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ewoooc/services/competitor_price_feeder.py
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V10.591 拆分比價型錄覆核狀態
2026-06-05 11:06:47 +08:00

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#!/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 os
import re
import time
from dataclasses import dataclass
from datetime import datetime, timedelta, timezone
from typing import Optional
from urllib.parse import quote_plus
from services.marketplace_product_matcher import FOCUSED_IDENTITY_TOTAL_PRICE_REASONS
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
EARLY_STOP_MATCH_SCORE = 0.90 # 搜尋候選池只有強同款才提前停止，避免次佳候選卡住後續精準搜尋詞
SEARCH_LIMIT = int(os.getenv("PCHOME_FEEDER_SEARCH_LIMIT", "20")) # 每個搜尋詞取 PChome 前 N 筆
MAX_SEARCH_TERMS = int(os.getenv("PCHOME_FEEDER_MAX_SEARCH_TERMS", "6")) # 每個 MOMO 商品最多嘗試幾組搜尋詞
SEARCH_MAX_PAGES = int(os.getenv("PCHOME_FEEDER_SEARCH_MAX_PAGES", "2")) # 每個搜尋詞最多掃描 PChome 搜尋頁數
BATCH_SIZE = 30 # 每批 DB 寫入筆數
RATE_DELAY = float(os.getenv("PCHOME_FEEDER_RATE_DELAY", "1.0")) # 每次 PChome 請求間隔（秒）
TTL_HOURS = int(os.getenv("PCHOME_FEEDER_TTL_HOURS", "48")) # competitor_prices 價格新鮮度有效期
REQUEST_TIMEOUT = float(os.getenv("PCHOME_FEEDER_TIMEOUT", "12")) # 避免外部搜尋 API 長時間卡住排程
BACKFILL_REQUEST_TIMEOUT = float(os.getenv("PCHOME_FEEDER_BACKFILL_TIMEOUT", str(min(REQUEST_TIMEOUT, 6.0))))
BACKFILL_MAX_SEARCH_TERMS = int(os.getenv("PCHOME_FEEDER_BACKFILL_MAX_SEARCH_TERMS", "2"))
BACKFILL_SEARCH_MAX_PAGES = int(os.getenv("PCHOME_FEEDER_BACKFILL_SEARCH_MAX_PAGES", "1"))
BACKFILL_MAX_SECONDS_PER_SKU = float(os.getenv("PCHOME_FEEDER_BACKFILL_MAX_SECONDS_PER_SKU", "18"))
SEARCH_COVERAGE_RESCUE_ENABLED = os.getenv(
"PCHOME_FEEDER_SEARCH_COVERAGE_RESCUE_ENABLED",
"true",
).lower() in {"1", "true", "yes", "on"}
VARIANT_RECALL_SORTS = ("sale/dc", "new/dc")
RECOVERABLE_LOW_SCORE_FLOOR = max(MIN_MATCH_SCORE - 0.03, 0.72)
GENERIC_RECALL_SAFE_PRODUCT_TYPES = {
"止汗噴霧",
"潔膚露",
"刮鬍刀",
"電動牙刷",
"洗手慕斯",
"足膜",
"化妝水",
"乳液",
"面霜",
"洗面乳",
"面膜",
"眼霜",
"卸妝",
}
GENERIC_RECALL_BLOCK_TERMS = (
"任選",
"多款",
"多色",
"色號",
"顏色",
"款式",
"香味",
"香調",
"香氛",
"精油",
"擴香",
"蠟燭",
"融蠟",
"融燭",
"美甲片",
"指甲油",
"指彩",
"唇釉",
"唇彩",
"唇膏",
"潤唇",
"眼影",
"腮紅",
"粉底",
"遮瑕",
"定妝",
"妝前",
"防曬",
"護手霜",
)
COVERAGE_RESCUE_NOISE_TERMS = (
"外出清潔",
"卸除髒汙",
"卸除防曬",
"卸防曬",
"交換禮物",
"送禮",
"節日",
"推薦",
)
GENERIC_RECALL_BLOCK_NAME_PATTERN = "|".join(re.escape(term) for term in GENERIC_RECALL_BLOCK_TERMS)
BROWSE_SH_DIAGNOSTIC_ENABLED = os.getenv("PCHOME_FEEDER_BROWSE_SH_DIAGNOSTIC_ENABLED", "true").lower() in {"1", "true", "yes", "on"}
BROWSE_SH_EXECUTE_ENABLED = os.getenv("PCHOME_FEEDER_BROWSE_SH_EXECUTE_ENABLED", "false").lower() in {"1", "true", "yes", "on"}
BROWSE_SH_TIMEOUT_SECONDS = int(os.getenv("PCHOME_FEEDER_BROWSE_SH_TIMEOUT", "20"))
BROWSE_SH_MAX_EXECUTIONS_PER_RUN = int(os.getenv("PCHOME_FEEDER_BROWSE_SH_MAX_PER_RUN", "3"))
BROWSE_SH_OUTPUT_PREVIEW_CHARS = int(os.getenv("PCHOME_FEEDER_BROWSE_SH_OUTPUT_PREVIEW_CHARS", "1200"))
RECOVERABLE_DIAGNOSTIC_REASONS = {
"strong_product_line_match",
"shared_identity_anchor",
"shared_identity_anchor_no_spec",
"shared_identity_anchor_packaging_variant",
"shared_identity_anchor_marketing_variant",
"shared_identity_anchor_core_line",
"shared_identity_anchor_variant_safe",
"shared_model_token",
"spec_name_alignment",
}
CATALOG_REVIEW_VARIANT_REASONS = {
"variant_selection_review",
"makeup_catalog_selection_gap",
}
CATALOG_REVIEW_UNIT_REASONS = {
"commercial_condition_gap",
"catalog_count_omission",
}
CATALOG_REVIEW_IDENTITY_REASONS = {
"strong_product_line_match",
"strong_exact_spec_match",
"shared_identity_anchor_exact_line",
"shared_identity_anchor_core_line",
"shared_identity_anchor_variant_safe",
"spec_name_alignment",
"shared_model_token",
}
CATALOG_REVIEW_BLOCK_REASONS = {
"brand_conflict",
"type_conflict",
"product_line_conflict",
"core_ingredient_line_conflict",
"variant_option_conflict",
"variant_descriptor_conflict",
"aroma_scent_variant_conflict",
"bath_additive_variant_gap",
"makeup_finish_conflict",
"makeup_usage_conflict",
"romand_lip_line_conflict",
"count_conflict",
"component_count_conflict",
"multi_component_conflict",
"multi_component_count_conflict",
"bundle_offer_conflict",
"refill_pack_conflict",
"accessory_case_conflict",
"named_component_quantity_conflict",
"price_ratio_extreme",
}
RECOVERABLE_SQL_REASON_LIST = ", ".join(f"'{reason}'" for reason in sorted(RECOVERABLE_DIAGNOSTIC_REASONS))
FOCUSED_TOTAL_PRICE_IDENTITY_DIAGNOSTIC_REASONS = {
f"focused_exact_identity_{reason}"
for reason in FOCUSED_IDENTITY_TOTAL_PRICE_REASONS
}
FOCUSED_TOTAL_PRICE_SAFE_DIAGNOSTIC_REASONS = {
"focused_exact_total_price_safe",
*FOCUSED_TOTAL_PRICE_IDENTITY_DIAGNOSTIC_REASONS,
}
FOCUSED_TOTAL_PRICE_IDENTITY_SQL_REASON_LIST = ", ".join(
f"'{reason}'" for reason in sorted(FOCUSED_TOTAL_PRICE_IDENTITY_DIAGNOSTIC_REASONS)
)
FOCUSED_TOTAL_PRICE_SAFE_SQL_REASON_LIST = ", ".join(
f"'{reason}'" for reason in sorted(FOCUSED_TOTAL_PRICE_SAFE_DIAGNOSTIC_REASONS)
)
REVALIDATABLE_REVIEW_GATE_REASONS = {
"strong_exact_spec_match",
"spec_name_alignment",
"shared_model_token",
}
REVALIDATABLE_REVIEW_BLOCK_REASONS = {
"commercial_condition_gap",
"variant_selection_review",
"variant_option_conflict",
"variant_descriptor_conflict",
"aroma_scent_variant_conflict",
"romand_lip_line_conflict",
"makeup_finish_conflict",
"makeup_usage_conflict",
"nail_tool_function_conflict",
"product_line_conflict",
"count_conflict",
"bundle_offer_conflict",
"multi_component_conflict",
"multi_component_count_conflict",
"refill_pack_conflict",
"accessory_case_conflict",
"named_component_quantity_conflict",
}
FOCUSED_REVALIDATABLE_REVIEW_BLOCK_REASONS = REVALIDATABLE_REVIEW_BLOCK_REASONS - {
"variant_selection_review",
}
REVALIDATABLE_REVIEW_SQL_REASON_LIST = ", ".join(
f"'{reason}'" for reason in sorted(REVALIDATABLE_REVIEW_GATE_REASONS)
)
REVALIDATABLE_REVIEW_BLOCK_SQL_REASON_LIST = ", ".join(
f"'{reason}'" for reason in sorted(REVALIDATABLE_REVIEW_BLOCK_REASONS)
)
FOCUSED_REVALIDATABLE_REVIEW_BLOCK_SQL_REASON_LIST = ", ".join(
f"'{reason}'" for reason in sorted(FOCUSED_REVALIDATABLE_REVIEW_BLOCK_REASONS)
)
STALE_IDENTITY_RECOVERY_BLOCK_REASONS = {
"accessory_case_conflict",
"aroma_lamp_style_selection_gap",
"aroma_scent_variant_conflict",
"bundle_offer_conflict",
"candle_catalog_selection_gap",
"catalog_count_omission",
"commercial_condition_gap",
"count_conflict",
"makeup_catalog_selection_gap",
"makeup_finish_conflict",
"makeup_usage_conflict",
"multi_component_conflict",
"multi_component_count_conflict",
"named_component_quantity_conflict",
"nail_tool_function_conflict",
"price_ratio_extreme",
"price_ratio_wide",
"product_line_conflict",
"refill_pack_conflict",
"romand_lip_line_conflict",
"unit_comparable",
"variant_descriptor_conflict",
"variant_option_conflict",
"variant_selection_review",
}
STALE_IDENTITY_RECOVERY_BLOCK_SQL_REASON_LIST = ", ".join(
f"'{reason}'" for reason in sorted(STALE_IDENTITY_RECOVERY_BLOCK_REASONS)
)
STALE_IDENTITY_RECOVERY_BLOCK_NAME_PATTERN = (
r"(任選|多款|色號|顏色|款式|香味|香調|即期|短效|航空版|"
r"融燭燈|融蠟燈|香氛蠟燭|精油蠟燭|蠟燭|限定|組合任選|"
r"\+|[xX]\s*\d|[*＊]\s*\d|湛藍|麋香|海洋|玫瑰|薰衣草|"
r"生理呵護|日用型|清爽|潤澤)"
)
def _json_list(value) -> list:
if isinstance(value, list):
return value
if isinstance(value, tuple):
return list(value)
if isinstance(value, str):
try:
parsed = json.loads(value)
return parsed if isinstance(parsed, list) else []
except Exception:
return []
return []
def _has_stale_identity_recovery_block(row: dict) -> bool:
reasons = set(str(reason) for reason in _json_list(row.get("diagnostic_reasons")))
if reasons & STALE_IDENTITY_RECOVERY_BLOCK_REASONS:
return True
haystack = f"{row.get('name') or ''} {row.get('competitor_product_name') or ''}"
return bool(re.search(STALE_IDENTITY_RECOVERY_BLOCK_NAME_PATTERN, haystack, flags=re.IGNORECASE))
# ── 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 _has_recoverable_identity_signal(diagnostics) -> bool:
if not diagnostics:
return False
reasons = set(getattr(diagnostics, "reasons", ()) or ())
if reasons & RECOVERABLE_DIAGNOSTIC_REASONS:
return True
return (
getattr(diagnostics, "brand_score", 0) >= 0.95
and getattr(diagnostics, "token_score", 0) >= 0.56
and getattr(diagnostics, "sequence_score", 0) >= 0.50
and getattr(diagnostics, "comparison_mode", "exact_identity") == "exact_identity"
)
def _is_multi_variant_listing_name(name: str) -> bool:
return bool(
re.search(
r"(多款任選|多款可選|多色任選|多色可選|多種香味|多種香氣|香味任選|香味可選|味道可選)",
name or "",
)
)
def _classify_low_score_attempt(score: float, diagnostics) -> str:
if getattr(diagnostics, "hard_veto", False):
return "identity_veto"
if "variant_selection_review" in set(getattr(diagnostics, "reasons", ()) or ()):
return "true_low_confidence"
if score >= RECOVERABLE_LOW_SCORE_FLOOR and _has_recoverable_identity_signal(diagnostics):
return "recoverable_low_score"
return "true_low_confidence"
def _is_auto_price_write_safe(diagnostics) -> bool:
"""Only exact, total-price identities may update the formal comparison cache."""
