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@@ -1101,3 +1101,360 @@ async def apply_decisions(
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except Exception as e:
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logger.error("apply_decisions 失败: %s", e, exc_info=True)
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return ToolResult(title="apply_decisions 失败", output=str(e))
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+
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+
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+# ═══════════════════════════════════════════
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+# 智能引擎工具 3：查询单个广告详情（Mode 2 支撑）
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+# ═══════════════════════════════════════════
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+
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+
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+@tool(description="查询单个广告的当前指标和历史数据")
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+async def query_ad_detail(
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+ ctx: ToolContext,
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+ ad_id: str,
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+ metrics_csv: str = "",
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+) -> ToolResult:
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+ """
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+ 查询单个广告的当前指标 + 全局分布上下文（Mode 2 定向操作用）。
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+
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+ Args:
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+ ctx: 工具上下文
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+ ad_id: 广告 ID（字符串或数字均可）
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+ metrics_csv: ROI 指标 CSV 路径（默认 outputs/metrics_temp.csv）
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+
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+ Returns:
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+ ToolResult，包含该广告的详细指标和全局上下文
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+ """
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+ import json
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+ import os
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+
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+ try:
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+ if not metrics_csv:
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+ metrics_csv = str(_MINI_DIR / "outputs" / "metrics_temp.csv")
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+
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+ metrics_path = Path(metrics_csv)
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+ if not metrics_path.exists():
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+ return ToolResult(
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+ title="query_ad_detail 失败",
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+ output=f"指标文件不存在: {metrics_csv}，请先执行 calculate_roi_metrics",
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+ )
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+
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+ # 检查数据新鲜度
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+ file_mtime = os.path.getmtime(metrics_path)
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+ age_hours = (datetime.now().timestamp() - file_mtime) / 3600
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+ freshness_warning = ""
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+ if age_hours > 24:
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+ freshness_warning = f"⚠️ 数据已过期（{age_hours:.1f}小时前更新），建议先执行 fetch_creative_data + calculate_roi_metrics 刷新数据。\n\n"
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+
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+ df = pd.read_csv(metrics_csv)
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+
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+ # 查找目标广告
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+ ad_id_int = int(ad_id)
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+ ad_row = df[df["ad_id"] == ad_id_int]
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+
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+ if ad_row.empty:
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+ return ToolResult(
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+ title="query_ad_detail",
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+ output=f"{freshness_warning}未找到广告 {ad_id}，共有 {len(df)} 个广告",
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+ )
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+
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+ row = ad_row.iloc[0]
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+
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+ # 计算广告年龄
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+ ad_age_days = _calculate_ad_age_days(row.get("create_time"))
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+
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+ # 全局 ROI 分布
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+ roi_series = df["动态ROI_7日均值"].dropna()
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+ roi_mean = float(roi_series.mean()) if len(roi_series) > 0 else 0.0
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+ cost_series = df["cost_7d_avg"].dropna()
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+ cost_median = float(cost_series.median()) if len(cost_series) > 0 else 0.0
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+
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+ roi_low_line = roi_mean * ROI_LOW_FACTOR if "ROI_LOW_FACTOR" in dir() else roi_mean * 0.5
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+ bid_down_line = roi_mean * BID_DOWN_ROI_FACTOR
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+ bid_up_line = roi_mean * BID_UP_ROI_FACTOR
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+
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+ # 构建广告详情
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+ f_roi = row.get("动态ROI_7日均值")
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+ ad_detail = {
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+ "ad_id": ad_id_int,
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+ "ad_name": str(row.get("ad_name", "")),
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+ "bid_amount": round(float(row.get("bid_amount", 0) or 0), 2),
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+ "动态ROI_7日均值": round(float(f_roi), 4) if not pd.isna(f_roi) else None,
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+ "cost_7d_avg": round(float(row.get("cost_7d_avg", 0) or 0), 2),
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+ "cost_7d_total": round(float(row.get("cost_7d_total", 0) or 0), 2),
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+ "ad_age_days": ad_age_days,
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+ "configured_status": str(row.get("configured_status", "")),
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+ }
