init: RAG 知识库服务初始提交

- 后端 API（Flask + Gunicorn）
- RAG 引擎（混合检索 + 云端 Reranker + 引用溯源）
- 文档解析（MinerU + 多格式支持）
- Docker 生产部署配置
- 排除前端项目、敏感配置、模型文件

core/__init__.py

@@ -0,0 +1,18 @@
+"""
+RAG 核心引擎模块
+
+包含：
+- engine: RAGEngine 单例类，管理模型和共享资源
+- agentic: AgenticRAG 智能问答
+- bm25_index: BM25 关键词检索索引
+- chunker: 文本分块器
+"""
+
+from .engine import RAGEngine, get_engine
+from .bm25_index import BM25Index
+from .chunker import split_text_with_limit, split_text
+
+__all__ = [
+    'RAGEngine', 'get_engine', 'BM25Index',
+    'split_text_with_limit', 'split_text'
+]

core/adaptive_topk.py

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+"""
+自适应 TopK 策略
+
+根据检索置信度动态调整 top_k：
+- 低置信度：扩大检索范围
+- 高置信度：缩小检索范围
+- 中等置信度：保持原样
+
+使用方式：
+    from core.adaptive_topk import AdaptiveTopK
+
+    strategy = AdaptiveTopK()
+    adjusted_k, should_retrieve = strategy.adjust(top_score, initial_k)
+"""
+
+from dataclasses import dataclass
+from typing import Tuple, Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class AdaptiveConfig:
+    """自适应配置"""
+    # 置信度阈值
+    low_confidence_threshold: float = 0.5    # 低于此值认为是低置信度
+    high_confidence_threshold: float = 0.8   # 高于此值认为是高置信度
+
+    # 扩展/收缩比例
+    expand_ratio: float = 2.0                # 低置信度时扩大倍数
+    shrink_ratio: float = 0.5                # 高置信度时缩小比例
+
+    # 限制
+    min_top_k: int = 3                       # 最小 top_k
+    max_top_k: int = 20                      # 最大 top_k
+
+    # 是否启用
+    enabled: bool = True
+
+
+class AdaptiveTopK:
+    """
+    自适应 TopK 策略
+
+    核心逻辑：
+    1. 第一次检索返回结果后，检查最高得分
+    2. 根据得分判断置信度
+    3. 决定是否需要调整 top_k 重新检索
+    """
+
+    def __init__(self, config: AdaptiveConfig = None):
+        """
+        初始化
+
+        Args:
+            config: 配置对象，如果为None则使用默认配置
+        """
+        self.config = config or AdaptiveConfig()
+
+    def adjust(
+        self,
+        top_score: float,
+        initial_k: int,
+        current_results_count: int = 0
+    ) -> Tuple[int, bool, str]:
+        """
+        根据置信度调整 top_k
+
+        Args:
+            top_score: 当前检索结果的最高得分 (0-1)
+            initial_k: 初始 top_k
+            current_results_count: 当前结果数量
+
+        Returns:
+            (adjusted_k, should_retrieve, reason)
+            - adjusted_k: 调整后的 top_k
+            - should_retrieve: 是否需要重新检索
+            - reason: 调整原因
+        """
+        if not self.config.enabled:
+            return initial_k, False, "disabled"
+
+        # 低置信度：扩大检索范围
+        if top_score < self.config.low_confidence_threshold:
+            adjusted_k = min(
+                int(initial_k * self.config.expand_ratio),
+                self.config.max_top_k
+            )
+            if adjusted_k > initial_k:
+                return adjusted_k, True, f"low_confidence({top_score:.2f}<{self.config.low_confidence_threshold})"
+
+        # 高置信度：可以缩小范围（但不重新检索）
+        elif top_score > self.config.high_confidence_threshold:
+            adjusted_k = max(
+                int(initial_k * self.config.shrink_ratio),
+                self.config.min_top_k
+            )
+            # 高置信度时不需要重新检索，只是返回时可以截断
+            return adjusted_k, False, f"high_confidence({top_score:.2f}>{self.config.high_confidence_threshold})"
+
+        # 中等置信度：保持原样
+        return initial_k, False, f"medium_confidence({top_score:.2f})"
+
+    def get_final_results(
+        self,
+        results: list,
+        adjusted_k: int,
+        reason: str
+    ) -> list:
+        """
+        获取最终结果
+
+        Args:
+            results: 检索结果列表
+            adjusted_k: 调整后的 top_k
+            reason: 调整原因
+
+        Returns:
+            截断后的结果列表
+        """
+        if "high_confidence" in reason:
+            # 高置信度时截断结果
+            return results[:adjusted_k]
+        return results
+
+    def get_config_dict(self) -> dict:
+        """获取配置字典"""
+        return {
+            "enabled": self.config.enabled,
+            "low_confidence_threshold": self.config.low_confidence_threshold,
+            "high_confidence_threshold": self.config.high_confidence_threshold,
+            "expand_ratio": self.config.expand_ratio,
+            "shrink_ratio": self.config.shrink_ratio,
+            "min_top_k": self.config.min_top_k,
+            "max_top_k": self.config.max_top_k
+        }
+
+
+class AdaptiveTopKWithStats(AdaptiveTopK):
+    """
+    带统计的自适应 TopK 策略
+
+    记录每次调整的统计信息，用于分析和优化
+    """
+
+    def __init__(self, config: AdaptiveConfig = None):
+        super().__init__(config)
+        self.stats = {
+            "total_queries": 0,
+            "low_confidence_count": 0,
+            "high_confidence_count": 0,
+            "medium_confidence_count": 0,
+            "re_retrieve_count": 0
+        }
+
+    def adjust(
+        self,
+        top_score: float,
+        initial_k: int,
+        current_results_count: int = 0
+    ) -> Tuple[int, bool, str]:
+        """带统计的调整"""
+        self.stats["total_queries"] += 1
+
+        adjusted_k, should_retrieve, reason = super().adjust(
+            top_score, initial_k, current_results_count
+        )
+
+        # 更新统计
+        if "low_confidence" in reason:
+            self.stats["low_confidence_count"] += 1
+        elif "high_confidence" in reason:
+            self.stats["high_confidence_count"] += 1
+        else:
+            self.stats["medium_confidence_count"] += 1
+
+        if should_retrieve:
+            self.stats["re_retrieve_count"] += 1
+
+        return adjusted_k, should_retrieve, reason
+
+    def get_stats(self) -> dict:
+        """获取统计信息"""
+        return self.stats.copy()
+
+    def reset_stats(self):
+        """重置统计"""
+        self.stats = {
+            "total_queries": 0,
+            "low_confidence_count": 0,
+            "high_confidence_count": 0,
+            "medium_confidence_count": 0,
+            "re_retrieve_count": 0
+        }
+
+
+# ==================== 便捷函数 ====================
+
+def create_adaptive_topk(
+    enabled: bool = True,
+    low_threshold: float = 0.5,
+    high_threshold: float = 0.8,
+    expand_ratio: float = 2.0,
+    shrink_ratio: float = 0.5
+) -> AdaptiveTopK:
+    """
+    创建自适应 TopK 策略
+
+    Args:
+        enabled: 是否启用
+        low_threshold: 低置信度阈值
+        high_threshold: 高置信度阈值
+        expand_ratio: 扩展比例
+        shrink_ratio: 收缩比例
+
+    Returns:
+        AdaptiveTopK 实例
+    """
+    config = AdaptiveConfig(
+        enabled=enabled,
+        low_confidence_threshold=low_threshold,
+        high_confidence_threshold=high_threshold,
+        expand_ratio=expand_ratio,
+        shrink_ratio=shrink_ratio
+    )
+    return AdaptiveTopK(config)
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    print("=" * 60)
+    print("自适应 TopK 策略测试")
+    print("=" * 60)
+
+    strategy = AdaptiveTopKWithStats()
+
+    test_cases = [
+        (0.3, 5, "低置信度 - 应该扩展"),
+        (0.6, 5, "中等置信度 - 保持"),
+        (0.9, 5, "高置信度 - 应该收缩"),
+        (0.45, 10, "边界低置信度"),
+        (0.8, 10, "边界高置信度"),
+    ]
+
+    for top_score, initial_k, description in test_cases:
+        adjusted_k, should_retrieve, reason = strategy.adjust(top_score, initial_k)
+        print(f"\n{description}")
+        print(f"  输入: top_score={top_score}, initial_k={initial_k}")
+        print(f"  输出: adjusted_k={adjusted_k}, should_retrieve={should_retrieve}")
+        print(f"  原因: {reason}")
+
+    print("\n" + "=" * 60)
+    print("统计信息:")
+    stats = strategy.get_stats()
+    for key, value in stats.items():
+        print(f"  {key}: {value}")

core/agentic.py

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+"""
+Agentic RAG - 知识库智能问答系统
+
+核心能力：
+1. 知识库检索 - 向量检索 + BM25 + Rerank
+2. 网络搜索 - 当知识库不足时自动搜索（需配置SERPER_API_KEY）
+3. 图谱检索 - 实体关系推理（需配置Neo4j）
+4. 多源融合 - 智能处理知识库和网络内容
+5. Agent决策 - 动态决定检索、改写、分解等操作
+
+使用方式：
+    from core.agentic import AgenticRAG
+
+    rag = AgenticRAG()
+    result = rag.process("你的问题")
+    print(result["answer"])
+"""
+
+import json
+import logging
+from openai import OpenAI
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+# 导入基础模块
+from core.engine import get_engine
+from core.llm_utils import call_llm, quick_yes_no, parse_json_from_response
+
+# 导入基础常量和配置
+from .agentic_base import (
+    API_KEY, BASE_URL, MODEL,
+    HAS_SERPER,
+    HAS_BUDGET, SEMANTIC_CACHE_ENABLED,
+    MAX_CONTEXT_TOKENS, MAX_CONTEXT_COUNT, RERANK_THRESHOLD,
+    SOURCE_KB, SOURCE_WEB,
+)
+
+# 尝试导入语义缓存
+try:
+    from core.semantic_cache import SemanticCache
+except ImportError:
+    SemanticCache = None
+
+# 导入 Mixin 类
+from .agentic_query import QueryRewriteMixin
+from .agentic_search import SearchMixin
+from .agentic_answer import AnswerMixin
+from .agentic_citation import CitationMixin
+from .agentic_media import RichMediaMixin
+from .agentic_quality import QualityMixin
+from .agentic_context import ContextMixin
+from .agentic_meta import MetaQuestionMixin
+
+
+class AgenticRAG(
+    QueryRewriteMixin,
+    SearchMixin,
+    AnswerMixin,
+    CitationMixin,
+    RichMediaMixin,
+    QualityMixin,
+    ContextMixin,
+    MetaQuestionMixin
+):
+    """
+    Agentic RAG - 知识库智能问答
+
+    通过 Mixin 模式组合功能：
+    - QueryRewriteMixin: 查询重写
+    - SearchMixin: 检索功能
+    - AnswerMixin: 答案生成
+    - CitationMixin: 引用处理
+    - RichMediaMixin: 富媒体处理
+    - QualityMixin: 质量评估
+    - ContextMixin: 上下文处理
+    - MetaQuestionMixin: 元问题处理
+    """
+
+    def __init__(
+        self,
+        max_iterations: int = 3,
+        enable_web_search: bool = True,
+        **kwargs
+    ):
+        """初始化"""
+        self.max_iterations = max_iterations
+        self.enable_web_search = enable_web_search and HAS_SERPER
+        self.client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
+
+        # 信息来源标记
+        self.SOURCE_KB = SOURCE_KB
+        self.SOURCE_WEB = SOURCE_WEB
+
+        # 初始化置信度门控
+        try:
+            from core.confidence_gate import create_gate
+            self.confidence_gate = create_gate()
+        except ImportError:
+            self.confidence_gate = None
+
+        # 初始化多维质量评估器
+        try:
+            from core.quality_assessor import create_assessor
+            self.quality_assessor = create_assessor()
+        except ImportError:
+            self.quality_assessor = None
+
+        # 初始化推理反思器
+        try:
+            from core.reasoning_reflector import create_reflector
+            self.reasoning_reflector = create_reflector()
+        except ImportError:
+            self.reasoning_reflector = None
+
+        # 初始化循环防护器
+        try:
+            from core.loop_guard import create_guard
+            self.loop_guard = create_guard(max_iterations=max_iterations)
+        except ImportError:
+            self.loop_guard = None
+
+        # 初始化语义缓存
+        self.semantic_cache = None
+        self.embedding_model = None
+        if SEMANTIC_CACHE_ENABLED and SemanticCache:
+            try:
+                engine = get_engine()
+                if engine and hasattr(engine, 'embedding_model'):
+                    self.embedding_model = engine.embedding_model
+                    emb_dim = 768
+                    # 优先使用新 API，兼容旧版本
+                    if hasattr(self.embedding_model, 'get_embedding_dimension'):
+                        emb_dim = self.embedding_model.get_embedding_dimension()
+                    elif hasattr(self.embedding_model, 'get_sentence_embedding_dimension'):
+                        emb_dim = self.embedding_model.get_sentence_embedding_dimension()
+                    self.semantic_cache = SemanticCache(
+                        dim=emb_dim,
+                        threshold=0.92,
+                        max_size=5000
+                    )
+                    logger.info(f"语义缓存已启用，维度={emb_dim}")
+            except Exception as e:
+                logger.warning(f"语义缓存初始化失败: {e}")
+                self.semantic_cache = None
+
+        # Context Compression 配置
+        self.MAX_CONTEXT_TOKENS = MAX_CONTEXT_TOKENS
+        self.MAX_CONTEXT_COUNT = MAX_CONTEXT_COUNT
+        self.RERANK_THRESHOLD = RERANK_THRESHOLD
+
+        # Answer Grounding 配置
+        self.MAX_GROUNDING_RETRY = 1
+        self.grounding_retry_count = 0
+
+    def should_rewrite(self, query: str, history: list = None) -> bool:
+        """判断是否需要重写查询"""
+        # 口语化表达模式
+        colloquial_patterns = [
+            "这个", "那个", "它", "这", "那",
+            "上面", "下面", "刚才", "之前",
+            "能不能", "可以吗", "行不行",
+            "怎么办", "怎么弄", "咋整"
+        ]
+
+        for pattern in colloquial_patterns:
+            if pattern in query:
+                return True
+
+        # 查询太短
+        if len(query) < 5:
+            return True
+
+        # 有对话历史时，可能需要实体补全
+        if history:
+            for msg in reversed(history[-3:]):
+                if msg.get("role") == "user":
+                    prev_query = msg.get("content", "")
+                    # 如果当前查询缺少主语，可能需要补全
+                    if any(kw in query for kw in ["标准", "规定", "流程", "制度"]):
+                        if not any(kw in query for kw in ["报销", "出差", "请假", "工资", "合同"]):
+                            return True
+
+        return False
+
+    def process(self, query: str, verbose: bool = True, history: list = None,
+                allowed_levels: list = None, role: str = None, department: str = None,
+                emit_log=None) -> dict:
+        """
+        主流程：智能问答
+
+        Args:
+            query: 用户问题
+            verbose: 是否打印详细过程
+            history: 对话历史
+            allowed_levels: 允许访问的安全级别
+            role: 用户角色
+            department: 用户部门
+            emit_log: 日志发射函数（流式输出）
+
+        Returns:
+            {
+                "answer": str,
+                "sources": list,
+                "images": list,
+                "tables": list,
+                "citations": list,
+                "log_trace": list
+            }
+        """
+        log_trace = []
+
+        # 1. 检查元问题
+        if self._is_meta_question(query):
+            answer = self._answer_meta_question(query, allowed_levels, role, department)
+            return {
+                "answer": answer,
+                "sources": [],
+                "images": [],
+                "tables": [],
+                "citations": [],
+                "log_trace": [{"phase": "meta_question", "query": query}]
+            }
+
+        # 2. 查询重写
+        current_query = query
+        if self.should_rewrite(query, history):
+            current_query = self._rewrite_query(query, history)
+            log_trace.append({"phase": "rewrite", "original": query, "rewritten": current_query})
+            if emit_log:
+                emit_log(f"📝 查询重写: {query} → {current_query}")
+
+        # 3. 知识库检索
+        contexts = []
+        try:
+            engine = get_engine()
+            if not engine._initialized:
+                engine.initialize()
+
+            # 获取用户可访问的向量库
+            from knowledge.manager import get_kb_manager
+            kb_mgr = get_kb_manager()
+            accessible = kb_mgr.get_accessible_collections(role or 'user', department or '', 'read')
+
+            # 统一使用 search_knowledge() — 生产路径的同一 API
+            # search_knowledge() 返回 dict: {ids, documents, metadatas, distances}，每项为 list[list]
+            # top_k 与生产路径对齐（30），确保 Rerank 后仍有足够结果
+            results = engine.search_knowledge(
+                query=current_query,
+                top_k=30,
+                collections=accessible if accessible else None,
+            )
+
+            docs = results.get('documents', [[]])[0]
+            metas = results.get('metadatas', [[]])[0]
+            dists = results.get('distances', [[]])[0]
+
+            for doc, meta, score in zip(docs, metas, dists):
+                contexts.append({
+                    'doc': doc,
+                    'meta': meta,
+                    'score': 1 - score if score <= 1 else 1 / (1 + score),  # 距离→相似度
+                    'source_type': self.SOURCE_KB,
+                    'query': current_query
+                })
+
+            log_trace.append({"phase": "kb_search", "query": current_query, "results": len(contexts)})
+            if emit_log:
+                emit_log(f"🔍 知识库检索: {len(contexts)} 条结果")
+
+        except Exception as e:
+            logger.error(f"知识库检索失败: {e}")
+            log_trace.append({"phase": "kb_search", "error": str(e)})
+
+        # 3.5 图片独立检索 + 打分选择（与生产路径对齐）
+        selected_images = []
+        try:
+            from api.chat_routes import select_images
+            selected_images = select_images(contexts, current_query)
+            if selected_images and emit_log:
+                emit_log(f"🖼️ 图片选择: {len(selected_images)} 张相关图片")
+            log_trace.append({"phase": "image_selection", "count": len(selected_images)})
+        except Exception as e:
+            logger.debug(f"图片选择失败: {e}")
+
+        # 4. 上下文压缩
+        contexts = self._compress_contexts(current_query, contexts)
+
+        # 5. 网络搜索（如果需要）
+        web_contexts = []
+        if self.enable_web_search and (
+            not contexts or
+            not self._is_kb_result_sufficient(current_query, [c['doc'] for c in contexts]) or
+            self._should_web_search(current_query)
+        ):
+            web_contexts = self._web_search_flow(current_query, log_trace, emit_log, verbose, allowed_levels)
+            contexts.extend(web_contexts)
+
+        # 7. 生成答案（注入图片描述到上下文）
+        if contexts:
+            # 将选中图片的描述注入上下文，让 LLM 能"看到"图片内容
+            if selected_images:
+                image_contexts = []
+                for i, img in enumerate(selected_images, 1):
+                    full_desc = img.get('full_description', '') or img.get('description', '')
+                    if full_desc:
+                        img_source = img.get('source', '')
+                        img_page = img.get('page', '')
+                        source_info = f"（来源：{img_source} 第{img_page}页）" if img_source and img_page else ""
+                        image_contexts.append({
+                            'doc': f"【图片{i}】{full_desc}{source_info}",
+                            'meta': {'source': img_source, 'page': img_page, 'chunk_type': img.get('type', 'image')},
+                            'score': img.get('score', 0),
+                            'source_type': self.SOURCE_KB,
+                            'query': current_query
+                        })
+                # 图片上下文追加到知识库上下文前面（让 LLM 优先看到图片信息）
+                contexts = image_contexts + contexts
+
+            answer = self._generate_fused_answer(current_query, contexts, allowed_levels)
+
+            # 答案验证（防止幻觉）
+            if self.grounding_retry_count < self.MAX_GROUNDING_RETRY:
+                answer = self._verify_and_refine_answer(current_query, answer, contexts)
+        else:
+            answer = self._generate_no_context_answer(current_query, allowed_levels)
+
+        # 8. 构建引用
+        citations = self._attach_citations(answer, contexts)
+
+        # 9. 图片结果：优先使用 select_images 的结构化结果（含 URL + 打分）
+        #    回退到 _extract_rich_media（从 metadata 提取）
+        if selected_images:
+            images_result = selected_images
+        else:
+            rich_media = self._extract_rich_media(contexts)
+            images_result = rich_media.get("images", [])
+
+        return {
+            "answer": answer,
+            "sources": citations.get("sources", []),
+            "images": images_result,
+            "tables": citations.get("tables", []) if isinstance(citations, dict) else [],
+            "citations": citations.get("citations", []),
+            "log_trace": log_trace
+        }
+
+    def chat_search(self, query: str, history: list = None, role: str = None,
+                    department: str = None, allowed_levels: list = None) -> dict:
+        """聊天式检索接口"""
+        return self.process(
+            query,
+            verbose=False,
+            history=history,
+            role=role,
+            department=department,
+            allowed_levels=allowed_levels
+        )
+
+    def chat(self):
+        """命令行交互模式"""
+        print("🤖 Agentic RAG 已启动，输入 'quit' 退出")
+        print("-" * 50)
+
+        history = []
+        while True:
+            try:
+                query = input("\n👤 你: ").strip()
+                if not query:
+                    continue
+                if query.lower() in ['quit', 'exit', 'q']:
+                    print("👋 再见！")
+                    break
+
+                result = self.process(query, verbose=True, history=history)
+                print(f"\n🤖 AI: {result['answer']}")
+
+                if result['sources']:
+                    print("\n📚 来源:")
+                    for src in result['sources'][:3]:
+                        print(f"  - {src['source']}")
+
+                history.append({"role": "user", "content": query})
+                history.append({"role": "assistant", "content": result['answer']})
+
+            except KeyboardInterrupt:
+                print("\n👋 再见！")
+                break
+            except Exception as e:
+                print(f"❌ 错误: {e}")

core/agentic_answer.py

@@ -0,0 +1,214 @@
+"""
+Agentic RAG - 答案生成 Mixin
+
+包含答案生成、上下文构建、融合回答等方法
+"""
+
+import logging
+
+from .agentic_base import logger, MODEL, SOURCE_KB, SOURCE_WEB
+from core.llm_utils import call_llm
+
+logger = logging.getLogger(__name__)
+
+
+class AnswerMixin:
+    """答案生成方法"""
+
+    def _generate_fused_answer(self, query: str, contexts: list, allowed_levels: list = None) -> str:
+        """生成融合答案 - 智能处理多源信息"""
+        # 分离不同来源
+        kb_contexts = [c for c in contexts if c.get('source_type') == self.SOURCE_KB]
+        web_contexts = [c for c in contexts if c.get('source_type') == self.SOURCE_WEB]
+
+        # 如果没有任何上下文，检测是否因权限限制
+        if not contexts:
+            return self._generate_no_context_answer(query, allowed_levels)
+
+        # 正常生成答案
+        context_str = self._build_context_string(kb_contexts, web_contexts)
+        prompt = self._build_normal_answer_prompt(query, context_str, kb_contexts, web_contexts)
+
+        result = call_llm(
+            self.client, prompt, MODEL,
+            temperature=0.7,
+            max_tokens=2000
+        )
+        return result or f"生成答案失败"
+
+    def _build_context_string(self, kb_contexts, web_contexts):
+        """构建上下文字符串 - FAQ 优先策略，按分数排序"""
+        # 分离 FAQ 和普通知识库内容
+        faq_contexts = [c for c in kb_contexts if c.get('meta', {}).get('chunk_type') == 'faq']
+        regular_contexts = [c for c in kb_contexts if c.get('meta', {}).get('chunk_type') != 'faq']
+
+        # 按分数降序排列，确保最相关的内容优先展示
+        regular_contexts.sort(key=lambda c: c.get('score', 0), reverse=True)
+
+        # FAQ 部分（优先展示）
+        faq_parts = []
+        for i, c in enumerate(faq_contexts[:3], 1):
+            meta = c['meta']
+            answer = meta.get('faq_answer', c['doc'])
+            faq_parts.append(f"[FAQ-{i}] 常见问题\n问题：{c['doc']}\n标准答案：{answer}")
+
+        # 普通知识库部分（用 12 条，提升覆盖率）
+        kb_parts = []
+        for i, c in enumerate(regular_contexts[:12], 1):
+            meta = c['meta']
+            source_str = meta.get('source', '未知')
+            section = meta.get('section', '')
+            source_info = f"{source_str}"
+            if section:
+                source_info += f" > {section[:60]}"
+            kb_parts.append(f"[知识库-{i}] {source_info}\n{c['doc']}")
+
+        web_parts = []
+        for i, c in enumerate(web_contexts[:5], 1):
+            meta = c['meta']
+            web_parts.append(f"[网络-{i}] {meta.get('title', '')}\n来源:{meta.get('source', '')}\n{c['doc']}")
+
+        return "\n\n".join(faq_parts + kb_parts + web_parts)
+
+    def _build_normal_answer_prompt(self, query, context_str, kb_contexts, web_contexts):
+        """构建正常回答的提示词（与生产路径 generate_answer_stream 对齐）"""
+        # 检测是否有图片上下文
+        has_images = any(c.get('meta', {}).get('chunk_type') in ('image', 'chart', 'table')
+                        for c in kb_contexts)
+
+        image_instruction = ""
+        if has_images:
+            image_instruction = "\n5. 如果参考资料中包含【图片N】信息，请在回答中简要介绍每张图片的内容和用途"
+
+        return f"""你是一个严谨的知识库问答助手。你必须且只能根据用户提供的【参考资料】回答问题。
+
+【参考资料】
+{context_str}
+
+【用户问题】
+{query}
+
+【回答要求】
+1. 如果参考资料中有答案，必须引用对应内容回答，并在回答末尾标注引用编号（如[1]、[2]）
+2. 如果参考资料中确实没有相关信息，简短说明"参考资料中没有相关信息"即可，不要编造或补充资料外的内容
+3. 禁止使用参考资料以外的知识进行补充或推测
+4. 分点列举，条理清晰，语言简洁{image_instruction}
+
+请仔细阅读以上全部参考资料后回答："""
+
+    def _build_answer_prompt_with_permission(self, query, context_str, levels_str, sources_str, kb_contexts, web_contexts):
+        """构建带权限提示的回答提示词"""
+        return f"""你是一个严谨的智能助手。
+
+【用户问题】
+{query}
+
+【重要提示】
+检测到与用户问题更相关的信息可能存在于「{levels_str}」级别的文档中，但用户当前的权限级别无法访问。
+
+【可访问的信息来源】
+{context_str}
+
+【回答要求】
+1. 首先明确告知用户：当前回答基于您有权限访问的文档，可能不完整
+2. 基于现有信息如实回答
+3. 建议用户如需完整信息，请联系管理员申请相应权限
+
+请回答："""
+
+    def _generate_no_context_answer(self, query: str, allowed_levels: list = None) -> str:
+        """无上下文时的回答 — 诚实告知，不编造"""
+        return "参考资料中没有找到与该问题相关的信息，无法根据现有知识库内容回答您的问题。"
+
+    def _verify_and_refine_answer(self, query: str, answer: str, contexts: list) -> str:
+        """验证并精炼答案 - 防止幻觉
+
+        返回值始终是干净的答案文本，不包含验证推理过程。
+        """
+        prompt = f"""请检查以下回答是否存在"幻觉"（与参考信息不符的内容）。
+
+【用户问题】
+{query}
+
+【参考信息】
+{chr(10).join([f"[{i+1}] {c['doc'][:200]}" for i, c in enumerate(contexts[:8])])}
+
+【AI回答】
+{answer}
+
+【检查规则】
+1. 逐条核对回答中的事实是否能在参考信息中找到依据
+2. 如果没有幻觉，只回复一个英文单词：PASS
+3. 如果有幻觉，只输出修正后的完整回答（不要输出检查过程、不要加标题、不要加"检查结果"等前缀）
+
+修正后的回答："""
+
+        try:
+            result = call_llm(
+                self.client, prompt, MODEL,
+                temperature=0.1,
+                max_tokens=2000
+            )
+            if not result:
+                return answer
+            # 如果返回 PASS 或很短的确认，说明无幻觉
+            cleaned = result.strip()
+            if cleaned.upper() == "PASS" or len(cleaned) < 10:
+                return answer
+            # 有幻觉时，返回修正后的干净答案（去掉可能的前缀）
+            for prefix in ["修正后的回答：", "修正后回答：", "修正回答：", "修正后："]:
+                if cleaned.startswith(prefix):
+                    cleaned = cleaned[len(prefix):].strip()
+            return cleaned
+        except Exception as e:
+            logger.warning(f"答案验证失败: {e}")
+            return answer
+
+    def _generate_uncertain_answer(self, query: str, contexts: list) -> str:
+        """生成不确定性回答"""
+        context_str = "\n".join([c['doc'][:200] for c in contexts[:3]])
+
+        prompt = f"""用户问题：{query}
+
+找到的信息可能不够完整或相关性不高：
+{context_str}
+
+请基于这些信息给出一个谨慎的回答，明确说明哪些部分是有依据的，哪些部分可能需要更多验证。
+
+回答："""
+
+        try:
+            result = call_llm(
+                self.client, prompt, MODEL,
+                temperature=0.7,
+                max_tokens=1000
+            )
+            return result or "根据现有信息无法确定答案。"
+        except Exception as e:
+            logger.error(f"生成不确定性回答失败: {e}")
+            return "根据现有信息无法确定答案。"
+
+    def _direct_answer(self, query: str, history: list = None) -> str:
+        """直接使用 LLM 回答（无知识库检索）"""
+        messages = [
+            {"role": "system", "content": "你是一个专业的助手，请用中文回答用户的问题。"}
+        ]
+
+        if history:
+            for h in history[-4:]:
+                if h.get("role") in ["user", "assistant"]:
+                    messages.append({"role": h["role"], "content": h.get("content", "")})
+
+        messages.append({"role": "user", "content": query})
+
+        try:
+            result = call_llm(
+                self.client, "", MODEL,
+                temperature=0.7,
+                max_tokens=1500,
+                messages=messages
+            )
+            return result or "抱歉，我无法回答这个问题。"
+        except Exception as e:
+            logger.error(f"直接回答失败: {e}")
+            return f"回答生成失败：{str(e)}"

