优化意图识别API，移除同步意图识别器，改为使用异步意图识别器，更新相关逻辑以支持异步处理，增强错误处理和日志记录，同时更新请求和响应模型以适应新的API结构。

rag2_0/demo/intent_recognition_example.py

@@ -21,7 +21,7 @@ from typing import List, Dict, Any
 from langchain.output_parsers import PydanticOutputParser
 from pydantic import BaseModel, Field
 sys.path.append(os.getcwd())
-from rag2_0.intent_recognition import IntentRecognizer, AsyncIntentRecognizer
+from rag2_0.intent_recognition import AsyncIntentRecognizer
 from rag2_0.dify.DifyQueryRetrieval import DifyQueryRetrieval
 from rag2_0.intent_recognition.DataModels import Classification
 from rag2_0.tool.ModelTool import OpenAiLLM
@@ -78,8 +78,6 @@ class QueryRewriteProcessor:
         self.api_key = api_key or os.getenv("OPENAI_API_KEY")
         self.base_url = base_url or os.getenv("OPENAI_API_BASE")
         self.model_name = model_name or os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo")
-        
-        self.recognizer = IntentRecognizer(api_key=self.api_key, base_url=self.base_url, model_name=self.model_name)
         # 使用asyncio.run()运行异步create方法
         self.recognizer_async = asyncio.run(AsyncIntentRecognizer.create(
             api_key=self.api_key, 
@@ -198,12 +196,12 @@ class QueryRewriteProcessor:
         
         while retry_count <= max_retries:
             try:
-                # 使用process_query方法处理查询
+                result = asyncio.run(self.recognizer_async.process_query_async(query, 
-                result = self.recognizer.process_query(query, 
                                                   conversation_context=conversation_context, 
                                                   chat_history=chat_history, 
                                                   previous_slots=previous_slots, 
-                                                  enable_query_expansion=True)
+                                                  enable_query_expansion=True))
+
                 # 提取分类信息
                 classification = result["classification"]
                 original_query = result["rewrite"]["rewrite"]
@@ -238,9 +236,9 @@ class QueryRewriteProcessor:
                 if slot_filling and "filled_data" in slot_filling:
                     # 格式化槽位填充结果
                     slot_filling_str = json.dumps({
-                        "是否完整": slot_filling.get("is_complete", False),
+                        "is_complete": slot_filling.get("is_complete", False),
-                        "缺失槽位": slot_filling.get("missing_slots", {}),
+                        "missing_slots": slot_filling.get("missing_slots", {}),
-                        "填充数据": slot_filling.get("filled_data", {})
+                        "filled_data": slot_filling.get("filled_data", {})
                     }, ensure_ascii=False, indent=2)
 
                 # 处理成功，返回结果
@@ -442,9 +440,12 @@ def main():
         for idx, query in enumerate(examples):
             if query.strip() == "":
                 continue
+            query="储能C1软件如何新建工程？"
+            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(
                 query, 
+                conversation_context=conversation_context,
                 enable_retrieval=True
             ), ensure_ascii=False, indent=2))
 

rag2_0/dify/DifyCompareTest.py

@@ -186,7 +186,6 @@ class DifyComparisonTester:
 要求
 1、分析待评估的回答与标准答案的匹配程度(包括内容、步骤、主体等)
 2、如果待评估的回答与标准答案在核心内容和关键信息(步骤)上一致，即使表达方式不同，也应判定为"正确"。
-3、只要大体描述一致，即使缺失了一些步骤，也应判定为"正确"。
 3、如果待评估的回答存在明显的错误信息，应判定为"错误"。
 4、请严格按json格式输出：
 {{

rag2_0/dify/intent_recognition_api.py

@@ -1,14 +1,14 @@
-from gevent import monkey
-monkey.patch_all()
-
-from flask import Flask, request, Response
 import os
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+from typing import Dict, List, Any, Optional
+import asyncio
+
 from dotenv import load_dotenv
 import json
 import time
-from gevent.lock import RLock
-
-
 import datetime
 import logging
 # 加载环境变量
@@ -16,7 +16,7 @@ load_dotenv()
 
