強化 Dashing Diva 美甲片同款比對

config.py

@@ -323,7 +323,7 @@ YOUTUBE_API_KEY = os.getenv('YOUTUBE_API_KEY', '')
 # ==========================================
 # 系統版本與路徑
 # ==========================================
-SYSTEM_VERSION = "V10.362"
+SYSTEM_VERSION = "V10.363"
 LOG_FILE_PATH = os.path.join(BASE_DIR, 'logs/system.log')
 public_url = PUBLIC_URL  # 用於模板顯示
 

docs/memory/history_logs.md

@@ -13,6 +13,7 @@
 ## 📅 詳細更新日誌 (考古存檔)
 
 ### 2026-05-21：瀏覽器測試守門與 PChome 熱路徑優化
+- **V10.363 Dashing Diva variant-safe search**: PChome/MOMO matcher 針對 Dashing Diva 美甲片補「商品頁目錄有 30片/盒、MOMO 標題省略片數」的安全豁免，只限同品牌、同美甲片線、同具名款式錨點；搜尋詞也優先帶出 `月影柔霧`、`銀絲柔彩` 等款式名，降低同系列不同款式互撞。
 - **V10.362 111 fallback shrink-to-3B**: 111 Mac 實測 `hermes3` / `qwen2.5-coder` 雖是 7B/8B，但 large context runner 仍會佔用 6-10GB RSS 並推高 swap；111 fallback 改為所有 7B+、vision 與 long-context 文字生成都降級到 `llama3.2:latest`，`ai_calls.model` 也會記錄實際降級模型並把原請求模型放入 `meta.requested_model`。
 - **V10.361 111 fallback resource guard**: 實測 111 Mac 高 load 主要來自 Codex app / WindowServer 前台負載，且 Ollama 曾因 fallback 載入 `qwen3:14b` 造成 16GB RAM / swap 壓力；已手動 unload 111 上的重模型，並讓 `OllamaService.generate()` 落到 111 時自動把 14B+ 模型降到 `OLLAMA_111_MODEL_FALLBACK`、`keep_alive` 縮至 `OLLAMA_111_KEEP_ALIVE=5m`、timeout 封頂 `OLLAMA_111_MAX_TIMEOUT=45`。GCP-A/GCP-B 仍可跑 `qwen3:14b`，111 只做短時最後備援。
 - **V10.360 browser smoke guard**: `tests/test_image_fetch.py` 改為預設 skip，只有 `RUN_MOMO_BROWSER_TESTS=1` 才會打開外部 MOMO 網站；手動執行時預設 headless，並關閉 Chrome password manager/autofill，避免一般 pytest 觸發瀏覽器與密碼允許提示。

services/marketplace_product_matcher.py

@@ -956,6 +956,36 @@ def _has_hard_count_unit_conflict(left: ProductIdentity, right: ProductIdentity)
     return False
 
 
+def _allow_catalog_count_omission(left: ProductIdentity, right: ProductIdentity) -> bool:
+    """Allow catalog-side piece counts for Dashing Diva nail lines when MOMO omits pack count."""
+    left_has_counts = bool(left.counts)
+    right_has_counts = bool(right.counts)
+    if left_has_counts == right_has_counts:
+        return False
+
+    shared_brand_tokens = {token.lower() for token in left.brand_tokens} & {
+        token.lower() for token in right.brand_tokens
+    }
+    if not ({"dashing", "diva"} <= shared_brand_tokens):
+        return False
+
+    searchable_pair = f"{left.searchable_name} {right.searchable_name}"
+    if "美甲片" not in searchable_pair:
+        return False
+
+    counted = left if left_has_counts else right
+    omitted = right if left_has_counts else left
+    if omitted.counts:
+        return False
+    if (counted.total_piece_count or 0) < 20:
+        return False
+
+    return any(
+        anchor in searchable_pair
+        for anchor in ("時尚潮流美甲片", "頂級璀燦美甲片", "薄型經典美甲片")
+    )
+
+
 def _count_score(left: ProductIdentity, right: ProductIdentity) -> tuple[float, bool]:
     left_counts = [count for count, _unit in left.counts]
     right_counts = [count for count, _unit in right.counts]
@@ -985,6 +1015,8 @@ def _count_score(left: ProductIdentity, right: ProductIdentity) -> tuple[float,
             if ratio >= 1.5:
                 return 0.0, True
         return 0.35, False
+    if _allow_catalog_count_omission(left, right):
+        return 0.55, False
     if (left_counts and max(left_counts) > 1) or (right_counts and max(right_counts) > 1):
         return 0.0, True
     return 0.5, False
@@ -1819,9 +1851,25 @@ def _ranked_search_core_phrases(identity: ProductIdentity, limit: int = 4) -> li
     return phrases
 
