#!/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 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", "5")) # 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 = 6 # competitor_prices 快取有效期 REQUEST_TIMEOUT = float(os.getenv("PCHOME_FEEDER_TIMEOUT", "12")) # 避免外部搜尋 API 長時間卡住排程 VARIANT_RECALL_SORTS = ("sale/dc", "new/dc") RECOVERABLE_LOW_SCORE_FLOOR = max(MIN_MATCH_SCORE - 0.03, 0.72) 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", } # ── 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 _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'[^\w\u4e00-\u9fff]+', ' ', value) return re.sub(r'\s+', ' ', value).strip() def _dedupe_terms(terms: list) -> list: result = [] seen = set() for term in terms: cleaned = _clean_search_text(term) if len(cleaned) < 2: continue key = cleaned.lower() if key in seen: continue seen.add(key) result.append(cleaned[:36]) if len(result) >= MAX_SEARCH_TERMS: break return result def _build_search_keywords(momo_name: str) -> list: """ 用多組商品身份線索搜尋 PChome，提高命中率，但仍交給身份比對門檻把關。 """ try: from services.marketplace_product_matcher import build_search_terms terms = build_search_terms(momo_name, max_terms=MAX_SEARCH_TERMS) 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]] primary_terms = _dedupe_terms(terms[: max(1, MAX_SEARCH_TERMS - 1)]) original_terms = _dedupe_terms([momo_name]) for term in original_terms: if term.lower() not in {existing.lower() for existing in primary_terms}: primary_terms.append(term) break return _dedupe_terms(primary_terms) 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", "")): 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 ()), } 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 _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, p.name, 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) -> 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() search_limit = SEARCH_LIMIT * max(1, SEARCH_MAX_PAGES) active_keywords = keywords or _build_search_keywords(momo_name) search_plan = _build_variant_recall_search_plan(momo_name, active_keywords) for keyword, sort in search_plan: if sort: ok, _, products = crawler.search_products( keyword, limit=search_limit, max_pages=SEARCH_MAX_PAGES, sort=sort, ) else: ok, _, products = crawler.search_products(keyword, limit=search_limit, max_pages=SEARCH_MAX_PAGES) 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 = "", ) -> 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, ) 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", [ ("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"), ]) 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_momo AS ( SELECT p.id AS product_id, p.i_code AS sku, p.name, p.category, pr.price AS momo_price, ROW_NUMBER() OVER (PARTITION BY p.id ORDER BY pr.timestamp DESC) AS rn FROM products p JOIN price_records pr ON pr.product_id = p.id WHERE p.status = 'ACTIVE' ) SELECT lm.product_id, lm.sku, lm.name, lm.category, lm.momo_price FROM latest_momo lm LEFT JOIN competitor_prices cp ON cp.sku = lm.sku AND cp.source = 'pchome' AND (cp.expires_at IS NULL OR cp.expires_at > CURRENT_TIMESTAMP) AND COALESCE(cp.match_score, 0) >= :match_score_floor AND COALESCE(cp.tags, '[]'::jsonb) ? 'identity_v2' WHERE lm.rn = 1 AND cp.sku IS NULL ORDER BY lm.momo_price DESC NULLS LAST, lm.sku LIMIT :limit """) with self.engine.connect() as conn: rows = conn.execute( sql, {"limit": max(1, min(int(limit), 300)), "match_score_floor": MIN_MATCH_SCORE}, ).fetchall() return [dict(r._mapping) for r in rows] def _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。 這條路徑不重新搜尋，只用前次留下的 PChome product_id 批次查詢最新商品資料， 適合把舊 scorer 卡在 0.70~0.759 的真同款重新推進正式比價。 僅重跑明顯有回收價值的候選；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(""" WITH latest_momo AS ( SELECT p.id AS product_id, p.i_code AS sku, p.name, p.category, pr.price AS momo_price, ROW_NUMBER() OVER (PARTITION BY p.id ORDER BY pr.timestamp DESC) AS rn FROM products p JOIN price_records pr ON pr.product_id = p.id WHERE p.status = 'ACTIVE' ), 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.attempted_at FROM competitor_match_attempts cma WHERE cma.source = 'pchome' ORDER BY cma.sku, cma.attempted_at DESC, cma.id DESC ) SELECT lm.product_id, lm.sku, lm.name, lm.category, lm.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 latest_momo lm JOIN latest_attempt la ON la.sku = lm.sku LEFT JOIN competitor_prices cp ON cp.sku = lm.sku AND cp.source = 'pchome' AND (cp.expires_at IS NULL OR cp.expires_at > CURRENT_TIMESTAMP) AND COALESCE(cp.match_score, 0) >= :match_score_floor AND COALESCE(cp.tags, '[]'::jsonb) ? 'identity_v2' WHERE lm.rn = 1 AND cp.sku IS NULL AND la.attempt_status IN ( 'low_score', 'refresh_low_score', 'recoverable_low_score', 'true_low_confidence', 'unit_comparable', 'refresh_unit_comparable', 'identity_veto' ) AND 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 OR la.attempt_status = 'identity_veto' ) ORDER BY la.best_match_score DESC NULLS LAST, lm.momo_price DESC NULLS LAST, lm.sku LIMIT :limit """) with self.engine.connect() as conn: rows = conn.execute( sql, { "limit": max(1, min(int(limit), 300)), "min_score": float(min_score), "match_score_floor": MIN_MATCH_SCORE, }, ).fetchall() return [dict(r._mapping) for r in rows] 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(""" WITH latest_momo AS ( SELECT p.id AS product_id, p.i_code AS sku, p.name, p.category, pr.price AS momo_price, ROW_NUMBER() OVER (PARTITION BY p.id ORDER BY pr.timestamp DESC) AS rn FROM products p JOIN price_records pr ON pr.product_id = p.id WHERE p.status = 'ACTIVE' ) SELECT lm.product_id, lm.sku, lm.name, lm.category, lm.momo_price, cp.competitor_product_id, cp.competitor_product_name, cp.match_score, cp.expires_at FROM latest_momo lm JOIN competitor_prices cp ON cp.sku = lm.sku AND cp.source = 'pchome' AND cp.competitor_product_id IS NOT NULL AND cp.competitor_product_id <> '' AND cp.expires_at IS NOT NULL AND cp.expires_at <= CURRENT_TIMESTAMP AND COALESCE(cp.match_score, 0) >= :match_score_floor AND COALESCE(cp.tags, '[]'::jsonb) ? 'identity_v2' WHERE lm.rn = 1 ORDER BY cp.expires_at ASC, lm.momo_price DESC NULLS LAST, lm.sku LIMIT :limit """) with self.engine.connect() as conn: rows = conn.execute( sql, {"limit": max(1, min(int(limit), 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 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") -> FeederResult: start = time.time() if source != "pchome": logger.warning(f"[Feeder] 尚未支援 source={source}，跳過") return FeederResult(0, 0, 0, 0, 0, 0.0) from services.pchome_crawler import PChomeCrawler crawler = PChomeCrawler(timeout=REQUEST_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: 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) 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", ) -> 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 = PChomeCrawler(timeout=REQUEST_TIMEOUT, delay=RATE_DELAY) 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: 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, ) 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): 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 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 not getattr(diagnostics, "hard_veto", False): 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, ) if recovered: recovered_product, recovered_score, recovered_diagnostics = recovered if recovered_score > score: 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") 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), 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") 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") # ───────────────────────────────────────────── # 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" )