1. 减少一列sell points。有时候大模型输出会将这最后两列混淆，因此干脆去掉一个

2. 优化缓存，缓存粒度为商品级，每次只对batch中未cache的重新计算；key使用每个商品输入的hash

1. 减少一列sell points。有时候大模型输出会将这最后两列混淆，因此干脆去掉一个
2. 优化缓存，缓存粒度为商品级，每次只对batch中未cache的重新计算；key使用每个商品输入的hash
tangwang
1 parent a73a751f
Showing 6 changed files with 119 additions and 72 deletions Show diff stats
indexer/ANCHORS_AND_SEMANTIC_ATTRIBUTES.md
indexer/product_enrich.py
indexer/product_enrich_prompts.py
tests/ci/test_service_api_contracts.py
tests/test_process_products_batching.py
tests/test_product_enrich_partial_mode.py
@@ -135,7 +135,6 @@ SUPPORTED_LANGS = set(LANG_LABELS.keys())
   "key_attributes": "<逗号分隔的关键属性>",
   "material": "<逗号分隔的材质说明>",
   "features": "<逗号分隔的功能特点>",
-  "selling_points": "<一句话卖点>",
   "anchor_text": "<逗号分隔的锚文本短语>",
   # 若发生错误，还会附带:
   # "error": "<异常信息>"
@@ -425,8 +425,7 @@ def parse_markdown_table(markdown_content: str) -&gt; List[Dict[str, str]]:
                     "key_attributes": parts[7] if len(parts) > 7 else "",  # 关键属性
                     "material": parts[8] if len(parts) > 8 else "",  # 材质说明
                     "features": parts[9] if len(parts) > 9 else "",  # 功能特点
-                    "selling_points": parts[10] if len(parts) > 10 else "",  # 商品卖点
-                    "anchor_text": parts[11] if len(parts) > 11 else "",  # 锚文本
+                    "anchor_text": parts[10] if len(parts) > 10 else "",  # 锚文本
                 }
                 data.append(row)
@@ -503,7 +502,6 @@ def process_batch(
                 "key_attributes": "",
                 "material": "",
                 "features": "",
-                "selling_points": "",
                 "anchor_text": "",
                 "error": f"prompt_creation_failed: unsupported target_lang={target_lang}",
             }
@@ -544,7 +542,6 @@ def process_batch(
                     "key_attributes": parsed_item.get("key_attributes", ""),  # 关键属性
                     "material": parsed_item.get("material", ""),  # 材质说明
                     "features": parsed_item.get("features", ""),  # 功能特点
-                    "selling_points": parsed_item.get("selling_points", ""),  # 商品卖点
                     "anchor_text": parsed_item.get("anchor_text", ""),  # 锚文本
                 }
                 results_with_ids.append(result)
@@ -586,7 +583,6 @@ def process_batch(
                 "key_attributes": "",
                 "material": "",
                 "features": "",
-                "selling_points": "",
                 "anchor_text": "",
                 "error": str(e),
             }
@@ -614,39 +610,48 @@ def analyze_products(
     if not products:
         return []
-    # 简单路径：索引阶段通常 batch_size=1，这里优先做单条缓存命中
-    if len(products) == 1:
-        p = products[0]
-        title = str(p.get("title") or "").strip()
-        if title:
-            cached = _get_cached_anchor_result(title, target_lang, tenant_id=tenant_id)
-            if cached:
-                logger.info(
-                    f"[analyze_products] Cache hit for title='{title[:50]}...', "
-                    f"lang={target_lang}, tenant_id={tenant_id or 'global'}"
-                )
-                return [cached]
+    results_by_index: List[Optional[Dict[str, Any]]] = [None] * len(products)
+    uncached_items: List[Tuple[int, Dict[str, str]]] = []
+
+    for idx, product in enumerate(products):
+        title = str(product.get("title") or "").strip()
+        if not title:
+            uncached_items.append((idx, product))
+            continue
+
