96927a789d
- 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）
140 lines
4.5 KiB
Python
140 lines
4.5 KiB
Python
"""
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沙箱执行器 —— 执行 SQL，只返回聚合结果
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"""
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import sqlite3
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import re
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from typing import Any
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from config import SANDBOX_RULES, DB_PATH
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class SandboxError(Exception):
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"""沙箱安全违规"""
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pass
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class SandboxExecutor:
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def __init__(self, db_path: str = DB_PATH):
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self.db_path = db_path
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self.rules = SANDBOX_RULES
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self.execution_log: list[dict] = []
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def execute(self, sql: str) -> dict[str, Any]:
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"""
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执行 SQL，返回脱敏后的聚合结果。
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如果违反安全规则，抛出 SandboxError。
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"""
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# 验证 SQL 安全性
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self._validate(sql)
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conn = sqlite3.connect(self.db_path)
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conn.row_factory = sqlite3.Row
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cur = conn.cursor()
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try:
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cur.execute(sql)
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rows = cur.fetchall()
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# 转为字典列表
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columns = [desc[0] for desc in cur.description] if cur.description else []
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results = [dict(row) for row in rows]
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# 脱敏处理
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sanitized = self._sanitize(results, columns)
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# 记录执行日志
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self.execution_log.append({
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"sql": sql,
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"rows_returned": len(results),
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"columns": columns,
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})
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return {
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"success": True,
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"columns": columns,
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"rows": sanitized,
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"row_count": len(sanitized),
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"sql": sql,
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}
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except sqlite3.Error as e:
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return {
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"success": False,
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"error": str(e),
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"sql": sql,
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}
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finally:
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conn.close()
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def _validate(self, sql: str):
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"""SQL 安全验证"""
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sql_upper = sql.upper().strip()
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# 1. 检查禁止的关键字
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for banned in self.rules["banned_keywords"]:
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if banned.upper() in sql_upper:
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raise SandboxError(f"禁止的 SQL 关键字: {banned}")
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# 2. 只允许 SELECT（不能有多语句）
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statements = [s.strip() for s in sql.split(";") if s.strip()]
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if len(statements) > 1:
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raise SandboxError("禁止多语句执行")
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if not sql_upper.startswith("SELECT"):
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raise SandboxError("只允许 SELECT 查询")
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# 3. 检查是否使用了聚合函数或 GROUP BY（可选要求）
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if self.rules["require_aggregation"]:
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agg_keywords = ["COUNT", "SUM", "AVG", "MIN", "MAX", "GROUP BY",
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"DISTINCT", "HAVING", "ROUND", "CAST"]
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has_agg = any(kw in sql_upper for kw in agg_keywords)
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has_limit = "LIMIT" in sql_upper
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if not has_agg and not has_limit:
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raise SandboxError(
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"要求使用聚合函数 (COUNT/SUM/AVG/MIN/MAX/GROUP BY) 或 LIMIT"
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)
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# 4. LIMIT 检查
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limit_match = re.search(r'LIMIT\s+(\d+)', sql_upper)
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if limit_match:
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limit_val = int(limit_match.group(1))
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if limit_val > self.rules["max_result_rows"]:
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raise SandboxError(
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f"LIMIT {limit_val} 超过最大允许值 {self.rules['max_result_rows']}"
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)
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def _sanitize(self, rows: list[dict], columns: list[str]) -> list[dict]:
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"""对结果进行脱敏处理"""
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if not rows:
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return rows
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# 1. 限制行数
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rows = rows[:self.rules["max_result_rows"]]
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# 2. 浮点数四舍五入
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for row in rows:
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for col in columns:
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val = row.get(col)
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if isinstance(val, float):
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row[col] = round(val, self.rules["round_floats"])
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# 3. 小样本抑制（k-anonymity）
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# 如果某个分组的 count 小于阈值，标记为 "<n"
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for row in rows:
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for col in columns:
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if col.lower() in ("count", "cnt", "n", "total"):
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val = row.get(col)
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if isinstance(val, (int, float)) and val < self.rules["suppress_small_n"]:
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row[col] = f"<{self.rules['suppress_small_n']}"
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return rows
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def get_execution_summary(self) -> str:
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"""获取执行摘要"""
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if not self.execution_log:
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return "尚未执行任何查询"
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lines = [f"共执行 {len(self.execution_log)} 条查询:"]
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for i, log in enumerate(self.execution_log, 1):
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lines.append(f" {i}. {log['sql'][:80]}... → {log['rows_returned']} 行结果")
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return "\n".join(lines)
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