 import sys
 sys.path.append(os.getcwd())
-from rag2_0.intent_recognition import IntentRecognizer
+from rag2_0.intent_recognition import AsyncIntentRecognizer
 
 
 logging.basicConfig(
@@ -31,32 +31,87 @@ logging.getLogger('openai').setLevel(logging.WARNING)
 
 logger = logging.getLogger(__name__)
 
-app = Flask(__name__)
 
+
+
+# 定义请求模型
+class IntentRecognizeRequest(BaseModel):
+    query: str
+    conversation_context: str = ""
+    chat_history: Optional[List] = None
+    previous_slots: str | Dict = None
+
+# 定义槽位填充响应模型
+class SlotFillingResponse(BaseModel):
+    is_complete: bool = False
+    missing_slots: Dict[str, Any] = Field(default_factory=dict)
+    filled_data: Dict[str, Any] = Field(default_factory=dict)
+
+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)
+    multi_questions: Dict[str, Any] = Field(default_factory=Dict)
+
+# 定义响应模型
+class IntentRecognizeResponse(BaseModel):
+    source_query: str
+    source_query_keys: List[str]
+    vertical_classification: str
+    sub_classification: str
+    rewrite_query: str
+    keywords: List[Dict[str, str]] = Field(default_factory=list)
+    has_slot_filling: bool = False
+    slot_filling: SlotFillingResponse = Field(default_factory=SlotFillingResponse)
+    query_expand: QueryExpandResponse = Field(default_factory=QueryExpandResponse)
+
+# 创建FastAPI应用
+app = FastAPI(
+    title="意图识别服务",
+    description="基于LLM的意图识别和问题改写服务",
+    version="2.0"
+)
+
+# 添加CORS中间件
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 全局变量存储AsyncIntentRecognizer实例
+_instance = None
+
+# 应用启动事件
+@app.on_event("startup")
+async def startup_event():
+    global _instance
+    # 初始化AsyncIntentRecognizer实例
     api_key = os.getenv("OPENAI_API_KEY")
     base_url = os.getenv("OPENAI_API_BASE")
     model_name = os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo")
-_instance = IntentRecognizer(api_key=api_key, base_url=base_url, model_name=model_name)
+    _instance = await AsyncIntentRecognizer.create(api_key=api_key, base_url=base_url, model_name=model_name)
+    logger.info("AsyncIntentRecognizer初始化完成")
 
-@app.route('/intent_recognize', methods=['POST'])
+@app.post("/intent_recognize", response_model=IntentRecognizeResponse, summary="意图识别", description="识别用户查询的意图并进行问题改写")
-def intent_recognize():
+async def intent_recognize(request: IntentRecognizeRequest):
     try:
-        data = request.get_json(force=True)
+        if not request.query:
-        query = data.get('query')
+            raise HTTPException(status_code=400, detail="缺少query参数")
-        conversation_context = data.get('conversation_context', "")
-        chat_history = data.get('chat_history', None)
-        previous_slots = data.get('previous_slots', None)
-        if not query:
-            return Response(json.dumps({"error": "缺少query参数"}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=400)
         
         start_time = time.time()
         
-        result = _instance.process_query(query=query, 
+        result = await _instance.process_query_async(
-                                          conversation_context=conversation_context, 
+            query=request.query, 
-                                          chat_history=chat_history, 
+            conversation_context=request.conversation_context, 
-                                          previous_slots=previous_slots,
+            chat_history=request.chat_history, 
+            previous_slots=request.previous_slots,
             use_jieba=False,
-                                          enable_query_expansion=True)
+            enable_query_expansion=True
+        )
         
         end_time = time.time()
         current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S %z")
@@ -67,41 +122,61 @@ def intent_recognize():
         
