""" 意图理解与推理模块 (React模式) Intent Understanding & Reasoning Module (React Pattern) 这个模块实现了 React (Reasoning + Acting) 模式的意图理解节点，用于： 1. 理解用户的查询意图 2. 判断是否需要调用 RAG 检索 3. 判断是否需要重新检索 4. 决定下一步的行动 5. 支持条件路由扩展 核心组件： - ReasoningAction: 推理动作枚举 - ReasoningResult: 推理结果数据类 - ReactIntentReasoner: React 模式意图推理器 """ import re from typing import Dict, Any, Optional, List, Set, Tuple from dataclasses import dataclass, field from enum import Enum, auto from abc import ABC, abstractmethod class ReasoningAction(Enum): """推理动作枚举 - 决定下一步做什么""" DIRECT_RESPONSE = auto() # 直接回答，不需要额外信息 RETRIEVE_RAG = auto() # 需要调用 RAG 检索 RERIEVE_RAG = auto() # 需要重新检索 (优化前版本，兼容保留) RE_RETRIEVE_RAG = auto() # 需要重新检索 (修正拼写) CALL_TOOL = auto() # 需要调用其他工具 CLARIFY = auto() # 需要澄清用户的问题 ROUTE_SUBGRAPH = auto() # 需要路由到子图 UNKNOWN = auto() # 未知动作 @dataclass class RetrievalConfig: """检索配置""" need_retrieval: bool = False # 是否需要检索 need_re_retrieval: bool = False # 是否需要重新检索 retrieval_query: Optional[str] = None # 优化后的检索查询 collection_name: Optional[str] = None # 检索的集合名称 k: int = 5 # 返回数量 score_threshold: float = 0.3 # 相似度阈值 metadata: Dict[str, Any] = field(default_factory=dict) @dataclass class ReasoningResult: """推理结果数据类""" action: ReasoningAction = ReasoningAction.UNKNOWN # 决定的动作 confidence: float = 0.0 # 置信度 reasoning: str = "" # 推理过程说明 retrieval_config: RetrievalConfig = field(default_factory=RetrievalConfig) extracted_entities: Dict[str, Any] = field(default_factory=dict) # 提取的实体 next_hints: List[str] = field(default_factory=list) # 下一步提示 original_query: str = "" # 原始查询 metadata: Dict[str, Any] = field(default_factory=dict) class BaseIntentReasoner(ABC): """意图推理器基类""" @abstractmethod def reason( self, query: str, context: Optional[Dict[str, Any]] = None ) -> ReasoningResult: """ 推理意图，决定下一步动作 Args: query: 用户查询 context: 上下文信息，可能包括： - messages: 对话历史 - retrieved_docs: 已检索的文档 - previous_actions: 之前的动作 - user_id: 用户ID - etc. Returns: ReasoningResult: 推理结果 """ pass class RuleBasedReactReasoner(BaseIntentReasoner): """基于规则的 React 推理器""" def __init__(self): # 检索触发关键词 self._retrieval_keywords = { "什么", "怎么", "如何", "为什么", "哪", "谁", "多少", "介绍", "解释", "说明", "资料", "文档", "查询", "搜索", "find", "search", "what", "how", "why", "where", "who", "tell me", "explain", "about", "information" } # 重新检索触发关键词 self._re_retrieval_keywords = { "再", "重新", "更多", "不够", "不足", "其他", "另外", "没找到", "找不到", "没有", "不对", "不是", "again", "more", "another", "other", "didn't find", "not enough" } # 澄清触发关键词 self._clarify_keywords = { "?", "？", "哪个", "哪些", "哪位", "什么意思", "请问", "能详细", "具体点", "举个例子" } # 工具调用关键词 self._tool_keywords = { "天气", "weather", "邮件", "email", "联系人", "contact", "翻译", "translate", "词典", "dictionary" } # 子图路由关键词映射 self._subgraph_keywords = { "contact": {"通讯录", "联系人", "contact", "email", "邮件"}, "dictionary": {"词典", "单词", "翻译", "dictionary", "translate"}, "news_analysis": {"资讯", "新闻", "分析", "news", "report"}, } # 直接回答模式（问候、感谢等） self._direct_response_patterns = [ (r'^(你好|您好|hi|hello|hey|早上好|下午好|晚上好|哈喽)', ReasoningAction.DIRECT_RESPONSE), (r'^(谢谢|感谢|多谢|thanks|thank you)', ReasoningAction.DIRECT_RESPONSE), (r'^(再见|拜拜|bye|goodbye|回见)', ReasoningAction.DIRECT_RESPONSE), ] def reason( self, query: str, context: Optional[Dict[str, Any]] = None ) -> ReasoningResult: """ 基于规则的推理 """ context = context or {} query_lower = query.lower() result = ReasoningResult(original_query=query) # 1. 