diff --git a/.gitignore b/.gitignore
index da56a35..ac13a73 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,9 +5,12 @@ __pycache__/
# 忽略数据文件
data/excel/*
-rag2_0/demo/Test.py
+rag2_0/demo/Test*
+rag2_0/demo/test*
data/excel/*.xlsx
rag2_0/demo/ProfessionalTermAnalyzer.py
data/logs/*
rag2_0/dify/Test.py
data/query_logs/*
+data/conversations/*
+data/test*
\ No newline at end of file
diff --git a/rag2_0/demo/dialogue_to_workorder.py b/rag2_0/demo/dialogue_to_workorder.py
index 3a7deb1..6eecffc 100755
--- a/rag2_0/demo/dialogue_to_workorder.py
+++ b/rag2_0/demo/dialogue_to_workorder.py
@@ -34,30 +34,33 @@ 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"],
+"1116":["夏剑媛", "新能源"],
+"1201":["曹美芳", "配网造价及清单"],
+"1202":["彭珊珊", "主网造价及清单"],
+"1230":["龚青", "配网造价及清单"],
+"1544":["黄婷", "主网造价及清单"],
+"1546":["严琼辉", "配网造价及清单"],
+"1552":["吴园妹", "主网造价及清单"],
+"1555":["魏怡璠", "配网造价及清单"],
+"1789":["冷琛", "主网造价及清单"],
+"2142":["余国庆", "配网造价及清单"],
+"2144":["卢光辉", "技改检修"],
+"2145":["万志星", "技改检修"],
+"2233":["徐雨萍", "主网造价及清单"],
+"2262":["刘雨微", "主网造价及清单"],
+"2591":["揭敏", "主网造价及清单"],
+"3035":["杨玲", "主网造价及清单"],
+"3416":["杨苏文", "配网造价及清单"],
+"3417":["王琴", "配网造价及清单"],
+"439":["赵莉", "技改检修"],
+"8340":["熊磊娇", "新能源"],
+"8442":["胡月", "配网造价及清单"],
+"8443":["杨淑玲", "主网造价及清单"],
+"8555":["胡青艳", "主网造价及清单"],
+"8762":["周丽华", "主网造价及清单"],
+"1553":["郝中华", "技改检修"],
+"8817":["赵雅馨", "技改检修"],
+"2590":["李琴", "技改检修"],
}
# ================ 模型定义 ================
@@ -65,6 +68,10 @@ class UserQuestionAndSolution(BaseModel):
user_question: str = Field(description="用户的核心问题")
solution: str = Field(description="坐席提供的解决方案,解决方案如果存在多个步骤，使用中文分号隔开")
+class ProductInfo:
+ product_line:str
+ product_name:str
+
class UserQuestionAndSolutionList(BaseModel):
user_question_list: list[UserQuestionAndSolution] = Field(description="客户问题列表")
@@ -133,49 +140,113 @@ class DialogueToWorkorder:
self.is_complaint_parser = PydanticOutputParser(pydantic_object=IsComplaint)
self.product_name_and_module_name_parser = PydanticOutputParser(pydantic_object=ProductNameAndModuleName)
self.product_line_parser = PydanticOutputParser(pydantic_object=ProductLine)
-
# 初始化LLM模型
- # self.llm_params = llm_params or {
- # "temperature": 0.2,
- # "top_p":0.95,
- # "model": "deepseek-ai/DeepSeek-R1",
- # "api_key": os.getenv("OPENAI_API_KEY"),
- # "base_url": os.getenv("OPENAI_API_BASE"),
- # "timeout": httpx.Timeout(600.0)
- # }
- self.api_key = "25t%Syu6I9yxX2IuTN"
self.llm_params = llm_params or {
"temperature": 0.2,
"top_p":0.95,
- "model": "deepseek-r1",
- "api_key": "25t%Syu6I9yxX2IuTN",
- "base_url": "http://10.1.0.154:8000/v1",
+ "model": "deepseek-ai/DeepSeek-R1",
+ "api_key": os.getenv("OPENAI_API_KEY"),
+ "base_url": os.getenv("OPENAI_API_BASE"),
"timeout": httpx.Timeout(600.0)
}
+ # self.llm_params = llm_params or {
+ # "temperature": 0.2,
+ # "top_p":0.95,
+ # "model": "deepseek-r1",
+ # "api_key": "25t%Syu6I9yxX2IuTN",
+ # "base_url": "http://10.1.0.154:8000/v1",
+ # "timeout": httpx.Timeout(600.0)
+ # }
self.llm = self._get_llm_instance()
def _get_llm_instance(self):
"""获取LLM实例"""
return OpenAiLLM(**self.llm_params)
-
- def parse_product_detail_excel(self, file_path):
- """解析产品详情Excel文件"""
- df = pd.read_excel(file_path)
- product_dict = {}
+
+ def get_product_info(self, conversation_context:str, skill_group:str)->ProductInfo:
+ product_info = ProductInfo()
- for _, row in df.iterrows():
- product_line = str(row['产品线']).strip() if pd.notna(row['产品线']) else ''
- product_name = str(row['产品名称']).strip() if pd.notna(row['产品名称']) else ''
- module_name = str(row['模块名称']).strip() if pd.notna(row['模块名称']) else ''
-
- if product_line not in product_dict:
- product_dict[product_line] = {}
- if product_name not in product_dict[product_line]:
- product_dict[product_line][product_name] = []
- product_dict[product_line][product_name].append(module_name)
