召回限定：must条件补充主干词命中逻辑。baseline的主干词提取方法。

TODO-keywords限定-done.txt

召回限定：must条件补充主干词命中逻辑。baseline的主干词提取方法。
TODO-keywords限定-done.txt
tangwang
1 parent ed13851c
Showing 8 changed files with 394 additions and 24 deletions Show diff stats
config/config.yaml
docs/TODO-keywords限定-done.txt
query/__init__.py
query/keyword_extractor.py
query/query_parser.py
search/es_query_builder.py
tests/test_es_query_builder_text_recall_languages.py
tests/test_keywords_query.py
@@ -100,16 +100,19 @@ query_config:
   # 查询翻译模型（须与 services.translation.capabilities 中某项一致）
   # 源语种在租户 index_languages 内：主召回可打在源语种字段，用下面三项。
-  # zh_to_en_model: "opus-mt-zh-en"
-  # en_to_zh_model: "opus-mt-en-zh"
-  # default_translation_model: "nllb-200-distilled-600m"
-  zh_to_en_model: "deepl"
-  en_to_zh_model: "deepl"
-  default_translation_model: "deepl"
+  zh_to_en_model: "nllb-200-distilled-600m" #  "opus-mt-zh-en"
+  en_to_zh_model: "nllb-200-distilled-600m" #  "opus-mt-en-zh"
+  default_translation_model: "nllb-200-distilled-600m"
+  # zh_to_en_model: "deepl"
+  # en_to_zh_model: "deepl"
+  # default_translation_model: "deepl"
   # 源语种不在 index_languages：翻译对可检索文本更关键，可单独指定（缺省则与上一组相同）
-  zh_to_en_model__source_not_in_index: "deepl"
-  en_to_zh_model__source_not_in_index: "deepl"
-  default_translation_model__source_not_in_index: "deepl"
+  zh_to_en_model__source_not_in_index: "nllb-200-distilled-600m"
+  en_to_zh_model__source_not_in_index: "nllb-200-distilled-600m"
+  default_translation_model__source_not_in_index: "nllb-200-distilled-600m"
+  # zh_to_en_model__source_not_in_index: "deepl"
+  # en_to_zh_model__source_not_in_index: "deepl"
+  # default_translation_model__source_not_in_index: "deepl"
   # 查询解析阶段：翻译与 query 向量并发执行，共用同一等待预算（毫秒）。
   # 检测语言已在租户 index_languages 内：较短；不在索引语言内：较长（翻译对召回更关键）。
@@ -153,8 +156,8 @@ query_config:
   # 统一文本召回策略（主查询 + 翻译查询）
   text_query_strategy:
-    base_minimum_should_match: "75%"
-    translation_minimum_should_match: "75%"
+    base_minimum_should_match: "60%"
+    translation_minimum_should_match: "60%"
     translation_boost: 0.75
     tie_breaker_base_query: 0.5
     best_fields_boost: 2.0
@@ -207,8 +210,8 @@ query_config:
     - skus
   # KNN：文本向量与多模态（图片）向量各自 boost 与召回（k / num_candidates）
-  knn_text_boost: 20
-  knn_image_boost: 20
+  knn_text_boost: 4
+  knn_image_boost: 4
   knn_text_k: 150
   knn_text_num_candidates: 400
@@ -247,7 +250,7 @@ rerank:
     knn_image_weight: 1.0
     knn_tie_breaker: 0.1
     knn_bias: 0.6
-    knn_exponent: 0.2
+    knn_exponent: 0.0
 # 可扩展服务/provider 注册表（单一配置源）
 services:
@@ -0,0 +1,93 @@
+@query/query_parser.py @scripts/es_debug_search.py 
+原始query、以及每一个翻译，都要有一个对应的keywords_query（token分词后，得到名词）
+参考这段代码，获取每一个长度大于 1 的名词，然后用空格拼接起来，作为keywords_query
+import hanlp
+from typing import List, Tuple, Dict, Any
+
+class KeywordExtractor:
+    """
+    基于 HanLP 的名词关键词提取器
+    """
+    def __init__(self):
+        # 加载带位置信息的分词模型（细粒度）
+        self.tok = hanlp.load(hanlp.pretrained.tok.CTB9_TOK_ELECTRA_BASE_CRF)
+        self.tok.config.output_spans = True   # 启用位置输出
+        
+        # 加载词性标注模型
+        self.pos_tag = hanlp.load(hanlp.pretrained.pos.CTB9_POS_ELECTRA_SMALL)
+        
+    def extract_keywords(self, query: str) -> str:
+        """
+        从查询中提取关键词（名词，长度 ≥ 2）
+        
+        Args:
+            query: 输入文本
+            
+        Returns:
+            拼接后的关键词字符串，非连续词之间自动插入空格
+        """
+        query = query.strip()
+        # 分词结果带位置：[[word, start, end], ...]