先检查是否是直接回答模式 for pattern, action in self._direct_response_patterns: if re.match(pattern, query, re.IGNORECASE): result.action = action result.confidence = 0.95 result.reasoning = "检测到问候、感谢或告别语，直接回答" return result # 2. 检查是否需要路由到子图（优先级高于重新检索，避免"有没有"误触发） for subgraph, keywords in self._subgraph_keywords.items(): if any(kw in query_lower for kw in keywords): result.action = ReasoningAction.ROUTE_SUBGRAPH result.confidence = 0.9 result.reasoning = f"检测到 {subgraph} 子图意图" result.metadata["target_subgraph"] = subgraph return result # 3. 检查是否需要重新检索 has_re_retrieval = any(kw in query_lower for kw in self._re_retrieval_keywords) # 同时检查上下文中是否有之前的检索结果但不够好 previous_retrieval = context.get("retrieved_docs") if has_re_retrieval or (previous_retrieval and len(previous_retrieval) < 2): result.action = ReasoningAction.RE_RETRIEVE_RAG result.confidence = 0.85 if has_re_retrieval else 0.7 result.reasoning = "检测到需要重新检索的意图" result.retrieval_config = RetrievalConfig( need_retrieval=True, need_re_retrieval=True, retrieval_query=self._optimize_retrieval_query(query), k=10 # 重新检索时返回更多结果 ) return result # 4. 检查是否需要调用工具 has_tool = any(kw in query_lower for kw in self._tool_keywords) if has_tool: result.action = ReasoningAction.CALL_TOOL result.confidence = 0.8 result.reasoning = "检测到工具调用意图" return result # 5. 检查是否需要澄清 has_clarify = any(kw in query_lower for kw in self._clarify_keywords) # 或者查询太短、太模糊 if has_clarify or len(query.strip()) < 3: result.action = ReasoningAction.CLARIFY result.confidence = 0.75 result.reasoning = "检测到需要澄清的意图" result.next_hints = [ "请提供更多细节", "您想了解什么方面的内容？", "能否具体说明一下？" ] return result # 6. 检查是否需要 RAG 检索 has_retrieval = any(kw in query_lower for kw in self._retrieval_keywords) if has_retrieval or len(query.strip()) > 5: result.action = ReasoningAction.RETRIEVE_RAG result.confidence = 0.85 if has_retrieval else 0.6 result.reasoning = "检测到需要检索知识库的意图" result.retrieval_config = RetrievalConfig( need_retrieval=True, retrieval_query=self._optimize_retrieval_query(query), k=5 ) return result # 7. 默认直接回答 result.action = ReasoningAction.DIRECT_RESPONSE result.confidence = 0.6 result.reasoning = "默认直接回答模式" return result def _optimize_retrieval_query(self, query: str) -> str: """优化检索查询，去掉不必要的语气词""" # 去掉常见的前缀 prefixes_to_remove = [ "请告诉我", "帮我查一下", "我想知道", "能不能告诉我", "请问", "你知道", "帮我找", "搜索一下", "查询一下" ] optimized = query for prefix in prefixes_to_remove: if optimized.startswith(prefix): optimized = optimized[len(prefix):] # 去掉常见的后缀 suffixes_to_remove = ["吗？", "呢？", "吧？", "吗", "呢", "吧", "？", "?"] for