react_agent/ agent_tools/middleware

.gitignore

@@ -0,0 +1,2 @@
+.git
+.idea

.idea/agent_proj.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="RAG" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="edu" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.12 (Eula)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="RAG" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="edu" project-jdk-type="Python SDK" />
 </project>

agent/react_agent.py

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+from langchain.agents import create_agent
+from model.factory import chat_model
+from utils.prompt_loader import load_system_prompts
+from agent.tools.agent_tools import (rag_summarize, get_weather, get_user_id,
+                                     get_user_location, get_current_month,
+                                     fill_context_for_report, fetch_external_data)
+from agent.tools.middleware import monitor_tool, log_before_model, repoet_prompt_switch
+
+
+class ReactAgent:
+    def __init__(self):
+        self.agent = create_agent(
+            model=chat_model,
+            system_prompt=load_system_prompts(),
+            tools=[rag_summarize, get_weather, get_user_location, get_user_id,
+                   get_current_month, fetch_external_data, fill_context_for_report],
+            middleware=[monitor_tool, log_before_model, repoet_prompt_switch],
+        )
+
+    def excute_stream(self, query: str):
+        input_dict = {
+            "messages": [
+                {"role": "user", "content": query}
+            ]
+        }
+
+        # 上下文runtime信息，做提示词切换标记
+        response = self.agent.stream(input_dict, stream_mode="values", context={"report": False})
+        for chunk in response:
+            latest_message = chunk["messages"][-1]
+            if latest_message.content:
+                yield latest_message.content.strip() + "\n"
+
+
+if __name__ == '__main__':
+    agent = ReactAgent();
+    stream = agent.excute_stream("扫地机器人在我所在地的气温下如何保养")
+    for chunk in stream:
+        print(chunk, end="", flush=True)

agent/tools/agent_tools.py

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+import pandas
+from langchain_core.tools import tool
+from rag.rag_service import RagSummarizeService
+import random
+from utils.config_handler import agent_conf
+from utils.path_tool import get_abs_path
+from utils.logger_handler import logger
+import os
+
+rag = RagSummarizeService()
+
+user_ids = [str(i) for i in range(1001, 1011, 1)]
+month_arr = [f"2025-{str(i).zfill(2)}" for i in range(1, 13, 1)]
+external_data = {}
+
+
+@tool(description="向量存储中检索参考资料")
+def rag_summarize(query: str) -> str:
+    return rag.rag_summarize(query)
+
+
+@tool(description="获取指定城市天气，消息以字符串方式返回")
+def get_weather(city: str) -> str:
+    return f"城市{city}天气为晴天，温度为26摄氏度，湿度50%，南风1级，AQI21，最近6小时降雨概率极低"
+
+
+@tool(description="获取用户所在城市名称，以纯字符串形式返回")
+def get_user_location() -> str:
+    return random.choice(["深圳", "合肥", "杭州"])
+
+
+@tool(description="获取用户ID，以纯字符串形式返回")
+def get_user_id() -> str:
+    return random.choice(user_ids)
+
+
+@tool(description="获取当前月份，以纯字符串形式返回")
+def get_current_month() -> str:
+    return random.choice(month_arr)
+
+
+def generate_external_data():
+    """得到用户字典，
+     {
+        "user_id": {
+            "month": {"特征: ..., "效率:}
+        }
+    }"""
+
+    if not external_data:
+        external_data_path = agent_conf['external_data_path']
+        external_data_path = get_abs_path(external_data_path)
+
+        if not os.path.exists(external_data_path):
+            raise FileExistsError(f"外部文件{external_data_path}不存在")
+
+        with open(external_data_path, 'r', encoding='utf-8') as f:
+            for line in f.readlines():
+                arr = line.strip().split(',')
+                user_id = arr[0].replace('"', "")
+                feature = arr[1].replace('"', "")
+                efficiency = arr[2].replace('"', "")
+                consumables = arr[3].replace('"', "")
+                comparison = arr[4].replace('"', "")
+                time = arr[5].replace('"', "")
+
+                if user_id not in external_data:
+                    external_data[user_id] = {}
+
+                external_data[user_id][time] = {
+                    "特征": feature,
+                    "效率": efficiency,
+                    "耗材": consumables,
+                    "对比": comparison,
+                }
+    return external_data
+
+
+@tool(description="从外部系统获取用户使用记录，以春字符串形式返回，如果未检索到，返回空字符串")
+def fetch_external_data(user_id: str, month: str) -> str:
+    generate_external_data()
+
+    try:
+        return external_data[user_id][month]
+    except KeyError:
+        logger.warning(f"[fetch_external_data]未检索到用户:{user_id}在{month}的使用数据")
+        return ""
+
+
+@tool(description="无入参，无返回值，调用后自动触发中间件，自动为报告生成动态场景上下文"
+                  "+")
+def fill_context_for_report():
+    return "fill_context_for_report已调用"
+
+# if __name__ == '__main__':
+#     user_id = "1005"
+#     month = "2025-06"
+#     res = fetch_external_data(user_id, month)
+#     print(res)

