调整启动脚本的权限，优化screen会话检查逻辑，修复DifyQueryRetrieval_api.py中的top_k参数，删除IntentRecognition.py中的冗余代码，优化异步处理流程，移除不必要的注释和日志记录。

rag2_0/dify/DifyQueryRetrieval.py

@@ -190,7 +190,6 @@ class DifyQueryRetrieval:
                         hit_doc_titles_set.add(doc_title)
                         query_hit_stats[query_type].append(doc_title)
 
-        logging.info(f"查询命中统计: {json.dumps(query_hit_stats, ensure_ascii=False)}")
         
         return {
             "documents": processed_documents,

rag2_0/dify/DifyQueryRetrieval_api.py

@@ -99,7 +99,7 @@ async def retrieve(request: RetrieveRequest):
             query_list, 
             data_set_list,
             query_expand_dict=query_expand_dict,
-            top_k=5
+            top_k=4
         )
         end_time = time.time()
         

rag2_0/intent_recognition/IntentRecognition.py

@@ -22,14 +22,14 @@ import threading
 from .PromptTemplates import (classification_prompt, query_rewrite_prompt_pro, 
                               extract_nouns_prompt, classification_info, 
                               slot_filling_prompt, step_back_prompt, 
-                              follow_up_questions_prompt, hyde_prompt, multi_questions_prompt)
+                              hyde_prompt)
 
 from .DataModels import (
     Classification, QueryRewrite, Term, TermList,
     SoftwareFunctionSlots, SoftwareTroubleShootingSlots, ProfessionalConsultingSlots, 
     DataProblemSlots, FileExtensionConsultingSlots, SoftwareLockSlots, 
     InstallationDownloadSlots, ProblemDiagnosisSlots, OtherSlots, IntentAndSlotResult,
-    StepBackPrompt, FollowUpQuestions, HypotheticalDocument, MultiQuestions
+    StepBackPrompt, HypotheticalDocument
 )
 from .ProfessionalNounVector import ProfessionalNounRetriever, AsyncProfessionalNounRetriever
 from rag2_0.tool.ModelTool import XinferenceReRankerModel, OpenAiLLM, SiliconFlowReRankerModel
@@ -147,7 +147,6 @@ class AsyncIntentRecognizer:
         Returns:
             分类结果
         """
-        classification_start_time = time.time()
         classification_parser = PydanticOutputParser(pydantic_object=Classification)
         formatted_prompt = classification_prompt.format(user_input=query, 
                                                         classification_info=classification_info, 
@@ -159,10 +158,6 @@ class AsyncIntentRecognizer:
             # 异步调用LLM
             response = await self._llm.invoke_async(formatted_prompt, False)
 
-            classification_end_time = time.time()
-            classification_time = classification_end_time - classification_start_time
-            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
             response.content = response.content.strip()
             clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
@@ -356,7 +351,6 @@ class AsyncIntentRecognizer:
             改写结果
         """
 
-        rewrite_start_time = time.time()
         # 准备问题改写提示
         terms_dict = [term.model_dump(exclude={"description"}) for term in keywords.terms] 
         keywords_str = json.dumps(terms_dict, ensure_ascii=False)
@@ -373,9 +367,6 @@ class AsyncIntentRecognizer:
             response.content = response.content.strip()
             clean_output = re.sub(r'<think>.*?</think>', '', response.content, flags=re.DOTALL)
             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")
             return parsed_output
         except Exception as e:
             raise RuntimeError(f"解析问题改写结果时出错: {e}") from e
@@ -423,26 +414,20 @@ class AsyncIntentRecognizer:
         if enable_query_expansion:
             # 创建异步任务并立即开始执行
             query_expand_tasks = [
-                # 5.1: 后退提示
+                # 后退提示
                 asyncio.create_task(self._generate_step_back_prompt_async(query, chat_history, conversation_context)),
                 
