更新.gitignore以忽略查询日志文件，优化意图识别逻辑，新增锁相关咨询处理方法，并调整后退提示的返回结构以支持固定话术类问题。

.gitignore

@@ -10,3 +10,4 @@ data/excel/*.xlsx
 rag2_0/demo/ProfessionalTermAnalyzer.py
 data/logs/*
 rag2_0/dify/Test.py
+data/query_logs/*

pyproject.toml

@@ -11,10 +11,12 @@ dependencies = [
     "flask>=3.1.1",
     "gevent>=25.5.1",
     "gunicorn>=23.0.0",
+    "ijson>=3.4.0",
     "jieba>=0.42.1",
     "langchain>=0.3.25",
     "langchain-community>=0.3.25",
     "langchain-openai>=0.3.24",
+    "langfuse==2.51.3",
     "markdownify==0.13.1",
     "openpyxl>=3.1.5",
     "pandas>=2.3.0",

rag2_0/demo/dialogue_to_workorder.py

@@ -499,7 +499,7 @@ class DialogueToWorkorder:
         
         return workorder_list
     
-    def analyze_conversation_data(self, conversation_excel_path, product_detail_excel_path, max_workers=10):
+    def analyze_conversation_data(self, conversation_excel_path, product_detail_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)
@@ -511,21 +511,29 @@ 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)
-        # 限制处理的会话数量为前2000个
+        logger.info(f"会话总数为 {len(conversation_dict)}，处理全部会话")
-        if len(conversation_dict) > 2000:
+        
-            logger.info(f"会话总数为 {len(conversation_dict)}，限制处理前2000个会话")
-            # 获取所有会话ID
-            conversation_ids = list(conversation_dict.keys())
-            # 只保留前2000个会话
-            limited_conversation_dict = {
-                conversation_id: conversation_dict[conversation_id]
-                for conversation_id in conversation_ids[:2000]
-            }
-            conversation_dict = limited_conversation_dict
-        else:
-            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
         workorder_dict_list = []
         with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
@@ -629,6 +637,10 @@ 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,
+                        help='开始日期，格式为YYYY-MM-DD')
+    parser.add_argument('--end_date', type=str, required=False,
+                        help='结束日期，格式为YYYY-MM-DD')
     
     return parser.parse_args()
 
@@ -649,9 +661,21 @@ def main():
     workorder_dict_list = processor.analyze_conversation_data(
         conversation_excel_path, 
         product_detail_excel_path, 
-        max_workers=args.max_workers
+        max_workers=args.max_workers,
+        start_date=args.start_date,
+        end_date=args.end_date
     )
-    output_file = conversation_excel_path.replace('.xlsx', '_转工单.xlsx')
+    
+    # 生成输出文件名
+    if args.start_date and args.end_date:
+        output_file = conversation_excel_path.replace('.xlsx', f'_{args.start_date}至{args.end_date}_转工单.xlsx')
+    elif args.start_date:
+        output_file = conversation_excel_path.replace('.xlsx', f'_从{args.start_date}起_转工单.xlsx')
+    elif args.end_date:
+        output_file = conversation_excel_path.replace('.xlsx', f'_至{args.end_date}_转工单.xlsx')
+    else:
+        output_file = conversation_excel_path.replace('.xlsx', '_转工单.xlsx')
+    
     # 保存结果
     processor.save_results_to_excel(workorder_dict_list, output_file)
 

rag2_0/demo/intent_recognition_example.py

@@ -200,7 +200,8 @@ class QueryRewriteProcessor:
                                                   conversation_context=conversation_context, 
                                                   chat_history=chat_history, 
                                                   previous_slots=previous_slots, 
-                                                  enable_query_expansion=True))
+                                                  enable_query_expansion=True,
+                                                  use_jieba=True))
 