if not diagnostics or getattr(diagnostics, "hard_veto", False):
return False
if getattr(diagnostics, "comparison_mode", "") != "exact_identity":
return False
if getattr(diagnostics, "match_type", "") != "exact":
return False
if getattr(diagnostics, "price_basis", "") != "total_price":
return False
if getattr(diagnostics, "alert_tier", "") != "price_alert_exact":
return False
if "variant_selection_review" in set(getattr(diagnostics, "reasons", ()) or ()):
return False
return True
def _classify_catalog_review_attempt(score: float, diagnostics) -> Optional[str]:
"""Split high-confidence manual gates into operational review lanes."""
if getattr(diagnostics, "hard_veto", False):
return None
if score < MIN_MATCH_SCORE:
return None
reasons = set(getattr(diagnostics, "reasons", ()) or ())
if reasons & CATALOG_REVIEW_BLOCK_REASONS:
return None
has_identity_signal = bool(reasons & CATALOG_REVIEW_IDENTITY_REASONS) or score >= 0.95
if not has_identity_signal:
return None
if reasons & CATALOG_REVIEW_VARIANT_REASONS:
return "catalog_variant_review"
if reasons & CATALOG_REVIEW_UNIT_REASONS:
return "catalog_unit_review"
if (
getattr(diagnostics, "comparison_mode", "") == "exact_identity"
and getattr(diagnostics, "match_type", "") in {"exact", "comparable"}
and getattr(diagnostics, "price_basis", "") in {"manual_review", "total_price"}
and getattr(diagnostics, "alert_tier", "") in {"identity_review", "price_alert_exact"}
):
return "catalog_identity_review"
return None
def _classify_auto_write_block_attempt(score: float, diagnostics) -> str:
if getattr(diagnostics, "hard_veto", False):
return "identity_veto"
catalog_status = _classify_catalog_review_attempt(score, diagnostics)
if catalog_status:
return catalog_status
if score >= MIN_MATCH_SCORE:
return "true_low_confidence"
return _classify_low_score_attempt(score, diagnostics)
def _has_variant_selection_gap(
momo_name: str,
ranked_matches: list[tuple],
best_score: float,
) -> bool:
"""True when source lacks explicit variant selection but top candidates require one."""
try:
from services.marketplace_product_matcher import (
_explicit_variant_option_tokens,
parse_product_identity,
)
except Exception:
return False
source_identity = parse_product_identity(momo_name)
source_options = set(_explicit_variant_option_tokens(source_identity))
source_multi_listing = _is_multi_variant_listing_name(momo_name)
if re.search(r"任選\s*[一二兩三四五六七八九十0-9]+\s*款", momo_name):
source_options -= {str(value) for value in range(1, 11)}
source_options -= {f"{value:02d}" for value in range(1, 11)}
if source_options and not source_multi_listing:
return False
threshold = max(best_score - 0.02, RECOVERABLE_LOW_SCORE_FLOOR)
option_buckets: set[str] = set()
for product, score, diagnostics in ranked_matches[:5]:
if getattr(diagnostics, "hard_veto", False) or score < threshold:
continue
candidate_name = getattr(product, "name", "") or ""
if source_multi_listing != _is_multi_variant_listing_name(candidate_name):
return True
candidate_identity = parse_product_identity(candidate_name)
options = _explicit_variant_option_tokens(candidate_identity)
if len(options) >= 2:
return True
option_buckets.update(options)
if len(option_buckets) >= 2:
return True
return False
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 _extend_match_tags(tags: list, diagnostics, extra: list[str] = None) -> list:
"""Attach matcher evidence tags in one place so all feeder lanes agree."""
tags = list(tags or [])
tags.extend(getattr(diagnostics, "tags", []) or [])
for reason in getattr(diagnostics, "reasons", ()) or ():
tags.append(f"match_{reason}")
if extra:
tags.extend(extra)
return list(dict.fromkeys(tag for tag in tags if tag))
def _clean_search_text(value: str) -> str:
value = re.sub(r'[（）()]', ' ', value or '')
value = re.sub(r'[【】\[\]]', ' ', value)
value = re.sub(r"(?<=\d)\.(?=\d)", "DECIMALPOINT", value)
value = re.sub(r'[^\w\u4e00-\u9fff]+', ' ', value)
value = value.replace("DECIMALPOINT", ".").replace("decimalpoint", ".")
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 _identity_spec_terms(identity) -> list[str]:
terms: list[str] = []
for value in getattr(identity, "volumes_ml", ()) or ():
terms.append(f"{float(value):g}ml")
for value in getattr(identity, "weights_g", ()) or ():
terms.append(f"{float(value):g}g")
for value in getattr(identity, "dosages_mg", ()) or ():
terms.append(f"{float(value):g}mg")
total_piece_count = getattr(identity, "total_piece_count", None)
if total_piece_count:
terms.append(f"{int(total_piece_count)}入")
return list(dict.fromkeys(terms))
def _identity_core_rescue_phrases(identity, limit: int = 3) -> list[str]:
product_type = getattr(identity, "product_type", "") or ""
phrases: list[str] = []
for token in sorted(
(getattr(identity, "core_tokens", set()) or set()),
key=lambda value: (
product_type not in str(value),
not bool(re.search(r"[\u4e00-\u9fff]", str(value))),
-len(str(value)),
str(value),
),
):
phrase = _clean_search_text(str(token))
compact = phrase.replace(" ", "")
if len(compact) < 2:
continue
if product_type and compact == str(product_type).replace(" ", ""):
continue
if compact.isdigit() or re.fullmatch(r"\d+(?:\.\d+)?(?:ml|g|mg|kg|l|入|片|支|瓶|盒|包)?", compact, re.I):
continue
if (
any(term in compact for term in GENERIC_RECALL_BLOCK_TERMS)
and not _is_generic_recall_safe(identity)
):
continue
if any(term in compact for term in COVERAGE_RESCUE_NOISE_TERMS):
continue
if phrase.lower() in {existing.lower() for existing in phrases}:
continue
phrases.append(phrase)
if len(phrases) >= limit:
break
return phrases
def _build_coverage_rescue_keywords(momo_name: str) -> list[str]:
"""Add narrow identity fallback terms for pending/no-result coverage recovery."""
if not SEARCH_COVERAGE_RESCUE_ENABLED:
return []
try:
from services.marketplace_product_matcher import parse_product_identity
identity = parse_product_identity(momo_name)
except Exception:
return []
brand_phrases = _coverage_brand_phrases(identity)
product_type = getattr(identity, "product_type", "") or ""
spec_part = " ".join(_identity_spec_terms(identity))
core_phrases = _identity_core_rescue_phrases(identity, limit=3)
primary_core = core_phrases[0] if core_phrases else product_type
secondary_core = core_phrases[1] if len(core_phrases) > 1 else ""
terms: list[str] = []
for brand in brand_phrases:
terms.extend([
" ".join(part for part in (brand, primary_core, spec_part) if part),
" ".join(part for part in (brand, primary_core, secondary_core, spec_part) if part),
" ".join(part for part in (brand, product_type, spec_part) if part)
if _is_generic_recall_safe(identity)
else "",
])
terms.append(" ".join(part for part in (primary_core, spec_part) if part))
return _dedupe_terms(terms)
def _append_search_term(target: list[str], term: str, *, max_terms: int) -> None:
if len(target) >= max_terms:
return
cleaned_terms = _dedupe_terms([term])
if not cleaned_terms:
return
cleaned = cleaned_terms[0]
if cleaned.lower() in {existing.lower() for existing in target}:
return
target.append(cleaned)
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)
terms.append(momo_name)
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]]
max_terms = max(1, MAX_SEARCH_TERMS)
primary_terms = _dedupe_terms(terms[: max(1, max_terms - 2)])
rescue_terms = _build_coverage_rescue_keywords(momo_name)
original_terms = _dedupe_terms([momo_name])
selected_terms: list[str] = []
for term in primary_terms:
_append_search_term(selected_terms, term, max_terms=max(1, max_terms - 1))
for term in rescue_terms:
_append_search_term(selected_terms, term, max_terms=max(1, max_terms - 1))
for term in original_terms:
if term.lower() not in {existing.lower() for existing in selected_terms}:
if len(selected_terms) >= max_terms:
selected_terms = selected_terms[: max(0, max_terms - 1)]
selected_terms.append(term)
break
return _dedupe_terms(selected_terms)
def _primary_brand_phrase(identity) -> str:
brand_tokens = {token.lower() for token in getattr(identity, "brand_tokens", set())}
if {"3w", "clinic"} <= brand_tokens:
return "3w clinic"
if {"dashing", "diva"} <= brand_tokens:
return "dashing diva"
if {"rom", "nd"} <= brand_tokens:
return "romand"
if {"im", "meme"} <= brand_tokens:
return "im meme"
if {"recipe", "box"} <= brand_tokens:
return "recipe box"
chinese = sorted(
(
token for token in getattr(identity, "brand_tokens", set())
if re.search(r"[\u4e00-\u9fff]", token)
),
key=lambda token: (-len(token), token),
)
if chinese:
return chinese[0]
latin = sorted(
(
token for token in getattr(identity, "brand_tokens", set())
if re.search(r"[a-z]", token, re.I) and len(token) >= 3
),
key=lambda token: (" " not in token and "-" not in token, -len(token), token),
)
if latin:
return latin[0].lower()
short_latin = sorted(
(
token for token in getattr(identity, "brand_tokens", set())
if re.search(r"[a-z]", token, re.I) and len(token) >= 2
),
key=lambda token: (" " not in token and "-" not in token, -len(token), token),
)
return short_latin[0].lower() if short_latin else ""
def _coverage_brand_phrases(identity) -> list[str]:
brand_tokens = {str(token).lower() for token in getattr(identity, "brand_tokens", set())}
chinese = sorted(
(
token for token in getattr(identity, "brand_tokens", set())
if re.search(r"[\u4e00-\u9fff]", str(token))
),
key=lambda token: (-len(str(token)), str(token)),
)
latin = sorted(
(
token for token in getattr(identity, "brand_tokens", set())
if re.search(r"[a-z]", str(token), re.I) and len(str(token)) >= 2
),
key=lambda token: (-len(str(token)), str(token)),
)
phrases: list[str] = []
if latin and chinese:
phrases.append(f"{str(latin[0]).lower()} {chinese[0]}")
primary = _primary_brand_phrase(identity)
if primary:
phrases.append(primary)
for token in (
"cerave",
"embryolisse",
"neogence",
"tunemakers",
"nivea",
"romand",
"lactacyd",
"pavaruni",
"solone",
):
if token in brand_tokens:
phrases.append(token)
return _dedupe_terms(phrases)
def _is_generic_recall_safe(identity) -> bool:
product_type = getattr(identity, "product_type", None)
if product_type not in GENERIC_RECALL_SAFE_PRODUCT_TYPES:
return False
searchable_name = getattr(identity, "searchable_name", "") or ""
return not any(term in searchable_name for term in GENERIC_RECALL_BLOCK_TERMS)
def _build_generic_recall_keywords(identity) -> list[str]:
if not _is_generic_recall_safe(identity):
return []
brand = _primary_brand_phrase(identity)
product_type = getattr(identity, "product_type", "") or ""
if not brand or not product_type:
return []
return _dedupe_terms([f"{brand} {product_type}"])
def _build_variant_recall_search_plan(momo_name: str, keywords: list[str]) -> list[tuple[str, str | None]]:
plan = [(keyword, None) for keyword in (keywords or [])]
try:
from services.marketplace_product_matcher import parse_product_identity
identity = parse_product_identity(momo_name)
except Exception:
return plan
brand_tokens = {token.lower() for token in getattr(identity, "brand_tokens", set())}
if not ({"dashing", "diva"} <= brand_tokens and "美甲片" in getattr(identity, "searchable_name", "")):
seen = {(keyword.lower(), sort) for keyword, sort in plan}
for keyword in _build_generic_recall_keywords(identity):
key = (keyword.lower(), None)
if key in seen:
continue
seen.add(key)
plan.append((keyword, None))
return plan
searchable_name = getattr(identity, "searchable_name", "")
broad_keywords = []
if "足部時尚潮流美甲片" in searchable_name:
broad_keywords.append("dashing diva 足部時尚潮流美甲片")
elif "頂級璀燦美甲片" in searchable_name:
broad_keywords.append("dashing diva 頂級璀燦美甲片")
elif "時尚潮流美甲片" in searchable_name:
broad_keywords.append("dashing diva 時尚潮流美甲片")
elif "薄型經典美甲片" in searchable_name:
broad_keywords.append("dashing diva 薄型經典美甲片")
broad_keywords.extend((
"dashing diva magicpress",
"dashing diva 美甲片",
))
seen = {(keyword.lower(), sort) for keyword, sort in plan}
def append(keyword: str, sort: str | None = None) -> None:
key = (keyword.lower(), sort)
if key in seen:
return
seen.add(key)
plan.append((keyword, sort))
for broad_keyword in broad_keywords:
append(broad_keyword, None)
if broad_keywords:
for sort in VARIANT_RECALL_SORTS:
append(broad_keywords[0], sort)
return plan
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"mode={getattr(diagnostics, 'comparison_mode', 'exact_identity')}; "
f"match_type={getattr(diagnostics, 'match_type', '')}; "
f"price_basis={getattr(diagnostics, 'price_basis', '')}; "
f"alert_tier={getattr(diagnostics, 'alert_tier', '')}; "
f"reasons={reasons}"
)
def _match_diagnostics_payload(diagnostics) -> dict:
"""Serialize matcher diagnostics for review/report consumers."""