+ # 默认为其他
+ product_info.product_line = "其他"
+ product_info.product_name = ""
- return product_dict
-
+ # 主网造价及清单技能组
+ if skill_group == "主网造价及清单":
+ if "西藏" in conversation_context:
+ product_info.product_line = "博微西藏计价通Z1"
+ if "建安预算" in conversation_context or "全口径预算" in conversation_context:
+ product_info.product_name = "施工图预算"
+ elif "招标" in conversation_context or "投标" in conversation_context:
+ product_info.product_name = "招投标"
+ elif "清单" in conversation_context and "结算" in conversation_context:
+ product_info.product_name = "清单结算"
+ else:
+ product_info.product_name = "概预算"
+ elif "造价2016" in conversation_context or "造价2014" in conversation_context or "造价2008" in conversation_context:
+ product_info.product_line = "主网造价系列"
+ elif "清单2016" in conversation_context or "清单2015" in conversation_context:
+ product_info.product_line = "主网清单系列"
+ elif "大结算" in conversation_context or "结算2018" in conversation_context:
+ product_info.product_line = "管理或辅助工具软件"
+ product_info.product_name = "结算应用2018"
+ elif "清标" in conversation_context or "数字化" in conversation_context:
+ product_info.product_line = "管理或辅助工具软件"
+ product_info.product_name = "数字化清标工具"
+ else:
+ product_info.product_line = "博微电力建设计价通软件"
+ if "建安预算" in conversation_context or "全口径预算" in conversation_context:
+ product_info.product_name = "施工图预算"
+ elif "招标" in conversation_context or "投标" in conversation_context:
+ product_info.product_name = "招投标"
+ elif any(keyword in conversation_context for keyword in ["清单结算", "基准价", "结算条款", "结算市场价", "结算审核"]):
+ product_info.product_name = "清单结算"
+ else:
+ product_info.product_name = "概预算"
+
+ # 技改检修技能组
+ elif skill_group == "技改检修":
+ if "技改2015" in conversation_context or "技改2016" in conversation_context:
+ product_info.product_line = "技改检修系列"
+ else:
+ product_info.product_line = "博微技改检修计价通T1软件"
+ if "T1" in conversation_context and "清单" in conversation_context:
+ product_info.product_name = "清单"
+ else:
+ product_info.product_name = "概预算"
+
+ # 配网造价及清单技能组
+ elif skill_group == "配网造价及清单":
+ if "配网2017" in conversation_context or "09配网" in conversation_context or "老配网" in conversation_context:
+ product_info.product_line = "配网系列"
+ else:
+ product_info.product_line = "博微配网计价通D3"
+ if "南网" in conversation_context:
+ product_info.product_name = "南网版"
+ elif "清单" in conversation_context and "南网" not in conversation_context:
+ product_info.product_name = "行业清单计价"
+ else:
+ product_info.product_name = "概预算"
+
+ # 新能源技能组
+ elif skill_group == "新能源":
+ product_info.product_line = "新能源系列"
+ if "核电" in conversation_context:
+ product_info.product_name = "核电清单2018"
+ elif "光伏" in conversation_context and "2018" in conversation_context:
+ product_info.product_name = "光伏计价2018"
+ elif "储能" in conversation_context:
+ product_info.product_name = "新型储能电站计价通C1"
+ elif "营销" in conversation_context:
+ product_info.product_name = "陕西电力营销计价通M1"
+ elif "新能源" in conversation_context or "光伏清单" in conversation_context or "风电" in conversation_context:
+ product_info.product_name = "新能源计价通N1"
+
+ # 经济评价及其他技能组
+ elif "经济评价" in conversation_context:
+ product_info.product_line = "经济评价系列"
+
+ return product_info
+
def get_workorder_dict(self, rows):
"""从会话行中提取工单基本信息"""
# 预设字段
@@ -220,6 +291,13 @@ class DialogueToWorkorder:
create_time_str = str(workorder_dict["创建时间"]).strip()
dt = datetime.strptime(create_time_str, "%Y-%m-%d %H:%M:%S")
workorder_dict["工单编号"] = dt.strftime("%Y%m%d%H%M%S")
+
+ # 获取产品线和产品名称
+ dialogue_str = self.get_dialogue_str(rows)
+ skill_group = workorder_dict.get("处理技能组", "")
+ product_info = self.get_product_info(dialogue_str, skill_group)
+ workorder_dict["产品线"] = product_info.product_line
+ workorder_dict["产品名称"] = product_info.product_name
return workorder_dict
@@ -291,7 +369,7 @@ class DialogueToWorkorder:
