日志，时间记录

src/agent/tool_monitor.py

+import logging
 from typing import Dict, Any, Optional, List
 from pydantic import BaseModel, Field
 from langchain_core.tools import BaseTool

src/agent/tool_rate.py

 import time
+import logging
 from typing import Dict, List, Tuple, Any, Optional
 from pydantic import BaseModel, Field
 from typing import Type

src/server/classify.py

@@ -2,7 +2,7 @@ from pydantic import BaseModel,Field
 from typing import Literal
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import PydanticOutputParser
-
+from ..utils.logger import get_logger
 
 class RouterQuery(BaseModel):
     """Route a user query to the most relevant datasource."""
@@ -43,9 +43,16 @@ class RouterLLM:
             partial_variables={"format_instructions": parser.get_format_instructions()},
         )
         self.router = prompt | llm | parser
-
+        self.logger = get_logger(self.__class__.__name__)
     def invoke(self, question):
-        return self.router.invoke({"query": question})
+        self.logger.info(f"路由用户查询: {question}")
+        try:
+            result = self.router.invoke({"query": question})
+            self.logger.info(f"路由用户查询结果: {result}")
+            return result
+        except Exception as e:
+            self.logger.error(f"路由用户查询失败: {e}", exc_info=True)
+            return {'datasource': 'none'}
 
 
 def new_router_llm(llm):

src/server/rewrite.py

@@ -84,7 +84,7 @@ from pydantic import BaseModel,Field
 from langchain_core.prompts import PromptTemplate
 from langchain_core.output_parsers import PydanticOutputParser
 import re
-
+from ..utils.logger import get_logger
 
 class ReWriterModel(BaseModel):
     rewriter: str = Field(
@@ -102,10 +102,11 @@ class ReWriteLLM:
         )
         self.router = prompt | llm
         self.parser = parser
-
+        self.logger = get_logger(self.__class__.__name__)
     def invoke(self, question, history):
-        response = self.router.invoke({"query": question, "histories": history})
+        self.logger.info(f"重写用户查询: {question}")
         try:
+            response = self.router.invoke({"query": question, "histories": history})
             result = self.parser.invoke(response)
         except:
             # 定义正则表达式模式
@@ -119,6 +120,7 @@ class ReWriteLLM:
                     return match[1].strip()
 
             return response.content
+        self.logger.info(f"重写后的问题: {result.rewriter}")
         return result.rewriter
 
     def extend_history(self, history):

src/server/tool_picker.py

 from typing import List, Dict
 from datetime import datetime
+import time
 
 from langchain_core.prompts import PromptTemplate,ChatPromptTemplate,SystemMessagePromptTemplate,MessagesPlaceholder,HumanMessagePromptTemplate
 from langchain.tools.render import  render_text_description_and_args
@@ -91,33 +92,56 @@ class ToolRunner:
         self.chain = prompt | self.llm 
 
     def run(self, input: str, tool_name: str, params: Dict):
-        self.logger.info(f"Running tool '{tool_name}' with params: {params}")
+        start_time = time.time()
+        self.logger.info(f"开始执行工具 '{tool_name}'，参数: {params}")
         
         if tool_name not in self.tools:
-            self.logger.error(f"Tool {tool_name} not found")
+            self.logger.error(f"工具 {tool_name} 未找到")
             raise ValueError(f"Tool {tool_name} not found")
             
         try:
+            # 工具执行
+            tool_start = time.time()
             tool = self.tools[tool_name]
-            self.logger.info(f"Invoking tool {tool_name}")
+            self.logger.info(f"调用工具 {tool_name}")
             result = tool.invoke(params)
-            self.logger.debug(f"Tool result: {result}")
+            tool_time = time.time() - tool_start
+            self.logger.info(f"工具执行完成，耗时: {tool_time:.2f}秒")
+            self.logger.debug(f"工具执行结果: {result}")
             
+            # 提取 markdown 表格
             table = ""
             if "markdown" in result:
                 table = result["markdown"]
                 del result["markdown"]
             
-            self.logger.info("Getting LLM interpretation")
+            # LLM 解释结果
+            llm_start = time.time()
+            self.logger.info("开始 LLM 结果解释")
             llm_result = self.chain.invoke({"input": input, "result": result})
+            llm_time = time.time() - llm_start
+            self.logger.info(f"LLM 解释完成，耗时: {llm_time:.2f}秒")
             
+            # 组装响应
             response = {
                 "output": llm_result.content,
                 "table": table
             }
-            self.logger.info("Tool execution completed successfully")
+            
+            total_time = time.time() - start_time
+            self.logger.info(f"工具执行完成，总耗时: {total_time:.2f}秒，其中工具执行: {tool_time:.2f}秒，LLM解释: {llm_time:.2f}秒")
+            
+            # 记录性能指标
+            self.logger.info(
+                "性能指标 - "
+                f"总耗时: {total_time:.2f}秒, "
+                f"工具执行: {tool_time:.2f}秒 ({(tool_time/total_time)*100:.1f}%), "
+                f"LLM解释: {llm_time:.2f}秒 ({(llm_time/total_time)*100:.1f}%)"
+            )
+            
             return response
             
         except Exception as e:
-            self.logger.error(f"Error running tool: {str(e)}")
+            error_time = time.time() - start_time
+            self.logger.error(f"工具执行失败，耗时: {error_time:.2f}秒，错误: {str(e)}", exc_info=True)
             raise