新增异步意图识别器和相关功能，优化意图识别和槽位填充逻辑，支持异步处理和多线程检索，改进API调用的错误处理和日志记录，增强文档检索和关键词提取功能。

rag2_0/intent_recognition/IntentRecognition.py

@@ -9,12 +9,14 @@ Description: 意图分类、改写核心逻辑
 
 import logging
 import os
+import asyncio
 from langchain.output_parsers import PydanticOutputParser
 import json
 from typing import List, Tuple, Dict, Any, Optional
 import re
 import jieba
 import time
+import threading
 
 from .PromptTemplates import (classification_prompt, query_rewrite_prompt, 
                               extract_nouns_prompt, classification_info, 
@@ -33,7 +35,7 @@ from .DataModels import (
     InstallationDownloadSlots, ProblemDiagnosisSlots, OtherSlots, IntentAndSlotResult,
     StepBackPrompt, FollowUpQuestions, HypotheticalDocument, MultiQuestions
 )
-from .ProfessionalNounVector import ProfessionalNounRetriever
+from .ProfessionalNounVector import ProfessionalNounRetriever, AsyncProfessionalNounRetriever
 from rag2_0.tool.ModelTool import XinferenceReRankerModel, OpenAiLLM, SiliconFlowReRankerModel
 
 
@@ -448,10 +450,10 @@ class IntentRecognizer:
                 "slot_filling": slot_filling_result
             }
 
-        # 等待所有greenlet完成
+        # 等待所有线程完成
         start_time = time.time()
-        for greenlet, _ in threads_and_results:
-            greenlet.join()
+        for thread, _ in threads_and_results:
+            thread.join()
         end_time = time.time()
         logging.info(f"问题扩展环节耗时统计 - 总耗时: {end_time - start_time:.2f}秒")
         
@@ -750,7 +752,7 @@ class IntentRecognizer:
     
     def _run_in_thread(self, func, args=(), kwargs={}):
         """
-        在greenlet中执行函数并返回结果
+        在线程中执行函数并返回结果
         
         Args:
             func: 要执行的函数
@@ -758,22 +760,21 @@ class IntentRecognizer:
             kwargs: 函数的关键字参数
             
         Returns:
-            (greenlet, result_container): greenlet对象和存放结果的容器
+            (thread, result_container): 线程对象和存放结果的容器
         """
-        from gevent import Greenlet
         result_container = []
         
-        def greenlet_target():
+        def thread_target():
             try:
                 result = func(*args, **kwargs)
                 result_container.append(result)
             except Exception as e:
-                logging.error(f"greenlet执行函数 {func.__name__} 时出错: {e}", exc_info=True)
+                logging.error(f"线程执行函数 {func.__name__} 时出错: {e}", exc_info=True)
                 result_container.append(None)
         
-        greenlet = Greenlet(greenlet_target)
-        greenlet.start()
-        return greenlet, result_container
+        thread = threading.Thread(target=thread_target)
+        thread.start()
+        return thread, result_container
 
             
     def _process_intent_and_slot(self, user_input: str, conversation_context: str = "", 
@@ -866,4 +867,813 @@ class IntentRecognizer:
             return result
             
