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ADR-012 核心設計： - 4 級信任邊界：L0 直出 / L1 Hermes 觀察 / L2 NemoTron 診斷執行 / L3 OpenClaw HITL - 通知鏈絕不中斷：每級失敗立即降級，保底 L0 模板 + 🟡 標記 - Audit Trail：每次 dispatch 自動寫 ai_insights (insight_type=agent_action) - 安全白名單：L2 可呼叫 6 個安全 action（retry/query_km/silence + 3 個既有 NemoTron tool） 新增檔案： - services/event_router.py — 事件分流入口，按 severity × event_type 分 Tier - services/agent_actions.py — 安全 action 白名單（Phase 1 stub + 完整介面） - docs/adr/ADR-012-agent-action-ladder.md — 完整設計 + 分階段計畫 Phase 1 狀態： - L0 直出完整可用 ✅ - L1 Hermes / L2 NemoTron 為 stub（Phase 2/3 填實作） - Fallback 降級鏈已完整 ✅ - 靜音檢查（is_silenced）+ Audit Trail 已就緒 ✅ 處理既有 TODO： - services/openclaw_strategist_service.py::_notify_telegram_group() 改用 telegram_templates.report() 統一週報格式 全景盤點（新 memory）： - reference_telegram_endpoints_map.md — 21 個 Telegram 發送點 - feedback_agent_action_ladder.md — 操作規範 （+ 既有 ADR-011 跨專案隔離規範一併生效） Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
476 lines
18 KiB
Python
476 lines
18 KiB
Python
import os
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import requests
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import json
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from datetime import datetime, timedelta
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from services.logger_manager import SystemLogger
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from database.manager import get_session
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from sqlalchemy import text
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from services.openclaw_learning_service import build_rag_context_by_date, store_insight
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def _build_citation_footer(start_date: str, end_date: str) -> str:
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"""
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查詢 ai_insights 中 [start_date, end_date] 區間的洞察來源，
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回傳結構化引用區塊供週報末尾附加。
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"""
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session = get_session()
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try:
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rows = session.execute(text("""
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SELECT
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DATE(created_at)::text AS day,
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insight_type,
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COUNT(*) AS cnt
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FROM ai_insights
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WHERE DATE(created_at) BETWEEN :s AND :e
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AND status NOT IN ('archived')
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GROUP BY DATE(created_at), insight_type
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ORDER BY DATE(created_at), insight_type
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"""), {"s": start_date, "e": end_date}).fetchall()
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if not rows:
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return ""
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TYPE_LABEL = {
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"price_alert": "競價告警",
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"human_review": "人工覆核",
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"recommendation": "推薦商品",
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"km_price_competition": "KM競價情報",
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"km_sales_anomaly": "KM銷量異常",
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"km_promotion_opportunity": "KM促銷機會",
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"km_market_trend": "KM市場趨勢",
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"relearn_event": "重新學習事件",
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"backup_status": "備份狀態",
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"price_recommendation": "降價建議",
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"price_decision_feedback": "降價決策回饋",
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"weekly_meta": "週報策略",
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"meta_analysis": "Meta 分析",
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}
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lines = ["\n\n---", "📚 **本報告引用來源：**"]
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for day, itype, cnt in rows:
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label = TYPE_LABEL.get(itype, itype)
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lines.append(f"• {day} 的「{label}」洞察（{cnt} 筆）")
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lines.append(f"\n> 資料區間：{start_date} ～ {end_date}，"
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f"由 Hermes / NemoTron / OpenClaw 三層 AI 系統自動蒐集")
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return "\n".join(lines)
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except Exception as e:
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sys_log.warning(f"[OCStrategist] citation footer 查詢失敗: {e}")
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return ""
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finally:
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session.close()
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sys_log = SystemLogger("OCStrategist").get_logger()
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try:
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from dotenv import load_dotenv
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load_dotenv()
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except ImportError:
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pass
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# === Gemini API 配置 ===
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GEMINI_API_KEY = os.getenv('GEMINI_API_KEY', '')
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GEMINI_BASE_URL = 'https://generativelanguage.googleapis.com/v1beta/models'
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GEMINI_MODEL = 'gemini-2.0-flash'
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def _call_gemini_flash(prompt: str) -> str:
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""" 內部調用 Gemini 2.0 Flash API 的通用方法 """
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if not GEMINI_API_KEY:
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sys_log.error("[OCStrategist] 未設定 GEMINI_API_KEY，無法呼叫 Gemini API。")
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return "⚠️ 生成失敗：未設定 GEMINI_API_KEY"
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payload = {
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"contents": [{"role": "user", "parts": [{"text": prompt}]}],