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+
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+ # 检测干预信号
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+ try:
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+ end_date = (datetime.now() - timedelta(days=1)).strftime("%Y%m%d")
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+ raw_dir = _MINI_DIR / "outputs" / "raw"
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+ ad_status_dir = _MINI_DIR / "outputs" / "ad_status"
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+ decay_signals = _detect_decay_signals(
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+ ad_ids=[ad_id_int],
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+ raw_dir=raw_dir,
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+ ad_status_dir=ad_status_dir,
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+ end_date=end_date,
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+ )
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+ if not decay_signals.empty:
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+ ds_row = decay_signals.iloc[0]
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+ ad_detail["bid_increased_7d"] = bool(ds_row.get("bid_increased_7d", False))
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+ ad_detail["creative_changed_7d"] = bool(ds_row.get("creative_changed_7d", False))
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+ except Exception as e:
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+ logger.warning("检测干预信号失败: %s", e)
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+
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+ # 全局上下文
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+ global_context = {
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+ "全体动态ROI均值": round(roi_mean, 4),
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+ "ROI关停线": round(roi_mean * 0.5, 4),
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+ "ROI降价线": round(bid_down_line, 4),
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+ "ROI提价线": round(bid_up_line, 4),
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+ "全体消耗中位数": round(cost_median, 2),
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+ }
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+
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+ result = {
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+ "ad_detail": ad_detail,
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+ "global_context": global_context,
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+ }
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+
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+ output = freshness_warning + json.dumps(result, ensure_ascii=False, indent=2)
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+
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+ return ToolResult(
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+ title=f"广告 {ad_id} 详情",
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+ output=output,
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+ metadata=result,
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+ )
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+
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+ except Exception as e:
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+ logger.error("query_ad_detail 失败: %s", e, exc_info=True)
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+ return ToolResult(title="query_ad_detail 失败", output=str(e))
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+
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+
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+# ═══════════════════════════════════════════
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+# 智能引擎工具 4：修改已有决策（Mode 3 支撑）
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+# ═══════════════════════════════════════════
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+
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+
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+@tool(description="修改已有决策：修改指定广告的操作或调幅，也可新增决策")
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+async def modify_decisions(
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+ ctx: ToolContext,
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+ modifications: str,
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+ decisions_csv: str = "",
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+ end_date: str = "yesterday",
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+) -> ToolResult:
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+ """
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+ 修改已有 llm_decisions_{date}.csv 中的决策（Mode 3 反馈修改用）。
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+
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+ 支持两种修改方式：
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+ 1. 按 ad_id 精确修改/新增（upsert）：
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+ [{"ad_id": "90289631207", "new_action": "bid_down", "new_change_pct": -0.05}]
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+ 2. 按过滤器批量修改：
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+ [{"filter": "all_bid_down", "new_change_pct": -0.03}]
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+ 支持: all_pause / all_bid_down / all_bid_up / all_llm
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+
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+ Args:
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+ ctx: 工具上下文
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+ modifications: JSON 字符串，修改列表
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+ decisions_csv: 决策 CSV 路径（默认自动查找最新）
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+ end_date: 结束日期（用于查找默认 CSV）
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+
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+ Returns:
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+ ToolResult，包含修改日志和新的 action 分布
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+ """
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+ import json
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+ import glob as glob_mod
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+
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+ try:
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+ if end_date == "yesterday":
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+ end_date = (datetime.now() - timedelta(days=1)).strftime("%Y%m%d")
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+
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+ # 解析修改列表
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+ try:
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+ mod_list = json.loads(modifications)
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+ except json.JSONDecodeError as e:
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+ return ToolResult(title="modify_decisions 失败", output=f"modifications 不是合法 JSON: {e}")
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+
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+ if not isinstance(mod_list, list):