+        tok_result_with_position = self.tok(query)
+        tok_result = [x[0] for x in tok_result_with_position]
+        
+        # 词性标注
+        pos_tag_result = list(zip(tok_result, self.pos_tag(tok_result)))
+        
+        # 需要忽略的词
+        ignore_keywords = ['玩具']
+        
+        keywords = []
+        last_end_pos = 0
+        
+        for (word, postag), (_, start_pos, end_pos) in zip(pos_tag_result, tok_result_with_position):
+            if len(word) >= 2 and postag.startswith('N'):
+                if word in ignore_keywords:
+                    continue
+                # 如果当前词与上一个词在原文中不连续，插入空格
+                if start_pos != last_end_pos and keywords:
+                    keywords.append(" ")
+                keywords.append(word)
+                last_end_pos = end_pos
+            # 可选：打印调试信息
+            # print(f'分词: {word} | 词性: {postag} | 起始: {start_pos} | 结束: {end_pos}')
+        
+        return "".join(keywords).strip()
+
+
+最后，在组织检索表达式时，目前是每一个 query （base_query base_query_trans_en base_query_trans_zh 三种情况）。 会组成一个bool查询，以base_query为例：
+    "bool": {
+      "should": [
+        {
+          "bool": {
+            "_name": "base_query",
+            "must": [
+              {
+                "combined_fields": {
+...
+                }
+              }
+            ],
+            "should": [
+              {
+                "multi_match": {
+...                  "type": "best_fields",
+...
+              },
+              {
+                "multi_match": {
+...
+                  "type": "phrase",
+...
+                }
+              }
+            ]
+          }
+        },
+
+base_query_trans_en base_query_trans_zh 也是同样
+
+在这个布尔查询的must里面加一项：keywords，搜索的字段和combined_fields一样，命中比例要求50%
+
+
+结合现有代码做出合理的设计，呈现简单清晰的数据接口，而不是打补丁
 \ No newline at end of file
@@ -2,6 +2,7 @@
 from .language_detector import LanguageDetector
 from .query_rewriter import QueryRewriter, QueryNormalizer
+from .keyword_extractor import KEYWORDS_QUERY_BASE_KEY
 from .query_parser import QueryParser, ParsedQuery
 __all__ = [
@@ -10,4 +11,5 @@ __all__ = [
     'QueryNormalizer',
     'QueryParser',
     'ParsedQuery',
+    'KEYWORDS_QUERY_BASE_KEY',
 ]
@@ -0,0 +1,86 @@
+"""
+HanLP-based noun keyword string for lexical constraints (token POS starts with N, length >= 2).
+
+``ParsedQuery.keywords_queries`` uses the same key layout as text variants:
+``KEYWORDS_QUERY_BASE_KEY`` for the rewritten source query, and ISO-like language
+codes for each ``ParsedQuery.translations`` entry (non-empty extractions only).
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+import hanlp  # type: ignore
+
+# Aligns with ``rewritten_query`` / ES ``base_query`` (not a language code).