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"generationConfig": {"temperature": 0.4, "maxOutputTokens": 4096},
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}
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try:
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url = f"{GEMINI_BASE_URL}/{GEMINI_MODEL}:generateContent?key={GEMINI_API_KEY}"
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resp = requests.post(url, headers={"Content-Type": "application/json"}, json=payload, timeout=60)
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resp.raise_for_status()
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data = resp.json()
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candidate = data.get("candidates", [{}])[0]
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parts = candidate.get("content", {}).get("parts", [])
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text_out = "".join(p.get("text", "") for p in parts)
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return text_out.strip()
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except Exception as e:
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sys_log.error(f"[OCStrategist] Gemini API 呼叫失敗: {e}")
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return f"⚠️ 呼叫 Gemini 失敗：{e}"
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def get_sales_summary_last_7d(start_date: str, end_date: str) -> str:
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""" 獲取近 7 天的業績概況，轉為文字供 Gemini 參考 """
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session = get_session()
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try:
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# daily_sales_snapshot 是動態表，嘗試查詢，若失敗則忽視
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sql = """
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SELECT
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snapshot_date,
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SUM(COALESCE("銷售金額"::numeric, 0)) as revenue,
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SUM(COALESCE("數量"::numeric, 0)) as qty
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FROM daily_sales_snapshot
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WHERE snapshot_date >= :start_date AND snapshot_date <= :end_date
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GROUP BY snapshot_date
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ORDER BY snapshot_date ASC
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"""
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results = session.execute(text(sql), {"start_date": start_date, "end_date": end_date}).fetchall()
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if not results:
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return "查無 daily_sales_snapshot 的近 7 天數據。"
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summary_parts = []
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for row in results:
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summary_parts.append(f"日期: {row[0]}, 總業績: {row[1]}, 總銷量: {row[2]}")
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return "\n".join(summary_parts)
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except Exception as e:
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sys_log.warning(f"[OCStrategist] 獲取 7 天業績總結時發生異常 (資料表可能未備妥): {e}")
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return "近 7 天業績數據暫無法取得。"
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finally:
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session.close()
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def generate_weekly_strategy_report(force_tg_alert: bool = False) -> str:
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"""
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產生 EwoooC 高階經營決策週報 (Gemini 策略師)
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核心流程：
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1. 算出前 7 天的日期區間
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2. 從 RAG 與 Sales DB 拉取過去一週的所有原始洞察與銷售數字
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3. 利用 Gemini 分析與總結出決策報告
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4. 將報表寫入 ai_insights 儲存 (Dual-Write)
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5. 若 force_tg_alert=True 則推送至 Telegram
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"""
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sys_log.info("[OCStrategist] 開始產生每週策略報表...")
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# 1. 決定時間區間 (看過去 7 天)
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now = datetime.now()
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end_dt = now - timedelta(days=1)
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start_dt = end_dt - timedelta(days=6)
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start_date_str = start_dt.strftime("%Y-%m-%d")
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end_date_str = end_dt.strftime("%Y-%m-%d")
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period_str = f"{now.year}-W{now.isocalendar()[1]}" # e.g., 2026-W16
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# 2. 獲取 RAG & Sales 上下文
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insights_context = build_rag_context_by_date(start_date_str, end_date_str)
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sales_context = get_sales_summary_last_7d(start_date_str, end_date_str)
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if not insights_context.strip():
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insights_context = "(過去 7 天內無 AI 洞察告警紀錄)"
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prompt = f"""
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你是一位頂尖的電商行銷與定價策略師（代號：OpenClaw Gemini）。
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你的任務是根據「過去 7 天系統紀錄的 AI 價格告警洞察」與「業績走勢」，撰寫一份給高階主管閱讀的【行銷與定價策略週報】。
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### 資料區間
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{start_date_str} ~ {end_date_str} ({period_str})
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### [資料一] 過去 7 天業績走勢：
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{sales_context}
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### [資料二] 過去 7 天市場異常告警與洞察紀錄 (由 NemoTron & Hermes 提供)：
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{insights_context}
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### 報告產出格式要求 (請嚴格遵守以 Markdown 輸出)：
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# 【EwoooC 每週 AI 策略報告】 ({period_str})
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## 一、本週業績與市場總結
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(概括過去 7 天的宏觀銷量表現與整體市場威脅概況)
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## 二、關鍵威脅商品與定價挑戰
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(從資料二中挑選出最嚴重的 3~5 項威脅商品，以條列式列出並指出競品價格差與我方影響)
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## 三、行銷與操作建議 (下週 Action Items)