output_format = self.user_question_and_solution_parser.get_format_instructions()
llm_prompt = prompt.format(output_format=output_format, dialogue_str=dialogue_str)
- response = self.llm.invoke(user_prompt=llm_prompt, need_retry=False, api_key=self.api_key)
+ response = self.llm.invoke(user_prompt=llm_prompt, need_retry=False)
clean_output = re.sub(r'.*?', '', response.content, flags=re.DOTALL)
try:
if clean_output.count('user_question') == 1:
@@ -321,75 +399,13 @@ class DialogueToWorkorder:
except Exception as e:
output_format = self.user_question_and_solution_list_parser.get_format_instructions()
llm_prompt = prompt.format(output_format=output_format, dialogue_str=dialogue_str)
- response = self.llm.invoke(user_prompt=llm_prompt, need_retry=False, api_key=self.api_key)
+ response = self.llm.invoke(user_prompt=llm_prompt, need_retry=False)
clean_output = re.sub(r'.*?', '', response.content, flags=re.DOTALL)
user_question_and_solution_temp = self.user_question_and_solution_list_parser.parse(clean_output)
return user_question_and_solution_temp.user_question_list
return [user_question_and_solution]
- @retry_llm_call(max_retries=3, delay=2)
- def get_product_name_and_module_name(self, product_line, conversation_rows, product_detail_dict, user_question_str, solution_str):
- """分析产品名称和模块名称"""
- if product_line == '':
- return '', ''
-
- json_str = json.dumps(product_detail_dict[product_line])
- dialogue_str = self.get_dialogue_str(conversation_rows)
-
- prompt = f"""
-请根据以下对话内容分析所属产品名称和模块名称，按优先级找出最相关的1-3个分类标签及置信度（0-1），最后返回置信度最高的分类标签。
-
-要求：
-1. 如果对话记录中存在多个产品名称和模块名称，则根据"{user_question_str}"和"{solution_str}"判断最可能的产品名称和模块名称。
-2. 如果对话记录中只存在一个产品名称和模块名称，则直接返回该产品名称和模块名称。
-
-输出格式：
-{self.product_name_and_module_name_parser.get_format_instructions()}
-
-产品名称列表及模块名称列表：
-{json_str}
-
-对话记录：
-{dialogue_str}
- """
-
- response = self.llm.invoke(user_prompt=prompt, need_retry=False, api_key=self.api_key)
- clean_output = re.sub(r'.*?', '', response.content, flags=re.DOTALL)
- product_name_and_module_name = self.product_name_and_module_name_parser.parse(clean_output)
-
- return product_name_and_module_name.product_name, product_name_and_module_name.module_name
-
- @retry_llm_call(max_retries=3, delay=2)
- def get_product_line(self, conversation_rows, product_detail_dict, user_question_str, solution_str):
- """分析产品线"""
- dialogue_str = self.get_dialogue_str(conversation_rows)
- product_line_list = list(product_detail_dict.keys())
-
- prompt = f"""
-请根据以下对话内容分析所属产品线，按优先级找出最相关的1-3个分类标签及置信度（0-1），最后返回置信度最高的分类标签。
-无法判断时，返回空字符串。即product_line=""
-
-要求：
-1. 如果对话记录中存在多个产品线，则根据"{user_question_str}"和"{solution_str}"判断最可能的产品线。
-2. 如果对话记录中只存在一个产品线，则直接返回该产品线。
-
-输出格式：
-{self.product_line_parser.get_format_instructions()}
-
-产品线列表：
-{product_line_list}
-
-对话记录：
-{dialogue_str}
- """
-
- response = self.llm.invoke(user_prompt=prompt, need_retry=False, api_key=self.api_key)
- clean_output = re.sub(r'.*?', '', response.content, flags=re.DOTALL)
- product_line = self.product_line_parser.parse(clean_output)
-
- return product_line.product_line
-
@retry_llm_call(max_retries=3, delay=2)
def get_problem_type(self, conversation_rows, user_question_str, solution_str):
"""分析问题类型"""
@@ -421,7 +437,7 @@ class DialogueToWorkorder:
{dialogue_str}
"""
- response = self.llm.invoke(user_prompt=prompt, need_retry=False, api_key=self.api_key)
+ response = self.llm.invoke(user_prompt=prompt, need_retry=False)
clean_output = re.sub(r'.*?', '', response.content, flags=re.DOTALL)
question_type = self.question_type_parser.parse(clean_output)
@@ -458,7 +474,7 @@ class DialogueToWorkorder:
"""
- response = self.llm.invoke(user_prompt=prompt, need_retry=False, api_key=self.api_key)
+ response = self.llm.invoke(user_prompt=prompt, need_retry=False)
clean_output = re.sub(r'.*?', '', response.content, flags=re.DOTALL)
