905 lines
35 KiB
Python
905 lines
35 KiB
Python
#!/usr/bin/env python3
|
||
# -*- coding: utf-8 -*-
|
||
"""
|
||
競品價格補給線 Worker (Competitor Price Feeder)
|
||
|
||
角色：獨立背景 Worker（生產者端）
|
||
架構位置：
|
||
[本 Worker — 每 4 小時跑一次] → competitor_prices DB 表（最新快取）
|
||
→ competitor_price_history DB 表（歷史快照）
|
||
↓
|
||
[AI Pipeline] → fetch_candidates() LEFT JOIN competitor_prices（消費者端）
|
||
|
||
設計原則：
|
||
- 與 AI Pipeline 完全解耦：本 Worker 掛了不影響核心大腦
|
||
- 自帶重試機制，不阻塞主排程
|
||
- 語意化標籤 (tags) 讓 Hermes 獲得更豐富的情境
|
||
|
||
爬取邏輯：
|
||
MOMO 商品名稱 → PChome 關鍵字搜尋 → 模糊比對最佳匹配 → 寫入 competitor_prices + competitor_price_history
|
||
|
||
依賴：
|
||
services/pchome_crawler.py — 搜尋 + 批量 API
|
||
services/price_comparison.py — ProductNameParser + 模糊比對
|
||
"""
|
||
|
||
import json
|
||
import logging
|
||
import re
|
||
import time
|
||
from dataclasses import dataclass
|
||
from datetime import datetime, timedelta, timezone
|
||
from typing import Optional
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
# ── 比對參數 ─────────────────────────────────────────
|
||
MIN_MATCH_SCORE = 0.76 # 低於此分數不寫入；核心比價寧可待審也不能錯配
|
||
REPLACE_DIFFERENT_PRODUCT_SCORE = 0.84 # 已有不同 PChome 商品時，需超高信心才覆蓋
|
||
SEARCH_LIMIT = 12 # 每個搜尋詞取 PChome 前 N 筆
|
||
MAX_SEARCH_TERMS = 3 # 每個 MOMO 商品最多嘗試幾組搜尋詞
|
||
BATCH_SIZE = 30 # 每批 DB 寫入筆數
|
||
RATE_DELAY = 0.8 # 每次 PChome 請求間隔（秒）
|
||
TTL_HOURS = 6 # competitor_prices 快取有效期
|
||
|
||
# ── Feeder 結果 ───────────────────────────────────────
|
||
@dataclass
|
||
class FeederResult:
|
||
total_skus: int
|
||
matched: int
|
||
skipped_no_result: int
|
||
skipped_low_score: int
|
||
errors: int
|
||
duration_sec: float
|
||
history_written: int = 0
|
||
attempts_written: int = 0
|
||
|
||
|
||
def _extract_tags(pchome_product) -> list:
|
||
"""
|
||
從 PChomeProduct 物件提取語意標籤
|
||
|
||
標籤設計：
|
||
- "on_sale" — is_on_sale = True
|
||
- "discount_10pct" — 折扣 10~19%
|
||
- "discount_20pct" — 折扣 20~29%
|
||
- "discount_30pct" — 折扣 ≥ 30%
|
||
- "low_stock" — 庫存 < 10
|
||
- "high_rating" — 評分 ≥ 4.5
|
||
"""
|
||
tags = []
|
||
|
||
if pchome_product.is_on_sale:
|
||
tags.append("on_sale")
|
||
|
||
try:
|
||
disc = int(pchome_product.discount or 0)
|
||
except (ValueError, TypeError):
|
||
disc = 0
|
||
if disc >= 30:
|
||
tags.append("discount_30pct")
|
||
elif disc >= 20:
|
||
tags.append("discount_20pct")
|
||
elif disc >= 10:
|
||
tags.append("discount_10pct")
|
||
|
||
try:
|
||
stock = int(pchome_product.stock) if pchome_product.stock is not None else None
|
||
if stock is not None and 0 < stock < 10:
|
||
tags.append("low_stock")
|
||
except (ValueError, TypeError):
|
||
pass
|
||
|
||
try:
|
||
if pchome_product.rating and float(pchome_product.rating) >= 4.5:
|
||
tags.append("high_rating")
|
||
except (ValueError, TypeError):
|
||
pass
|
||
|
||
return tags
|
||
|
||
|
||
def _clean_search_text(value: str) -> str:
|
||
value = re.sub(r'[（(][^)）]*[)）]', ' ', value or '')
|
||
value = re.sub(r'[【\[].*?[】\]]', ' ', value)
|
||
value = re.sub(r'[^\w\u4e00-\u9fff]+', ' ', value)
|
||
return re.sub(r'\s+', ' ', value).strip()
|
||
|
||
|
||
def _dedupe_terms(terms: list) -> list:
|
||
result = []
|
||
seen = set()
|
||
for term in terms:
|
||
cleaned = _clean_search_text(term)
|
||
if len(cleaned) < 2:
|
||
continue
|
||
key = cleaned.lower()
|
||
if key in seen:
|
||
continue
|
||
seen.add(key)
|
||
result.append(cleaned[:36])
|
||
if len(result) >= MAX_SEARCH_TERMS:
|
||
break
|
||
return result
|
||
|
||
|
||
def _build_search_keywords(momo_name: str) -> list:
|
||
"""
