修改_extract_combined_knn_score相关的代码以及配置，

重排融合：之前有knn的配置bias和exponential。现在，文本和图片的embedding相似需要融合，融合方式是dis_max，因此需要配置： 1）各自的权重和tie_breaker 2）整个向量方面的权重（bias和exponential）

修改_extract_combined_knn_score相关的代码以及配置，
重排融合：之前有knn的配置bias和exponential。现在，文本和图片的embedding相似需要融合，融合方式是dis_max，因此需要配置： 1）各自的权重和tie_breaker 2）整个向量方面的权重（bias和exponential）
tangwang
1 parent dc403578
Showing 6 changed files with 87 additions and 8 deletions Show diff stats
config/config.yaml
config/loader.py
config/schema.py
search/rerank_client.py
search/searcher.py
tests/test_rerank_client.py
@@ -169,7 +169,7 @@ query_config:
  
   # Embedding字段名称
   text_embedding_field: "title_embedding"
-  image_embedding_field: null
+  image_embedding_field: "image_embedding.vector"
  
   # 返回字段配置（_source includes）
   # null表示返回所有字段，[]表示不返回任何字段，列表表示只返回指定字段
@@ -225,13 +225,19 @@ rerank:
   rerank_query_template: "{query}"
   rerank_doc_template: "{title}"
   # 乘法融合：fused = Π (max(score,0) + bias) ** exponent（rerank / text / knn 三项）
+  # 其中 knn_score 先做一层 dis_max：
+  #   max(knn_text_weight * text_knn, knn_image_weight * image_knn)
+  #   + knn_tie_breaker * 另一侧较弱信号
   fusion:
     rerank_bias: 0.00001
     rerank_exponent: 1.0
     text_bias: 0.1
     text_exponent: 0.35
+    knn_text_weight: 1.0
+    knn_image_weight: 1.0
+    knn_tie_breaker: 0.1
     knn_bias: 0.6
-    knn_exponent: 0.0
+    knn_exponent: 0.2
  
 # 可扩展服务/provider 注册表（单一配置源）
 services:
@@ -477,6 +477,9 @@ class AppConfigLoader:
                     rerank_exponent=float(fusion_raw.get("rerank_exponent", 1.0)),
                     text_bias=float(fusion_raw.get("text_bias", 0.1)),
                     text_exponent=float(fusion_raw.get("text_exponent", 0.35)),
+                    knn_text_weight=float(fusion_raw.get("knn_text_weight", 1.0)),
+                    knn_image_weight=float(fusion_raw.get("knn_image_weight", 1.0)),
+                    knn_tie_breaker=float(fusion_raw.get("knn_tie_breaker", 0.0)),
                     knn_bias=float(fusion_raw.get("knn_bias", 0.6)),
                     knn_exponent=float(fusion_raw.get("knn_exponent", 0.2)),
                 ),
@@ -104,6 +104,9 @@ class RerankFusionConfig:
     rerank_exponent: float = 1.0
     text_bias: float = 0.1
     text_exponent: float = 0.35
+    knn_text_weight: float = 1.0
+    knn_image_weight: float = 1.0
+    knn_tie_breaker: float = 0.0
     knn_bias: float = 0.6
     knn_exponent: float = 0.2
  
@@ -152,11 +152,30 @@ def _extract_named_query_score(matched_queries: Any, name: str) -&gt; float:
     return 0.0
  
  
-def _extract_combined_knn_score(matched_queries: Any) -> float:
-    return max(
-        _extract_named_query_score(matched_queries, "knn_query"),
-        _extract_named_query_score(matched_queries, "image_knn_query"),
-    )
+def _collect_knn_score_components(
+    matched_queries: Any,
+    fusion: RerankFusionConfig,
+) -> Dict[str, float]:
+    text_knn_score = _extract_named_query_score(matched_queries, "knn_query")
+    image_knn_score = _extract_named_query_score(matched_queries, "image_knn_query")
+
+    weighted_text_knn_score = text_knn_score * float(fusion.knn_text_weight)
+    weighted_image_knn_score = image_knn_score * float(fusion.knn_image_weight)
+    weighted_components = [weighted_text_knn_score, weighted_image_knn_score]
+
+    primary_knn_score = max(weighted_components)
+    support_knn_score = sum(weighted_components) - primary_knn_score
+    knn_score = primary_knn_score + float(fusion.knn_tie_breaker) * support_knn_score
+
+    return {
+        "text_knn_score": text_knn_score,
+        "image_knn_score": image_knn_score,
+        "weighted_text_knn_score": weighted_text_knn_score,
+        "weighted_image_knn_score": weighted_image_knn_score,
+        "primary_knn_score": primary_knn_score,
+        "support_knn_score": support_knn_score,
+        "knn_score": knn_score,
+    }
  