if not diagnostics:
return {}
return {
"score": getattr(diagnostics, "score", None),
"brand_score": getattr(diagnostics, "brand_score", None),
"token_score": getattr(diagnostics, "token_score", None),
"spec_score": getattr(diagnostics, "spec_score", None),
"sequence_score": getattr(diagnostics, "sequence_score", None),
"type_score": getattr(diagnostics, "type_score", None),
"price_penalty": getattr(diagnostics, "price_penalty", None),
"hard_veto": bool(getattr(diagnostics, "hard_veto", False)),
"comparison_mode": getattr(diagnostics, "comparison_mode", "exact_identity"),
"match_type": getattr(diagnostics, "match_type", None),
"price_basis": getattr(diagnostics, "price_basis", None),
"alert_tier": getattr(diagnostics, "alert_tier", None),
"evidence_flags": list(getattr(diagnostics, "evidence_flags", ()) or ()),
"reasons": list(getattr(diagnostics, "reasons", ()) or ()),
"identity_evidence": getattr(diagnostics, "identity_evidence", None) or {},
"offer_evidence": getattr(diagnostics, "offer_evidence", None) or {},
}
def _pchome_search_url(keyword: str) -> str:
return f"https://ecshweb.pchome.com.tw/search/v3.3/?q={quote_plus(keyword or '')}"
def _build_browse_sh_diagnostic_payload(
momo_name: str,
search_terms: list[str] = None,
reason: str = "unknown",
best_product=None,
best_score: float = None,
diagnostics=None,
candidate_count: int = 0,
) -> dict:
"""Build a read-only browse.sh probe plan for low-confidence PChome cases."""
if not BROWSE_SH_DIAGNOSTIC_ENABLED:
return {}
terms = _dedupe_terms(search_terms or _build_search_keywords(momo_name))[:3]
urls = [_pchome_search_url(term) for term in terms]
product_url = getattr(best_product, "product_url", None)
if product_url:
urls.append(product_url)
urls = list(dict.fromkeys(url for url in urls if url))
primary_url = urls[0] if urls else _pchome_search_url(momo_name)
diagnostic_payload = _match_diagnostics_payload(diagnostics)
return {
"tool": "browse.sh",
"mode": "execute_on_demand" if BROWSE_SH_EXECUTE_ENABLED else "plan_only",
"reason": reason,
"execute_enabled": BROWSE_SH_EXECUTE_ENABLED,
"timeout_seconds": BROWSE_SH_TIMEOUT_SECONDS,
"candidate_count": int(candidate_count or 0),
"momo_name": (momo_name or "")[:300],
"search_terms": terms,
"urls": urls,
"suggested_commands": [
{
"purpose": "static_fetch_first_page",
"args": ["get", primary_url],
},
{
"purpose": "manual_browser_probe",
"args": ["open", primary_url],
},
],
"best_candidate": {
"product_id": getattr(best_product, "product_id", None),
"name": (getattr(best_product, "name", None) or "")[:300] or None,
"price": getattr(best_product, "price", None),
"url": product_url,
"score": best_score,
} if best_product else None,
"diagnostic_codes": diagnostic_payload.get("reasons") or [],
"comparison_mode": diagnostic_payload.get("comparison_mode"),
"hard_veto": diagnostic_payload.get("hard_veto"),
"execution": {"status": "disabled"},
}
def _product_snapshot_payload(product) -> dict:
payload = {
"competitor_product_url": None,
"competitor_image_url": None,
"competitor_stock": None,
}
if not product:
return payload
payload.update({
"competitor_product_url": getattr(product, "product_url", None),
"competitor_image_url": getattr(product, "image_url", None),
"competitor_stock": getattr(product, "stock", None),
})
return payload
def _product_id_key(product_id: str) -> str:
"""Normalize PChome IDs for comparing cached IDs with API-returned IDs."""
return re.sub(r"[^A-Z0-9]", "", str(product_id or "").upper())
def _candidate_match_name(product) -> str:
"""Return the identity-rich PChome text used for scoring, falling back to display name."""
return (getattr(product, "match_name", None) or getattr(product, "name", None) or "").strip()
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
"""
ranked = _rank_match_details(momo_name, pchome_products, momo_price=momo_price)
return ranked[0] if ranked else None
def _rank_match_details(
momo_name: str,
pchome_products: list,
momo_price: float = None,
) -> list[tuple]:
"""Score all PChome candidates and return them from strongest to weakest."""
from services.marketplace_product_matcher import score_marketplace_match
ranked = []
for p in pchome_products:
diagnostics = score_marketplace_match(
momo_name,
_candidate_match_name(p),
momo_price=momo_price,
competitor_price=getattr(p, "price", None),
)
ranked.append((p, diagnostics.score, diagnostics))
return sorted(ranked, key=lambda item: item[1], reverse=True)
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,
max_terms: int | None = None,
max_pages: int | None = None,
max_seconds: float | None = None,
) -> list:
"""以多組搜尋詞擴大 PChome 候選池，只在強同款時提前停止。"""
candidates = []
seen_ids = set()
page_cap = max(1, int(max_pages or SEARCH_MAX_PAGES))
search_limit = SEARCH_LIMIT * page_cap
active_keywords = keywords or _build_search_keywords(momo_name)
search_plan = _build_variant_recall_search_plan(momo_name, active_keywords)
if max_terms is not None:
search_plan = search_plan[:max(1, int(max_terms))]
deadline = (time.monotonic() + max_seconds) if max_seconds and max_seconds > 0 else None
for keyword, sort in search_plan:
if deadline and time.monotonic() >= deadline:
logger.info("[Feeder] PChome search budget exhausted before keyword=%s", keyword)
break
if sort:
ok, _, products = crawler.search_products(
keyword,
limit=search_limit,
max_pages=page_cap,
sort=sort,
)
else:
ok, _, products = crawler.search_products(keyword, limit=search_limit, max_pages=page_cap)
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] >= EARLY_STOP_MATCH_SCORE:
break
return candidates
def _recover_low_score_with_fresh_search(
crawler,
momo_name: str,
momo_price: float = None,
existing_product_id: str = "",
max_terms: int | None = None,
max_pages: int | None = None,
max_seconds: float | None = None,
) -> tuple[Optional[tuple], list[str], int]:
"""
當 legacy / known-id 候選重評仍低分時，再跑一次 fresh keyword search，
嘗試把舊錯配洗成新的真同款。
"""
keywords = _build_search_keywords(momo_name)
candidates = _search_pchome_candidates(
crawler,
momo_name,
keywords=keywords,
momo_price=momo_price,
max_terms=max_terms,
max_pages=max_pages,
max_seconds=max_seconds,
)
if existing_product_id:
existing_key = _product_id_key(existing_product_id)
fresh_candidates = [
candidate
for candidate in candidates
if _product_id_key(getattr(candidate, "product_id", "")) != existing_key
]
if fresh_candidates:
candidates = fresh_candidates
best = _find_best_match_detail(momo_name, candidates, momo_price=momo_price)
return best, keywords, len(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
self._price_table_columns_ready = False
self._browse_sh_executions = 0
def _ensure_table_columns(self, conn, table: str, column_specs: list[tuple[str, str]]) -> None:
"""補齊既有表欄位；避免正式端舊表在新 INSERT 時炸掉。"""
from sqlalchemy import inspect, text
inspector = inspect(conn)
if not inspector.has_table(table):
return
existing = {column["name"] for column in inspector.get_columns(table)}
for column_name, column_type in column_specs:
if column_name in existing:
continue
conn.execute(text(f"ALTER TABLE {table} ADD COLUMN {column_name} {column_type}"))
existing.add(column_name)
def _ensure_competitor_prices_columns(self, conn) -> None:
if self._price_table_columns_ready:
return
self._ensure_table_columns(conn, "competitor_prices", [
("competitor_product_url", "TEXT"),
("competitor_image_url", "TEXT"),
("competitor_stock", "INTEGER"),
("match_diagnostic_json", "JSONB" if conn.dialect.name == "postgresql" else "TEXT"),
("comparison_mode", "VARCHAR(40)"),
("hard_veto", "BOOLEAN"),
("diagnostic_codes", "JSONB" if conn.dialect.name == "postgresql" else "TEXT"),
])
self._price_table_columns_ready = True
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,
competitor_product_url TEXT,
competitor_image_url TEXT,
competitor_stock INTEGER,
match_score NUMERIC(4,3),
tags JSONB DEFAULT '[]'::jsonb,
match_diagnostic_json JSONB,
comparison_mode VARCHAR(40),
hard_veto BOOLEAN,
diagnostic_codes 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,
competitor_product_url TEXT,
competitor_image_url TEXT,
competitor_stock INTEGER,
match_score NUMERIC(4,3),
tags TEXT DEFAULT '[]',
match_diagnostic_json TEXT,
comparison_mode VARCHAR(40),
hard_veto BOOLEAN,
diagnostic_codes TEXT,
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._ensure_table_columns(conn, "competitor_price_history", [
("competitor_product_url", "TEXT"),
("competitor_image_url", "TEXT"),
("competitor_stock", "INTEGER"),
("match_diagnostic_json", "JSONB" if conn.dialect.name == "postgresql" else "TEXT"),
("comparison_mode", "VARCHAR(40)"),
("hard_veto", "BOOLEAN"),
("diagnostic_codes", "JSONB" if conn.dialect.name == "postgresql" else "TEXT"),
])
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,
competitor_product_url TEXT,
competitor_image_url TEXT,
competitor_stock INTEGER,
best_competitor_price NUMERIC(10,2),
best_match_score NUMERIC(4,3),
match_diagnostic_json JSONB,
comparison_mode VARCHAR(40),
hard_veto BOOLEAN,
diagnostic_codes JSONB,
browse_diagnostic_json JSONB,
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,
competitor_product_url TEXT,
competitor_image_url TEXT,
competitor_stock INTEGER,
best_competitor_price NUMERIC(10,2),
best_match_score NUMERIC(4,3),
match_diagnostic_json TEXT,
comparison_mode VARCHAR(40),
hard_veto BOOLEAN,
diagnostic_codes TEXT,
browse_diagnostic_json TEXT,
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._ensure_table_columns(conn, "competitor_match_attempts", [
("search_terms", "JSONB" if conn.dialect.name == "postgresql" else "TEXT"),
("competitor_product_url", "TEXT"),
("competitor_image_url", "TEXT"),
("competitor_stock", "INTEGER"),
("match_diagnostic_json", "JSONB" if conn.dialect.name == "postgresql" else "TEXT"),
("comparison_mode", "VARCHAR(40)"),
("hard_veto", "BOOLEAN"),
("diagnostic_codes", "JSONB" if conn.dialect.name == "postgresql" else "TEXT"),
("browse_diagnostic_json", "JSONB" if conn.dialect.name == "postgresql" else "TEXT"),
("error_message", "TEXT"),
])
self._attempt_table_ready = True
def _prepare_browse_diagnostic(
self,
momo_name: str,
search_terms: list = None,
reason: str = "unknown",
best_product=None,
best_score: float = None,
diagnostics=None,
candidate_count: int = 0,
) -> dict:
"""Return browse.sh diagnostic evidence; CLI execution remains opt-in and rate-limited."""