                 # 提取分类信息
                 classification = result["classification"]
@@ -440,7 +441,7 @@ def main():
         for idx, query in enumerate(examples):
             if query.strip() == "":
                 continue
-            query="储能C1软件如何新建工程？"
+            query="811619150828能看一下这个锁是16的马"
             conversation_context="当前使用软件：配网计价通D3软件"
             # 在调试模式下使用完整的参数
             print(json.dumps(processor.process_query(

rag2_0/dify/AnswerType.py

@@ -84,17 +84,18 @@ async def health_check():
     return {"status": "ok"}
 
 @app.get("/query_type", summary="异步检索API")
-async def query_type(query: str, query_type: str):
+async def query_type(query: str, query_type: str, workflow_run_id:str):
     try:
         # 记录请求
-        logger.info(f"接收到请求: {query}, 类型: {query_type}")
+        logger.info(f"接收到请求: {query}, 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
         timestamp = datetime.datetime.now().isoformat()
         query_data = {
             "query": query,
             "query_type": query_type,
-            "timestamp": timestamp
+            "timestamp": timestamp,
+            "workflow_run_id": workflow_run_id
         }
         success = True
         try:

rag2_0/dify/DifyQueryRetrieval.py

@@ -197,6 +197,8 @@ class DifyQueryRetrieval:
         # 将去重后的文档转换为列表
         deduplicated_documents = list(unique_documents.values())
         
+        if len(deduplicated_documents) == 0:
+            return []
         # 对所有检索出来的文档进行重排序
         time_start = time.time()
         processed_documents = await self.data_post_processor_async(original_query, deduplicated_documents, top_k)

rag2_0/dify/dify_tool.py

@@ -244,6 +244,29 @@ class PgSql:
                 raise Exception(f"Error while getting conversation_messages: {error}")
             return rating
 
+    def get_workflow_run_info(self, workflow_run_id):
+        """
+        通过msg_id从message_feedbacks中找到对应的rating。
+        :param msg_id: 消息ID (UUID格式)
+        :return: rating 字符串
+        """
+        with self.pg_sql_lock:
+            rating = None
+            try:
+                with self.connection.cursor() as cursor:
+                    # 构建查询语句
+                    cursor.execute("""
+                        SELECT * FROM workflow_runs WHERE id=%s;
+                    """,
+                    (workflow_run_id,))
+                    # 执行查询
+                    result = cursor.fetchone()
+                    if result:
+                        colnames = [desc[0] for desc in cursor.description]
+                        return dict(zip(colnames, result))
+            except (Exception, psycopg2.Error) as error:
+                raise Exception(f"Error while getting conversation_messages: {error}")
+            return None
 
 class DifyTool:
     """
@@ -337,6 +360,9 @@ class DifyTool:
     def get_message_rating(self, msg_id):
         return self.dify_pgsql.get_message_rating(msg_id)
 
+    def get_workflow_run_info(self, workflow_run_id):
+        return self.dify_pgsql.get_workflow_run_info(workflow_run_id)
+
 class BaseWorkflowChat:
     """
     工作流对话基类，封装了与Dify API交互的基本功能

rag2_0/dify/intent_recognition_api.py

@@ -51,7 +51,7 @@ class QueryExpandResponse(BaseModel):
     all: List[str] = Field(default_factory=list)
     step_back: Dict[str, Any] = Field(default_factory=Dict)
     follow_up: Dict[str, Any] = Field(default_factory=Dict)
-    hyde: Dict[str, Any] = Field(default_factory=Dict)
+    # hyde: Dict[str, Any] = Field(default_factory=Dict)
     multi_questions: Dict[str, Any] = Field(default_factory=Dict)
 
 # 定义响应模型
@@ -109,7 +109,7 @@ async def intent_recognize(request: IntentRecognizeRequest):
             conversation_context=request.conversation_context, 
             chat_history=request.chat_history, 
             previous_slots=request.previous_slots,
-            use_jieba=False,
+            use_jieba=True,
             enable_query_expansion=True
         )
         