|
||
用多組商品身份線索搜尋 PChome，提高命中率，但仍交給身份比對門檻把關。
|
||
"""
|
||
try:
|
||
from services.marketplace_product_matcher import build_search_terms
|
||
terms = build_search_terms(momo_name, max_terms=MAX_SEARCH_TERMS)
|
||
except Exception:
|
||
logger.debug(
|
||
"[Feeder] marketplace matcher failed while building search keywords; "
|
||
"fallback to cleaned product name",
|
||
exc_info=True,
|
||
)
|
||
cleaned = _clean_search_text(momo_name)
|
||
terms = [cleaned[:36], cleaned[:24]]
|
||
|
||
return _dedupe_terms(terms)
|
||
|
||
|
||
def _format_match_diagnostics(diagnostics) -> str:
|
||
if not diagnostics:
|
||
return ""
|
||
reasons = ",".join(getattr(diagnostics, "reasons", ()) or ())
|
||
return (
|
||
f"score={diagnostics.score}; brand={diagnostics.brand_score}; "
|
||
f"token={diagnostics.token_score}; spec={diagnostics.spec_score}; "
|
||
f"seq={diagnostics.sequence_score}; type={diagnostics.type_score}; "
|
||
f"penalty={diagnostics.price_penalty}; veto={diagnostics.hard_veto}; "
|
||
f"reasons={reasons}"
|
||
)
|
||
|
||
|
||
def _find_best_match_detail(
|
||
momo_name: str,
|
||
pchome_products: list,
|
||
momo_price: float = None,
|
||
) -> Optional[tuple]:
|
||
"""
|
||
從 PChome 搜尋結果中找出與 MOMO 商品名稱最接近的一筆
|
||
|
||
Args:
|
||
momo_name: MOMO 商品名稱
|
||
pchome_products: PChomeProduct 列表
|
||
|
||
Returns:
|
||
(PChomeProduct, score, diagnostics) or None
|
||
"""
|
||
from services.marketplace_product_matcher import score_marketplace_match
|
||
|
||
best, best_score, best_diagnostics = None, 0.0, None
|
||
for p in pchome_products:
|
||
diagnostics = score_marketplace_match(
|
||
momo_name,
|
||
p.name,
|
||
momo_price=momo_price,
|
||
competitor_price=getattr(p, "price", None),
|
||
)
|
||
score = diagnostics.score
|
||
if score > best_score:
|
||
best, best_score, best_diagnostics = p, score, diagnostics
|
||
|
||
return (best, best_score, best_diagnostics) if best else None
|
||
|
||
|
||
def _find_best_match(momo_name: str, pchome_products: list) -> Optional[tuple]:
|
||
"""Backward-compatible helper for smoke scripts."""
|
||
result = _find_best_match_detail(momo_name, pchome_products)
|
||
if not result:
|
||
return None
|
||
best, score, _diagnostics = result
|
||
return best, score
|
||
|
||
|
||
def _search_pchome_candidates(crawler, momo_name: str, keywords: list = None, momo_price: float = None) -> list:
|
||
"""以多組搜尋詞擴大 PChome 候選池，找到可信候選後提早停止。"""
|
||
candidates = []
|
||
seen_ids = set()
|
||
for keyword in keywords or _build_search_keywords(momo_name):
|
||
ok, _, products = crawler.search_products(keyword, limit=SEARCH_LIMIT)
|
||
if not ok or not products:
|
||
continue
|
||
for product in products:
|
||
if product.product_id in seen_ids:
|
||
continue
|
||
seen_ids.add(product.product_id)
|
||
candidates.append(product)
|
||
best = _find_best_match_detail(momo_name, candidates, momo_price=momo_price)
|
||
if best and best[1] >= 0.76:
|
||
break
|
||
return candidates
|
||
|
||
|
||
def _structural_similarity(momo_p, pchome_p) -> float:
|
||
"""
|
||
結構化相似度計算（品牌 + 規格 + 關鍵字）
|
||
|
||
權重：品牌匹配 0.4 + 規格匹配 0.3 + 關鍵字相似 0.3
|
||
"""
|
||
from difflib import SequenceMatcher
|
||
|
||
score = 0.0
|
||
|
||
# 品牌比對 (0.4)
|
||
if momo_p.brand and pchome_p.brand:
|
||
if momo_p.brand == pchome_p.brand:
|
||
score += 0.4
|
||
elif momo_p.brand in pchome_p.brand or pchome_p.brand in momo_p.brand:
|
||
score += 0.2
|
||
elif not momo_p.brand and not pchome_p.brand:
|
||
score += 0.1 # 都沒有品牌，不扣分
|
||
|
||
# 規格比對 (0.3) — 容量/克重
|
||
