add README
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# 默认忽略的文件
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/shelf/
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/workspace.xml
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# 基于编辑器的 HTTP 客户端请求
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/httpRequests/
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# Datasource local storage ignored files
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/dataSources/
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/dataSources.local.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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</component>
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<orderEntry type="sourceFolder" forTests="false" />
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</module>
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<component name="InspectionProjectProfileManager">
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<profile version="1.0">
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<option name="myName" value="Project Default" />
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<inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
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<option name="ignoredPackages">
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<value>
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<list size="18">
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<item index="0" class="java.lang.String" itemvalue="pandas" />
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<item index="1" class="java.lang.String" itemvalue="pyyaml" />
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<item index="2" class="java.lang.String" itemvalue="scipy" />
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<item index="3" class="java.lang.String" itemvalue="future" />
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<item index="4" class="java.lang.String" itemvalue="torchdiffeq" />
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<item index="5" class="java.lang.String" itemvalue="pytorch" />
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<item index="6" class="java.lang.String" itemvalue="numpy" />
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<item index="7" class="java.lang.String" itemvalue="tqdm" />
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<item index="8" class="java.lang.String" itemvalue="sktime" />
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<item index="9" class="java.lang.String" itemvalue="patool" />
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<item index="10" class="java.lang.String" itemvalue="scikit-learn" />
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<item index="11" class="java.lang.String" itemvalue="PyWavelets" />
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<item index="12" class="java.lang.String" itemvalue="matplotlib" />
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<item index="13" class="java.lang.String" itemvalue="reformer-pytorch" />
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<item index="14" class="java.lang.String" itemvalue="torch" />
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<item index="15" class="java.lang.String" itemvalue="einops" />
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<item index="16" class="java.lang.String" itemvalue="local-attention" />
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<item index="17" class="java.lang.String" itemvalue="sympy" />
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<list>
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<option value="N803" />
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<option value="N806" />
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<inspection_tool class="SpellCheckingInspection" enabled="false" level="TYPO" enabled_by_default="false">
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<option name="processCode" value="true" />
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<option name="processLiterals" value="true" />
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<option name="processComments" value="true" />
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="Black">
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<option name="sdkName" value="Python 3.12 (Eula)" />
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/agent_proj.iml" filepath="$PROJECT_DIR$/.idea/agent_proj.iml" />
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="$PROJECT_DIR$" vcs="Git" />
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</project>
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26
01_Agent.py
26
01_Agent.py
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from langchain.agents import create_agent
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from langchain_community.chat_models.tongyi import ChatTongyi
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from langchain_core.tools import tool
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@tool(description="查询天气")
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def get_weather():
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return "晴天"
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agent = create_agent(
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model=ChatTongyi(model="qwen3-max"),
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tools=[get_weather],
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system_prompt="你是一个聊天助手，可以回答用户问题"
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)
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res = agent.invoke(
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{
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"messages": [
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{"role": "user", "content": "明天深圳的天气如何"},
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]
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}
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)
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for msg in res["messages"]:
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print(f"{type(msg).__name__}: {msg.content}")
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37
02_Stream.py
37
02_Stream.py
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from langchain.agents import create_agent
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from langchain_community.chat_models.tongyi import ChatTongyi
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from langchain_core.tools import tool
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@tool(description="查询股票价格")
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def get_price(name: str) -> str:
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return f"股票{name}的价格是20元"
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@tool(description="查询股票信息")
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def get_info(name: str) -> str:
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return f"股票{name}是一家A股上市公司"
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agent = create_agent(
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model=ChatTongyi(model="qwen3-max"),
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tools=[get_price, get_info],
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system_prompt="你是一个智能助手，可以回答股票相关问题，请告知我思考过程，让我知道你为什么调用某个工具"
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)
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res = agent.stream(
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{"messages": [{
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"role": "user", "content": "kk公司股价多少，并介绍一下"
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}]},
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stream_mode="values"
