更新日志目录创建逻辑，调整对话到工单的日期参数默认值，新增对话记录分析功能，优化API密钥管理器中的购买余额计算，并添加多个API密钥。同时，新增数据处理和分析模块以支持工单问答数据的上传和处理。
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@@ -21,14 +21,14 @@ sys.path.append(os.getcwd())
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from rag2_0.tool.ModelTool import OpenAiLLM
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load_dotenv()
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os.makedirs("data/logs", exist_ok=True)
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.StreamHandler(),
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logging.FileHandler('dialogue_to_workorder.log', encoding='utf-8')
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logging.FileHandler('data/logs/dialogue_to_workorder.log', encoding='utf-8')
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]
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)
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logger = logging.getLogger("dialogue_to_workorder")
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@@ -565,7 +565,7 @@ class DialogueToWorkorder:
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# 按照指定的列顺序重新排列DataFrame的列
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columns_order = [
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'工单编号', '产品线', '产品名称', '模块名称', '问题类型',
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'客户问题', '解决方案', '是否抱怨', '是否投诉', '抱怨级别',
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'客户问题', '解决方案', '是否抱怨', "抱怨内容", '是否投诉', '抱怨级别',
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'会话id', '访客昵称', '处理坐席', '创建时间'
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]
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@@ -609,6 +609,7 @@ class DialogueToWorkorder:
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'客户问题': 20,
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'解决方案': 30,
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'是否抱怨': 9,
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'抱怨内容': 30,
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'是否投诉': 9,
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'抱怨级别': 9,
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'会话id': 9,
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@@ -639,9 +640,9 @@ def parse_arguments():
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help='产品详情Excel文件路径')
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parser.add_argument('--max_workers', type=int, default=16,
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help='并发处理线程数，默认为16')
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parser.add_argument('--start_date', type=str, required=False,default="2025-05-25 00:00:00",
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parser.add_argument('--start_date', type=str, required=False,default="2025-05-01 00:00:00",
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help='开始日期，格式为YYYY-MM-DD')
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parser.add_argument('--end_date', type=str, required=False,default="2025-05-30 15:54",
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parser.add_argument('--end_date', type=str, required=False,default="2025-05-24 23:59:59",
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help='结束日期，格式为YYYY-MM-DD')
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return parser.parse_args()
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@@ -29,7 +29,7 @@ logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler('./data/log/mariadb_client.log'),
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logging.FileHandler('./data/logs/mariadb_client.log'),
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logging.StreamHandler()
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]
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)
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@@ -267,6 +267,9 @@ class DataProcessor:
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# 转换为字典列表
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result = []
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for record in filtered_df.to_dict('records'):
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# 如果上一条消息和当前消息的发送者、创建时间、消息内容相同，则跳过
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if result and result[-1]['会话id'] == record['SESSION_ID'] and result[-1]['消息发送者'] == record['message_sender'] and result[-1]['创建时间'] == record['CREATE_TIME'] and result[-1]['消息内容'] == record['processed_content']:
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continue
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result.append({
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"账号id": record["ACCOUNT"],
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"会话id": record["SESSION_ID"],
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@@ -0,0 +1,116 @@
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import pandas as pd
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import json
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from regex import search
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import ijson
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df = pd.read_excel("data/excel/已分析数据汇总（第一轮）.xlsx")
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df=df[df["评价"]=="dislike"]
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msg_id_list = df["msg_id"].tolist()
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msg_debug_list = {}
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# 流式解析 JSON 数组
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with open("data/excel/msg_debug_list.json", "r", encoding="utf-8") as f:
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# 使用ijson.items直接获取顶层键值对
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for msg_id, data in ijson.kvitems(f, ''):
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if msg_id in msg_id_list:
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msg_debug_list[msg_id] = data
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def get_rewrite_query(intent_node_execution_info)->str:
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outputs_result =json.loads(intent_node_execution_info['outputs'])
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return outputs_result['optimize_query']
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def judge_error_node_and_reason(intent_node_execution_info, knowledge_filter_node_execution_info_list, answer_wiki_name)->dict:
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result = {"问题改写结果":None, "错误环节":None, "错误原因":None, "具体描述":None}
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if answer_wiki_name is None or pd.isna(answer_wiki_name):
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return result
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outputs_result =json.loads(intent_node_execution_info['outputs'])
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result["问题改写结果"] = outputs_result['optimize_query']
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if outputs_result['is_complete'] == False:
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result["错误环节"] = "槽点填充"
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result["错误原因"] = f"槽点缺失"
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result["具体描述"] = f"缺失内容：{outputs_result['missing_slots']}"
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return result
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if len(knowledge_filter_node_execution_info_list) == 0:
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return result
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knowledge_filter_node_execution_info=knowledge_filter_node_execution_info_list[0]
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# 获取检索到的所有词条
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knowledge_filter_outputs = json.loads(knowledge_filter_node_execution_info['outputs'])
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source_knowledge = knowledge_filter_outputs['source_kno']
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source_knowledge_title ="\n".join([item['title'] for item in source_knowledge])
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if answer_wiki_name not in source_knowledge_title:
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result["错误环节"] = "知识检索"
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result["错误原因"] = f"未检索到对应词条"
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# 获取词条名称及对应评分
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result["具体描述"] = "检索到的词条如下：\n"
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for index, item in enumerate(source_knowledge):
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result["具体描述"] += f"词条名称：{item['title'].split('/')[-1]}，重排评分：{item['metadata']['score']:.2f}\n"