@@ -150,7 +150,7 @@ async def intent_recognize(request: IntentRecognizeRequest):
                 all=result["query_expand"]["all"],
                 step_back=result["query_expand"]["step_back"],
                 follow_up=result["query_expand"]["follow_up"],
-                hyde=result["query_expand"]["hyde"],
+                # hyde=result["query_expand"]["hyde"],
                 multi_questions=result["query_expand"]["multi_questions"]
             )
         )
@@ -175,7 +175,7 @@ if __name__ == "__main__":
         "rag2_0.dify.intent_recognition_api:app", 
         host="0.0.0.0", 
         port=8001, 
-        reload=False,  # 开发环境启用热重载
+        reload=True,  # 开发环境启用热重载
         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:

rag2_0/intent_recognition/DataModels.py

@@ -224,15 +224,13 @@ class DataProblemSlots(SlotBase):
 # 3.1 后缀名咨询
 class FileExtensionConsultingSlots(SlotBase):
     file_extension: str = Field(default="", description="文件后缀名")
-    operation_purpose: str = Field(default="", description="操作目的(了解对应软件,对应工程)")
+    operation_purpose: Optional[str] = Field(default="", description="操作目的(了解对应软件,对应工程)")
     
     def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
         """检查必填槽位是否都存在"""
         missing_slots = {}
         if not self.file_extension:
             missing_slots["file_extension"] = FileExtensionConsultingSlots.model_fields["file_extension"].description
-        if not self.operation_purpose:
-            missing_slots["operation_purpose"] = FileExtensionConsultingSlots.model_fields["operation_purpose"].description
         return len(missing_slots) == 0, missing_slots
 
 # 3.2 软件锁类
@@ -311,7 +309,8 @@ class IntentAndSlotResult(BaseModel):
 class StepBackPrompt(BaseModel):
     """后退提示数据模型"""
     original_query: str = Field(description="原始查询")
-    step_back_query: str = Field(description="后退提示生成的抽象查询")
+    can_use_back_prompt: bool = Field(description="原始查询是否可以进行后退提示(True/False),如果原始查询没有限定词或其他限定词语，则不能进行后退提示")
+    step_back_query: List[str] = Field(description="后退提示生成的抽象查询(多个)")
 
 class FollowUpQuestions(BaseModel):
     """后续问题数据模型"""

rag2_0/intent_recognition/IntentRecognition.py

@@ -155,7 +155,9 @@ class AsyncIntentRecognizer:
             logging.info(f"异步意图分类耗时统计 - 总耗时: {classification_time:.2f}秒")
 
             # 尝试直接解析JSON响应
-            parsed_output = classification_parser.parse(response.content.strip())
+            response.content = response.content.strip()
+            clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
+            parsed_output = classification_parser.parse(clean_output)
             return parsed_output
         except Exception as e:
             raise RuntimeError(f"解析分类结果时出错: {e}") from e
@@ -216,7 +218,9 @@ class AsyncIntentRecognizer:
             response = await self._llm.invoke_async(formatted_prompt, False)
             
             # 尝试使用Pydantic解析器解析TermList
-            parsed_output = terms_list_parser.parse(response.content)
+            response.content = response.content.strip()
+            clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
+            parsed_output = terms_list_parser.parse(clean_output)
             return parsed_output.terms
 
     
@@ -344,9 +348,9 @@ class AsyncIntentRecognizer:
         try:
             # 异步调用LLM
             response = await self._llm.invoke_async(formatted_prompt, False)
-        
+            response.content = response.content.strip()
-            # 尝试直接解析JSON响应
+            clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
-            parsed_output = query_rewrite_parser.parse(response.content)
+            parsed_output = query_rewrite_parser.parse(clean_output)
             rewrite_end_time = time.time()
             rewrite_time = rewrite_end_time - rewrite_start_time
             logging.info(f"异步问题改写耗时统计 - 总耗时: {rewrite_time:.2f}秒")
@@ -374,6 +378,18 @@ class AsyncIntentRecognizer:
         
         return bool(matched_suffixes), matched_suffixes
 
+    def _process_lock_related_query(self, query: str) -> str:
+        """
+        特殊处理锁相关咨询
+        """
+        pattern = r'(?<!\d)(?:8116(?:\s*\d){8}|\d{2}\s*-\s*\d{6})(?!\d)'
+        matches = re.findall(pattern, query)
+        if not matches:
+            return query
+        lock_number = "、".join(matches)
+        return f"通过博微软件助手查询软件锁信息，锁注册号为{lock_number}"
+
+
     async def process_query_async(self, query: str, conversation_context: str = "", 
                       chat_history: List[Dict[str, str]] = None, 
                       previous_slots: Dict[str, Any] = None,
@@ -410,7 +426,7 @@ class AsyncIntentRecognizer:
                 asyncio.create_task(self._generate_follow_up_questions_async(query, chat_history, conversation_context)),
                 