 
+def _variant_primary_phrase(identity: ProductIdentity) -> str:
+    text = identity.searchable_name
+    for anchor in ("時尚潮流美甲片", "頂級璀燦美甲片", "薄型經典美甲片", "足部時尚潮流美甲片"):
+        pattern = rf"{re.escape(anchor)}[-_ ]*([\u4e00-\u9fff]{{2,8}})"
+        match = re.search(pattern, text)
+        if not match:
+            continue
+        phrase = _clean_search_phrase(match.group(1))
+        compact = phrase.replace(" ", "")
+        if compact and compact not in SEARCH_NOISE_TOKENS:
+            return phrase
+    variant_descriptors = sorted(_variant_descriptors(identity), key=lambda token: (len(token), token))
+    return variant_descriptors[0] if variant_descriptors else ""
+
+
 def build_search_terms(name: str, max_terms: int = 3) -> list[str]:
     identity = parse_product_identity(name)
     terms: list[str] = []
+    is_dashing_diva_nail_line = {"dashing", "diva"} <= identity.brand_tokens and "美甲片" in identity.searchable_name
 
     def primary_brand_phrase() -> str:
         if {"dashing", "diva"} <= identity.brand_tokens:
@@ -1863,8 +1911,7 @@ def build_search_terms(name: str, max_terms: int = 3) -> list[str]:
     modifier_with_primary = " ".join(
         part for part in (chinese_detail_phrases[0] if chinese_detail_phrases else "", core_primary) if part
     )
-    variant_descriptors = sorted(_variant_descriptors(identity), key=lambda token: (len(token), token))
-    variant_primary = variant_descriptors[0] if variant_descriptors else ""
+    variant_primary = _variant_primary_phrase(identity)
     variant_options = sorted(_explicit_variant_option_tokens(identity), key=lambda token: (len(token), token))
     variant_option_part = " ".join(variant_options[:2])
     model_phrases = [
@@ -1878,6 +1925,9 @@ def build_search_terms(name: str, max_terms: int = 3) -> list[str]:
     )
     variant_sensitive = any(keyword in identity.searchable_name for keyword in VARIANT_SENSITIVE_KEYWORDS)
     for value in (
+        " ".join(part for part in (brand_part, core_primary, variant_primary, spec_part) if part)
+        if is_dashing_diva_nail_line and variant_sensitive and variant_primary
+        else "",
         " ".join(part for part in (brand_part, core_primary, variant_option_part, spec_part) if part)
         if variant_sensitive and variant_option_part
         else "",

tests/test_marketplace_product_matcher.py

@@ -591,6 +591,21 @@ def test_marketplace_matcher_promotes_variant_safe_exact_option():
     assert "shared_identity_anchor_variant_safe" in diagnostics.reasons
 
 
+def test_marketplace_matcher_allows_catalog_piece_count_for_exact_dashing_diva_variant():
+    from services.marketplace_product_matcher import score_marketplace_match
+
+    diagnostics = score_marketplace_match(
+        "【DASHING DIVA】MAGICPRESS 時尚潮流美甲片-銀河綠波(貓眼 漸層)",
+        "Dashing Diva/F 時尚潮流美甲片-銀河綠波 30片/盒 MDF5F012AG",
+        momo_price=399,
+        competitor_price=420,
+    )
+
+    assert diagnostics.score >= 0.76
+    assert diagnostics.hard_veto is False
+    assert "count_conflict" not in diagnostics.reasons
+
+
 def test_marketplace_matcher_rejects_explicit_shade_option_mismatch():
     from services.marketplace_product_matcher import score_marketplace_match
 
@@ -860,6 +875,22 @@ def test_marketplace_search_terms_keep_variant_descriptor_for_sensitive_lines():
     assert romand_terms[0] == "romand 勝過眼皮十色眼影盤"
 
 
+def test_marketplace_search_terms_prioritize_exact_dashing_diva_variant_name():
+    from services.marketplace_product_matcher import build_search_terms
+
+    moon_terms = build_search_terms(
+        "【DASHING DIVA】MAGICPRESS 時尚潮流美甲片-月影柔霧(貓眼 金色 漸層)",
+        max_terms=5,
+    )
+    silk_terms = build_search_terms(
+        "【DASHING DIVA】MAGICPRESS 時尚潮流美甲片-銀絲柔彩(紫色 漸層 貓眼)",
+        max_terms=5,
+    )
+
+    assert moon_terms[0] == "dashing diva 時尚潮流美甲片 月影柔霧"
+    assert silk_terms[0] == "dashing diva 時尚潮流美甲片 銀絲柔彩"
+
+
 def test_marketplace_search_terms_prioritize_private_wash_identity_phrase():
     from services.marketplace_product_matcher import build_search_terms