momo_specs = momo_p.specs or {}
|
||
pchome_specs = pchome_p.specs or {}
|
||
if momo_specs and pchome_specs:
|
||
matching_specs = sum(
|
||
1 for k, v in momo_specs.items()
|
||
if pchome_specs.get(k) == v
|
||
)
|
||
total_specs = max(len(momo_specs), len(pchome_specs), 1)
|
||
score += 0.3 * (matching_specs / total_specs)
|
||
elif not momo_specs and not pchome_specs:
|
||
score += 0.15
|
||
|
||
# 關鍵字相似度 (0.3)
|
||
momo_kws = " ".join(momo_p.keywords or [])
|
||
pchome_kws = " ".join(pchome_p.keywords or [])
|
||
if momo_kws and pchome_kws:
|
||
kw_sim = SequenceMatcher(None, momo_kws.lower(), pchome_kws.lower()).ratio()
|
||
score += 0.3 * kw_sim
|
||
|
||
return round(score, 3)
|
||
|
||
|
||
class CompetitorPriceFeeder:
|
||
"""
|
||
競品價格補給線 Worker
|
||
|
||
用法：
|
||
feeder = CompetitorPriceFeeder(engine=db_engine)
|
||
result = feeder.run(source="pchome")
|
||
"""
|
||
|
||
def __init__(self, engine=None):
|
||
self.engine = engine
|
||
self._history_table_ready = False
|
||
self._attempt_table_ready = False
|
||
|
||
def _ensure_competitor_price_history_table(self, conn):
|
||
"""確保競品價格歷史表存在；排程可自癒補表，不依賴手動 migration。"""
|
||
if self._history_table_ready:
|
||
return
|
||
|
||
from sqlalchemy import text
|
||
if conn.dialect.name == "postgresql":
|
||
conn.execute(text("""
|
||
CREATE TABLE IF NOT EXISTS competitor_price_history (
|
||
id BIGSERIAL PRIMARY KEY,
|
||
sku VARCHAR(50) NOT NULL,
|
||
source VARCHAR(30) NOT NULL DEFAULT 'pchome',
|
||
momo_product_id INTEGER,
|
||
momo_price NUMERIC(10,2),
|
||
price NUMERIC(10,2) NOT NULL,
|
||
original_price NUMERIC(10,2),
|
||
discount_pct INTEGER,
|
||
competitor_product_id VARCHAR(100),
|
||
competitor_product_name TEXT,
|
||
match_score NUMERIC(4,3),
|
||
tags JSONB DEFAULT '[]'::jsonb,
|
||
crawled_at TIMESTAMP NOT NULL DEFAULT NOW()
|
||
)
|
||
"""))
|
||
conn.execute(text("""
|
||
CREATE INDEX IF NOT EXISTS idx_comp_price_history_sku_source_time
|
||
ON competitor_price_history (sku, source, crawled_at DESC)
|
||
"""))
|
||
conn.execute(text("""
|
||
CREATE INDEX IF NOT EXISTS idx_comp_price_history_competitor_id
|
||
ON competitor_price_history (competitor_product_id)
|
||
"""))
|
||
else:
|
||
conn.execute(text("""
|
||
CREATE TABLE IF NOT EXISTS competitor_price_history (
|
||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||
sku VARCHAR(50) NOT NULL,
|
||
source VARCHAR(30) NOT NULL DEFAULT 'pchome',
|
||
momo_product_id INTEGER,
|
||
momo_price NUMERIC(10,2),
|
||
price NUMERIC(10,2) NOT NULL,
|
||
original_price NUMERIC(10,2),
|
||
discount_pct INTEGER,
|
||
competitor_product_id VARCHAR(100),
|
||
competitor_product_name TEXT,
|
||
match_score NUMERIC(4,3),
|
||
tags TEXT DEFAULT '[]',
|
||
crawled_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP
|
||
)
|
||
"""))
|
||
conn.execute(text("""
|
||
CREATE INDEX IF NOT EXISTS idx_comp_price_history_sku_source_time
|
||
ON competitor_price_history (sku, source, crawled_at DESC)
|
||
"""))
|
||
conn.execute(text("""
|
||
CREATE INDEX IF NOT EXISTS idx_comp_price_history_competitor_id
|
||
ON competitor_price_history (competitor_product_id)
|
||
"""))
|
||
|
||
self._history_table_ready = True
|
||
|
||
def _ensure_competitor_match_attempts_table(self, conn):
|
||
"""確保 PChome 比對嘗試表存在；成功、低分、無結果與錯誤都要留痕。"""
|
||
if self._attempt_table_ready:
|
||
return
|
||
|
||
from sqlalchemy import text
|
||
if conn.dialect.name == "postgresql":
|
||
conn.execute(text("""
|
||
CREATE TABLE IF NOT EXISTS competitor_match_attempts (
|
||
id BIGSERIAL PRIMARY KEY,
