新增多个启动脚本以支持不同服务的后台运行，优化对话到工单的处理逻辑，增加人力信息映射，调整日志记录机制以支持异步处理。

rag2_0/demo/dialogue_to_workorder.py

@@ -33,6 +33,33 @@ logging.basicConfig(
 )
 logger = logging.getLogger("dialogue_to_workorder")
 
+human_info={
+"1116":["夏剑媛",	"储能"],
+"1201":["曹美芳",	"配网"],
+"1202":["彭珊珊",	"主网"],
+"1230":["龚青",	"配网"],
+"1544":["黄婷",	"主网"],
+"1546":["严琼辉",	"配网"],
+"1552":["吴园妹",	"主网"],
+"1555":["魏怡璠",	"配网"],
+"1789":["冷琛",	"主网"],
+"2142":["余国庆",	"配网"],
+"2144":["卢光辉",	"技改"],
+"2145":["万志星",	"技改"],
+"2233":["徐雨萍",	"主网"],
+"2262":["刘雨微",	"主网"],
+"2591":["揭敏",	"主网"],
+"3035":["杨玲",	"主网"],
+"3416":["杨苏文",	"配网"],
+"3417":["王琴",	"配网"],
+"439":["赵莉",	"技改"],
+"8340":["熊磊娇",	"储能"],
+"8442":["胡月",	"配网"],
+"8443":["杨淑玲",	"主网"],
+"8555":["胡青艳",	"主网"],
+"8762":["周丽华",	"主网"],
+}
+
 # ================ 模型定义 ================
 class UserQuestionAndSolution(BaseModel):
     user_question: str = Field(description="用户的核心问题")
@@ -143,6 +170,7 @@ class DialogueToWorkorder:
     
     def get_workorder_dict(self, rows):
         """从会话行中提取工单基本信息"""
+        # 预设字段
         workorder_dict = {}
         
         # 创建时间
@@ -158,6 +186,10 @@ class DialogueToWorkorder:
             sender_nickname = row['发送者昵称']
             if sender == "坐席" and pd.notna(sender_nickname) and str(sender_nickname).strip() != '':
                 workorder_dict["处理坐席"] = sender_nickname
+                sender_num = re.findall(r'客服(\d+)', sender_nickname)
+                if len(sender_num) > 0 and sender_num[0] in human_info:
+                    workorder_dict["处理人"] = human_info[sender_num[0]][0]
+                    workorder_dict["处理技能组"] = human_info[sender_num[0]][1]
                 break
         
         # 访客昵称
@@ -463,7 +495,28 @@ class DialogueToWorkorder:
         
 
         # 更新工单字典
-        base_workorder_dict.update({
+        # base_workorder_dict.update({
+        #         "产品线": product_line,
+        #         "产品名称": product_name,
+        #         "模块名称": module_name,
+        #         "客户问题": user_question_str,
+        #         "问题类型": problem_type,
+        #         "是否抱怨": "是" if is_dissatisfaction else '否',
+        #         "抱怨内容": dissatisfaction_reasoning if is_dissatisfaction else '',
+        #         "抱怨级别": dissatisfaction_level if is_dissatisfaction else '',
+        #         "是否投诉": "是" if is_complaint else '否',
+        #         "解决方案": solution_str
+        #     })
+        # workorder_list.append(base_workorder_dict)
+        for user_question in user_question_list:
+            user_question_str = user_question.user_question
+            solution_str = user_question.solution
+            
+            # 创建新的工单字典，复制基本信息
+            workorder_dict = base_workorder_dict.copy()
+
+            # 更新工单字典
+            workorder_dict.update({
                 "产品线": product_line,
                 "产品名称": product_name,
                 "模块名称": module_name,
@@ -475,29 +528,9 @@ class DialogueToWorkorder:
                 "是否投诉": "是" if is_complaint else '否',
                 "解决方案": solution_str
             })
-        workorder_list.append(base_workorder_dict)
-        # for user_question in user_question_list:
-        #     user_question_str = user_question.user_question
-        #     solution_str = user_question.solution
             