payload = _build_browse_sh_diagnostic_payload(
momo_name,
search_terms=search_terms,
reason=reason,
best_product=best_product,
best_score=best_score,
diagnostics=diagnostics,
candidate_count=candidate_count,
)
if not payload or not BROWSE_SH_EXECUTE_ENABLED:
return payload
if self._browse_sh_executions >= BROWSE_SH_MAX_EXECUTIONS_PER_RUN:
payload["execution"] = {"status": "rate_limited"}
return payload
command_args = tuple((payload.get("suggested_commands") or [{}])[0].get("args") or ())
if not command_args:
payload["execution"] = {"status": "missing_command"}
return payload
try:
from services.browse_sh_tool import BrowseShTool
self._browse_sh_executions += 1
result = BrowseShTool(timeout_seconds=BROWSE_SH_TIMEOUT_SECONDS).run(
command_args,
timeout_seconds=BROWSE_SH_TIMEOUT_SECONDS,
)
payload["execution"] = {
"status": "ok" if result.ok else "failed",
"returncode": result.returncode,
"timed_out": result.timed_out,
"unavailable_reason": result.unavailable_reason,
"stdout_preview": (result.stdout or "")[:BROWSE_SH_OUTPUT_PREVIEW_CHARS],
"stderr_preview": (result.stderr or "")[:BROWSE_SH_OUTPUT_PREVIEW_CHARS],
}
except Exception as exc:
payload["execution"] = {
"status": "error",
"error": str(exc)[:500],
}
return payload
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,
diagnostics=None,
browse_diagnostic: dict = 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"
json_cast = "CAST(:match_diagnostic_json AS jsonb)" if conn.dialect.name == "postgresql" else ":match_diagnostic_json"
codes_cast = "CAST(:diagnostic_codes AS jsonb)" if conn.dialect.name == "postgresql" else ":diagnostic_codes"
browse_cast = "CAST(:browse_diagnostic_json AS jsonb)" if conn.dialect.name == "postgresql" else ":browse_diagnostic_json"
diagnostic_payload = _match_diagnostics_payload(diagnostics)
diagnostic_codes = diagnostic_payload.get("reasons") or []
product_payload = _product_snapshot_payload(best_product)
browse_diagnostic_json = (
json.dumps(browse_diagnostic, ensure_ascii=False)
if browse_diagnostic
else None
)
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,
competitor_product_url, competitor_image_url, competitor_stock,
best_competitor_price, best_match_score,
match_diagnostic_json, comparison_mode, hard_veto, diagnostic_codes,
browse_diagnostic_json,
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,
:competitor_product_url, :competitor_image_url, :competitor_stock,
:best_price, :best_score,
{json_cast}, :comparison_mode, :hard_veto, {codes_cast},
{browse_cast},
: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,
**product_payload,
"best_price": getattr(best_product, "price", None),
"best_score": best_score,
"match_diagnostic_json": json.dumps(diagnostic_payload, ensure_ascii=False) if diagnostic_payload else None,
"comparison_mode": diagnostic_payload.get("comparison_mode"),
"hard_veto": diagnostic_payload.get("hard_veto"),
"diagnostic_codes": json.dumps(diagnostic_codes, ensure_ascii=False) if diagnostic_codes else None,
"browse_diagnostic_json": browse_diagnostic_json,
"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_attempt AS (
SELECT DISTINCT ON (cma.sku)
cma.sku,
cma.attempt_status,
cma.attempted_at
FROM competitor_match_attempts cma
WHERE cma.source = 'pchome'
ORDER BY cma.sku, cma.attempted_at DESC, cma.id DESC
)
SELECT
p.id AS product_id,
p.i_code AS sku,
p.name,
p.category,
latest_price.price AS momo_price,
COALESCE(la.attempt_status, 'never_attempted') AS latest_attempt_status
FROM products p
JOIN LATERAL (
SELECT pr.price
FROM price_records pr
WHERE pr.product_id = p.id
ORDER BY pr.timestamp DESC, pr.id DESC
LIMIT 1
) latest_price ON TRUE
LEFT JOIN latest_attempt la
ON la.sku = p.i_code
LEFT JOIN competitor_prices cp
ON cp.sku = p.i_code
AND cp.source = 'pchome'
AND cp.expires_at > CURRENT_TIMESTAMP
AND COALESCE(cp.match_score, 0) >= :match_score_floor
AND COALESCE(cp.tags, '[]'::jsonb) ? 'identity_v2'
WHERE p.status = 'ACTIVE'
AND cp.sku IS NULL
ORDER BY
CASE
WHEN la.attempt_status IN ('no_result', 'refresh_no_result')
AND p.name !~* :generic_recall_block_pattern THEN 0
WHEN la.sku IS NULL THEN 1
WHEN la.attempt_status IN ('no_result', 'refresh_no_result') THEN 2
WHEN la.attempt_status IN ('low_score', 'refresh_low_score', 'recoverable_low_score') THEN 3
ELSE 4
END,
latest_price.price DESC NULLS LAST,
p.i_code
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,
"generic_recall_block_pattern": GENERIC_RECALL_BLOCK_NAME_PATTERN,
},
).fetchall()
return [dict(r._mapping) for r in rows]
def _fetch_retryable_candidate_skus(self, limit: int = 80, min_score: float = 0.70) -> list:
"""
取得近門檻候選，供 matcher 升級後重新評分。
這條路徑優先用前次留下的 PChome product_id 批次查詢最新商品資料；
若 product_id 過期或重評仍低分，才走受控 fresh search recovery。
自動隊列只收「近門檻、無 hard veto、仍在 exact_identity 軌道、已有同品線證據」
的候選；identity_veto、unit_comparable 不在自動回刷主戰場。
true_low_confidence 只允許目前已補 focused exact 規則的品線進入窄門重評，
且必須沒有 commercial / variant / count / bundle 類阻擋理由。
最後仍由現行 matcher 重新判斷，不因舊 attempt_status 自動寫入正式比價。
"""
if self.engine is None:
raise RuntimeError("需要注入 SQLAlchemy engine")
from sqlalchemy import text
sql = text(f"""
WITH latest_attempt AS (
SELECT DISTINCT ON (cma.sku)
cma.sku,
cma.best_competitor_product_id,
cma.best_competitor_product_name,
cma.best_match_score,
cma.attempt_status,
cma.hard_veto,
cma.match_diagnostic_json,
cma.attempted_at
FROM competitor_match_attempts cma
WHERE cma.source = 'pchome'
ORDER BY cma.sku, cma.attempted_at DESC, cma.id DESC
),
legacy_unmasked_attempt AS (
SELECT DISTINCT ON (cma.sku, cma.best_competitor_product_id)
cma.sku,
cma.best_competitor_product_id,
cma.best_competitor_product_name,
cma.best_match_score,
cma.attempt_status,
cma.hard_veto,
cma.match_diagnostic_json,
cma.attempted_at
FROM competitor_match_attempts cma
JOIN latest_attempt current_la
ON current_la.sku = cma.sku
WHERE cma.source = 'pchome'
AND current_la.attempt_status IN (
'refresh_no_result',
'no_result',
'expired_match'
)
AND cma.attempt_status IN (
'low_score',
'refresh_low_score',
'recoverable_low_score',
'true_low_confidence',
'rescore_accepted_current'
)
AND cma.attempted_at < current_la.attempted_at
ORDER BY cma.sku, cma.best_competitor_product_id, cma.attempted_at DESC, cma.id DESC
),
candidate_attempt AS (
SELECT DISTINCT ON (la.sku, la.best_competitor_product_id)
la.*
FROM (
SELECT * FROM latest_attempt
UNION ALL
SELECT * FROM legacy_unmasked_attempt
) la
WHERE la.best_competitor_product_id IS NOT NULL
AND la.best_competitor_product_id <> ''
AND COALESCE(la.best_match_score, 0) >= :min_score
AND COALESCE(la.hard_veto, false) = false
AND COALESCE(la.match_diagnostic_json->>'comparison_mode', 'exact_identity') = 'exact_identity'
ORDER BY la.sku, la.best_competitor_product_id, la.attempted_at DESC
)
SELECT
p.id AS product_id,
p.i_code AS sku,
p.name,
p.category,
latest_price.price AS momo_price,
la.best_competitor_product_id AS competitor_product_id,
la.best_competitor_product_name AS competitor_product_name,
la.best_match_score,
la.attempt_status
FROM candidate_attempt la
JOIN products p
ON p.i_code = la.sku
AND p.status = 'ACTIVE'
JOIN LATERAL (
SELECT pr.price
FROM price_records pr
WHERE pr.product_id = p.id
ORDER BY pr.timestamp DESC, pr.id DESC
LIMIT 1
) latest_price ON TRUE
LEFT JOIN competitor_prices cp
ON cp.sku = p.i_code
AND cp.source = 'pchome'
AND cp.expires_at > CURRENT_TIMESTAMP
AND COALESCE(cp.match_score, 0) >= :match_score_floor
AND COALESCE(cp.tags, '[]'::jsonb) ? 'identity_v2'
WHERE cp.sku IS NULL
AND (
(
la.attempt_status IN (
'low_score',
'refresh_low_score',
'recoverable_low_score'
)
AND (
la.attempt_status = 'recoverable_low_score'
OR COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb) ?| array[{RECOVERABLE_SQL_REASON_LIST}]
)
)
OR (
la.attempt_status IN (
'low_score',
'refresh_low_score',
'true_low_confidence',
'rescore_accepted_current'
)
AND COALESCE(la.best_match_score, 0) >= :min_score
AND COALESCE(la.hard_veto, false) = false
AND COALESCE(la.match_diagnostic_json->>'comparison_mode', 'exact_identity') = 'exact_identity'
AND (
(
COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb) ? 'focused_exact_total_price_safe'
AND COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb) ?| array[{FOCUSED_TOTAL_PRICE_IDENTITY_SQL_REASON_LIST}]
)
OR (
COALESCE(la.best_match_score, 0) >= :match_score_floor
AND COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb) ?| array[{FOCUSED_TOTAL_PRICE_IDENTITY_SQL_REASON_LIST}]
)
)
AND NOT (
COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb)
?| array[{REVALIDATABLE_REVIEW_BLOCK_SQL_REASON_LIST}]
)
)
OR (
la.attempt_status = 'true_low_confidence'
AND COALESCE(la.best_match_score, 0) >= 0.95
AND COALESCE(la.match_diagnostic_json->>'match_type', '') = 'exact'
AND COALESCE(la.match_diagnostic_json->>'price_basis', '') = 'manual_review'
AND COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb) ? 'strong_product_line_match'
AND NOT (
COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb)
?| array[{REVALIDATABLE_REVIEW_BLOCK_SQL_REASON_LIST}]
)
AND (
(
lower(COALESCE(p.name, '')) LIKE '%bioneo%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%bioneo%'
AND COALESCE(p.name, '') LIKE '%微煥膚藻晶去角質凝膠%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%微煥膚藻晶去角質凝膠%'
AND lower(COALESCE(p.name, '')) ~* '150\\s*ml\\s*[x*＊]\\s*2'
AND lower(COALESCE(la.best_competitor_product_name, '')) ~* '150\\s*ml\\s*[x*＊]\\s*2'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%cetaphil%'
OR COALESCE(p.name, '') LIKE '%舒特膚%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%cetaphil%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%舒特膚%'
)
AND COALESCE(p.name, '') LIKE '%舒新雪潤安撫水%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%舒新雪潤安撫水%'
AND lower(COALESCE(p.name, '')) ~* '150\\s*ml\\s*[x*＊]\\s*2'
AND lower(COALESCE(la.best_competitor_product_name, '')) ~* '150\\s*ml\\s*[x*＊]\\s*2'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%avene%'
OR COALESCE(p.name, '') LIKE '%雅漾%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%avene%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%雅漾%'
)
AND COALESCE(p.name, '') LIKE '%舒護活泉水%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%舒護活泉水%'
AND lower(COALESCE(p.name, '')) ~* '300\\s*ml\\s*[x*＊]\\s*4'
AND lower(COALESCE(la.best_competitor_product_name, '')) ~* '300\\s*ml\\s*[x*＊]\\s*4'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%schick%'
OR COALESCE(p.name, '') LIKE '%舒適牌%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%schick%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%舒適牌%'
)
AND (
(
COALESCE(p.name, '') LIKE '%超捍輕便刀%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%超捍輕便刀%'
)
OR (
COALESCE(p.name, '') LIKE '%高級防滑輕便刀%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%高級防滑輕便刀%'
AND COALESCE(p.name, '') LIKE '%滋潤型%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%滋潤型%'
)
)
AND lower(COALESCE(p.name, '')) ~* '2\\s*\\+\\s*1\\s*入'
AND lower(COALESCE(la.best_competitor_product_name, '')) ~* '2\\s*\\+\\s*1\\s*入'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%kose%'
OR COALESCE(p.name, '') LIKE '%高絲%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%kose%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%高絲%'
)
AND COALESCE(p.name, '') LIKE '%雪肌精限量豪華禮盒%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%雪肌精限量豪華禮盒%'
AND lower(COALESCE(p.name, '')) ~* '500\\s*ml\\s*[x*＊]\\s*2'
AND lower(COALESCE(la.best_competitor_product_name, '')) ~* '500\\s*ml\\s*[x*＊]\\s*2'
)
)
)
OR (
la.attempt_status = 'true_low_confidence'
AND COALESCE(la.best_match_score, 0) >= 0.95
AND COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb) ? 'strong_exact_spec_match'
AND COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb) ?| array[{REVALIDATABLE_REVIEW_SQL_REASON_LIST}]
AND NOT (
COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb)