+KEYWORDS_QUERY_BASE_KEY = "base"
+
+
+class KeywordExtractor:
+    """基于 HanLP 的名词关键词提取器（与分词位置对齐，非连续名词间插入空格）。"""
+
+    def __init__(
+        self,
+        tokenizer: Optional[Any] = None,
+        *,
+        ignore_keywords: Optional[List[str]] = None,
+    ):
+        if tokenizer is not None:
+            self.tok = tokenizer
+        else:
+            self.tok = hanlp.load(hanlp.pretrained.tok.CTB9_TOK_ELECTRA_BASE_CRF)
+            self.tok.config.output_spans = True
+        self.pos_tag = hanlp.load(hanlp.pretrained.pos.CTB9_POS_ELECTRA_SMALL)
+        self.ignore_keywords = frozenset(ignore_keywords or ["玩具"])
+
+    def extract_keywords(self, query: str) -> str:
+        """
+        从查询中提取关键词（名词，长度 ≥ 2），以空格分隔非连续片段。
+        """
+        query = (query or "").strip()
+        if not query:
+            return ""
+        tok_result_with_position = self.tok(query)
+        tok_result = [x[0] for x in tok_result_with_position]
+        if not tok_result:
+            return ""
+        pos_tags = self.pos_tag(tok_result)
+        pos_tag_result = list(zip(tok_result, pos_tags))
+        keywords: List[str] = []
+        last_end_pos = 0
+        for (word, postag), (_, start_pos, end_pos) in zip(pos_tag_result, tok_result_with_position):
+            if len(word) >= 2 and str(postag).startswith("N"):
+                if word in self.ignore_keywords:
+                    continue
+                if start_pos != last_end_pos and keywords:
+                    keywords.append(" ")
+                keywords.append(word)
+                last_end_pos = end_pos
+        return "".join(keywords).strip()
+
+
+def collect_keywords_queries(
+    extractor: KeywordExtractor,
+    rewritten_query: str,
+    translations: Dict[str, str],
+) -> Dict[str, str]:
+    """
+    Build the keyword map for all lexical variants (base + translations).
+
+    Omits entries when extraction yields an empty string.
+    """
+    out: Dict[str, str] = {}
+    base_kw = extractor.extract_keywords(rewritten_query)
+    if base_kw:
+        out[KEYWORDS_QUERY_BASE_KEY] = base_kw
+    for lang, text in translations.items():
+        lang_key = str(lang or "").strip().lower()
+        if not lang_key or not (text or "").strip():
+            continue
+        kw = extractor.extract_keywords(text)
+        if kw:
+            out[lang_key] = kw
+    return out
@@ -27,6 +27,7 @@ from .product_title_exclusion import (
 from .query_rewriter import QueryRewriter, QueryNormalizer
 from .style_intent import StyleIntentDetector, StyleIntentProfile, StyleIntentRegistry
 from .tokenization import extract_token_strings, simple_tokenize_query
+from .keyword_extractor import KeywordExtractor, collect_keywords_queries
 logger = logging.getLogger(__name__)
@@ -59,7 +60,14 @@ def rerank_query_text(
 @dataclass(slots=True)
 class ParsedQuery:
-    """Container for query parser facts."""
+    """
+    Container for query parser facts.
+
+    ``keywords_queries`` parallels text variants: key ``base`` (see
+    ``keyword_extractor.KEYWORDS_QUERY_BASE_KEY``) for ``rewritten_query``,
+    and the same language codes as ``translations`` for each translated string.
+    Entries with no extracted nouns are omitted.
