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

layers/insights.py

@@ -0,0 +1,148 @@
+"""
+Layer 3: 洞察引擎
+"""
+import json
+from typing import Any
+
+from core.config import LLM_CONFIG
+from core.utils import get_llm_client, extract_json_array
+from layers.explorer import ExplorationStep
+
+
+INSIGHT_SYSTEM = """你是一个数据洞察专家。你会收到探索过程的所有结果，你需要：
+
+1. 从结果中发现异常和有趣现象
+2. 对比不同维度，找出差异
+3. 输出用户可能没问但值得知道的洞察
+
+## 输出格式（严格 JSON 数组）
+```json
+[
+  {
+    "type": "outlier" | "trend" | "distribution" | "correlation" | "recommendation",
+    "severity": "high" | "medium" | "low",
+    "title": "简短标题",
+    "detail": "详细描述，包含具体数字",
+    "evidence": "支撑这个洞察的数据来源"
+  }
+]
+```
+
+## 分析原则
+- 每个洞察必须有具体数字支撑
+- 用对比来说话（A 比 B 高 X%）
+- 关注异常，不描述平淡的事实
+- 如果没有异常，返回空数组"""
+
+
+class Insight:
+    """单条洞察"""
+    def __init__(self, data: dict):
+        self.type = data.get("type", "unknown")
+        self.severity = data.get("severity", "low")
+        self.title = data.get("title", "")
+        self.detail = data.get("detail", "")
+        self.evidence = data.get("evidence", "")
+
+    @property
+    def emoji(self) -> str:
+        return {"outlier": "⚠️", "trend": "📈", "distribution": "📊",
+                "correlation": "🔗", "recommendation": "💡"}.get(self.type, "📌")
+
+    @property
+    def severity_emoji(self) -> str:
+        return {"high": "🔴", "medium": "🟡", "low": "🟢"}.get(self.severity, "")
+
+    def __str__(self):
+        return f"{self.emoji} {self.severity_emoji} {self.title}: {self.detail}"
+
+
+class InsightEngine:
+    """洞察引擎"""
+
+    def __init__(self):
+        self.client, self.model = get_llm_client(LLM_CONFIG)
+
+    def analyze(self, steps: list[ExplorationStep], question: str) -> list[Insight]:
+        if not steps:
+            return []
+
+        history = self._build_history(steps)
+        response = self.client.chat.completions.create(
+            model=self.model,
+            messages=[
+                {"role": "system", "content": INSIGHT_SYSTEM},
+                {"role": "user", "content": f"## 用户问题\n{question}\n\n## 探索历史\n{history}\n\n请分析以上数据，输出异常和洞察。"},
+            ],
+            temperature=0.3, max_tokens=2048,
+        )
+        content = response.choices[0].message.content.strip()
+        return [Insight(d) for d in extract_json_array(content)]
+
+    def format_insights(self, insights: list[Insight]) -> str:
+        if not insights:
+            return ""
+        severity_order = {"high": 0, "medium": 1, "low": 2}
+        sorted_insights = sorted(insights, key=lambda i: severity_order.get(i.severity, 9))
+        lines = ["## 💡 主动洞察", "", "_以下是你没问但数据告诉我们的事：_\n"]
+        for insight in sorted_insights:
+            lines.append(f"**{insight.emoji} {insight.title}** {insight.severity_emoji}")
+            lines.append(f"  {insight.detail}")
+            lines.append(f"  _数据来源: {insight.evidence}_")
+            lines.append("")
+        return "\n".join(lines)
+
+    def _build_history(self, steps: list[ExplorationStep]) -> str:
+        parts = []
+        for step in steps:
+            if step.action == "done":
+                parts.append(f"### 结束\n{step.reasoning}")
+            elif step.success:
+                parts.append(
+                    f"### 第 {step.round_num} 轮：{step.purpose}\n"
+                    f"SQL: `{step.sql}`\n结果 ({step.row_count} 行):\n"
+                    f"数据: {json.dumps(step.rows, ensure_ascii=False)}"
+                )
+            else:
+                parts.append(f"### 第 {step.round_num} 轮：{step.purpose}\nSQL: `{step.sql}`\n失败: {step.error}")
+        return "\n\n".join(parts)
+
+
+def quick_detect(steps: list[ExplorationStep]) -> list[str]:
+    """基于规则的快速异常检测，不调 LLM"""
+    alerts = []
+    seen = set()  # 去重
+
+    for step in steps:
+        if not step.success or not step.rows:
+            continue
+
+        for col in step.columns:
+            vals = [r.get(col) for r in step.rows if isinstance(r.get(col), (int, float))]
+            if not vals:
+                continue
+
+            col_lower = col.lower()
+
+            # 占比列：某个分组占比过高
+            if col_lower in ("pct", "percent", "percentage", "占比"):
+                for v in vals:
+                    if v > 50:
+                        key = f"pct_{step.purpose}"
+                        if key not in seen:
+                            seen.add(key)
+                            alerts.append(f"⚠️ {step.purpose} 中某个分组占比 {v}%，集中度过高")
+                        break
+
+            # 计数列：极值差异
+            if col_lower in ("count", "cnt", "n", "total", "order_count") and len(vals) >= 3:
+                avg = sum(vals) / len(vals)
+                if avg > 0:
+                    ratio = max(vals) / avg
+                    if ratio > 3:
+                        key = f"count_{step.purpose}"
+                        if key not in seen:
+                            seen.add(key)
+                            alerts.append(f"⚠️ {step.purpose} 中最大值是均值的 {ratio:.1f} 倍")
+
+    return alerts