                 # 5.3: HyDE
-                asyncio.create_task(self._generate_hypothetical_document_async(query, chat_history, conversation_context)),
+                # asyncio.create_task(self._generate_hypothetical_document_async(query, chat_history, conversation_context)),
                 
                 # 5.4: 多问题查询
                 asyncio.create_task(self._generate_multi_questions_async(query, chat_history, conversation_context))
@@ -438,11 +454,18 @@ class AsyncIntentRecognizer:
         classification_task = self._classify_intent_async(rewrite.rewrite, conversation_context, chat_history, previous_slots)
         classification = await classification_task
 
+        # 特殊处理 锁相关咨询
+        if classification.vertical_classification == "安装下载注册" and classification.sub_classification == "软件锁类":
+            process_lock_start_time = time.time()
+            rewrite.rewrite = self._process_lock_related_query(rewrite.rewrite)
+            process_lock_end_time = time.time()
+            process_lock_time = process_lock_end_time - process_lock_start_time
+            logging.info(f"锁相关咨询正则匹配 - 总耗时: {process_lock_time:.2f}秒")
         # 步骤4: 进行槽位填充   
         # 如果是有效分类，进行槽位填充
         slot_filling_result = {}
-        if classification.vertical_classification not in ["其他", "闲聊"] and classification.sub_classification not in ["其他", "闲聊"]:
+        # if classification.vertical_classification not in ["其他", "闲聊"] and classification.sub_classification not in ["其他", "闲聊"]:
-            slot_filling_result = await self._fill_slots_async(rewrite.rewrite, classification, conversation_context, chat_history, previous_slots)
+        #     slot_filling_result = await self._fill_slots_async(rewrite.rewrite, classification, conversation_context, chat_history, previous_slots)
         
         if not enable_query_expansion:
             return {
@@ -460,23 +483,24 @@ class AsyncIntentRecognizer:
         logging.info(f"异步问题扩展环节耗时统计 - 总耗时: {end_time - start_time:.2f}秒")
         
         # 收集结果
-        step_back_result = query_expand_results[0] if query_expand_results[0] else StepBackPrompt(original_query=query, step_back_query=query)
+        step_back_result = query_expand_results[0] if query_expand_results[0] else StepBackPrompt(original_query=query, can_use_back_prompt=False, step_back_query=[query])
         follow_up_result = query_expand_results[1] if query_expand_results[1] else FollowUpQuestions(original_query=query, follow_up_query=query)
-        hyde_result = query_expand_results[2] if query_expand_results[2] else HypotheticalDocument(original_query=query, hypothetical_answer="")
+        # hyde_result = query_expand_results[2] if query_expand_results[2] else HypotheticalDocument(original_query=query, hypothetical_answer="")
-        multi_questions_result = query_expand_results[3] if query_expand_results[3] else MultiQuestions(original_query=query, sub_questions=[query])
+        multi_questions_result = query_expand_results[2] if query_expand_results[2] else MultiQuestions(original_query=query, sub_questions=[query])
         
         all_questions = multi_questions_result.sub_questions
         all_questions.append(query)
-        all_questions.append(step_back_result.step_back_query)
+        all_questions.append(rewrite.rewrite)
+        all_questions.extend(step_back_result.step_back_query)
         all_questions.append(follow_up_result.follow_up_query)
-        all_questions.append(hyde_result.hypothetical_answer)
+        # all_questions.append(hyde_result.hypothetical_answer)
         all_questions = list(set(all_questions))
 
         query_expand = {
             "all": all_questions,
             "step_back": step_back_result.model_dump(),
             "follow_up": follow_up_result.model_dump(),
-            "hyde": hyde_result.model_dump(),
+            # "hyde": hyde_result.model_dump(),
             "multi_questions": multi_questions_result.model_dump()
         }
         
