenAICompat


@tool
def multiply(x_and_y: str) -> str:
    """Multiply two integers given as 'x y'."""
    try:
        a, b = [int(s) for s in x_and_y.strip().split()]
    except Exception:
        return "Please provide two integers: 'x y'"
    return str(a * b)


def main():
    base = ChatQwenOpenAICompat(temperature=0)
    model = base.bind_tools([multiply]).bind(tool_choice="auto")

    agent = create_react_agent(model, [multiply])
    res = agent.invoke({"messages": [HumanMessage(content="计算 6 和 7 的乘积，然后解释你的步骤。")]} )
    print("\n=== Final ===\n")
    print(res["messages"][-1].content)


if __name__ == "__main__":
    main()