44 lines
1.0 KiB
Python
44 lines
1.0 KiB
Python
from typing import TypedDict
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from langgraph.graph import StateGraph, START
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from langchain_core.messages import HumanMessage
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from langgraph_qwen import ChatQwenOpenAICompat
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class SimpleState(TypedDict):
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topic: str
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joke: str
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model = ChatQwenOpenAICompat(temperature=0)
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def call_model(state: SimpleState):
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llm_response = model.invoke([HumanMessage(content=f"Generate a joke about {state['topic']}")])
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return {"joke": llm_response.content}
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graph = (
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StateGraph(SimpleState)
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.add_node("call_model", call_model)
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.add_edge(START, "call_model")
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.compile()
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)
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def main():
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print("=" * 60)
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print("💬 LangGraph stream_mode='messages' 示例（捕获 LLM tokens）")
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print("=" * 60)
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for msg, meta in graph.stream({"topic": "cats"}, stream_mode="messages"):
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if hasattr(msg, "content") and msg.content:
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node = meta.get("langgraph_node", "unknown")
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print(f"[{node}] {msg.content}", end="", flush=True)
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print("\n\n✅ 完成！")
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if __name__ == "__main__":
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main()
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