?| array[{REVALIDATABLE_REVIEW_BLOCK_SQL_REASON_LIST}]
)
AND (
(
lower(COALESCE(p.name, '')) LIKE '%beauty foot%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%beauty foot%'
AND COALESCE(p.name, '') LIKE '%足膜%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%足膜%'
)
OR (
COALESCE(p.name, '') LIKE '%足足稱奇%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%足足稱奇%'
AND COALESCE(p.name, '') LIKE '%足膜%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%足膜%'
)
OR (
lower(COALESCE(p.name, '')) LIKE '%ts6%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%ts6%'
AND (
COALESCE(p.name, '') LIKE '%蜜愛潤滑液%'
OR COALESCE(p.name, '') LIKE '%蜜桃煥白凝膠%'
OR COALESCE(p.name, '') LIKE '%極淨白私密潔膚露%'
)
AND (
COALESCE(la.best_competitor_product_name, '') LIKE '%蜜愛潤滑液%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%蜜桃煥白凝膠%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%極淨白私密潔膚露%'
)
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%vaseline%'
OR COALESCE(p.name, '') LIKE '%凡士林%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%vaseline%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%凡士林%'
)
AND COALESCE(p.name, '') LIKE '%嬰兒高純修護凝膠%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%嬰兒高純修護凝膠%'
)
)
)
OR (
la.attempt_status IN (
'low_score',
'refresh_low_score',
'true_low_confidence'
)
AND COALESCE(la.best_match_score, 0) >= :min_score
AND COALESCE(la.hard_veto, false) = false
AND COALESCE(la.match_diagnostic_json->>'comparison_mode', 'exact_identity') = 'exact_identity'
AND NOT (
COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb)
?| array[{REVALIDATABLE_REVIEW_BLOCK_SQL_REASON_LIST}]
)
AND (
(
(
lower(COALESCE(p.name, '')) LIKE '%herb24%'
OR COALESCE(p.name, '') LIKE '%草本24%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%herb24%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%草本24%'
)
AND COALESCE(p.name, '') LIKE '%晨霧純精油擴香儀%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%晨霧純精油擴香儀%'
AND COALESCE(p.name, '') LIKE '%黑%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%黑%'
)
OR (
lower(COALESCE(p.name, '')) LIKE '%pavaruni%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%pavaruni%'
AND COALESCE(p.name, '') LIKE '%天然植物%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%天然植物%'
AND COALESCE(p.name, '') LIKE '%精油%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%精油%'
AND COALESCE(p.name, '') LIKE '%40%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%40%'
AND lower(COALESCE(p.name, '')) ~* '10\\s*ml'
AND lower(COALESCE(la.best_competitor_product_name, '')) ~* '10\\s*ml'
)
OR (
lower(COALESCE(p.name, '')) LIKE '%pavaruni%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%pavaruni%'
AND COALESCE(p.name, '') LIKE '%香氛蠟燭%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%香氛蠟燭%'
AND COALESCE(p.name, '') LIKE '%20%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%20%'
AND lower(COALESCE(p.name, '')) ~* '450\\s*g'
AND lower(COALESCE(la.best_competitor_product_name, '')) ~* '450\\s*g'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%derma%'
OR COALESCE(p.name, '') LIKE '%德瑪%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%derma%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%德瑪%'
)
AND COALESCE(p.name, '') LIKE '%大地%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%大地%'
AND COALESCE(p.name, '') LIKE '%有機植萃%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%有機植萃%'
AND COALESCE(p.name, '') LIKE '%護膚油%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%護膚油%'
AND lower(COALESCE(p.name, '')) ~* '150\\s*ml'
AND lower(COALESCE(la.best_competitor_product_name, '')) ~* '150\\s*ml'
AND NOT (
COALESCE(p.name, '') ~* '(2\\s*入|2\\s*瓶|x\\s*2|\\*\\s*2|＊\\s*2)'
OR COALESCE(la.best_competitor_product_name, '') ~* '(2\\s*入|2\\s*瓶|x\\s*2|\\*\\s*2|＊\\s*2)'
)
)
)
)
OR (
(
(
la.attempt_status = 'true_low_confidence'
AND COALESCE(la.best_match_score, 0) >= 0.95
)
OR (
la.attempt_status = 'rescore_accepted_current'
AND COALESCE(la.best_match_score, 0) >= 0.76
)
)
AND COALESCE(la.hard_veto, false) = false
AND COALESCE(la.match_diagnostic_json->>'comparison_mode', 'exact_identity') = 'exact_identity'
AND NOT (
COALESCE(la.match_diagnostic_json->'reasons', '[]'::jsonb)
?| array[{FOCUSED_REVALIDATABLE_REVIEW_BLOCK_SQL_REASON_LIST}]
)
AND (
(
COALESCE(p.name, '') LIKE '%花美水%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%花美水%'
AND lower(COALESCE(p.name, '')) LIKE '%relax%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%relax%'
AND COALESCE(p.name, '') LIKE '%薰衣草%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%薰衣草%'
AND COALESCE(p.name, '') LIKE '%潤滑凝膠%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%潤滑凝膠%'
)
OR (
COALESCE(p.name, '') LIKE '%聖克萊爾%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%聖克萊爾%'
AND COALESCE(p.name, '') LIKE '%私密呼呼溫和潔淨慕斯%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%私密呼呼溫和潔淨慕斯%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%biopeutic%'
OR COALESCE(p.name, '') LIKE '%葆療美%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%biopeutic%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%葆療美%'
)
AND COALESCE(p.name, '') LIKE '%果酸煥膚水凝乳%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%果酸煥膚水凝乳%'
)
OR (
COALESCE(p.name, '') LIKE '%台塑生醫%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%台塑生醫%'
AND COALESCE(p.name, '') LIKE '%嬰兒沐浴洗髮%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%嬰兒沐浴洗髮%'
AND COALESCE(p.name, '') LIKE '%嬰兒沐浴精%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%嬰兒沐浴精%'
AND COALESCE(p.name, '') LIKE '%嬰幼童洗髮精%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%嬰幼童洗髮精%'
)
OR (
COALESCE(p.name, '') LIKE '%雅頓%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%雅頓%'
AND COALESCE(p.name, '') LIKE '%八小時潤澤護唇膏%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%八小時潤澤護唇膏%'
AND lower(COALESCE(p.name, '')) LIKE '%spf15%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%spf15%'
)
OR (
COALESCE(p.name, '') LIKE '%理膚寶水%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%理膚寶水%'
AND COALESCE(p.name, '') LIKE '%全面修復潤唇膏%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%全面修復潤唇膏%'
)
OR (
lower(COALESCE(p.name, '')) LIKE '%3w%'
AND lower(COALESCE(p.name, '')) LIKE '%clinic%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%3w%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%clinic%'
AND COALESCE(p.name, '') LIKE '%膠原蛋白粉底液%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%膠原蛋白粉底液%'
AND COALESCE(p.name, '') ~* '50\\s*ml'
AND COALESCE(la.best_competitor_product_name, '') ~* '50\\s*ml'
)
OR (
COALESCE(p.name, '') LIKE '%花美水%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%花美水%'
AND lower(COALESCE(p.name, '')) LIKE '%moisture%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%moisture%'
AND COALESCE(p.name, '') LIKE '%保濕修護%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%保濕修護%'
AND COALESCE(p.name, '') LIKE '%精華凝膠%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%精華凝膠%'
AND COALESCE(p.name, '') LIKE '%原黃金%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%原黃金%'
)
OR (
COALESCE(p.name, '') LIKE '%花美水%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%花美水%'
AND lower(COALESCE(p.name, '')) LIKE '%inclear%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%inclear%'
AND COALESCE(p.name, '') LIKE '%櫻克麗兒%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%櫻克麗兒%'
AND COALESCE(p.name, '') LIKE '%私密淨化凝膠%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%私密淨化凝膠%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%kussen%'
OR COALESCE(p.name, '') LIKE '%葵森%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%kussen%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%葵森%'
)
AND COALESCE(p.name, '') LIKE '%寶寶益菌屁屁膏%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%寶寶益菌屁屁膏%'
AND COALESCE(p.name, '') LIKE '%50%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%50%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%lab52%'
OR COALESCE(p.name, '') LIKE '%齒妍堂%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%lab52%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%齒妍堂%'
)
AND COALESCE(p.name, '') LIKE '%牙刷%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%牙刷%'
AND (
COALESCE(p.name, '') LIKE '%嬰幼兒%'
OR COALESCE(p.name, '') LIKE '%幼兒%'
OR COALESCE(p.name, '') LIKE '%汪汪隊%'
)
AND (
COALESCE(la.best_competitor_product_name, '') LIKE '%嬰幼兒%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%幼兒%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%汪汪隊%'
)
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%flortte%'
OR COALESCE(p.name, '') LIKE '%花洛莉亞%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%flortte%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%花洛莉亞%'
)
AND COALESCE(p.name, '') LIKE '%水果沙拉系列彩色防水眼線液筆%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%水果沙拉系列彩色防水眼線液筆%'
AND COALESCE(p.name, '') LIKE '%色號任選%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%色號任選%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%neutrogena%'
OR COALESCE(p.name, '') LIKE '%露得清%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%neutrogena%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%露得清%'
)
AND COALESCE(p.name, '') LIKE '%護手霜%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%護手霜%'
AND COALESCE(p.name, '') LIKE '%無香%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%無香%'
AND COALESCE(p.name, '') LIKE '%有香%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%有香%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%kanebo%'
OR COALESCE(p.name, '') LIKE '%佳麗寶%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%kanebo%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%佳麗寶%'
)
AND lower(COALESCE(p.name, '')) LIKE '%allie%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%allie%'
AND COALESCE(p.name, '') LIKE '%持采亮化UV防曬水凝乳%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%持采亮化UV防曬水凝乳%'
AND COALESCE(p.name, '') LIKE '%任選%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%任選%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%o.p.i%'
OR lower(COALESCE(p.name, '')) LIKE '%opi%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%o.p.i%'
OR lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%opi%'
)
AND COALESCE(p.name, '') LIKE '%類光繚%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%類光繚%'
AND (
COALESCE(p.name, '') LIKE '%指甲油%'
OR COALESCE(p.name, '') LIKE '%指彩%'
)
AND (
COALESCE(la.best_competitor_product_name, '') LIKE '%指甲油%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%指彩%'
)
AND COALESCE(p.name, '') ~* 'isl[a-z0-9]*[0-9]{2,3}'
AND COALESCE(la.best_competitor_product_name, '') ~* 'isl[a-z0-9]*[0-9]{2,3}'
)
OR (
lower(COALESCE(p.name, '')) LIKE '%ordinary%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%ordinary%'
AND COALESCE(p.name, '') LIKE '%咖啡因%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%咖啡因%'
AND lower(COALESCE(p.name, '')) LIKE '%egcg%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%egcg%'
AND COALESCE(p.name, '') LIKE '%兒茶眼部配方%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%兒茶眼部配方%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%sk-ii%'
OR lower(COALESCE(p.name, '')) LIKE '%skii%'
OR lower(COALESCE(p.name, '')) LIKE '%sk2%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%sk-ii%'
OR lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%skii%'
OR lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%sk2%'
)
AND COALESCE(p.name, '') LIKE '%青春露%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%青春露%'
AND COALESCE(p.name, '') LIKE '%330%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%330%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%amiino%'
OR COALESCE(p.name, '') LIKE '%安美諾%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%amiino%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%安美諾%'