         except Exception as e:
-            raise RuntimeError(f"process_intent_and_slot error:{e}") from e
+            raise RuntimeError(f"process_intent_and_slot error:{e}") from e
+    
+
+
+class AsyncIntentRecognizer:
+    """
+    异步意图识别和问题改写类
+    """
+    def __init__(self, api_key: str = None, base_url: str = None, model_name: str = "gpt-3.5-turbo", vector_index_dir: str = None):
+        """
+        初始化异步意图识别器
+        
+        Args:
+            api_key: OpenAI API密钥，如果为None则从环境变量获取
+            base_url: OpenAI API基础URL，如果为None则使用默认URL
+            model_name: 要使用的模型名称
+            vector_index_dir: 向量索引目录，如果为None则使用默认目录
+        """
+        # 初始化LLM
+        llm_params = {
+            "temperature": 0.2,  # 降低随机性，使结果更确定
+            "top_p": 0.7,
+            "model": model_name
+        }
+        
+        # 如果提供了API密钥，则使用提供的密钥
+        if api_key:
+            llm_params["api_key"] = api_key
+        
+        # 如果提供了自定义URL，则使用提供的URL
+        if base_url:
+            llm_params["base_url"] = base_url
+        
+        self._llm = OpenAiLLM(**llm_params) 
+        
+        # 加载suffix关键词
+        self._suffix_keywords = self._load_suffix_keywords()
+        
+        # 异步检索器将在create方法中初始化
+        self._noun_retriever = None
+        self._api_key = api_key
+        self._vector_index_dir = vector_index_dir
+    
+    @classmethod
+    async def create(cls, api_key: str = None, base_url: str = None, model_name: str = "gpt-3.5-turbo", vector_index_dir: str = None):
+        """
+        异步工厂方法：创建并初始化异步意图识别器实例
+        
+        Args:
+            api_key: OpenAI API密钥，如果为None则从环境变量获取
+            base_url: OpenAI API基础URL，如果为None则使用默认URL
+            model_name: 要使用的模型名称
+            vector_index_dir: 向量索引目录，如果为None则使用默认目录
+            
+        Returns:
+            初始化完成的AsyncIntentRecognizer实例
+        """
+        instance = cls(api_key, base_url, model_name, vector_index_dir)
+        # 异步初始化名词检索器
+        instance._noun_retriever = await AsyncProfessionalNounRetriever.create(
+            api_key=api_key,
+            index_dir=vector_index_dir
+        )
+        return instance
+    
+    def _load_suffix_keywords(self, filepath: str = None) -> List[str]:
+        """
+        加载后缀关键词列表
+        
+        Args:
+            filepath: 后缀关键词文件路径，默认为None使用默认路径
+            
+        Returns:
+            后缀关键词列表
+        """
+        try:
+            # 如果未指定路径，使用默认路径
+            if filepath is None:
+                current_dir = os.path.dirname(os.path.abspath(__file__))
+                filepath = os.path.join(current_dir, "..", "..", "data", "nouns", "suffix_keywords.json")
+            
+            # 读取JSON文件
+            with open(filepath, "r", encoding="utf-8") as f:
+                suffix_data = json.load(f)
+            
+            # 添加额外的固定后缀
+            return suffix_data
+            
+        except Exception as e:
+            raise RuntimeError(f"加载后缀关键词失败: {e}") from e
+    
+    async def _classify_intent_async(self, query: str, conversation_context: str = "", 
+                      chat_history: List[Dict[str, str]] = None, 
+                      previous_slots: Dict[str, Any] = None) -> Classification:
+        """
+        异步对用户输入进行意图分类
+        
+        Args:
+            content: 用户输入内容
+            keywords: 匹配到的关键词列表
+            rewrite: 重写的问题
+        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, 
+                                                        output_format=classification_parser.get_format_instructions(),
+                                                        conversation_context=conversation_context,
+                                                        chat_history=json.dumps(chat_history, ensure_ascii=False))
+        # 解析输出
+        try:
+            # 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
+            parsed_output = classification_parser.parse(response.content.strip())
+            return parsed_output
+        except Exception as e:
+            raise RuntimeError(f"解析分类结果时出错: {e}") from e
+    
+    def _tokenize_with_jieba(self, query: str) -> List[str]:
+        """
+        使用jieba分词器对查询进行分词
+        
+        Args:
+            query: 用户查询
+            
+        Returns:
+            分词后的词语列表
+        """
+        # 使用jieba进行分词
+        seg_list = jieba.cut(query, cut_all=False)