core/agentic_base.py

@@ -0,0 +1,62 @@
+"""
+Agentic RAG - 基础模块
+
+包含常量、导入和共享配置
+"""
+
+import logging
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+# 尝试导入搜索API配置
+try:
+    from config import SERPER_API_KEY
+    HAS_SERPER = True
+except ImportError:
+    HAS_SERPER = False
+    SERPER_API_KEY = None
+
+# LLM 预算控制
+try:
+    from core.llm_budget import get_budget_controller, should_use_agent, CallType
+    HAS_BUDGET = True
+except ImportError:
+    HAS_BUDGET = False
+    CallType = None
+
+# 语义缓存
+try:
+    from config import SEMANTIC_CACHE_ENABLED, SEMANTIC_CACHE_THRESHOLD
+    HAS_SEMANTIC_CACHE_CONFIG = True
+except ImportError:
+    SEMANTIC_CACHE_ENABLED = False
+    SEMANTIC_CACHE_THRESHOLD = 0.92
+    HAS_SEMANTIC_CACHE_CONFIG = False
+
+try:
+    from core.semantic_cache import SemanticCache, get_semantic_cache
+    HAS_SEMANTIC_CACHE = True
+except ImportError:
+    HAS_SEMANTIC_CACHE = False
+    SemanticCache = None
+
+# LLM 配置
+try:
+    from config import API_KEY, BASE_URL, MODEL
+except ImportError:
+    API_KEY = None
+    BASE_URL = None
+    MODEL = None
+
+# 来源标记
+SOURCE_KB = "知识库"
+SOURCE_WEB = "网络搜索"
+
+# Context Compression 配置
+MAX_CONTEXT_TOKENS = 8000  # 最大上下文 token 数（与生产路径对齐）
+MAX_CONTEXT_COUNT = 20     # 最大上下文数量
+RERANK_THRESHOLD = 0.3    # Rerank 过滤阈值
+
+# Answer Grounding 配置
+MAX_GROUNDING_RETRY = 1   # 幻觉修正最多重试次数

core/agentic_citation.py

@@ -0,0 +1,237 @@
+"""
+Agentic RAG - 引用处理 Mixin
+
+包含来源提取、引用构建、引用附加等方法
+"""
+
+import json
+import logging
+
+from .agentic_base import logger, SOURCE_KB
+
+logger = logging.getLogger(__name__)
+
+
+class CitationMixin:
+    """引用处理方法"""
+
+    def _extract_sources(self, contexts: list) -> list:
+        """提取来源列表，返回结构化定位信息"""
+        source_map = {}
+
+        for c in contexts:
+            meta = c.get('meta', {})
+            source_type = c.get('source_type', '未知')
+
+            if source_type == self.SOURCE_KB:
+                source_key = meta.get('source', '未知')
+                page = meta.get('page')
+                page_end = meta.get('page_end', page)
+                section = meta.get('section', '')
+                doc_type = meta.get('doc_type', 'other')
+                preview = meta.get('preview', '')
+                section_chunk_id = meta.get('section_chunk_id')
+            else:
+                source_key = meta.get('title', meta.get('source', '未知'))
+                page = None
+                page_end = None
+                section = ''
+                doc_type = 'other'
+                preview = ''
+                section_chunk_id = None
+
+            if source_key not in source_map:
+                source_map[source_key] = {
+                    "source": source_key,
+                    "type": source_type,
+                    "doc_type": doc_type,
+                    "count": 0,
+                    "pages": [],
+                    "sections": set(),
+                    "previews": [],
+                    "section_chunk_ids": set()
+                }
+
+            source_map[source_key]["count"] += 1
+
+            if page:
+                page_range = (page, page_end if page_end else page)
+                if page_range not in source_map[source_key]["pages"]:
+                    source_map[source_key]["pages"].append(page_range)
+
+            if section:
+                source_map[source_key]["sections"].add(section)
+
+            if preview and len(source_map[source_key]["previews"]) < 3:
+                if preview not in source_map[source_key]["previews"]:
+                    source_map[source_key]["previews"].append(preview)
+
+            if section_chunk_id:
+                source_map[source_key]["section_chunk_ids"].add(section_chunk_id)
+
+        sources = []
+        for key, info in source_map.items():
+            source_str = info["source"]
+            doc_type = info.get("doc_type", "other")
+            location_parts = []
+
+            if doc_type == 'pdf':
+                if info["pages"]:
+                    valid_pages = [(s, e) for s, e in info["pages"] if s > 1 or e > 1]
+                    if valid_pages or not info["sections"]:
+                        page_strs = []
+                        for start, end in sorted(info["pages"], key=lambda x: x[0]):
+                            if start == end:
+                                page_strs.append(f"第{start}页")
+                            else:
+                                page_strs.append(f"第{start}-{end}页")
+                        location_parts.append(", ".join(page_strs))
+
+                if info["sections"]:
+                    sections_list = sorted(info["sections"])[:3]
+                    sections_str = "、".join(sections_list)
+                    if len(info["sections"]) > 3:
+                        sections_str += f"等{len(info['sections'])}个章节"
+                    location_parts.append(sections_str)
+
+            elif doc_type == 'word':
+                if info["sections"]:
+                    sections_list = sorted(info["sections"])[:3]
+                    sections_str = "、".join(sections_list)
+                    if len(info["sections"]) > 3:
+                        sections_str += f"等{len(info['sections'])}个章节"
+                    location_parts.append(sections_str)
+
+                if info.get("section_chunk_ids"):
+                    chunk_ids = sorted(info["section_chunk_ids"])[:5]
+                    if chunk_ids:
+                        chunk_str = f"第{chunk_ids[0]}"
+                        if len(chunk_ids) > 1:
+                            chunk_str = f"第{chunk_ids[0]}-{chunk_ids[-1]}段"
+                        location_parts.append(chunk_str)
+
+            elif doc_type == 'excel':
+                if info["sections"]:
+                    sections_list = sorted(info["sections"])[:3]
+                    sections_str = "、".join(sections_list)
+                    location_parts.append(sections_str)
+
+            else:
+                if info["pages"]:
+                    valid_pages = [(s, e) for s, e in info["pages"] if s > 1 or e > 1]
+                    if valid_pages or not info["sections"]:
+                        page_strs = []
+                        for start, end in sorted(info["pages"], key=lambda x: x[0]):
+                            if start == end:
+                                page_strs.append(f"第{start}页")
+                            else:
+                                page_strs.append(f"第{start}-{end}页")
+                        location_parts.append(", ".join(page_strs))
+
+                if info["sections"]:
+                    sections_list = sorted(info["sections"])[:3]
+                    sections_str = "、".join(sections_list)
+                    if len(info["sections"]) > 3:
+                        sections_str += f"等{len(info['sections'])}个章节"
+                    location_parts.append(sections_str)
+
+            if location_parts:
+                source_str = f"{source_str} ({' | '.join(location_parts)})"
+
+            sources.append({
+                "source": source_str,
+                "type": info["type"],
+                "count": info["count"],
+                "doc_type": doc_type,
+                "previews": info.get("previews", []),
+                "section_chunk_ids": sorted(info.get("section_chunk_ids", []))[:5]
+            })
+
+        return sources
+
+    def _build_citation(self, meta: dict) -> dict:
+        """根据文档类型构建定位信息"""
+        # 从 chunk_id 中提取全局切片序号（格式: "filename_N"）
+        chunk_id_raw = meta.get('chunk_id', '')
+        chunk_index = None
+        if chunk_id_raw and '_' in str(chunk_id_raw):
+            try:
+                chunk_index = int(str(chunk_id_raw).rsplit('_', 1)[-1])
+            except (ValueError, IndexError):
+                chunk_index = meta.get('chunk_index')
+        else:
+            chunk_index = meta.get('chunk_index')
+
+        citation = {
+            "chunk_id": chunk_id_raw,
+            "chunk_index": chunk_index,  # 全局切片序号，用于前端文档预览跳转
+            "source": meta.get('source', ''),
+            "collection": meta.get('_collection', ''),  # 所属向量库，用于前端文档预览跳转
+            "doc_type": meta.get('doc_type', 'other'),
+            "section": meta.get('section', ''),
+            "preview": meta.get('preview', ''),
+            "content": meta.get('preview', ''),  # 初始用 preview，_attach_citations 中会用完整内容覆盖
+            "chunk_type": meta.get('chunk_type', 'text'),
+        }
+
+        doc_type = meta.get('doc_type', 'other')
+
+        if doc_type == 'pdf':
+            bbox_raw = meta.get('bbox')
+            bbox = None
+            if bbox_raw:
+                try:
+                    bbox = json.loads(bbox_raw) if isinstance(bbox_raw, str) else bbox_raw
+                except (json.JSONDecodeError, TypeError):
+                    bbox = bbox_raw
+
+            citation.update({
+                "page": meta.get('page'),
+                "page_end": meta.get('page_end'),
+                "bbox": bbox,
+                "bbox_mode": meta.get('bbox_mode'),
+            })
+        elif doc_type == 'word':
+            citation.update({
+                "section_chunk_id": meta.get('section_chunk_id'),
+            })
+        elif doc_type == 'excel':
+            citation.update({
+                "page": meta.get('page'),
+            })
+        else:
+            bbox_raw = meta.get('bbox')
+            bbox = None
+            if bbox_raw:
+                try:
+                    bbox = json.loads(bbox_raw) if isinstance(bbox_raw, str) else bbox_raw
+                except (json.JSONDecodeError, TypeError):
+                    bbox = bbox_raw
+
+            citation.update({
+                "page": meta.get('page'),
+                "page_end": meta.get('page_end'),
+                "bbox": bbox,
+                "bbox_mode": meta.get('bbox_mode'),
+            })
+
+        return citation
+
+    def _attach_citations(self, answer: str, contexts: list) -> dict:
+        """将引用信息附加到答案"""
+        citations = []
+
+        for c in contexts:
+            meta = c.get('meta', {})
+            full_content = c.get('doc', '')
+            citation = self._build_citation(meta)
+            # 用上下文中的完整文档内容覆盖 content 字段
+            if full_content:
+                citation['content'] = full_content[:300]
+            citations.append(citation)
+
+        return {
+            "answer": answer,
+            "citations": citations,
+            "sources": self._extract_sources(contexts)
+        }

core/agentic_context.py

@@ -0,0 +1,111 @@
+"""
+Agentic RAG - 上下文处理 Mixin
+
+包含上下文压缩、去重、Token 控制等方法
+"""
+
+import logging
+
+from .agentic_base import logger, MAX_CONTEXT_TOKENS, RERANK_THRESHOLD
+
+logger = logging.getLogger(__name__)
+
+
+class ContextMixin:
+    """上下文处理方法"""
+
+    def _compress_contexts(self, query: str, contexts: list) -> list:
+        """上下文压缩三步走：Rerank 过滤 → 去重 → Token 控制"""
+        if not contexts:
+            return contexts
+
+        # Step 1: Rerank 过滤
+        filtered = self._rerank_filter(contexts)
+
+        # Step 2: 去重
+        deduped = self._deduplicate_contexts(filtered)
+
+        # Step 3: Token 控制
+        result = self._truncate_to_tokens(deduped, self.MAX_CONTEXT_TOKENS)
+
+        return result
+
+    def _rerank_filter(self, contexts: list) -> list:
+        """Rerank 过滤 - 保留相关性分数 >= 阈值的上下文"""
+        scored_contexts = [c for c in contexts if c.get('score') is not None]
+
+        if scored_contexts:
+            filtered = [c for c in contexts if c.get('score', 0) >= self.RERANK_THRESHOLD]
+            return filtered if filtered else contexts
+
+        return contexts
+
+    def _deduplicate_contexts(self, contexts: list, threshold: float = 0.9) -> list:
+        """去重 - 基于内容相似度去重"""
+        if len(contexts) <= 1:
+            return contexts
+
+        result = []
+        seen_keys = set()
+
+        for c in contexts:
+            doc = c.get('doc', '')
+            key = doc[:100] if doc else ''
+
+            meta = c.get('meta', {})
+            source = meta.get('source', '')
+            page = meta.get('page', '')
+
+            composite_key = f"{source}|{page}|{key}"
+
+            if composite_key not in seen_keys:
+                seen_keys.add(composite_key)
+                result.append(c)
+
+        return result
+
+    def _truncate_to_tokens(self, contexts: list, max_tokens: int) -> list:
+        """Token 控制 - 截断到最大 Token 数"""
+        result = []
+        total_tokens = 0
+
+        for c in contexts:
+            doc = c.get('doc', '')
+            # 简单估算：1 token ≈ 1.5 中文字符
+            tokens = len(doc) // 1.5
+
+            if total_tokens + tokens <= max_tokens:
+                result.append(c)
+                total_tokens += tokens
+            else:
+                break
+
+        return result
+
+    def _merge_and_deduplicate(self, old_contexts: list, new_contexts: list) -> list:
+        """合并并去重两个上下文列表"""
+        result = list(old_contexts)
+        seen_keys = set()
+
+        # 记录已有上下文的 key
+        for c in old_contexts:
+            doc = c.get('doc', '')
+            key = doc[:100] if doc else ''
+            meta = c.get('meta', {})
+            source = meta.get('source', '')
+            composite_key = f"{source}|{key}"
+            seen_keys.add(composite_key)
+
+        # 添加新上下文（去重）
+        for c in new_contexts:
+            doc = c.get('doc', '')
+            key = doc[:100] if doc else ''
+            meta = c.get('meta', {})
+            source = meta.get('source', '')
+            composite_key = f"{source}|{key}"
+
+            if composite_key not in seen_keys:
+                seen_keys.add(composite_key)
+                result.append(c)
+
+        return result

core/agentic_media.py

@@ -0,0 +1,200 @@
+"""
+Agentic RAG - 富媒体处理 Mixin
+
+包含图表查找、图片提取、富媒体附加等方法
+"""
+
+import re
+import json
+import logging
+
+from .agentic_base import logger
+
+logger = logging.getLogger(__name__)
+
+
+class RichMediaMixin:
+    """富媒体处理方法"""
+
+    def _find_figure(self, query: str, contexts: list, source: str = None) -> dict:
+        """精确查找图表，带 fallback"""
+        patterns = [
+            r'图\s*(\d+[\.\-]\d+)',
+            r'Fig\.?\s*(\d+[\.\-]\d+)',
+            r'Figure\s*(\d+[\.\-]\d+)',
+        ]
+
+        target_figure = None
+        for pattern in patterns:
+            match = re.search(pattern, query, re.IGNORECASE)
+            if match:
+                target_figure = match.group(1).replace('-', '.')
+                break
+
+        if not target_figure:
+            return {"found": False}
+
+        # 从 contexts 中查找
+        for ctx in contexts:
+            meta = ctx.get('meta', {})
+            fig_num = meta.get('figure_number', '')
+            if fig_num == target_figure:
+                if not source or meta.get('source') == source:
+                    return {
+                        "found": True,
+                        "chunk_id": meta.get('chunk_id'),
+                        "source": meta.get('source'),
+                        "page": meta.get('page'),
+                        "caption": meta.get('caption'),
+                        "image_path": meta.get('image_path'),
+                    }
+
+        # Fallback: 直接查向量库
+        try:
+            from knowledge.manager import get_kb_manager
+            kb_mgr = get_kb_manager()
+            coll = kb_mgr.get_collection('public_kb')
+
+            if coll:
+                where_conditions = [{'chunk_type': {'$in': ['image', 'chart']}}]
+                if source:
+                    where_conditions.append({'source': source})
+
+                result = coll.get(
+                    where={'$and': where_conditions} if len(where_conditions) > 1 else where_conditions[0],
+                    include=['metadatas', 'documents']
+                )
+
+                for meta, doc in zip(result.get('metadatas', []), result.get('documents', [])):
+                    if meta.get('figure_number') == target_figure:
+                        return {
+                            "found": True,
+                            "chunk_id": meta.get('chunk_id'),
+                            "source": meta.get('source'),
+                            "page": meta.get('page'),
+                            "caption": meta.get('caption'),
+                            "image_path": meta.get('image_path'),
+                        }
+                    caption = meta.get('caption', '') or (doc if doc else '')
+                    if f"图{target_figure}" in caption or f"图 {target_figure}" in caption:
+                        return {
+                            "found": True,
+                            "chunk_id": meta.get('chunk_id'),
+                            "source": meta.get('source'),
+                            "page": meta.get('page'),
+                            "caption": meta.get('caption'),
+                            "image_path": meta.get('image_path'),
+                        }
+        except Exception as e:
+            logger.warning(f"_find_figure fallback 查询失败: {e}")
+
+        return {"found": False}
+
+    def _get_images_for_source(self, source: str, collections: list = None) -> list:
+        """直接从向量库获取指定文件的所有图片"""
+        try:
+            from knowledge.manager import get_kb_manager
+            kb_mgr = get_kb_manager()
+        except ImportError:
+            return []
+
+        images = []
+        seen_ids = set()
+
+        target_collections = collections or ['public_kb']
+
+        for kb_name in target_collections:
+            try:
+                coll = kb_mgr.get_collection(kb_name)
+                if not coll:
+                    continue
+
+                result = coll.get(
+                    where={'source': source},
+                    include=['metadatas']
+                )
+
+                for meta in result.get('metadatas', []):
+                    images_json = meta.get('images_json')
+                    if images_json:
+                        try:
+                            imgs = json.loads(images_json)
+                            for img in imgs:
+                                img_id = img.get('id')
+                                if img_id and img_id not in seen_ids:
+                                    seen_ids.add(img_id)
+                                    images.append({
+                                        "id": img_id,
+                                        "caption": img.get("caption", ""),
+                                        "url": f"/images/{img_id}",
+                                        "page": img.get("page") or meta.get("page"),
+                                        "source": source,
+                                        "width": img.get("width"),
+                                        "height": img.get("height")
+                                    })
+                        except (json.JSONDecodeError, TypeError):
+                            pass
+            except Exception as e:
+                logger.warning(f"从 {kb_name} 获取图片失败: {e}")
+                continue
+
+        return images
+
+    def _extract_rich_media(self, contexts: list, sources_filter: list = None, max_images: int = 10,
+                           max_tables: int = 5) -> dict:
+        """从检索结果中提取富媒体（图片、表格）"""
+        images = []
+        tables = []
+        seen_image_ids = set()
+        seen_table_ids = set()
+
+        for ctx in contexts:
+            meta = ctx.get('meta', {})
+            source = meta.get('source', '')
+
+            # 过滤来源
+            if sources_filter and source not in sources_filter:
+                continue
+
+            # 提取图片
+            images_json = meta.get('images_json')
+            if images_json:
+                try:
+                    imgs = json.loads(images_json)
+                    for img in imgs:
+                        img_id = img.get('id')
+                        if img_id and img_id not in seen_image_ids:
+                            seen_image_ids.add(img_id)
+                            images.append({
+                                "id": img_id,
+                                "caption": img.get("caption", ""),
+                                "url": f"/images/{img_id}",
+                                "page": img.get("page") or meta.get("page"),
+                                "source": source,
+                                "type": img.get("type", "image")
+                            })
+                except (json.JSONDecodeError, TypeError):
+                    pass
+
+            # 提取表格
+            table_json = meta.get('table_json')
+            if table_json:
+                try:
+                    tbl = json.loads(table_json)
+                    tbl_id = tbl.get('id') or meta.get('chunk_id')
+                    if tbl_id and tbl_id not in seen_table_ids:
+                        seen_table_ids.add(tbl_id)
+                        tables.append({
+                            "id": tbl_id,
+                            "caption": tbl.get("caption", ""),
+                            "markdown": tbl.get("markdown", ""),
+                            "page": meta.get("page"),
+                            "source": source
+                        })
+                except (json.JSONDecodeError, TypeError):
+                    pass
+
+        return {
+            "images": images[:max_images],
+            "tables": tables[:max_tables]
+        }

core/agentic_meta.py

@@ -0,0 +1,133 @@
+"""
+Agentic RAG - 元问题处理 Mixin
+
+包含元问题判断和知识库元数据回答方法
+"""
+
+import logging
+
+from .agentic_base import logger
+
+logger = logging.getLogger(__name__)
+
+
+class MetaQuestionMixin:
+    """元问题处理方法"""
+
+    def _is_meta_question(self, query: str) -> bool:
+        """判断是否为元问题（关于知识库本身的问题）"""
+        meta_patterns = [
+            "有哪些文件", "什么文件", "哪些文件", "文件列表", "文件目录",
+            "可以查看", "能查看", "有权限查看", "权限查看",
+            "能访问", "可以访问", "有权限访问",
+            "我的权限", "用户权限", "查看权限", "访问权限",
+            "权限能", "权限可以", "有什么权限", "有哪些权限",
+            "我能看", "我可以看", "我能查", "我可以查",
+            "能看到什么", "能查到什么", "可以看什么", "可以查什么",
+            "知识库有哪些", "库里有", "文档有哪些", "有哪些文档",
+            "有什么文档", "有什么文件", "包含什么", "包含哪些",
+            "你知道什么", "你都知道", "你能回答什么",
+            "系统里有什么", "库里有什么",
+            "public_kb", "dept_tech", "dept_hr", "dept_finance", "dept_operation",
+            "kb里", "向量库", "有哪些库", "库列表", "kb有哪些"
+        ]
+        query_lower = query.lower()
+        return any(kw in query_lower for kw in meta_patterns)
+
+    def _answer_meta_question(self, query: str, allowed_levels: list = None,
+                              role: str = None, department: str = None) -> str:
+        """回答元问题（关于知识库本身的问题）"""
+        try:
+            source_map = {}
+
+            try:
+                from knowledge.manager import get_kb_manager
+                from auth.gateway import get_accessible_collections as _get_accessible
+
+                kb_mgr = get_kb_manager()
+                accessible = _get_accessible(role or 'user', department or '', 'read')
+
+                for kb_name in accessible:
+                    coll = kb_mgr.get_collection(kb_name)
+                    if not coll:
+                        continue
+                    try:
+                        result = coll.get(include=['metadatas'])
+                    except Exception as e:
+                        logger.debug(f"获取{kb_name}元数据失败: {e}")
+                        continue
+
+                    for meta in result.get('metadatas', []):
+                        source = meta.get('source', '未知')
+                        level = meta.get('security_level', 'public')
+                        page = meta.get('page')
+
+                        if source not in source_map:
+                            source_map[source] = {
+                                'count': 0, 'levels': set(),
+                                'pages': set(), 'collections': set()
+                            }
+
+                        source_map[source]['count'] += 1
+                        source_map[source]['levels'].add(level)
+                        source_map[source]['collections'].add(kb_name)
+                        if page:
+                            source_map[source]['pages'].add(page)
+
+            except ImportError:
+                from core.engine import get_engine
+                all_docs = get_engine().collection.get(include=['metadatas'])
+                for meta in all_docs.get('metadatas', []):
+                    source = meta.get('source', '未知')
+                    level = meta.get('security_level', 'public')
+                    page = meta.get('page')
+
+                    if source not in source_map:
+                        source_map[source] = {
+                            'count': 0, 'levels': set(),
+                            'pages': set(), 'collections': set()
+                        }
+
+                    source_map[source]['count'] += 1
+                    source_map[source]['levels'].add(level)
+                    if page:
+                        source_map[source]['pages'].add(page)
+
+            # 根据安全级别过滤
+            if allowed_levels:
+                allowed_set = set(allowed_levels)
+                filtered_sources = {}
+                for source, info in source_map.items():
+                    if info['levels'] & allowed_set:
+                        filtered_sources[source] = info
+                source_map = filtered_sources
+
+            if not source_map:
+                return "抱歉，您当前没有权限查看任何文档，或者知识库为空。"
+
+            sorted_sources = sorted(source_map.items(), key=lambda x: x[1]['count'], reverse=True)
+
+            answer_parts = [f"📚 **知识库文档列表**（共 {len(sorted_sources)} 个文档）\n"]
+
+            for i, (source, info) in enumerate(sorted_sources, 1):
+                colls = info.get('collections', set())
+                coll_str = f"，所属: {', '.join(sorted(colls))}" if colls else ""
+                pages_str = ''
+                if info['pages']:
+                    pages_list = sorted(info['pages'])
+                    if len(pages_list) <= 5:
+                        pages_str = f"，页码: {', '.join(map(str, pages_list))}"
+                    else:
+                        pages_str = f"，共 {len(info['pages'])} 页"
+
+                answer_parts.append(f"{i}. **{source}** ({info['count']} 条片段{coll_str}{pages_str})")
+
+            answer_parts.append(f"\n**总计**: {sum(s[1]['count'] for s in sorted_sources)} 条知识片段")
+            answer_parts.append(f"\n**您的权限级别**: {', '.join(allowed_levels) if allowed_levels else '全部'}")
+
+            answer_parts.append("\n\n💡 **提示**: 您可以直接提问关于这些文档内容的问题。")
+
+            return '\n'.join(answer_parts)
+
+        except Exception as e:
+            return f"获取文档列表时出错: {str(e)}\n\n您可以直接提问，我会尝试从知识库中检索相关信息。"

core/agentic_quality.py

@@ -0,0 +1,137 @@
+"""
+Agentic RAG - 质量评估 Mixin
+
+包含置信度门控、质量评估、推理反思等方法
+"""
+
+import logging
+
+from .agentic_base import logger
+
+logger = logging.getLogger(__name__)
+
+
+class QualityMixin:
+    """质量评估方法"""
+
+    def _check_confidence_gate(self, query: str, docs: list, verbose: bool = True,
+                               precomputed_scores: list = None):
+        """检查置信度门控
+
+        Args:
+            query: 用户查询
+            docs: 文档列表
+            verbose: 是否详细输出
+            precomputed_scores: 预计算的 Rerank 分数（可选，避免重复推理）
+        """
+        if not self.confidence_gate:
+            return {"passed": True, "reason": "no_gate"}
+
+        try:
+            result = self.confidence_gate.evaluate(query, docs,
+                                                    precomputed_scores=precomputed_scores)
+            return result
+        except Exception as e:
+            logger.warning(f"置信度门控检查失败: {e}")
+            return {"passed": True, "reason": "error"}
+
+    def _assess_quality(self, query: str, docs: list, metas: list = None,
+                       verbose: bool = True) -> dict:
+        """多维质量评估"""
+        if not self.quality_assessor:
+            return {"overall_score": 0.5, "dimensions": {}}
+
+        try:
+            result = self.quality_assessor.assess(query, docs, metas)
+            return result
+        except Exception as e:
+            logger.warning(f"质量评估失败: {e}")
+            return {"overall_score": 0.5, "dimensions": {}}
+
+    def _reflect_on_answer(self, query: str, answer: str, contexts: list,
+                          verbose: bool = True) -> dict:
+        """推理反思"""
+        if not self.reasoning_reflector:
+            return {"needs_reflection": False, "issues": []}
+
+        try:
+            result = self.reasoning_reflector.reflect(query, answer, contexts)
+            return result
+        except Exception as e:
+            logger.warning(f"推理反思失败: {e}")
+            return {"needs_reflection": False, "issues": []}
+
+    def _think(self, original_query: str, current_query: str,
+               iteration: int, contexts: list, verbose: bool = True) -> dict:
+        """
+        Agent 思考：决定下一步行动
+
+        Returns:
+            {
+                "action": "answer" | "rewrite" | "search_web" | "decompose",
+                "reason": "...",
+                "rewrite_query": "..."  # 如果 action == "rewrite"
+            }
+        """
+        from core.llm_utils import call_llm, parse_json_from_response
+        from .agentic_base import MODEL
+
+        # 构建思考提示
+        context_summary = ""
+        if contexts:
+            for i, ctx in enumerate(contexts[:3], 1):
+                meta = ctx.get('meta', {})
+                source = meta.get('source', '未知')
+                doc_preview = ctx.get('doc', '')[:100]
+                context_summary += f"{i}. [{source}] {doc_preview}...\n"
+
+        prompt = f"""你是一个 RAG 系统的决策 Agent，需要判断下一步行动。
+
+【原始问题】
+{original_query}
+
+【当前问题】
+{current_query}
+
+【迭代轮次】
+{iteration} / {self.max_iterations}
+
+【已检索到的上下文】
+{context_summary if context_summary else "（无）"}
+
+【可选行动】
+1. answer - 已有足够信息，可以回答
+2. rewrite - 查询不够清晰，需要重写
+3. search_web - 知识库信息不足，需要网络搜索
+4. decompose - 问题太复杂，需要分解
+
+【决策要求】
+- 如果上下文足够回答问题，选择 answer
+- 如果上下文不足且迭代未超限，选择 search_web 或 rewrite
+- 返回 JSON 格式
+
+请决策："""
+
+        try:
+            result = call_llm(
+                self.client, prompt, MODEL,
+                temperature=0.3,
+                max_tokens=200
+            )
+
+            decision = parse_json_from_response(result) if result else {}
+
+            # 默认决策
+            if not decision or "action" not in decision:
+                if contexts and len(contexts) >= 2:
+                    decision = {"action": "answer", "reason": "有足够上下文"}
+                else:
+                    decision = {"action": "rewrite", "reason": "上下文不足"}
+
+            return decision
+
+        except Exception as e:
+            logger.warning(f"Agent 思考失败: {e}")
+            if contexts:
+                return {"action": "answer", "reason": "默认回答"}
+            return {"action": "rewrite", "reason": "默认重写"}