         # 提取关键词信息
         keywords = result["keywords"]
-        keywords_str = ""
+        keywords_list = []
         if keywords and keywords.get("terms"):
-            term_details = []
             for term in keywords["terms"]:
-                term_info = {
+                keywords_list.append({
-                    "名称": term["name"],
+                    "名称": term["name"]
-                }
+                })
-                term_details.append(term_info)
-            keywords_str = term_details
         
         # 提取槽位填充信息
         slot_filling = result.get("slot_filling", {})
         
-        response_result = {
+        # 构建响应
-            "source_query": query,
+        response = IntentRecognizeResponse(
-            "source_query_keys": result["query_keys"],
+            source_query=request.query,
-            "vertical_classification": classification["vertical_classification"],
+            source_query_keys=result["query_keys"],
-            "sub_classification": classification["sub_classification"],
+            vertical_classification=classification["vertical_classification"],
-            "rewrite_query": result["rewrite"]["rewrite"],
+            sub_classification=classification["sub_classification"],
-            "keywords": keywords_str,
+            rewrite_query=result["rewrite"]["rewrite"],
-            "has_slot_filling": len(slot_filling)!=0,
+            keywords=keywords_list,
-            "slot_filling": {
+            has_slot_filling=len(slot_filling) != 0,
-                "is_complete": slot_filling.get("is_complete", False),
+            slot_filling=SlotFillingResponse(
-                "missing_slots": slot_filling.get("missing_slots", {}),
+                is_complete=slot_filling.get("is_complete", False),
-                "filled_data": slot_filling.get("filled_data", {})
+                missing_slots=slot_filling.get("missing_slots", {}),
-            },
+                filled_data=slot_filling.get("filled_data", {})
-            "query_expand": result["query_expand"]
+            ),
-        }
+            query_expand=QueryExpandResponse(
-        return Response(json.dumps(response_result, ensure_ascii=False), content_type='application/json; charset=utf-8')
+                all=result["query_expand"]["all"],
+                step_back=result["query_expand"]["step_back"],
+                follow_up=result["query_expand"]["follow_up"],
+                hyde=result["query_expand"]["hyde"],
+                multi_questions=result["query_expand"]["multi_questions"]
+            )
+        )
+        
+        return response
+        
+    except HTTPException as e:
+        raise e
     except Exception as e:
         logger.error(f"意图识别出错: {str(e)}", exc_info=True)
-        return Response(json.dumps({"error": str(e)}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=500)
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 添加健康检查端点
+@app.get("/health", summary="健康检查")
+async def health_check():
+    return {"status": "ok"}
 
 if __name__ == "__main__":
-    # 开发环境使用Flask内置服务器
+    # 使用uvicorn启动服务
-    # 生产环境使用gunicorn支持高并发 uv run gunicorn -w 10 -k gevent -b 0.0.0.0:8001 rag2_0.dify.intent_recognition_api:app
+    import uvicorn
-    # uv run gunicorn -w 10 -k gevent --preload -b 0.0.0.0:8001 rag2_0.dify.intent_recognition_api:app
+    uvicorn.run(
-    app.run(host="0.0.0.0", port=8001, threaded=True) 
+        "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
+        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:
+    # uvicorn rag2_0.dify.intent_recognition_api:app --host 0.0.0.0 --port 8001 --workers 10 

rag2_0/intent_recognition/DataModels.py

@@ -225,8 +225,6 @@ class DataProblemSlots(SlotBase):
 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)")
-    file_source: Optional[str] = Field(default="", description="文件来源场景")
-    related_software: Optional[str] = Field(default="",  description="相关软件名称")
     
     def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
         """检查必填槽位是否都存在"""
@@ -239,7 +237,7 @@ class FileExtensionConsultingSlots(SlotBase):
 