+        
+        # 过滤掉停用词和标点符号
+        filtered_tokens = []
+        for token in seg_list:
+            # 过滤掉空格和标点符号
+            if token.strip() and not re.match(r'^[^\w\s]$', token):
+                filtered_tokens.append(token)
+        
+        return filtered_tokens
+    
+    async def _extract_keywords_with_llm_async(self, query: str, use_jieba: bool = False) -> List[Term]:
+        """
+        异步使用LLM从用户查询中提取专业关键词
+        
+        Args:
+            query: 用户查询
+            use_jieba: 是否使用jieba分词辅助提取关键词
+            
+        Returns:
+            提取的术语列表
+        """
+        # 如果使用jieba分词
+        if use_jieba:
+            # 先使用jieba分词
+            tokens = self._tokenize_with_jieba(query)
+            
+            # 构建术语列表
+            terms = []
+            for token in tokens:
+                if len(token) > 1:  # 过滤掉单字词
+                    terms.append(Term(name=token, synonymous=[], description=""))
+            
+            return terms
+        else:
+            # 使用LLM提取关键词
+            # 准备提示词
+            formatted_prompt = extract_nouns_prompt.replace("{content}", query)
+            terms_list_parser = PydanticOutputParser(pydantic_object=TermList)
+            formatted_prompt = formatted_prompt.replace("{output_format}", terms_list_parser.get_format_instructions())
+            
+            # 异步调用LLM
+            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)
+            return parsed_output.terms
+
+    
+    async def _rerank_matched_terms_async(self, query_key: str, matched_terms: set, top_k: int = 2, rerank_score:float = 0.6) -> List[Term]:
+        """
+        异步对召回的专业术语进行重排序，按与用户查询的相关性排序
+        
+        Args:
+            query: 用户查询
+            matched_terms: 匹配到的专业术语集合
+            query_keys: 用户查询中提取的关键词列表
+            
+        Returns:
+            重排序后的专业术语列表
+        """
+        if not matched_terms:
+            return []
+        
+        if len(matched_terms) <= top_k:
+            return list(matched_terms)
+
+        try:
+            # 将每个术语转换为可用于重排序的文本表示
+            term_texts = ["名称:" + term.name + "|" + "同义词:" + ";".join(term.synonymous) for term in matched_terms]
+            
+            # 使用异步重排序模型
+            rerank_results = await XinferenceReRankerModel.rerank_async(query_key, term_texts, top_k=top_k)
+            
+            # 将matched_terms转换为列表以便按索引访问
+            matched_terms_list = list(matched_terms)
+            
+            # 根据重排序结果获取排序后的术语列表
+            reranked_terms = [matched_terms_list[result["index"]] for result in rerank_results if result["score"] >= rerank_score]
+            
+            return reranked_terms
+            
+        except Exception as e:
+            raise RuntimeError(f"异步_rerank_matched_terms重排失败：{e}") from e
+
+    async def _match_keywords_async(self, query: str, use_jieba: bool = False) -> Tuple[TermList, List[str]]:
+        """
+        异步从用户问题中匹配关键词，结合LLM提取和向量检索
+        
+        Args:
+            query: 用户问题
+            use_jieba: 是否使用jieba分词辅助提取关键词
+            
+        Returns:
+            匹配到的关键词列表
+        """
+        start_time = time.time()
+        query_keys=[]
+        # 步骤1: 使用LLM提取查询中的关键词
+        try:
+            llm_start_time = time.time()
+            extracted_terms = await self._extract_keywords_with_llm_async(query, use_jieba)
+            for term in extracted_terms:
+                query_keys.append(term.name)
+            llm_end_time = time.time()
+            llm_time = llm_end_time - llm_start_time
+        except Exception as e:
+            raise RuntimeError(f"异步LLM关键词提取失败: {e}") from e
+        
+        matched_terms = []  # 存储匹配到的Term对象       
+        # 步骤2: 使用向量检索找到相似的专业名词
+        try:
+            vector_start_time = time.time()
+            # 对matched_terms中的每个关键字进行向量检索
+            for current_key in query_keys:
+                vector_results = await self._noun_retriever.query_async(current_key, top_k=5, use_intersection=False)
+                current_key_terms = set()
+                # 添加向量检索结果
+                for result in vector_results:
+                    if isinstance(result.get('synonymous', []), str):
+                        result['synonymous'] = result['synonymous'].split(';')
+                    term = Term(
+                        name=result.get('name'),