+        cached = _get_cached_anchor_result(title, target_lang, tenant_id=tenant_id)
+        if cached:
+            logger.info(
+                f"[analyze_products] Cache hit for title='{title[:50]}...', "
+                f"lang={target_lang}, tenant_id={tenant_id or 'global'}"
+            )
+            results_by_index[idx] = cached
+            continue
+
+        uncached_items.append((idx, product))
+
+    if not uncached_items:
+        return [item for item in results_by_index if item is not None]
     # call_llm 一次处理上限固定为 BATCH_SIZE（默认 20）：
     # - 尽可能攒批处理；
     # - 即便调用方传入更大的 batch_size，也会自动按上限拆批。
     req_bs = BATCH_SIZE if batch_size is None else int(batch_size)
     bs = max(1, min(req_bs, BATCH_SIZE))
-    all_results: List[Dict[str, Any]] = []
-    total_batches = (len(products) + bs - 1) // bs
+    total_batches = (len(uncached_items) + bs - 1) // bs
-    for i in range(0, len(products), bs):
+    for i in range(0, len(uncached_items), bs):
         batch_num = i // bs + 1
-        batch = products[i : i + bs]
+        batch_slice = uncached_items[i : i + bs]
+        batch = [item for _, item in batch_slice]
         logger.info(
             f"[analyze_products] Processing batch {batch_num}/{total_batches}, "
             f"size={len(batch)}, target_lang={target_lang}"
         )
         batch_results = process_batch(batch, batch_num=batch_num, target_lang=target_lang)
-        all_results.extend(batch_results)
-        # 写入缓存
-        for item in batch_results:
+        for (original_idx, _), item in zip(batch_slice, batch_results):
+            results_by_index[original_idx] = item
             title_input = str(item.get("title_input") or "").strip()
             if not title_input:
                 continue
@@ -659,4 +664,4 @@ def analyze_products(
                 # 已在内部记录 warning
                 pass
-    return all_results
+    return [item for item in results_by_index if item is not None]
@@ -19,8 +19,7 @@ SHARED_ANALYSIS_INSTRUCTION = &quot;&quot;&quot;Analyze each input product title and fill these
 7. Key attributes
 8. Material description
 9. Functional features
-10. Selling point: one concise core selling phrase
-11. Anchor text: a set of search-oriented words or phrases covering category, attributes, scenes, and demand
+10. Anchor text: a search-focused set of keywords, selling points, and phrases covering categories, attributes, usage scenarios, and user intent
 Rules:
 - Keep the input order and row count exactly the same.
@@ -46,7 +45,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Key attributes",
     "Material",
     "Features",
-    "Selling point",
     "Anchor text"
   ],
   "zh": [
@@ -60,7 +58,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "关键属性",
     "材质说明",
     "功能特点",
-    "商品卖点",
     "锚文本"
   ],
   "zh_tw": [
@@ -74,7 +71,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "關鍵屬性",
     "材質說明",
     "功能特點",
-    "商品賣點",
     "錨文本"
   ],
   "ru": [
@@ -88,7 +84,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Ключевые атрибуты",
     "Материал",
     "Особенности",
-    "Преимущество товара",
     "Анкорный текст"
   ],
   "ja": [
@@ -102,7 +97,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "主要属性",
     "素材",
     "機能特徴",
-    "商品の訴求点",
     "アンカーテキスト"
   ],
   "ko": [
@@ -116,7 +110,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "핵심 속성",
     "소재",
     "기능 특징",
-    "상품 포인트",
     "앵커 텍스트"
   ],
   "es": [
@@ -130,7 +123,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Atributos clave",