 # 3.2 软件锁类
 class SoftwareLockSlots(SlotBase):
-    lock_type: str = Field(default="", description="锁类型(单机锁、网络锁)")
+    lock_type: str = Field(default="单机锁", description="锁类型(单机锁、网络锁)")
     operation_purpose: str = Field(default="", description="操作目的(查询锁信息、无法激活、无法注册)")
     lock_number: Optional[str] = Field(default="",  description="软件锁编号/注册号")
     
@@ -259,7 +257,6 @@ class InstallationDownloadSlots(SlotBase):
     file_name: str = Field(default="", description="文件名，与software_name二选一")
     operation_stage: str = Field(default="", description="操作阶段(下载、安装等)")
     os_version: Optional[str] = Field(default="",  description="操作系统版本")
-    package_source: Optional[str] = Field(default="",  description="安装包来源/版本号")
     
     def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
         """检查必填槽位是否都存在"""

rag2_0/intent_recognition/IntentRecognition.py

@@ -38,839 +38,6 @@ from .DataModels import (
 from .ProfessionalNounVector import ProfessionalNounRetriever, AsyncProfessionalNounRetriever
 from rag2_0.tool.ModelTool import XinferenceReRankerModel, OpenAiLLM, SiliconFlowReRankerModel
 
-
-class IntentRecognizer:
-    """
-    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()
-        
-        # 初始化向量检索器
-        self._noun_retriever = ProfessionalNounRetriever(api_key=api_key, index_dir=vector_index_dir)
-    
-    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
-    
-    def _classify_intent(self, query: str, conversation_context: str = "", 
-                      chat_history: List[Dict[str, str]] = None, 
-                      previous_slots: Dict[str, Any] = None) -> Classification:
-        """
-        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:
-            # 调用LLM
-            response = self._llm.invoke(formatted_prompt, False)
-
-            classification_end_time = time.time()
-            classification_time = classification_end_time - classification_start_time
-            logging.info(f"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
-    
-    def _extract_keywords_with_llm(self, query: str, use_jieba: bool = False) -> List[Term]:
-        """
-        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())
-            
-            # 调用LLM
-            response = self._llm.invoke(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
-
-    
-    def _rerank_matched_terms(self, query_key: str, matched_terms: set, top_k: int = 2, rerank_score:float = 0.6) -> List[Term]:
-        """
-        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) + "|" + "描述:" + term.description for term in matched_terms]
-            term_texts = ["名称:" + term.name + "|" + "同义词:" + ";".join(term.synonymous) for term in matched_terms]
-            
-            # 使用重排序模型
-            xinference_reranker = XinferenceReRankerModel()
-            rerank_results = xinference_reranker.rerank(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"_rerank_matched_terms重排失败：{e}") from e
-
-    def _match_keywords(self, query: str, use_jieba: bool = False) -> Tuple[TermList, List[str]]:
-        """
-        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 = self._extract_keywords_with_llm(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"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 = self._noun_retriever.query(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 = self._rerank_matched_terms(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"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"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
-    
-    def _rewrite_query(self, query: str, keywords: TermList, query_keys:List[str], chat_history: List[Dict[str, str]] = None, context: str = "") -> QueryRewrite:
-        """
-        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] 
-        # terms_dict = [term.model_dump() 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.format(query=query, 
-        #                                                output_format=query_rewrite_parser.get_format_instructions(),
-        #                                                keywords=keywords_str)
-        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:
-            # 调用LLM
-            response = self._llm.invoke(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"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
-
-    def process_query(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]:
-        """
-        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
-            
-        Returns:
-            包含分类、关键词、改写和槽位填充结果的字典
-        """
-        # 是否是扩展名
-        # is_suffix, matched_suffixes = self._judge_define_suffix(query)
-        # if is_suffix:
-        #     # 将所有匹配到的后缀名作为Term添加到结果中
-        #     suffix_terms = []
-        #     for suffix in matched_suffixes:
-        #         term_dict = next((item for item in self._suffix_keywords if item['name'].lower() == suffix.lower()), None)
-        #         if term_dict:
-        #             suffix_term = Term(
-        #                 name=term_dict.get('name'),
-        #                 synonymous=term_dict.get('synonymous', []),
-        #                 description=json.dumps(term_dict.get('description', ''), ensure_ascii=False)
-        #             )
-        #         suffix_terms.append(suffix_term)
-   
-        #     return Classification(vertical_classification="安装下载", sub_classification="查询"), TermList(terms=suffix_terms), QueryRewrite(rewrite=query), matched_suffixes
-        
-        if chat_history is None:
-            chat_history = []
-        if previous_slots is None:
-            previous_slots = {}
-        
-        # 步骤: 并行执行提问扩展
-        if enable_query_expansion:
-            # 创建线程和结果容器
-            threads_and_results = [
-                # 5.1: 后退提示
-                self._run_in_thread(self._generate_step_back_prompt, args=(query, chat_history, conversation_context)),
-                
-                # 5.2: Follow Up Questions
-                self._run_in_thread(self._generate_follow_up_questions, args=(query, chat_history, conversation_context)),
-                
-                # 5.3: HyDE
-                self._run_in_thread(self._generate_hypothetical_document, args=(query, chat_history, conversation_context)),
-                
-                # 5.4: 多问题查询
-                self._run_in_thread(self._generate_multi_questions, args=(query, chat_history, conversation_context))
-            ]
-
-        # 步骤1: 匹配关键词
-        keywords_terms, query_keys = self._match_keywords(query, use_jieba)
-
-        # 步骤2: 问题改写
-        rewrite = self._rewrite_query(
-            query=query, 
-            keywords=keywords_terms,
-            query_keys=query_keys,
-            chat_history=chat_history,
-            context=conversation_context
-        )
-        
-        # 步骤3: 进行意图识别和槽位填充
-        # result = self._process_intent_and_slot(rewrite.rewrite, conversation_context, chat_history, previous_slots)
-        # result.update({"keywords": keywords_terms.model_dump(),
-        #                       "rewrite": rewrite.model_dump(),
-        #                       "query_keys": query_keys})
-        # return result
-        # 步骤3: 进行意图分类
-        classification = self._classify_intent(rewrite.rewrite, conversation_context, chat_history, previous_slots)
-
-        # 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 = self._fill_slots(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
-            }
-
-        # 等待所有线程完成
-        start_time = time.time()
-        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")
-        
-        # 收集结果
-        step_back_result = threads_and_results[0][1][0] if threads_and_results[0][1] else StepBackPrompt(original_query=query, step_back_query=query)
-        follow_up_result = threads_and_results[1][1][0] if threads_and_results[1][1] else FollowUpQuestions(original_query=query, follow_up_query=query)
-        hyde_result = threads_and_results[2][1][0] if threads_and_results[2][1] else HypotheticalDocument(original_query=query, hypothetical_answer="")
-        multi_questions_result = threads_and_results[3][1][0] if threads_and_results[3][1] 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
-        }
-
-    
-    def _fill_slots(self, query: str, classification: Classification, conversation_context: str = "", 
-                      chat_history: List[Dict[str, str]] = None, 
-                      previous_slots: Dict[str, Any] = None,) -> Dict[str, Any]:
-        """
-        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
-            keywords: 匹配的关键词列表
-            
-        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 = self._fill_slots_with_llm(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"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
-
-    def _fill_slots_with_llm(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:
-        """
-        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:
-            # 调用LLM
-            response = self._llm.invoke(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
-    
-    def _generate_step_back_prompt(self, query: str, chat_history: List[Dict[str, str]] = None, conversation_context: str = "") -> StepBackPrompt:
-        """
-        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:
-            # 调用LLM
-            response = self._llm.invoke(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"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"后退提示生成失败: {e}", exc_info=True)
-            return StepBackPrompt(original_query=query, step_back_query=query)
-    
-    def _generate_follow_up_questions(self, query: str, chat_history: List[Dict[str, str]] = None, conversation_context: str = "") -> FollowUpQuestions:
-        """
-        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:
-            # 调用LLM
-            response = self._llm.invoke(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"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"后续问题生成失败: {e}", exc_info=True)
-            return FollowUpQuestions(original_query=query, follow_up_query=query)
-    
-    def _generate_hypothetical_document(self, query: str, chat_history: List[Dict[str, str]] = None, conversation_context: str = "") -> HypotheticalDocument:
-        """
-        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:
-            # 调用LLM
-            response = self._llm.invoke(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"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"假设性文档生成失败: {e}", exc_info=True)
-            return HypotheticalDocument(original_query=query, hypothetical_answer="")
-    
-    def _generate_multi_questions(self, query: str, chat_history: List[Dict[str, str]] = None, conversation_context: str = "") -> MultiQuestions:
-        """
-        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:
-            # 调用LLM
-            response = self._llm.invoke(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"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"多角度问题生成失败: {e}",exc_info=True)
-            return MultiQuestions(original_query=query, sub_questions=[query])
-    
-    def _run_in_thread(self, func, args=(), kwargs={}):
-        """
-        在线程中执行函数并返回结果
-        
-        Args:
-            func: 要执行的函数
-            args: 函数的位置参数
-            kwargs: 函数的关键字参数
-            
-        Returns:
-            (thread, result_container): 线程对象和存放结果的容器
-        """
-        result_container = []
-        
-        def thread_target():
-            try:
-                result = func(*args, **kwargs)
-                result_container.append(result)
-            except Exception as e:
-                logging.error(f"线程执行函数 {func.__name__} 时出错: {e}", exc_info=True)
-                result_container.append(None)
-        
-        thread = threading.Thread(target=thread_target)
-        thread.start()
-        return thread, result_container
-
-            
-    def _process_intent_and_slot(self, user_input: str, conversation_context: str = "", 
-                              chat_history: List[Dict[str, str]] = None, 
-                              previous_slots: Dict[str, Any] = None) -> Dict[str, Any]:
-        """
-        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
-        )
-        
-        # 调用LLM
-        llm_start_time = time.time()
-        response = self._llm.invoke(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 = self._classify_intent(user_input, conversation_context, chat_history, previous_slots)
-                fill_slots = self._fill_slots(user_input, classification, conversation_context, chat_history, previous_slots)
-                
-                result = {
-                    "classification": classification.model_dump(),
-                    "slot_filling": fill_slots
-                }
-                logging.warning(f"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 = self._fill_slots(user_input, classification, conversation_context, chat_history, previous_slots)
-                result = {
-                    "classification": classification.model_dump(),
-                    "slot_filling": slot_filling
-                }
-                logging.warning(f"重新填充槽点")
-                return result
-            
-            logging.info(f"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"process_intent_and_slot error:{e}") from e
-    
-
-
 class AsyncIntentRecognizer:
     """
     异步意图识别和问题改写类
@@ -976,7 +143,7 @@ class AsyncIntentRecognizer:
         formatted_prompt = classification_prompt.format(user_input=query, 
                                                         classification_info=classification_info, 
                                                         output_format=classification_parser.get_format_instructions(),
-                                                        conversation_context=conversation_context,
+                                                        # conversation_context=conversation_context,
                                                         chat_history=json.dumps(chat_history, ensure_ascii=False))
         # 解析输出
         try:

rag2_0/intent_recognition/PromptTemplates.py

@@ -56,12 +56,9 @@ classification_info="""【垂直领域分类】：
 1. 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，将其归类为以下垂直领域之一：
+    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：
     {classification_info}
 
-    ## 【会话背景信息】
-   {conversation_context}
-
    ## 【历史对话记录】
    {chat_history}
 

rag2_0/intent_recognition/__init__.py

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