+                        synonymous=result.get('synonymous', []),
+                        description=result.get('description', '')
+                    )
+                    current_key_terms.add(term)
+                if len(current_key_terms) > 0:
+                    reranked_terms = await self._rerank_matched_terms_async(current_key, current_key_terms)
+                    matched_terms.extend(reranked_terms)
+            vector_end_time = time.time()
+            vector_time = vector_end_time - vector_start_time
+        except Exception as e:
+            raise RuntimeError(f"异步向量检索关键词时出错: {e}") from e
+        
+        # 提取所有Term对象的名称并排序
+        # 将set类型的matched_terms转换为TermList类型
+        term_list = TermList(terms=list(matched_terms))
+        end_time = time.time()
+        total_time = end_time - start_time
+
+        # 输出整合的时间日志
+        logging.info(f"异步关键词匹配耗时统计 - 总耗时: {total_time:.2f}秒, 问题关键词提取: {llm_time:.2f}秒, 向量检索+重排序: {vector_time:.2f}秒")
+        
+        return term_list, query_keys
+    
+    async def _rewrite_query_async(self, query: str, keywords: TermList, query_keys:List[str], chat_history: List[Dict[str, str]] = None, context: str = "") -> QueryRewrite:
+        """
+        异步对用户问题进行改写
+        
+        Args:
+            query: 用户原始问题
+            keywords: 匹配到的关键词列表
+            query_keys: 用户查询中提取的关键词列表
+
+        Returns:
+            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)
+        query_rewrite_parser = PydanticOutputParser(pydantic_object=QueryRewrite)
+        formatted_prompt = query_rewrite_prompt_pro.format(query=query, 
+                                                         output_format=query_rewrite_parser.get_format_instructions(),
+                                                         keywords=keywords_str,
+                                                         chat_history=chat_history,
+                                                         context=context)
+        # 解析输出
+        try:
+            # 异步调用LLM
+            response = await self._llm.invoke_async(formatted_prompt, False)
+        
+            # 尝试直接解析JSON响应
+            parsed_output = query_rewrite_parser.parse(response.content)
+            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
+    
+    def _judge_define_suffix(self, input_str: str) -> Tuple[bool, List[str]]:
+        """
+        判断输入字符串是否包含定义的后缀，并返回所有匹配到的后缀名列表
+        
+        Args:
+            input_str: 输入字符串
+            
+        Returns:
+            Tuple[bool, List[str]]: (是否包含定义的后缀, 匹配到的后缀名列表)
+        """
+        
+        # 构建正则表达式模式，匹配大小写不敏感且前面可能带有.
+        pattern = r'(?:\.?)(' + '|'.join(re.escape(field.get('name')) for field in self._suffix_keywords) + r')'
+        
+        # 使用 re.IGNORECASE 标志来忽略大小写，findall找到所有匹配
+        matches = re.finditer(pattern, input_str, re.IGNORECASE)
+        matched_suffixes = [match.group(1) for match in matches]
+        
+        return bool(matched_suffixes), matched_suffixes
+
+    async def process_query_async(self, query: str, conversation_context: str = "", 
+                      chat_history: List[Dict[str, str]] = None, 
+                      previous_slots: Dict[str, Any] = None,
+                      use_jieba: bool = False,
+                      enable_query_expansion: bool = False) -> Dict[str, Any]:
+        """
+        异步处理用户问题的完整流程
+        
+        Args:
+            query: 用户原始问题
+            conversation_context: 会话背景信息
+            chat_history: 历史对话记录，格式为[{"user": "content"}, {"assistant": "content"}]
+            previous_slots: 历史槽位信息
+            use_jieba: 是否使用jieba分词辅助提取关键词
+            enable_query_expansion: 是否启用查询扩展
+            
+        Returns:
+            包含分类、关键词、改写和槽位填充结果的字典
+        """
+        if chat_history is None:
+            chat_history = []
+        if previous_slots is None:
+            previous_slots = {}
+        
+        # 步骤: 并行执行提问扩展
+        query_expand_tasks = []
+        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
+                asyncio.create_task(self._generate_step_back_prompt_async(query, chat_history, conversation_context)),
+                
+                # 5.2: Follow Up Questions
+                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)),
+                
+                # 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