core/agentic_query.py

@@ -0,0 +1,271 @@
+"""
+Agentic RAG - 查询重写 Mixin
+
+包含查询改写、实体补全、专业术语映射等方法
+"""
+
+import re
+import logging
+
+from .agentic_base import logger, MODEL
+
+logger = logging.getLogger(__name__)
+
+
+class QueryRewriteMixin:
+    """查询重写方法"""
+
+    def _rewrite_query(self, query: str, history: list = None,
+                       strategy: str = "professional") -> str:
+        """
+        增强版查询重写：将口语化表达转为专业术语
+
+        Args:
+            query: 原始查询
+            history: 对话历史（用于实体补全）
+            strategy: 重写策略
+                - professional: 口语化→专业术语
+                - expand: 扩展关键词
+                - clarify: 消歧义
+                - entity: 实体补全
+
+        Returns:
+            str: 重写后的查询
+        """
+        # 尝试多种策略组合
+        rewritten = query
+
+        # 策略1: 口语化→专业术语映射
+        if strategy in ["professional", "all"]:
+            rewritten = self._apply_professional_mapping(rewritten)
+
+        # 策略2: 实体补全（利用对话历史）
+        if strategy in ["entity", "all"] and history:
+            rewritten = self._complete_entities(rewritten, history)
+
+        # 策略3: LLM 深度重写（仅在需要时调用）
+        if strategy in ["professional", "all"]:
+            llm_rewritten = self._llm_rewrite(rewritten)
+            if llm_rewritten and len(llm_rewritten) > len(rewritten) * 0.5:
+                rewritten = llm_rewritten
+
+        return rewritten
+
+    def _apply_professional_mapping(self, query: str) -> str:
+        """应用口语化→专业术语映射"""
+        TERM_MAPPING = {
+            "报销": "差旅报销 费用报销 报销审批",
+            "请假": "休假申请 请假审批 考勤管理",
+            "加班": "加班申请 工时管理 加班审批",
+            "工资": "薪酬管理 工资发放 薪资结构",
+            "合同": "合同管理 合同签署 合同审批",
+            "流程": "审批流程 业务流程 工作流",
+            "制度": "管理制度 规章制度 企业规范",
+            "规定": "管理规定 制度规定 政策要求",
+            "几天": "时限 审批时限 办理时限",
+            "多久": "处理时效 审批周期 办理周期",
+            "多少": "标准 额度 限额 标准",
+            "能不能": "是否允许 是否可以 权限",
+            "人事": "人力资源 HR 人力部门",
+            "财务": "财务部 财务部门 财务管理",
+            "技术": "技术部 研发部 IT部门",
+        }
+
+        result = query
+        for colloquial, professional in TERM_MAPPING.items():
+            if colloquial in query:
+                result = result.replace(colloquial, f"{colloquial} {professional.split()[0]}")
+
+        return result
+
+    def _complete_entities(self, query: str, history: list) -> str:
+        """实体补全：利用对话历史补充缺失的实体"""
+        if not history:
+            return query
+
+        # 图片指代识别
+        image_reference = self._detect_image_reference(query, history)
+        if image_reference:
+            return image_reference
+
+        # 获取最近用户消息
+        last_user_msg = None
+        for msg in reversed(history):
+            if msg.get("role") == "user":
+                last_user_msg = msg.get("content", "")
+                break
+
+        if not last_user_msg:
+            return query
+
+        # 检查当前查询是否缺少主语
+        BUSINESS_KEYWORDS = ["报销", "出差", "请假", "工资", "合同", "审批", "流程",
+                           "制度", "规定", "标准", "金额", "时间"]
+
+        has_subject = any(kw in query for kw in BUSINESS_KEYWORDS)
+
+        if not has_subject:
+            try:
+                import jieba
+                entities = []
+                for word in jieba.cut(last_user_msg):
+                    word = word.strip()
+                    if len(word) >= 2 and any(kw in word for kw in BUSINESS_KEYWORDS):
+                        entities.append(word)
+
+                if entities:
+                    return f"{entities[0]} {query}"
+            except ImportError:
+                pass
+
+        return query
+
+    def _detect_image_reference(self, query: str, history: list) -> str:
+        """检测图片指代查询并重写"""
+        IMAGE_REFERENCE_PATTERNS = [
+            r'这[张些]图片', r'那[张些]图片', r'上面的图片', r'刚才的图片',
+            r'这[张些]图', r'那[张些]图', r'上面的图', r'刚才的图',
+            r'解释一下这[张些]图', r'说明一下这[张些]图',
+            r'这[张些]是什么图', r'图[里内]是什么', r'图片[里内]是什么',
+        ]
+
+        is_image_reference = False
+        for pattern in IMAGE_REFERENCE_PATTERNS:
+            if re.search(pattern, query):
+                is_image_reference = True
+                break
+
+        if not is_image_reference:
+            return ""
+
+        last_images = []
+        for msg in reversed(history):
+            if msg.get("role") == "assistant":
+                metadata = msg.get("metadata", {})
+                if isinstance(metadata, dict):
+                    images = metadata.get("images", [])
+                    if images:
+                        for img in images[:5]:
+                            if isinstance(img, dict):
+                                desc = img.get("description", "")
+                                img_type = img.get("type", "图片")
+                                if desc:
+                                    last_images.append(f"{img_type}：{desc}")
+                            elif isinstance(img, str):
+                                last_images.append(f"图片：{img}")
+
+                if not last_images:
+                    content = msg.get("content", "")
+                    if "图片" in content or "图表" in content or "图" in content:
+                        sentences = content.split("。")
+                        for sentence in sentences:
+                            if "图片" in sentence or "图表" in sentence:
+                                last_images.append(sentence.strip())
+
+                if last_images:
+                    break
+
+        if last_images:
+            image_context = " ".join(last_images[:3])
+            question_intent = re.sub(
+                r'这[张些]图片?|那[张些]图片?|上面的图片?|刚才的图片?|解释一下|说明一下',
+                '', query
+            ).strip()
+
+            if question_intent:
+                return f"{image_context} {question_intent}"
+            else:
+                return f"详细解释：{image_context}"
+
+        return query
+
+    def _extract_image_context_from_history(self, history: list) -> str:
+        """从对话历史中提取图片上下文"""
+        if not history:
+            return ""
+
+        for msg in reversed(history):
+            if msg.get("role") == "assistant":
+                metadata = msg.get("metadata", {})
+                images = metadata.get("images", [])
+                content = msg.get("content", "")
+
+                image_descriptions = []
+
+                if images:
+                    for i, img in enumerate(images[:5], 1):
+                        if isinstance(img, dict):
+                            desc = img.get("description", "")
+                            img_type = img.get("type", "图片")
+                            source = img.get("source", "")
+                            page = img.get("page", "")
+
+                            img_info = f"图片{i}：{img_type}"
+                            if desc:
+                                img_info += f"，描述：{desc}"
+                            if source:
+                                img_info += f"，来源：{source}"
+                            if page:
+                                img_info += f"，第{page}页"
+                            image_descriptions.append(img_info)
+
+                if not image_descriptions:
+                    if "图片" in content or "图表" in content:
+                        sentences = content.split("。")
+                        for sentence in sentences:
+                            if "图片" in sentence or "图表" in sentence:
+                                image_descriptions.append(sentence.strip())
+                                if len(image_descriptions) >= 3:
+                                    break
+
+                if image_descriptions:
+                    return "\n".join(image_descriptions)
+
+        return ""
+
+    def _answer_image_reference(self, enhanced_query: str, history: list) -> str:
+        """回答图片引用问题"""
+        from core.llm_utils import call_llm
+
+        messages = [
+            {"role": "system", "content": "你是一个专业的助手，请根据提供的图片信息回答用户的问题。"}
+        ]
+
+        for h in history[-4:]:
+            if h.get("role") in ["user", "assistant"]:
+                messages.append({"role": h["role"], "content": h.get("content", "")})
+
+        messages.append({"role": "user", "content": enhanced_query})
+
+        try:
+            result = call_llm(
+                self.client, "", MODEL,
+                temperature=0.3,
+                max_tokens=1000,
+                messages=messages
+            )
+            return result or ""
+        except Exception as e:
+            logger.error(f"图片引用回答失败: {e}")
+            return f"抱歉，回答图片问题时出现错误：{str(e)}"
+
+    def _llm_rewrite(self, query: str) -> str:
+        """LLM 深度重写查询"""
+        from core.llm_utils import call_llm
+
+        prompt = f"""请将以下用户问题改写为更专业、更清晰的表达，保持原意不变。
+
+原问题：{query}
+
+改写后的问题："""
+
+        try:
+            rewritten = call_llm(
+                self.client, prompt, MODEL,
+                temperature=0.3,
+                max_tokens=100
+            )
+            return rewritten.strip() if rewritten else query
+        except Exception as e:
+            logger.warning(f"LLM 重写失败: {e}")
+            return query

core/agentic_search.py

@@ -0,0 +1,152 @@
+"""
+Agentic RAG - 检索 Mixin
+
+包含知识库检索、网络搜索等方法
+"""
+
+import json
+import logging
+import requests
+
+from .agentic_base import (
+    logger, HAS_SERPER, SERPER_API_KEY,
+    SOURCE_KB, SOURCE_WEB
+)
+
+logger = logging.getLogger(__name__)
+
+
+class SearchMixin:
+    """检索功能方法"""
+
+    def _web_search(self, query: str, top_k: int = 5) -> list:
+        """网络搜索（使用Serper API）"""
+        if not HAS_SERPER:
+            return []
+
+        try:
+            url = "https://google.serper.dev/search"
+            payload = json.dumps({
+                "q": query,
+                "gl": "cn",
+                "hl": "zh-cn",
+                "num": top_k
+            })
+            headers = {
+                'X-API-KEY': SERPER_API_KEY,
+                'Content-Type': 'application/json'
+            }
+
+            response = requests.post(url, headers=headers, data=payload, timeout=10)
+            response.raise_for_status()
+            data = response.json()
+
+            results = []
+            for item in data.get('organic', [])[:top_k]:
+                results.append({
+                    'title': item.get('title', ''),
+                    'link': item.get('link', ''),
+                    'snippet': item.get('snippet', ''),
+                    'date': item.get('date', '')
+                })
+
+            return results
+
+        except Exception as e:
+            logger.warning(f"网络搜索失败: {e}")
+            return []
+
+    def _should_web_search(self, query: str) -> bool:
+        """判断是否需要网络搜索"""
+        realtime_keywords = [
+            "今天", "最新", "今日", "当前", "现在",
+            "天气", "新闻", "股价", "行情", "汇率",
+            "最近", "近期", "这周", "本月", "今年",
+            "实时", "动态", "热点", "发生"
+        ]
+
+        query_lower = query.lower()
+        return any(kw in query_lower for kw in realtime_keywords)
+
+    def _web_search_flow(self, query: str, log_trace: list, emit_log, verbose: bool,
+                         allowed_levels: list = None) -> list:
+        """
+        网络搜索流程
+
+        Args:
+            query: 查询
+            log_trace: 日志追踪列表
+            emit_log: 日志发射函数
+            verbose: 是否详细输出
+            allowed_levels: 允许的安全级别
+
+        Returns:
+            网络搜索结果列表
+        """
+        if not self.enable_web_search or not HAS_SERPER:
+            return []
+
+        if emit_log:
+            emit_log("🌐 触发网络搜索...")
+
+        web_results = self._web_search(query, top_k=5)
+
+        if not web_results:
+            if emit_log:
+                emit_log("⚠️ 网络搜索未返回结果")
+            return []
+
+        # 转换为统一上下文格式
+        web_contexts = []
+        for item in web_results:
+            web_contexts.append({
+                'doc': f"{item.get('title', '')}\n{item.get('snippet', '')}",
+                'meta': {
+                    'source': self.SOURCE_WEB,
+                    'link': item.get('link', ''),
+                    'date': item.get('date', '')
+                },
+                'source_type': self.SOURCE_WEB,
+                'query': query
+            })
+
+        log_trace.append({
+            'phase': 'web_search',
+            'query': query,
+            'results_count': len(web_contexts)
+        })
+
+        if emit_log:
+            emit_log(f"✅ 网络搜索返回 {len(web_contexts)} 条结果")
+
+        return web_contexts
+
+    def _is_kb_result_sufficient(self, query: str, docs: list) -> bool:
+        """判断知识库检索结果是否充分"""
+        if not docs:
+            return False
+
+        # 结果数量检查
+        if len(docs) >= 3:
+            # 至少3条结果，检查相关性
+            high_rel_count = 0
+            for doc in docs:
+                score = doc.get('score', 0) or doc.get('distance', 1)
+                # cosine 距离转相似度
+                if isinstance(score, (int, float)):
+                    sim = 1 - score if score <= 1 else score
+                    if sim >= 0.6:
+                        high_rel_count += 1
+
+            if high_rel_count >= 2:
+                return True
+
+        # 有高质量结果
+        for doc in docs[:2]:
+            score = doc.get('score', 0) or doc.get('distance', 1)
+            if isinstance(score, (int, float)):
+                sim = 1 - score if score <= 1 else score
+                if sim >= 0.8:
+                    return True
+
+        return False

core/bm25_index.py

@@ -0,0 +1,139 @@
+"""
+BM25 关键词检索索引
+
+使用 rank_bm25 + jieba 分词实现中文关键词检索。
+支持索引的序列化/反序列化。
+
+使用方式：
+    from core.bm25_index import BM25Index
+
+    index = BM25Index()
+    index.add_documents(ids, documents, metadatas)
+    results = index.search("查询内容", top_k=5)
+"""
+
+import os
+import pickle
+import numpy as np
+from rank_bm25 import BM25Okapi
+import jieba
+import logging
+from core.constants import get_empty_result
+
+logger = logging.getLogger(__name__)
+
+
+class BM25Index:
+    """BM25索引管理器，用于关键词检索"""
+
+    def __init__(self):
+        self.bm25 = None
+        self.documents = []  # 原始文档
+        self.metadatas = []  # 元数据
+        self.ids = []  # 文档ID
+
+    def tokenize(self, text):
+        """中文分词"""
+        return list(jieba.cut(text))
+
+    def add_documents(self, ids, documents, metadatas):
+        """添加文档到索引"""
+        self.ids = ids
+        self.documents = documents
+        self.metadatas = metadatas
+
+        # 分词并构建BM25索引
+        tokenized_docs = [self.tokenize(doc) for doc in documents]
+        self.bm25 = BM25Okapi(tokenized_docs)
+
+    def search(self, query, top_k=10):
+        """BM25检索"""
+        if not self.bm25:
+            return get_empty_result()
+
+        tokenized_query = self.tokenize(query)
+        scores = self.bm25.get_scores(tokenized_query)
+
+        # 获取top_k个结果
+        top_indices = np.argsort(scores)[::-1][:top_k]
+
+        return {
+            'ids': [[self.ids[i] for i in top_indices]],
+            'documents': [[self.documents[i] for i in top_indices]],
+            'metadatas': [[self.metadatas[i] for i in top_indices]],
+            'distances': [[float(scores[i]) for i in top_indices]]
+        }
+
+    def save(self, path):
+        """保存索引到文件"""
+        data = {
+            'ids': self.ids,
+            'documents': self.documents,
+            'metadatas': self.metadatas
+        }
+        with open(path, 'wb') as f:
+            pickle.dump(data, f)
+        logger.info(f"BM25索引已保存: {path}")
+
+    def load(self, path):
+        """从文件加载索引"""
+        if not os.path.exists(path):
+            return False
+
+        with open(path, 'rb') as f:
+            data = pickle.load(f)
+
+        self.ids = data['ids']
+        self.documents = data['documents']
+        self.metadatas = data['metadatas']
+
+        # 重建BM25索引
+        tokenized_docs = [self.tokenize(doc) for doc in self.documents]
+        self.bm25 = BM25Okapi(tokenized_docs)
+
+        logger.info(f"BM25索引已加载: {len(self.documents)} 个文档")
+        return True
+
+    def clear(self):
+        """清空索引"""
+        self.bm25 = None
+        self.documents = []
+        self.metadatas = []
+        self.ids = []
+
+
+# ==================== 全局 BM25 索引管理器 ====================
+
+_bm25_indexer: BM25Index = None
+
+
+def get_bm25_indexer() -> BM25Index:
+    """
+    获取全局 BM25 索引器实例
+
+    Returns:
+        BM25Index 实例
+    """
+    global _bm25_indexer
+    if _bm25_indexer is None:
+        _bm25_indexer = BM25Index()
+    return _bm25_indexer
+
+
+def init_bm25_indexer(ids=None, documents=None, metadatas=None) -> BM25Index:
+    """
+    初始化 BM25 索引器并添加文档
+
+    Args:
+        ids: 文档 ID 列表
+        documents: 文档内容列表
+        metadatas: 元数据列表
+
+    Returns:
+        初始化后的 BM25Index 实例
+    """
+    global _bm25_indexer
+    _bm25_indexer = BM25Index()
+    if ids and documents:
+        _bm25_indexer.add_documents(ids, documents, metadatas or [])
+    return _bm25_indexer

core/cache.py

@@ -0,0 +1,443 @@
+# -*- coding: utf-8 -*-
+"""
+RAG 三层缓存模块
+
+缓存层次：
+1. Query Cache: 完整问答结果缓存
+2. Embedding Cache: 向量化结果缓存
+3. Rerank Cache: 重排序分数缓存
+
+缓存失效：基于知识库版本号（kb_version）的自动失效机制
+"""
+
+import hashlib
+import time
+import threading
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Any, Tuple
+from collections import OrderedDict
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class CacheEntry:
+    """缓存条目"""
+    key: str
+    value: Any
+    created_at: float
+    ttl: float  # 秒
+    hits: int = 0
+    kb_version: int = 0
+
+    def is_expired(self) -> bool:
+        return time.time() - self.created_at > self.ttl
+
+
+@dataclass
+class CacheStats:
+    """缓存统计"""
+    total_entries: int = 0
+    hits: int = 0
+    misses: int = 0
+    evictions: int = 0
+
+    @property
+    def hit_rate(self) -> float:
+        total = self.hits + self.misses
+        return self.hits / total if total > 0 else 0.0
+
+
+class LRUCache:
+    """线程安全的 LRU 缓存实现"""
+
+    def __init__(self, max_size: int = 1000, default_ttl: float = 3600):
+        self.max_size = max_size
+        self.default_ttl = default_ttl
+        self._cache: OrderedDict[str, CacheEntry] = OrderedDict()
+        self._lock = threading.RLock()
+        self._stats = CacheStats()
+
+    def get(self, key: str) -> Optional[Any]:
+        """获取缓存值"""
+        with self._lock:
+            if key not in self._cache:
+                self._stats.misses += 1
+                return None
+
+            entry = self._cache[key]
+
+            # 检查过期
+            if entry.is_expired():
+                del self._cache[key]
+                self._stats.misses += 1
+                self._stats.evictions += 1
+                return None
+
+            # LRU 更新
+            self._cache.move_to_end(key)
+            entry.hits += 1
+            self._stats.hits += 1
+
+            return entry.value
+
+    def set(self, key: str, value: Any, ttl: float = None,
+            kb_version: int = 0) -> None:
+        """设置缓存值"""
+        with self._lock:
+            if key in self._cache:
+                del self._cache[key]
+
+            entry = CacheEntry(
+                key=key,
+                value=value,
+                created_at=time.time(),
+                ttl=ttl or self.default_ttl,
+                kb_version=kb_version
+            )
+
+            self._cache[key] = entry
+
+            # LRU 淘汰
+            while len(self._cache) > self.max_size:
+                oldest_key = next(iter(self._cache))
+                del self._cache[oldest_key]
+                self._stats.evictions += 1
+
+            self._stats.total_entries = len(self._cache)
+
+    def invalidate_by_version(self, kb_version: int) -> int:
+        """失效指定版本的所有缓存"""
+        count = 0
+        with self._lock:
+            keys_to_delete = [
+                k for k, v in self._cache.items()
+                if v.kb_version == kb_version
+            ]
+            for key in keys_to_delete:
+                del self._cache[key]
+                count += 1
+            self._stats.evictions += count
+            self._stats.total_entries = len(self._cache)
+        return count
+
+    def clear(self) -> None:
+        """清空缓存"""
+        with self._lock:
+            self._cache.clear()
+            self._stats.total_entries = 0
+
+    def get_stats(self) -> CacheStats:
+        """获取统计信息"""
+        with self._lock:
+            return self._stats
+
+
+class RAGCacheManager:
+    """RAG 三层缓存管理器"""
+
+    # 默认配置（可从 config 覆盖）
+    DEFAULT_QUERY_CACHE_SIZE = 500
+    DEFAULT_QUERY_CACHE_TTL = 3600  # 1小时
+
+    DEFAULT_EMBEDDING_CACHE_SIZE = 2000
+    DEFAULT_EMBEDDING_CACHE_TTL = 86400  # 24小时
+
+    DEFAULT_RERANK_CACHE_SIZE = 1000
+    DEFAULT_RERANK_CACHE_TTL = 3600  # 1小时
+
+    def __init__(
+        self,
+        query_cache_size: int = None,
+        query_cache_ttl: float = None,
+        embedding_cache_size: int = None,
+        embedding_cache_ttl: float = None,
+        rerank_cache_size: int = None,
+        rerank_cache_ttl: float = None,
+        kb_versions: Dict[str, int] = None
+    ):
+        """初始化缓存管理器"""
+        self.query_cache = LRUCache(
+            max_size=query_cache_size or self.DEFAULT_QUERY_CACHE_SIZE,
+            default_ttl=query_cache_ttl or self.DEFAULT_QUERY_CACHE_TTL
+        )
+        self.embedding_cache = LRUCache(
+            max_size=embedding_cache_size or self.DEFAULT_EMBEDDING_CACHE_SIZE,
+            default_ttl=embedding_cache_ttl or self.DEFAULT_EMBEDDING_CACHE_TTL
+        )
+        self.rerank_cache = LRUCache(
+            max_size=rerank_cache_size or self.DEFAULT_RERANK_CACHE_SIZE,
+            default_ttl=rerank_cache_ttl or self.DEFAULT_RERANK_CACHE_TTL
+        )
+
+        self._kb_versions: Dict[str, int] = kb_versions or {}
+        self._version_lock = threading.Lock()
+
+    def get_kb_version(self, kb_name: str) -> int:
+        """获取知识库当前版本号"""
+        with self._version_lock:
+            return self._kb_versions.get(kb_name, 0)
+
+    def increment_kb_version(self, kb_name: str) -> int:
+        """递增知识库版本号（文档更新时调用）"""
+        with self._version_lock:
+            old_version = self._kb_versions.get(kb_name, 0)
+            self._kb_versions[kb_name] = old_version + 1
+            new_version = self._kb_versions[kb_name]
+
+        # 失效旧版本缓存
+        self.query_cache.invalidate_by_version(old_version)
+        self.embedding_cache.invalidate_by_version(old_version)
+
+        logger.info(f"知识库 {kb_name} 版本更新: {old_version} -> {new_version}")
+        return new_version
+
+    # ==================== Query Cache 方法 ====================
+
+    @staticmethod
+    def _make_query_cache_key(query: str, kb_name: str, kb_version: int, doc_hash: str = "") -> str:
+        """
+        生成查询缓存 key
+
+        Args:
+            query: 查询文本
+            kb_name: 知识库名称
+            kb_version: 知识库版本号
+            doc_hash: 相关文档版本哈希（细粒度失效）
+
+        Returns:
+            缓存 key
+        """
+        if doc_hash:
+            # 细粒度：只失效相关文档的缓存
+            return hashlib.md5(
+                f"query:{query}:{kb_name}:{doc_hash}".encode()
+            ).hexdigest()
+        else:
+            # 粗粒度：整个知识库版本变化时失效
+            return hashlib.md5(
+                f"query:{query}:{kb_name}:{kb_version}".encode()
+            ).hexdigest()
+
+    def get_query_result(self, query: str, kb_name: str, doc_ids: List[str] = None) -> Optional[Dict]:
+        """
+        获取查询缓存结果
+
+        Args:
+            query: 查询文本
+            kb_name: 知识库名称
+            doc_ids: 相关文档 ID 列表（用于细粒度缓存 key）
+        """
+        kb_version = self.get_kb_version(kb_name)
+
+        # 计算文档哈希（如果提供了 doc_ids）
+        doc_hash = ""
+        if doc_ids:
+            doc_hash = self._compute_doc_hash(kb_name, doc_ids)
+
+        key = self._make_query_cache_key(query, kb_name, kb_version, doc_hash)
+        return self.query_cache.get(key)
+
+    def set_query_result(self, query: str, kb_name: str, result: Dict, doc_ids: List[str] = None) -> None:
+        """
+        设置查询缓存结果
+
+        Args:
+            query: 查询文本
+            kb_name: 知识库名称
+            result: 缓存结果
+            doc_ids: 相关文档 ID 列表（用于细粒度失效）
+        """
+        kb_version = self.get_kb_version(kb_name)
+
+        # 计算相关文档的版本哈希（细粒度失效）
+        doc_hash = ""
+        if doc_ids:
+            doc_hash = self._compute_doc_hash(kb_name, doc_ids)
+
+        key = self._make_query_cache_key(query, kb_name, kb_version, doc_hash)
+        self.query_cache.set(key, result, kb_version=kb_version)
+
+    def _compute_doc_hash(self, kb_name: str, doc_ids: List[str]) -> str:
+        """
+        计算文档版本哈希
+
+        用于细粒度缓存失效：只失效相关文档变化时的缓存
+        """
+        if not doc_ids:
+            return ""
+
+        # 从文档 ID 中提取 source（文件名）
+        sources = set()
+        for doc_id in doc_ids:
+            # doc_id 格式通常为 "filename_text_0" 或类似
+            parts = doc_id.split('_')
+            if parts:
+                sources.add(parts[0])
+
+        # 生成哈希
+        sources_str = ','.join(sorted(sources))
+        return hashlib.md5(f"docs:{sources_str}".encode()).hexdigest()
+
+    # ==================== Embedding Cache 方法 ====================
+
+    @staticmethod
+    def _make_embedding_key(text: str) -> str:
+        return hashlib.md5(f"emb:{text}".encode()).hexdigest()
+
+    def get_embedding(self, text: str) -> Optional[List[float]]:
+        """获取文本的 Embedding 缓存"""
+        key = self._make_embedding_key(text)
+        return self.embedding_cache.get(key)
+
+    def set_embedding(self, text: str, embedding: List[float],
+                      kb_version: int = 0) -> None:
+        """设置 Embedding 缓存"""
+        key = self._make_embedding_key(text)
+        self.embedding_cache.set(key, embedding, kb_version=kb_version)
+
+    def get_embeddings_batch(self, texts: List[str]) -> Tuple[List[Optional[List[float]]], List[int]]:
+        """
+        批量获取 Embedding
+
+        Returns:
+            (embeddings, missed_indices): 命中的 embedding 列表（未命中为 None）和未命中的索引列表
+        """
+        embeddings: List[Optional[List[float]]] = []
+        missed_indices: List[int] = []
+
+        for i, text in enumerate(texts):
+            emb = self.get_embedding(text)
+            if emb is not None:
+                embeddings.append(emb)
+            else:
+                embeddings.append(None)
+                missed_indices.append(i)
+
+        return embeddings, missed_indices
+
+    # ==================== Rerank Cache 方法 ====================
+
+    @staticmethod
+    def _make_rerank_key(query: str, doc_ids: List[str]) -> str:
+        sorted_ids = sorted(doc_ids)
+        return hashlib.md5(
+            f"rerank:{query}:{':'.join(sorted_ids)}".encode()
+        ).hexdigest()
+
+    def get_rerank_scores(self, query: str, doc_ids: List[str]) -> Optional[Dict[str, float]]:
+        """获取 Rerank 分数缓存
+
+        返回 {doc_id: score} 映射（而非位置列表），调用方按当前 doc_ids 顺序查表，
+        避免同一组文档以不同顺序返回时分数错位。
+        """
+        key = self._make_rerank_key(query, doc_ids)
+        return self.rerank_cache.get(key)
+
+    def set_rerank_scores(self, query: str, doc_ids: List[str],
+                          scores: List[float]) -> None:
+        """设置 Rerank 分数缓存
+
+        以 {doc_id: score} 映射存储，保证顺序无关的正确性。
+        """
+        key = self._make_rerank_key(query, doc_ids)
+        score_map = {doc_id: float(s) for doc_id, s in zip(doc_ids, scores)}
+        self.rerank_cache.set(key, score_map)
+
+    # ==================== 统计方法 ====================
+
+    def get_all_stats(self) -> Dict[str, CacheStats]:
+        """获取所有缓存的统计信息"""
+        return {
+            "query_cache": self.query_cache.get_stats(),
+            "embedding_cache": self.embedding_cache.get_stats(),
+            "rerank_cache": self.rerank_cache.get_stats()
+        }
+
+    def clear_all(self) -> None:
+        """清空所有缓存"""
+        self.query_cache.clear()
+        self.embedding_cache.clear()
+        self.rerank_cache.clear()
+        logger.info("所有缓存已清空")
+
+
+# ==================== 全局缓存实例 ====================
+
+_cache_manager: Optional[RAGCacheManager] = None
+_cache_lock = threading.Lock()
+
+
+def get_cache_manager() -> RAGCacheManager:
+    """获取全局缓存管理器实例（单例模式）"""
+    global _cache_manager
+    if _cache_manager is None:
+        with _cache_lock:
+            if _cache_manager is None:
+                # 尝试从配置加载参数
+                try:
+                    from config import (
+                        QUERY_CACHE_SIZE, QUERY_CACHE_TTL,
+                        EMBEDDING_CACHE_SIZE, EMBEDDING_CACHE_TTL,
+                        RERANK_CACHE_SIZE, RERANK_CACHE_TTL
+                    )
+                    _cache_manager = RAGCacheManager(
+                        query_cache_size=QUERY_CACHE_SIZE,
+                        query_cache_ttl=QUERY_CACHE_TTL,
+                        embedding_cache_size=EMBEDDING_CACHE_SIZE,
+                        embedding_cache_ttl=EMBEDDING_CACHE_TTL,
+                        rerank_cache_size=RERANK_CACHE_SIZE,
+                        rerank_cache_ttl=RERANK_CACHE_TTL
+                    )
+                except ImportError:
+                    # 使用默认配置
+                    _cache_manager = RAGCacheManager()
+    return _cache_manager
+
+
+def reset_cache_manager() -> None:
+    """重置全局缓存管理器（主要用于测试）"""
+    global _cache_manager
+    with _cache_lock:
+        if _cache_manager is not None:
+            _cache_manager.clear_all()
+        _cache_manager = None
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    print("=" * 60)
+    print("缓存模块测试")
+    print("=" * 60)
+
+    cache = RAGCacheManager()
+
+    # 测试 Query Cache
+    print("\n1. Query Cache 测试")
+    cache.set_query_result("什么是Python?", "public_kb", {"answer": "Python是一种编程语言"})
+    result = cache.get_query_result("什么是Python?", "public_kb")
+    print(f"   缓存命中: {result}")
+
+    # 测试版本号失效
+    print("\n2. 版本号失效测试")
+    cache.increment_kb_version("public_kb")
+    result = cache.get_query_result("什么是Python?", "public_kb")
+    print(f"   版本更新后缓存失效: {result is None}")
+
+    # 测试 Embedding Cache
+    print("\n3. Embedding Cache 测试")
+    cache.set_embedding("测试文本", [0.1, 0.2, 0.3])
+    emb = cache.get_embedding("测试文本")
+    print(f"   Embedding 缓存: {emb}")
+
+    # 测试统计
+    print("\n4. 缓存统计")
+    stats = cache.get_all_stats()
+    for name, stat in stats.items():
+        print(f"   {name}: hits={stat.hits}, misses={stat.misses}, hit_rate={stat.hit_rate:.2%}")