)
AND COALESCE(p.name, '') LIKE '%美白修護霜%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%美白修護霜%'
AND COALESCE(p.name, '') LIKE '%30%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%30%'
)
OR (
lower(COALESCE(p.name, '')) LIKE '%natures%'
AND lower(COALESCE(p.name, '')) LIKE '%care%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%natures%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%care%'
AND COALESCE(p.name, '') LIKE '%綿羊油%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%綿羊油%'
)
OR (
lower(COALESCE(p.name, '')) LIKE '%tomoon%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%tomoon%'
AND COALESCE(p.name, '') LIKE '%德國奔月%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%德國奔月%'
AND COALESCE(p.name, '') LIKE '%豪華套裝組%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%豪華套裝組%'
AND COALESCE(p.name, '') LIKE '%指甲%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%指甲%'
)
OR (
lower(COALESCE(p.name, '')) LIKE '%hh%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%hh%'
AND COALESCE(p.name, '') LIKE '%私密植萃抗菌潔淨露%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%私密植萃抗菌潔淨露%'
AND COALESCE(p.name, '') LIKE '%私密衣物抗菌手洗精%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%私密衣物抗菌手洗精%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%sebamed%'
OR COALESCE(p.name, '') LIKE '%施巴%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%sebamed%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%施巴%'
)
AND COALESCE(p.name, '') LIKE '%護潔露%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%護潔露%'
AND COALESCE(p.name, '') LIKE '%200%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%200%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%yes%'
OR COALESCE(p.name, '') LIKE '%德悅氏%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%yes%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%德悅氏%'
)
AND COALESCE(p.name, '') LIKE '%鋒利窄弧型剪刀%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%鋒利窄弧型剪刀%'
AND lower(COALESCE(p.name, '')) LIKE '%9cm%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%9cm%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%yes%'
OR COALESCE(p.name, '') LIKE '%德悅氏%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%yes%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%德悅氏%'
)
AND COALESCE(p.name, '') LIKE '%腳指甲剪刀%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%腳指甲剪刀%'
AND lower(COALESCE(p.name, '')) LIKE '%10.5cm%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%10.5cm%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%yes%'
OR COALESCE(p.name, '') LIKE '%德悅氏%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%yes%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%德悅氏%'
)
AND COALESCE(p.name, '') LIKE '%極細指甲緣硬皮剪刀%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%極細指甲緣硬皮剪刀%'
AND lower(COALESCE(p.name, '')) LIKE '%9cm%'
AND lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%9cm%'
)
OR (
(
lower(COALESCE(p.name, '')) LIKE '%yes%'
OR COALESCE(p.name, '') LIKE '%德悅氏%'
)
AND (
lower(COALESCE(la.best_competitor_product_name, '')) LIKE '%yes%'
OR COALESCE(la.best_competitor_product_name, '') LIKE '%德悅氏%'
)
AND (
(
COALESCE(p.name, '') LIKE '%指甲剪附除垢銼刀%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%指甲剪附除垢銼刀%'
)
OR (
COALESCE(p.name, '') LIKE '%腳皮銼腳板%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%腳皮銼腳板%'
)
OR (
COALESCE(p.name, '') LIKE '%藍寶石銼刀%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%藍寶石銼刀%'
)
OR (
COALESCE(p.name, '') LIKE '%指甲拋光棒%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%指甲拋光棒%'
)
OR (
COALESCE(p.name, '') LIKE '%指甲剪%'
AND COALESCE(la.best_competitor_product_name, '') LIKE '%指甲剪%'
AND COALESCE(p.name, '') NOT LIKE '%附除垢%'
AND COALESCE(la.best_competitor_product_name, '') NOT LIKE '%附除垢%'
AND COALESCE(p.name, '') NOT LIKE '%腳指甲%'
AND COALESCE(la.best_competitor_product_name, '') NOT LIKE '%腳指甲%'
)
)
)
)
)
)
ORDER BY la.best_match_score DESC NULLS LAST, latest_price.price DESC NULLS LAST, p.i_code
LIMIT :limit
""")
with self.engine.connect() as conn:
rows = conn.execute(
sql,
{
"limit": max(1, min(int(limit), 300)),
"min_score": float(min_score),
"match_score_floor": MIN_MATCH_SCORE,
},
).fetchall()
return [dict(r._mapping) for r in rows]
def preview_retryable_candidate_revalidation(self, limit: int = 80, min_score: float = 0.70) -> dict:
"""
只讀預覽下一批可重評候選。
這個方法刻意復用正式重評的候選 SQL，但只回傳受 limit 限制的摘要；
不啟動 PChome 搜尋、不呼叫 LLM、不寫 competitor_match_attempts / competitor_prices。
"""
preview_limit = max(1, min(int(limit), 300))
rows = self._fetch_retryable_candidate_skus(limit=preview_limit, min_score=min_score)
status_counts: dict[str, int] = {}
examples: list[dict] = []
review_gated_count = 0
for row in rows:
status = str(row.get("attempt_status") or "unknown")
status_counts[status] = status_counts.get(status, 0) + 1
if status == "true_low_confidence":
review_gated_count += 1
if len(examples) < 5:
score = row.get("best_match_score")
examples.append({
"sku": row.get("sku"),
"momo_name": row.get("name"),
"competitor_product_id": row.get("competitor_product_id"),
"competitor_product_name": row.get("competitor_product_name"),
"best_match_score": float(score) if score is not None else None,
"attempt_status": status,
})
return {
"available": True,
"preview_limit": preview_limit,
"candidate_count": len(rows),
"has_more": len(rows) >= preview_limit,
"review_gated_count": review_gated_count,
"status_counts": status_counts,
"examples": examples,
"min_score": float(min_score),
"boundary": "read_only_no_crawl_no_llm_no_db_write",
}
def preview_expired_identity_recovery(self, limit: int = 40) -> dict:
"""
只讀預覽過期 identity_v2 搜尋救援候選。
這裡只查本地 DB 中已建立 identity_v2、但價格已過期的 SKU；
不查 PChome、不重新比對、不寫 attempts / prices。
"""
preview_limit = max(1, min(int(limit), 120))
rows = self._fetch_expired_identity_recovery_skus(limit=preview_limit)
examples: list[dict] = []
for row in rows[:5]:
examples.append({
"sku": row.get("sku"),
"momo_name": row.get("name"),
"competitor_product_id": row.get("competitor_product_id"),
"competitor_product_name": row.get("competitor_product_name"),
"match_score": float(row.get("match_score")) if row.get("match_score") is not None else None,
"expires_at": str(row.get("expires_at")) if row.get("expires_at") is not None else None,
})
return {
"available": True,
"preview_limit": preview_limit,
"candidate_count": len(rows),
"has_more": len(rows) >= preview_limit,
"examples": examples,
"boundary": "read_only_no_crawl_no_llm_no_db_write",
}
def _fetch_expired_identity_recovery_skus(self, limit: int = 40) -> list:
"""
取得適合 fresh-search recovery 的過期 identity_v2 商品。
這比一般 expired refresh 更窄：只收過去已是 exact / total_price /
price_alert_exact 的正式配對，且排除款式、香味、型態、入數、商業狀態等
高風險診斷或名稱訊號。避免把本來應該人工覆核的 stale pair 送進慢速搜尋。
"""
if self.engine is None:
raise RuntimeError("需要注入 SQLAlchemy engine")
from sqlalchemy import text
candidate_limit = max(1, min(int(limit) * 6, 400))
target_limit = max(1, min(int(limit), 120))
sql = text(f"""
WITH expired_competitor AS (
SELECT
cp.sku,
cp.competitor_product_id,
cp.competitor_product_name,
cp.match_score,
cp.expires_at,
COALESCE(cp.match_diagnostic_json->'reasons', '[]'::jsonb) AS diagnostic_reasons
FROM competitor_prices cp
WHERE cp.source = 'pchome'
AND cp.competitor_product_id IS NOT NULL
AND cp.competitor_product_id <> ''
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'
AND (
COALESCE(cp.tags, '[]'::jsonb) ? 'price_basis_total_price'
OR cp.match_diagnostic_json->>'price_basis' = 'total_price'
)
AND (
COALESCE(cp.tags, '[]'::jsonb) ? 'alert_tier_price_alert_exact'
OR cp.match_diagnostic_json->>'alert_tier' = 'price_alert_exact'
)
AND COALESCE(cp.match_diagnostic_json->>'comparison_mode', 'exact_identity') = 'exact_identity'
AND COALESCE(cp.hard_veto, false) = false
ORDER BY
CASE WHEN cp.expires_at IS NULL THEN 0 ELSE 1 END,
cp.expires_at ASC NULLS FIRST,
cp.sku
LIMIT :candidate_limit
)
SELECT
p.id AS product_id,
p.i_code AS sku,
p.name,
p.category,
latest_price.price AS momo_price,
ec.competitor_product_id,
ec.competitor_product_name,
ec.match_score,
ec.expires_at,
ec.diagnostic_reasons
FROM expired_competitor ec
JOIN products p
ON p.i_code = ec.sku
AND p.status = 'ACTIVE'
JOIN LATERAL (
SELECT pr.price
FROM price_records pr
WHERE pr.product_id = p.id
ORDER BY pr.timestamp DESC, pr.id DESC
LIMIT 1
) latest_price ON TRUE
ORDER BY
CASE WHEN ec.expires_at IS NULL THEN 0 ELSE 1 END,
ec.expires_at ASC NULLS FIRST,
latest_price.price DESC NULLS LAST,
p.i_code
""")
with self.engine.connect() as conn:
rows = conn.execute(
sql,
{
"candidate_limit": candidate_limit,
"match_score_floor": MIN_MATCH_SCORE,
},
).fetchall()
filtered = []
for row in rows:
payload = dict(row._mapping)
if _has_stale_identity_recovery_block(payload):
continue
payload.pop("diagnostic_reasons", None)
filtered.append(payload)
if len(filtered) >= target_limit:
break
return filtered
def _fetch_expired_identity_skus(self, limit: int = 120) -> list:
"""
取得 identity_v2 已確認、但 PChome 價格快取過期的商品。
這些商品不需重新 keyword search，先用既有 PChome product_id 批次刷新價格。
"""
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,
latest_price.price AS momo_price,
cp.competitor_product_id,
cp.competitor_product_name,
cp.match_score,
cp.expires_at
FROM competitor_prices cp
JOIN products p
ON p.i_code = cp.sku
AND p.status = 'ACTIVE'
JOIN LATERAL (
SELECT pr.price
FROM price_records pr
WHERE pr.product_id = p.id
ORDER BY pr.timestamp DESC, pr.id DESC
LIMIT 1
) latest_price ON TRUE
WHERE cp.source = 'pchome'
AND cp.competitor_product_id IS NOT NULL
AND cp.competitor_product_id <> ''
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'
ORDER BY
CASE
WHEN cp.expires_at IS NULL THEN -1
WHEN (
COALESCE(cp.tags, '[]'::jsonb) ? 'price_basis_total_price'
OR cp.match_diagnostic_json->>'price_basis' = 'total_price'
)
AND (
COALESCE(cp.tags, '[]'::jsonb) ? 'alert_tier_price_alert_exact'
OR cp.match_diagnostic_json->>'alert_tier' = 'price_alert_exact'
)
THEN 0
ELSE 1
END,
cp.expires_at ASC NULLS FIRST,
latest_price.price DESC NULLS LAST,
p.i_code
LIMIT :limit
""")
with self.engine.connect() as conn:
rows = conn.execute(
sql,
{"limit": max(1, min(int(limit), 500)), "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,
diagnostics=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)
diagnostic_payload = _match_diagnostics_payload(diagnostics)
diagnostic_codes = diagnostic_payload.get("reasons") or []
product_payload = _product_snapshot_payload(product)
with self.engine.begin() as conn:
self._ensure_competitor_price_history_table(conn)
self._ensure_competitor_prices_columns(conn)
json_expr = "CAST(:match_diagnostic_json AS jsonb)" if conn.dialect.name == "postgresql" else ":match_diagnostic_json"
codes_expr = "CAST(:diagnostic_codes AS jsonb)" if conn.dialect.name == "postgresql" else ":diagnostic_codes"
conn.execute(text("""
INSERT INTO competitor_prices
(sku, source, price, original_price, discount_pct,
competitor_product_id, competitor_product_name,
competitor_product_url, competitor_image_url, competitor_stock,
match_score, tags, match_diagnostic_json,
comparison_mode, hard_veto, diagnostic_codes,
crawled_at, expires_at)
VALUES
(:sku, :source, :price, :original_price, :discount_pct,
:comp_id, :comp_name,
:competitor_product_url, :competitor_image_url, :competitor_stock,
:match_score, :tags, {json_expr},
:comparison_mode, :hard_veto, {codes_expr},
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,
competitor_product_url = EXCLUDED.competitor_product_url,
competitor_image_url = EXCLUDED.competitor_image_url,
competitor_stock = EXCLUDED.competitor_stock,
match_score = EXCLUDED.match_score,
tags = EXCLUDED.tags,
match_diagnostic_json = EXCLUDED.match_diagnostic_json,
comparison_mode = EXCLUDED.comparison_mode,
hard_veto = EXCLUDED.hard_veto,
diagnostic_codes = EXCLUDED.diagnostic_codes,