-        #     # 创建新的工单字典，复制基本信息
+            # 将工单添加到列表中
-        #     workorder_dict = base_workorder_dict.copy()
+            workorder_list.append(workorder_dict)
-
-        #     # 更新工单字典
-        #     workorder_dict.update({
-        #         "产品线": product_line,
-        #         "产品名称": product_name,
-        #         "模块名称": module_name,
-        #         "客户问题": user_question_str,
-        #         "问题类型": problem_type,
-        #         "是否抱怨": "是" if is_dissatisfaction else '否',
-        #         "抱怨级别": dissatisfaction_level if is_dissatisfaction else '',
-        #         "是否投诉": "是" if is_complaint else '否',
-        #         "解决方案": (solution_str + '\n存在抱怨:' + dissatisfaction_reasoning) if is_dissatisfaction else solution_str
-        #     })
-            
-        #     # 将工单添加到列表中
-        #     workorder_list.append(workorder_dict)
         
         return workorder_list
     
@@ -513,27 +546,32 @@ class DialogueToWorkorder:
         # 解析产品详情
         product_detail_dict = self.parse_product_detail_excel(product_detail_excel_path)
         
-        # 如果指定了时间范围，则过滤数据
-        if start_date or end_date:
-            # 确保创建时间列为日期时间类型
-            if '创建时间' in df.columns:
-                df['创建时间'] = pd.to_datetime(df['创建时间'], errors='coerce')
-                
-                # 按时间范围过滤
-                if start_date:
-                    start_date = pd.to_datetime(start_date)
-                    df = df[df['创建时间'] >= start_date]
-                    logger.info(f"过滤开始时间 {start_date}，剩余数据行数: {len(df)}")
-                
-                if end_date:
-                    end_date = pd.to_datetime(end_date)
-                    df = df[df['创建时间'] <= end_date]
-                    logger.info(f"过滤结束时间 {end_date}，剩余数据行数: {len(df)}")
-            else:
-                logger.warning("数据中没有'创建时间'列，无法按时间范围过滤")
-        
         # 按会话ID分组
         conversation_dict = self.group_conversations_by_id(df)
+
+        # 如果指定了时间范围，则过滤数据
+        if start_date or end_date:
+            logging.info(f"过滤时间范围: {start_date} 至 {end_date}")
+            # 将字符串日期转换为datetime对象
+            start_date_dt = datetime.strptime(start_date, "%Y-%m-%d %H:%M:%S") if start_date else None
+            end_date_dt = datetime.strptime(end_date, "%Y-%m-%d %H:%M:%S") if end_date else None
+            
+            new_conversation_dict = {}
+            for conversation_id, conversation_rows in conversation_dict.items():
+                # 获取会话创建时间并转换为datetime对象
+                create_time_str = conversation_rows[0]["创建时间"]
+                if isinstance(create_time_str, str):
+                    create_time_dt = datetime.strptime(create_time_str, "%Y-%m-%d %H:%M:%S")
+                else:
+                    # 如果已经是datetime对象则直接使用
+                    create_time_dt = create_time_str
+                
+                # 使用datetime对象进行比较
+                if (start_date_dt and create_time_dt < start_date_dt) or (end_date_dt and create_time_dt > end_date_dt):
+                    continue
+                new_conversation_dict[conversation_id] = conversation_rows
+            conversation_dict = new_conversation_dict
+
         logger.info(f"会话总数为 {len(conversation_dict)}，处理全部会话")
         
         # 使用线程池处理每个会话
@@ -566,7 +604,7 @@ class DialogueToWorkorder:
         columns_order = [
             '工单编号', '产品线', '产品名称', '模块名称', '问题类型', 
             '客户问题', '解决方案', '是否抱怨', "抱怨内容", '是否投诉', '抱怨级别', 
-            '会话id', '访客昵称', '处理坐席', '创建时间'
+            '会话id', '访客昵称', '处理坐席', "处理人", "处理技能组",'创建时间'
         ]
         
         # 确保所有列都存在，如果不存在则添加空列
@@ -615,6 +653,8 @@ class DialogueToWorkorder:
                 '会话id': 9,
                 '访客昵称': 9,
                 '处理坐席': 9,
+                '处理人': 9,
+                '处理技能组': 9,
                 '创建时间': 9
             }
             
