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/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