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+ return ToolResult(title="modify_decisions 失败", output="modifications 必须是 JSON 数组")
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+
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+ # 定位决策 CSV
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+ if not decisions_csv:
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+ reports_dir = _MINI_DIR / "outputs" / "reports"
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+ # 先找当天的，再找最新的
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+ target_path = reports_dir / f"llm_decisions_{end_date}.csv"
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+ if target_path.exists():
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+ decisions_csv = str(target_path)
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+ else:
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+ # 查找最新的 llm_decisions_*.csv
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+ pattern = str(reports_dir / "llm_decisions_*.csv")
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+ files = sorted(glob_mod.glob(pattern), reverse=True)
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+ if files:
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+ decisions_csv = files[0]
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+ else:
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+ return ToolResult(
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+ title="modify_decisions 失败",
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+ output="未找到任何已有决策文件（llm_decisions_*.csv），请先执行全量分析",
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+ )
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+
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+ decisions_path = Path(decisions_csv)
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+ if not decisions_path.exists():
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+ return ToolResult(title="modify_decisions 失败", output=f"决策文件不存在: {decisions_csv}")
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+
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+ df = pd.read_csv(decisions_csv)
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+ if df.empty:
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+ return ToolResult(title="modify_decisions 失败", output="决策文件为空")
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+
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+ # 加载 metrics 获取 bid_amount
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+ metrics_csv_path = str(_MINI_DIR / "outputs" / "metrics_temp.csv")
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+ bid_map = {}
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+ try:
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+ df_metrics = pd.read_csv(metrics_csv_path)
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+ bid_map = dict(zip(df_metrics["ad_id"].astype(int), df_metrics["bid_amount"].fillna(0)))
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+ except Exception as e:
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+ logger.warning("加载 metrics 获取 bid_amount 失败: %s", e)
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+
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+ change_log = []
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+ new_rows = []
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+
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+ for mod in mod_list:
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+ if "filter" in mod:
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+ # 批量修改
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+ filter_type = mod["filter"]
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+ filter_map = {
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+ "all_pause": "pause",
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+ "all_bid_down": "bid_down",
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+ "all_bid_up": "bid_up",
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+ "all_llm": None, # 所有 LLM 决策
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+ }
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+ if filter_type not in filter_map:
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+ change_log.append(f"⚠️ 未知 filter: {filter_type}，跳过")
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+ continue
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+
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+ target_action = filter_map[filter_type]
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+ if target_action:
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+ mask = df["action"] == target_action
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+ else:
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+ mask = df["source"] == "llm"
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+
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+ matched = mask.sum()
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+ if matched == 0:
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+ change_log.append(f"filter={filter_type}: 无匹配行")
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+ continue
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+
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+ # 应用修改
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+ if "new_action" in mod:
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+ df.loc[mask, "action"] = mod["new_action"]
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+ if "new_change_pct" in mod:
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+ df.loc[mask, "recommended_change_pct"] = mod["new_change_pct"]
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+ # 重算 recommended_bid
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+ for idx in df[mask].index:
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+ ad_id_val = int(df.at[idx, "ad_id"])
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+ bid = bid_map.get(ad_id_val, 0)
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+ if bid > 0:
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+ new_bid = round(bid * (1 + mod["new_change_pct"]), 2)
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+ new_bid = max(new_bid, BID_FLOOR_YUAN)
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+ new_bid = min(new_bid, BID_CEILING_YUAN)
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+ df.at[idx, "recommended_bid"] = new_bid
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+ df.at[idx, "current_bid"] = round(bid, 2)
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+ if "new_dimension" in mod:
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+ df.loc[mask, "dimension"] = mod["new_dimension"]
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+ if "new_reason" in mod:
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+ df.loc[mask, "reason"] = mod["new_reason"]
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+
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+ df.loc[mask, "source"] = "llm_modified"