@@ -640,9 +680,9 @@ def parse_arguments():
                         help='产品详情Excel文件路径')
     parser.add_argument('--max_workers', type=int, default=16,
                         help='并发处理线程数，默认为16')
-    parser.add_argument('--start_date', type=str, required=False,default="2025-05-01 00:00:00",
+    parser.add_argument('--start_date', type=str, required=False,default="2025-06-10 16:08:00",
                         help='开始日期，格式为YYYY-MM-DD')
-    parser.add_argument('--end_date', type=str, required=False,default="2025-05-24 23:59:59",
+    parser.add_argument('--end_date', type=str, required=False,default="2025-06-30 23:59:59",
                         help='结束日期，格式为YYYY-MM-DD')
     
     return parser.parse_args()

rag2_0/dify/AnswerType.py

@@ -8,6 +8,8 @@ from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel, Field
 from typing import Dict, List, Any, Optional
 import asyncio
+import threading
+import queue
 
 from dotenv import load_dotenv
 import json
@@ -32,20 +34,92 @@ from rag2_0.dify.DifyQueryRetrieval import DifyQueryRetrieval
 # 定义文件锁和JSON文件路径
 file_lock = asyncio.Lock()
 QUERY_LOG_DIR = os.path.join(os.getcwd(), "data", "query_logs")
-QUERY_LOG_FILE = os.path.join(QUERY_LOG_DIR, "answer_type_logs.json")
+QUERY_DATA_FILE = os.path.join(QUERY_LOG_DIR, "answer_type_logs.json")
 
-logging.basicConfig(
+# 创建异步日志队列和工作线程
-    level=logging.INFO,
+log_queue = queue.Queue()
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+worker_thread = None
-    handlers=[
+
-        logging.StreamHandler()
+# 后台工作线程函数
-    ]
+def log_worker():
+    while True:
+        try:
+            # 从队列获取数据，设置超时以允许线程退出
+            data = log_queue.get(timeout=1.0)
+            if data is None:  # 接收到退出信号
+                # 处理剩余数据后再退出
+                while not log_queue.empty():
+                    data = log_queue.get_nowait()
+                    if data is None:  # 跳过额外的停止信号
+                        continue
+                    process_log_data(data)
+                break
+                
+            process_log_data(data)
+            log_queue.task_done()
+        except queue.Empty:
+            continue
+        except Exception as e:
+            logger.error(f"保存查询数据时出错: {str(e)}", exc_info=True)
+
+# 提取数据处理逻辑到单独函数
+def process_log_data(data):
+    try:
+        # 确保目录存在
+        os.makedirs(os.path.dirname(QUERY_DATA_FILE), exist_ok=True)
+        
+        # 读取现有数据
+        existing_data = []
+        if os.path.exists(QUERY_DATA_FILE) and os.path.getsize(QUERY_DATA_FILE) > 0:
+            with open(QUERY_DATA_FILE, 'r', encoding='utf-8') as f:
+                try:
+                    existing_data = json.load(f)
+                except json.JSONDecodeError:
+                    logger.error(f"JSON文件解析错误，将创建新文件: {QUERY_DATA_FILE}")
+                    existing_data = []
+        
+        # 添加新数据
+        existing_data.append(data)
+        
+        # 写入文件
+        with open(QUERY_DATA_FILE, 'w', encoding='utf-8') as f:
+            json.dump(existing_data, f, ensure_ascii=False, indent=2)
+            
+        logger.info(f"成功保存查询数据到: {QUERY_DATA_FILE}")
+    except Exception as e:
+        logger.error(f"处理日志数据时出错: {str(e)}", exc_info=True)
+
+# 创建日志目录
+os.makedirs(QUERY_LOG_DIR, exist_ok=True)
+
+# 配置日志 - 同时输出到控制台和文件
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+# 创建控制台处理器
+console_handler = logging.StreamHandler()
+console_handler.setLevel(logging.INFO)
+
+# 创建文件处理器
+file_handler = logging.FileHandler(
+    os.path.join(QUERY_LOG_DIR, "answer_type_service.log"),
+    encoding='utf-8'
 )
+file_handler.setLevel(logging.INFO)
+
+# 创建日志格式
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+console_handler.setFormatter(formatter)
+file_handler.setFormatter(formatter)
+
+# 添加处理器到日志器
+logger.addHandler(console_handler)
+logger.addHandler(file_handler)
+
+# 设置其他库的日志级别
 logging.getLogger('httpx').setLevel(logging.WARNING)
 logging.getLogger('openai').setLevel(logging.WARNING)
 