agent/tools/middleware.py

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+from langchain.agents.middleware import wrap_tool_call, before_model, dynamic_prompt, ModelRequest
+from langchain.tools.tool_node import ToolCallRequest
+from typing import Callable
+from langchain_core.messages import ToolMessage
+from langgraph.types import Command
+from utils.logger_handler import logger
+from langchain.agents import AgentState
+from langgraph.runtime import Runtime
+from utils.prompt_loader import load_system_prompts, load_report_prompts
+
+@wrap_tool_call
+def monitor_tool(
+        # 函数
+        request: ToolCallRequest,
+        # 入参
+        handler: Callable[[ToolCallRequest], ToolMessage | Command],
+) -> ToolMessage | Command:
+    logger.info(f"[tool monitor]执行工具：{request.tool_call['name']}")
+    logger.info(f"[tool monitor]传入参数：{request.tool_call['args']}")
+
+    try:
+        result = handler(request)
+        logger.info(f"[tool minitor]工具{request.tool_call['name']}调用成功")
+
+        if request.tool_call['name'] == 'fill_context_for_report':
+            request.runtime.context["report"] = True
+
+        return result
+    except Exception as e:
+        logger.error(f"工具{request.tool_call['name']}调用失败，原因: {str(e)}")
+        raise e
+
+
+@before_model
+def log_before_model(
+        state: AgentState,  # 状态记录
+        runtine: Runtime,  # 执行过程 上下文信息
+):
+    logger.info(f"[log_before_model]即将调用模型，带有{len(state["messages"])}条消息")
+    logger.debug(f"[log_before_model]{type(state["messages"][-1]).__name__} | "
+                 f"消息内容：{state["messages"][-1].content.strip()}")
+    return None
+
+
+@dynamic_prompt  # 每一次提示词生成前，调用
+def repoet_prompt_switch(request: ModelRequest):
+    is_report = request.runtime.context.get("report", False)
+    if is_report:
+        return load_report_prompts()
+
+    return load_system_prompts()

config/agent.yaml

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+external_data_path: data/external/records.csv