-                # 5.2: Follow Up Questions
+                # 文档查询
-                asyncio.create_task(self._generate_follow_up_questions_async(query, chat_history, conversation_context)),
-                
-                # 5.3: 文档查询
                 asyncio.create_task(self._find_matching_software_docs_async(query, cur_soft_name, chat_history)),
-
-                # 5.4: 多问题查询
-                asyncio.create_task(self._generate_multi_questions_async(query, chat_history, conversation_context))
             ]
 
-        # 步骤1-3: 并行执行关键词匹配、问题改写和意图分类
+        # 执行关键词匹配
         keywords_task = self._match_keywords_async(query, use_jieba)
         
         # 等待关键词匹配完成
         keywords_terms, query_keys = await keywords_task
         
-        # 步骤2: 问题改写
+        # 步骤2-3: 并行执行问题改写和意图分类
         rewrite_task = self._rewrite_query_async(
             query=query, 
             keywords=keywords_terms,
@@ -450,13 +435,13 @@ class AsyncIntentRecognizer:
             chat_history=chat_history,
             context=conversation_context
         )
+        classification_task = self._classify_intent_async(query, conversation_context, chat_history, previous_slots)
         
-        # 等待问题改写完成
+        # 并行等待问题改写和意图分类完成
-        rewrite = await rewrite_task
+        start_time = time.time()
-        
+        rewrite, classification = await asyncio.gather(rewrite_task, classification_task)
-        # 步骤3: 进行意图分类
+        end_time = time.time()
-        classification_task = self._classify_intent_async(rewrite.rewrite, conversation_context, chat_history, previous_slots)
+        logging.info(f"意图分类耗时统计 - 总耗时: {end_time - start_time:.2f}秒")
-        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":
@@ -465,11 +450,8 @@ class AsyncIntentRecognizer:
             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"]:
-        #     slot_filling_result = await self._fill_slots_async(rewrite.rewrite, classification, conversation_context, chat_history, previous_slots)
        
         if not enable_query_expansion:
             return {
@@ -484,28 +466,22 @@ class AsyncIntentRecognizer:
         start_time = time.time()
         query_expand_results = await asyncio.gather(*query_expand_tasks)
         end_time = time.time()
-        logging.info(f"异步问题扩展环节耗时统计 - 总耗时: {end_time - start_time:.2f}秒")
+        logging.info(f"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, 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)
+        wiki_result = query_expand_results[1] if query_expand_results[1] else []
-        wiki_result = query_expand_results[2] if query_expand_results[2] else []
-        multi_questions_result = query_expand_results[3] if query_expand_results[3] else MultiQuestions(original_query=query, sub_questions=[query])
         
         all_questions=[]
         all_questions.append(query)
         all_questions.append(rewrite.rewrite)
         all_questions.extend(wiki_result)
         all_questions.extend(step_back_result.step_back_query)
-        all_questions.append(follow_up_result.follow_up_query)
-        all_questions.extend(multi_questions_result.sub_questions)        
         all_questions = list(set(all_questions))
 