@@ -597,9 +621,10 @@ class AsyncIntentRecognizer:
         try:
             # 异步调用LLM
             response = await self._llm.invoke_async(formatted_prompt, False)
-        
+            response.content = response.content.strip()
+            clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
             # 尝试解析LLM响应
-            parsed_output = slot_parser.parse(response.content)
+            parsed_output = slot_parser.parse(clean_output)
             return parsed_output
         except Exception as e:
             # 如果解析失败，创建一个空的模型实例
@@ -633,7 +658,9 @@ class AsyncIntentRecognizer:
             response = await self._llm.invoke_async(formatted_prompt, False)
             
             # 解析输出
-            parsed_output = step_back_parser.parse(response.content)
+            response.content = response.content.strip()
+            clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
+            parsed_output = step_back_parser.parse(clean_output)
             step_back_end_time = time.time()
             step_back_time = step_back_end_time - step_back_start_time
             logging.debug(f"异步后退提示生成耗时统计 - 总耗时: {step_back_time:.2f}秒")
@@ -641,7 +668,7 @@ class AsyncIntentRecognizer:
         except Exception as e:
             # 如果解析失败，返回原始查询作为后退提示
             logging.error(f"异步后退提示生成失败: {e}", exc_info=True)
-            return StepBackPrompt(original_query=query, step_back_query=query)
+            return StepBackPrompt(original_query=query, can_use_back_prompt=False, step_back_query=[query])
     
     async def _generate_follow_up_questions_async(self, query: str, chat_history: List[Dict[str, str]] = None, conversation_context: str = "") -> FollowUpQuestions:
         """
@@ -670,7 +697,9 @@ class AsyncIntentRecognizer:
             response = await self._llm.invoke_async(formatted_prompt, False)
             
             # 解析输出
-            parsed_output = follow_up_parser.parse(response.content)
+            response.content = response.content.strip()
+            clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
+            parsed_output = follow_up_parser.parse(clean_output)
             follow_up_end_time = time.time()
             follow_up_time = follow_up_end_time - follow_up_start_time
             logging.debug(f"异步后续问题生成耗时统计 - 总耗时: {follow_up_time:.2f}秒")
@@ -707,7 +736,9 @@ class AsyncIntentRecognizer:
             response = await self._llm.invoke_async(formatted_prompt, False)
             
             # 解析输出
-            parsed_output = hyde_parser.parse(response.content)
+            response.content = response.content.strip()
+            clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
+            parsed_output = hyde_parser.parse(clean_output)
             hyde_end_time = time.time()
             hyde_time = hyde_end_time - hyde_start_time
             logging.debug(f"异步假设性文档生成耗时统计 - 总耗时: {hyde_time:.2f}秒")
@@ -744,7 +775,9 @@ class AsyncIntentRecognizer:
             response = await self._llm.invoke_async(formatted_prompt, False)
             
             # 解析输出
-            parsed_output = multi_questions_parser.parse(response.content)
+            response.content = response.content.strip()
+            clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
+            parsed_output = multi_questions_parser.parse(clean_output)
             multi_questions_end_time = time.time()
             multi_questions_time = multi_questions_end_time - multi_questions_start_time
             logging.debug(f"异步多角度问题生成耗时统计 - 总耗时: {multi_questions_time:.2f}秒")
@@ -799,7 +832,9 @@ class AsyncIntentRecognizer:
 
         try:
             # 解析LLM响应为JSON
-            result_json = parser.parse(response.content)
+            response.content = response.content.strip()
+            clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
+            result_json = parser.parse(clean_output)
             classification = result_json.classification
             slot_filling = result_json.slots
             is_complete, missing_slots = slot_filling.check_required_slots()

rag2_0/intent_recognition/PromptTemplates.py

@@ -31,7 +31,8 @@ classification_info="""【垂直领域分类】：
 1. 软件问题 -- 指涉及软件使用、功能询问、软件故障排查等方面的提问或请求。
 2. 业务问题 -- 指涉及电力造价领域专业知识、造价费用计算等电力造价业务知识
 3. 安装下载注册 -- 指涉及软件(或插件)安装下载、注册、激活等操作类问题。
-4. 其他 -- 指与软件或电力造价专业无关的日常对话、问候、感慨、情绪表达等。
+4. 固定话术类 -- 指涉及需要固定话术回答的问题，如：规费咨询
+5. 其他 -- 指与软件或电力造价专业无关的日常对话、问候、感慨、情绪表达等。
 