core/chunker.py

@@ -0,0 +1,258 @@
+# -*- coding: utf-8 -*-
+"""
+文本分块模块
+
+提供带硬性上限的分块函数，确保切片不会超过 max_length。
+
+核心特性：
+- 基于 LangChain RecursiveCharacterTextSplitter
+- Markdown 结构感知分块
+- 硬性上限保护（max_length=1200）
+- 最小切片约束（min_length=200）
+- 相邻切片合并（过短切片）
+"""
+
+from typing import List
+import re
+
+
+def split_text_with_limit(
+    text: str,
+    chunk_size: int = 1000,
+    overlap: int = 100,
+    max_length: int = 1200,
+    min_length: int = 200
+) -> List[str]:
+    """
+    带硬性上限和下限的分块函数
+
+    确保切片不会超过 max_length，且不会低于 min_length（尝试合并）。
+
+    Args:
+        text: 待分块文本
+        chunk_size: 目标分块大小
+        overlap: 分块重叠字符数
+        max_length: 硬性上限
+        min_length: 硬性下限（过短则尝试合并）
+
+    Returns:
+        分块列表
+    """
+    if not text or not text.strip():
+        return []
+
+    try:
+        from langchain_text_splitters import RecursiveCharacterTextSplitter
+    except ImportError:
+        raise ImportError("请安装 langchain-text-splitters: pip install langchain-text-splitters")
+
+    # Markdown 分隔符优先级
+    separators = [
+        "\n#{1,6} ",  # 标题
+        "\n```\n",    # 代码块
+        "\n|",        # 表格
+        "\n\n",       # 段落
+        "\n",         # 行
+        " ",          # 词
+        ""            # 字符
+    ]
+
+    splitter = RecursiveCharacterTextSplitter(
+        separators=separators,
+        chunk_size=chunk_size,
+        chunk_overlap=overlap,
+        length_function=len,
+        keep_separator=True
+    )
+
+    chunks = splitter.split_text(text)
+
+    # 第一轮：硬性上限保护
+    result = []
+    for chunk in chunks:
+        if len(chunk) > max_length:
+            # 尝试在句子边界截断
+            last_boundary = max(
+                chunk.rfind('。', 0, max_length),
+                chunk.rfind('？', 0, max_length),
+                chunk.rfind('！', 0, max_length),
+                chunk.rfind('.', 0, max_length),
+                chunk.rfind('\n', 0, max_length)
+            )
+            if last_boundary > max_length // 2:
+                result.append(chunk[:last_boundary + 1])
+            else:
+                result.append(chunk[:max_length])
+        else:
+            result.append(chunk)
+
+    # 第二轮：合并过短的切片
+    result = merge_short_chunks(result, min_length, max_length)
+
+    return result
+
+
+def merge_short_chunks(chunks: List[str], min_length: int, max_length: int) -> List[str]:
+    """
+    合并过短的相邻切片
+
+    Args:
+        chunks: 原始切片列表
+        min_length: 最小长度
+        max_length: 最大长度
+
+    Returns:
+        合并后的切片列表
+    """
+    if not chunks or min_length <= 0:
+        return chunks
+
+    result = []
+    i = 0
+
+    while i < len(chunks):
+        current = chunks[i]
+
+        # 如果当前切片过短，尝试与下一个合并
+        while len(current) < min_length and i + 1 < len(chunks):
+            next_chunk = chunks[i + 1]
+            merged = current + "\n" + next_chunk
+
+            # 检查合并后是否超过上限
+            if len(merged) <= max_length:
+                current = merged
+                i += 1
+            else:
+                # 合并后超限，停止合并
+                break
+
+        result.append(current)
+        i += 1
+
+    return result
+
+
+def filter_chunks_by_section(
+    chunks: List[dict],
+    query: str,
+    section_keywords: List[str] = None
+) -> List[dict]:
+    """
+    根据查询中的章节信息过滤切片
+
+    Args:
+        chunks: 切片列表，每个切片需包含 metadata
+        query: 用户查询
+        section_keywords: 章节关键词列表
+
+    Returns:
+        过滤后的切片列表
+    """
+    if not section_keywords:
+        section_keywords = [
+            "第一章", "第二章", "第三章", "第四章", "第五章",
+            "第1章", "第2章", "第3章", "第4章", "第5章",
+            "一、", "二、", "三、", "四、", "五、",
+            "1.", "2.", "3.", "4.", "5."
+        ]
+
+    # 从查询中提取章节关键词
+    mentioned_sections = []
+    for keyword in section_keywords:
+        if keyword in query:
+            mentioned_sections.append(keyword)
+
+    if not mentioned_sections:
+        return chunks
+
+    # 过滤切片
+    result = []
+    for chunk in chunks:
+        metadata = chunk.get('metadata', chunk)
+        section_path = metadata.get('section', metadata.get('section_path', ''))
+
+        # 检查是否匹配任一章节
+        for section in mentioned_sections:
+            if section in section_path or section in chunk.get('content', ''):
+                result.append(chunk)
+                break
+
+    # 如果过滤后结果为空，返回原始列表
+    return result if result else chunks
+
+
+def extract_section_mention(query: str) -> str:
+    """
+    从查询中提取章节提及
+
+    Args:
+        query: 用户查询
+
+    Returns:
+        提取的章节字符串，如 "第一章"
+    """
+    patterns = [
+        r'第[一二三四五六七八九十\d]+章',
+        r'第\s*\d+\s*章',
+        r'[一二三四五六七八九十]+、',
+        r'\d+\.',
+    ]
+
+    for pattern in patterns:
+        match = re.search(pattern, query)
+        if match:
+            return match.group()
+
+    return ""
+
+
+# 兼容性别名
+split_text = split_text_with_limit
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    # 测试分块
+    test_text = """
+一、适用范围
+
+适用于全省地市公司货源投放工作所涉及的基础工作。
+
+二、总体要求
+
+货源投放是烟草营销的核心业务，总体要求是：
+1.坚持市场导向、供需匹配；
+2.坚持总量控制、稍紧平衡。
+
+三、投放方法
+
+主要有六种投放方法。
+"""
+
+    print("=" * 60)
+    print("分块测试")
+    print("=" * 60)
+
+    chunks = split_text_with_limit(test_text, chunk_size=100, min_length=50)
+    for i, chunk in enumerate(chunks, 1):
+        print(f"\n[{i}] (len={len(chunk)})")
+        print(chunk.strip()[:100])
+
+    print("\n" + "=" * 60)
+    print("章节过滤测试")
+    print("=" * 60)
+
+    test_chunks = [
+        {"content": "内容1", "metadata": {"section": "一、适用范围"}},
+        {"content": "内容2", "metadata": {"section": "二、总体要求"}},
+        {"content": "内容3", "metadata": {"section": "三、投放方法"}},
+    ]
+
+    filtered = filter_chunks_by_section(test_chunks, "适用范围是什么？")
+    print(f"查询: '适用范围是什么？'")
+    print(f"匹配结果: {[c['metadata']['section'] for c in filtered]}")

core/confidence_gate.py

@@ -0,0 +1,367 @@
+"""
+置信度门控模块
+
+基于 Reranker 分数判断检索结果质量，低于阈值则拦截并触发补救流程。
+
+核心功能：
+1. 使用 Reranker 计算检索结果的置信度分数
+2. 根据阈值判断结果质量
+3. 决定是继续生成还是触发补救流程
+
+使用方式：
+    from core.confidence_gate import ConfidenceGate, create_gate
+
+    gate = create_gate()
+    result = gate.evaluate(query, documents)
+
+    if result.action == GateAction.REWRITE:
+        # 触发查询重写或网络搜索
+        ...
+"""
+
+from dataclasses import dataclass
+from typing import List, Optional
+from enum import Enum
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class GateAction(Enum):
+    """门控动作"""
+    PASS = "pass"                    # 通过，继续生成
+    REWRITE = "rewrite"              # 需要查询重写
+    WEB_SEARCH = "web_search"        # 触发网络搜索
+    FALLBACK = "fallback"            # 降级处理（无结果）
+
+
+@dataclass
+class GateResult:
+    """门控结果"""
+    action: GateAction
+    confidence: float          # 综合置信度
+    top_score: float          # Top-1 分数
+    avg_score: float          # Top-3 平均分数
+    reason: str               # 决策原因
+    suggested_action: str     # 建议的后续动作
+    scores: List[float] = None  # 所有分数
+
+
+class ConfidenceGate:
+    """
+    置信度门控器
+
+    基于 Reranker 分数判断检索结果质量，决定是否继续生成或触发补救。
+
+    阈值设计（基于 Agentic RAG 优化报告）：
+    - PASS_THRESHOLD = 0.35: 通过阈值，低于此值需要补救
+    - GOOD_THRESHOLD = 0.5: 良好阈值，高质量结果
+    - EXCELLENT_THRESHOLD = 0.7: 优秀阈值，可直接生成
+    """
+
+    # 关键阈值
+    # 2026-04-15: PASS_THRESHOLD 从 0.35 降低到 0.2，减少误判导致补救流程
+    PASS_THRESHOLD = 0.2      # 通过阈值（降低以减少误判）
+    GOOD_THRESHOLD = 0.4      # 良好阈值（从 0.5 降低）
+    EXCELLENT_THRESHOLD = 0.7 # 优秀阈值
+
+    def __init__(self, reranker=None):
+        """
+        初始化门控器
+
+        Args:
+            reranker: CrossEncoder 重排序模型
+        """
+        self.reranker = reranker
+
+    def evaluate(self, query: str, documents: List[str],
+                 metadatas: List[dict] = None,
+                 precomputed_scores: List[float] = None) -> GateResult:
+        """
+        评估检索结果质量
+
+        Args:
+            query: 用户查询
+            documents: 检索到的文档列表
+            metadatas: 文档元数据（可选，用于更精确评估）
+            precomputed_scores: 预计算的 Rerank 分数（可选，避免重复推理）
+                                如果主检索管线已执行 Rerank，可直接传入分数
+
+        Returns:
+            GateResult: 门控决策结果
+        """
+        # 无结果情况
+        if not documents:
+            return GateResult(
+                action=GateAction.FALLBACK,
+                confidence=0.0,
+                top_score=0.0,
+                avg_score=0.0,
+                reason="无检索结果",
+                suggested_action="尝试网络搜索或告知用户无相关信息"
+            )
+
+        # 使用预计算分数或重新计算
+        if precomputed_scores is not None:
+            scores = precomputed_scores
+            logger.debug(f"置信度门控: 复用预计算 Rerank 分数 (跳过重复推理)")
+        else:
+            scores = self._compute_scores(query, documents)
+
+        top_score = max(scores) if scores else 0.0
+        avg_score = sum(scores[:3]) / min(3, len(scores)) if len(scores) >= 1 else 0.0
+
+        # 决策逻辑
+        if top_score >= self.GOOD_THRESHOLD:
+            # 高置信度，直接通过
+            return GateResult(
+                action=GateAction.PASS,
+                confidence=top_score,
+                top_score=top_score,
+                avg_score=avg_score,
+                reason=f"检索结果置信度高 ({top_score:.3f} >= {self.GOOD_THRESHOLD})，可直接生成回答",
+                suggested_action="继续生成答案",
+                scores=scores
+            )
+
+        elif top_score >= self.PASS_THRESHOLD:
+            # 中等置信度，可以通过但建议关注
+            return GateResult(
+                action=GateAction.PASS,
+                confidence=top_score,
+                top_score=top_score,
+                avg_score=avg_score,
+                reason=f"检索结果置信度中等 ({top_score:.3f})，可能需要补充信息",
+                suggested_action="继续生成答案，但需标注不确定性",
+                scores=scores
+            )
+
+        else:
+            # 低置信度，需要补救
+            # 判断是触发查询重写还是网络搜索
+            if avg_score < self.PASS_THRESHOLD:
+                # 平均分也很低，直接网络搜索
+                return GateResult(
+                    action=GateAction.WEB_SEARCH,
+                    confidence=top_score,
+                    top_score=top_score,
+                    avg_score=avg_score,
+                    reason=f"Top-1 置信度 {top_score:.3f} 低于阈值 {self.PASS_THRESHOLD}，平均置信度 {avg_score:.3f} 也很低",
+                    suggested_action="触发网络搜索作为补充",
+                    scores=scores
+                )
+            else:
+                # 尝试查询重写
+                return GateResult(
+                    action=GateAction.REWRITE,
+                    confidence=top_score,
+                    top_score=top_score,
+                    avg_score=avg_score,
+                    reason=f"Top-1 置信度 {top_score:.3f} 低于阈值 {self.PASS_THRESHOLD}，尝试查询重写",
+                    suggested_action="触发查询重写或补充检索",
+                    scores=scores
+                )
+
+    def _compute_scores(self, query: str, documents: List[str]) -> List[float]:
+        """
+        计算 Reranker 分数
+
+        Args:
+            query: 用户查询
+            documents: 文档列表
+
+        Returns:
+            分数列表
+        """
+        if not self.reranker:
+            # 无 Reranker，使用向量相似度降级
+            return self._vector_similarity_fallback(query, documents)
+
+        try:
+            import numpy as np
+            pairs = [(query, doc) for doc in documents]
+            scores = self.reranker.predict(pairs)
+            # 确保返回 float 列表
+            return [float(s) for s in scores]
+        except Exception as e:
+            logger.warning(f"Reranker 计算失败: {e}")
+            return self._vector_similarity_fallback(query, documents)
+
+    def _vector_similarity_fallback(self, query: str, documents: List[str]) -> List[float]:
+        """
+        向量相似度降级方案
+
+        当 Reranker 不可用时，使用向量相似度计算置信度。
+        比关键词匹配更可靠。
+
+        Args:
+            query: 用户查询
+            documents: 文档列表
+
+        Returns:
+            分数列表（归一化到 0-1 范围）
+        """
+        try:
+            import numpy as np
+            from core.engine import get_engine
+
+            engine = get_engine()
+            if not engine or not engine.embedding_model:
+                return self._keyword_fallback(query, documents)
+
+            # 计算查询向量
+            query_vec = np.array(engine.embedding_model.encode(query))
+            query_norm = np.linalg.norm(query_vec)
+
+            if query_norm == 0:
+                return self._keyword_fallback(query, documents)
+
+            scores = []
+            for doc in documents:
+                # 计算文档向量
+                doc_vec = np.array(engine.embedding_model.encode(doc))
+                doc_norm = np.linalg.norm(doc_vec)
+
+                # 余弦相似度
+                if doc_norm > 0:
+                    similarity = np.dot(query_vec, doc_vec) / (query_norm * doc_norm)
+                else:
+                    similarity = 0.0
+
+                # 归一化到 0-1 范围（余弦相似度在 -1 到 1）
+                normalized_score = (similarity + 1) / 2
+                scores.append(float(normalized_score))
+
+            return scores
+
+        except Exception as e:
+            logger.warning(f"向量相似度降级失败: {e}")
+            return self._keyword_fallback(query, documents)
+
+    def _keyword_fallback(self, query: str, documents: List[str]) -> List[float]:
+        """
+        关键词匹配降级方案
+
+        当 Reranker 不可用时，使用关键词匹配作为降级方案。
+        返回归一化到 0-1 范围的分数。
+        """
+        try:
+            import jieba
+        except ImportError:
+            # jieba 不可用，返回中等分数
+            return [0.5] * len(documents)
+
+        # 提取查询关键词
+        query_words = set()
+        for word in jieba.cut(query):
+            word = word.strip()
+            if len(word) >= 2:
+                query_words.add(word.lower())
+
+        if not query_words:
+            return [0.5] * len(documents)
+
+        scores = []
+        for doc in documents:
+            doc_lower = doc.lower()
+            matched = sum(1 for word in query_words if word in doc_lower)
+            # 归一化到 0-1
+            score = matched / len(query_words)
+            # 映射到类似 Reranker 的范围（关键词匹配通常分数较低，需要放大）
+            score = min(score * 0.8, 1.0)
+            scores.append(score)
+
+        return scores
+
+    def get_threshold_info(self) -> dict:
+        """获取阈值信息"""
+        return {
+            "pass_threshold": self.PASS_THRESHOLD,
+            "good_threshold": self.GOOD_THRESHOLD,
+            "excellent_threshold": self.EXCELLENT_THRESHOLD,
+            "has_reranker": self.reranker is not None
+        }
+
+
+def create_gate() -> ConfidenceGate:
+    """
+    创建门控器实例
+
+    自动从 RAG Engine 获取 Reranker 模型。
+
+    Returns:
+        ConfidenceGate: 门控器实例
+    """
+    try:
+        from core.engine import get_engine
+        engine = get_engine()
+        return ConfidenceGate(reranker=engine.reranker)
+    except Exception as e:
+        logger.warning(f"创建门控器失败，使用降级模式: {e}")
+        return ConfidenceGate(reranker=None)
+
+
+# ==================== 便捷函数 ====================
+
+def check_confidence(query: str, documents: List[str]) -> GateResult:
+    """
+    便捷函数：检查检索结果置信度
+
+    Args:
+        query: 用户查询
+        documents: 检索到的文档列表
+
+    Returns:
+        GateResult: 门控决策结果
+    """
+    gate = create_gate()
+    return gate.evaluate(query, documents)
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    print("=" * 60)
+    print("置信度门控测试")
+    print("=" * 60)
+
+    # 测试用例
+    test_cases = [
+        # (query, documents, expected_action)
+        (
+            "公司报销制度是怎样的？",
+            ["报销制度规定员工可以报销差旅费用，需提供发票...", "根据公司规定，报销需在30天内提交..."],
+            GateAction.PASS  # 应该高分通过
+        ),
+        (
+            "宇宙的终极答案是什么？",
+            ["文档中提到了一些技术细节...", "另一个不相关的内容..."],
+            GateAction.REWRITE  # 低置信度，需要补救
+        ),
+        (
+            "测试空结果",
+            [],
+            GateAction.FALLBACK  # 无结果
+        ),
+    ]
+
+    gate = ConfidenceGate()  # 不使用 Reranker 的测试
+
+    print(f"\n阈值配置: {gate.get_threshold_info()}")
+    print()
+
+    for i, (query, docs, expected) in enumerate(test_cases, 1):
+        result = gate.evaluate(query, docs)
+        status = "[OK]" if result.action == expected else "[WARN]"
+        print(f"测试 {i}: {status}")
+        print(f"  查询: {query}")
+        print(f"  文档数: {len(docs)}")
+        print(f"  动作: {result.action.value}")
+        print(f"  置信度: {result.confidence:.3f}")
+        print(f"  Top分数: {result.top_score:.3f}")
+        print(f"  原因: {result.reason}")
+        print()

core/constants.py

@@ -0,0 +1,35 @@
+"""
+公共常量定义
+
+统一管理 RAG 系统中的常量值，避免重复定义。
+"""
+
+from copy import deepcopy
+
+# ==================== 检索结果常量 ====================
+
+# 空检索结果模板（不要直接返回，使用 get_empty_result()）
+_EMPTY_RESULT_TEMPLATE = {
+    'ids': [[]],
+    'documents': [[]],
+    'metadatas': [[]],
+    'distances': [[]]
+}
+
+# 空检索结果模板（无嵌套列表形式）
+_EMPTY_RESULT_FLAT_TEMPLATE = {
+    'ids': [],
+    'documents': [],
+    'metadatas': [],
+    'distances': []
+}
+
+
+def get_empty_result() -> dict:
+    """返回空检索结果的深拷贝，避免副作用"""
+    return deepcopy(_EMPTY_RESULT_TEMPLATE)
+
+
+def get_empty_result_flat() -> dict:
+    """返回扁平空检索结果的深拷贝"""
+    return deepcopy(_EMPTY_RESULT_FLAT_TEMPLATE)