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)
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for chunk in res:
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last_msg = chunk["messages"][-1]
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if last_msg.content:
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print(type(last_msg).__name__, last_msg.content)
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if type(last_msg).__name__ == "AIMessage":
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if last_msg.tool_calls:
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tool_list = [tc['name'] for tc in last_msg.tool_calls]
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print(f"工具调佣 {tool_list}")
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39
03_ReAct.py
39
03_ReAct.py
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from langchain.agents import create_agent
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from langchain_community.chat_models.tongyi import ChatTongyi
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from langchain_core.tools import tool
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@tool(description="获取体重，返回值整数，单位千克")
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def get_weight() -> int:
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return 93
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@tool(description="获取身高，返回值整数，单位厘米")
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def get_height() -> int:
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return 185
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agent = create_agent(
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model=ChatTongyi(model="qwen3-max"),
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tools=[get_height, get_weight],
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system_prompt="""你是严格遵循ReAct框架的智能体，必须按[思考，行动，观察，再思考]的流程解决问题
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每轮仅能思考并调用1个工具，禁止单词调用多个工具。并告知我你的思考过程，工具调用的原因，按思考、行动
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、观察三个结构告知我"""
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)
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res = agent.stream(
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{"messages": [{
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"role": "user", "content": "计算我的BMI"
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}]},
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stream_mode="values"
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)
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for chunk in res:
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last_msg = chunk["messages"][-1]
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if last_msg.content:
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print(type(last_msg).__name__, last_msg.content)
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if type(last_msg).__name__ == "AIMessage":
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if last_msg.tool_calls:
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tool_list = [tc['name'] for tc in last_msg.tool_calls]
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print(f"工具调佣 {tool_list}")
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"""
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agent 前后
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model 前后
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工具 中
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模型 中
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"""
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from langchain.agents import create_agent, AgentState
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from langchain.agents.middleware import before_agent, after_agent, before_model, after_model, wrap_model_call, \
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wrap_tool_call
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from langchain_community.chat_models.tongyi import ChatTongyi
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from langchain_core.tools import tool
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from langgraph.runtime import Runtime
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@tool(description="查询天气, 传入城市名称字符串，返回字符串天气信息")
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def get_weather(city: str) -> str:
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return f"{city} : 晴天"
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@before_agent
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def log_before_agent(state: AgentState, runtime: Runtime) -> None:
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print(f"before agent: info_num: {len(state["messages"])}")
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@after_agent
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def log_after_agent(state: AgentState, runtime: Runtime) -> None:
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print(f"after agent: info_num: {len(state["messages"])}")
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@before_model
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def log_before_model(state: AgentState, runtime: Runtime) -> None:
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print(f"before model: info_num: {len(state["messages"])}")
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@after_model
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def log_after_model(state: AgentState, runtime: Runtime) -> None:
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print(f"after model: info_num: {len(state["messages"])}")
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@wrap_model_call
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def model_call_hook(request, handler):
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print(f"model call: {request}")
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return handler(request)
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@wrap_tool_call
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def model_tool_hook(request, handler):
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print(f"model tool: {request.tool_call['name']}")
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print(f"args: {request.tool_call['args']}")
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return handler(request)
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agent = create_agent(
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model=ChatTongyi(model="qwen3-max"),
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tools=[get_weather],
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middleware=[model_call_hook, model_tool_hook, log_before_model,
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log_after_model, log_before_agent, log_after_agent],
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system_prompt="""你是严格遵循ReAct框架的智能体，必须按[思考，行动，观察，再思考]的流程解决问题
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每轮仅能思考并调用1个工具，禁止单词调用多个工具。并告知我你的思考过程，工具调用的原因，按思考、行动
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、观察三个结构告知我"""
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)
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res = agent.stream(
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{"messages": [{
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"role": "user", "content": "查询北京的天气"
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}]},
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stream_mode="values"
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)
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for chunk in res:
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print(chunk["messages"][-1].content)
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