57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
"""
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Streaming example with LangGraph + Qwen. Prints incremental model tokens and
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tool call events.
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Prereqs:
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pip install -U langgraph langchain langchain-openai
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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: # older versions
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from langchain_core.tools import tool # type: ignore
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from langgraph_qwen import ChatQwenOpenAICompat, bind_qwen_tools
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@tool
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def calc(expr: str) -> str:
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"""Safely evaluate a simple arithmetic expression."""
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import math
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allowed = {k: getattr(math, k) for k in ["sqrt", "sin", "cos"]}
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try:
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return str(eval(expr, {"__builtins__": {}}, allowed))
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except Exception as e:
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return f"error: {e}"
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def main():
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base = ChatQwenOpenAICompat(temperature=0)
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model = bind_qwen_tools(base, [calc], tool_choice="auto")
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agent = create_react_agent(model, [calc])
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inputs = {"messages": [HumanMessage(content="先说一句你好，然后计算 sqrt(144)。")]}
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print("Streaming events (tokens and tool calls):\n")
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for ev in agent.stream(inputs, stream_mode="values"):
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# ev is the partial state (dict with messages)
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msg = ev["messages"][-1]
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role = getattr(msg, "type", getattr(msg, "role", ""))
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content = getattr(msg, "content", "")
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# Filter to only incremental assistant outputs
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if role in ("ai", "assistant"):
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print(content, end="", flush=True)
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print("\n\nDone.")
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if __name__ == "__main__":
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main()
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