47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
"""
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Use the custom BaseChatModel-like class ChatQwenOpenAICompat with LangGraph.
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Prereqs:
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pip install -U langgraph langchain httpx
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Env:
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export QWEN_API_KEY=sk-...
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export QWEN_BASE_URL=https://dashscope-intl.aliyuncs.com/compatible-mode/v1
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export QWEN_MODEL=qwen3-coder-flash
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"""
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from langgraph.prebuilt import create_react_agent
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from langchain_core.messages import HumanMessage
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try:
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from langchain.tools import tool
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except Exception:
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from langchain_core.tools import tool # type: ignore
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from langgraph_qwen import ChatQwenOpenAICompat
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@tool
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def multiply(x_and_y: str) -> str:
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"""Multiply two integers given as 'x y'."""
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try:
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a, b = [int(s) for s in x_and_y.strip().split()]
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except Exception:
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return "Please provide two integers: 'x y'"
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return str(a * b)
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def main():
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base = ChatQwenOpenAICompat(temperature=0)
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model = base.bind_tools([multiply]).bind(tool_choice="auto")
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agent = create_react_agent(model, [multiply])
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res = agent.invoke({"messages": [HumanMessage(content="计算 6 和 7 的乘积，然后解释你的步骤。")]} )
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print("\n=== Final ===\n")
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print(res["messages"][-1].content)
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if __name__ == "__main__":
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main()
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