299 lines
13 KiB
Python
299 lines
13 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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services/mcp_collector_service.py
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MCP 外部情報收集層
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透過 Gemini Google Search Grounding 收集外部市場情報，供 OpenClaw 戰略分析使用：
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- 台灣電商市場趨勢
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- 節日 / 促銷行事曆
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- 季節性消費洞察
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- 競品動態（蝦皮/PChome/momo/Yahoo）
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- 消費者情緒與熱銷品類
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結果快取至 ai_insights（type='mcp_cache'），24h TTL 避免重複呼叫。
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"""
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import json
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import logging
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import os
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import time
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional
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from database.manager import get_session
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from sqlalchemy import text
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logger = logging.getLogger(__name__)
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GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "")
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MCP_CACHE_TTL_HOURS = int(os.getenv("MCP_CACHE_TTL_HOURS", "24"))
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MCP_MODEL = os.getenv("MCP_GEMINI_MODEL", "gemini-2.0-flash")
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# ── 查詢主題定義 ────────────────────────────────────────────────────────────
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_SEARCH_TOPICS = {
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"market_trends": (
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"台灣電商 momo購物網 2026年熱銷商品趨勢 消費者行為 美妝保養 家電 生活用品"
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),
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"holiday_calendar": (
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"2026年台灣重要節日促銷行事曆 母親節 618購物節 雙11 雙12 中秋 跨年 電商大促"
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),
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"seasonal_insights": (
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"台灣電商季節性銷售趨勢 換季商品 夏季防曬 冬季保暖 Q3 Q4 消費高峰"
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),
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"competitor_intel": (
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"momo購物網 PChome 蝦皮 Yahoo購物 2026年競爭策略 促銷活動 物流比較"
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),
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"consumer_sentiment": (
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"台灣消費者 2026 購物偏好 低價高CP 品牌忠誠度 直播購物 社群電商 KOL影響"
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),
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"pricing_strategy": (
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"台灣電商定價策略 動態定價 競品比價 心理定價 促銷折扣最佳時機"
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),
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}
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class MCPCollectorService:
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"""
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外部情報收集服務（MCP 節點）
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使用 Gemini Search Grounding 抓取即時市場資訊
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"""
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def __init__(self):
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self._initialized = False
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self._genai = None
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def _ensure_init(self) -> bool:
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if self._initialized:
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return True
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if not GEMINI_API_KEY:
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logger.warning("[MCP] GEMINI_API_KEY 未設定，跳過外部情報收集")
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return False
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try:
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import google.generativeai as genai
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genai.configure(api_key=GEMINI_API_KEY)
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self._genai = genai
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self._initialized = True
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return True
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except ImportError:
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logger.error("[MCP] google-generativeai 未安裝")
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return False
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except Exception as e:
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logger.error("[MCP] Gemini 初始化失敗: %s", e)
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return False
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# ── 快取讀寫 ────────────────────────────────────────────────────────────
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def _read_cache(self, topic: str) -> Optional[str]:
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session = get_session()
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try:
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row = session.execute(
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text(f"""
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SELECT content FROM ai_insights
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WHERE insight_type = 'mcp_cache'
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AND created_by = 'mcp_collector'
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AND metadata_json::jsonb ->> 'topic' = :topic
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AND created_at >= NOW() - INTERVAL '{MCP_CACHE_TTL_HOURS} hours'
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ORDER BY created_at DESC LIMIT 1
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"""),
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{"topic": topic},
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).fetchone()
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if row:
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logger.debug("[MCP] 快取命中: %s", topic)
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content = row[0]
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if self._looks_unreliable(content):
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logger.warning("[MCP] 快取內容含占位文字，略過 topic=%s", topic)
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return None
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return content
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return None
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except Exception:
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return None
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finally:
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session.close()
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def _write_cache(self, topic: str, content: str) -> None:
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session = get_session()
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try:
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row = session.execute(text("""
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INSERT INTO ai_insights
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(insight_type, content, confidence, created_by, status, metadata_json)
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VALUES ('mcp_cache', :content, 0.9, 'mcp_collector', 'active', :meta)
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RETURNING id
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"""), {
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"content": content[:4000],
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"meta": json.dumps({"topic": topic, "model": MCP_MODEL, "cached_at": datetime.now().isoformat()})
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}).fetchone()
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session.commit()
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if row:
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try:
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from services.openclaw_learning_service import enqueue_insight_embedding
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enqueue_insight_embedding(row[0], "mcp_cache", content[:4000])
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except Exception as embed_err:
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logger.warning("[MCP] embedding queue enqueue failed: %s", embed_err)
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except Exception as e:
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logger.warning("[MCP] 快取寫入失敗: %s", e)
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session.rollback()
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finally:
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session.close()
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# ── 單主題搜尋 ──────────────────────────────────────────────────────────
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def _search_topic(self, topic: str, query: str) -> str:
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cached = self._read_cache(topic)
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if cached:
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return cached
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if not self._ensure_init():
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return self._fallback_topic_content(topic, "GEMINI_API_KEY 未設定，使用本地行銷情報。")
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try:
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prompt = f"請用繁體中文整理以下主題的最新資訊，提供具體數據與洞察，500字以內：\n{query}"
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response = None
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last_error = None
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for tools in (["google_search"], ["google_search_retrieval"], None):
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try:
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kwargs = {"model_name": MCP_MODEL}
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if tools:
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kwargs["tools"] = tools
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model = self._genai.GenerativeModel(**kwargs)
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response = model.generate_content(prompt)
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break
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except Exception as tool_err:
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last_error = tool_err
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continue
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if response is None:
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raise last_error or RuntimeError("Gemini response empty")
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content = response.text or ""
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if self._looks_unreliable(content):
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return self._fallback_topic_content(topic, "即時搜尋內容含占位數字或待更新文字，已改用本地行銷情報。")
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if content:
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self._write_cache(topic, content)
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return content
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return self._fallback_topic_content(topic, "Gemini 回傳空內容，使用本地行銷情報。")
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except Exception as e:
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logger.warning("[MCP] 搜尋失敗 topic=%s: %s", topic, e)
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return self._fallback_topic_content(topic, f"即時外部搜尋暫不可用：{type(e).__name__}")
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@staticmethod
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def _looks_unreliable(content: str) -> bool:
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"""避免將模型產生的占位數字或待補文字當成真實情報。"""
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if not content:
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return False
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markers = (
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"XX",
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"請自行更新",
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"待補",
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"資料待查",
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"自行查詢",
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"示例數據",
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"範例數據",
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)
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return any(marker in content for marker in markers)
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def _fallback_topic_content(self, topic: str, reason: str = "") -> str:
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"""外部搜尋失敗時的穩定回覆，避免 Telegram 按鈕空白或像壞掉。"""
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holiday = self.get_holiday_context()
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seasonal = self.get_seasonal_context()
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fallback_map = {
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"market_trends": [
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"台灣電商營運觀察：美妝保養、保健食品、母嬰與個人清潔仍適合用週期性促銷與組合包拉升轉換。",
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"建議優先檢查近期高業績品類、毛利率與庫存週轉，將活動資源集中在高轉換商品。",
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],
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"competitor_intel": [
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"競品情報 fallback：請優先比較 momo / PChome / 蝦皮同款商品的售價、庫存、到貨速度與組合優惠。",
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"若 PChome 價格優勢明顯，可強化文案中的即時到貨、價格透明與組合折扣。",
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],
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"consumer_sentiment": [
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"消費者聲量 fallback：高 CP 值、到貨速度、真實評價與成分/規格透明度通常會影響購買意願。",
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"建議把負評來源拆成價格、物流、規格不符、售後服務四類追蹤。",
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],
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"pricing_strategy": [
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"定價策略 fallback：先鎖定高流量高轉換商品做競品價差監控，再用加價購、滿額折與組合包保護毛利。",
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],
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"holiday_calendar": [holiday],
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"seasonal_insights": [seasonal],
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}
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lines = fallback_map.get(topic, [holiday, seasonal])
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if reason:
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lines.append(f"資料狀態：{reason}")
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return "\n".join(line for line in lines if line)
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# ── 公開介面 ────────────────────────────────────────────────────────────
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def collect_all(self) -> Dict[str, str]:
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"""
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收集所有外部情報主題，回傳 {topic: content} 字典。
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各主題獨立失敗不影響整體。
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"""
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results = {}
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for topic, query in _SEARCH_TOPICS.items():
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try:
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results[topic] = self._search_topic(topic, query)
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time.sleep(0.5) # 避免 Gemini rate limit
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except Exception as e:
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logger.error("[MCP] topic=%s 收集失敗: %s", topic, e)
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results[topic] = ""
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logger.info("[MCP] 收集完成，有效主題=%d/%d", sum(1 for v in results.values() if v), len(results))
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return results
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def collect_topic(self, topic: str) -> str:
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"""收集單一主題"""
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query = _SEARCH_TOPICS.get(topic, topic)
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return self._search_topic(topic, query)
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def get_holiday_context(self) -> str:
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"""取得節日行事曆（供 Prompt 注入）"""
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now = datetime.now()
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month = now.month
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day = now.day
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# 靜態台灣電商節日知識庫
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static_calendar = {
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1: "元旦促銷、農曆新年備貨期",
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2: "農曆新年、情人節（2/14）",
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3: "婦女節（3/8）、開學季",
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4: "清明連假（4月初）、春季大促、換季服飾高峰",
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5: "母親節（5月第2週，年度大促）、520情人節、勞動節（5/1）",
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6: "618購物節（年中最大促銷）、端午節",
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7: "暑假開端、父親節前哨站",
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8: "父親節（8/8）、七夕情人節",
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9: "中秋節、開學季、百貨週年慶預熱",
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10: "雙10國慶、百貨週年慶高峰期",
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11: "雙11光棍節（全年最強電商季）、黑五大促",
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12: "雙12年終慶、聖誕節、跨年備貨",
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}
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current_focus = static_calendar.get(month, "")
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next_month = (month % 12) + 1
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next_focus = static_calendar.get(next_month, "")
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# 月底優化：若超過 20 號，自動將焦點轉向「下月預告」，避免產生如「4月底還在過清明節」的幻覺
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if day > 20:
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header = f"當前日期：{now.strftime('%Y/%m/%d')} (月底轉場期)"
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body = f"本月即將結束，目前重點已轉向：{next_focus}"
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footer = f"下月詳細預告：{next_focus}"
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else:
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header = f"當前日期：{now.strftime('%Y/%m/%d')}"
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body = f"本月電商重點：{current_focus}"
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footer = f"下月預告：{next_focus}"
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return f"{header}\n{body}\n{footer}"
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def get_seasonal_context(self) -> str:
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"""季節性消費情境"""
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month = datetime.now().month
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seasons = {
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(3, 4, 5): "春季：換季保養、外出服飾、春遊裝備",
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(6, 7, 8): "夏季：防曬/美白、涼感寢具、戶外運動、冷氣清潔",
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(9, 10, 11): "秋季：保濕修護、秋冬服飾、保健養生、熱飲週邊",
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(12, 1, 2): "冬季：保暖寢具、暖身家電、年節禮品、養生補品",
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}
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for months, desc in seasons.items():
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if month in months:
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return desc
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return ""
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# 模組單例
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mcp_collector = MCPCollectorService()
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