+    """
     original_query: str
     query_normalized: str
@@ -69,6 +77,7 @@ class ParsedQuery:
     query_vector: Optional[np.ndarray] = None
     image_query_vector: Optional[np.ndarray] = None
     query_tokens: List[str] = field(default_factory=list)
+    keywords_queries: Dict[str, str] = field(default_factory=dict)
     style_intent_profile: Optional[StyleIntentProfile] = None
     product_title_exclusion_profile: Optional[ProductTitleExclusionProfile] = None
@@ -91,6 +100,7 @@ class ParsedQuery:
             "has_query_vector": self.query_vector is not None,
             "has_image_query_vector": self.image_query_vector is not None,
             "query_tokens": self.query_tokens,
+            "keywords_queries": dict(self.keywords_queries),
             "style_intent_profile": (
                 self.style_intent_profile.to_dict() if self.style_intent_profile is not None else None
             ),
@@ -138,6 +148,7 @@ class QueryParser:
         self.language_detector = LanguageDetector()
         self.rewriter = QueryRewriter(config.query_config.rewrite_dictionary)
         self._tokenizer = tokenizer or self._build_tokenizer()
+        self._keyword_extractor = KeywordExtractor(tokenizer=self._tokenizer)
         self.style_intent_registry = StyleIntentRegistry.from_query_config(config.query_config)
         self.style_intent_detector = StyleIntentDetector(
             self.style_intent_registry,
@@ -523,6 +534,16 @@ class QueryParser:
             if translations and context:
                 context.store_intermediate_result("translations", translations)
+        keywords_queries: Dict[str, str] = {}
+        try:
+            keywords_queries = collect_keywords_queries(
+                self._keyword_extractor,
+                query_text,
+                translations,
+            )
+        except Exception as e:
+            log_info(f"Keyword extraction failed | Error: {e}")
+
         # Build result
         base_result = ParsedQuery(
             original_query=query,
@@ -533,6 +554,7 @@ class QueryParser:
             query_vector=query_vector,
             image_query_vector=image_query_vector,
             query_tokens=query_tokens,
+            keywords_queries=keywords_queries,
         )
         style_intent_profile = self.style_intent_detector.detect(base_result)
         product_title_exclusion_profile = self.product_title_exclusion_detector.detect(base_result)
@@ -555,6 +577,7 @@ class QueryParser:
             query_vector=query_vector,
             image_query_vector=image_query_vector,
             query_tokens=query_tokens,
+            keywords_queries=keywords_queries,
             style_intent_profile=style_intent_profile,
             product_title_exclusion_profile=product_title_exclusion_profile,
         )
@@ -12,6 +12,7 @@ from typing import Dict, Any, List, Optional, Tuple
 import numpy as np
 from config import FunctionScoreConfig
+from query.keyword_extractor import KEYWORDS_QUERY_BASE_KEY
 class ESQueryBuilder:
@@ -39,6 +40,7 @@ class ESQueryBuilder:
         knn_image_num_candidates: int = 400,
         base_minimum_should_match: str = "70%",
         translation_minimum_should_match: str = "70%",
+        keywords_minimum_should_match: str = "50%",
         translation_boost: float = 0.4,
         tie_breaker_base_query: float = 0.9,
         best_fields_boosts: Optional[Dict[str, float]] = None,
@@ -85,6 +87,7 @@ class ESQueryBuilder:
         self.knn_image_num_candidates = int(knn_image_num_candidates)
         self.base_minimum_should_match = base_minimum_should_match
         self.translation_minimum_should_match = translation_minimum_should_match
+        self.keywords_minimum_should_match = str(keywords_minimum_should_match)
         self.translation_boost = float(translation_boost)
         self.tie_breaker_base_query = float(tie_breaker_base_query)
         default_best_fields = {
@@ -505,6 +508,7 @@ class ESQueryBuilder:
         clause_name: str,
         *,
         is_source: bool,
+        keywords_query: Optional[str] = None,
     ) -> Optional[Dict[str, Any]]:
         combined_fields = self._match_field_strings(lang)
         if not combined_fields:
@@ -512,6 +516,26 @@ class ESQueryBuilder:
         minimum_should_match = (
             self.base_minimum_should_match if is_source else self.translation_minimum_should_match
         )
+        must_clauses: List[Dict[str, Any]] = [
+            {
+                "combined_fields": {
+                    "query": lang_query,
+                    "fields": combined_fields,
+                    "minimum_should_match": minimum_should_match,
+                }
+            }
+        ]
+        kw = (keywords_query or "").strip()
+        if kw:
+            must_clauses.append(
