乘法公式配置化

tangwang
1 parent 581dafae
Showing 9 changed files with 168 additions and 227 deletions Show diff stats
config/__init__.py
config/config.yaml
config/loader.py
config/schema.py
frontend/static/js/app.js
search/rerank_client.py
search/searcher.py
tests/test_rerank_client.py
tests/test_search_rerank_window.py
@@ -8,6 +8,7 @@ from config.schema import (
     IndexConfig,
     QueryConfig,
     RerankConfig,
+    RerankFusionConfig,
     SPUConfig,
     SearchConfig,
     ServicesConfig,
@@ -36,6 +37,7 @@ __all__ = [
     "IndexConfig",
     "QueryConfig",
     "RerankConfig",
+    "RerankFusionConfig",
     "SPUConfig",
     "SearchConfig",
     "ServicesConfig",
@@ -219,6 +219,14 @@ rerank:
   weight_ai: 0.6
   rerank_query_template: "{query}"
   rerank_doc_template: "{title}"
+  # 乘法融合：fused = Π (max(score,0) + bias) ** exponent（rerank / text / knn 三项）
+  fusion:
+    rerank_bias: 0.00001
+    rerank_exponent: 1.0
+    text_bias: 0.1
+    text_exponent: 0.35
+    knn_bias: 0.6
+    knn_exponent: 0.2
 # 可扩展服务/provider 注册表（单一配置源）
 services:
@@ -37,6 +37,7 @@ from config.schema import (
     ProductEnrichConfig,
     RedisSettings,
     RerankConfig,
+    RerankFusionConfig,
     RerankServiceConfig,
     RuntimeConfig,
     SearchConfig,
@@ -393,6 +394,7 @@ class AppConfigLoader:
         function_score_cfg = raw.get("function_score") if isinstance(raw.get("function_score"), dict) else {}
         rerank_cfg = raw.get("rerank") if isinstance(raw.get("rerank"), dict) else {}
+        fusion_raw = rerank_cfg.get("fusion") if isinstance(rerank_cfg.get("fusion"), dict) else {}
         spu_cfg = raw.get("spu_config") if isinstance(raw.get("spu_config"), dict) else {}
         return SearchConfig(
@@ -412,6 +414,14 @@ class AppConfigLoader:
                 weight_ai=float(rerank_cfg.get("weight_ai", 0.6)),
                 rerank_query_template=str(rerank_cfg.get("rerank_query_template") or "{query}"),
                 rerank_doc_template=str(rerank_cfg.get("rerank_doc_template") or "{title}"),
+                fusion=RerankFusionConfig(
+                    rerank_bias=float(fusion_raw.get("rerank_bias", 0.00001)),
+                    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_bias=float(fusion_raw.get("knn_bias", 0.6)),
+                    knn_exponent=float(fusion_raw.get("knn_exponent", 0.2)),
+                ),
             ),
             spu_config=SPUConfig(
                 enabled=bool(spu_cfg.get("enabled", False)),
@@ -91,6 +91,21 @@ class FunctionScoreConfig:
 @dataclass(frozen=True)
+class RerankFusionConfig:
+    """
+    Multiplicative fusion: fused = Π (max(score_i, 0) + bias_i) ** exponent_i
+    for rerank / text / knn terms respectively.
+    """
+
+    rerank_bias: float = 0.00001
+    rerank_exponent: float = 1.0
+    text_bias: float = 0.1
+    text_exponent: float = 0.35
+    knn_bias: float = 0.6
+    knn_exponent: float = 0.2
+
+
+@dataclass(frozen=True)
 class RerankConfig:
     """Search-time rerank configuration."""