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(根據上述現象，具體給出下週該執行的行銷加碼、特價活動或降價策略)
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請用繁體中文，語氣保持專業、精煉、具備行動力。
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在報告的每個具體數據或告警描述後，若來自「資料二」，請在句末標注【引用自 {start_date_str} ～ {end_date_str} 的洞察】。
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---
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在報告最末尾，**必須**輸出以下標記行與 JSON（若本週無明確降價建議則輸出空陣列）：
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PRICE_DECISIONS_JSON:
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[
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{{
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"product_sku": "貨號（若無則填空字串）",
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"product_name": "商品名稱",
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"current_price": 現價數字,
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"suggested_price": 建議降至數字,
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"reason": "一句話理由（中文）"
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}}
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]
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只輸出純 JSON 陣列，不加 markdown 代碼塊，不加任何其他說明文字。
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"""
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# 3. 呼叫 Gemini
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report_md = _call_gemini_flash(prompt)
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# 附加 KM 引用來源區塊（無論 Gemini 是否成功，皆嘗試附加）
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citation_footer = _build_citation_footer(start_date_str, end_date_str)
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if citation_footer:
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report_md = report_md + citation_footer
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# 4. Dual-Write 存入 ai_insights 知識庫
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if not report_md.startswith("⚠️ 呼叫 Gemini 失敗"):
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insight_id = store_insight(
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insight_type='weekly_meta',
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content=report_md,
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period=period_str,
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metadata={"start_date": start_date_str, "end_date": end_date_str, "generated_by": "Gemini-2.0-Flash"}
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)
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sys_log.info(f"[OCStrategist] 週報產出成功並已雙寫存入 AI 知識庫 (ID: {insight_id})")
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# 5. 解析降價決策並推送 Telegram Inline Keyboard
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price_recs = _parse_price_recommendations(report_md)
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if price_recs:
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_send_price_decision_requests(price_recs, period_str, source_insight_id=insight_id)
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# 6. 週報摘要通知 Telegram
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if force_tg_alert:
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_notify_telegram_group(report_md, period_str)
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return report_md
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def _parse_price_recommendations(report_md: str) -> list:
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"""從 Gemini 週報中解析 PRICE_DECISIONS_JSON 區塊，回傳降價建議清單。"""
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marker = "PRICE_DECISIONS_JSON:"
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idx = report_md.find(marker)
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if idx == -1:
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return []
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raw = report_md[idx + len(marker):].strip()
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# 找最後一個 ] 確保取完整陣列（欄位值含 ] 時 find 會截斷）
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end = raw.rfind("]")
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if end == -1:
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return []
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raw = raw[: end + 1]
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try:
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recs = json.loads(raw)
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if not isinstance(recs, list):
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return []
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valid = []
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for r in recs:
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if all(k in r for k in ("product_name", "current_price", "suggested_price", "reason")):
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r.setdefault("product_sku", "")
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try:
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r["current_price"] = float(r["current_price"])
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r["suggested_price"] = float(r["suggested_price"])
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except (ValueError, TypeError):
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continue
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if r["suggested_price"] < r["current_price"]:
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valid.append(r)
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return valid
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except json.JSONDecodeError as e:
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sys_log.warning(f"[OCStrategist] price_recs JSON 解析失敗: {e}")
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return []
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def _send_price_decision_requests(recs: list, period_str: str, source_insight_id: int = None):
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"""
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對每筆降價建議：
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1. 寫入 ai_insights（insight_type='price_recommendation'）取得 insight_id
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2. 查詢所有 is_admin=true 的 Telegram 用戶
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3. 用 TELEGRAM_BOT_TOKEN 發送含 ✅/❌ inline keyboard 的訊息
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"""
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bot_token = os.getenv('TELEGRAM_BOT_TOKEN')
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if not bot_token:
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sys_log.warning("[OCStrategist] TELEGRAM_BOT_TOKEN 未設定，略過降價決策通知")
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return
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# 查管理員 chat_id