+        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
+        rewrite_task = self._rewrite_query_async(
+            query=query, 
+            keywords=keywords_terms,
+            query_keys=query_keys,
+            chat_history=chat_history,
+            context=conversation_context
+        )
+        
+        # 等待问题改写完成
+        rewrite = await rewrite_task
+        
+        # 步骤3: 进行意图分类
+        classification_task = self._classify_intent_async(rewrite.rewrite, conversation_context, chat_history, previous_slots)
+        classification = await classification_task
+
+        # 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 {
+                "classification": classification.model_dump(),
+                "keywords": keywords_terms.model_dump(),
+                "rewrite": rewrite.model_dump(),
+                "query_keys": query_keys,
+                "slot_filling": slot_filling_result
+            }
+
+        # 等待所有query_expand_tasks完成
+        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")
+        
+        # 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)
+        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="")
+        multi_questions_result = query_expand_results[3] if query_expand_results[3] 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(follow_up_result.follow_up_query)
+        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(),
+            "multi_questions": multi_questions_result.model_dump()
+        }
+        
+        # 返回所有结果
+        return {
+            "classification": classification.model_dump(),
+            "keywords": keywords_terms.model_dump(),
+            "rewrite": rewrite.model_dump(),
+            "query_keys": query_keys,
+            "slot_filling": slot_filling_result,
+            "query_expand": query_expand
+        }
+    
+    async def _fill_slots_async(self, query: str, classification: Classification, conversation_context: str = "", 
+                      chat_history: List[Dict[str, str]] = None, 
+                      previous_slots: Dict[str, Any] = None,) -> Dict[str, Any]:
+        """
+        异步根据分类结果对问题进行槽位填充
+        
+        Args:
+            query: 用户原始问题
+            classification: 意图分类结果
+            
+        Returns:
+            填充后的槽位数据模型
+        """
+        # 根据分类结果选择对应的数据模型
+        slot_model = self._get_slot_model(classification)
+        if not slot_model:
+            raise RuntimeError("未找到匹配的槽位模型")
+        
+        fill_slots_start_time = time.time()
+        # 使用LLM进行槽位填充
+        filled_slots = await self._fill_slots_with_llm_async(query, classification, slot_model, conversation_context, chat_history, previous_slots)
+        fill_slots_end_time = time.time()
+        fill_slots_time = fill_slots_end_time - fill_slots_start_time
+        logging.info(f"异步槽位填充耗时统计 - 总耗时: {fill_slots_time:.2f}秒")
+        
+        # 检查必填槽位是否都已填充
+        is_complete, missing_slots = filled_slots.check_required_slots()
+        
+        return {
+            "is_complete": is_complete,
+            "missing_slots": missing_slots,
+            "filled_data": filled_slots.model_dump()
+        }
+    
+    def _get_slot_model(self, classification: Classification) -> Optional[type]:
+        """
+        根据分类结果获取对应的槽位模型类，用于统一提示词处理
+        
+        Args:
+            classification: 意图分类结果
+            
+        Returns:
+            对应的槽位模型类
+        """
+        # 软件问题
+        if classification.vertical_classification == "软件问题":
+            if classification.sub_classification == "软件功能":
+                return SoftwareFunctionSlots
+            elif classification.sub_classification == "故障排查":
+                return SoftwareTroubleShootingSlots
+        
+        # 业务问题
+        elif classification.vertical_classification == "业务问题":
+            if classification.sub_classification == "专业咨询":
+                return ProfessionalConsultingSlots
+            elif classification.sub_classification == "数据问题":
+                return DataProblemSlots
+        
+        # 安装下载注册
+        elif classification.vertical_classification == "安装下载注册":
+            if classification.sub_classification == "后缀名咨询":
+                return FileExtensionConsultingSlots
+            elif classification.sub_classification == "软件锁类":
+                return SoftwareLockSlots
