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

core/schema.py

@@ -0,0 +1,98 @@
+"""
+Schema 提取器 —— 只提取表结构，不碰数据
+"""
+import sqlite3
+from typing import Any
+
+
+def extract_schema(db_path: str) -> dict[str, Any]:
+    """从数据库提取 Schema，只返回结构信息"""
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+    cur = conn.cursor()
+
+    cur.execute("SELECT name FROM sqlite_master WHERE type='table' AND name NOT LIKE 'sqlite_%'")
+    tables = [row["name"] for row in cur.fetchall()]
+
+    schema = {"tables": []}
+
+    for table in tables:
+        cur.execute(f"PRAGMA table_info('{table}')")
+        columns = []
+        for col in cur.fetchall():
+            columns.append({
+                "name": col["name"],
+                "type": col["type"],
+                "nullable": col["notnull"] == 0,
+                "is_primary_key": col["pk"] == 1,
+            })
+
+        cur.execute(f"PRAGMA foreign_key_list('{table}')")
+        fks = [
+            {"column": fk["from"], "references_table": fk["table"], "references_column": fk["to"]}
+            for fk in cur.fetchall()
+        ]
+
+        cur.execute(f"SELECT COUNT(*) AS cnt FROM '{table}'")
+        row_count = cur.fetchone()["cnt"]
+
+        data_profile = {}
+        for col in columns:
+            col_name = col["name"]
+            col_type = (col["type"] or "").upper()
+
+            if any(t in col_type for t in ("VARCHAR", "TEXT", "CHAR")):
+                cur.execute(f'SELECT DISTINCT "{col_name}" FROM "{table}" WHERE "{col_name}" IS NOT NULL LIMIT 20')
+                vals = [row[0] for row in cur.fetchall()]
+                if len(vals) <= 20:
+                    data_profile[col_name] = {"type": "enum", "distinct_count": len(vals), "values": vals}
+            elif any(t in col_type for t in ("INT", "REAL", "FLOAT", "DOUBLE", "DECIMAL", "NUMERIC")):
+                cur.execute(f'''
+                    SELECT MIN("{col_name}") AS min_val, MAX("{col_name}") AS max_val,
+                           AVG("{col_name}") AS avg_val, COUNT(DISTINCT "{col_name}") AS distinct_count
+                    FROM "{table}" WHERE "{col_name}" IS NOT NULL
+                ''')
+                row = cur.fetchone()
+                if row and row["min_val"] is not None:
+                    data_profile[col_name] = {
+                        "type": "numeric",
+                        "min": round(row["min_val"], 2), "max": round(row["max_val"], 2),
+                        "avg": round(row["avg_val"], 2), "distinct_count": row["distinct_count"],
+                    }
+
+        schema["tables"].append({
+            "name": table, "columns": columns, "foreign_keys": fks,
+            "row_count": row_count, "data_profile": data_profile,
+        })
+
+    conn.close()
+    return schema
+
+
+def schema_to_text(schema: dict) -> str:
+    """将 Schema 转为可读文本，供 LLM 理解"""
+    lines = ["=== 数据库 Schema ===\n"]
+    for table in schema["tables"]:
+        lines.append(f"📋 表: {table['name']} (共 {table['row_count']} 行)")
+        lines.append("  列:")
+        for col in table["columns"]:
+            pk = " [PK]" if col["is_primary_key"] else ""
+            null = " NULL" if col["nullable"] else " NOT NULL"
+            lines.append(f'    - {col["name"]}: {col["type"]}{pk}{null}')
+        if table["foreign_keys"]:
+            lines.append("  外键:")
+            for fk in table["foreign_keys"]:
+                lines.append(f'    - {fk["column"]} → {fk["references_table"]}.{fk["references_column"]}')
+        if table["data_profile"]:
+            lines.append("  数据画像:")
+            for col_name, profile in table["data_profile"].items():
+                if profile["type"] == "enum":
+                    vals = ", ".join(str(v) for v in profile["values"][:10])
+                    lines.append(f'    - {col_name}: 枚举值({profile["distinct_count"]}个) = [{vals}]')
+                elif profile["type"] == "numeric":
+                    lines.append(
+                        f'    - {col_name}: 范围[{profile["min"]} ~ {profile["max"]}], '
+                        f'均值{profile["avg"]}, {profile["distinct_count"]}个不同值'
+                    )
+        lines.append("")
+    return "\n".join(lines)