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session = get_session()
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try:
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rows = session.execute(
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text("SELECT telegram_id FROM telegram_users WHERE is_active = true AND is_admin = true")
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).fetchall()
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except Exception as e:
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sys_log.error(f"[OCStrategist] 查詢管理員失敗: {e}")
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return
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finally:
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session.close()
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if not rows:
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sys_log.info("[OCStrategist] 無 is_admin 管理員，略過降價決策通知")
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return
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admin_ids = [row[0] for row in rows]
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tg_url = f"https://api.telegram.org/bot{bot_token}/sendMessage"
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for rec in recs:
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# 寫 KM
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meta = {**rec, "period": period_str}
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if source_insight_id:
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meta["source_weekly_meta_id"] = source_insight_id
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rec_insight_id = store_insight(
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insight_type="price_recommendation",
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content=f"建議 {rec['product_name']} 從 ${rec['current_price']:,.0f} 降至 ${rec['suggested_price']:,.0f}：{rec['reason']}",
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period=period_str,
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product_sku=rec["product_sku"] or None,
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metadata=meta,
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)
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if not rec_insight_id:
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sys_log.warning(f"[OCStrategist] store_insight 失敗，略過 {rec['product_name']}")
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continue
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from services.telegram_templates import price_decision
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msg, keyboard = price_decision(
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product_name=rec["product_name"],
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product_sku=rec["product_sku"],
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current_price=rec["current_price"],
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suggested_price=rec["suggested_price"],
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reason=rec["reason"],
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insight_id=rec_insight_id,
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)
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for chat_id in admin_ids:
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try:
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resp = requests.post(tg_url, json={
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"chat_id": chat_id,
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"text": msg,
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"parse_mode": "Markdown",
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"reply_markup": keyboard,
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}, timeout=10)
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if not resp.ok:
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sys_log.warning(f"[OCStrategist] TG send 失敗 chat_id={chat_id}: {resp.text[:100]}")
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except Exception as e:
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sys_log.error(f"[OCStrategist] TG send 例外 chat_id={chat_id}: {e}")
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sys_log.info(f"[OCStrategist] 降價決策推送 insight_id={rec_insight_id} → {len(admin_ids)} 位管理員")
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def _notify_telegram_group(report_md: str, period_str: str, report_type: str = "週報") -> None:
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"""
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推送策略報告至 Telegram 群組（已套用 telegram_templates.report() 統一格式）。
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ADR-012 備註：週報類為 L3 OpenClaw 的週期性輸出，不經 event_router。
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"""
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bot_token = os.getenv("TELEGRAM_BOT_TOKEN") or os.getenv("OPENCLAW_BOT_TOKEN")
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chat_id = os.getenv("OPENCLAW_GROUP_ID", "-1003940688311")
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if not bot_token:
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sys_log.warning("[OCStrategist] TELEGRAM_BOT_TOKEN 未設定，略過週報推播")
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return
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from services.telegram_templates import report as render_report
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msg = render_report(
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title="AI 策略報告已出爐",
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report_type=report_type,
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period=period_str,
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content_md=report_md,
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)
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try:
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requests.post(
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f"https://api.telegram.org/bot{bot_token}/sendMessage",
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json={"chat_id": chat_id, "text": msg, "parse_mode": "Markdown"},
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timeout=10,
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)
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||
sys_log.info(f"[OCStrategist] Telegram {report_type}推送成功")
|
||
except Exception as e:
|
||
sys_log.error(f"[OCStrategist] Telegram {report_type}推送失敗: {e}")
|
||
|
||
def generate_meta_analysis_report() -> str:
|
||
"""
|
||
週日 02:00 OpenClaw 綜合 Meta-Analysis。
|
||
分析本週 AI 系統的「學習模式」與「告警效能」：
|
||
- 哪些 SKU 反覆觸發告警（高頻威脅）
|
||
- relearn 事件集中在哪些商品類型
|
||
- 各 Agent 分工占比（Hermes/NemoTron/OpenClaw 貢獻度）
|
||
- 對下週 AI 排程策略的建議
|
||
輸出雙寫 ai_insights（insight_type='meta_analysis'）並推送 Telegram。
|
||
"""
|
||
sys_log.info("[OCStrategist] 開始產生週日 Meta-Analysis...")