+                {
+                    "combined_fields": {
+                        "query": kw,
+                        "fields": combined_fields,
+                        "minimum_should_match": self.keywords_minimum_should_match,
+                    }
+                }
+            )
         should_clauses = [
             clause
             for clause in (
@@ -523,15 +547,7 @@ class ESQueryBuilder:
         clause: Dict[str, Any] = {
             "bool": {
                 "_name": clause_name,
-                "must": [
-                    {
-                        "combined_fields": {
-                            "query": lang_query,
-                            "fields": combined_fields,
-                            "minimum_should_match": minimum_should_match,
-                        }
-                    }
-                ],
+                "must": must_clauses,
             }
         }
         if should_clauses:
@@ -572,6 +588,11 @@ class ESQueryBuilder:
         base_query_text = (
             getattr(parsed_query, "rewritten_query", None) if parsed_query else None
         ) or query_text
+        kw_by_variant: Dict[str, str] = (
+            getattr(parsed_query, "keywords_queries", None) or {}
+            if parsed_query
+            else {}
+        )
         if base_query_text:
             base_clause = self._build_lexical_language_clause(
@@ -579,6 +600,7 @@ class ESQueryBuilder:
                 base_query_text,
                 "base_query",
                 is_source=True,
+                keywords_query=(kw_by_variant.get(KEYWORDS_QUERY_BASE_KEY) or "").strip(),
             )
             if base_clause:
                 should_clauses.append(base_clause)
@@ -590,11 +612,13 @@ class ESQueryBuilder:
                 continue
             if normalized_lang == source_lang and normalized_text == base_query_text:
                 continue
+            trans_kw = (kw_by_variant.get(normalized_lang) or "").strip()
             trans_clause = self._build_lexical_language_clause(
                 normalized_lang,
                 normalized_text,
                 f"base_query_trans_{normalized_lang}",
                 is_source=False,
+                keywords_query=trans_kw,
             )
             if trans_clause:
                 should_clauses.append(trans_clause)
@@ -11,6 +11,7 @@ from typing import Any, Dict, List
 import numpy as np
+from query.keyword_extractor import KEYWORDS_QUERY_BASE_KEY
 from search.es_query_builder import ESQueryBuilder
@@ -129,6 +130,29 @@ def test_zh_query_index_zh_en_includes_base_zh_and_trans_en():
     assert "title.en" in _title_fields(idx["base_query_trans_en"])
+def test_keywords_combined_fields_second_must_same_fields_and_50pct():
+    """When ParsedQuery.keywords_queries is set, must includes a second combined_fields."""
+    qb = _builder_multilingual_title_only(default_language="en")
+    parsed = SimpleNamespace(
+        rewritten_query="连衣裙",
+        detected_language="zh",
+        translations={"en": "red dress"},
+        keywords_queries={KEYWORDS_QUERY_BASE_KEY: "连衣 裙", "en": "dress"},
+    )
+    q = qb.build_query(query_text="连衣裙", parsed_query=parsed, enable_knn=False)
+    idx = _clauses_index(q)
+    base = idx["base_query"]
+    assert len(base["must"]) == 2
+    assert base["must"][0]["combined_fields"]["query"] == "连衣裙"
+    assert base["must"][1]["combined_fields"]["query"] == "连衣 裙"
+    assert base["must"][1]["combined_fields"]["minimum_should_match"] == "50%"
+    assert base["must"][1]["combined_fields"]["fields"] == base["must"][0]["combined_fields"]["fields"]
+    trans = idx["base_query_trans_en"]
+    assert len(trans["must"]) == 2
+    assert trans["must"][1]["combined_fields"]["query"] == "dress"
+    assert trans["must"][1]["combined_fields"]["minimum_should_match"] == "50%"
+
+
 def test_en_query_index_zh_en_includes_base_en_and_trans_zh():
     qb = _builder_multilingual_title_only(default_language="en")
     q = _build(
@@ -0,0 +1,115 @@
+import hanlp
+from typing import List, Tuple, Dict, Any
+
+class KeywordExtractor:
+    """
+    基于 HanLP 的名词关键词提取器
+    """
+    def __init__(self):
+        # 加载带位置信息的分词模型（细粒度）
+        self.tok = hanlp.load(hanlp.pretrained.tok.CTB9_TOK_ELECTRA_BASE_CRF)
+        self.tok.config.output_spans = True   # 启用位置输出
+        
+        # 加载词性标注模型
+        self.pos_tag = hanlp.load(hanlp.pretrained.pos.CTB9_POS_ELECTRA_SMALL)
+        
+    def extract_keywords(self, query: str) -> str:
+        """
+        从查询中提取关键词（名词，长度 ≥ 2）
+        
+        Args:
+            query: 输入文本
+            
+        Returns:
+            拼接后的关键词字符串，非连续词之间自动插入空格
+        """
+        query = query.strip()
+        # 分词结果带位置：[[word, start, end], ...]