+            elif classification.sub_classification == "安装下载类":
+                return InstallationDownloadSlots
+            elif classification.sub_classification == "问题排查类":
+                return ProblemDiagnosisSlots
+        
+        # 其他
+        elif classification.vertical_classification == "其他":
+            return OtherSlots
+            
+        return None
+
+    async def _fill_slots_with_llm_async(self, query: str, 
+                             classification: Classification, 
+                             slot_model_class: type, 
+                             conversation_context: str = "", 
+                            chat_history: List[Dict[str, str]] = None, 
+                            previous_slots: Dict[str, Any] = None) -> Any:
+        """
+        异步使用LLM进行槽位填充
+        
+        Args:
+            query: 用户原始问题
+            classification: 意图分类结果
+            slot_model_class: 槽位模型类
+            
+        Returns:
+            填充后的槽位数据模型实例
+        """
+        # 准备提示词
+        slot_parser = PydanticOutputParser(pydantic_object=slot_model_class)
+        
+        formatted_prompt = slot_filling_prompt.format(
+            query=query,
+            vertical_classification=classification.vertical_classification,
+            sub_classification=classification.sub_classification,
+            output_format=slot_parser.get_format_instructions(),
+            conversation_context=conversation_context,
+            chat_history=json.dumps(chat_history,ensure_ascii=False),
+            previous_slots=json.dumps(previous_slots,ensure_ascii=False),
+        )
+        try:
+            # 异步调用LLM
+            response = await self._llm.invoke_async(formatted_prompt, False)
+        
+            # 尝试解析LLM响应
+            parsed_output = slot_parser.parse(response.content)
+            return parsed_output
+        except Exception as e:
+            # 如果解析失败，创建一个空的模型实例
+            empty_instance = slot_model_class()
+            return empty_instance
+    
+    async def _generate_step_back_prompt_async(self, query: str, chat_history: List[Dict[str, str]] = None, conversation_context: str = "") -> StepBackPrompt:
+        """
+        异步生成后退提示
+        
+        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:
+            后退提示结果
+        """
+        step_back_start_time = time.time()
+        # 准备提示词
+        step_back_parser = PydanticOutputParser(pydantic_object=StepBackPrompt)
+        formatted_prompt = step_back_prompt.format(
+            query=query,
+            chat_history=json.dumps(chat_history, ensure_ascii=False) if chat_history else "[]",
+            conversation_context=conversation_context,
+            output_format=step_back_parser.get_format_instructions()
+        )
+        
+        try:
+            # 异步调用LLM
+            response = await self._llm.invoke_async(formatted_prompt, False)
+            
+            # 解析输出
+            parsed_output = step_back_parser.parse(response.content)
+            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")
+            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)
+            return StepBackPrompt(original_query=query, 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
+            parsed_output = follow_up_parser.parse(response.content)
+            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 _generate_hypothetical_document_async(self, query: str, chat_history: List[Dict[str, str]] = None, conversation_context: str = "") -> HypotheticalDocument:
+        """
+        异步生成假设性文档
+        
+        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:
+            假设性文档结果
+        """
+        hyde_start_time = time.time()
+        # 准备提示词
+        hyde_parser = PydanticOutputParser(pydantic_object=HypotheticalDocument)
+        formatted_prompt = hyde_prompt.format(
+            query=query,
+            chat_history=json.dumps(chat_history, ensure_ascii=False) if chat_history else "[]",
+            conversation_context=conversation_context,
+            output_format=hyde_parser.get_format_instructions()
+        )
+        
+        try:
+            # 异步调用LLM
+            response = await self._llm.invoke_async(formatted_prompt, False)
+            
+            # 解析输出
+            parsed_output = hyde_parser.parse(response.content)