|
||
sku VARCHAR(50) NOT NULL,
|
||
source VARCHAR(30) NOT NULL DEFAULT 'pchome',
|
||
momo_product_id INTEGER,
|
||
momo_product_name TEXT,
|
||
momo_price NUMERIC(10,2),
|
||
search_terms JSONB DEFAULT '[]'::jsonb,
|
||
candidate_count INTEGER DEFAULT 0,
|
||
attempt_status VARCHAR(30) NOT NULL,
|
||
best_competitor_product_id VARCHAR(100),
|
||
best_competitor_product_name TEXT,
|
||
best_competitor_price NUMERIC(10,2),
|
||
best_match_score NUMERIC(4,3),
|
||
error_message TEXT,
|
||
attempted_at TIMESTAMP NOT NULL DEFAULT NOW()
|
||
)
|
||
"""))
|
||
conn.execute(text("""
|
||
CREATE INDEX IF NOT EXISTS idx_comp_match_attempts_sku_source_time
|
||
ON competitor_match_attempts (sku, source, attempted_at DESC)
|
||
"""))
|
||
conn.execute(text("""
|
||
CREATE INDEX IF NOT EXISTS idx_comp_match_attempts_status_time
|
||
ON competitor_match_attempts (attempt_status, attempted_at DESC)
|
||
"""))
|
||
else:
|
||
conn.execute(text("""
|
||
CREATE TABLE IF NOT EXISTS competitor_match_attempts (
|
||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||
sku VARCHAR(50) NOT NULL,
|
||
source VARCHAR(30) NOT NULL DEFAULT 'pchome',
|
||
momo_product_id INTEGER,
|
||
momo_product_name TEXT,
|
||
momo_price NUMERIC(10,2),
|
||
search_terms TEXT DEFAULT '[]',
|
||
candidate_count INTEGER DEFAULT 0,
|
||
attempt_status VARCHAR(30) NOT NULL,
|
||
best_competitor_product_id VARCHAR(100),
|
||
best_competitor_product_name TEXT,
|
||
best_competitor_price NUMERIC(10,2),
|
||
best_match_score NUMERIC(4,3),
|
||
error_message TEXT,
|
||
attempted_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP
|
||
)
|
||
"""))
|
||
conn.execute(text("""
|
||
CREATE INDEX IF NOT EXISTS idx_comp_match_attempts_sku_source_time
|
||
ON competitor_match_attempts (sku, source, attempted_at DESC)
|
||
"""))
|
||
conn.execute(text("""
|
||
CREATE INDEX IF NOT EXISTS idx_comp_match_attempts_status_time
|
||
ON competitor_match_attempts (attempt_status, attempted_at DESC)
|
||
"""))
|
||
|
||
self._attempt_table_ready = True
|
||
|
||
def _record_match_attempt(
|
||
self,
|
||
sku: str,
|
||
momo_name: str,
|
||
momo_product_id: int = None,
|
||
momo_price: float = None,
|
||
search_terms: list = None,
|
||
candidate_count: int = 0,
|
||
attempt_status: str = "unknown",
|
||
best_product=None,
|
||
best_score: float = None,
|
||
error_message: str = None,
|
||
source: str = "pchome",
|
||
) -> None:
|
||
"""追加一筆 PChome 比對嘗試紀錄，讓待比對/低信心也能回溯。"""
|
||
from sqlalchemy import text
|
||
|
||
with self.engine.begin() as conn:
|
||
self._ensure_competitor_match_attempts_table(conn)
|
||
search_terms_expr = "CAST(:search_terms AS jsonb)" if conn.dialect.name == "postgresql" else ":search_terms"
|
||
conn.execute(text(f"""
|
||
INSERT INTO competitor_match_attempts
|
||
(sku, source, momo_product_id, momo_product_name, momo_price,
|
||
search_terms, candidate_count, attempt_status,
|
||
best_competitor_product_id, best_competitor_product_name,
|
||
best_competitor_price, best_match_score, error_message,
|
||
attempted_at)
|
||
VALUES
|
||
(:sku, :source, :momo_product_id, :momo_product_name, :momo_price,
|
||
{search_terms_expr}, :candidate_count, :attempt_status,
|
||
:best_id, :best_name,
|
||
:best_price, :best_score, :error_message,
|
||
CURRENT_TIMESTAMP)
|
||
"""), {
|
||
"sku": sku,
|
||
"source": source,
|
||
"momo_product_id": momo_product_id,
|
||
"momo_product_name": momo_name,
|
||
"momo_price": momo_price,
|
||
"search_terms": json.dumps(search_terms or [], ensure_ascii=False),
