调参

tangwang
1 parent a7cc9078
Showing 5 changed files with 30 additions and 15 deletions Show diff stats
config/loader.py
config/schema.py
docs/TODO.txt
docs/常用查询 - ES.md
search/es_query_builder.py
@@ -281,8 +281,8 @@ class AppConfigLoader:
                     ["title", "brief", "vendor", "category_name_text"],
                 )
             ),
-            base_minimum_should_match=str(text_strategy.get("base_minimum_should_match", "75%")),
-            translation_minimum_should_match=str(text_strategy.get("translation_minimum_should_match", "75%")),
+            base_minimum_should_match=str(text_strategy.get("base_minimum_should_match", "70%")),
+            translation_minimum_should_match=str(text_strategy.get("translation_minimum_should_match", "70%")),
             translation_boost=float(text_strategy.get("translation_boost", 0.4)),
             translation_boost_when_source_missing=float(
                 text_strategy.get("translation_boost_when_source_missing", 1.0)
@@ -51,8 +51,8 @@ class QueryConfig:
     core_multilingual_fields: List[str] = field(
         default_factory=lambda: ["title", "brief", "vendor", "category_name_text"]
     )
-    base_minimum_should_match: str = "75%"
-    translation_minimum_should_match: str = "75%"
+    base_minimum_should_match: str = "70%"
+    translation_minimum_should_match: str = "70%"
     translation_boost: float = 0.4
     translation_boost_when_source_missing: float = 1.0
     source_boost_when_missing: float = 0.6
@@ -236,10 +236,13 @@ config/environments/&lt;env&gt;.yaml
  
  
  
+筛选SKU： 先只筛选第一个维度，但考虑到用户搜索词可能带了尺码，所以第二、三个维度也要考虑
  
  
-
-
+引入图片的相关性：
+图片的向量最好做SKU维度，用 SPU 维度还是 SKU 维度？
+1. SKU维度（主款式，option1维度），如果用户搜索“蓝色 T恤”，这种图片相关性会比较有价值。
+2. 我不考虑颜色的差异，其余的款式一般是大小之类的。这些图片，项链细粉到 SKU 维度，可能价值不大，性价比偏低
  
  
  
@@ -654,3 +654,18 @@ GET /search_products_tenant_170/_search
         }
     }
 }
+
+
+检查某个字段是否存在
+curl -u 'saas:4hOaLaf41y2VuI8y' -X POST \
+  'http://localhost:9200/search_products_tenant_163/_count' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "query": {
+      "bool": {
+        "filter": [
+          { "exists": { "field": "title_embedding" } }
+        ]
+      }
+    }
+  }'
 \ No newline at end of file
@@ -33,8 +33,8 @@ class ESQueryBuilder:
         function_score_config: Optional[FunctionScoreConfig] = None,
         default_language: str = "en",
         knn_boost: float = 0.25,
-        base_minimum_should_match: str = "75%",
-        translation_minimum_should_match: str = "75%",
+        base_minimum_should_match: str = "70%",
+        translation_minimum_should_match: str = "70%",
         translation_boost: float = 0.4,
         translation_boost_when_source_missing: float = 1.0,
         source_boost_when_missing: float = 0.6,
@@ -261,16 +261,13 @@ class ESQueryBuilder:
             if parsed_query:
                 query_tokens = getattr(parsed_query, 'query_tokens', None) or []
                 token_count = len(query_tokens)
-                if token_count <= 2:
-                    knn_k, knn_num_candidates = 30, 100
-                    knn_boost = self.knn_boost * 0.6  # Lower weight for short queries
-                elif token_count >= 5:
-                    knn_k, knn_num_candidates = 80, 300
+                if token_count >= 5:
+                    knn_k, knn_num_candidates = 160, 500
                     knn_boost = self.knn_boost * 1.4  # Higher weight for long queries
                 else:
-                    knn_k, knn_num_candidates = 50, 200
+                    knn_k, knn_num_candidates = 120, 400
             else:
-                knn_k, knn_num_candidates = 50, 200
+                knn_k, knn_num_candidates = 120, 400
             knn_clause = {
                 "field": self.text_embedding_field,
                 "query_vector": query_vector.tolist(),