         query_expand = {
             "all": all_questions,
             "step_back": step_back_result.step_back_query,
-            "follow_up": [follow_up_result.follow_up_query],
-            "multi_questions": multi_questions_result.sub_questions,
             "wiki_title": wiki_result,
             "original_query":query,
             "rewrite_query":rewrite.rewrite
@@ -670,52 +646,13 @@ class AsyncIntentRecognizer:
             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")
+            logging.info(f"后退提示生成耗时统计 - 总耗时: {step_back_time:.2f}秒")
             return parsed_output
         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)
+            logging.error(f"后退提示生成失败: {e}", exc_info=True)
             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:
-        """
-        异步生成后续问题
-        
-        Args:
-            query: 用户原始问题
-            chat_history: 历史对话记录
-            conversation_context: 会话背景信息
-            
-        Returns:
-            后续问题结果
-        """
-        follow_up_start_time = time.time()
-        # 准备提示词
-        follow_up_parser = PydanticOutputParser(pydantic_object=FollowUpQuestions)
-        formatted_prompt = follow_up_questions_prompt.format(
-            query=query,
-            chat_history=json.dumps(chat_history, ensure_ascii=False) if chat_history else "[]",
-            # conversation_context=conversation_context,
-            output_format=follow_up_parser.get_format_instructions()
-        )
-        
-        try:
-            # 异步调用LLM
-            response = await self._llm.invoke_async(formatted_prompt, False)
-            
-            # 解析输出
-            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")
-            return parsed_output
-        except Exception as e:
-            # 如果解析失败，返回原始查询作为后续问题
-            logging.error(f"异步后续问题生成失败: {e}", exc_info=True)
-            return FollowUpQuestions(original_query=query, follow_up_query=query)
-    
     async def _find_matching_software_docs_async(self, query: str, soft_name: str, 
                                         chat_history: List[Dict[str, str]] = None, 
                                         top_k: int = 3) -> List[str]:
@@ -771,7 +708,7 @@ class AsyncIntentRecognizer:
                 json_response = json_parser.parse(response.content)
                 for match in json_response:
                     wiki_names.append(match["content"])
-                logging.debug(f"软件文档匹配耗时: {end_time - start_time:.2f}秒")
+                logging.info(f"软件文档匹配耗时: {end_time - start_time:.2f}秒")
                 return wiki_names
                 
             except json.JSONDecodeError as e:
@@ -782,42 +719,3 @@ class AsyncIntentRecognizer:
             logging.error(f"查找匹配软件文档时出错: {e}", exc_info=True)
             # 出错时返回空列表
             return []
-
-    async def _generate_multi_questions_async(self, query: str, chat_history: List[Dict[str, str]] = None, conversation_context: str = "") -> MultiQuestions:
-        """
-        异步生成多角度问题
-        
-        Args:
-            query: 用户原始问题
-            chat_history: 历史对话记录
-            conversation_context: 会话背景信息
-            
-        Returns:
-            多角度问题结果
-        """
-        multi_questions_start_time = time.time()
-        # 准备提示词
-        multi_questions_parser = PydanticOutputParser(pydantic_object=MultiQuestions)
-        formatted_prompt = multi_questions_prompt.format(
-            query=query,
-            chat_history=json.dumps(chat_history, ensure_ascii=False) if chat_history else "[]",
-            # conversation_context=conversation_context,
-            output_format=multi_questions_parser.get_format_instructions()
-        )
-        
-        try:
-            # 异步调用LLM
-            response = await self._llm.invoke_async(formatted_prompt, False)
-            
-            # 解析输出
-            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")
-            return parsed_output
-        except Exception as e:
-            # 如果解析失败，返回原始查询作为唯一子问题
-            logging.error(f"异步多角度问题生成失败: {e}", exc_info=True)
-            return MultiQuestions(original_query=query, sub_questions=[query])

start_AnswerType.sh

@@ -4,14 +4,14 @@
 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
+if screen -ls | grep -q "\.AnswerType\s"; 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 '
+    screen -dmS AnswerType bash -c "
-        cd $SCRIPT_DIR
+        cd \"$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."

start_DifyQueryRetrieval_api.sh

@@ -4,12 +4,12 @@
 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
+if screen -ls | grep -q "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 $SCRIPT_DIR
+        cd \"$SCRIPT_DIR\"
         uv run uvicorn rag2_0.dify.DifyQueryRetrieval_api:app --host 0.0.0.0 --port 8002 --workers 25
     '
 

start_intent_recognition_api.sh

@@ -7,11 +7,11 @@ SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 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
+    # 启动一个名为intent_recognition_api的screen会话，并在其中执行后续命令
-    screen -dmS intent_recognition_api bash -c '
+    screen -dmS intent_recognition_api bash -c "
-        cd $SCRIPT_DIR
+        cd \"$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."