core/intent_analyzer.py

@@ -0,0 +1,584 @@
+"""
+意图分析器 - 改写 + 双层判断
+
+核心功能：
+1. 问题改写：指代消解、省略补全
+2. 双层判断：
+   - 历史是否可答
+   - 是否需要外部知识
+3. 输出：决策（use_context / need_retrieval）
+4. 语义缓存：相似问题复用结果
+
+设计原则：
+- 使用轻量 LLM 完成改写和判断
+- 替代硬编码规则，更灵活可维护
+- 语义缓存减少 LLM 调用
+"""
+
+import json
+import logging
+import hashlib
+from dataclasses import dataclass, asdict
+from typing import List, Dict, Optional, Tuple
+
+from config import INTENT_TEMPERATURE, INTENT_MAX_TOKENS, INTENT_HISTORY_WINDOW, INTENT_MODEL
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class IntentAnalysis:
+    """意图分析结果"""
+    rewritten_query: str           # 改写后的完整问题
+    use_context: bool              # 是否使用上下文回答
+    need_retrieval: bool           # 是否需要检索
+    confidence: float              # 置信度
+    reason: str                    # 判断理由
+    context_images: List[dict]     # 上下文中的图片信息
+    sub_queries: List[str]         # 用于检索的子查询列表
+    intent: str                    # 意图类型: factual/comparison/reasoning/instruction/other
+
+    def to_dict(self) -> dict:
+        """转换为字典（用于缓存）"""
+        return {
+            "rewritten_query": self.rewritten_query,
+            "use_context": self.use_context,
+            "need_retrieval": self.need_retrieval,
+            "confidence": self.confidence,
+            "reason": self.reason,
+            "context_images": self.context_images,
+            "sub_queries": self.sub_queries,
+            "intent": self.intent
+        }
+
+    @classmethod
+    def from_dict(cls, data: dict) -> "IntentAnalysis":
+        """从字典创建（用于缓存读取）"""
+        return cls(
+            rewritten_query=data.get("rewritten_query", ""),
+            use_context=data.get("use_context", False),
+            need_retrieval=data.get("need_retrieval", True),
+            confidence=data.get("confidence", 0.5),
+            reason=data.get("reason", ""),
+            context_images=data.get("context_images", []),
+            sub_queries=data.get("sub_queries", []),
+            intent=data.get("intent", "factual")
+        )
+
+
+class IntentAnalyzer:
+    """
+    意图分析器
+
+    使用 LLM 一次调用完成：
+    1. 问题改写（指代消解）
+    2. 意图判断（是否需要检索）
+    """
+
+    SYSTEM_PROMPT = """你是一个意图分析助手。你的任务是分析用户问题，判断它需要什么类型的回答。
+
+## 任务说明
+
+根据对话历史和当前用户消息，输出一个 JSON 对象，包含以下字段：
+
+1. **rewritten_query**: 改写后的完整问题
+   - 如果问题包含指代（如"这两张图片"、"继续说"），将其改写为完整、独立的问题
+   - 例如："分析一下这两张图片" → "分析一下对话历史中提到的图片"
+   - 如果问题本身已经完整，直接返回原文
+
+2. **use_context**: 布尔值
+   - true: 问题依赖历史对话中的信息，答案已经在历史回答中
+   - false: 问题是新问题，需要新的回答
+
+3. **need_retrieval**: 布尔值
+   - true: 需要从知识库检索信息才能回答
+   - false: 可以直接使用历史上下文或自己的知识回答
+
+4. **reason**: 简短说明判断理由
+
+5. **sub_queries**: 用于检索的子查询列表
+   - 对比类（intent="comparison"）：生成最多3个子查询
+     * 关于A的一个最佳检索查询
+     * 关于B的一个最佳检索查询
+     * 直接对比A和B的检索查询
+   - 推理类（intent="reasoning"）：生成最多2个子查询
+     * 原问题的检索查询
+     * 一个补充角度的检索查询（如原因、背景、影响等），帮助获取更全面的上下文
+   - 其他类（factual/instruction/other）：严格只生成1个子查询（原问题）
+   - 不要为同一实体生成语义重叠的查询
+   - 子查询应保持原问题的关键词，长度20-60字符为宜
+
+6. **intent**: 意图分类（5种类型，请严格按以下定义判断）
+
+   **"comparison"（对比查询）** —— 明确对比两个或多个事物的差异
+   - 关键特征：问题中出现两个以上被比较的对象，且问的是它们之间的差异、异同或优劣
+   - 关键词：区别、不同、差异、对比、相比、各自区别、A和B有什么不同、哪个更好
+   - ⚠️ 注意：问"如何定义和区分"不是在对比，是在问定义标准 → factual
+   - ⚠️ 注意："各自有什么"如果只是分别描述而非对比差异 → factual
+
+   **"reasoning"（推理查询）** —— 需要因果分析、原因推断或影响评估
+   - 关键特征：问题要求分析"为什么"、推导因果关系、或评估某事的影响/后果
+   - 关键词：为什么、原因、导致、影响、后果、为何、怎样导致、如何影响、分析原因
+
+   **"instruction"（操作指导）** —— 询问具体操作步骤、执行方法或实施流程
+   - 关键特征：问题要求的是"怎么做某事"——需要具体的步骤、方法或可执行的指导
+   - 关键词：怎么做、如何操作、如何实施、步骤是什么、操作流程、配置方法、如何申请、如何办理、升级路径、需要满足哪些条件
+   - ⚠️ 注意：问"某机制是怎样的"如果涉及评价标准和执行流程 → instruction
+
+   **"factual"（事实查询）** —— 查询事实、定义、标准、规定、数据、枚举项
+   - 关键特征：问题要求的是文档中明确记载的事实信息
+   - 关键词：是什么、有哪些、包括什么、多少、规定、标准、要求、内容、原则、办法、类型、种类、评分标准、占比限制、时限
+   - 这是最常见的类型——问"某东西包含哪些内容/原则/办法/标准"都属于事实查询
+
+   **"other"（其他）** —— 闲聊、问候、不属于以上类型的问题
+
+## intent 判断流程（按优先级依次检查）
+
+1. 问题是否在对比两个事物的差异？→ comparison
+2. 问题是否在问"为什么"或分析原因/影响？→ reasoning
+3. 问题是否在问具体怎么做、操作步骤、申请流程、升级路径？→ instruction
+4. 以上都不是 → factual（大多数知识库查询属于此类）
+
+### 常见易混淆情况：
+- "如何定义和区分" → factual（问的是定义和标准，不是对比差异）
+- "有哪些原则/办法/标准" → factual（枚举事实，不是操作指导）
+- "如何优化/如何实施" → instruction（问的是具体做法和步骤）
+- "评价标准和退出机制是怎样的" → instruction（涉及评价流程和退出流程）
+- "升级路径是什么，需要满足哪些条件" → instruction（涉及升级步骤和条件）
+- "A和B有什么区别/不同" → comparison（明确对比两者差异）
+- "分别包含什么/各自含义" → factual（分别描述，不是对比差异）
+
+## 判断原则（两个判断是独立的！）
+
+### use_context = true 的情况：
+- 用户引用了历史对话中的内容（"这两张图片"、"上面的数据"、"继续说"）
+- 问题是对历史回答的追问或深入询问
+- 答案可以从历史回答中直接得出
+- **重复提问**：用户再次问相同问题
+
+### need_retrieval = true 的情况：
+- 问题询问新的事实、数据、流程
+- 问题提到了具体的图号、表号、章节
+- 问题需要知识库中的文档来回答
+- 用户明确要求查看知识库内容
+
+## ⚠️ 重要规则：重复提问必须检索！
+
+**当用户重复提问相同或相似问题时：**
+- `use_context` = true（因为确实依赖历史上下文）
+- `need_retrieval` = **true**（必须重新检索！）
+
+**原因：**
+1. 用户重复提问说明之前的回答不满意或不正确
+2. 历史回答可能包含错误信息，不能直接复用
+3. 必须重新检索确保回答准确性
+
+**判断重复提问的方法：**
+- 当前问题与历史中的用户问题语义相同或高度相似
+- 例如：用户之前问"发电量"，现在又问"发电量统计"
+- 例如：用户之前问过某问题，现在换个方式再问
+
+### 两者都可以为 false：
+- 简单的闲聊、问候
+- 基于附件/图片的视觉分析请求（图片信息在上下文中）
+- 常识性问题
+
+## 输出格式
+
+只输出一个 JSON 对象，不要其他内容：
+```json
+{
+    "rewritten_query": "改写后的问题",
+    "use_context": true/false,
+    "need_retrieval": true/false,
+    "reason": "判断理由",
+    "sub_queries": ["检索子查询1", "检索子查询2"],
+    "intent": "factual"
+}
+```
+
+示例：
+- 对比类："新现代终端和普通现代终端有什么区别？" → sub_queries: ["新现代终端的定义和特点", "普通现代终端的定义和特点", "新现代终端与普通现代终端的区别"], intent: "comparison"
+- 推理类："为什么市场状态评估结果与实际投放不一致？" → sub_queries: ["市场状态评估结果与实际投放不一致的原因"], intent: "reasoning"
+- 操作指导："从普通终端升级到加盟终端需要怎么做？" → sub_queries: ["从普通终端升级到加盟终端的流程和条件"], intent: "instruction"
+- 事实类（枚举）："货源投放工作有哪些原则？" → sub_queries: ["货源投放工作的原则"], intent: "factual"
+- 事实类（定义）："市场状态有哪几种类型？" → sub_queries: ["市场状态的类型和评分标准"], intent: "factual"
+"""
+
+    def __init__(self, model: str = None, use_cache: bool = True):
+        """
+        初始化意图分析器
+
+        Args:
+            model: 使用的 LLM 模型（默认从配置读取）
+            use_cache: 是否启用语义缓存
+        """
+        self.model = model
+        self.use_cache = use_cache
+        self._client = None
+        self._cache = None
+        self._embedding_model = None
+        # 精确匹配缓存（后备方案，不依赖 embedding）
+        self._exact_cache: Dict[str, IntentAnalysis] = {}
+        self._exact_cache_max = 500
+
+    def _get_client(self):
+        """获取 LLM 客户端"""
+        if self._client is None:
+            from config import get_llm_client
+            self._client = get_llm_client()
+        return self._client
+
+    def _get_cache(self):
+        """获取语义缓存"""
+        if self._cache is None and self.use_cache:
+            try:
+                from core.semantic_cache import get_semantic_cache
+                self._cache = get_semantic_cache()
+            except Exception as e:
+                logger.warning(f"语义缓存初始化失败: {e}")
+                self._cache = None
+        return self._cache
+
+    def _get_embedding(self, text: str):
+        """获取文本向量，优先复用 engine 已加载的模型"""
+        if self._embedding_model is None:
+            # 优先从 engine 复用已加载的 embedding 模型
+            try:
+                from core.engine import get_engine
+                engine = get_engine()
+                self._embedding_model = engine.embedding_model
+                if self._embedding_model is not None:
+                    logger.info("意图分析缓存: 复用 engine 的 embedding 模型")
+                else:
+                    logger.warning("意图分析缓存: engine.embedding_model 为 None")
+            except Exception as e:
+                # fallback: 单独加载
+                logger.warning(f"意图分析缓存: 无法获取 engine 的 embedding 模型: {e}，尝试单独加载")
+                try:
+                    from sentence_transformers import SentenceTransformer
+                    self._embedding_model = SentenceTransformer('models/bge-base-zh-v1.5')
+                    logger.info("意图分析缓存: 单独加载 embedding 模型成功")
+                except Exception as e2:
+                    logger.warning(f"意图分析缓存: 嵌入模型初始化失败: {e2}")
+                    return None
+
+        try:
+            import numpy as np
+            emb = self._embedding_model.encode(text)
+            return np.array(emb, dtype='float32')
+        except Exception as e:
+            logger.warning(f"向量化失败: {e}")
+            return None
+
+    def analyze(
+        self,
+        query: str,
+        history: List[dict],
+        context_images: List[dict] = None
+    ) -> IntentAnalysis:
+        """
+        分析用户意图
+
+        Args:
+            query: 用户当前问题
+            history: 对话历史 [{"role": "user/assistant", "content": "...", "metadata": {...}}]
+            context_images: 上下文中的图片信息
+
+        Returns:
+            IntentAnalysis: 分析结果
+        """
+        # 构建历史摘要
+        history_summary = self._build_history_summary(history, context_images)
+
+        # 1. 精确匹配缓存（快速路径，不依赖 embedding）
+        exact_key = self._build_cache_key(query, history)
+        if exact_key in self._exact_cache:
+            logger.info(f"意图分析精确缓存命中: {exact_key[:50]}")
+            return self._exact_cache[exact_key]
+
+        # 2. 尝试从语义缓存获取
+        cache = self._get_cache()
+        if cache:
+            # 使用 query + 历史关键信息作为缓存键
+            cache_key = self._build_cache_key(query, history)
+            cache_emb = self._get_embedding(cache_key)
+
+            if cache_emb is not None:
+                cached = cache.get(cache_emb)
+                if cached:
+                    logger.info(f"意图分析缓存命中: {cached.get('reason', '')[:50]}")
+                    return IntentAnalysis.from_dict(cached)
+                else:
+                    logger.debug(f"意图分析缓存未命中，缓存状态: {cache.get_stats()}")
+            else:
+                logger.warning("意图分析缓存: _get_embedding 返回 None，跳过缓存")
+        else:
+            logger.warning("意图分析缓存: _get_cache 返回 None，缓存不可用")
+
+        # 构建 prompt
+        user_prompt = f"""## 对话历史
+
+{history_summary}
+
+## 当前用户消息
+
+{query}
+
+请分析用户的意图，输出 JSON 对象。"""
+
+        try:
+            client = self._get_client()
+            model = self.model or self._get_default_model()
+
+            from core.llm_utils import call_llm
+            content = call_llm(
+                client,
+                "",
+                model,
+                temperature=INTENT_TEMPERATURE,
+                max_tokens=INTENT_MAX_TOKENS,
+                messages=[
+                    {"role": "system", "content": self.SYSTEM_PROMPT},
+                    {"role": "user", "content": user_prompt}
+                ]
+            )
+
+            # 解析 JSON
+            result = self._parse_json(content)
+
+            if result:
+                # 提取子查询，默认使用原始查询
+                sub_queries = result.get("sub_queries", [query])
+                if not sub_queries:
+                    sub_queries = [query]
+
+                # 非对比类查询强制截断子查询（LLM 可能不遵守规则）
+                intent_type = result.get("intent", "factual")
+                if intent_type == "comparison":
+                    # 对比类：确保原始查询始终作为第一个子查询（避免拆分后丢失核心信息）
+                    if query not in sub_queries:
+                        sub_queries = [query] + sub_queries
+                    sub_queries = sub_queries[:3]  # 最多3个
+                elif intent_type == "reasoning":
+                    sub_queries = sub_queries[:2]  # 推理类最多2个子查询
+                elif len(sub_queries) > 1:
+                    sub_queries = sub_queries[:1]  # 其他类只保留1个
+
+                analysis = IntentAnalysis(
+                    rewritten_query=result.get("rewritten_query", query),
+                    use_context=result.get("use_context", False),
+                    need_retrieval=result.get("need_retrieval", True),
+                    confidence=0.9,
+                    reason=result.get("reason", ""),
+                    context_images=context_images or [],
+                    sub_queries=sub_queries,
+                    intent=intent_type
+                )
+
+                # 存入语义缓存
+                if cache and cache_emb is not None:
+                    cache.set(cache_emb, analysis.to_dict())
+
+                # 存入精确匹配缓存
+                if len(self._exact_cache) < self._exact_cache_max:
+                    self._exact_cache[exact_key] = analysis
+
+                return analysis
+
+        except Exception as e:
+            logger.warning(f"意图分析失败: {e}，使用默认值")
+
+        # 默认值：需要检索
+        return IntentAnalysis(
+            rewritten_query=query,
+            use_context=False,
+            need_retrieval=True,
+            confidence=0.5,
+            reason="意图分析失败，默认走检索流程",
+            context_images=context_images or [],
+            sub_queries=[query],
+            intent="factual"
+        )
+
+    def _build_cache_key(self, query: str, history: List[dict]) -> str:
+        """
+        构建缓存键
+
+        结合 query 和历史关键信息，确保相同上下文下的相同问题能命中缓存
+        """
+        parts = [query]
+
+        # 添加最近历史的关键信息
+        if history:
+            for msg in history[-2:]:  # 最近1轮
+                role = msg.get("role", "")
+                content = msg.get("content", "")[:100]  # 截断
+                parts.append(f"{role[:1]}:{content}")
+
+        return " | ".join(parts)
+
+    def _build_history_summary(
+        self,
+        history: List[dict],
+        context_images: List[dict] = None
+    ) -> str:
+        """
+        构建历史摘要
+
+        Args:
+            history: 对话历史
+            context_images: 上下文中的图片
+
+        Returns:
+            历史摘要文本
+        """
+        if not history:
+            return "（无历史对话）"
+
+        parts = []
+
+        # 提取最近 3 轮对话
+        recent_history = history[-INTENT_HISTORY_WINDOW:]
+
+        for msg in recent_history:
+            role = "用户" if msg.get("role") == "user" else "助手"
+            content = msg.get("content", "")
+
+            # 截断过长的内容
+            if len(content) > 500:
+                content = content[:500] + "..."
+
+            parts.append(f"【{role}】{content}")
+
+            # 提取图片信息
+            metadata = msg.get("metadata", {})
+            if isinstance(metadata, dict):
+                images = metadata.get("images", [])
+                if images:
+                    for img in images[:3]:
+                        if isinstance(img, dict):
+                            desc = img.get("description", "")[:100]
+                            img_type = img.get("type", "图片")
+                            parts.append(f"  └─ {img_type}: {desc}")
+
+        # 添加图片上下文
+        if context_images:
+            parts.append("\n【上下文中的图片】")
+            for img in context_images[:5]:
+                desc = img.get("description", "")[:100]
+                img_type = img.get("type", "图片")
+                parts.append(f"  - {img_type}: {desc}")
+
+        return "\n".join(parts)
+
+    def _parse_json(self, content: str) -> Optional[dict]:
+        """解析 JSON 响应"""
+        # 尝试直接解析
+        try:
+            return json.loads(content)
+        except json.JSONDecodeError:
+            pass
+
+        # 尝试提取 JSON 块
+        import re
+        json_match = re.search(r'\{[\s\S]*\}', content)
+        if json_match:
+            try:
+                return json.loads(json_match.group())
+            except json.JSONDecodeError:
+                pass
+
+        return None
+
+    def _get_default_model(self) -> str:
+        """获取意图分析专用模型"""
+        try:
+            from config import INTENT_MODEL
+            return INTENT_MODEL
+        except (ImportError, NameError):
+            return INTENT_MODEL
+
+
+# ==================== 便捷函数 ====================
+
+_analyzer = None
+
+def get_intent_analyzer() -> IntentAnalyzer:
+    """获取意图分析器单例"""
+    global _analyzer
+    if _analyzer is None:
+        _analyzer = IntentAnalyzer()
+    return _analyzer
+
+
+def analyze_intent(
+    query: str,
+    history: List[dict],
+    context_images: List[dict] = None
+) -> IntentAnalysis:
+    """
+    便捷函数：分析用户意图
+
+    Args:
+        query: 用户问题
+        history: 对话历史
+        context_images: 上下文中的图片
+
+    Returns:
+        IntentAnalysis: 分析结果
+    """
+    analyzer = get_intent_analyzer()
+    return analyzer.analyze(query, history, context_images)
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    # 测试用例
+    test_cases = [
+        {
+            "query": "分析一下这两张图片",
+            "history": [
+                {"role": "user", "content": "三峡工程有什么图片"},
+                {"role": "assistant", "content": "我找到了三峡大坝的示意图...", "metadata": {"images": [{"type": "示意图", "description": "三峡大坝全景"}]}},
+            ],
+            "expected": {"use_context": True, "need_retrieval": False}
+        },
+        {
+            "query": "三峡水库补水调度统计",
+            "history": [],
+            "expected": {"use_context": False, "need_retrieval": True}
+        },
+        {
+            "query": "继续说",
+            "history": [
+                {"role": "user", "content": "介绍一下三峡工程"},
+                {"role": "assistant", "content": "三峡工程是..."},
+            ],
+            "expected": {"use_context": True, "need_retrieval": False}
+        },
+    ]
+
+    print("=" * 60)
+    print("意图分析器测试")
+    print("=" * 60)
+
+    analyzer = IntentAnalyzer()
+
+    for i, case in enumerate(test_cases, 1):
+        print(f"\n--- 测试 {i} ---")
+        print(f"问题: {case['query']}")
+
+        result = analyzer.analyze(case["query"], case["history"])
+
+        print(f"改写后: {result.rewritten_query}")
+        print(f"use_context: {result.use_context}")
+        print(f"need_retrieval: {result.need_retrieval}")
+        print(f"理由: {result.reason}")
+        print(f"子查询: {result.sub_queries}")
+        print(f"意图: {result.intent}")

core/llm_budget.py

@@ -0,0 +1,351 @@
+# -*- coding: utf-8 -*-
+"""
+LLM 调用预算控制器
+
+控制每次查询的 LLM 调用次数，防止过度消耗
+
+功能：
+- 每次查询最大调用次数限制
+- 特定类型调用次数限制（如重写、反思）
+- 调用统计与监控
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Optional, Dict
+from enum import Enum
+import time
+import threading
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class CallType(Enum):
+    """LLM 调用类型"""
+    CLASSIFY = "classify"      # 查询分类
+    REWRITE = "rewrite"        # 查询重写
+    GENERATE = "generate"      # 答案生成
+    REFLECT = "reflect"        # 推理反思
+    DECOMPOSE = "decompose"    # 查询分解
+    WEB_SEARCH = "web_search"  # 网络搜索
+
+
+@dataclass
+class CallRecord:
+    """调用记录"""
+    call_type: CallType
+    timestamp: float
+    tokens_used: int = 0
+    success: bool = True
+    description: str = ""
+
+
+@dataclass
+class BudgetConfig:
+    """预算配置"""
+    max_calls_per_query: int = 2      # 每次查询最大调用次数
+    max_tokens_per_query: int = 8000  # 每次查询最大 token 数
+    max_rewrites: int = 1             # 最多重写次数
+    max_reflects: int = 1             # 最多反思次数
+    max_decomposes: int = 1           # 最多分解次数
+
+
+class LLMBudgetController:
+    """LLM 调用预算控制器"""
+
+    DEFAULT_CONFIG = BudgetConfig()
+
+    def __init__(self, config: BudgetConfig = None):
+        self.config = config or self.DEFAULT_CONFIG
+        self._current_query_calls: List[CallRecord] = []
+        self._lock = threading.Lock()
+        self._total_stats = {
+            "total_queries": 0,
+            "total_calls": 0,
+            "total_tokens": 0,
+            "budget_exceeded": 0
+        }
+
+    def start_query(self) -> None:
+        """开始新查询（重置计数）"""
+        with self._lock:
+            self._current_query_calls.clear()
+
+    def can_call(self, call_type: CallType) -> bool:
+        """
+        检查是否可以进行指定类型的调用
+
+        Args:
+            call_type: 调用类型
+
+        Returns:
+            是否允许调用
+        """
+        with self._lock:
+            # 检查总调用次数
+            if len(self._current_query_calls) >= self.config.max_calls_per_query:
+                logger.debug(f"LLM 预算超限: 已调用 {len(self._current_query_calls)} 次")
+                return False
+
+            # 检查特定类型限制
+            type_count = sum(
+                1 for c in self._current_query_calls
+                if c.call_type == call_type
+            )
+
+            if call_type == CallType.REWRITE and type_count >= self.config.max_rewrites:
+                logger.debug(f"重写次数超限: 已重写 {type_count} 次")
+                return False
+
+            if call_type == CallType.REFLECT and type_count >= self.config.max_reflects:
+                logger.debug(f"反思次数超限: 已反思 {type_count} 次")
+                return False
+
+            if call_type == CallType.DECOMPOSE and type_count >= self.config.max_decomposes:
+                logger.debug(f"分解次数超限: 已分解 {type_count} 次")
+                return False
+
+            return True
+
+    def record_call(self, call_type: CallType, tokens_used: int = 0,
+                    success: bool = True, description: str = "") -> CallRecord:
+        """
+        记录一次调用
+
+        Args:
+            call_type: 调用类型
+            tokens_used: 使用的 token 数
+            success: 是否成功
+            description: 调用描述
+
+        Returns:
+            调用记录
+        """
+        record = CallRecord(
+            call_type=call_type,
+            timestamp=time.time(),
+            tokens_used=tokens_used,
+            success=success,
+            description=description
+        )
+
+        with self._lock:
+            self._current_query_calls.append(record)
+            self._total_stats["total_calls"] += 1
+            self._total_stats["total_tokens"] += tokens_used
+
+        return record
+
+    def end_query(self) -> Dict:
+        """
+        结束当前查询，返回统计信息
+
+        Returns:
+            本次查询的统计信息
+        """
+        with self._lock:
+            stats = {
+                "calls": len(self._current_query_calls),
+                "tokens": sum(c.tokens_used for c in self._current_query_calls),
+                "call_types": {}
+            }
+
+            for call in self._current_query_calls:
+                type_name = call.call_type.value
+                stats["call_types"][type_name] = stats["call_types"].get(type_name, 0) + 1
+
+            if stats["calls"] >= self.config.max_calls_per_query:
+                self._total_stats["budget_exceeded"] += 1
+
+            self._total_stats["total_queries"] += 1
+            self._current_query_calls.clear()
+
+            return stats
+
+    def get_current_stats(self) -> Dict:
+        """获取当前查询的调用统计"""
+        with self._lock:
+            return {
+                "total_calls": len(self._current_query_calls),
+                "max_calls": self.config.max_calls_per_query,
+                "remaining_calls": self.config.max_calls_per_query - len(self._current_query_calls),
+                "tokens_used": sum(c.tokens_used for c in self._current_query_calls),
+                "call_types": {
+                    call.call_type.value: sum(1 for c in self._current_query_calls if c.call_type == call)
+                    for call in CallType
+                }
+            }
+
+    def get_total_stats(self) -> Dict:
+        """获取总体统计信息"""
+        with self._lock:
+            return {
+                **self._total_stats,
+                "avg_calls_per_query": (
+                    self._total_stats["total_calls"] / self._total_stats["total_queries"]
+                    if self._total_stats["total_queries"] > 0 else 0
+                ),
+                "avg_tokens_per_query": (
+                    self._total_stats["total_tokens"] / self._total_stats["total_queries"]
+                    if self._total_stats["total_queries"] > 0 else 0
+                )
+            }
+
+    def reset_stats(self) -> None:
+        """重置统计信息"""
+        with self._lock:
+            self._total_stats = {
+                "total_queries": 0,
+                "total_calls": 0,
+                "total_tokens": 0,
+                "budget_exceeded": 0
+            }
+
+
+# ==================== 全局预算控制器 ====================
+
+_budget_controller: Optional[LLMBudgetController] = None
+_budget_lock = threading.Lock()
+
+
+def get_budget_controller() -> LLMBudgetController:
+    """获取全局预算控制器实例（单例模式）"""
+    global _budget_controller
+    if _budget_controller is None:
+        with _budget_lock:
+            if _budget_controller is None:
+                # 尝试从配置加载参数
+                try:
+                    from config import MAX_LLM_CALLS_PER_QUERY, MAX_QUERY_REWRITES
+                    config = BudgetConfig(
+                        max_calls_per_query=MAX_LLM_CALLS_PER_QUERY,
+                        max_rewrites=MAX_QUERY_REWRITES
+                    )
+                    _budget_controller = LLMBudgetController(config)
+                except ImportError:
+                    _budget_controller = LLMBudgetController()
+    return _budget_controller
+
+
+def reset_budget_controller() -> None:
+    """重置全局预算控制器（主要用于测试）"""
+    global _budget_controller
+    with _budget_lock:
+        if _budget_controller is not None:
+            _budget_controller.reset_stats()
+        _budget_controller = None
+
+
+# ==================== Agent 使用判断 ====================
+
+def should_use_agent(query: str, query_type: str = None,
+                      classified_result=None) -> bool:
+    """
+    判断是否需要使用 Agent 流程
+
+    规则：
+    - META/SIMPLE/FILE_SPECIFIC: 不需要 Agent
+    - COMPARISON/PROCESS: 需要 Agent
+    - FACT: 根据复杂度判断
+
+    Args:
+        query: 用户查询
+        query_type: 查询类型字符串
+        classified_result: QueryClassifier.classify() 的返回结果
+
+    Returns:
+        是否需要 Agent 流程
+    """
+    # 如果有分类结果，使用它
+    if classified_result is not None:
+        # 检查是否跳过 LLM
+        if hasattr(classified_result, 'skip_llm_decision') and classified_result.skip_llm_decision:
+            return False
+
+        # 获取查询类型
+        qt = classified_result.query_type.value if hasattr(classified_result.query_type, 'value') \
+            else str(classified_result.query_type)
+
+        # 不需要 Agent 的类型
+        if qt in ['META', 'SIMPLE', 'FILE_SPECIFIC', 'REALTIME']:
+            return False
+
+        # 需要 Agent 的类型
+        if qt in ['COMPARISON', 'PROCESS']:
+            return True
+
+        # FACT 类型：根据复杂度判断
+        if qt == 'FACT':
+            # 查询长度
+            if len(query) > 50:
+                return True
+            # 关键词数量
+            if hasattr(classified_result, 'keywords') and len(classified_result.keywords) > 4:
+                return True
+
+        return False
+
+    # 没有分类结果，使用简单规则
+    if query_type:
+        if query_type in ['META', 'SIMPLE', 'FILE_SPECIFIC']:
+            return False
+        if query_type in ['COMPARISON', 'PROCESS']:
+            return True
+
+    # 默认：简单查询（短查询）不走 Agent
+    return len(query) > 30
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    print("=" * 60)
+    print("LLM 预算控制器测试")
+    print("=" * 60)
+
+    controller = LLMBudgetController(BudgetConfig(max_calls_per_query=3))
+    controller.start_query()
+
+    # 模拟调用
+    print("\n1. 检查调用限制")
+    print(f"   can_call(REWRITE): {controller.can_call(CallType.REWRITE)}")
+    print(f"   can_call(GENERATE): {controller.can_call(CallType.GENERATE)}")
+
+    # 记录调用
+    controller.record_call(CallType.REWRITE, tokens_used=500, description="查询重写")
+    controller.record_call(CallType.GENERATE, tokens_used=1500, description="答案生成")
+
+    print(f"\n2. 当前统计: {controller.get_current_stats()}")
+
+    # 再次检查
+    print(f"\n3. 再次检查（已调用2次）")
+    print(f"   can_call(REWRITE): {controller.can_call(CallType.REWRITE)}")  # 应该 False（重写限制1次）
+    print(f"   can_call(GENERATE): {controller.can_call(CallType.GENERATE)}")  # 应该 True
+
+    # 第三次调用后检查
+    controller.record_call(CallType.GENERATE, tokens_used=1000)
+    print(f"\n4. 调用达到上限后: {controller.can_call(CallType.GENERATE)}")  # 应该 False
+
+    # 结束查询
+    stats = controller.end_query()
+    print(f"\n5. 本次查询统计: {stats}")
+
+    # Agent 判断测试
+    print("\n" + "=" * 60)
+    print("Agent 使用判断测试")
+    print("=" * 60)
+
+    test_cases = [
+        ("什么是Python?", "SIMPLE"),
+        ("比较 Python 和 Java 的区别", "COMPARISON"),
+        ("请列出文档中的所有文件", "META"),
+        ("如何部署这个服务？", "PROCESS"),
+    ]
+
+    for query, qtype in test_cases:
+        result = should_use_agent(query, qtype)
+        print(f"   '{query[:30]}...' [{qtype}] -> use_agent: {result}")

core/llm_utils.py

@@ -0,0 +1,290 @@
+"""
+LLM 调用工具函数
+
+统一封装 LLM 调用模式，减少代码重复。
+"""
+
+import json
+import re
+import logging
+from typing import List, Optional, Union, Iterator, Callable
+
+logger = logging.getLogger(__name__)
+
+
+def call_llm(
+    client,
+    prompt: str,
+    model: str,
+    temperature: float = 0.3,
+    max_tokens: int = 1000,
+    messages: List[dict] = None,
+    stream: bool = False,
+    **kwargs
+) -> Union[str, Iterator, None]:
+    """
+    统一的 LLM 调用封装
+
+    Args:
+        client: OpenAI 客户端实例
+        prompt: 用户提示（当 messages 为 None 时使用）
+        model: 模型名称
+        temperature: 温度参数 (0-1)
+        max_tokens: 最大 token 数
+        messages: 完整消息列表（优先于 prompt）
+        stream: 是否启用流式输出
+        **kwargs: 其他参数（如 response_format, tools 等）
+
+    Returns:
+        流式模式返回 stream 迭代器，否则返回响应内容字符串
+        调用失败返回 None
+
+    Example:
+        # 简单调用
+        response = call_llm(client, "你好", model, temperature=0.3)
+
+        # 使用消息列表
+        response = call_llm(client, "", model, messages=[
+            {"role": "system", "content": "你是助手"},
+            {"role": "user", "content": "你好"}
+        ])
+
+        # 流式调用
+        for chunk in call_llm(client, prompt, model, stream=True):
+            if chunk.choices and chunk.choices[0].delta.content:
+                yield chunk.choices[0].delta.content
+    """
+    if messages is None:
+        messages = [{"role": "user", "content": prompt}]
+
+    try:
+        response = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            stream=stream,
+            **kwargs
+        )
+
+        if stream:
+            return response
+
+        content = response.choices[0].message.content
+        
+        # 推理模型兼容：content 为空时尝试从 reasoning_content 提取
+        if not content or not content.strip():
+            reasoning = getattr(response.choices[0].message, 'reasoning_content', None)
+            if reasoning and reasoning.strip():
+                # 从思维链中提取 JSON 块作为内容
+                json_match = re.search(r'\{[\s\S]*\}', reasoning)
+                if json_match:
+                    logger.info("LLM: content为空，从reasoning_content提取JSON")
+                    return json_match.group().strip()
+            logger.warning("LLM 返回空 content（可能需要增大 max_tokens）")
+            return None
+        
+        return content.strip()
+    except Exception as e:
+        logger.warning(f"LLM 调用失败: {e}")
+        return None
+
+
+def call_llm_stream(
+    client,
+    prompt: str,
+    model: str,
+    temperature: float = 0.3,
+    max_tokens: int = 1000,
+    messages: List[dict] = None,
+    error_prefix: str = "[错误]",
+    **kwargs
+) -> Iterator[str]:
+    """
+    流式 LLM 调用（生成器封装）
+
+    自动处理流式响应，逐块 yield 文本内容。
+
+    Args:
+        client: OpenAI 客户端实例
+        prompt: 用户提示
+        model: 模型名称
+        temperature: 温度参数
+        max_tokens: 最大 token 数
+        messages: 完整消息列表
+        error_prefix: 错误时的前缀
+        **kwargs: 其他参数
+
+    Yields:
+        响应文本片段
+
+    Example:
+        for text in call_llm_stream(client, "讲个故事", model):
+            print(text, end="", flush=True)
+    """
+    if messages is None:
+        messages = [{"role": "user", "content": prompt}]
+
+    try:
+        stream = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            stream=True,
+            **kwargs
+        )
+
+        for chunk in stream:
+            if chunk.choices and chunk.choices[0].delta.content:
+                yield chunk.choices[0].delta.content
+
+    except Exception as e:
+        logger.error(f"LLM 流式调用失败: {e}")
+        yield f"{error_prefix} 调用大模型失败: {str(e)}"
+
+
+def call_llm_with_retry(
+    client,
+    prompt: str,
+    model: str,
+    max_retries: int = 3,
+    retry_delay: float = 1.0,
+    **kwargs
+) -> Optional[str]:
+    """
+    带重试的 LLM 调用
+
+    Args:
+        client: OpenAI 客户端实例
+        prompt: 用户提示
+        model: 模型名称
+        max_retries: 最大重试次数
+        retry_delay: 重试间隔（秒）
+        **kwargs: 传递给 call_llm 的其他参数
+
+    Returns:
+        响应内容，全部失败返回 None
+    """
+    import time
+
+    for attempt in range(max_retries):
+        result = call_llm(client, prompt, model, **kwargs)
+        if result is not None:
+            return result
+
+        if attempt < max_retries - 1:
+            logger.debug(f"LLM 调用重试 {attempt + 1}/{max_retries}")
+            time.sleep(retry_delay)
+
+    logger.warning(f"LLM 调用失败，已重试 {max_retries} 次")
+    return None
+
+
+def parse_json_from_response(content: str) -> Optional[dict]:
+    """
+    从 LLM 响应中解析 JSON
+
+    自动处理 ```json 或 ``` 代码块格式
+
+    Args:
+        content: LLM 返回的原始内容
+
+    Returns:
+        解析后的字典，失败返回 None
+    """
+    if not content:
+        return None
+
+    # 提取 JSON 代码块（支持 ```json 或 ```）
+    json_match = re.search(r'```(?:json)?\s*([\s\S]*?)\s*```', content)
+    json_str = json_match.group(1) if json_match else content
+
+    try:
+        return json.loads(json_str.strip())
+    except (json.JSONDecodeError, TypeError, ValueError):
+        return None
+
+
+def parse_json_list_from_response(content: str) -> Optional[List[dict]]:
+    """
+    从 LLM 响应中解析 JSON 数组
+
+    Args:
+        content: LLM 返回的原始内容
+
+    Returns:
+        解析后的列表，失败返回 None
+    """
+    if not content:
+        return None
+
+    # 提取 JSON 代码块
+    json_match = re.search(r'```(?:json)?\s*([\s\S]*?)\s*```', content)
+    json_str = json_match.group(1) if json_match else content
+
+    try:
+        result = json.loads(json_str.strip())
+        if isinstance(result, list):
+            return result
+        # 如果是 {"items": [...]} 格式，提取 items
+        if isinstance(result, dict):
+            for key in ['items', 'data', 'results', 'questions']:
+                if key in result and isinstance(result[key], list):
+                    return result[key]
+        return None
+    except (json.JSONDecodeError, TypeError, ValueError):
+        return None
+
+
+# ==================== 便捷函数 ====================
+
+def quick_ask(
+    client,
+    prompt: str,
+    model: str,
+    temperature: float = 0.3
+) -> str:
+    """
+    快速提问（简化版）
+
+    Args:
+        client: OpenAI 客户端
+        prompt: 问题
+        model: 模型
+        temperature: 温度
+
+    Returns:
+        回答内容，失败返回空字符串
+    """
+    result = call_llm(client, prompt, model, temperature=temperature)
+    return result or ""
+
+
+def quick_yes_no(
+    client,
+    prompt: str,
+    model: str,
+    keywords: List[str] = None
+) -> bool:
+    """
+    快速是/否判断
+
+    Args:
+        client: OpenAI 客户端
+        prompt: 问题（需包含判断标准）
+        model: 模型
+        keywords: 判断为 True 的关键词（默认 ["是", "需要", "yes", "true"]）
+
+    Returns:
+        布尔判断结果
+    """
+    if keywords is None:
+        keywords = ["是", "需要", "yes", "true"]
+
+    result = call_llm(client, prompt, model, temperature=0, max_tokens=10)
+    if result is None:
+        return False
+
+    result_lower = result.lower()
+    return any(kw.lower() in result_lower for kw in keywords)

