feat: 四层架构数据分析 Agent

- Layer 1 Planner: 意图规划，将问题转为结构化分析计划
- Layer 2 Explorer: 自适应探索循环，多轮迭代动态生成 SQL
- Layer 3 InsightEngine: 异常检测 + 主动洞察
- Layer 4 ContextManager: 多轮对话上下文记忆

安全设计：AI 只看 Schema + 聚合结果，不接触原始数据。
支持任意 OpenAI 兼容 API（OpenAI / Ollama / DeepSeek / vLLM）

sandbox_executor.py

@@ -0,0 +1,139 @@
+"""
+沙箱执行器 —— 执行 SQL，只返回聚合结果
+"""
+import sqlite3
+import re
+from typing import Any
+from config import SANDBOX_RULES, DB_PATH
+
+
+class SandboxError(Exception):
+    """沙箱安全违规"""
+    pass
+
+
+class SandboxExecutor:
+    def __init__(self, db_path: str = DB_PATH):
+        self.db_path = db_path
+        self.rules = SANDBOX_RULES
+        self.execution_log: list[dict] = []
+
+    def execute(self, sql: str) -> dict[str, Any]:
+        """
+        执行 SQL，返回脱敏后的聚合结果。
+        如果违反安全规则，抛出 SandboxError。
+        """
+        # 验证 SQL 安全性
+        self._validate(sql)
+
+        conn = sqlite3.connect(self.db_path)
+        conn.row_factory = sqlite3.Row
+        cur = conn.cursor()
+
+        try:
+            cur.execute(sql)
+            rows = cur.fetchall()
+
+            # 转为字典列表
+            columns = [desc[0] for desc in cur.description] if cur.description else []
+            results = [dict(row) for row in rows]
+
+            # 脱敏处理
+            sanitized = self._sanitize(results, columns)
+
+            # 记录执行日志
+            self.execution_log.append({
+                "sql": sql,
+                "rows_returned": len(results),
+                "columns": columns,
+            })
+
+            return {
+                "success": True,
+                "columns": columns,
+                "rows": sanitized,
+                "row_count": len(sanitized),
+                "sql": sql,
+            }
+
+        except sqlite3.Error as e:
+            return {
+                "success": False,
+                "error": str(e),
+                "sql": sql,
+            }
+        finally:
+            conn.close()
+
+    def _validate(self, sql: str):
+        """SQL 安全验证"""
+        sql_upper = sql.upper().strip()
+
+        # 1. 检查禁止的关键字
+        for banned in self.rules["banned_keywords"]:
+            if banned.upper() in sql_upper:
+                raise SandboxError(f"禁止的 SQL 关键字: {banned}")
+
+        # 2. 只允许 SELECT（不能有多语句）
+        statements = [s.strip() for s in sql.split(";") if s.strip()]
+        if len(statements) > 1:
+            raise SandboxError("禁止多语句执行")
+        if not sql_upper.startswith("SELECT"):
+            raise SandboxError("只允许 SELECT 查询")
+
+        # 3. 检查是否使用了聚合函数或 GROUP BY（可选要求）
+        if self.rules["require_aggregation"]:
+            agg_keywords = ["COUNT", "SUM", "AVG", "MIN", "MAX", "GROUP BY",
+                            "DISTINCT", "HAVING", "ROUND", "CAST"]
+            has_agg = any(kw in sql_upper for kw in agg_keywords)
+            has_limit = "LIMIT" in sql_upper
+
+            if not has_agg and not has_limit:
+                raise SandboxError(
+                    "要求使用聚合函数 (COUNT/SUM/AVG/MIN/MAX/GROUP BY) 或 LIMIT"
+                )
+
+        # 4. LIMIT 检查
+        limit_match = re.search(r'LIMIT\s+(\d+)', sql_upper)
+        if limit_match:
+            limit_val = int(limit_match.group(1))
+            if limit_val > self.rules["max_result_rows"]:
+                raise SandboxError(
+                    f"LIMIT {limit_val} 超过最大允许值 {self.rules['max_result_rows']}"
+                )
+
+    def _sanitize(self, rows: list[dict], columns: list[str]) -> list[dict]:
+        """对结果进行脱敏处理"""
+        if not rows:
+            return rows
+
+        # 1. 限制行数
+        rows = rows[:self.rules["max_result_rows"]]
+
+        # 2. 浮点数四舍五入
+        for row in rows:
+            for col in columns:
+                val = row.get(col)
+                if isinstance(val, float):
+                    row[col] = round(val, self.rules["round_floats"])
+
+        # 3. 小样本抑制（k-anonymity）
+        # 如果某个分组的 count 小于阈值，标记为 "<n"
+        for row in rows:
+            for col in columns:
+                if col.lower() in ("count", "cnt", "n", "total"):
+                    val = row.get(col)
+                    if isinstance(val, (int, float)) and val < self.rules["suppress_small_n"]:
+                        row[col] = f"<{self.rules['suppress_small_n']}"
+
+        return rows
+
+    def get_execution_summary(self) -> str:
+        """获取执行摘要"""
+        if not self.execution_log:
+            return "尚未执行任何查询"
+
+        lines = [f"共执行 {len(self.execution_log)} 条查询:"]
+        for i, log in enumerate(self.execution_log, 1):
+            lines.append(f"  {i}. {log['sql'][:80]}... → {log['rows_returned']} 行结果")
+        return "\n".join(lines)