|
||
"candidate_count": candidate_count,
|
||
"attempt_status": attempt_status,
|
||
"best_id": getattr(best_product, "product_id", None),
|
||
"best_name": (getattr(best_product, "name", None) or "")[:300] or None,
|
||
"best_price": getattr(best_product, "price", None),
|
||
"best_score": best_score,
|
||
"error_message": (error_message or "")[:1000] or None,
|
||
})
|
||
|
||
def _fetch_active_skus(self) -> list:
|
||
"""
|
||
從 products 表取得待監控的 ACTIVE 商品清單
|
||
|
||
Returns:
|
||
list of {"sku": str, "name": str, "category": str}
|
||
"""
|
||
if self.engine is None:
|
||
raise RuntimeError("需要注入 SQLAlchemy engine")
|
||
|
||
from sqlalchemy import text
|
||
sql = text("""
|
||
SELECT
|
||
p.id AS product_id,
|
||
p.i_code AS sku,
|
||
p.name,
|
||
p.category,
|
||
(
|
||
SELECT pr.price
|
||
FROM price_records pr
|
||
WHERE pr.product_id = p.id
|
||
ORDER BY pr.timestamp DESC
|
||
LIMIT 1
|
||
) AS momo_price
|
||
FROM products p
|
||
WHERE p.status = 'ACTIVE'
|
||
AND EXISTS (
|
||
SELECT 1
|
||
FROM price_records pr
|
||
WHERE pr.product_id = p.id
|
||
)
|
||
ORDER BY p.i_code
|
||
""")
|
||
with self.engine.connect() as conn:
|
||
rows = conn.execute(sql).fetchall()
|
||
return [dict(r._mapping) for r in rows]
|
||
|
||
def _fetch_unmatched_priority_skus(self, limit: int = 80) -> list:
|
||
"""
|
||
取得目前沒有有效 PChome 配對的高價 ACTIVE 商品，供補強流程優先處理。
|
||
"""
|
||
if self.engine is None:
|
||
raise RuntimeError("需要注入 SQLAlchemy engine")
|
||
|
||
from sqlalchemy import text
|
||
sql = text("""
|
||
WITH latest_momo AS (
|
||
SELECT
|
||
p.id AS product_id,
|
||
p.i_code AS sku,
|
||
p.name,
|
||
p.category,
|
||
pr.price AS momo_price,
|
||
ROW_NUMBER() OVER (PARTITION BY p.id ORDER BY pr.timestamp DESC) AS rn
|
||
FROM products p
|
||
JOIN price_records pr ON pr.product_id = p.id
|
||
WHERE p.status = 'ACTIVE'
|
||
)
|
||
SELECT
|
||
lm.product_id,
|
||
lm.sku,
|
||
lm.name,
|
||
lm.category,
|
||
lm.momo_price
|
||
FROM latest_momo lm
|
||
LEFT JOIN competitor_prices cp
|
||
ON cp.sku = lm.sku
|
||
AND cp.source = 'pchome'
|
||
AND (cp.expires_at IS NULL OR cp.expires_at > CURRENT_TIMESTAMP)
|
||
AND COALESCE(cp.match_score, 0) >= :match_score_floor
|
||
AND COALESCE(cp.tags, '[]'::jsonb) ? 'identity_v2'
|
||
WHERE lm.rn = 1
|
||
AND cp.sku IS NULL
|
||
ORDER BY lm.momo_price DESC NULLS LAST, lm.sku
|
||
LIMIT :limit
|
||
""")
|
||
with self.engine.connect() as conn:
|
||
rows = conn.execute(
|
||
sql,
|
||
{"limit": max(1, min(int(limit), 300)), "match_score_floor": MIN_MATCH_SCORE},
|
||
).fetchall()
|
||
return [dict(r._mapping) for r in rows]
|
||
|
||
def _upsert_competitor_price(
|
||
self,
|
||
sku: str,
|
||
product, # PChomeProduct
|
||
match_score: float,
|
||
tags: list,
|
||
momo_product_id: int = None,
|
||
momo_price: float = None,
|
||
source: str = "pchome",
|
||
):
|
||
"""單筆寫入/更新最新快取，並追加一筆歷史快照。"""
|
||
from sqlalchemy import text
|
||
_taipei = timezone(timedelta(hours=8))
|
||
expires_at = (datetime.now(_taipei) + timedelta(hours=TTL_HOURS)).strftime("%Y-%m-%d %H:%M:%S")
|
||
tags_json = json.dumps(tags, ensure_ascii=False)
|
||
with self.engine.begin() as conn:
|
||
self._ensure_competitor_price_history_table(conn)
|
||
conn.execute(text("""
|
||
INSERT INTO competitor_prices
|
||
(sku, source, price, original_price, discount_pct,
|
||
competitor_product_id, competitor_product_name,
|
||
match_score, tags, crawled_at, expires_at)
|
||
VALUES
|
||
(:sku, :source, :price, :original_price, :discount_pct,
|
||
