update
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@@ -0,0 +1,54 @@
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"""
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This code is supported by the website: https://www.guanjihuan.com
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The newest version of this code is on the web page: https://www.guanjihuan.com/archives/47909
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"""
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import os
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import dotenv
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from langchain_openai import ChatOpenAI
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from langchain_core.tools import tool
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from langchain.agents import create_agent
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from langchain_core.globals import set_debug # ✅ 用于开启详细日志
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# 开启调试日志输出
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set_debug(True)
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# 加载环境变量
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dotenv.load_dotenv()
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# 定义工具
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@tool
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def get_current_time() -> str:
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"""获取当前日期和时间"""
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from datetime import datetime
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return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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@tool
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def add_numbers(a: float, b: float) -> float:
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"""将两个数字相加"""
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return a + b
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tools = [get_current_time, add_numbers]
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# 创建 LLM
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llm = ChatOpenAI(
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("DASHSCOPE_BASE_URL"),
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model="qwen-plus",
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temperature=0.7,
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)
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# 创建 agent
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agent = create_agent(llm, tools)
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# 执行调用(此时会打印详细步骤)
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response = agent.invoke(
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{
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"messages": [
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{"role": "user", "content": "现在几点了?然后把 123 和 456 加起来。"}
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]
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}
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)
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print("\n---\n")
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print(response["messages"][-1].content)
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@@ -0,0 +1,91 @@
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"""
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This code is supported by the website: https://www.guanjihuan.com
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The newest version of this code is on the web page: https://www.guanjihuan.com/archives/47925
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"""
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import os
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import dotenv
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from langchain_openai import ChatOpenAI
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from langchain_core.tools import tool
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from langchain.agents import create_agent
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from langchain_core.globals import set_debug
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# 开启调试日志输出
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set_debug(True)
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dotenv.load_dotenv()
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# -----------------------
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# 定义工具
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# -----------------------
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@tool
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def execute_python(code: str) -> str:
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"""执行 Python 代码并返回输出"""
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import sys, io
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old_stdout = sys.stdout
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sys.stdout = mystdout = io.StringIO()
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try:
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exec(code, {})
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except Exception as e:
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return f"Error: {e}"
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finally:
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sys.stdout = old_stdout
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return mystdout.getvalue().strip()
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tools = [execute_python]
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# -----------------------
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# 创建 LLM
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# -----------------------
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llm = ChatOpenAI(
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("DASHSCOPE_BASE_URL"),
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model="qwen-plus",
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temperature=0,
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)
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# -----------------------
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# 创建 Agent
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# -----------------------
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agent = create_agent(llm, tools)
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# -----------------------
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# 系统消息,模拟 ReAct 风格
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# -----------------------
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system_message = """
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你是一个严谨的 Python 编程助手,必须通过工具执行代码来验证逻辑。
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每次只能执行一个 Action,所有 Python 代码必须通过工具执行,不得假设输出结果。
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Action Input 必须是纯 Python 代码,不要包含 Markdown 代码块符号。
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如果测试失败(如 assert 报错),请根据错误信息修正代码并重新测试。
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输出必须严格遵循以下格式:
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Thought: <你的思考>
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Action: <工具名称>
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Action Input: <纯 Python 代码>
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Observation: <工具返回结果>
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Final Answer: <最终答案>
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"""
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# -----------------------
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# 用户问题:编写 Fibonacci 函数
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# -----------------------
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user_message = """
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请编写一个 Python 函数 fibonacci(n),返回第 n 个斐波那契数。
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先测试代码正确性,如果测试通过再进行优化,并给出最终代码。
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"""
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# -----------------------
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# 调用 Agent
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# -----------------------
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response = agent.invoke(
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{
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"messages": [
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{"role": "system", "content": system_message},
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{"role": "user", "content": user_message}
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]
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}
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)
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# -----------------------
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# 输出结果
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# -----------------------
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print("\n--- Agent 输出 ---\n")
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print(response["messages"][-1].content)
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