logs/Agent_20260301.log

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+2026-03-01 00:10:11,186 - Agent - WARNING - agent_tools.py:86 - [fetch_external_data]未检索到用户:1004在2月的使用数据
+2026-03-01 00:10:15,880 - Agent - WARNING - agent_tools.py:86 - [fetch_external_data]未检索到用户:1008在8月的使用数据
+2026-03-01 11:06:16,324 - Agent - INFO - middleware.py:39 - [log_before_model]即将调用模型，带有1条消息
+2026-03-01 11:06:16,324 - Agent - DEBUG - middleware.py:40 - [log_before_model]HumanMessage | 消息内容：扫地机器人在我所在地的气温下如何保养
+2026-03-01 11:06:50,251 - Agent - INFO - middleware.py:39 - [log_before_model]即将调用模型，带有1条消息
+2026-03-01 11:06:50,251 - Agent - DEBUG - middleware.py:40 - [log_before_model]HumanMessage | 消息内容：扫地机器人在我所在地的气温下如何保养
+2026-03-01 11:08:04,788 - Agent - INFO - middleware.py:39 - [log_before_model]即将调用模型，带有1条消息
+2026-03-01 11:08:04,789 - Agent - DEBUG - middleware.py:40 - [log_before_model]HumanMessage | 消息内容：扫地机器人在我所在地的气温下如何保养
+2026-03-01 11:08:09,924 - Agent - INFO - middleware.py:18 - [tool monitor]执行工具：get_user_location
+2026-03-01 11:08:09,925 - Agent - INFO - middleware.py:19 - [tool monitor]传入参数：{}
+2026-03-01 11:08:09,926 - Agent - INFO - middleware.py:23 - [tool minitor]工具get_user_location调用成功
+2026-03-01 11:08:09,927 - Agent - INFO - middleware.py:39 - [log_before_model]即将调用模型，带有3条消息
+2026-03-01 11:08:09,927 - Agent - DEBUG - middleware.py:40 - [log_before_model]ToolMessage | 消息内容：杭州
+2026-03-01 11:08:13,076 - Agent - INFO - middleware.py:18 - [tool monitor]执行工具：get_weather
+2026-03-01 11:08:13,076 - Agent - INFO - middleware.py:19 - [tool monitor]传入参数：{'city': '杭州'}
+2026-03-01 11:08:13,078 - Agent - INFO - middleware.py:23 - [tool minitor]工具get_weather调用成功
+2026-03-01 11:08:13,079 - Agent - INFO - middleware.py:39 - [log_before_model]即将调用模型，带有5条消息
+2026-03-01 11:08:13,079 - Agent - DEBUG - middleware.py:40 - [log_before_model]ToolMessage | 消息内容：城市杭州天气为晴天，温度为26摄氏度，湿度50%，南风1级，AQI21，最近6小时降雨概率极低
+2026-03-01 11:08:16,346 - Agent - INFO - middleware.py:18 - [tool monitor]执行工具：rag_summarize
+2026-03-01 11:08:16,346 - Agent - INFO - middleware.py:19 - [tool monitor]传入参数：{'query': '扫地机器人在26摄氏度温度和50%湿度环境下的保养方法'}
+2026-03-01 11:08:19,183 - Agent - INFO - middleware.py:23 - [tool minitor]工具rag_summarize调用成功
+2026-03-01 11:08:19,185 - Agent - INFO - middleware.py:39 - [log_before_model]即将调用模型，带有7条消息
+2026-03-01 11:08:19,185 - Agent - DEBUG - middleware.py:40 - [log_before_model]ToolMessage | 消息内容：参考资料中未提及扫地机器人在26摄氏度温度和50%湿度环境下的具体保养方法。
+2026-03-01 11:08:21,538 - Agent - INFO - middleware.py:18 - [tool monitor]执行工具：rag_summarize
+2026-03-01 11:08:21,539 - Agent - INFO - middleware.py:19 - [tool monitor]传入参数：{'query': '扫地机器人日常保养方法'}
+2026-03-01 11:08:25,493 - Agent - INFO - middleware.py:23 - [tool minitor]工具rag_summarize调用成功
+2026-03-01 11:08:25,494 - Agent - INFO - middleware.py:39 - [log_before_model]即将调用模型，带有9条消息
+2026-03-01 11:08:25,494 - Agent - DEBUG - middleware.py:40 - [log_before_model]ToolMessage | 消息内容：定期清理滚刷和边刷上的缠绕物，清空尘盒，擦拭传感器和充电触点，检查并清理滤网，避免在潮湿或有水区域使用，及时更换磨损配件。