:comp_id, :comp_name,
|
||
:match_score, :tags, CURRENT_TIMESTAMP, :expires_at)
|
||
ON CONFLICT (sku, source) DO UPDATE
|
||
SET price = EXCLUDED.price,
|
||
original_price = EXCLUDED.original_price,
|
||
discount_pct = EXCLUDED.discount_pct,
|
||
competitor_product_id = EXCLUDED.competitor_product_id,
|
||
competitor_product_name = EXCLUDED.competitor_product_name,
|
||
match_score = EXCLUDED.match_score,
|
||
tags = EXCLUDED.tags,
|
||
crawled_at = CURRENT_TIMESTAMP,
|
||
expires_at = :expires_at
|
||
"""), {
|
||
"sku": sku,
|
||
"source": source,
|
||
"price": product.price,
|
||
"original_price":product.original_price,
|
||
"discount_pct": product.discount,
|
||
"comp_id": product.product_id,
|
||
"comp_name": product.name[:200],
|
||
"match_score": match_score,
|
||
"tags": tags_json,
|
||
"expires_at": expires_at,
|
||
})
|
||
conn.execute(text("""
|
||
INSERT INTO competitor_price_history
|
||
(sku, source, momo_product_id, momo_price,
|
||
price, original_price, discount_pct,
|
||
competitor_product_id, competitor_product_name,
|
||
match_score, tags, crawled_at)
|
||
VALUES
|
||
(:sku, :source, :momo_product_id, :momo_price,
|
||
:price, :original_price, :discount_pct,
|
||
:comp_id, :comp_name,
|
||
:match_score, :tags, CURRENT_TIMESTAMP)
|
||
"""), {
|
||
"sku": sku,
|
||
"source": source,
|
||
"momo_product_id": momo_product_id,
|
||
"momo_price": momo_price,
|
||
"price": product.price,
|
||
"original_price": product.original_price,
|
||
"discount_pct": product.discount,
|
||
"comp_id": product.product_id,
|
||
"comp_name": product.name[:200],
|
||
"match_score": match_score,
|
||
"tags": tags_json,
|
||
})
|
||
|
||
def _should_upsert_competitor_price(
|
||
self,
|
||
sku: str,
|
||
product,
|
||
match_score: float,
|
||
source: str = "pchome",
|
||
) -> tuple[bool, str]:
|
||
"""
|
||
保護正式 competitor_prices：若既有配對是不同 PChome 商品，
|
||
只有超高信心才允許覆蓋，避免新 matcher 一次污染核心比價資料。
|
||
"""
|
||
from sqlalchemy import text
|
||
|
||
with self.engine.connect() as conn:
|
||
row = conn.execute(text("""
|
||
SELECT competitor_product_id, match_score, tags
|
||
FROM competitor_prices
|
||
WHERE sku = :sku
|
||
AND source = :source
|
||
LIMIT 1
|
||
"""), {"sku": sku, "source": source}).mappings().first()
|
||
|
||
if not row:
|
||
return True, "new_match"
|
||
|
||
existing_id = str(row.get("competitor_product_id") or "")
|
||
incoming_id = str(getattr(product, "product_id", "") or "")
|
||
try:
|
||
existing_score = float(row.get("match_score") or 0)
|
||
except (TypeError, ValueError):
|
||
existing_score = 0.0
|
||
existing_tags = row.get("tags") or []
|
||
if isinstance(existing_tags, str):
|
||
try:
|
||
existing_tags = json.loads(existing_tags)
|
||
except Exception:
|
||
existing_tags = []
|
||
if "identity_v2" not in existing_tags:
|
||
return True, "replace_legacy_unverified"
|
||
|
||
if not existing_id or existing_id == incoming_id:
|
||
return True, "same_or_empty_existing"
|
||
if existing_score < MIN_MATCH_SCORE:
|
||
return True, f"replace_low_existing_score={existing_score:.3f}"
|
||
if match_score >= REPLACE_DIFFERENT_PRODUCT_SCORE:
|
||
return True, f"replace_high_confidence_score={match_score:.3f}"
|
||
return (
|
||
False,
|
||
f"existing_match_conflict;existing_id={existing_id};"
|
||
f"incoming_id={incoming_id};existing_score={existing_score:.3f};"
|
||
f"incoming_score={match_score:.3f}",
|
||
)
|
||
|
||
def _run_sku_items(self, skus: list, source: str = "pchome", label: str = "PChome 競品價格") -> FeederResult:
|
||
start = time.time()