|
||
|
||
now = datetime.now()
|
||
end_dt = now - timedelta(days=1) # 昨天
|
||
start_dt = end_dt - timedelta(days=6)
|
||
start_str = start_dt.strftime("%Y-%m-%d")
|
||
end_str = end_dt.strftime("%Y-%m-%d")
|
||
period_str = f"{now.year}-W{now.isocalendar()[1]}-meta"
|
||
|
||
# 從 DB 抽取本週 ai_insights 統計摘要
|
||
session = get_session()
|
||
stats_text = ""
|
||
try:
|
||
rows = session.execute(text("""
|
||
SELECT insight_type, product_sku, COUNT(*) AS cnt, AVG(avg_quality) AS avg_q
|
||
FROM ai_insights
|
||
WHERE DATE(created_at) BETWEEN :s AND :e
|
||
GROUP BY insight_type, product_sku
|
||
ORDER BY cnt DESC
|
||
LIMIT 30
|
||
"""), {"s": start_str, "e": end_str}).fetchall()
|
||
|
||
relearn_count = session.execute(text("""
|
||
SELECT COUNT(*) FROM ai_insights
|
||
WHERE status = 'relearn'
|
||
AND DATE(updated_at) BETWEEN :s AND :e
|
||
"""), {"s": start_str, "e": end_str}).scalar() or 0
|
||
|
||
archived_count = session.execute(text("""
|
||
SELECT COUNT(*) FROM ai_insights
|
||
WHERE status = 'archived'
|
||
AND DATE(updated_at) BETWEEN :s AND :e
|
||
"""), {"s": start_str, "e": end_str}).scalar() or 0
|
||
|
||
total_insights = session.execute(text("""
|
||
SELECT COUNT(*) FROM ai_insights
|
||
WHERE DATE(created_at) BETWEEN :s AND :e
|
||
"""), {"s": start_str, "e": end_str}).scalar() or 0
|
||
|
||
lines = [f"總洞察數：{total_insights} 筆 | relearn 標記：{relearn_count} 筆 | 本週歸檔：{archived_count} 筆"]
|
||
for itype, sku, cnt, avg_q in rows:
|
||
sku_str = f"SKU={sku}" if sku else "（無 SKU）"
|
||
lines.append(f"• {itype} / {sku_str}：{cnt} 次 (avg_quality={avg_q:.2f})")
|
||
stats_text = "\n".join(lines)
|
||
except Exception as e:
|
||
stats_text = f"（DB 統計查詢失敗：{e}）"
|
||
finally:
|
||
session.close()
|
||
|
||
prompt = f"""
|
||
你是 OpenClaw Gemini — EwoooC 三層 AI 競情系統的元分析師。
|
||
每週日凌晨，你負責審視本週 AI 系統自身的「學習效能」與「告警品質」，
|
||
並對下週的 AI 排程策略提出建議。
|
||
|
||
### 本週資料區間
|
||
{start_str} ~ {end_str} ({period_str})
|
||
|
||
### 本週 ai_insights 統計摘要（系統自動產生）：
|
||
{stats_text}
|
||
|
||
### 請依以下格式產出 Meta-Analysis 報告（繁體中文）：
|
||
# 【EwoooC AI 系統週報 Meta-Analysis】 ({period_str})
|
||
## 一、本週 AI 告警效能總覽
|
||
（總洞察量、各類型占比、品質分布概述）
|
||
|
||
## 二、高頻威脅 SKU 分析
|
||
（哪些 SKU 反覆觸發告警，是否已超出正常競價範圍）
|
||
|
||
## 三、relearn 事件洞察
|
||
（哪些商品類型的洞察被推翻，代表什麼市場信號）
|
||
|
||
## 四、AI 系統調優建議（下週）
|
||
（根據本週數據，建議調整 Hermes 閾值、NIM 配額分配、或 relearn 觸發條件）
|
||
|
||
語氣：分析師視角，精準、客觀，不誇大。
|
||
"""
|
||
|
||
report_md = _call_gemini_flash(prompt)
|
||
citation_footer = _build_citation_footer(start_str, end_str)
|
||
if citation_footer:
|
||
report_md = report_md + citation_footer
|
||
|
||
if not report_md.startswith("⚠️ 呼叫 Gemini 失敗"):
|
||
store_insight(
|
||
insight_type="meta_analysis",
|
||
content=report_md,
|
||
period=period_str,
|
||
metadata={
|
||
"start_date": start_str, "end_date": end_str,
|
||
"generated_by": "Gemini-2.0-Flash",
|
||
"total_insights": total_insights if 'total_insights' in dir() else 0,
|
||
},
|
||
)
|
||
_notify_telegram_group(report_md, period_str)
|
||
sys_log.info("[OCStrategist] Meta-Analysis 完成並推送 Telegram")
|
||
|
||
return report_md
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# 手動測試用
|
||
print(generate_weekly_strategy_report(force_tg_alert=False))
|