core/loop_guard.py

@@ -0,0 +1,373 @@
+"""
+循环检索防护模块
+
+防止 Agentic RAG 陷入无限检索循环，确保检索效率和质量。
+
+核心机制：
+1. 最大迭代数限制（max_iterations=3）
+2. 置信度递增检查（每次迭代置信度必须提升）
+3. 重复查询检测（避免重复检索相同内容）
+4. 循环中断决策（判断是否应该停止迭代）
+
+使用方式：
+    from core.loop_guard import LoopGuard, GuardDecision
+
+    guard = LoopGuard(max_iterations=3)
+    guard.record_iteration(query, confidence, results_count)
+
+    decision = guard.should_continue()
+    if decision == GuardDecision.STOP:
+        # 终止迭代
+        ...
+"""
+
+from dataclasses import dataclass
+from typing import List, Optional
+from enum import Enum
+import time
+
+
+class GuardDecision(Enum):
+    """循环防护决策"""
+    CONTINUE = "continue"       # 继续迭代
+    STOP_MAX_ITER = "stop_max_iter"  # 达到最大迭代数
+    STOP_NO_PROGRESS = "stop_no_progress"  # 无进展（置信度未提升）
+    STOP_DUPLICATE = "stop_duplicate"  # 重复查询
+    STOP_SUFFICIENT = "stop_sufficient"  # 结果已足够
+
+
+@dataclass
+class IterationRecord:
+    """迭代记录"""
+    iteration: int              # 迭代次数
+    query: str                  # 查询内容
+    confidence: float           # 置信度分数
+    results_count: int          # 检索结果数量
+    timestamp: float            # 时间戳
+    query_type: str             # 查询类型（可选）
+
+
+@dataclass
+class GuardResult:
+    """防护检查结果"""
+    decision: GuardDecision     # 决策
+    reason: str                 # 原因
+    current_iteration: int      # 当前迭代次数
+    confidence_trend: str       # 置信度趋势（"improving"/"stable"/"declining"）
+    recommendation: str         # 建议
+
+
+class LoopGuard:
+    """
+    循环检索防护器
+
+    监控迭代过程，防止无限循环和无效迭代。
+    """
+
+    # 默认参数
+    DEFAULT_MAX_ITERATIONS = 3
+    MIN_CONFIDENCE_IMPROVEMENT = 0.05  # 最小置信度提升阈值
+
+    def __init__(self, max_iterations: int = None,
+                 min_confidence_improvement: float = None):
+        """
+        初始化防护器
+
+        Args:
+            max_iterations: 最大迭代次数
+            min_confidence_improvement: 最小置信度提升阈值
+        """
+        self.max_iterations = max_iterations or self.DEFAULT_MAX_ITERATIONS
+        self.min_confidence_improvement = min_confidence_improvement or self.MIN_CONFIDENCE_IMPROVEMENT
+
+        # 迭代历史
+        self.iterations: List[IterationRecord] = []
+        self.query_history: List[str] = []
+
+    def record_iteration(self, query: str, confidence: float,
+                        results_count: int, query_type: str = None) -> IterationRecord:
+        """
+        记录一次迭代
+
+        Args:
+            query: 查询内容
+            confidence: 置信度分数
+            results_count: 检索结果数量
+            query_type: 查询类型
+
+        Returns:
+            IterationRecord: 迭代记录
+        """
+        record = IterationRecord(
+            iteration=len(self.iterations) + 1,
+            query=query,
+            confidence=confidence,
+            results_count=results_count,
+            timestamp=time.time(),
+            query_type=query_type
+        )
+
+        self.iterations.append(record)
+        self.query_history.append(query.lower().strip())
+
+        return record
+
+    def should_continue(self, current_confidence: float = None) -> GuardResult:
+        """
+        判断是否应该继续迭代
+
+        Args:
+            current_confidence: 当前置信度（可选，使用最近记录）
+
+        Returns:
+            GuardResult: 防护检查结果
+        """
+        # 检查最大迭代数
+        if len(self.iterations) >= self.max_iterations:
+            return GuardResult(
+                decision=GuardDecision.STOP_MAX_ITER,
+                reason=f"已达到最大迭代次数 {self.max_iterations}",
+                current_iteration=len(self.iterations),
+                confidence_trend=self._get_confidence_trend(),
+                recommendation="使用当前结果生成答案"
+            )
+
+        # 检查是否有记录
+        if not self.iterations:
+            return GuardResult(
+                decision=GuardDecision.CONTINUE,
+                reason="首次迭代",
+                current_iteration=0,
+                confidence_trend="unknown",
+                recommendation="继续执行首次检索"
+            )
+
+        # 获取当前置信度
+        if current_confidence is None:
+            current_confidence = self.iterations[-1].confidence
+
+        # 检查置信度是否足够高
+        if current_confidence >= 0.7:  # 高置信度阈值
+            return GuardResult(
+                decision=GuardDecision.STOP_SUFFICIENT,
+                reason=f"置信度已足够高 ({current_confidence:.3f})",
+                current_iteration=len(self.iterations),
+                confidence_trend="good",
+                recommendation="结果质量良好，可以生成答案"
+            )
+
+        # 检查置信度趋势
+        trend = self._get_confidence_trend()
+
+        if trend == "declining":
+            return GuardResult(
+                decision=GuardDecision.STOP_NO_PROGRESS,
+                reason="置信度下降，继续迭代无益",
+                current_iteration=len(self.iterations),
+                confidence_trend=trend,
+                recommendation="停止迭代，使用最佳历史结果"
+            )
+
+        if trend == "stable" and len(self.iterations) >= 2:
+            # 检查是否有显著提升
+            recent_improvement = self._get_recent_improvement()
+            if recent_improvement < self.min_confidence_improvement:
+                return GuardResult(
+                    decision=GuardDecision.STOP_NO_PROGRESS,
+                    reason=f"置信度提升不明显 ({recent_improvement:.3f} < {self.min_confidence_improvement})",
+                    current_iteration=len(self.iterations),
+                    confidence_trend=trend,
+                    recommendation="检索结果趋于稳定，停止迭代"
+                )
+
+        return GuardResult(
+            decision=GuardDecision.CONTINUE,
+            reason="迭代正常进行中",
+            current_iteration=len(self.iterations),
+            confidence_trend=trend,
+            recommendation="继续执行下一次检索"
+        )
+
+    def is_duplicate_query(self, query: str, similarity_threshold: float = 0.9) -> bool:
+        """
+        检测是否为重复查询
+
+        Args:
+            query: 待检测查询
+            similarity_threshold: 相似度阈值
+
+        Returns:
+            bool: 是否为重复查询
+        """
+        query_lower = query.lower().strip()
+
+        # 完全匹配
+        if query_lower in self.query_history:
+            return True
+
+        # 简单相似度检查（基于共同词比例）
+        try:
+            import jieba
+            query_words = set(w for w in jieba.cut(query_lower) if len(w) >= 2)
+
+            for hist_query in self.query_history:
+                hist_words = set(w for w in jieba.cut(hist_query) if len(w) >= 2)
+                if not query_words or not hist_words:
+                    continue
+
+                intersection = len(query_words & hist_words)
+                union = len(query_words | hist_words)
+                similarity = intersection / union if union > 0 else 0
+
+                if similarity >= similarity_threshold:
+                    return True
+        except ImportError:
+            pass
+
+        return False
+
+    def get_best_iteration(self) -> Optional[IterationRecord]:
+        """获取最佳迭代记录（置信度最高）"""
+        if not self.iterations:
+            return None
+        return max(self.iterations, key=lambda r: r.confidence)
+
+    def get_summary(self) -> dict:
+        """获取迭代摘要"""
+        if not self.iterations:
+            return {
+                "total_iterations": 0,
+                "best_confidence": 0,
+                "avg_confidence": 0,
+                "total_results": 0
+            }
+
+        confidences = [r.confidence for r in self.iterations]
+        return {
+            "total_iterations": len(self.iterations),
+            "best_confidence": max(confidences),
+            "avg_confidence": sum(confidences) / len(confidences),
+            "total_results": sum(r.results_count for r in self.iterations),
+            "confidence_trend": self._get_confidence_trend(),
+            "iterations": [
+                {
+                    "iteration": r.iteration,
+                    "confidence": r.confidence,
+                    "results_count": r.results_count
+                }
+                for r in self.iterations
+            ]
+        }
+
+    def _get_confidence_trend(self) -> str:
+        """获取置信度趋势"""
+        if len(self.iterations) < 2:
+            return "unknown"
+
+        confidences = [r.confidence for r in self.iterations]
+
+        # 计算趋势
+        improving_count = 0
+        declining_count = 0
+
+        for i in range(1, len(confidences)):
+            diff = confidences[i] - confidences[i-1]
+            if diff > 0.02:
+                improving_count += 1
+            elif diff < -0.02:
+                declining_count += 1
+
+        if declining_count > improving_count:
+            return "declining"
+        elif improving_count > declining_count:
+            return "improving"
+        else:
+            return "stable"
+
+    def _get_recent_improvement(self) -> float:
+        """获取最近的置信度提升"""
+        if len(self.iterations) < 2:
+            return 0.0
+
+        return self.iterations[-1].confidence - self.iterations[-2].confidence
+
+    def reset(self):
+        """重置防护器状态"""
+        self.iterations.clear()
+        self.query_history.clear()
+
+    def get_config(self) -> dict:
+        """获取配置信息"""
+        return {
+            "max_iterations": self.max_iterations,
+            "min_confidence_improvement": self.min_confidence_improvement
+        }
+
+
+def create_guard(max_iterations: int = 3) -> LoopGuard:
+    """
+    创建循环防护器实例
+
+    Args:
+        max_iterations: 最大迭代次数
+
+    Returns:
+        LoopGuard: 防护器实例
+    """
+    return LoopGuard(max_iterations=max_iterations)
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    print("=" * 60)
+    print("循环检索防护测试")
+    print("=" * 60)
+
+    guard = LoopGuard(max_iterations=3)
+
+    print(f"\n配置: {guard.get_config()}")
+
+    # 模拟迭代
+    test_iterations = [
+        ("公司报销制度", 0.3, 5),
+        ("报销流程规定", 0.45, 8),
+        ("报销审批标准", 0.42, 6),  # 置信度下降
+    ]
+
+    print("\n模拟迭代过程:")
+    for query, confidence, count in test_iterations:
+        # 检查重复
+        is_dup = guard.is_duplicate_query(query)
+        if is_dup:
+            print(f"  ⚠️ 检测到重复查询: {query}")
+
+        # 记录迭代
+        guard.record_iteration(query, confidence, count)
+
+        # 检查是否继续
+        result = guard.should_continue()
+
+        print(f"  迭代 {result.current_iteration}: {query}")
+        print(f"    置信度: {confidence:.3f}, 趋势: {result.confidence_trend}")
+        print(f"    决策: {result.decision.value}")
+        print(f"    原因: {result.reason}")
+
+        if result.decision != GuardDecision.CONTINUE:
+            print(f"    🛑 停止迭代")
+            break
+
+    print(f"\n迭代摘要:")
+    summary = guard.get_summary()
+    print(f"  总迭代数: {summary['total_iterations']}")
+    print(f"  最佳置信度: {summary['best_confidence']:.3f}")
+    print(f"  平均置信度: {summary['avg_confidence']:.3f}")
+    print(f"  总结果数: {summary['total_results']}")
+
+    print("\n" + "=" * 60)
+    print("✅ 测试完成")
+    print("=" * 60)

core/mmr.py

@@ -0,0 +1,223 @@
+# -*- coding: utf-8 -*-
+"""
+MMR（Max Marginal Relevance）去重模块
+
+功能：
+- 平衡相关性和多样性
+- 避免重复内容占据 top_k 结果
+- 前置到 rerank 之前，减少 rerank 输入量
+
+使用场景：
+召回 100 个 → MMR 去重取 30 个 → rerank 取 top_k
+"""
+
+import numpy as np
+from typing import List, Dict, Tuple, Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def cosine_similarity(vec1: np.ndarray, vec2: np.ndarray) -> float:
+    """计算两个向量的余弦相似度"""
+    norm1 = np.linalg.norm(vec1)
+    norm2 = np.linalg.norm(vec2)
+    if norm1 == 0 or norm2 == 0:
+        return 0.0
+    return float(np.dot(vec1, vec2) / (norm1 * norm2))
+
+
+def mmr_rerank(
+    query_emb: np.ndarray,
+    candidates: List[Dict],
+    top_k: int = 30,
+    lambda_param: float = 0.5,
+    emb_key: str = 'embedding'
+) -> List[Dict]:
+    """
+    Max Marginal Relevance 去重
+
+    公式: MMR = λ * Relevance - (1-λ) * Max_Similarity
+
+    Args:
+        query_emb: 查询向量
+        candidates: 候选文档列表，每个文档需包含 embedding
+        top_k: 返回数量
+        lambda_param: 相关性/多样性权衡参数 (0-1)
+            - 1.0: 只考虑相关性
+            - 0.5: 平衡相关性和多样性
+            - 0.0: 只考虑多样性
+        emb_key: embedding 在候选文档中的 key
+
+    Returns:
+        去重后的候选文档列表
+    """
+    if not candidates:
+        return []
+
+    if len(candidates) <= top_k:
+        return candidates
+
+    selected = []
+    remaining = candidates.copy()
+
+    while len(selected) < top_k and remaining:
+        mmr_scores = []
+
+        for i, cand in enumerate(remaining):
+            # 获取候选文档的 embedding
+            cand_emb = cand.get(emb_key)
+            if cand_emb is None:
+                # 没有 embedding，跳过或使用默认分数
+                mmr_scores.append(-float('inf'))
+                continue
+
+            cand_emb = np.array(cand_emb)
+
+            # 1. 相关性：与查询的相似度
+            relevance = cosine_similarity(query_emb, cand_emb)
+
+            # 2. 冗余度：与已选文档的最大相似度
+            if selected:
+                # 过滤出有 embedding 的已选文档
+                selected_with_emb = [s for s in selected if s.get(emb_key) is not None]
+                if selected_with_emb:
+                    max_sim = max(
+                        cosine_similarity(cand_emb, np.array(s.get(emb_key)))
+                        for s in selected_with_emb
+                    )
+                else:
+                    max_sim = 0.0
+            else:
+                max_sim = 0.0
+
+            # 3. MMR 分数 = λ * 相关性 - (1-λ) * 冗余度
+            mmr_score = lambda_param * relevance - (1 - lambda_param) * max_sim
+            mmr_scores.append(mmr_score)
+
+        # 选择 MMR 分数最高的
+        if mmr_scores:
+            best_idx = np.argmax(mmr_scores)
+            if mmr_scores[best_idx] > -float('inf'):
+                selected.append(remaining.pop(best_idx))
+            else:
+                # 所有候选都没有 embedding，直接取前 top_k
+                selected.extend(remaining[:top_k - len(selected)])
+                break
+
+    return selected
+
+
+def mmr_filter_by_content(
+    candidates: List[Dict],
+    top_k: int = 30,
+    similarity_threshold: float = 0.9
+) -> List[Dict]:
+    """
+    基于内容相似度的去重（简化版，不需要 embedding）
+
+    适用于：
+    - 没有 embedding 的情况
+    - 快速去重场景
+
+    Args:
+        candidates: 候选文档列表
+        top_k: 返回数量
+        similarity_threshold: 相似度阈值，超过则视为重复
+
+    Returns:
+        去重后的候选文档列表
+    """
+    if not candidates:
+        return []
+
+    if len(candidates) <= top_k:
+        return candidates
+
+    selected = []
+    remaining = candidates.copy()
+
+    while len(selected) < top_k and remaining:
+        current = remaining.pop(0)
+
+        # 检查是否与已选内容重复
+        is_duplicate = False
+        current_content = current.get('content', current.get('document', ''))[:200]
+
+        for s in selected:
+            s_content = s.get('content', s.get('document', ''))[:200]
+
+            # 简单的 Jaccard 相似度
+            words1 = set(current_content)
+            words2 = set(s_content)
+            if words1 and words2:
+                intersection = len(words1 & words2)
+                union = len(words1 | words2)
+                similarity = intersection / union if union > 0 else 0
+
+                if similarity > similarity_threshold:
+                    is_duplicate = True
+                    break
+
+        if not is_duplicate:
+            selected.append(current)
+
+    return selected
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    print("=" * 60)
+    print("MMR 去重测试")
+    print("=" * 60)
+
+    # 模拟候选文档
+    np.random.seed(42)
+
+    def random_embedding():
+        emb = np.random.randn(768)
+        return emb / np.linalg.norm(emb)
+
+    query_emb = random_embedding()
+
+    # 创建 10 个候选，前 5 个相似
+    base_emb = random_embedding()
+    candidates = []
+
+    for i in range(10):
+        if i < 5:
+            # 前 5 个与 query 相似
+            emb = query_emb + np.random.randn(768) * 0.1
+        else:
+            # 后 5 个与 query 不太相似
+            emb = random_embedding()
+
+        candidates.append({
+            'id': f'doc_{i}',
+            'content': f'文档内容 {i}',
+            'embedding': emb / np.linalg.norm(emb)
+        })
+
+    print(f"\n候选数量: {len(candidates)}")
+
+    # MMR 去重
+    selected = mmr_rerank(query_emb, candidates, top_k=5, lambda_param=0.5)
+
+    print(f"MMR 选择数量: {len(selected)}")
+    print(f"选择的文档 ID: {[c['id'] for c in selected]}")
+
+    # 计算多样性
+    embs = [c['embedding'] for c in selected]
+    diversity_scores = []
+    for i in range(len(embs)):
+        for j in range(i + 1, len(embs)):
+            sim = cosine_similarity(embs[i], embs[j])
+            diversity_scores.append(sim)
+
+    avg_similarity = np.mean(diversity_scores) if diversity_scores else 0
+    print(f"平均相似度（越低多样性越高）: {avg_similarity:.3f}")