-logger = logging.getLogger(__name__)
-
 # 定义请求模型
 class AnswerTypeRequest(BaseModel):
     query: str
@@ -70,13 +144,32 @@ app.add_middleware(
 # 应用启动事件
 @app.on_event("startup")
 async def startup_event():
+    global worker_thread
     # 确保日志目录存在
     os.makedirs(QUERY_LOG_DIR, exist_ok=True)
     # 确保日志文件存在
-    if not os.path.exists(QUERY_LOG_FILE):
+    if not os.path.exists(QUERY_DATA_FILE):
-        async with file_lock:
+        with open(QUERY_DATA_FILE, 'w', encoding='utf-8') as f:
-            with open(QUERY_LOG_FILE, 'w', encoding='utf-8') as f:
+            json.dump([], f, ensure_ascii=False)
-                json.dump([], f, ensure_ascii=False)
+    
+    # 启动后台工作线程
+    worker_thread = threading.Thread(target=log_worker, daemon=True)
+    worker_thread.start()
+    logger.info("后台日志工作线程已启动")
+
+# 应用关闭事件
+@app.on_event("shutdown")
+def shutdown_event():
+    global worker_thread
+    if worker_thread:
+        # 发送退出信号
+        log_queue.put(None)
+        # 等待工作线程处理剩余数据
+        worker_thread.join(timeout=10.0)
+        if worker_thread.is_alive():
+            logger.warning("工作线程未在超时时间内退出")
+        else:
+            logger.info("后台日志工作线程已停止")
 
 # 添加健康检查端点
 @app.get("/health", summary="健康检查")
@@ -89,41 +182,22 @@ async def query_type(query_type: str, workflow_run_id:str):
         # 记录请求
         logger.info(f"接收到请求: 类型: {query_type}, workflow_run_id: {workflow_run_id}")
         
-        # 保存 提问、问题类型、当前时间戳到json
+        # 准备数据
         timestamp = datetime.datetime.now().isoformat()
         query_data = {
             "query_type": query_type,
             "timestamp": timestamp,
             "workflow_run_id": workflow_run_id
         }
-        success = True
+        
+        # 将数据放入队列
         try:
-            # 使用锁保护文件读写操作
+            log_queue.put(query_data)
-            async with file_lock:
+            success = True
-                # 确保目录存在
+            logger.info(f"查询数据已加入队列，当前队列大小: {log_queue.qsize()}")
-                os.makedirs(os.path.dirname(QUERY_LOG_FILE), exist_ok=True)
-                
-                # 读取现有数据
-                existing_data = []
-                if os.path.exists(QUERY_LOG_FILE) and os.path.getsize(QUERY_LOG_FILE) > 0:
-                    with open(QUERY_LOG_FILE, 'r', encoding='utf-8') as f:
-                        try:
-                            existing_data = json.load(f)
-                        except json.JSONDecodeError:
-                            logger.error(f"JSON文件解析错误，将创建新文件: {QUERY_LOG_FILE}")
-                            existing_data = []
-                
-                # 添加新数据
-                existing_data.append(query_data)
-                
-                # 写入文件
-                with open(QUERY_LOG_FILE, 'w', encoding='utf-8') as f:
-                    json.dump(existing_data, f, ensure_ascii=False, indent=2)
-                
-                logger.info(f"成功保存查询数据到: {QUERY_LOG_FILE}")
         except Exception as e:
             success = False
-            logger.error(f"保存查询数据时出错: {str(e)}", exc_info=True)
+            logger.error(f"加入队列时出错: {str(e)}", exc_info=True)
         