 【软件问题包括以下两类】：
 1. 软件功能：询问软件功能的使用、功能操作(调整)、功能位置、如何设置、如何转换等
@@ -49,11 +50,19 @@ classification_info="""【垂直领域分类】：
 "用哪个软件打开.BDY3文件？",
 "BDD3是什么"
 2. 软件锁类：询问软件锁信息、锁注册号查询、许可证查询、锁激活问题等软件锁相关问题
+形如: 8116 开头连续的12位数字 或者 形如 xx-xxxxxx 的数字格式 的内容为锁的注册号，提问出现对应内容时，归类为软件锁类
 3. 安装下载类：安装下载咨询、组件(插件)选择、环境配置、安装包下载、政策文件(规范文件)下载等
 4. 问题排查类：软件安装下载失败、报错，系统兼容性问题等
 
+【固定话术类包括以下一类】：
+1. 规费咨询：所有涉及规费咨询等问题(规费、社保费、公积金费等)
+
 【其他】:
-1. 其他"""
+1. 其他
+
+分类优先级：
+ 固定话术类 > 软件问题 、 业务问题 、 安装下载注册 > 其他
+"""
 
 classification_prompt="""
     用户正在使用电力造价软件或想询问电力造价领域相关知识，你需要根据用户的输入内容集合历史对话(如果存在)，将其归类为以下垂直领域之一：
@@ -211,13 +220,12 @@ step_back_prompt = """
 ## 任务说明
 1. 分析用户的原始问题，理解其核心意图和需求
 2. 考虑历史对话和会话背景，理解用户当前问题的上下文
-3. 生成一个更抽象、更高层次的问题，称为"后退问题"
+3. 生成更抽象、更高层次的问题，称为"后退问题",后退问题可以生成多个，依次后退到更抽象、更高层次的问题
 4. 后退问题应该：
-   - 更加通用和抽象
+   - 更加通用和抽象，不应包含原始问题的具体细节(包括场景限定、界面限定等其他限定词语)
    - 涵盖原始问题的核心主题
-   - 去除过于具体的限制条件（如时间、地点、特定版本等）
+   - 去除过于具体的限制条件（如时间、地点、特定版本、特定工程等）
    - 保持在同一领域和主题范围内
-   - 考虑历史对话中的相关信息
 
 ## 输入
 用户原始问题: {query}
@@ -229,10 +237,21 @@ step_back_prompt = """
 
 ## 示例
 原始问题: "配网D3软件2023版本如何在Windows 11系统上导入单位工程量清单？"
-后退问题: "配网D3软件如何导入工程量清单？"
+后退问题: 
+{{
+    "original_query": "配网D3软件2023版本如何在Windows 11系统上导入单位工程量清单？",
+    "can_use_back_prompt": True,
+    "step_back_query": ["配网D3软件如何导入工程量清单？", "如何导入单位工程量清单？"]
+}}
 
 原始问题: "技改T1软件中的某个设备更换后，如何在系统中更新对应的定额？"
-后退问题: "技改T1软件中如何更新设备对应的定额？"
+后退问题: 
+{{
+    "original_query": "技改T1软件中的某个设备更换后，如何在系统中更新对应的定额？",
+    "can_use_back_prompt": True,
+    "step_back_query": ["技改T1软件中如何更新设备对应的定额？", "如何更新设备对应的定额？"]
+}}
+
 """
 
 follow_up_questions_prompt = """

uv.lock

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+version = "2.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/47/d7/5bbeb12c44d7c4f2fb5b56abce497eb5ed9f34d85701de869acedd602619/backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba", size = 17001, upload-time = "2022-10-05T19:19:32.061Z" }
+wheels = [
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+]
+
 [[package]]
 name = "beautifulsoup4"
 version = "4.13.4"
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 ]
 
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