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return result
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# 获取检索到的词条的metadata
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knowledge_filter = knowledge_filter_outputs['knowledge_list_metadata']
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knowledge_filter_title ="\n".join([item['title'] for item in knowledge_filter])
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if answer_wiki_name not in knowledge_filter_title:
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result["错误环节"] = "知识过滤"
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result["错误原因"] = f"词条被过滤"
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result["具体描述"] = "检索到的词条如下：\n"
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for index, item in enumerate(source_knowledge):
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result["具体描述"] += f"词条名称：{item['title'].split('/')[-1]}，重排评分：{item['metadata']['score']:.2f}\n"
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return result
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# 检索正确，回答错误
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result["错误环节"] = "生成错误"
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result["错误原因"] = f""
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result["具体描述"] = f""
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return result
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df["问题改写结果"] = None
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df["错误环节"] = None
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df["错误原因"] = None
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df["具体描述"] = None
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for index, row in df.iterrows():
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try:
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msg_id = row["msg_id"]
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answer = row["回答"]
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query = row["提问"]
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rating = row["评价"]
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class_type = row["问题分类"]
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dislike_reason = row["点踩原因"]
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if dislike_reason is None or pd.isna(dislike_reason):
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continue
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answer_wiki_name = row["关联词条"]
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search_wiki = row["检索到的词条"]
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node_executions_info = msg_debug_list[msg_id]
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intent_node_execution_info = [node_execution_info for node_execution_info in node_executions_info
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if node_execution_info["title"] == "意图识别结果解析"]
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knowledge_filter_node_execution_info_list = [node_execution_info for node_execution_info in node_executions_info
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if node_execution_info["title"] == "提取处理后的知识"]
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if len(intent_node_execution_info) == 0:
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print(f"msg_id: {msg_id} 缺少节点信息")
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continue
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rewrite_query = get_rewrite_query(intent_node_execution_info[0])
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df.loc[index, "问题改写结果"] = rewrite_query
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if "有词条" not in dislike_reason:
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continue
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result = judge_error_node_and_reason(intent_node_execution_info[0], knowledge_filter_node_execution_info_list, answer_wiki_name)
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for key, value in result.items():
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df.loc[index, key] = value
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except Exception as e:
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print(f"msg_id: {msg_id} 处理失败: {e}")
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continue
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df.to_excel("data/excel/已分析数据汇总（第一轮）_分析.xlsx", index=False)
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@@ -0,0 +1,29 @@
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import os
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import sys
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sys.path.append(os.getcwd())
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import rag2_0.dify.dify_client.dify_api as DifyApi
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import pandas as pd
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pd_data = pd.read_excel("data/excel/2025年5月30日到6月10号对话记录_转工单.xlsx")
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dify_api = DifyApi.DifyApi()
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dataset_id = dify_api.get_or_create_dataset_by_name("工单问答数据")
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document_id = dify_api.upload_text_to_document(text_name="5月30日到6月10号对话工单", text="", dataset_id=dataset_id)
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segments_list=[]
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for index, row in pd_data.iterrows():
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query = row["客户问题"]
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answer = row["解决方案"]
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if "存在抱怨" in answer:
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answer = answer.split("存在抱怨")[0]
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content = f"问题：{query}\n解决方案：{answer}"
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segments_list.append({
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"content": str(content),
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"answer": "",
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"keywords": []
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})
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dify_api.add_document_segments(dataset_id=dataset_id, document_id=document_id, segments_list=segments_list)
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@@ -275,17 +275,18 @@ if __name__ == "__main__":
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stats = instance.get_usage_stats()
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all_balance=0.0
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buy_balance=19 * 10 * 14 # 购买18次，一次10条api_key，每个api_key有14元
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buy_balance=24 * 10 * 14 # 购买18次，一次10条api_key，每个api_key有14元
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invalid_api_keys = []
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for key, data in stats.items():
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usage_stats = APIKeyManager.get_key_usage_stats(key)
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all_balance+=float(usage_stats['data']['balance'])
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valid,err_info = APIKeyManager.get_valid_api_keys(key)
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if not valid:
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print(f"api_key:{key}---赠送余额：{usage_stats['data']['balance']}元---报错信息：{err_info}")
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# invalid_api_keys.append(key)
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else:
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print(f"api_key:{key}---赠送余额：{usage_stats['data']['balance']}元")
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# valid,err_info = APIKeyManager.get_valid_api_keys(key)
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# if not valid:
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# print(f"api_key:{key}---赠送余额：{usage_stats['data']['balance']}元---报错信息：{err_info}")
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# # invalid_api_keys.append(key)
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# else:
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# print(f"api_key:{key}---赠送余额：{usage_stats['data']['balance']}元")
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print(f"api_key:{key}---赠送余额：{usage_stats['data']['balance']}元")
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if float(usage_stats['data']['balance']) == 0:
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invalid_api_keys.append(key)
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