core/quality_assessor.py

@@ -0,0 +1,576 @@
+"""
+多维质量评估模块
+
+对检索结果进行 4 维质量评估：
+1. 相关性（Relevance）：答案是否切题
+2. 完整性（Completeness）：信息是否充分
+3. 准确性（Accuracy）：是否有冲突信息
+4. 覆盖率（Coverage）：多角度覆盖
+
+总阈值：32/40 (80%)
+
+使用方式：
+    from core.quality_assessor import QualityAssessor, assess_quality
+
+    assessor = QualityAssessor(llm_client)
+    result = assessor.assess(query, documents)
+
+    if result.total_score >= 32:
+        # 质量合格，继续生成
+        ...
+"""
+
+from dataclasses import dataclass
+from typing import List, Optional
+from enum import Enum
+from config import RAG_CHAT_MODEL
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class QualityDimension(Enum):
+    """质量评估维度"""
+    RELEVANCE = "relevance"         # 相关性：答案是否切题
+    COMPLETENESS = "completeness"   # 完整性：信息是否充分
+    ACCURACY = "accuracy"           # 准确性：是否有冲突
+    COVERAGE = "coverage"           # 覆盖率：多角度覆盖
+
+
+@dataclass
+class DimensionScore:
+    """单个维度的评分"""
+    dimension: QualityDimension
+    score: int              # 0-10 分
+    reason: str             # 评分原因
+    issues: List[str]       # 发现的问题
+
+
+@dataclass
+class QualityAssessment:
+    """质量评估结果"""
+    relevance: DimensionScore
+    completeness: DimensionScore
+    accuracy: DimensionScore
+    coverage: DimensionScore
+    total_score: int        # 总分（0-40）
+    is_sufficient: bool     # 是否达标（>= 32）
+    summary: str            # 评估总结
+    recommendations: List[str]  # 改进建议
+
+
+class QualityAssessor:
+    """
+    多维质量评估器
+
+    基于报告建议的 4 维评估框架，对检索结果进行全面质量检查。
+    """
+
+    # 质量阈值（报告建议值）
+    QUALITY_THRESHOLD = 32      # 总分阈值（40分制，80%）
+    DIMENSION_WEIGHTS = {
+        QualityDimension.RELEVANCE: 1.0,
+        QualityDimension.COMPLETENESS: 1.0,
+        QualityDimension.ACCURACY: 1.0,
+        QualityDimension.COVERAGE: 1.0,
+    }
+
+    def __init__(self, llm_client=None, model: str = None):
+        """
+        初始化评估器
+
+        Args:
+            llm_client: LLM 客户端（用于语义评估）
+            model: 模型名称
+        """
+        self.llm_client = llm_client
+        self.model = model or RAG_CHAT_MODEL
+
+    def assess(self, query: str, documents: List[str],
+               metadatas: List[dict] = None) -> QualityAssessment:
+        """
+        评估检索结果质量
+
+        Args:
+            query: 用户查询
+            documents: 检索到的文档列表
+            metadatas: 文档元数据（可选）
+
+        Returns:
+            QualityAssessment: 质量评估结果
+        """
+        if not documents:
+            return self._empty_assessment()
+
+        # 使用 LLM 进行语义评估
+        if self.llm_client:
+            return self._llm_assess(query, documents, metadatas)
+        else:
+            # 降级：基于规则的评估
+            return self._rule_based_assess(query, documents, metadatas)
+
+    def _llm_assess(self, query: str, documents: List[str],
+                    metadatas: List[dict] = None) -> QualityAssessment:
+        """
+        使用 LLM 进行语义质量评估
+        """
+        # 构建文档摘要
+        doc_summary = self._summarize_documents(documents, metadatas)
+
+        prompt = f"""请对以下检索结果进行多维质量评估。
+
+## 用户查询
+{query}
+
+## 检索到的文档摘要
+{doc_summary}
+
+## 评估要求
+请从 4 个维度进行评分（每个维度 0-10 分）：
+
+1. **相关性（Relevance）**：文档内容是否直接回答用户问题？
+   - 10分：完全相关，直接回答
+   - 7-9分：高度相关，核心内容匹配
+   - 4-6分：部分相关，有侧面信息
+   - 0-3分：几乎无关
+
+2. **完整性（Completeness）**：信息是否充分完整？
+   - 10分：信息完整，可直接回答
+   - 7-9分：基本完整，缺少次要细节
+   - 4-6分：部分完整，缺少关键信息
+   - 0-3分：信息严重不足
+
+3. **准确性（Accuracy）**：文档之间是否有矛盾冲突？
+   - 10分：信息一致，无冲突
+   - 7-9分：有轻微表述差异但不影响理解
+   - 4-6分：有明显矛盾需要辨别
+   - 0-3分：严重冲突，信息不可靠
+
+4. **覆盖率（Coverage）**：是否从多个角度/来源覆盖问题？
+   - 10分：多来源、多角度全面覆盖
+   - 7-9分：有多个相关来源
+   - 4-6分：单一来源但有不同方面
+   - 0-3分：覆盖角度单一
+
+请以 JSON 格式返回评估结果：
+```json
+{{
+    "relevance": {{"score": 8, "reason": "原因", "issues": ["问题1"]}},
+    "completeness": {{"score": 7, "reason": "原因", "issues": []}},
+    "accuracy": {{"score": 9, "reason": "原因", "issues": []}},
+    "coverage": {{"score": 6, "reason": "原因", "issues": ["问题1"]}},
+    "summary": "整体评估总结",
+    "recommendations": ["建议1", "建议2"]
+}}
+```"""
+
+        try:
+            from core.llm_utils import call_llm
+            content = call_llm(
+                self.llm_client,
+                prompt,
+                self.model,
+                temperature=0.1,
+                max_tokens=800
+            )
+            if content is None:
+                return self._rule_based_assess(query, documents, metadatas)
+            return self._parse_llm_response(content)
+
+        except Exception as e:
+            logger.warning(f"LLM 质量评估失败: {e}")
+            return self._rule_based_assess(query, documents, metadatas)
+
+    def _parse_llm_response(self, content: str) -> QualityAssessment:
+        """解析 LLM 返回的 JSON"""
+        import json
+        import re
+
+        # 提取 JSON 块
+        json_match = re.search(r'```json\s*([\s\S]*?)\s*```', content)
+        if json_match:
+            json_str = json_match.group(1)
+        else:
+            # 尝试直接解析
+            json_str = content
+
+        try:
+            data = json.loads(json_str)
+        except json.JSONDecodeError:
+            # 解析失败，返回默认评估
+            return self._default_assessment()
+
+        # 构建维度评分
+        relevance = DimensionScore(
+            dimension=QualityDimension.RELEVANCE,
+            score=min(10, max(0, data.get("relevance", {}).get("score", 5))),
+            reason=data.get("relevance", {}).get("reason", ""),
+            issues=data.get("relevance", {}).get("issues", [])
+        )
+
+        completeness = DimensionScore(
+            dimension=QualityDimension.COMPLETENESS,
+            score=min(10, max(0, data.get("completeness", {}).get("score", 5))),
+            reason=data.get("completeness", {}).get("reason", ""),
+            issues=data.get("completeness", {}).get("issues", [])
+        )
+
+        accuracy = DimensionScore(
+            dimension=QualityDimension.ACCURACY,
+            score=min(10, max(0, data.get("accuracy", {}).get("score", 5))),
+            reason=data.get("accuracy", {}).get("reason", ""),
+            issues=data.get("accuracy", {}).get("issues", [])
+        )
+
+        coverage = DimensionScore(
+            dimension=QualityDimension.COVERAGE,
+            score=min(10, max(0, data.get("coverage", {}).get("score", 5))),
+            reason=data.get("coverage", {}).get("reason", ""),
+            issues=data.get("coverage", {}).get("issues", [])
+        )
+
+        total_score = relevance.score + completeness.score + accuracy.score + coverage.score
+
+        return QualityAssessment(
+            relevance=relevance,
+            completeness=completeness,
+            accuracy=accuracy,
+            coverage=coverage,
+            total_score=total_score,
+            is_sufficient=total_score >= self.QUALITY_THRESHOLD,
+            summary=data.get("summary", ""),
+            recommendations=data.get("recommendations", [])
+        )
+
+    def _rule_based_assess(self, query: str, documents: List[str],
+                           metadatas: List[dict] = None) -> QualityAssessment:
+        """
+        基于规则的质量评估（降级方案）
+        """
+        # 相关性：基于关键词匹配
+        relevance_score = self._assess_relevance(query, documents)
+
+        # 完整性：基于文档长度和数量
+        completeness_score = self._assess_completeness(documents)
+
+        # 准确性：假设一致（降级方案无法检测冲突）
+        accuracy_score = 8  # 默认较高分数
+
+        # 覆盖率：基于来源多样性
+        coverage_score = self._assess_coverage(documents, metadatas)
+
+        total_score = relevance_score + completeness_score + accuracy_score + coverage_score
+
+        return QualityAssessment(
+            relevance=DimensionScore(
+                dimension=QualityDimension.RELEVANCE,
+                score=relevance_score,
+                reason=f"关键词匹配评估（规则降级）",
+                issues=[]
+            ),
+            completeness=DimensionScore(
+                dimension=QualityDimension.COMPLETENESS,
+                score=completeness_score,
+                reason=f"基于文档长度和数量评估（规则降级）",
+                issues=[]
+            ),
+            accuracy=DimensionScore(
+                dimension=QualityDimension.ACCURACY,
+                score=accuracy_score,
+                reason="降级方案：假设信息一致",
+                issues=[]
+            ),
+            coverage=DimensionScore(
+                dimension=QualityDimension.COVERAGE,
+                score=coverage_score,
+                reason=f"基于来源多样性评估（规则降级）",
+                issues=[]
+            ),
+            total_score=total_score,
+            is_sufficient=total_score >= self.QUALITY_THRESHOLD,
+            summary="基于规则的质量评估（LLM 不可用）",
+            recommendations=["建议启用 LLM 进行更精确的语义评估"]
+        )
+
+    def _assess_relevance(self, query: str, documents: List[str]) -> int:
+        """评估相关性（关键词匹配）"""
+        try:
+            import jieba
+
+            # 提取查询关键词
+            query_words = set()
+            for word in jieba.cut(query):
+                word = word.strip()
+                if len(word) >= 2:
+                    query_words.add(word.lower())
+
+            if not query_words:
+                return 5
+
+            # 计算每个文档的关键词覆盖率
+            coverages = []
+            for doc in documents:
+                doc_lower = doc.lower()
+                matched = sum(1 for word in query_words if word in doc_lower)
+                coverages.append(matched / len(query_words))
+
+            avg_coverage = sum(coverages) / len(coverages) if coverages else 0
+
+            # 映射到 0-10 分
+            if avg_coverage >= 0.8:
+                return 9
+            elif avg_coverage >= 0.6:
+                return 7
+            elif avg_coverage >= 0.4:
+                return 5
+            elif avg_coverage >= 0.2:
+                return 3
+            else:
+                return 1
+
+        except ImportError:
+            return 5
+
+    def _assess_completeness(self, documents: List[str]) -> int:
+        """评估完整性（基于文档长度和数量）"""
+        if not documents:
+            return 0
+
+        # 文档数量评估
+        doc_count_score = min(3, len(documents))  # 最多 3 分
+
+        # 文档长度评估
+        total_length = sum(len(doc) for doc in documents)
+        if total_length >= 2000:
+            length_score = 7
+        elif total_length >= 1000:
+            length_score = 5
+        elif total_length >= 500:
+            length_score = 3
+        else:
+            length_score = 1
+
+        return min(10, doc_count_score + length_score)
+
+    def _assess_coverage(self, documents: List[str],
+                         metadatas: List[dict] = None) -> int:
+        """评估覆盖率（来源多样性）"""
+        if not metadatas:
+            # 无元数据，基于文档内容差异度
+            if len(documents) >= 3:
+                return 7
+            elif len(documents) >= 2:
+                return 5
+            else:
+                return 3
+
+        # 统计来源多样性
+        sources = set()
+        for meta in metadatas:
+            if isinstance(meta, dict):
+                source = meta.get("source", "")
+                if source:
+                    sources.add(source)
+
+        source_count = len(sources)
+        if source_count >= 3:
+            return 9
+        elif source_count >= 2:
+            return 7
+        elif source_count == 1:
+            return 5
+        else:
+            return 3
+
+    def _summarize_documents(self, documents: List[str],
+                            metadatas: List[dict] = None) -> str:
+        """生成文档摘要用于 LLM 评估"""
+        summary_parts = []
+
+        for i, doc in enumerate(documents[:5], 1):  # 最多 5 个文档
+            source = ""
+            if metadatas and i <= len(metadatas):
+                meta = metadatas[i - 1]
+                if isinstance(meta, dict):
+                    source = meta.get("source", "未知来源")
+                    page = meta.get("page", "")
+                    if page:
+                        source += f" (第{page}页)"
+
+            content = doc[:300] + "..." if len(doc) > 300 else doc
+            summary_parts.append(f"### 文档 {i} ({source})\n{content}")
+
+        return "\n\n".join(summary_parts)
+
+    def _empty_assessment(self) -> QualityAssessment:
+        """空评估结果"""
+        return QualityAssessment(
+            relevance=DimensionScore(
+                dimension=QualityDimension.RELEVANCE,
+                score=0,
+                reason="无检索结果",
+                issues=[]
+            ),
+            completeness=DimensionScore(
+                dimension=QualityDimension.COMPLETENESS,
+                score=0,
+                reason="无检索结果",
+                issues=[]
+            ),
+            accuracy=DimensionScore(
+                dimension=QualityDimension.ACCURACY,
+                score=0,
+                reason="无检索结果",
+                issues=[]
+            ),
+            coverage=DimensionScore(
+                dimension=QualityDimension.COVERAGE,
+                score=0,
+                reason="无检索结果",
+                issues=[]
+            ),
+            total_score=0,
+            is_sufficient=False,
+            summary="无检索结果可供评估",
+            recommendations=["请尝试其他查询方式"]
+        )
+
+    def _default_assessment(self) -> QualityAssessment:
+        """默认评估结果（解析失败时）"""
+        return QualityAssessment(
+            relevance=DimensionScore(
+                dimension=QualityDimension.RELEVANCE,
+                score=5,
+                reason="评估解析失败，使用默认分数",
+                issues=[]
+            ),
+            completeness=DimensionScore(
+                dimension=QualityDimension.COMPLETENESS,
+                score=5,
+                reason="评估解析失败，使用默认分数",
+                issues=[]
+            ),
+            accuracy=DimensionScore(
+                dimension=QualityDimension.ACCURACY,
+                score=5,
+                reason="评估解析失败，使用默认分数",
+                issues=[]
+            ),
+            coverage=DimensionScore(
+                dimension=QualityDimension.COVERAGE,
+                score=5,
+                reason="评估解析失败，使用默认分数",
+                issues=[]
+            ),
+            total_score=20,
+            is_sufficient=False,
+            summary="LLM 评估解析失败",
+            recommendations=["请检查 LLM 响应格式"]
+        )
+
+    def get_threshold_info(self) -> dict:
+        """获取阈值信息"""
+        return {
+            "quality_threshold": self.QUALITY_THRESHOLD,
+            "max_score": 40,
+            "pass_percentage": f"{self.QUALITY_THRESHOLD / 40 * 100}%",
+            "dimensions": ["relevance", "completeness", "accuracy", "coverage"]
+        }
+
+
+def create_assessor() -> QualityAssessor:
+    """
+    创建质量评估器实例
+
+    自动从配置获取 LLM 客户端。
+
+    Returns:
+        QualityAssessor: 质量评估器实例
+    """
+    try:
+        from openai import OpenAI
+        try:
+            from config import API_KEY, BASE_URL, RAG_CHAT_MODEL
+            MODEL = RAG_CHAT_MODEL
+        except ImportError:
+            from config import API_KEY, BASE_URL
+            MODEL = "qwen3.5-flash"  # fallback
+
+        client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
+        return QualityAssessor(llm_client=client, model=MODEL)
+
+    except Exception as e:
+        logger.warning(f"创建质量评估器失败，使用降级模式: {e}")
+        return QualityAssessor()
+
+
+def assess_quality(query: str, documents: List[str],
+                   metadatas: List[dict] = None) -> QualityAssessment:
+    """
+    便捷函数：评估检索结果质量
+
+    Args:
+        query: 用户查询
+        documents: 检索到的文档列表
+        metadatas: 文档元数据（可选）
+
+    Returns:
+        QualityAssessment: 质量评估结果
+    """
+    assessor = create_assessor()
+    return assessor.assess(query, documents, metadatas)
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    print("=" * 60)
+    print("多维质量评估测试")
+    print("=" * 60)
+
+    # 测试用例
+    test_cases = [
+        {
+            "query": "公司报销制度是怎样的？",
+            "documents": [
+                "公司报销制度规定员工可以报销差旅费用，需提供发票和审批单。报销流程：提交申请 -> 部门审批 -> 财务审核 -> 打款。",
+                "差旅报销标准：高铁一等座、飞机经济舱、住宿每天500元内。超过标准需特批。",
+                "报销时限：费用发生后30天内提交，逾期不予受理。"
+            ],
+            "description": "高质量检索结果"
+        },
+        {
+            "query": "量子计算的基本原理",
+            "documents": [
+                "文档中提到了一些技术细节...",
+                "另一个不相关的内容..."
+            ],
+            "description": "低质量检索结果"
+        }
+    ]
+
+    assessor = QualityAssessor()  # 不使用 LLM 的规则评估
+
+    print(f"\n阈值配置: {assessor.get_threshold_info()}")
+    print()
+
+    for i, case in enumerate(test_cases, 1):
+        print(f"测试 {i}: {case['description']}")
+        print(f"查询: {case['query']}")
+        print(f"文档数: {len(case['documents'])}")
+
+        result = assessor.assess(case['query'], case['documents'])
+
+        print(f"\n评分结果:")
+        print(f"  相关性: {result.relevance.score}/10 - {result.relevance.reason}")
+        print(f"  完整性: {result.completeness.score}/10 - {result.completeness.reason}")
+        print(f"  准确性: {result.accuracy.score}/10 - {result.accuracy.reason}")
+        print(f"  覆盖率: {result.coverage.score}/10 - {result.coverage.reason}")
+        print(f"\n  总分: {result.total_score}/40")
+        print(f"  达标: {'✅ 是' if result.is_sufficient else '❌ 否'} (阈值: 32)")
+        print(f"  总结: {result.summary}")
+        print()

core/query_classifier.py

@@ -0,0 +1,26 @@
+"""
+查询分类工具
+
+当前仅保留枚举查询检测函数（被生产管线使用）。
+原有的 QueryClassifier 类和 classify() 方法未被生产路径调用，已清理。
+
+使用方式：
+    from core.query_classifier import is_enumeration_query
+
+    if is_enumeration_query(query):
+        # 枚举类查询需要连续上下文
+        ...
+"""
+import re
+
+
+def is_enumeration_query(query: str) -> bool:
+    """Return True for list/numbered-clause questions that need contiguous context."""
+    if not query:
+        return False
+
+    strong_markers = ("哪些", "有哪些", "列出", "严禁", "禁止", "不得", "包括", "要求")
+    if any(marker in query for marker in strong_markers):
+        return True
+
+    return bool(re.search(r"(哪几|几类|几种|多少项|第[一二三四五六七八九十\d]+条)", query))

core/query_decomposer.py

@@ -0,0 +1,454 @@
+"""
+Query 拆分器 - 复杂查询分解
+
+核心功能：
+1. 识别需要拆分的复杂查询
+2. 将对比类、推理类问题拆分为子查询
+3. 支持并行检索和结果合并
+
+使用方式：
+    from core.query_decomposer import QueryDecomposer
+
+    decomposer = QueryDecomposer()
+    result = decomposer.decompose("Transformer 和 CNN 的区别是什么？")
+    print(result.sub_queries)  # ["Transformer的核心原理", "CNN的核心原理", "两者的主要区别"]
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Optional, Tuple
+import re
+
+
+@dataclass
+class DecomposedQuery:
+    """拆分后的查询结果"""
+    original_query: str              # 原始查询
+    sub_queries: List[str]           # 子查询列表
+    query_type: str                  # 拆分类型 (comparison, multi_concept, reasoning)
+    entities: List[str]              # 识别的实体
+    needs_merge: bool                # 是否需要合并答案
+    merge_strategy: str              # 合并策略 (compare, summarize, synthesize)
+
+    def to_dict(self) -> dict:
+        return {
+            "original_query": self.original_query,
+            "sub_queries": self.sub_queries,
+            "query_type": self.query_type,
+            "entities": self.entities,
+            "needs_merge": self.needs_merge,
+            "merge_strategy": self.merge_strategy
+        }
+
+
+class QueryDecomposer:
+    """
+    查询拆分器
+
+    拆分策略：
+    1. 对比类查询：拆分为各实体的独立查询 + 对比查询
+    2. 多概念查询：拆分为各概念的独立查询
+    3. 推理类查询：拆分为前提验证 + 推理步骤
+
+    触发条件：
+    - 包含"区别"、"对比"、"比较"等关键词
+    - 包含多个实体
+    - 复杂推理问题
+    """
+
+    # 对比类关键词
+    COMPARISON_KEYWORDS = [
+        "区别", "对比", "比较", "差异", "不同", "差别",
+        "哪个更好", "哪个更", "vs", "VS", "还是",
+        "一样吗", "有什么不同", "有什么区别",
+        "优缺点", "利弊", "优劣", "相比"
+    ]
+
+    # 多实体连接词
+    ENTITY_CONNECTORS = ["和", "与", "跟", "及", "以及", "和", "同"]
+
+    # 推理类关键词
+    REASONING_KEYWORDS = [
+        "为什么", "原因", "怎么导致", "如何影响",
+        "怎么会", "为何", "是什么导致"
+    ]
+
+    # 列举类关键词
+    LIST_KEYWORDS = [
+        "有哪些", "有什么", "列举", "分别",
+        "都有哪些", "各有什么"
+    ]
+
+    def __init__(self, llm_client=None, llm_model: str = None):
+        """
+        初始化拆分器
+
+        Args:
+            llm_client: LLM客户端（可选，用于复杂拆分）
+            llm_model: 模型名称
+        """
+        self.llm_client = llm_client
+        self.llm_model = llm_model
+
+    def should_decompose(self, query: str) -> Tuple[bool, str]:
+        """
+        判断是否需要拆分
+
+        Args:
+            query: 用户查询
+
+        Returns:
+            (needs_decompose, decompose_type)
+        """
+        # 对比类查询
+        if any(kw in query for kw in self.COMPARISON_KEYWORDS):
+            # 检查是否有多个实体
+            entities = self._extract_entities_for_comparison(query)
+            if len(entities) >= 2:
+                return True, "comparison"
+
+        # 推理类查询（复杂）
+        if any(kw in query for kw in self.REASONING_KEYWORDS):
+            # 简单推理不拆分
+            if len(query) > 30:  # 长推理问题
+                return True, "reasoning"
+
+        return False, ""
+
+    def decompose(self, query: str) -> DecomposedQuery:
+        """
+        拆分查询
+
+        Args:
+            query: 用户查询
+
+        Returns:
+            DecomposedQuery: 拆分结果
+        """
+        needs_decompose, decompose_type = self.should_decompose(query)
+
+        if not needs_decompose:
+            return DecomposedQuery(
+                original_query=query,
+                sub_queries=[query],
+                query_type="simple",
+                entities=[],
+                needs_merge=False,
+                merge_strategy="none"
+            )
+
+        if decompose_type == "comparison":
+            return self._decompose_comparison(query)
+        elif decompose_type == "reasoning":
+            return self._decompose_reasoning(query)
+
+        return DecomposedQuery(
+            original_query=query,
+            sub_queries=[query],
+            query_type="unknown",
+            entities=[],
+            needs_merge=False,
+            merge_strategy="none"
+        )
+
+    def _decompose_comparison(self, query: str) -> DecomposedQuery:
+        """
+        拆分对比类查询
+
+        示例：
+            "A和B的区别是什么？" → ["A的核心原理是什么？", "B的核心原理是什么？", "A和B的主要区别有哪些？"]
+        """
+        entities = self._extract_entities_for_comparison(query)
+        entities = [e for e in entities if len(e) >= 2]  # 过滤短实体
+
+        if len(entities) < 2:
+            # 无法识别实体，返回原查询
+            return DecomposedQuery(
+                original_query=query,
+                sub_queries=[query],
+                query_type="comparison_fallback",
+                entities=[],
+                needs_merge=False,
+                merge_strategy="none"
+            )
+
+        sub_queries = []
+
+        # 为每个实体创建独立查询
+        for entity in entities:
+            sub_queries.append(f"{entity}是什么？")
+            sub_queries.append(f"{entity}的主要特点有哪些？")
+
+        # 添加对比查询
+        entity_str = "和".join(entities[:2])  # 最多两个实体
+        sub_queries.append(f"{entity_str}的主要区别有哪些？")
+
+        # 去重
+        sub_queries = list(dict.fromkeys(sub_queries))
+
+        return DecomposedQuery(
+            original_query=query,
+            sub_queries=sub_queries,
+            query_type="comparison",
+            entities=entities,
+            needs_merge=True,
+            merge_strategy="compare"
+        )
+
+    def _decompose_reasoning(self, query: str) -> DecomposedQuery:
+        """
+        拆分推理类查询
+
+        示例：
+            "为什么A会导致B？" → ["A是什么？", "B是什么？", "A和B的关系是什么？", "A导致B的原因是什么？"]
+        """
+        # 简单实现：提取关键概念
+        # 尝试使用LLM进行更精确的拆分
+        if self.llm_client:
+            return self._llm_decompose(query, "reasoning")
+
+        # 降级：返回原查询 + 相关概念查询
+        return DecomposedQuery(
+            original_query=query,
+            sub_queries=[query, f"{query[:10]}...的相关背景"],
+            query_type="reasoning",
+            entities=[],
+            needs_merge=True,
+            merge_strategy="synthesize"
+        )
+
+    def _llm_decompose(self, query: str, query_type: str) -> DecomposedQuery:
+        """
+        使用LLM进行查询拆分
+
+        Args:
+            query: 原始查询
+            query_type: 查询类型
+
+        Returns:
+            DecomposedQuery
+        """
+        if not self.llm_client:
+            return DecomposedQuery(
+                original_query=query,
+                sub_queries=[query],
+                query_type=query_type,
+                entities=[],
+                needs_merge=False,
+                merge_strategy="none"
+            )
+
+        prompt = f"""请将以下复杂查询拆分为多个简单的子查询，便于分别检索。
+
+原始查询：{query}
+
+要求：
+1. 每个子查询应该简单明确，便于检索
+2. 子查询应该覆盖原查询的关键信息需求
+3. 返回JSON格式：{{"sub_queries": ["子查询1", "子查询2", ...], "entities": ["实体1", "实体2"], "merge_strategy": "compare/summarize/synthesize"}}
+
+只返回JSON，不要其他内容。"""
+
+        try:
+            from core.llm_utils import call_llm
+            result_text = call_llm(
+                self.llm_client,
+                prompt,
+                self.llm_model,
+                temperature=0.1,
+                max_tokens=300
+            )
+            if result_text is None:
+                return DecomposedQuery(
+                    original_query=query,
+                    sub_queries=[query],
+                    query_type=query_type,
+                    entities=[],
+                    needs_merge=False,
+                    merge_strategy="none"
+                )
+
+            import json
+
+            # 解析JSON
+            json_match = re.search(r'\{[^}]+\}', result_text, re.DOTALL)
+            if json_match:
+                data = json.loads(json_match.group())
+                sub_queries = data.get('sub_queries', [query])
+                entities = data.get('entities', [])
+                merge_strategy = data.get('merge_strategy', 'synthesize')
+
+                return DecomposedQuery(
+                    original_query=query,
+                    sub_queries=sub_queries if sub_queries else [query],
+                    query_type=query_type,
+                    entities=entities,
+                    needs_merge=True,
+                    merge_strategy=merge_strategy
+                )
+        except Exception as e:
+            # LLM拆分失败，返回原查询
+            pass
+
+        return DecomposedQuery(
+            original_query=query,
+            sub_queries=[query],
+            query_type=query_type,
+            entities=[],
+            needs_merge=False,
+            merge_strategy="none"
+        )
+
+    def _extract_entities_for_comparison(self, query: str) -> List[str]:
+        """
+        从对比类查询中提取实体
+
+        示例：
+            "A和B的区别" → ["A", "B"]
+            "年假和病假有什么不同" → ["年假", "病假"]
+        """
+        entities = []
+
+        # 预处理：移除常见的疑问后缀
+        query_clean = query
+        suffixes = ["有什么区别", "的区别是什么", "有什么不同", "的不同是什么",
+                   "的对比是什么", "的比较是什么", "的差异是什么",
+                   "的区别", "的不同", "的对比", "的比较", "的差异",
+                   "对比", "比较", "哪个更好", "哪个更"]
+        for suffix in suffixes:
+            if query_clean.endswith(suffix):
+                query_clean = query_clean[:-len(suffix)]
+                break
+
+        # 方法1：使用连接词分割
+        for connector in self.ENTITY_CONNECTORS:
+            if connector in query_clean:
+                parts = query_clean.split(connector)
+                if len(parts) >= 2:
+                    # 提取第一个部分的主语
+                    first = self._clean_entity(parts[0])
+                    # 提取第二个部分的主语
+                    second = self._clean_entity(parts[1])
+
+                    if first and 2 <= len(first) <= 15:
+                        entities.append(first)
+                    if second and 2 <= len(second) <= 15:
+                        entities.append(second)
+                break
+
+        # 方法2：使用正则匹配常见模式
+        if not entities:
+            # 模式：实体1和实体2
+            match = re.search(r'([^\s，。？!！?？和与跟及]+)[和与跟及]([^\s，。？!！?？的]+)', query)
+            if match:
+                e1 = match.group(1).strip()
+                e2 = match.group(2).strip()
+                if len(e1) >= 2 and len(e1) <= 15:
+                    entities.append(e1)
+                if len(e2) >= 2 and len(e2) <= 15:
+                    entities.append(e2)
+
+        # 去重
+        entities = list(dict.fromkeys(entities))
+
+        return entities[:3]  # 最多3个实体
+
+    def _clean_entity(self, text: str) -> str:
+        """
+        清理实体文本
+
+        移除常见的修饰词和标点
+        """
+        text = text.strip()
+
+        # 移除开头的修饰词
+        prefixes = ["请问", "我想知道", "帮我查", "查一下"]
+        for prefix in prefixes:
+            if text.startswith(prefix):
+                text = text[len(prefix):]
+
+        # 移除结尾的标点和疑问词
+        text = re.sub(r'[？?！!。，,、]+$', '', text)
+        text = re.sub(r'(是什么|有什么|有哪些|怎么样|如何|有什么区别|有什么不同)$', '', text)
+
+        # 移除结尾的"区别"、"不同"等
+        text = re.sub(r'(的区别|的不同|区别|不同)$', '', text)
+
+        return text.strip()
+
+    # ==================== 扩展接口 ====================
+
+    def decompose_with_context(
+        self,
+        query: str,
+        history: List[dict] = None,
+        context: str = None
+    ) -> DecomposedQuery:
+        """
+        带上下文的查询拆分
+
+        Args:
+            query: 用户查询
+            history: 对话历史
+            context: 额外上下文
+
+        Returns:
+            DecomposedQuery
+        """
+        # 当前实现不需要上下文，预留接口
+        return self.decompose(query)
+
+
+# ==================== 便捷函数 ====================
+
+def decompose_query(query: str, llm_client=None, llm_model: str = None) -> DecomposedQuery:
+    """
+    便捷函数：拆分查询
+
+    Args:
+        query: 用户查询
+        llm_client: LLM客户端（可选）
+        llm_model: 模型名称
+
+    Returns:
+        DecomposedQuery: 拆分结果
+    """
+    decomposer = QueryDecomposer(llm_client=llm_client, llm_model=llm_model)
+    return decomposer.decompose(query)
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    # 测试用例
+    test_queries = [
+        # 对比类
+        "年假和病假有什么区别？",
+        "Transformer和CNN的对比",
+        "A和B哪个更好？",
+        "主导品规和护卫品规有什么区别？",
+
+        # 推理类
+        "为什么2022年三峡电站发电量较低？",
+
+        # 简单查询（不需要拆分）
+        "货源投放的总体要求是什么？",
+        "报销标准",
+    ]
+
+    decomposer = QueryDecomposer()
+
+    print("=" * 60)
+    print("查询拆分器测试")
+    print("=" * 60)
+
+    for query in test_queries:
+        result = decomposer.decompose(query)
+        print(f"\n原始查询: {query}")
+        print(f"拆分类型: {result.query_type}")
+        print(f"实体: {result.entities}")
+        print(f"子查询: {result.sub_queries}")
+        print(f"需要合并: {result.needs_merge} ({result.merge_strategy})")

core/query_expansion.py

@@ -0,0 +1,271 @@
+# -*- coding: utf-8 -*-
+"""
+Query Expansion 模块（安全版）
+
+功能：
+- 查询扩展：扩展查询词，提升召回率
+- 安全过滤：扩展词必须与原查询相似度 > threshold
+- 防止噪声词污染检索
+
+使用方式：
+    from core.query_expansion import expand_query_safe, expand_query_data_driven
+
+    # 方案A：相似度过滤
+    expansions = expand_query_safe(query, threshold=0.8)
+
+    # 方案B：数据驱动扩展
+    expansions = expand_query_data_driven(query, vector_store)
+"""
+
+import logging
+from typing import List, Dict, Optional, Set
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+# ==================== 领域术语词典 ====================
+# 可根据实际业务扩展
+
+DOMAIN_TERMS = {
+    # 报销相关
+    "报销": ["差旅报销", "费用报销", "报销审批", "报销标准", "报销流程"],
+    "出差": ["差旅", "出差申请", "出差审批", "差旅费"],
+    "请假": ["休假申请", "请假审批", "年假", "事假", "病假"],
+
+    # 人事相关
+    "入职": ["入职办理", "新员工", "入职流程", "试用期"],
+    "离职": ["离职办理", "辞职", "离职流程", "离职审批"],
+    "薪资": ["工资", "薪酬", "薪资结构", "绩效考核"],
+
+    # 通用
+    "流程": ["办理流程", "操作流程", "审批流程"],
+    "标准": ["标准规范", "规定", "制度"],
+    "申请": ["申请流程", "申请条件", "申请材料"],
+}
+
+
+def get_domain_terms(query: str) -> List[str]:
+    """
+    从领域词典获取扩展词
+
+    Args:
+        query: 用户查询
+
+    Returns:
+        扩展词列表
+    """
+    expansions = []
+
+    for keyword, terms in DOMAIN_TERMS.items():
+        if keyword in query:
+            expansions.extend(terms)
+
+    return expansions
+
+
+def cosine_similarity(vec1: np.ndarray, vec2: np.ndarray) -> float:
+    """计算余弦相似度"""
+    norm1 = np.linalg.norm(vec1)
+    norm2 = np.linalg.norm(vec2)
+    if norm1 == 0 or norm2 == 0:
+        return 0.0
+    return float(np.dot(vec1, vec2) / (norm1 * norm2))
+
+
+def expand_query_safe(
+    query: str,
+    embedding_model=None,
+    threshold: float = 0.8,
+    max_expansions: int = 5
+) -> List[str]:
+    """
+    安全的查询扩展（带相似度过滤）
+
+    Args:
+        query: 用户查询
+        embedding_model: embedding 模型（用于计算相似度）
+        threshold: 相似度阈值，扩展词必须 > threshold
+        max_expansions: 最大扩展数量
+
+    Returns:
+        扩展后的查询列表（包含原查询）
+    """
+    expansions = [query]
+
+    # 1. 从领域词典获取候选扩展词
+    domain_candidates = get_domain_terms(query)
+
+    if not domain_candidates:
+        return expansions
+
+    # 2. 如果没有 embedding 模型，直接返回领域词（但限制数量）
+    if embedding_model is None:
+        # 没有 embedding，只取前几个
+        expansions.extend(domain_candidates[:max_expansions])
+        return list(set(expansions))
+
+    # 3. 有 embedding 模型，做相似度过滤
+    try:
+        query_emb = embedding_model.encode(query)
+
+        scored_candidates = []
+        for candidate in domain_candidates:
+            cand_emb = embedding_model.encode(candidate)
+            similarity = cosine_similarity(query_emb, cand_emb)
+
+            if similarity > threshold:
+                scored_candidates.append((candidate, similarity))
+
+        # 按相似度排序，取前 max_expansions
+        scored_candidates.sort(key=lambda x: x[1], reverse=True)
+        filtered = [c[0] for c in scored_candidates[:max_expansions]]
+
+        expansions.extend(filtered)
+
+    except Exception as e:
+        logger.warning(f"Query expansion embedding 计算失败: {e}")
+        # 降级：直接使用领域词
+        expansions.extend(domain_candidates[:max_expansions])
+
+    return list(set(expansions))
+
+
+def expand_query_data_driven(
+    query: str,
+    search_func=None,
+    top_k: int = 3
+) -> List[str]:
+    """
+    数据驱动的查询扩展
+
+    从向量库中查找相似查询，而非使用规则词典
+
+    Args:
+        query: 用户查询
+        search_func: 向量检索函数
+        top_k: 扩展数量
+
+    Returns:
+        扩展后的查询列表
+    """
+    expansions = [query]
+
+    if search_func is None:
+        return expansions
+
+    try:
+        # 在向量库中搜索相似文档
+        # 取文档的前几个关键词作为扩展
+        results = search_func(query, top_k=top_k)
+
+        if results and results.get('documents') and results['documents'][0]:
+            for doc in results['documents'][0][:top_k]:
+                # 从文档中提取关键词
+                keywords = extract_keywords(doc, top_n=2)
+                expansions.extend(keywords)
+
+    except Exception as e:
+        logger.warning(f"数据驱动扩展失败: {e}")
+
+    return list(set(expansions))
+
+
+def extract_keywords(text: str, top_n: int = 3) -> List[str]:
+    """
+    从文本中提取关键词（简单实现）
+
+    Args:
+        text: 文本内容
+        top_n: 提取数量
+
+    Returns:
+        关键词列表
+    """
+    try:
+        import jieba
+        import jieba.analyse
+
+        keywords = jieba.analyse.extract_tags(text, topK=top_n)
+        return keywords
+    except ImportError:
+        # 没有 jieba，简单分词
+        words = text.split()[:top_n]
+        return [w for w in words if len(w) > 1]
+
+
+def merge_expansion_results(
+    query: str,
+    expansions: List[str],
+    search_func,
+    top_k_per_query: int = 3,
+    final_top_k: int = 10
+) -> List[Dict]:
+    """
+    合并多个扩展查询的检索结果
+
+    Args:
+        query: 原始查询
+        expansions: 扩展查询列表
+        search_func: 检索函数
+        top_k_per_query: 每个查询返回数量
+        final_top_k: 最终返回数量
+
+    Returns:
+        合并后的结果列表
+    """
+    all_results = []
+    seen_ids = set()
+
+    for q in expansions:
+        try:
+            results = search_func(q, top_k=top_k_per_query)
+
+            if results and results.get('ids') and results['ids'][0]:
+                for i, doc_id in enumerate(results['ids'][0]):
+                    if doc_id not in seen_ids:
+                        seen_ids.add(doc_id)
+                        all_results.append({
+                            'id': doc_id,
+                            'content': results['documents'][0][i] if results.get('documents') else '',
+                            'metadata': results['metadatas'][0][i] if results.get('metadatas') else {},
+                            'score': results['distances'][0][i] if results.get('distances') else 0,
+                            'query': q
+                        })
+        except Exception as e:
+            logger.warning(f"扩展查询检索失败: {q}, 错误: {e}")
+
+    # 按分数排序
+    all_results.sort(key=lambda x: x.get('score', 0), reverse=True)
+
+    return all_results[:final_top_k]
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    print("=" * 60)
+    print("Query Expansion 测试")
+    print("=" * 60)
+
+    # 测试领域词典扩展
+    test_queries = [
+        "报销标准是什么？",
+        "出差流程怎么走？",
+        "入职需要什么材料？"
+    ]
+
+    for query in test_queries:
+        print(f"\n原查询: {query}")
+
+        # 无 embedding 的扩展
+        expansions = expand_query_safe(query, embedding_model=None, threshold=0.8)
+        print(f"扩展词（无 embedding）: {expansions}")
+
+        # 领域词
+        domain_terms = get_domain_terms(query)
+        print(f"领域词: {domain_terms}")