crawled_at = CURRENT_TIMESTAMP,
expires_at = :expires_at
""".format(json_expr=json_expr, codes_expr=codes_expr)), {
"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],
**product_payload,
"match_score": match_score,
"tags": tags_json,
"match_diagnostic_json": json.dumps(diagnostic_payload, ensure_ascii=False) if diagnostic_payload else None,
"comparison_mode": diagnostic_payload.get("comparison_mode"),
"hard_veto": diagnostic_payload.get("hard_veto"),
"diagnostic_codes": json.dumps(diagnostic_codes, ensure_ascii=False) if diagnostic_codes else None,
"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,
competitor_product_url, competitor_image_url, competitor_stock,
match_score, tags, match_diagnostic_json,
comparison_mode, hard_veto, diagnostic_codes,
crawled_at)
VALUES
(:sku, :source, :momo_product_id, :momo_price,
:price, :original_price, :discount_pct,
:comp_id, :comp_name,
:competitor_product_url, :competitor_image_url, :competitor_stock,
:match_score, :tags, {json_expr},
:comparison_mode, :hard_veto, {codes_expr},
CURRENT_TIMESTAMP)
""".format(json_expr=json_expr, codes_expr=codes_expr)), {
"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],
**product_payload,
"match_score": match_score,
"tags": tags_json,
"match_diagnostic_json": json.dumps(diagnostic_payload, ensure_ascii=False) if diagnostic_payload else None,
"comparison_mode": diagnostic_payload.get("comparison_mode"),
"hard_veto": diagnostic_payload.get("hard_veto"),
"diagnostic_codes": json.dumps(diagnostic_codes, ensure_ascii=False) if diagnostic_codes else None,
})
def _should_upsert_competitor_price(
self,
sku: str,
product,
match_score: float,
momo_name: str | None = None,
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, competitor_product_name, 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 "")
existing_name = str(row.get("competitor_product_name") or "")
incoming_id = str(getattr(product, "product_id", "") or "")
incoming_name = str(getattr(product, "name", "") 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 momo_name and existing_name:
try:
from services.marketplace_product_matcher import score_marketplace_match
existing_current = score_marketplace_match(momo_name, existing_name)
except Exception:
existing_current = None
if (
existing_current
and (
existing_current.hard_veto
or existing_current.score < MIN_MATCH_SCORE
)
and match_score >= MIN_MATCH_SCORE
):
return (
True,
"replace_stale_existing_identity="
f"{existing_score:.3f}->{existing_current.score:.3f}->{match_score:.3f}",
)
if existing_name and incoming_name:
try:
from services.marketplace_product_matcher import score_marketplace_match
candidate_equivalence = score_marketplace_match(existing_name, incoming_name)
except Exception:
candidate_equivalence = None
if (
candidate_equivalence
and candidate_equivalence.score >= 0.88
and not candidate_equivalence.hard_veto
and candidate_equivalence.comparison_mode == "exact_identity"
and match_score >= max(MIN_MATCH_SCORE, existing_score + 0.015)
):
return (
True,
"replace_same_identity_better_score="
f"{existing_score:.3f}->{match_score:.3f}",
)
if existing_score >= match_score:
return (
False,
f"existing_match_conflict;stronger_existing;existing_id={existing_id};"
f"incoming_id={incoming_id};existing_score={existing_score:.3f};"
f"incoming_score={match_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 _fetch_latest_manual_review_for_candidate(
self,
sku: str,
competitor_product_id: str,
source: str = "pchome",
) -> Optional[dict]:
"""Read the latest human review for this exact candidate, if the table exists."""
if not competitor_product_id:
return None
from sqlalchemy import text
try:
with self.engine.connect() as conn:
row = conn.execute(text("""
SELECT review_action, review_reason, reviewer_identity, reviewed_at
FROM competitor_match_reviews
WHERE sku = :sku
AND source = :source
AND candidate_product_id = :candidate_id
ORDER BY reviewed_at DESC, id DESC
LIMIT 1
"""), {
"sku": sku,
"source": source,
"candidate_id": competitor_product_id,
}).mappings().first()
except Exception:
return None
return dict(row) if row else None
def _run_sku_items(
self,
skus: list,
source: str = "pchome",
label: str = "PChome 競品價格",
bounded_search: bool = False,
) -> 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_timeout = BACKFILL_REQUEST_TIMEOUT if bounded_search else REQUEST_TIMEOUT
crawler = PChomeCrawler(timeout=crawler_timeout, 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:
if bounded_search:
products = _search_pchome_candidates(
crawler,
momo_name,
search_terms,
momo_price=momo_price,
max_terms=BACKFILL_MAX_SEARCH_TERMS,
max_pages=BACKFILL_SEARCH_MAX_PAGES,
max_seconds=BACKFILL_MAX_SECONDS_PER_SKU,
)
else:
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")
browse_diagnostic = self._prepare_browse_diagnostic(
momo_name,
search_terms=search_terms,
reason="no_result",
candidate_count=0,
)
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",
browse_diagnostic=browse_diagnostic,
source=source,
)
attempts_written += 1
skipped_no += 1
continue
ranked_matches = _rank_match_details(momo_name, products, momo_price=momo_price)
if not ranked_matches:
browse_diagnostic = self._prepare_browse_diagnostic(
momo_name,
search_terms=search_terms,
reason="no_match",
candidate_count=len(products),
)
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",
browse_diagnostic=browse_diagnostic,
source=source,
)
attempts_written += 1
skipped_no += 1
continue
selected_match = None
manually_rejected_ids: list[str] = []
for candidate_product, candidate_score, candidate_diagnostics in ranked_matches:
candidate_review = self._fetch_latest_manual_review_for_candidate(
sku,
getattr(candidate_product, "product_id", None),
source=source,
)
if (candidate_review or {}).get("review_action") == "reject_identity":
manually_rejected_ids.append(str(getattr(candidate_product, "product_id", "") or ""))
continue
selected_match = (
candidate_product,
candidate_score,
candidate_diagnostics,
candidate_review,
)
break
if not selected_match:
best_product, score, diagnostics = ranked_matches[0]
rejected_note = ",".join(product_id for product_id in manually_rejected_ids if product_id)
logger.info(
f"[Feeder] {sku} 所有可信候選都已被人工否決，跳過正式寫入 | "
f"rejected_candidates={rejected_note}"
)
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="manual_rejected",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=(
f"manual_review_rejected; rejected_candidates={rejected_note}; "
f"{_format_match_diagnostics(diagnostics)}"
),
source=source,
)
attempts_written += 1
skipped_low += 1
continue
best_product, score, diagnostics, manual_review = selected_match
manual_action = (manual_review or {}).get("review_action")
if manual_action == "unit_price_required":
logger.info(
f"[Feeder] {sku} 候選已被人工標記為單位價比較，不寫正式總價差 | "
f"candidate={getattr(best_product, 'product_id', None)}"
)
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="manual_unit_price_required",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=f"manual_review_unit_price_required; {_format_match_diagnostics(diagnostics)}",
source=source,
)
attempts_written += 1
skipped_low += 1
continue
manual_accept_override = manual_action == "accept_identity"
if getattr(diagnostics, "comparison_mode", "") == "unit_comparable" and not manual_accept_override:
logger.info(
f"[Feeder] {sku} 候選屬單位價可比但非同販售組合，"
f"不寫入正式價差 | {_format_match_diagnostics(diagnostics)}"
)
browse_diagnostic = self._prepare_browse_diagnostic(
momo_name,
search_terms=search_terms,
reason="unit_comparable",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
candidate_count=len(products),
)
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="unit_comparable",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
browse_diagnostic=browse_diagnostic,
error_message=_format_match_diagnostics(diagnostics),
source=source,
)
attempts_written += 1
skipped_low += 1
continue
if score < MIN_MATCH_SCORE and not manual_accept_override:
attempt_status = _classify_low_score_attempt(score, diagnostics)
if (
attempt_status == "recoverable_low_score"
and _has_variant_selection_gap(momo_name, ranked_matches, score)
):
attempt_status = "true_low_confidence"
logger.debug(
f"[Feeder] {sku} 比對分數過低 ({score:.3f} < {MIN_MATCH_SCORE})，"
f"{_format_match_diagnostics(diagnostics)}"
)
browse_diagnostic = self._prepare_browse_diagnostic(
momo_name,
search_terms=search_terms,
reason=attempt_status,
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
candidate_count=len(products),
)
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=attempt_status,
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
browse_diagnostic=browse_diagnostic,
error_message=_format_match_diagnostics(diagnostics),
source=source,
)
attempts_written += 1
skipped_low += 1
continue
if manual_accept_override:
score = max(score, MIN_MATCH_SCORE)
if not manual_accept_override and not _is_auto_price_write_safe(diagnostics):
attempt_status = _classify_auto_write_block_attempt(score, diagnostics)
browse_diagnostic = self._prepare_browse_diagnostic(
momo_name,
search_terms=search_terms,
reason=attempt_status,
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
candidate_count=len(products),
)
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=attempt_status,
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
browse_diagnostic=browse_diagnostic,
error_message=f"auto_price_write_blocked; {_format_match_diagnostics(diagnostics)}",
source=source,
)
attempts_written += 1
skipped_low += 1
continue
tags = _extend_match_tags(_extract_tags(best_product), diagnostics)
if manual_accept_override:
tags.extend(["manual_review", "manual_accept"])
tags = [tag for tag in tags if tag != "identity_veto"]
tags = list(dict.fromkeys(tags))
should_write, write_reason = self._should_upsert_competitor_price(
sku,
best_product,
score,
momo_name=momo_name,
source=source,
)
if manual_accept_override and not should_write:
should_write = True
write_reason = "manual_accept_override"
if not should_write:
logger.info(f"[Feeder] {sku} 進入人工覆核，不覆蓋既有配對 | {write_reason}")
browse_diagnostic = self._prepare_browse_diagnostic(
momo_name,
search_terms=search_terms,
reason="protected_existing_match",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
candidate_count=len(products),
)
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="protected_existing_match",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
browse_diagnostic=browse_diagnostic,
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,
diagnostics=diagnostics,
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,
diagnostics=diagnostics,
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:
browse_diagnostic = self._prepare_browse_diagnostic(
momo_name,
search_terms=search_terms,
reason="crawler_error",
candidate_count=0,
)
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=search_terms,
attempt_status="error",
browse_diagnostic=browse_diagnostic,
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_known_identity_refresh_items(
self,
skus: list,
source: str = "pchome",
label: str = "已確認身份價格刷新",
allow_missing_recovery: bool = True,
bounded_recovery: bool = False,
) -> 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)
if not skus:
return FeederResult(0, 0, 0, 0, 0, 0.0)
from services.pchome_crawler import PChomeCrawler
crawler_timeout = BACKFILL_REQUEST_TIMEOUT if bounded_recovery else REQUEST_TIMEOUT
crawler = PChomeCrawler(timeout=crawler_timeout, delay=RATE_DELAY)
recovery_kwargs = (
{
"max_terms": BACKFILL_MAX_SEARCH_TERMS,
"max_pages": BACKFILL_SEARCH_MAX_PAGES,
"max_seconds": BACKFILL_MAX_SECONDS_PER_SKU,
}
if bounded_recovery
else {}
)
requested_ids = [
str(item.get("competitor_product_id") or "").strip()
for item in skus
if str(item.get("competitor_product_id") or "").strip()
]