suffix in suffixes_to_remove: if optimized.endswith(suffix): optimized = optimized[:-len(suffix)] return optimized.strip() class LLMReactReasoner(BaseIntentReasoner): """ 基于 LLM 的 React 推理器 使用大语言模型进行更智能的推理判断 """ def __init__(self, llm_client=None): """ 初始化 LLM 推理器 Args: llm_client: LLM 客户端，需要支持调用方法 """ self.llm_client = llm_client self.rule_based = RuleBasedReactReasoner() def reason( self, query: str, context: Optional[Dict[str, Any]] = None ) -> ReasoningResult: """ 使用 LLM 进行推理，失败时回退到规则推理 """ try: if self.llm_client: return self._reason_with_llm(query, context) except Exception: pass # LLM 不可用或失败，回退到规则推理 return self.rule_based.reason(query, context) def _reason_with_llm( self, query: str, context: Optional[Dict[str, Any]] = None ) -> ReasoningResult: """ 使用 LLM 进行推理（需要实现具体的 LLM 调用逻辑） """ # 这里是一个示例实现，实际项目需要连接真实的 LLM prompt = self._build_reasoning_prompt(query, context) # 模拟 LLM 返回（实际项目中替换为真实调用） # 这里我们还是先调用规则推理作为示例 return self.rule_based.reason(query, context) def _build_reasoning_prompt(self, query: str, context: Optional[Dict[str, Any]]) -> str: """构建推理提示词""" context_str = "" if context: context_lines = [] if "messages" in context: context_lines.append(f"对话历史: {len(context['messages'])} 条") if "retrieved_docs" in context: context_lines.append(f"已检索文档: {len(context['retrieved_docs'])} 条") context_str = "\n".join(context_lines) return f"""你是一个意图推理助手，需要判断用户的查询应该如何处理。 用户查询: {query} 上下文信息: {context_str or '无额外上下文'} 请判断下一步应该做什么，可选动作: 1. DIRECT_RESPONSE - 直接回答，不需要额外信息 2. RETRIEVE_RAG - 需要调用知识库检索 3. RE_RETRIEVE_RAG - 需要重新检索更多/更好的结果 4. CALL_TOOL - 需要调用其他工具 5. CLARIFY - 需要澄清用户的问题 6. ROUTE_SUBGRAPH - 需要路由到子图 请以 JSON 格式输出你的判断。 """ def create_react_reasoner( use_llm: bool = False, llm_client=None ) -> BaseIntentReasoner: """ 创建 React 模式意图推理器工厂函数 Args: use_llm: 是否使用 LLM 推理 llm_client: LLM 客户端实例 Returns: BaseIntentReasoner: 推理器实例 """ if use_llm: return LLMReactReasoner(llm_client) return RuleBasedReactReasoner() # 便捷函数 - 直接推理 def react_reason( query: str, context: Optional[Dict[str, Any]] = None, reasoner: Optional[BaseIntentReasoner] = None ) -> ReasoningResult: """ 便捷函数：直接进行 React 推理 Args: query: 用户查询 context: 上下文信息 reasoner: 可选的推理器实例 Returns: ReasoningResult: 推理结果 """ if reasoner is None: reasoner = create_react_reasoner() return reasoner.reason(query, context) # 条件路由辅助函数 def get_route_by_reasoning(result: ReasoningResult) -> str: """ 根据推理结果获取路由字符串 Args: result: 推理结果 Returns: str: 路由标识 """ action_to_route = { ReasoningAction.DIRECT_RESPONSE: "direct_response", ReasoningAction.RETRIEVE_RAG: "retrieve_rag", ReasoningAction.RE_RETRIEVE_RAG: "re_retrieve_rag", ReasoningAction.RERIEVE_RAG: "re_retrieve_rag", # 兼容旧拼写 ReasoningAction.CALL_TOOL: "call_tool", ReasoningAction.CLARIFY: "clarify", ReasoningAction.ROUTE_SUBGRAPH: result.metadata.get("target_subgraph", "unknown_subgraph"), ReasoningAction.UNKNOWN: "unknown", } return action_to_route.get(result.action, "unknown")