 """
 原始变量：
@@ -279,7 +298,8 @@ def fuse_scores_and_resort(
         es_score = _to_score(hit.get("_score"))
         rerank_score = _to_score(rerank_scores[idx])
         matched_queries = hit.get("matched_queries")
-        knn_score = _extract_combined_knn_score(matched_queries)
+        knn_components = _collect_knn_score_components(matched_queries, f)
+        knn_score = knn_components["knn_score"]
         text_components = _collect_text_score_components(matched_queries, es_score)
         text_score = text_components["text_score"]
         rerank_factor, text_factor, knn_factor, fused = _multiply_fusion_factors(
@@ -293,6 +313,8 @@ def fuse_scores_and_resort(
         hit["_rerank_score"] = rerank_score
         hit["_text_score"] = text_score
         hit["_knn_score"] = knn_score
+        hit["_text_knn_score"] = knn_components["text_knn_score"]
+        hit["_image_knn_score"] = knn_components["image_knn_score"]
         hit["_fused_score"] = fused
         hit["_style_intent_selected_sku_boost"] = style_boost
         if debug:
@@ -300,6 +322,8 @@ def fuse_scores_and_resort(
             hit["_text_translation_score"] = text_components["translation_score"]
             hit["_text_primary_score"] = text_components["primary_text_score"]
             hit["_text_support_score"] = text_components["support_text_score"]
+            hit["_knn_primary_score"] = knn_components["primary_knn_score"]
+            hit["_knn_support_score"] = knn_components["support_knn_score"]
  
         if debug:
             debug_entry = {
@@ -318,6 +342,12 @@ def fuse_scores_and_resort(
                     and text_components["source_score"] <= 0.0
                     and text_components["translation_score"] <= 0.0
                 ),
+                "text_knn_score": knn_components["text_knn_score"],
+                "image_knn_score": knn_components["image_knn_score"],
+                "weighted_text_knn_score": knn_components["weighted_text_knn_score"],
+                "weighted_image_knn_score": knn_components["weighted_image_knn_score"],
+                "knn_primary_score": knn_components["primary_knn_score"],
+                "knn_support_score": knn_components["support_knn_score"],
                 "knn_score": knn_score,
                 "rerank_factor": rerank_factor,
                 "text_factor": text_factor,
@@ -882,6 +882,7 @@ class Searcher:
                     "index_languages": index_langs,
                     "translations": context.query_analysis.translations,
                     "has_vector": context.query_analysis.query_vector is not None,
+                    "has_image_vector": getattr(parsed_query, "image_query_vector", None) is not None,
                     "query_tokens": getattr(parsed_query, "query_tokens", []),
                     "intent_detection": context.get_intermediate_result("style_intent_profile"),
                 },
@@ -168,3 +168,39 @@ def test_fuse_scores_and_resort_uses_max_of_text_and_image_knn_scores():
  
     assert isclose(hits[0]["_knn_score"], 0.7, rel_tol=1e-9)
     assert isclose(debug[0]["knn_score"], 0.7, rel_tol=1e-9)
+    assert isclose(debug[0]["text_knn_score"], 0.2, rel_tol=1e-9)
+    assert isclose(debug[0]["image_knn_score"], 0.7, rel_tol=1e-9)
+
+
+def test_fuse_scores_and_resort_applies_knn_dismax_weights_and_tie_breaker():
+    hits = [
+        {
+            "_id": "mm-hit",
+            "_score": 1.0,
+            "matched_queries": {
+                "base_query": 1.5,
+                "knn_query": 0.4,
+                "image_knn_query": 0.5,
+            },
+        }
+    ]
+    fusion = RerankFusionConfig(
+        rerank_bias=0.00001,
+        rerank_exponent=1.0,
+        text_bias=0.1,
+        text_exponent=0.35,
+        knn_text_weight=2.0,
+        knn_image_weight=1.0,
+        knn_tie_breaker=0.25,
+        knn_bias=0.0,
+        knn_exponent=1.0,
+    )
+
+    debug = fuse_scores_and_resort(hits, [0.8], fusion=fusion, debug=True)
+
+    expected_knn = 0.8 + 0.25 * 0.5
+    assert isclose(hits[0]["_knn_score"], expected_knn, rel_tol=1e-9)
+    assert isclose(debug[0]["weighted_text_knn_score"], 0.8, rel_tol=1e-9)
+    assert isclose(debug[0]["weighted_image_knn_score"], 0.5, rel_tol=1e-9)
+    assert isclose(debug[0]["knn_primary_score"], 0.8, rel_tol=1e-9)
+    assert isclose(debug[0]["knn_support_score"], 0.5, rel_tol=1e-9)