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+ change_log.append(f"filter={filter_type}: 修改 {matched} 行")
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+
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+ elif "ad_id" in mod:
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+ # 精确修改/新增（upsert）
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+ target_id = int(mod["ad_id"])
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+ mask = df["ad_id"] == target_id
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+
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+ if mask.any():
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+ # 修改已有行
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+ if "new_action" in mod:
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+ old_action = df.loc[mask, "action"].iloc[0]
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+ df.loc[mask, "action"] = mod["new_action"]
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+ change_log.append(f"ad_id={target_id}: action {old_action} → {mod['new_action']}")
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+ if "new_change_pct" in mod:
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+ df.loc[mask, "recommended_change_pct"] = mod["new_change_pct"]
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+ bid = bid_map.get(target_id, 0)
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+ if bid > 0:
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+ new_bid = round(bid * (1 + mod["new_change_pct"]), 2)
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+ new_bid = max(new_bid, BID_FLOOR_YUAN)
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+ new_bid = min(new_bid, BID_CEILING_YUAN)
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+ df.loc[mask, "recommended_bid"] = new_bid
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+ df.loc[mask, "current_bid"] = round(bid, 2)
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+ change_log.append(f"ad_id={target_id}: change_pct → {mod['new_change_pct']}")
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+ if "new_dimension" in mod:
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+ df.loc[mask, "dimension"] = mod["new_dimension"]
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+ if "new_reason" in mod:
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+ df.loc[mask, "reason"] = mod["new_reason"]
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+ df.loc[mask, "source"] = "llm_modified"
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+ else:
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+ # 新增行
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+ new_action = mod.get("new_action", "hold")
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+ change_pct = mod.get("new_change_pct")
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+ bid = bid_map.get(target_id, 0)
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+ new_bid = None
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+ if change_pct is not None and bid > 0:
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+ new_bid = round(bid * (1 + change_pct), 2)
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+ new_bid = max(new_bid, BID_FLOOR_YUAN)
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+ new_bid = min(new_bid, BID_CEILING_YUAN)
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+
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+ new_row = {
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+ "ad_id": target_id,
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+ "action": new_action,
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+ "dimension": mod.get("new_dimension", "用户指定"),
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+ "reason": mod.get("new_reason", "用户定向操作"),
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+ "confidence": mod.get("confidence", "high"),
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+ "source": "llm_modified",
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+ "recommended_change_pct": change_pct,
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+ "current_bid": round(bid, 2) if bid > 0 else None,
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+ "recommended_bid": new_bid,
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+ }
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+ new_rows.append(new_row)
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+ change_log.append(f"ad_id={target_id}: 新增 action={new_action}")
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+ else:
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+ change_log.append(f"⚠️ 修改项缺少 ad_id 或 filter，跳过: {mod}")
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+
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+ # 合并新增行
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+ if new_rows:
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+ df = pd.concat([df, pd.DataFrame(new_rows)], ignore_index=True)
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+
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+ # 保存（覆盖原文件）
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+ df.to_csv(decisions_csv, index=False, encoding="utf-8-sig")
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+
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+ # 统计新的 action 分布
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+ action_dist = df["action"].value_counts().to_dict()
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+
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+ output_parts = [
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+ f"决策已修改并保存: {decisions_csv}",
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+ "",
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+ "修改日志:",
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+ ]
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+ for log in change_log:
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+ output_parts.append(f" {log}")
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+
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+ output_parts.extend([
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+ "",
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+ "当前 action 分布:",
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+ ])
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+ for action, count in action_dist.items():
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+ output_parts.append(f" {action}: {count} 个")
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|
+ output_parts.append(f" 总计: {len(df)} 个")
|
|
|
+
|
|
|
+ return ToolResult(
|
|
|
+ title=f"决策修改完成（{len(change_log)}项变更）",
|
|
|
+ output="\n".join(output_parts),
|
|
|
+ metadata={
|
|
|
+ "csv_path": str(decisions_csv),
|
|
|
+ "changes": len(change_log),
|
|
|
+ "action_distribution": action_dist,
|
|
|
+ "total": len(df),
|
|
|
+ },
|
|
|
+ )
|
|
|
+
|
|
|
+ except Exception as e:
|
|
|
+ logger.error("modify_decisions 失败: %s", e, exc_info=True)
|
|
|
+ return ToolResult(title="modify_decisions 失败", output=str(e))
|
刘立冬