+            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")
+            return parsed_output
+        except Exception as e:
+            # 如果解析失败，返回空的假设性回答
+            logging.error(f"异步假设性文档生成失败: {e}", exc_info=True)
+            return HypotheticalDocument(original_query=query, hypothetical_answer="")
+    
+    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
+            parsed_output = multi_questions_parser.parse(response.content)
+            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])
+            
+    async def _process_intent_and_slot_async(self, user_input: str, conversation_context: str = "", 
+                              chat_history: List[Dict[str, str]] = None, 
+                              previous_slots: Dict[str, Any] = None) -> Dict[str, Any]:
+        """
+        异步使用统一提示词同时进行意图识别和槽位填充
+        
+        Args:
+            user_input: 当前用户输入
+            conversation_context: 会话背景信息
+            chat_history: 历史对话记录，格式为[{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
+            previous_slots: 历史槽位信息
+            
+        Returns:
+            包含意图分类和槽位填充结果的字典
+        """
+        # 初始化默认值
+        if chat_history is None:
+            chat_history = []
+        
+        if previous_slots is None:
+            previous_slots = {}
+            
+        # 生成槽位映射文档
+        slot_mapping_doc = generate_slot_mapping_doc()
+        
+        # 准备提示词
+        parser = PydanticOutputParser(pydantic_object=IntentAndSlotResult)
+        formatted_prompt = intent_and_slot_prompt.format(
+            conversation_context=conversation_context,
+            chat_history=json.dumps(chat_history, ensure_ascii=False),
+            previous_slots=json.dumps(previous_slots, ensure_ascii=False),
+            user_input=user_input,
+            slot_mapping_doc=slot_mapping_doc,
+            output_format=parser.get_format_instructions(),
+            classification_info=classification_info
+        )
+        
+        # 异步调用LLM
+        llm_start_time = time.time()
+        response = await self._llm.invoke_async(formatted_prompt + output_example, False)
+        llm_end_time = time.time()
+        llm_time = llm_end_time - llm_start_time
+
+        try:
+            # 解析LLM响应为JSON
+            result_json = parser.parse(response.content)
+            classification = result_json.classification
+            slot_filling = result_json.slots
+            is_complete, missing_slots = slot_filling.check_required_slots()
+            expected_slot_model = self._get_slot_model(classification)
+
+            # 添加容错处理，发生概率较低，但仍需处理
+            if expected_slot_model is None:
+                # 添加容错处理，应对LLM返回错误分类信息,一级分类跟二级分类错乱
+                # 重新分类
+                classification = await self._classify_intent_async(user_input, conversation_context, chat_history, previous_slots)
+                fill_slots = await self._fill_slots_async(user_input, classification, conversation_context, chat_history, previous_slots)
+                
+                result = {
+                    "classification": classification.model_dump(),
+                    "slot_filling": fill_slots
+                }
+                logging.warning(f"异步重新分类与槽点填充")
+                return result
+            elif expected_slot_model.__name__ != type(slot_filling).__name__:
+                # 添加容错处理，应对LLM槽位与分类不匹配。重新填充槽位
+                slot_filling = await self._fill_slots_async(user_input, classification, conversation_context, chat_history, previous_slots)
+                result = {
+                    "classification": classification.model_dump(),
+                    "slot_filling": slot_filling
+                }
+                logging.warning(f"异步重新填充槽点")
+                return result
+            
+            logging.info(f"异步意图识别+槽位LLM调用耗时: {llm_time:.2f}秒")
+
+            # 构建最终结果
+            result = {
+                "classification": classification.model_dump(),
+                "slot_filling": {
+                    "is_complete": is_complete,
+                    "missing_slots": missing_slots,
+                    "filled_data": slot_filling.model_dump()
+                }
+            }
+            
+            return result
+            
+        except Exception as e:
+            raise RuntimeError(f"异步process_intent_and_slot error:{e}") from e