|
||
|
||
if source != "pchome":
|
||
logger.warning(f"[Feeder] 尚未支援 source={source}，跳過")
|
||
return FeederResult(0, 0, 0, 0, 0, 0.0)
|
||
|
||
from services.pchome_crawler import PChomeCrawler
|
||
crawler = PChomeCrawler(timeout=30, delay=RATE_DELAY)
|
||
|
||
logger.info(f"[Feeder] 開始抓取 {len(skus)} 支商品的 {label}")
|
||
|
||
matched = 0
|
||
skipped_no = 0
|
||
skipped_low = 0
|
||
errors = 0
|
||
history_written = 0
|
||
attempts_written = 0
|
||
|
||
for item in skus:
|
||
sku = item["sku"]
|
||
momo_name = item["name"]
|
||
momo_product_id = item.get("product_id")
|
||
momo_price = item.get("momo_price")
|
||
search_terms = _build_search_keywords(momo_name)
|
||
|
||
try:
|
||
products = _search_pchome_candidates(crawler, momo_name, search_terms, momo_price=momo_price)
|
||
if not products:
|
||
logger.debug(f"[Feeder] {sku} 無搜尋結果，跳過")
|
||
self._record_match_attempt(
|
||
sku,
|
||
momo_name,
|
||
momo_product_id=momo_product_id,
|
||
momo_price=momo_price,
|
||
search_terms=search_terms,
|
||
candidate_count=0,
|
||
attempt_status="no_result",
|
||
source=source,
|
||
)
|
||
attempts_written += 1
|
||
skipped_no += 1
|
||
continue
|
||
|
||
result = _find_best_match_detail(momo_name, products, momo_price=momo_price)
|
||
if not result:
|
||
self._record_match_attempt(
|
||
sku,
|
||
momo_name,
|
||
momo_product_id=momo_product_id,
|
||
momo_price=momo_price,
|
||
search_terms=search_terms,
|
||
candidate_count=len(products),
|
||
attempt_status="no_match",
|
||
source=source,
|
||
)
|
||
attempts_written += 1
|
||
skipped_no += 1
|
||
continue
|
||
|
||
best_product, score, diagnostics = result
|
||
|
||
if score < MIN_MATCH_SCORE:
|
||
logger.debug(
|
||
f"[Feeder] {sku} 比對分數過低 ({score:.3f} < {MIN_MATCH_SCORE})，"
|
||
f"{_format_match_diagnostics(diagnostics)}"
|
||
)
|
||
self._record_match_attempt(
|
||
sku,
|
||
momo_name,
|
||
momo_product_id=momo_product_id,
|
||
momo_price=momo_price,
|
||
search_terms=search_terms,
|
||
candidate_count=len(products),
|
||
attempt_status="low_score",
|
||
best_product=best_product,
|
||
best_score=score,
|
||
error_message=_format_match_diagnostics(diagnostics),
|
||
source=source,
|
||
)
|
||
attempts_written += 1
|
||
skipped_low += 1
|
||
continue
|
||
|
||
tags = _extract_tags(best_product)
|
||
tags.extend(getattr(diagnostics, "tags", []))
|
||
for reason in getattr(diagnostics, "reasons", ()) or ():
|
||
tags.append(f"match_{reason}")
|
||
tags = list(dict.fromkeys(tags))
|
||
should_write, write_reason = self._should_upsert_competitor_price(
|
||
sku,
|
||
best_product,
|
||
score,
|
||
source=source,
|
||
)
|
||
if not should_write:
|
||
logger.info(f"[Feeder] {sku} 進入人工覆核，不覆蓋既有配對 | {write_reason}")
|
||
self._record_match_attempt(
|
||
sku,
|
||
momo_name,
|
||
momo_product_id=momo_product_id,
|
||
momo_price=momo_price,
|
||
search_terms=search_terms,
|
||
candidate_count=len(products),
|
||
attempt_status="needs_review",
|
||
best_product=best_product,
|
||
best_score=score,
|
||
error_message=f"{write_reason}; {_format_match_diagnostics(diagnostics)}",
|
||
source=source,
|
||
)
|
||
attempts_written += 1
|
||
skipped_low += 1
|
||
continue
|
||
|
||
tags.append(write_reason)
|
||
self._upsert_competitor_price(
|
||
sku,
|
||
best_product,
|
||
score,
|
||
tags,
|
||
momo_product_id=momo_product_id,
|
||
momo_price=momo_price,
|
||
source=source,
|
||
)
|
||
self._record_match_attempt(
|
||
sku,
|
||
momo_name,
|
||
momo_product_id=momo_product_id,
|
||
momo_price=momo_price,
|
||