is_complaint = self.is_complaint_parser.parse(clean_output)
@@ -467,7 +483,7 @@ class DialogueToWorkorder:
is_complaint.dissatisfaction_reasoning,
is_complaint.is_complaint)
- def process_conversation(self, conversation_id, conversation_rows, product_detail_dict):
+ def process_conversation(self, conversation_id, conversation_rows):
"""处理单个会话的函数，用于多线程并发"""
# if conversation_id!="b157aa91-3acb-11f0-a191-4fb224ef4b40":
# return []
@@ -494,34 +510,9 @@ class DialogueToWorkorder:
# 分析问题类型
problem_type = self.get_problem_type(conversation_rows, user_question_str, solution_str)
- # 分析产品线
- product_line = self.get_product_line(conversation_rows, product_detail_dict, user_question_str, solution_str)
- # 分析产品名称和模块名称
- if product_line != '':
- product_name, module_name = self.get_product_name_and_module_name(
- product_line, conversation_rows, product_detail_dict, user_question_str, solution_str)
- else:
- product_name = ''
- module_name = ''
-
# 创建工单列表
workorder_list = []
-
- # 更新工单字典
- # 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
@@ -531,9 +522,6 @@ class DialogueToWorkorder:
# 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',
@@ -551,7 +539,7 @@ class DialogueToWorkorder:
logger.error(f"处理会话ID: {conversation_id} 时发生错误: {e}")
return []
- def analyze_conversation_data(self, conversation_excel_path, product_detail_excel_path, max_workers=10, start_date=None, end_date=None):
+ def analyze_conversation_data(self, conversation_excel_path, max_workers=10, start_date=None, end_date=None):
"""分析会话数据主流程，使用多线程并发处理"""
# 读取Excel文件
df = pd.read_excel(conversation_excel_path)
@@ -560,9 +548,6 @@ class DialogueToWorkorder:
logger.info(f"Excel文件列名: {df.columns.tolist()}")
logger.info(f"数据总行数: {len(df)}")
- # 解析产品详情
- product_detail_dict = self.parse_product_detail_excel(product_detail_excel_path)
-
# 按会话ID分组
conversation_dict = self.group_conversations_by_id(df)
@@ -596,7 +581,7 @@ class DialogueToWorkorder:
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
# 创建任务
future_to_conversation = {
- executor.submit(self.process_conversation, conversation_id, conversation_rows, product_detail_dict): conversation_id
+ executor.submit(self.process_conversation, conversation_id, conversation_rows): conversation_id
for conversation_id, conversation_rows in conversation_dict.items()
}
@@ -694,13 +679,11 @@ def parse_arguments():
parser.add_argument('--conversation_file', type=str, required=False,
help='会话内容Excel文件路径')
- parser.add_argument('--product_detail_file', type=str, required=False,
- help='产品详情Excel文件路径')
- parser.add_argument('--max_workers', type=int, default=6,
+ parser.add_argument('--max_workers', type=int, default=40,
help='并发处理线程数，默认为16')
- parser.add_argument('--start_date', type=str, required=False,default="2025-06-10 16:08:00",
+ parser.add_argument('--start_date', type=str, required=False,default=None,
help='开始日期，格式为YYYY-MM-DD')
- parser.add_argument('--end_date', type=str, required=False,default="2025-06-30 23:59:59",
+ parser.add_argument('--end_date', type=str, required=False,default=None,
help='结束日期，格式为YYYY-MM-DD')
return parser.parse_args()
@@ -712,8 +695,7 @@ def main():
args = parse_arguments()
# 设置默认文件路径
- conversation_excel_path = args.conversation_file or os.path.join('data', 'excel', '2025年1月到6月所有对话记录.xlsx')
- product_detail_excel_path = args.product_detail_file or os.path.join('data', 'excel', '产品详情_工单.xlsx')
+ conversation_excel_path = args.conversation_file or os.path.join('data', 'excel', '客服对话记录7.1-7.22.xlsx')
# 创建处理实例
processor = DialogueToWorkorder()
@@ -721,7 +703,6 @@ def main():
# 分析会话数据
workorder_dict_list = processor.analyze_conversation_data(
conversation_excel_path,
- product_detail_excel_path,
max_workers=args.max_workers,
start_date=args.start_date,
end_date=args.end_date