     "Material",
     "Caracteristicas",
-    "Punto de venta",
     "Texto ancla"
   ],
   "fr": [
@@ -144,7 +136,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Attributs cles",
     "Matiere",
     "Caracteristiques",
-    "Argument de vente",
     "Texte d'ancrage"
   ],
   "pt": [
@@ -158,7 +149,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Atributos principais",
     "Material",
     "Caracteristicas",
-    "Ponto de venda",
     "Texto ancora"
   ],
   "de": [
@@ -172,7 +162,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Wichtige Attribute",
     "Material",
     "Funktionen",
-    "Verkaufsargument",
     "Ankertext"
   ],
   "it": [
@@ -186,7 +175,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Attributi chiave",
     "Materiale",
     "Caratteristiche",
-    "Punto di forza",
     "Testo ancora"
   ],
   "th": [
@@ -200,7 +188,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "คุณสมบัติสำคัญ",
     "วัสดุ",
     "คุณสมบัติการใช้งาน",
-    "จุดขายสินค้า",
     "แองเคอร์เท็กซ์"
   ],
   "vi": [
@@ -214,7 +201,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Thuoc tinh chinh",
     "Chat lieu",
     "Tinh nang",
-    "Diem ban hang",
     "Van ban neo"
   ],
   "id": [
@@ -228,7 +214,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Atribut utama",
     "Bahan",
     "Fitur",
-    "Nilai jual",
     "Teks jangkar"
   ],
   "ms": [
@@ -242,7 +227,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Atribut utama",
     "Bahan",
     "Ciri-ciri",
-    "Nilai jual",
     "Teks sauh"
   ],
   "ar": [
@@ -256,7 +240,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "السمات الرئيسية",
     "المادة",
     "الميزات",
-    "نقطة البيع",
     "نص الربط"
   ],
   "hi": [
@@ -270,7 +253,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "मुख्य गुण",
     "सामग्री",
     "विशेषताएं",
-    "बिक्री बिंदु",
     "एंकर टेक्स्ट"
   ],
   "he": [
@@ -284,7 +266,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "מאפיינים מרכזיים",
     "חומר",
     "תכונות",
-    "נקודת מכירה",
     "טקסט עוגן"
   ],
   "my": [
@@ -298,7 +279,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "အဓိကဂုဏ်သတ္တိများ",
     "ပစ္စည်း",
     "လုပ်ဆောင်ချက်များ",
-    "အရောင်းထူးခြားချက်",
     "အန်ကာစာသား"
   ],
   "ta": [
@@ -312,7 +292,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "முக்கிய பண்புகள்",
     "பொருள்",
     "அம்சங்கள்",
-    "விற்பனை அம்சம்",
     "ஆங்கர் உரை"
   ],
   "ur": [
@@ -326,7 +305,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "کلیدی خصوصیات",
     "مواد",
     "فیچرز",
-    "فروختی نقطہ",
     "اینکر ٹیکسٹ"
   ],
   "bn": [
@@ -340,7 +318,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "মূল বৈশিষ্ট্য",
     "উপাদান",
     "ফিচার",
-    "বিক্রয় পয়েন্ট",
     "অ্যাঙ্কর টেক্সট"
   ],
   "pl": [
@@ -354,7 +331,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Kluczowe atrybuty",
     "Material",
     "Cechy",
-    "Atut sprzedazowy",
     "Tekst kotwicy"
   ],
   "nl": [
@@ -368,7 +344,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Belangrijke kenmerken",
     "Materiaal",
     "Functies",
-    "Verkooppunt",
     "Ankertekst"
   ],
   "ro": [
@@ -382,7 +357,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Atribute cheie",
     "Material",
     "Caracteristici",
-    "Punct de vanzare",
     "Text ancora"
   ],
   "tr": [
@@ -396,7 +370,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Temel ozellikler",
     "Malzeme",
     "Ozellikler",
-    "Satis noktasi",
     "Capa metni"