+        tok_result_with_position = self.tok(query)
+        tok_result = [x[0] for x in tok_result_with_position]
+        
+        # 词性标注
+        pos_tag_result = list(zip(tok_result, self.pos_tag(tok_result)))
+        
+        # 需要忽略的词
+        ignore_keywords = ['玩具']
+        
+        keywords = []
+        last_end_pos = 0
+        
+        for (word, postag), (_, start_pos, end_pos) in zip(pos_tag_result, tok_result_with_position):
+            if len(word) >= 2 and postag.startswith('N'):
+                if word in ignore_keywords:
+                    continue
+                # 如果当前词与上一个词在原文中不连续，插入空格
+                if start_pos != last_end_pos and keywords:
+                    keywords.append(" ")
+                keywords.append(word)
+                last_end_pos = end_pos
+            # 可选：打印调试信息
+            # print(f'分词: {word} | 词性: {postag} | 起始: {start_pos} | 结束: {end_pos}')
+        
+        return "".join(keywords).strip()
+
+
+# 测试代码
+if __name__ == "__main__":
+    extractor = KeywordExtractor()
+    
+    test_queries = [
+        # 中文（保留 9 个代表性查询）
+        "2.4G遥控大蛇",
+        "充气的篮球",
+        "遥控 塑料 飞船 汽车 ",
+        "亚克力相框",
+        "8寸 搪胶蘑菇钉",
+        "7寸娃娃",
+        "太空沙套装",
+        "脚蹬工程车",
+        "捏捏乐钥匙扣",
+        
+        # 英文（新增）
+        "plastic toy car",
+        "remote control helicopter",
+        "inflatable beach ball",
+        "music keychain",
+        "sand play set",
+        # 常见商品搜索
+        "plastic dinosaur toy",
+        "wireless bluetooth speaker",
+        "4K action camera",
+        "stainless steel water bottle",
+        "baby stroller with cup holder",
+        
+        # 疑问式 / 自然语言
+        "what is the best smartphone under 500 dollars",
+        "how to clean a laptop screen",
+        "where can I buy organic coffee beans",
+        
+        # 含数字、特殊字符
+        "USB-C to HDMI adapter 4K",
+        "LED strip lights 16.4ft",
+        "Nintendo Switch OLED model",
+        "iPhone 15 Pro Max case",
+        
+        # 简短词组
+        "gaming mouse",
+        "mechanical keyboard",
+        "wireless earbuds",
+        
+        # 长尾词
+        "rechargeable AA batteries with charger",
+        "foldable picnic blanket waterproof",
+        
+        # 商品属性组合
+        "women's running shoes size 8",
+        "men's cotton t-shirt crew neck",
+
+            
+        # 其他语种（保留原样，用于多语言测试）
+        "свет USB с пультом дистанционного управления красочные",  # 俄语
+    ]
+    
+    for q in test_queries:
+        keywords = extractor.extract_keywords(q)
+        print(f"{q:30} => {keywords}")