         # 返回响应
         content = f"<strong>问题类型</strong>: {query_type}<br><strong>操作是否成功</strong>: {'成功' if success else '失败'}"
@@ -146,4 +220,4 @@ if __name__ == "__main__":
     #     workers=1     # 生产环境可以增加worker数量
     # )
     # 生产环境可以使用以下命令启动:
-    # uvicorn rag2_0.dify.AnswerType:app --host 0.0.0.0 --port 8003 --workers 20 
+    # uvicorn rag2_0.dify.AnswerType:app --host 0.0.0.0 --port 8003 --workers 1 

rag2_0/dify/WorkorderToDify.py

@@ -5,7 +5,7 @@ sys.path.append(os.getcwd())
 import rag2_0.dify.dify_client.dify_api as DifyApi
 
 import pandas as pd
-pd_data = pd.read_excel("data/excel/工单汇总（给AI）_2.xlsx")
+pd_data = pd.read_excel("data/excel/工单汇总(给AI)_工单拆分.xlsx")
 
 
 dify_api = DifyApi.DifyApi()
@@ -13,6 +13,7 @@ peiwang_dataset_id = dify_api.get_or_create_dataset_by_name("配网工单数据"
 zhuwang_dataset_id = dify_api.get_or_create_dataset_by_name("主网工单数据")
 jianga_dataset_id = dify_api.get_or_create_dataset_by_name("技改工单数据")
 chuneng_dataset_id = dify_api.get_or_create_dataset_by_name("储能工单数据")
+xizang_dataset_id = dify_api.get_or_create_dataset_by_name("西藏工单数据")
 
 
 soft_segments_list={}
@@ -39,6 +40,10 @@ for skill_group, segments_list in soft_segments_list.items():
         dataset_id = jianga_dataset_id
     elif skill_group == "储能":
         dataset_id = chuneng_dataset_id
+    elif skill_group == "西藏":
+        dataset_id = xizang_dataset_id
+    else:
+        continue
     document_id = dify_api.get_document_id(dataset_id=dataset_id, document_name=f"{skill_group}工单数据")
     if not document_id:
         document_id = dify_api.upload_text_to_document(text_name=f"{skill_group}工单数据", text="", dataset_id=dataset_id)

start_AnswerType.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+# 获取当前脚本所在的绝对路径
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+# 检查是否已经存在名为AnswerType的screen会话
+if screen -ls | grep "AnswerType"; then
+    echo "Screen session 'AnswerType' already exists."
+else
+    # 启动一个名为AnswerType的screen会话，并在其中执行后续命令
+    screen -dmS AnswerType bash -c '
+        cd $SCRIPT_DIR
+        uv run uvicorn rag2_0.dify.AnswerType:app --host 0.0.0.0 --port 8003 --workers 1 
+    '
+
+    # 输出提示信息
+    echo "Started screen session 'AnswerType' and executed the command."
+fi

start_DifyQueryRetrieval_api.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+# 获取当前脚本所在的绝对路径
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+# 检查是否已经存在名为DifyQueryRetrieval_api的screen会话
+if screen -ls | grep "DifyQueryRetrieval_api"; then
+    echo "Screen session 'DifyQueryRetrieval_api' already exists."
+else
+    # 启动一个名为DifyQueryRetrieval_api的screen会话，并在其中执行后续命令
+    screen -dmS DifyQueryRetrieval_api bash -c '
+        cd $SCRIPT_DIR
+        uv run uvicorn rag2_0.dify.DifyQueryRetrieval_api:app --host 0.0.0.0 --port 8002 --workers 25
+    '
+
+    # 输出提示信息
+    echo "Started screen session 'DifyQueryRetrieval_api' and executed the command."
+fi

start_intent_recognition_api.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+# 获取当前脚本所在的绝对路径
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+# 检查是否已经存在名为xinference的screen会话
+if screen -ls | grep "intent_recognition_api"; then
+    echo "Screen session 'intent_recognition_api' already exists."
+else
+    # 启动一个名为xinference的screen会话，并在其中执行后续命令
+    screen -dmS intent_recognition_api bash -c '
+        cd $SCRIPT_DIR
+        uv run uvicorn rag2_0.dify.intent_recognition_api:app --host 0.0.0.0 --port 8001 --workers 25
+    '
+
+    # 输出提示信息
+    echo "Started screen session 'intent_recognition_api' and executed the command."
+fi