SQLite 持久连接 — sandbox 不再每次查询开关连接，改为 __init__ 时建连、close() 时释放

Explorer 的 system prompt 明确告知 sandbox 规则 — "每条 SQL 必须包含聚合函数或 LIMIT"，减少 LLM 生成违规 SQL 浪费轮次
LLM 客户端单例 — 所有组件共享一个 openai.OpenAI 实例，不再各建各的
sanitize 顺序修复 — 小样本抑制放在 float round 之前，避免被 round 干扰
quick_detect 从 O(n²) 改为 O(n) — 按列聚合一次，加去重，不再对每行重复算整列统计
历史上下文实际生效 — get_context_for 的结果现在会注入到 Explorer 的初始 prompt 里，多轮分析时 LLM 能看到之前的发现

cli.py

@@ -1,27 +1,30 @@
 """
 交互式 CLI —— 四层架构自适应分析
-用法: python3 cli.py [数据库路径]
+用法: python cli.py [数据库路径]
 """
 import os
 import sys
 
 sys.path.insert(0, os.path.dirname(__file__))
 
-from config import DB_PATH, LLM_CONFIG, MAX_EXPLORATION_ROUNDS
+from core.config import DB_PATH, LLM_CONFIG, MAX_EXPLORATION_ROUNDS, PLAYBOOK_DIR
 from agent import DataAnalysisAgent
 
 
 def print_help():
     print("""
 可用命令:
-  <问题>           分析一个问题
+  <问题>             分析一个问题
   rounds=<N> <问题>  设置探索轮数
-  schema           查看数据库 Schema
-  history          查看分析历史
-  audit            查看 SQL 审计日志
-  clear            清空分析历史
-  help             显示帮助
-  quit / q         退出
+  report [主题]      整合所有分析，生成综合报告
+  schema             查看数据库 Schema
+  playbooks          查看已加载的预设剧本
+  regen              重新生成预设剧本
+  history            查看分析历史
+  audit              查看 SQL 审计日志
+  clear              清空分析历史
+  help               显示帮助
+  quit / q           退出
 """)
 
 
@@ -30,17 +33,12 @@ def main():
 
     if not os.path.exists(db_path):
         print(f"❌ 数据库不存在: {db_path}")
-        print(f"   请先运行 python3 demo.py 创建示例数据库，或指定已有数据库路径")
         sys.exit(1)
 
     if not LLM_CONFIG["api_key"]:
-        print("⚠️  未配置 LLM_API_KEY，请先设置环境变量：")
-        print("  export LLM_API_KEY=sk-xxx")
-        print("  export LLM_BASE_URL=https://api.openai.com/v1  # 或 Ollama 等")
-        print("  export LLM_MODEL=gpt-4o")
+        print("⚠️  未配置 LLM_API_KEY")
         sys.exit(1)
 
-    # 初始化 Agent
     agent = DataAnalysisAgent(db_path)
 
     print("=" * 60)
@@ -49,6 +47,7 @@ def main():
     print(f"\n🔗 LLM: {LLM_CONFIG['model']} @ {LLM_CONFIG['base_url']}")
     print(f"🔄 最大探索轮数: {MAX_EXPLORATION_ROUNDS}")
     print(f"💾 数据库: {db_path}")
+    print(f"📋 预设剧本: {len(agent.playbook_mgr.playbooks)} 个")
     print(f"\n💬 输入分析问题（help 查看命令）\n")
 
     while True:
@@ -68,44 +67,69 @@ def main():
             break
         elif cmd == "help":
             print_help()
-            continue
         elif cmd == "schema":
             print(agent.get_schema())
-            continue
         elif cmd == "history":
             print(agent.get_history())
-            continue
         elif cmd == "audit":
             print(agent.get_audit())
-            continue
         elif cmd == "clear":
             agent.clear_history()
             print("✅ 历史已清空")
-            continue
-
-        # 解析可选参数：rounds=3
-        max_rounds = MAX_EXPLORATION_ROUNDS
-        question = user_input
-        if "rounds=" in question.lower():
-            parts = question.split("rounds=")
-            question = parts[0].strip()
+        elif cmd.startswith("report"):
+            topic = user_input[6:].strip()
             try:
-                max_rounds = int(parts[1].strip().split()[0])
-            except (ValueError, IndexError):
-                pass
+                report = agent.full_report(question=topic)
+                print("\n" + report)
+                print("\n" + "~" * 60)
+            except Exception as e:
+                print(f"\n❌ 报告整合出错: {e}")
+                import traceback
+                traceback.print_exc()
+        elif cmd == "playbooks":
+            if not agent.playbook_mgr.playbooks:
+                print("（无预设剧本，输入 regen 让 AI 自动生成）")
+            else:
+                for i, pb in enumerate(agent.playbook_mgr.playbooks, 1):
+                    print(f"  {i}. 📋 {pb.name} — {pb.description} ({len(pb.preset_queries)} 条预设)")
+        elif cmd == "regen":
+            if os.path.isdir(PLAYBOOK_DIR):
+                for f in os.listdir(PLAYBOOK_DIR):
+                    if f.startswith("auto_") and f.endswith(".json"):
+                        os.remove(os.path.join(PLAYBOOK_DIR, f))
+            agent.playbook_mgr.playbooks.clear()
+            print("🤖 AI 正在重新生成预设剧本...")
+            generated = agent.playbook_mgr.auto_generate(agent.schema_text, save_dir=PLAYBOOK_DIR)
+            if generated:
+                print(f"✅ 生成 {len(generated)} 个剧本：")
+                for pb in generated:
+                    print(f"   📋 {pb.name} — {pb.description}")
+            else:
+                print("⚠️ 生成失败")
+        else:
+            # 解析 rounds=N
+            max_rounds = MAX_EXPLORATION_ROUNDS
+            question = user_input
+            if "rounds=" in question.lower():
+                parts = question.split("rounds=")
+                question = parts[0].strip()
+                try:
+                    max_rounds = int(parts[1].strip().split()[0])
+                except (ValueError, IndexError):
+                    pass
 
-        try:
-            report = agent.analyze(question, max_rounds=max_rounds)
-            print("\n" + report)
-            print("\n" + "~" * 60)
-        except Exception as e:
-            print(f"\n❌ 分析出错: {e}")
-            import traceback
-            traceback.print_exc()
+            try:
+                report = agent.analyze(question, max_rounds=max_rounds)
+                print("\n" + report)
+                print("\n" + "~" * 60)
+            except Exception as e:
+                print(f"\n❌ 分析出错: {e}")
+                import traceback
+                traceback.print_exc()
 
-    # 退出时显示审计
     print("\n📋 本次会话审计:")
     print(agent.get_audit())
+    agent.close()
 
 
 if __name__ == "__main__":