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- 新增 services/mcp_collector_service.py：Gemini Search Grounding 外部情報收集 - 重寫 services/openclaw_strategist_service.py：真實 Gemini 2.5 Flash 分析，DB 持久化 - scheduler.py：修復 generate_meta_analysis_report ImportError，串接 Meta-Analysis - elephant_alpha_autonomous_engine.py：新增 weekly_insight 觸發器路由 OpenClaw Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
226 lines
8.9 KiB
Python
226 lines
8.9 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-2.5-flash-preview-05-20")
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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 2025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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"2025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 2025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 2025 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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return row[0]
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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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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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"""), {
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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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})
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session.commit()
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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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if not self._ensure_init():
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return ""
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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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try:
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model = self._genai.GenerativeModel(
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model_name=MCP_MODEL,
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tools=["google_search_retrieval"],
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)
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response = model.generate_content(
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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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)
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content = response.text or ""
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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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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 ""
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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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# 靜態台灣電商節日知識庫（無需 API 呼叫）
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static_calendar = {
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1: "元旦促銷、農曆新年備貨期（1/20前後開始）",
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2: "農曆新年（年貨、禮盒熱賣）、情人節（2/14，保養/禮品衝量）",
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3: "婦女節（3/8）、春季換季保養、開學季",
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4: "清明連假、春季大促、換季服飾高峰",
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5: "母親節（5/2週前後，美妝/保健/家電最高峰）、520情人節",
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6: "618購物節（最大中年促銷，全平台衝量）、父親節備檔",
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7: "父親節（7/4週前後）、暑假家電/3C/旅遊用品高峰",
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8: "七夕情人節（8/10前後）、暑假尾聲出清",
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9: "中秋節（禮盒/食品衝量）、開學季3C/文具",
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10: "雙10國慶、品牌週年慶（百貨、電商 10月旺季）",
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11: "雙11光棍節（全年最大促銷）、品牌大促備貨",
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12: "雙12年終慶、聖誕節（12/25）、跨年（元旦備貨）",
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}
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base = static_calendar.get(month, "")
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# 加入下個月預告
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next_month = (month % 12) + 1
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next_base = static_calendar.get(next_month, "")
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return (
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f"當前月份：{now.strftime('%Y年%m月')}\n"
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f"本月電商重點：{base}\n"
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f"下月預告：{next_base}"
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)
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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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