rag2_0/intent_recognition/ProfessionalNounVector.py

@@ -10,11 +10,13 @@ Description: 专业名词向量化和检索的核心逻辑
 import os
 import json
 import shutil
+import asyncio
 from typing import List, Dict, Any, Tuple, Optional
 from langchain.embeddings.base import Embeddings
 from langchain_community.vectorstores import FAISS
 from rag2_0.tool.ModelTool import SiliconFlowEmbeddings
 import logging
+import httpx
 
 def get_embedding_model(api_key: str = None) -> Embeddings:
     """
@@ -350,4 +352,148 @@ class ProfessionalNounRetriever:
      
         except Exception as e:
             logging.error(f"查询FAISS索引失败: {e}", exc_info=True)
-            return [] 
+            return [] 
+
+
+class AsyncProfessionalNounRetriever:
+    """异步专业名词检索类"""
+    
+    def __init__(self, 
+                 embedding_model: Optional[Embeddings] = None, 
+                 api_key: str = None,
+                 index_dir: str = None):
+        """
+        初始化异步检索器
+        
+        Args:
+            embedding_model: 嵌入模型，如果为None则使用默认模型
+            api_key: SiliconFlow API密钥，仅在embedding_model为None时使用
+            index_dir: 索引目录路径，默认为None使用默认路径
+        """
+        # 设置嵌入模型
+        if embedding_model:
+            self.embedding_model = embedding_model
+        else:
+            self.embedding_model = get_embedding_model(api_key)
+        
+        # 设置索引路径
+        self.index_dir = index_dir
+        if self.index_dir is None:
+            current_dir = os.path.dirname(os.path.abspath(__file__))
+            self.index_dir = os.path.join(current_dir, "..", "..", "data", "nouns", "professional_nouns_index")
+        
+        # 初始化索引为None，不在构造函数中加载
+        self.faiss_index = None
+    
+    @classmethod
+    async def create(cls, 
+                    embedding_model: Optional[Embeddings] = None, 
+                    api_key: str = None,
+                    index_dir: str = None):
+        """
+        异步工厂方法：创建并初始化异步检索器实例
+        
+        Args:
+            embedding_model: 嵌入模型，如果为None则使用默认模型
+            api_key: SiliconFlow API密钥，仅在embedding_model为None时使用
+            index_dir: 索引目录路径，默认为None使用默认路径
+        
+        Returns:
+            初始化完成的AsyncProfessionalNounRetriever实例
+        """
+        instance = cls(embedding_model, api_key, index_dir)
+        await instance._load_index_async()
+        return instance
+    
+    async def _load_index_async(self) -> None:
+        """
+        异步从本地加载FAISS索引 (内部方法)
+        """
+        try:
+            # 由于FAISS加载可能是CPU密集型操作，使用线程池执行器来避免阻塞事件循环
+            self.faiss_index = await asyncio.to_thread(
+                FAISS.load_local,
+                folder_path=self.index_dir,
+                embeddings=self.embedding_model,
+                allow_dangerous_deserialization=True
+            )
+            logging.info(f"异步成功从 {self.index_dir} 加载FAISS索引")
+        except Exception as e:
+            logging.warning(f"异步加载FAISS索引失败: {e}")
+            self.faiss_index = None
+    
+    async def _invoke_retriever_async(self, retriever, query_text: str):
+        """
+        异步调用检索器 (内部方法)
+        
+        Args:
+            retriever: 检索器实例
+            query_text: 查询文本
+        
+        Returns:
+            检索结果
+        """
+        # 由于LangChain的retriever.invoke可能不是异步的，使用线程池执行器
+        return await asyncio.to_thread(retriever.invoke, query_text)
+    
+    async def query_async(self, query_text: str, top_k: int = 5, use_intersection: bool = True) -> List[Dict]:
+        """
+        异步查询FAISS索引，获取最相似的专业名词
+        
+        Args:
+            query_text: 查询文本
+            top_k: 返回的结果数量，默认为5
+            use_intersection: 是否使用三种检索方式的交集，默认为True
+        
+        Returns:
+            相似度最高的专业名词列表
+        """
+        try:
+            # 检查索引是否已加载
+            if self.faiss_index is None:
+                logging.warning("FAISS索引未加载，尝试加载...")
+                await self._load_index_async()
+                if self.faiss_index is None:
+                    logging.warning("异步加载FAISS索引失败，无法执行查询")
+                    return []
+            
+            # 使用三种检索方式并取交集
+            retriever1 = self.faiss_index.as_retriever(search_kwargs={"k": top_k})
+            retriever2 = self.faiss_index.as_retriever(
+                search_type="mmr",
+                search_kwargs={"k": top_k, "fetch_k": 3, "lambda_mult": 0.5}
+            )
+            retriever3 = self.faiss_index.as_retriever(
+                search_type="similarity_score_threshold",
+                search_kwargs={"score_threshold": 0.5}
+            )
+            
+            # 并行执行三个检索任务
+            results = await asyncio.gather(
+                self._invoke_retriever_async(retriever1, query_text),
+                self._invoke_retriever_async(retriever2, query_text),
+                self._invoke_retriever_async(retriever3, query_text)
+            )
+            
+            # 用json.dumps将dict转为字符串，便于取交集
+            set1 = set(json.dumps(i.metadata, sort_keys=True, ensure_ascii=False) for i in results[0])
+            set2 = set(json.dumps(i.metadata, sort_keys=True, ensure_ascii=False) for i in results[1])
+            set3 = set(json.dumps(i.metadata, sort_keys=True, ensure_ascii=False) for i in results[2])
+            
+            # 如果use_intersection为True，取交集；否则取并集
+            if use_intersection:
+                intersection = set1 & set2 & set3
+            else:
+                intersection = set1 | set2 | set3
+            
+            # 如果交集为空，使用第一种检索方式的结果
+            if not intersection:
+                logging.warning("三种检索方式无交集，使用普通检索结果")
+                return [json.loads(item) for item in set1]
+            
+            # 转回dict
+            return [json.loads(item) for item in intersection]
+     
+        except Exception as e:
+            logging.error(f"异步查询FAISS索引失败: {e}", exc_info=True)
+            return [] 

rag2_0/intent_recognition/__init__.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
 
 from .ProfessionalNounVector import ProfessionalNounVectorizer, ProfessionalNounRetriever
-from .IntentRecognition import IntentRecognizer
+from .IntentRecognition import IntentRecognizer, AsyncIntentRecognizer
 from .DataModels import Term, TermList, Classification, QueryRewrite