search_terms=search_terms,
|
||
candidate_count=len(products),
|
||
attempt_status="matched",
|
||
best_product=best_product,
|
||
best_score=score,
|
||
source=source,
|
||
)
|
||
matched += 1
|
||
history_written += 1
|
||
attempts_written += 1
|
||
logger.debug(
|
||
f"[Feeder] {sku} → PChome ${best_product.price} "
|
||
f"score={score:.3f} tags={tags}"
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"[Feeder] {sku} 處理失敗: {e}")
|
||
try:
|
||
self._record_match_attempt(
|
||
sku,
|
||
momo_name,
|
||
momo_product_id=momo_product_id,
|
||
momo_price=momo_price,
|
||
search_terms=search_terms,
|
||
attempt_status="error",
|
||
error_message=str(e),
|
||
source=source,
|
||
)
|
||
attempts_written += 1
|
||
except Exception as attempt_error:
|
||
logger.warning(f"[Feeder] {sku} 比對嘗試紀錄寫入失敗: {attempt_error}")
|
||
errors += 1
|
||
|
||
duration = round(time.time() - start, 2)
|
||
logger.info(
|
||
f"[Feeder] 完成 matched={matched} skipped_no={skipped_no} "
|
||
f"skipped_low={skipped_low} errors={errors} "
|
||
f"history_written={history_written} attempts_written={attempts_written} 耗時={duration}s"
|
||
)
|
||
return FeederResult(
|
||
total_skus=len(skus),
|
||
matched=matched,
|
||
skipped_no_result=skipped_no,
|
||
skipped_low_score=skipped_low,
|
||
errors=errors,
|
||
duration_sec=duration,
|
||
history_written=history_written,
|
||
attempts_written=attempts_written,
|
||
)
|
||
|
||
def run(self, source: str = "pchome") -> FeederResult:
|
||
"""
|
||
執行一輪競品價格抓取與寫入
|
||
|
||
Args:
|
||
source: 競品來源代碼（目前支援 'pchome'）
|
||
|
||
Returns:
|
||
FeederResult
|
||
"""
|
||
try:
|
||
skus = self._fetch_active_skus()
|
||
except Exception as e:
|
||
logger.error(f"[Feeder] 讀取商品清單失敗: {e}")
|
||
return FeederResult(0, 0, 0, 0, 1, 0.0)
|
||
|
||
return self._run_sku_items(skus, source=source, label="PChome 競品價格")
|
||
|
||
def run_unmatched_priority(self, limit: int = 80, source: str = "pchome") -> FeederResult:
|
||
"""優先補抓尚未有有效 PChome 配對的高價商品。"""
|
||
try:
|
||
skus = self._fetch_unmatched_priority_skus(limit=limit)
|
||
except Exception as e:
|
||
logger.error(f"[Feeder] 讀取待比對優先商品失敗: {e}")
|
||
return FeederResult(0, 0, 0, 0, 1, 0.0)
|
||
|
||
return self._run_sku_items(skus, source=source, label="待比對優先補抓")
|
||
|
||
|
||
# ─────────────────────────────────────────────
|
||
# CLI 測試（不依賴 DB，直接測試爬蟲 + 比對邏輯）
|
||
# python3 services/competitor_price_feeder.py
|
||
# ─────────────────────────────────────────────
|
||
if __name__ == "__main__":
|
||
logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
|
||
|
||
from services.pchome_crawler import PChomeCrawler
|
||
|
||
test_items = [
|
||
{"sku": "A003", "name": "舒特膚AD乳液200ml"},
|
||
{"sku": "A001", "name": "玻尿酸面膜10片裝"},
|
||
{"sku": "A009", "name": "美白化妝水150ml"},
|
||
]
|
||
|
||
crawler = PChomeCrawler(delay=0.8)
|
||
|
||
print("=== Competitor Price Feeder CLI 測試 ===\n")
|
||
for item in test_items:
|
||
keyword = item["name"][:20]
|
||
ok, msg, products = crawler.search_products(keyword, limit=10)
|
||
|
||
if not ok or not products:
|
||
print(f"[{item['sku']}] 無結果: {msg}")
|
||
continue
|
||
|
||
result = _find_best_match(item["name"], products)
|
||
if not result:
|
||
print(f"[{item['sku']}] 無法比對")
|
||
continue
|
||
|
||
best, score = result
|
||
tags = _extract_tags(best)
|
||
symbol = "✅" if score >= MIN_MATCH_SCORE else "⚠️ 低分"
|
||
print(
|
||
f"{symbol} [{item['sku']}] {item['name'][:25]}\n"
|
||
f" → PChome: {best.name[:40]}\n"
|
||
f" → 售價 ${best.price} | 分數 {score:.3f} | 標籤 {tags}\n"
|
||
)
|