   ],
   "km": [
@@ -410,7 +383,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "លក្ខណៈសម្បត្តិសំខាន់",
     "សម្ភារៈ",
     "មុខងារ",
-    "ចំណុចលក់",
     "អត្ថបទអង់ក័រ"
   ],
   "lo": [
@@ -424,7 +396,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "ຄຸນລັກສະນະສຳຄັນ",
     "ວັດສະດຸ",
     "ຄຸນສົມບັດ",
-    "ຈຸດຂາຍ",
     "ຂໍ້ຄວາມອັງເຄີ"
   ],
   "yue": [
@@ -438,7 +409,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "關鍵屬性",
     "材質說明",
     "功能特點",
-    "商品賣點",
     "錨文本"
   ],
   "cs": [
@@ -452,7 +422,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Klicove atributy",
     "Material",
     "Vlastnosti",
-    "Prodejni argument",
     "Kotvici text"
   ],
   "el": [
@@ -466,7 +435,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Βασικά χαρακτηριστικά",
     "Υλικό",
     "Λειτουργίες",
-    "Σημείο πώλησης",
     "Κείμενο άγκυρας"
   ],
   "sv": [
@@ -480,7 +448,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Viktiga attribut",
     "Material",
     "Funktioner",
-    "Saljpunkt",
     "Ankartext"
   ],
   "hu": [
@@ -494,7 +461,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Fo jellemzok",
     "Anyag",
     "Funkciok",
-    "Ertekesitesi elony",
     "Horgonyszoveg"
   ],
   "da": [
@@ -508,7 +474,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Nogleattributter",
     "Materiale",
     "Funktioner",
-    "Salgsargument",
     "Ankertekst"
   ],
   "fi": [
@@ -522,7 +487,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Keskeiset ominaisuudet",
     "Materiaali",
     "Ominaisuudet",
-    "Myyntivaltti",
     "Ankkuriteksti"
   ],
   "uk": [
@@ -536,7 +500,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Ключові атрибути",
     "Матеріал",
     "Особливості",
-    "Продаюча перевага",
     "Анкорний текст"
   ],
   "bg": [
@@ -550,7 +513,6 @@ LANGUAGE_MARKDOWN_TABLE_HEADERS: Dict[str, Dict[str, Any]] = {
     "Ключови атрибути",
     "Материал",
     "Характеристики",
-    "Търговско предимство",
     "Анкор текст"
   ]
 }
 \ No newline at end of file
@@ -366,7 +366,6 @@ def test_indexer_enrich_content_contract(indexer_client: TestClient, monkeypatch
                 "key_attributes": "",
                 "material": "",
                 "features": "",
-                "selling_points": "",
                 "anchor_text": f"{target_lang}-anchor-{p['id']}",
             }
             for p in products
@@ -29,7 +29,6 @@ def test_analyze_products_caps_batch_size_to_20(monkeypatch):
                 "key_attributes": "",
                 "material": "",
                 "features": "",
-                "selling_points": "",
                 "anchor_text": "",
             }
             for item in batch_data
@@ -69,7 +68,6 @@ def test_analyze_products_uses_min_batch_size_1(monkeypatch):
                 "key_attributes": "",
                 "material": "",
                 "features": "",
-                "selling_points": "",
                 "anchor_text": "",
             }
             for item in batch_data
@@ -195,9 +195,9 @@ def test_call_llm_logs_shared_context_once_and_verbose_contains_full_requests():
 def test_process_batch_reads_result_and_validates_expected_fields():
-    merged_markdown = """| 序号 | 商品标题 | 品类路径 | 细分标签 | 适用人群 | 使用场景 | 适用季节 | 关键属性 | 材质说明 | 功能特点 | 商品卖点 | 锚文本 |
-|----|----|----|----|----|----|----|----|----|----|----|----|
-| 1 | 法式连衣裙 | 女装>连衣裙 | 法式,收腰 | 年轻女性 | 通勤,约会 | 春季,夏季 | 中长款 | 聚酯纤维 | 透气 | 修身显瘦 | 法式收腰连衣裙 |
+    merged_markdown = """| 序号 | 商品标题 | 品类路径 | 细分标签 | 适用人群 | 使用场景 | 适用季节 | 关键属性 | 材质说明 | 功能特点 | 锚文本 |
