feat: 飞书机器人按租户路由 群组绑定租户 + 独立凭证 + 知识库隔离

1. 新增 resolve_tenant_by_chat_id() 根据飞书群 chat_id 查找绑定的租户
2. 新增 get_tenant_feishu_config() 获取租户级飞书凭证
3. FeishuService 支持传入自定义 app_id/app_secret（租户级别）
4. feishu_bot.py 收到消息时自动解析租户，使用租户凭证回复
5. feishu_longconn_service.py 同样按 chat_id 解析租户并传递 tenant_id
6. 租户管理 UI 新增飞书配置字段：App ID、App Secret、绑定群 Chat ID
7. 租户列表展示飞书绑定状态和群数量
8. 保存租户时同步更新飞书配置到 config JSON

.kiro/skills/kb-audit/scripts/kb_audit.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+知识库体检脚本
+
+对 KnowledgeEntry 做简单统计，供 kb-audit Skill 调用。
+"""
+
+import sys
+from datetime import datetime, timedelta
+from pathlib import Path
+
+
+def add_project_root_to_path():
+    # 假定脚本位于 .claude/skills/kb-audit/scripts/ 下
+    script_path = Path(__file__).resolve()
+    project_root = script_path.parents[4]
+    if str(project_root) not in sys.path:
+        sys.path.insert(0, str(project_root))
+
+
+def main():
+    add_project_root_to_path()
+
+    from src.core.database import db_manager
+    from src.core.models import KnowledgeEntry
+
+    print("=== 知识库健康检查 ===\n")
+
+    with db_manager.get_session() as session:
+        total = session.query(KnowledgeEntry).count()
+        print(f"知识条目总数: {total}")
+
+        # 低置信度（<0.7）
+        low_conf = (
+            session.query(KnowledgeEntry)
+            .filter(KnowledgeEntry.confidence_score.isnot(None))
+            .filter(KnowledgeEntry.confidence_score < 0.7)
+            .count()
+        )
+        print(f"低置信度条目数 (confidence_score < 0.7): {low_conf}")
+
+        # 使用次数极低（usage_count < 3 或为 NULL）
+        low_usage = (
+            session.query(KnowledgeEntry)
+            .filter(
+                (KnowledgeEntry.usage_count.is_(None))
+                | (KnowledgeEntry.usage_count < 3)
+            )
+            .count()
+        )
+        print(f"使用次数极低条目数 (usage_count < 3 或空): {low_usage}")
+
+        # 长期未更新（> 90 天）
+        cutoff = datetime.now() - timedelta(days=90)
+        old_entries = (
+            session.query(KnowledgeEntry)
+            .filter(
+                (KnowledgeEntry.updated_at.isnot(None))
+                & (KnowledgeEntry.updated_at < cutoff)
+            )
+            .count()
+        )
+        print(f"长期未更新条目数 (updated_at > 90 天未更新): {old_entries}")
+
+        print("\n示例问题条目（不含完整答案，仅展示前若干个）：")
+        sample_entries = (
+            session.query(KnowledgeEntry)
+            .order_by(KnowledgeEntry.created_at.desc())
+            .limit(5)
+            .all()
+        )
+        for e in sample_entries:
+            q_preview = (e.question or "")[:40]
+            print(
+                f"  ID={e.id}, category={e.category}, "
+                f"confidence={e.confidence_score}, usage={e.usage_count}, "
+                f"Q='{q_preview}...'"
+            )
+
+    print("\n提示：")
+    print("  - 建议优先审查低置信度且 usage_count 较高的条目；")
+    print("  - 对长期未更新且 usage_count 较高的条目，可考虑人工复查内容是否过时；")
+    print("  - 对 usage_count 极低且从未触发的条目，可考虑合并或归档。")
+
+
+if __name__ == "__main__":
+    main()
+