core/reasoning_reflector.py

@@ -0,0 +1,476 @@
+"""
+推理反思模块（Re²Search）
+
+在答案生成过程中，自动识别未经验证的声明，触发补充检索进行验证。
+
+核心机制：
+1. 从生成的答案中提取关键声明（claims）
+2. 识别哪些声明缺乏检索证据支持
+3. 对未验证声明触发补充检索
+4. 根据补充信息修正或增强答案
+
+使用方式：
+    from core.reasoning_reflector import ReasoningReflector, reflect_and_verify
+
+    reflector = ReasoningReflector(llm_client)
+    result = reflector.reflect(query, answer, contexts)
+
+    if result.has_unverified_claims:
+        # 触发补充检索
+        ...
+"""
+
+from dataclasses import dataclass
+from typing import List, Optional, Tuple
+from enum import Enum
+from config import RAG_CHAT_MODEL
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class ClaimType(Enum):
+    """声明类型"""
+    FACTUAL = "factual"         # 事实性声明（可验证）
+    OPINION = "opinion"         # 观点性声明（主观）
+    INFERENCE = "inference"     # 推论性声明（逻辑推导）
+    HEDGED = "hedged"          # 保留性声明（可能、也许）
+
+
+@dataclass
+class Claim:
+    """单个声明"""
+    content: str               # 声明内容
+    claim_type: ClaimType      # 声明类型
+    is_verified: bool          # 是否已验证
+    supporting_contexts: List[str]  # 支持该声明的上下文
+    confidence: float          # 置信度（0-1）
+
+
+@dataclass
+class ReflectionResult:
+    """反思结果"""
+    original_answer: str       # 原始答案
+    claims: List[Claim]        # 提取的声明列表
+    unverified_claims: List[Claim]  # 未验证的声明
+    has_unverified_claims: bool    # 是否存在未验证声明
+    verification_queries: List[str]  # 建议的验证查询
+    reflection_summary: str    # 反思总结
+    should_supplement: bool    # 是否需要补充检索
+
+
+class ReasoningReflector:
+    """
+    推理反思器
+
+    在答案生成后，分析答案中的声明，识别未经验证的部分，
+    触发补充检索以提高答案质量。
+    """
+
+    # 需要关注的关键词（可能表示未验证声明）
+    UNVERIFIED_MARKERS = [
+        "可能", "也许", "大概", "应该", "估计",
+        "通常", "一般", "往往", "多半",
+        "我认为", "我猜测", "似乎", "看起来"
+    ]
+
+    # 事实性声明关键词
+    FACTUAL_MARKERS = [
+        "是", "有", "包括", "规定", "要求", "标准",
+        "流程", "步骤", "时间", "金额", "数量"
+    ]
+
+    def __init__(self, llm_client=None, model: str = None):
+        """
+        初始化反思器
+
+        Args:
+            llm_client: LLM 客户端
+            model: 模型名称
+        """
+        self.llm_client = llm_client
+        self.model = model or RAG_CHAT_MODEL
+
+    def reflect(self, query: str, answer: str,
+                contexts: List[str] = None) -> ReflectionResult:
+        """
+        对答案进行推理反思
+
+        Args:
+            query: 用户查询
+            answer: 生成的答案
+            contexts: 检索上下文
+
+        Returns:
+            ReflectionResult: 反思结果
+        """
+        if not answer:
+            return self._empty_reflection()
+
+        # 使用 LLM 进行声明提取和验证
+        if self.llm_client:
+            return self._llm_reflect(query, answer, contexts)
+        else:
+            # 降级：基于规则的反思
+            return self._rule_based_reflect(query, answer, contexts)
+
+    def _llm_reflect(self, query: str, answer: str,
+                     contexts: List[str] = None) -> ReflectionResult:
+        """
+        使用 LLM 进行深度反思
+        """
+        # 构建上下文摘要
+        context_summary = ""
+        if contexts:
+            context_summary = "\n\n".join([
+                f"[上下文 {i+1}] {ctx[:300]}..."
+                for i, ctx in enumerate(contexts[:5])
+            ])
+
+        prompt = f"""请对以下答案进行推理反思分析。
+
+## 用户问题
+{query}
+
+## 生成的答案
+{answer}
+
+## 检索到的上下文
+{context_summary if context_summary else "（无检索上下文）"}
+
+## 分析要求
+1. 从答案中提取关键声明（claims）
+2. 判断每个声明是否有上下文支持
+3. 识别未验证的声明
+4. 生成验证查询建议
+
+请以 JSON 格式返回：
+```json
+{{
+    "claims": [
+        {{
+            "content": "声明内容",
+            "type": "factual/opinion/inference/hedged",
+            "verified": true/false,
+            "supporting_evidence": "支持证据或'无'",
+            "confidence": 0.8
+        }}
+    ],
+    "unverified_claims_count": 2,
+    "verification_queries": ["建议的验证查询1", "建议的验证查询2"],
+    "summary": "反思总结",
+    "should_supplement": true/false
+}}
+```"""
+
+        try:
+            from core.llm_utils import call_llm
+            content = call_llm(
+                self.llm_client,
+                prompt,
+                self.model,
+                temperature=0.1,
+                max_tokens=1000
+            )
+            if content is None:
+                return self._rule_based_reflect(query, answer, contexts)
+            return self._parse_llm_response(answer, content)
+
+        except Exception as e:
+            logger.warning(f"LLM 反思失败: {e}")
+            return self._rule_based_reflect(query, answer, contexts)
+
+    def _parse_llm_response(self, original_answer: str,
+                            content: str) -> ReflectionResult:
+        """解析 LLM 返回的 JSON"""
+        import json
+        import re
+
+        # 提取 JSON 块
+        json_match = re.search(r'```json\s*([\s\S]*?)\s*```', content)
+        if json_match:
+            json_str = json_match.group(1)
+        else:
+            json_str = content
+
+        try:
+            data = json.loads(json_str)
+        except json.JSONDecodeError:
+            return self._default_reflection(original_answer)
+
+        # 解析声明
+        claims = []
+        unverified_claims = []
+
+        for claim_data in data.get("claims", []):
+            claim_type_str = claim_data.get("type", "factual")
+            claim_type = {
+                "factual": ClaimType.FACTUAL,
+                "opinion": ClaimType.OPINION,
+                "inference": ClaimType.INFERENCE,
+                "hedged": ClaimType.HEDGED
+            }.get(claim_type_str, ClaimType.FACTUAL)
+
+            claim = Claim(
+                content=claim_data.get("content", ""),
+                claim_type=claim_type,
+                is_verified=claim_data.get("verified", False),
+                supporting_contexts=[claim_data.get("supporting_evidence", "")],
+                confidence=claim_data.get("confidence", 0.5)
+            )
+            claims.append(claim)
+
+            if not claim.is_verified:
+                unverified_claims.append(claim)
+
+        return ReflectionResult(
+            original_answer=original_answer,
+            claims=claims,
+            unverified_claims=unverified_claims,
+            has_unverified_claims=len(unverified_claims) > 0,
+            verification_queries=data.get("verification_queries", []),
+            reflection_summary=data.get("summary", ""),
+            should_supplement=data.get("should_supplement", False)
+        )
+
+    def _rule_based_reflect(self, query: str, answer: str,
+                            contexts: List[str] = None) -> ReflectionResult:
+        """
+        基于规则的反思（降级方案）
+        """
+        claims = []
+        unverified_claims = []
+        verification_queries = []
+
+        # 简单句子分割
+        sentences = self._split_sentences(answer)
+
+        for sentence in sentences:
+            if len(sentence.strip()) < 10:
+                continue
+
+            # 检测声明类型
+            claim_type = self._detect_claim_type(sentence)
+
+            # 检测是否已验证
+            is_verified = self._check_verification(sentence, contexts)
+            confidence = self._estimate_confidence(sentence, is_verified)
+
+            claim = Claim(
+                content=sentence.strip(),
+                claim_type=claim_type,
+                is_verified=is_verified,
+                supporting_contexts=[],
+                confidence=confidence
+            )
+            claims.append(claim)
+
+            if not is_verified and claim_type in [ClaimType.FACTUAL, ClaimType.INFERENCE]:
+                unverified_claims.append(claim)
+                # 生成验证查询
+                verification_queries.append(f"验证：{sentence.strip()[:50]}")
+
+        has_unverified = len(unverified_claims) > 0
+        should_supplement = has_unverified and len(unverified_claims) <= 3
+
+        summary = f"共提取 {len(claims)} 个声明，其中 {len(unverified_claims)} 个未验证"
+        if has_unverified:
+            summary += "，建议补充检索验证"
+
+        return ReflectionResult(
+            original_answer=answer,
+            claims=claims,
+            unverified_claims=unverified_claims,
+            has_unverified_claims=has_unverified,
+            verification_queries=verification_queries[:3],
+            reflection_summary=summary,
+            should_supplement=should_supplement
+        )
+
+    def _split_sentences(self, text: str) -> List[str]:
+        """分割句子"""
+        import re
+        # 按中英文标点分割
+        sentences = re.split(r'[。！？\.\!\?]\s*', text)
+        return [s.strip() for s in sentences if s.strip()]
+
+    def _detect_claim_type(self, sentence: str) -> ClaimType:
+        """检测声明类型"""
+        # 保留性声明
+        if any(marker in sentence for marker in self.UNVERIFIED_MARKERS):
+            return ClaimType.HEDGED
+
+        # 事实性声明
+        if any(marker in sentence for marker in self.FACTUAL_MARKERS):
+            return ClaimType.FACTUAL
+
+        # 默认为推论
+        return ClaimType.INFERENCE
+
+    def _check_verification(self, sentence: str,
+                           contexts: List[str] = None) -> bool:
+        """检查声明是否已验证"""
+        if not contexts:
+            return False
+
+        # 简单关键词匹配
+        keywords = self._extract_keywords(sentence)
+        if not keywords:
+            return False
+
+        for ctx in contexts:
+            matches = sum(1 for kw in keywords if kw in ctx)
+            if matches >= len(keywords) * 0.5:
+                return True
+
+        return False
+
+    def _extract_keywords(self, text: str) -> List[str]:
+        """提取关键词"""
+        try:
+            import jieba
+            keywords = []
+            for word in jieba.cut(text):
+                word = word.strip()
+                if len(word) >= 2 and word.isalpha():
+                    keywords.append(word)
+            return list(set(keywords))[:10]
+        except ImportError:
+            return []
+
+    def _estimate_confidence(self, sentence: str, is_verified: bool) -> float:
+        """估计置信度"""
+        base_confidence = 0.8 if is_verified else 0.4
+
+        # 保留性声明降低置信度
+        if any(marker in sentence for marker in self.UNVERIFIED_MARKERS):
+            base_confidence -= 0.2
+
+        return max(0.1, min(1.0, base_confidence))
+
+    def _empty_reflection(self) -> ReflectionResult:
+        """空反思结果"""
+        return ReflectionResult(
+            original_answer="",
+            claims=[],
+            unverified_claims=[],
+            has_unverified_claims=False,
+            verification_queries=[],
+            reflection_summary="无答案可供反思",
+            should_supplement=False
+        )
+
+    def _default_reflection(self, answer: str) -> ReflectionResult:
+        """默认反思结果（解析失败时）"""
+        return ReflectionResult(
+            original_answer=answer,
+            claims=[],
+            unverified_claims=[],
+            has_unverified_claims=False,
+            verification_queries=[],
+            reflection_summary="反思解析失败",
+            should_supplement=False
+        )
+
+    def get_info(self) -> dict:
+        """获取反思器信息"""
+        return {
+            "model": self.model,
+            "has_llm": self.llm_client is not None,
+            "unverified_markers_count": len(self.UNVERIFIED_MARKERS),
+            "factual_markers_count": len(self.FACTUAL_MARKERS)
+        }
+
+
+def create_reflector() -> ReasoningReflector:
+    """
+    创建反思器实例
+
+    Returns:
+        ReasoningReflector: 反思器实例
+    """
+    try:
+        from openai import OpenAI
+        try:
+            from config import API_KEY, BASE_URL, RAG_CHAT_MODEL
+            MODEL = RAG_CHAT_MODEL
+        except ImportError:
+            from config import API_KEY, BASE_URL
+            MODEL = "qwen3.5-flash"  # fallback
+
+        client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
+        return ReasoningReflector(llm_client=client, model=MODEL)
+
+    except Exception as e:
+        logger.warning(f"创建反思器失败，使用降级模式: {e}")
+        return ReasoningReflector()
+
+
+def reflect_and_verify(query: str, answer: str,
+                       contexts: List[str] = None) -> ReflectionResult:
+    """
+    便捷函数：反思并验证答案
+
+    Args:
+        query: 用户查询
+        answer: 生成的答案
+        contexts: 检索上下文
+
+    Returns:
+        ReflectionResult: 反思结果
+    """
+    reflector = create_reflector()
+    return reflector.reflect(query, answer, contexts)
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    print("=" * 60)
+    print("推理反思测试")
+    print("=" * 60)
+
+    # 测试用例
+    test_query = "公司的报销制度是怎样的？"
+    test_answer = """
+根据公司规定，员工可以报销差旅费用。
+
+报销流程通常包括：提交申请、部门审批、财务审核、打款。
+
+可能需要提供发票和审批单，大概在30天内完成。
+
+我认为公司对报销标准有明确要求，但具体金额我不太确定。
+"""
+    test_contexts = [
+        "公司报销制度规定员工可以报销差旅费用，需提供发票和审批单。",
+        "报销流程：提交申请 -> 部门审批 -> 财务审核 -> 打款。"
+    ]
+
+    reflector = ReasoningReflector()  # 不使用 LLM 的规则反思
+
+    print(f"\n反思器信息: {reflector.get_info()}")
+    print()
+
+    result = reflector.reflect(test_query, test_answer, test_contexts)
+
+    print(f"反思总结: {result.reflection_summary}")
+    print(f"需要补充检索: {'是' if result.should_supplement else '否'}")
+    print(f"\n声明分析:")
+    for i, claim in enumerate(result.claims, 1):
+        status = "✅ 已验证" if claim.is_verified else "⚠️ 未验证"
+        print(f"  {i}. [{claim.claim_type.value}] {status}")
+        print(f"     内容: {claim.content[:50]}...")
+        print(f"     置信度: {claim.confidence:.2f}")
+
+    if result.verification_queries:
+        print(f"\n建议验证查询:")
+        for q in result.verification_queries:
+            print(f"  - {q}")
+
+    print("\n" + "=" * 60)
+    print("✅ 测试完成")
+    print("=" * 60)

core/semantic_cache.py

@@ -0,0 +1,295 @@
+# -*- coding: utf-8 -*-
+"""
+语义缓存模块（FAISS 版）
+
+功能：
+- 使用 FAISS 向量索引实现 O(1) 查找
+- 语义级缓存：相似查询也能命中
+- 高性能：10万缓存量下查询 < 1ms
+
+使用方式：
+    from core.semantic_cache import SemanticCache
+
+    cache = SemanticCache(dim=768, threshold=0.92)
+
+    # 查找
+    result = cache.get(query_embedding)
+
+    # 存储
+    cache.set(query_embedding, result)
+"""
+
+import numpy as np
+import logging
+from typing import Dict, Optional, List, Any
+import threading
+
+logger = logging.getLogger(__name__)
+
+# FAISS 可选依赖
+try:
+    import faiss
+    FAISS_AVAILABLE = True
+except ImportError:
+    FAISS_AVAILABLE = False
+    logger.warning("FAISS 未安装，语义缓存将使用降级方案")
+
+
+class SemanticCache:
+    """
+    语义缓存（FAISS 向量索引）
+
+    使用 FAISS 实现高性能向量检索，支持：
+    - O(1) 查找复杂度
+    - 10万+ 缓存量
+    - 亚毫秒级响应
+    """
+
+    def __init__(
+        self,
+        dim: int = 768,
+        threshold: float = 0.92,
+        max_size: int = 10000
+    ):
+        """
+        初始化语义缓存
+
+        Args:
+            dim: 向量维度
+            threshold: 相似度阈值（cosine 相似度）
+            max_size: 最大缓存数量
+        """
+        self.dim = dim
+        self.threshold = threshold
+        self.max_size = max_size
+
+        self._lock = threading.RLock()
+        self._cache: Dict[int, Any] = {}  # id -> result
+        self._next_id = 0
+
+        # 统计信息
+        self._hits = 0
+        self._misses = 0
+
+        if FAISS_AVAILABLE:
+            # 使用内积索引（需要归一化向量）
+            self._index = faiss.IndexFlatIP(dim)
+            self._use_faiss = True
+            logger.info(f"语义缓存初始化（FAISS），维度={dim}，阈值={threshold}")
+        else:
+            # 降级方案：使用 numpy
+            self._embeddings: List[np.ndarray] = []
+            self._use_faiss = False
+            logger.warning("语义缓存降级为 numpy 方案")
+
+    def get(self, query_emb: np.ndarray) -> Optional[Dict]:
+        """
+        查找语义缓存
+
+        Args:
+            query_emb: 查询向量（已归一化）
+
+        Returns:
+            缓存结果，未命中返回 None
+        """
+        with self._lock:
+            if self._use_faiss:
+                return self._get_faiss(query_emb)
+            else:
+                return self._get_numpy(query_emb)
+
+    def _get_faiss(self, query_emb: np.ndarray) -> Optional[Dict]:
+        """FAISS 查找"""
+        if self._index.ntotal == 0:
+            self._misses += 1
+            return None
+
+        # 归一化并搜索
+        query = self._normalize(query_emb).reshape(1, -1).astype('float32')
+        D, I = self._index.search(query, k=1)
+
+        if D[0][0] > self.threshold:
+            cache_id = int(I[0][0])
+            self._hits += 1
+            logger.debug(f"语义缓存命中，相似度={D[0][0]:.3f}")
+            return self._cache.get(cache_id)
+
+        self._misses += 1
+        return None
+
+    def _get_numpy(self, query_emb: np.ndarray) -> Optional[Dict]:
+        """Numpy 降级查找"""
+        if not self._embeddings:
+            self._misses += 1
+            return None
+
+        query = self._normalize(query_emb)
+        best_score = 0
+        best_id = -1
+
+        for i, emb in enumerate(self._embeddings):
+            score = float(np.dot(query, emb))
+            if score > best_score:
+                best_score = score
+                best_id = i
+
+        if best_score > self.threshold:
+            self._hits += 1
+            logger.debug(f"语义缓存命中（numpy），相似度={best_score:.3f}")
+            return self._cache.get(best_id)
+
+        self._misses += 1
+        return None
+
+    def set(self, query_emb: np.ndarray, result: Dict) -> None:
+        """
+        存储到语义缓存
+
+        Args:
+            query_emb: 查询向量
+            result: 缓存结果
+        """
+        with self._lock:
+            if self._use_faiss:
+                self._set_faiss(query_emb, result)
+            else:
+                self._set_numpy(query_emb, result)
+
+    def _set_faiss(self, query_emb: np.ndarray, result: Dict) -> None:
+        """FAISS 存储"""
+        # 检查容量
+        if self._index.ntotal >= self.max_size:
+            # LRU 淘汰：重建索引（简单实现）
+            logger.debug("语义缓存已满，执行淘汰")
+            self.clear()
+
+        # 归一化并添加
+        query = self._normalize(query_emb).reshape(1, -1).astype('float32')
+        self._index.add(query)
+        self._cache[self._next_id] = result
+        self._next_id += 1
+
+    def _set_numpy(self, query_emb: np.ndarray, result: Dict) -> None:
+        """Numpy 存储"""
+        if len(self._embeddings) >= self.max_size:
+            # 淘汰最早的
+            self._embeddings.pop(0)
+            # 重建 cache（ID 偏移）
+            old_cache = self._cache
+            self._cache = {}
+            for i, (k, v) in enumerate(old_cache.items()):
+                if i > 0:
+                    self._cache[i - 1] = v
+
+        query = self._normalize(query_emb)
+        self._embeddings.append(query)
+        self._cache[len(self._embeddings) - 1] = result
+
+    def _normalize(self, emb: np.ndarray) -> np.ndarray:
+        """归一化向量"""
+        emb = np.array(emb, dtype='float32')
+        norm = np.linalg.norm(emb)
+        if norm > 0:
+            emb = emb / norm
+        return emb
+
+    def clear(self) -> None:
+        """清空缓存"""
+        with self._lock:
+            if self._use_faiss:
+                self._index = faiss.IndexFlatIP(self.dim)
+            else:
+                self._embeddings.clear()
+            self._cache.clear()
+            self._next_id = 0
+            logger.info("语义缓存已清空")
+
+    def get_stats(self) -> Dict:
+        """获取统计信息"""
+        with self._lock:
+            total = self._hits + self._misses
+            hit_rate = self._hits / total if total > 0 else 0
+
+            return {
+                "total_entries": self._index.ntotal if self._use_faiss else len(self._embeddings),
+                "max_size": self.max_size,
+                "hits": self._hits,
+                "misses": self._misses,
+                "hit_rate": hit_rate,
+                "use_faiss": self._use_faiss
+            }
+
+
+# ==================== 全局语义缓存实例 ====================
+
+_semantic_cache: Optional[SemanticCache] = None
+_semantic_cache_lock = threading.Lock()
+
+
+def get_semantic_cache(dim: int = 768) -> SemanticCache:
+    """获取全局语义缓存实例"""
+    global _semantic_cache
+    if _semantic_cache is None:
+        with _semantic_cache_lock:
+            if _semantic_cache is None:
+                try:
+                    from config import SEMANTIC_CACHE_THRESHOLD
+                    threshold = SEMANTIC_CACHE_THRESHOLD
+                except ImportError:
+                    threshold = 0.92
+
+                _semantic_cache = SemanticCache(dim=dim, threshold=threshold)
+
+    return _semantic_cache
+
+
+# ==================== 测试 ====================
+
+if __name__ == "__main__":
+    import sys
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    print("=" * 60)
+    print("语义缓存测试")
+    print("=" * 60)
+
+    cache = SemanticCache(dim=128, threshold=0.9, max_size=100)
+
+    # 生成测试向量
+    np.random.seed(42)
+
+    def random_embedding():
+        emb = np.random.randn(128)
+        return emb / np.linalg.norm(emb)
+
+    # 存储一些向量
+    print("\n存储测试向量...")
+    base_emb = random_embedding()
+    cache.set(base_emb, {"answer": "测试答案1", "confidence": 0.9})
+
+    for i in range(10):
+        emb = random_embedding()
+        cache.set(emb, {"answer": f"测试答案{i+2}", "confidence": 0.8})
+
+    print(f"缓存统计: {cache.get_stats()}")
+
+    # 测试精确命中
+    print("\n测试精确命中...")
+    result = cache.get(base_emb)
+    print(f"结果: {result}")
+
+    # 测试相似命中
+    print("\n测试相似命中...")
+    similar_emb = base_emb + np.random.randn(128) * 0.05
+    similar_emb = similar_emb / np.linalg.norm(similar_emb)
+    result = cache.get(similar_emb)
+    print(f"结果: {result}")
+
+    # 测试未命中
+    print("\n测试未命中...")
+    different_emb = random_embedding()
+    result = cache.get(different_emb)
+    print(f"结果: {result}")
+
+    print(f"\n最终统计: {cache.get_stats()}")

core/status_codes.py

@@ -0,0 +1,125 @@
+"""
+状态码定义
+
+统一的业务状态码，用于 API 响应中标识操作结果。
+状态码格式：4位数字，前两位表示类别，后两位表示具体状态
+- 10xx: 处理中
+- 20xx: 成功
+- 40xx: 客户端错误
+- 50xx: 服务端错误
+"""
+
+from typing import Dict
+
+# 状态码到消息的映射
+_STATUS_MESSAGES: Dict[int, str] = {
+    # 处理中状态 (10xx)
+    1000: "处理中",
+    1001: "文件接收中",
+    1002: "文件解析中",
+    1003: "向量化中",
+    1010: "同步进行中",
+    1020: "出题生成中",
+    1021: "批阅进行中",
+
+    # 成功状态 (20xx)
+    2000: "操作成功",
+    2001: "创建成功",
+    2002: "文件上传成功",
+    2003: "批量上传完成",
+    2004: "删除成功",
+    2005: "更新成功",
+    2010: "同步完成",
+    2020: "出题成功",
+    2021: "批阅完成",
+    2030: "图片描述更新成功",
+
+    # 客户端错误 (40xx)
+    4000: "请求参数错误",
+    4001: "未授权",
+    4002: "禁止访问",
+    4003: "资源不存在",
+    4004: "没有上传文件",
+    4005: "没有选择文件",
+    4006: "请指定目标向量库",
+    4007: "不支持的文件格式",
+    4008: "文件大小超过限制",
+    4009: "文档解析失败",
+    4010: "文件不存在",
+    4011: "向量库不存在",
+    4012: "文件内容为空",
+    4013: "权限不足",
+
+    # 服务端错误 (50xx)
+    5000: "服务器内部错误",
+    5001: "服务不可用",
+    5002: "向量化失败",
+    5003: "LLM调用失败",
+    5010: "同步失败",
+    5020: "出题失败",
+    5021: "批阅失败",
+    5030: "图片描述更新失败",
+}
+
+
+def get_status_message(status_code: int) -> str:
+    """
+    根据状态码获取默认消息
+
+    Args:
+        status_code: 业务状态码
+
+    Returns:
+        状态码对应的默认消息
+    """
+    return _STATUS_MESSAGES.get(status_code, "未知状态")
+
+
+# ==================== 常用状态码常量 ====================
+
+# 处理中 (10xx)
+PROCESSING = 1000
+FILE_RECEIVING = 1001
+FILE_PARSING = 1002
+VECTORIZING = 1003
+SYNC_RUNNING = 1010
+EXAM_GENERATING = 1020
+EXAM_GRADING = 1021
+
+# 成功 (20xx)
+SUCCESS = 2000
+CREATED = 2001
+UPLOAD_SUCCESS = 2002
+BATCH_UPLOAD_SUCCESS = 2003
+DELETE_SUCCESS = 2004
+UPDATE_SUCCESS = 2005
+SYNC_SUCCESS = 2010
+EXAM_SUCCESS = 2020
+GRADE_SUCCESS = 2021
+IMAGE_DESC_SUCCESS = 2030
+
+# 客户端错误 (40xx)
+BAD_REQUEST = 4000
+UNAUTHORIZED = 4001
+FORBIDDEN = 4002
+NOT_FOUND = 4003
+NO_FILE = 4004
+NO_FILE_SELECTED = 4005
+NO_COLLECTION = 4006
+UNSUPPORTED_FORMAT = 4007
+FILE_TOO_LARGE = 4008
+PARSE_ERROR = 4009
+FILE_NOT_FOUND = 4010
+COLLECTION_NOT_FOUND = 4011
+NO_CONTENT = 4012
+PERMISSION_DENIED = 4013
+
+# 服务端错误 (50xx)
+INTERNAL_ERROR = 5000
+SERVICE_UNAVAILABLE = 5001
+VECTORIZE_ERROR = 5002
+LLM_ERROR = 5003
+SYNC_ERROR = 5010
+EXAM_ERROR = 5020
+GRADE_ERROR = 5021
+IMAGE_DESC_ERROR = 5030