ok, message, products = crawler.fetch_product_details(requested_ids, batch_size=20)
product_map = {_product_id_key(product.product_id): product for product in products} if ok else {}
logger.info(
f"[Feeder] {label} product_id 批次查詢 | requested={len(requested_ids)} "
f"returned={len(product_map)} msg={message}"
)
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")
competitor_product_id = str(item.get("competitor_product_id") or "").strip()
search_terms = [f"known_product_id:{competitor_product_id}"] if competitor_product_id else []
try:
product = product_map.get(_product_id_key(competitor_product_id))
if not product:
if not allow_missing_recovery:
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="refresh_no_result",
error_message=(
f"PChome product_id not returned: {competitor_product_id}; "
"fresh_search_recovery_deferred"
),
source=source,
)
skipped_no += 1
attempts_written += 1
continue
recovered, recovery_terms, recovery_candidate_count = _recover_low_score_with_fresh_search(
crawler,
momo_name,
momo_price=momo_price,
existing_product_id=competitor_product_id,
**recovery_kwargs,
)
if recovered:
best_product, score, diagnostics = recovered
if getattr(diagnostics, "comparison_mode", "") == "unit_comparable":
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=search_terms + [term for term in recovery_terms if term not in search_terms],
candidate_count=max(1, recovery_candidate_count),
attempt_status="refresh_unit_comparable",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=_format_match_diagnostics(diagnostics),
source=source,
)
skipped_low += 1
attempts_written += 1
continue
if score >= MIN_MATCH_SCORE and not getattr(diagnostics, "hard_veto", False):
if not _is_auto_price_write_safe(diagnostics):
attempt_terms = search_terms + [term for term in recovery_terms if term not in search_terms]
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=attempt_terms,
candidate_count=max(1, recovery_candidate_count),
attempt_status=_classify_auto_write_block_attempt(score, diagnostics),
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=f"auto_price_write_blocked; {_format_match_diagnostics(diagnostics)}",
source=source,
)
skipped_low += 1
attempts_written += 1
continue
tags = _extend_match_tags(
_extract_tags(best_product),
diagnostics,
["refresh_known_identity", "fresh_search_recovery", "missing_known_product_id"],
)
should_write, write_reason = self._should_upsert_competitor_price(
sku,
best_product,
score,
momo_name=momo_name,
source=source,
)
attempt_terms = search_terms + [term for term in recovery_terms if term not in search_terms]
if not should_write:
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=attempt_terms,
candidate_count=max(1, recovery_candidate_count),
attempt_status="protected_existing_match",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=f"{write_reason}; {_format_match_diagnostics(diagnostics)}",
source=source,
)
skipped_low += 1
attempts_written += 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,
diagnostics=diagnostics,
source=source,
)
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=attempt_terms,
candidate_count=max(1, recovery_candidate_count),
attempt_status="matched",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
source=source,
)
matched += 1
history_written += 1
attempts_written += 1
continue
attempt_status = _classify_low_score_attempt(score, diagnostics)
if (
attempt_status == "recoverable_low_score"
and _has_variant_selection_gap(momo_name, [(best_product, score, diagnostics)], score)
):
attempt_status = "true_low_confidence"
attempt_terms = search_terms + [term for term in recovery_terms if term not in search_terms]
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=attempt_terms,
candidate_count=max(1, recovery_candidate_count),
attempt_status=attempt_status,
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=(
"missing_known_product_id_fresh_search_low_confidence; "
f"PChome product_id not returned: {competitor_product_id}; "
f"{_format_match_diagnostics(diagnostics)}"
),
source=source,
)
skipped_low += 1
attempts_written += 1
continue
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=search_terms + [term for term in recovery_terms if term not in search_terms],
candidate_count=max(0, recovery_candidate_count),
attempt_status="refresh_no_result",
error_message=f"PChome product_id not returned: {competitor_product_id}",
source=source,
)
skipped_no += 1
attempts_written += 1
continue
result = _find_best_match_detail(momo_name, [product], 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=1,
attempt_status="refresh_no_match",
source=source,
)
skipped_no += 1
attempts_written += 1
continue
best_product, score, diagnostics = result
if getattr(diagnostics, "comparison_mode", "") == "unit_comparable":
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=search_terms,
candidate_count=1,
attempt_status="refresh_unit_comparable",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=_format_match_diagnostics(diagnostics),
source=source,
)
skipped_low += 1
attempts_written += 1
continue
if score < MIN_MATCH_SCORE:
recovery_terms: list[str] = []
recovery_candidate_count = 0
if allow_missing_recovery:
recovered, recovery_terms, recovery_candidate_count = _recover_low_score_with_fresh_search(
crawler,
momo_name,
momo_price=momo_price,
existing_product_id=competitor_product_id,
**recovery_kwargs,
)
if recovered:
recovered_product, recovered_score, recovered_diagnostics = recovered
if (
recovered_score > score
or getattr(diagnostics, "hard_veto", False)
and not getattr(recovered_diagnostics, "hard_veto", False)
):
best_product, score, diagnostics = recovered_product, recovered_score, recovered_diagnostics
if score >= MIN_MATCH_SCORE:
extras = ["refresh_known_identity"]
if recovery_terms:
extras.append("fresh_search_recovery")
if not _is_auto_price_write_safe(diagnostics):
candidate_count = max(1, recovery_candidate_count or 1)
attempt_terms = search_terms + [term for term in recovery_terms if term not in search_terms]
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=attempt_terms,
candidate_count=candidate_count,
attempt_status=_classify_auto_write_block_attempt(score, diagnostics),
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=f"auto_price_write_blocked; {_format_match_diagnostics(diagnostics)}",
source=source,
)
skipped_low += 1
attempts_written += 1
continue
tags = _extend_match_tags(_extract_tags(best_product), diagnostics, extras)
should_write, write_reason = self._should_upsert_competitor_price(
sku,
best_product,
score,
momo_name=momo_name,
source=source,
)
candidate_count = max(1, recovery_candidate_count or 1)
attempt_terms = search_terms + [term for term in recovery_terms if term not in search_terms]
if not should_write:
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=attempt_terms,
candidate_count=candidate_count,
attempt_status="protected_existing_match",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=f"{write_reason}; {_format_match_diagnostics(diagnostics)}",
source=source,
)
skipped_low += 1
attempts_written += 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,
diagnostics=diagnostics,
source=source,
)
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=attempt_terms,
candidate_count=candidate_count,
attempt_status="matched",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
source=source,
)
matched += 1
history_written += 1
attempts_written += 1
continue
attempt_status = _classify_low_score_attempt(score, diagnostics)
if (
attempt_status == "recoverable_low_score"
and _has_variant_selection_gap(momo_name, [(best_product, score, diagnostics)], score)
):
attempt_status = "true_low_confidence"
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=search_terms + [term for term in recovery_terms if term not in search_terms],
candidate_count=max(1, recovery_candidate_count or 1),
attempt_status=attempt_status,
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=(
_format_match_diagnostics(diagnostics)
if allow_missing_recovery
else f"fresh_search_recovery_deferred; {_format_match_diagnostics(diagnostics)}"
),
source=source,
)
skipped_low += 1
attempts_written += 1
continue
if not _is_auto_price_write_safe(diagnostics):
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=search_terms,
candidate_count=1,
attempt_status=_classify_auto_write_block_attempt(score, diagnostics),
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=f"auto_price_write_blocked; {_format_match_diagnostics(diagnostics)}",
source=source,
)
skipped_low += 1
attempts_written += 1
continue
tags = _extend_match_tags(_extract_tags(best_product), diagnostics, ["refresh_known_identity"])
should_write, write_reason = self._should_upsert_competitor_price(
sku,
best_product,
score,
momo_name=momo_name,
source=source,
)
if not should_write:
self._record_match_attempt(
sku,
momo_name,
momo_product_id=momo_product_id,
momo_price=momo_price,
search_terms=search_terms,
candidate_count=1,
attempt_status="protected_existing_match",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
error_message=f"{write_reason}; {_format_match_diagnostics(diagnostics)}",
source=source,
)
skipped_low += 1
attempts_written += 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,
diagnostics=diagnostics,
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=1,
attempt_status="matched",
best_product=best_product,
best_score=score,
diagnostics=diagnostics,
source=source,
)
matched += 1
history_written += 1
attempts_written += 1
except Exception as e:
logger.error(f"[Feeder] {sku} 已確認身份刷新失敗: {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="refresh_error",
error_message=str(e),
source=source,
)
attempts_written += 1
except Exception as attempt_error:
logger.warning(f"[Feeder] {sku} 刷新嘗試紀錄寫入失敗: {attempt_error}")
errors += 1
duration = round(time.time() - start, 2)
logger.info(
f"[Feeder] {label} 完成 matched={matched}/{len(skus)} "
f"skip_no={skipped_no} skip_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_expired_identity_refresh(self, limit: int = 120, source: str = "pchome") -> FeederResult:
"""刷新已通過 identity_v2、但 PChome 價格快取過期的商品。"""
try:
skus = self._fetch_expired_identity_skus(limit=limit)
except Exception as e:
logger.error(f"[Feeder] 讀取過期 identity_v2 商品失敗: {e}")
return FeederResult(0, 0, 0, 0, 1, 0.0)
return self._run_known_identity_refresh_items(
skus,
source=source,
label="identity_v2 過期價格刷新",
allow_missing_recovery=False,
)
def run_expired_identity_search_recovery(self, limit: int = 40, source: str = "pchome") -> FeederResult:
"""
搜尋救援已建立 identity_v2、但價格過期或舊 PChome ID 失效的商品。
與一般 refresh-stale 不同，這條路徑允許在 known product_id 缺失或重評低分時，
走受控 fresh_search_recovery；最後仍會經過 hard veto、auto price write
safety、overwrite protection 才能寫入正式 competitor_prices。
"""
try:
skus = self._fetch_expired_identity_recovery_skus(limit=max(1, min(int(limit), 120)))
except Exception as e:
logger.error(f"[Feeder] 讀取過期 identity_v2 搜尋救援商品失敗: {e}")
return FeederResult(0, 0, 0, 0, 1, 0.0)
return self._run_known_identity_refresh_items(
skus,
source=source,
label="identity_v2 過期價格搜尋救援",
allow_missing_recovery=True,
bounded_recovery=True,
)
def run_retryable_candidate_revalidation(
self,
limit: int = 80,
min_score: float = 0.70,
source: str = "pchome",
) -> FeederResult:
"""重新評分近門檻候選，避免 matcher 升級後仍卡在舊的 low_score。"""
try:
skus = self._fetch_retryable_candidate_skus(limit=limit, min_score=min_score)
except Exception as e:
logger.error(f"[Feeder] 讀取近門檻候選失敗: {e}")
return FeederResult(0, 0, 0, 0, 1, 0.0)
return self._run_known_identity_refresh_items(
skus,
source=source,
label="近門檻候選重新評分",
)
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",
bounded_search=True,
)
# ─────────────────────────────────────────────
# 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"
)
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