+|----|----|----|----|----|----|----|----|----|----|----|
+| 1 | 法式连衣裙 | 女装>连衣裙 | 法式,收腰 | 年轻女性 | 通勤,约会 | 春季,夏季 | 中长款 | 聚酯纤维 | 透气 | 法式收腰连衣裙 |
 """
     with mock.patch.object(
@@ -225,5 +225,89 @@ def test_process_batch_reads_result_and_validates_expected_fields():
     assert row["key_attributes"] == "中长款"
     assert row["material"] == "聚酯纤维"
     assert row["features"] == "透气"
-    assert row["selling_points"] == "修身显瘦"
     assert row["anchor_text"] == "法式收腰连衣裙"
+
+
+def test_analyze_products_uses_product_level_cache_across_batch_requests():
+    cache_store = {}
+    process_calls = []
+
+    def fake_get_cached_anchor_result(title, target_lang, tenant_id=None):
+        return cache_store.get((tenant_id, target_lang, title))
+
+    def fake_set_cached_anchor_result(title, target_lang, result, tenant_id=None):
+        cache_store[(tenant_id, target_lang, title)] = result
+
+    def fake_process_batch(batch_data, batch_num, target_lang="zh"):
+        process_calls.append(
+            {
+                "batch_num": batch_num,
+                "target_lang": target_lang,
+                "titles": [item["title"] for item in batch_data],
+            }
+        )
+        return [
+            {
+                "id": item["id"],
+                "lang": target_lang,
+                "title_input": item["title"],
+                "title": f"normalized:{item['title']}",
+                "category_path": "cat",
+                "tags": "tags",
+                "target_audience": "audience",
+                "usage_scene": "scene",
+                "season": "season",
+                "key_attributes": "attrs",
+                "material": "material",
+                "features": "features",
+                "anchor_text": f"anchor:{item['title']}",
+            }
+            for item in batch_data
+        ]
+
+    products = [
+        {"id": "1", "title": "dress"},
+        {"id": "2", "title": "shirt"},
+    ]
+
+    with mock.patch.object(product_enrich, "API_KEY", "fake-key"), mock.patch.object(
+        product_enrich,
+        "_get_cached_anchor_result",
+        side_effect=fake_get_cached_anchor_result,
+    ), mock.patch.object(
+        product_enrich,
+        "_set_cached_anchor_result",
+        side_effect=fake_set_cached_anchor_result,
+    ), mock.patch.object(
+        product_enrich,
+        "process_batch",
+        side_effect=fake_process_batch,
+    ):
+        first = product_enrich.analyze_products(
+            [products[0]],
+            target_lang="zh",
+            tenant_id="170",
+        )
+        second = product_enrich.analyze_products(
+            products,
+            target_lang="zh",
+            tenant_id="170",
+        )
+        third = product_enrich.analyze_products(
+            products,
+            target_lang="zh",
+            tenant_id="170",
+        )
+
+    assert [row["title_input"] for row in first] == ["dress"]
+    assert [row["title_input"] for row in second] == ["dress", "shirt"]
+    assert [row["title_input"] for row in third] == ["dress", "shirt"]
+
+    assert process_calls == [
+        {"batch_num": 1, "target_lang": "zh", "titles": ["dress"]},
+        {"batch_num": 1, "target_lang": "zh", "titles": ["shirt"]},
+    ]
+    assert second[0]["anchor_text"] == "anchor:dress"
+    assert second[1]["anchor_text"] == "anchor:shirt"
+    assert third[0]["anchor_text"] == "anchor:dress"
+    assert third[1]["anchor_text"] == "anchor:shirt"