@@ -101,6 +116,7 @@ class RerankConfig:
     weight_ai: float = 0.6
     rerank_query_template: str = "{query}"
     rerank_doc_template: str = "{title}"
+    fusion: RerankFusionConfig = field(default_factory=RerankFusionConfig)
 @dataclass(frozen=True)
@@ -411,11 +411,6 @@ function displayResults(data) {
             const esNorm = typeof debug.es_score_normalized === 'number'
                 ? debug.es_score_normalized.toFixed(4)
                 : (debug.es_score_normalized == null ? '' : String(debug.es_score_normalized));
-
-            const esNormMinMax = typeof debug.es_score_norm === 'number'
-                ? debug.es_score_norm.toFixed(4)
-                : (debug.es_score_norm == null ? '' : String(debug.es_score_norm));
-
             const rerankScore = typeof debug.rerank_score === 'number'
                 ? debug.rerank_score.toFixed(4)
                 : (debug.rerank_score == null ? '' : String(debug.rerank_score));
@@ -437,13 +432,28 @@ function displayResults(data) {
             const resultJson = customStringify(result);
             const rawUrl = `${API_BASE_URL}/search/es-doc/${encodeURIComponent(spuId)}?tenant_id=${encodeURIComponent(tenantId)}`;
             const rerankInputHtml = debug.rerank_input
-                ? `<details><summary>Rerank input</summary><pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 220px;">${escapeHtml(customStringify(debug.rerank_input))}</pre></details>`
+                ? `
+                    <details>
+                        <summary>Rerank input</summary>
+                        <pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 220px;">${escapeHtml(customStringify(debug.rerank_input))}</pre>
+                    </details>
+                `
                 : '';
             const styleIntentHtml = debug.style_intent_sku
-                ? `<details><summary>Selected SKU</summary><pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 220px;">${escapeHtml(customStringify(debug.style_intent_sku))}</pre></details>`
+                ? `
+                    <details>
+                        <summary>Selected SKU</summary>
+                        <pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 220px;">${escapeHtml(customStringify(debug.style_intent_sku))}</pre>
+                    </details>
+                `
                 : '';
             const matchedQueriesHtml = debug.matched_queries
-                ? `<details><summary>matched_queries</summary><pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 220px;">${escapeHtml(customStringify(debug.matched_queries))}</pre></details>`
+                ? `
+                    <details>
+                        <summary>matched_queries</summary>
+                        <pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 220px;">${escapeHtml(customStringify(debug.matched_queries))}</pre>
+                    </details>
+                `
                 : '';
             debugHtml = `
@@ -453,9 +463,7 @@ function displayResults(data) {
                     <div class="product-debug-line">Position before rerank: ${escapeHtml(String(debug.initial_rank ?? ''))}</div>
                     <div class="product-debug-line">Position after rerank: ${escapeHtml(String(debug.final_rank ?? ''))}</div>
                     <div class="product-debug-line">ES score: ${esScore}</div>
-                    <div class="product-debug-line">ES normalized (score / initial ES max): ${esNorm}</div>
-                    <div class="product-debug-line">ES norm (min-max over initial ES window): ${esNormMinMax}</div>
-                    <div class="product-debug-line">ES score min/max: ${escapeHtml(String(debug.es_score_min ?? ''))} / ${escapeHtml(String(debug.es_score_max ?? ''))}</div>
+                    <div class="product-debug-line">ES normalized: ${esNorm}</div>
                     <div class="product-debug-line">Rerank score: ${rerankScore}</div>
                     <div class="product-debug-line">rerank_factor: ${escapeHtml(String(debug.rerank_factor ?? ''))}</div>
                     <div class="product-debug-line">text_score: ${escapeHtml(String(debug.text_score ?? ''))}</div>
@@ -910,9 +918,6 @@ function displayDebugInfo(data) {
         html += `<div>query_tokens: ${escapeHtml((debugInfo.query_analysis.query_tokens || []).join(', ') || 'N/A')}</div>`;
         html += `<div>domain: ${escapeHtml(debugInfo.query_analysis.domain || 'default')}</div>`;
         html += `<div>is_simple_query: ${debugInfo.query_analysis.is_simple_query ? 'yes' : 'no'}</div>`;
-        if (debugInfo.query_analysis.query_vector_summary) {
-            html += `<div>query_vector_summary: ${escapeHtml(customStringify(debugInfo.query_analysis.query_vector_summary))}</div>`;
-        }
         if (debugInfo.query_analysis.translations && Object.keys(debugInfo.query_analysis.translations).length > 0) {
             html += '<div>translations: ';
@@ -946,25 +951,21 @@ function displayDebugInfo(data) {
         html += `<div>took_ms: ${debugInfo.es_response.took_ms}ms</div>`;
         html += `<div>total_hits: ${debugInfo.es_response.total_hits}</div>`;
         html += `<div>max_score: ${debugInfo.es_response.max_score?.toFixed(3) || 0}</div>`;
-        html += `<div>initial_es_max_score: ${escapeHtml(String(debugInfo.es_response.initial_es_max_score ?? ''))}</div>`;
-        html += `<div>initial_es_min_score: ${escapeHtml(String(debugInfo.es_response.initial_es_min_score ?? ''))}</div>`;
+        html += `<div>es_score_normalization_factor: ${escapeHtml(String(debugInfo.es_response.es_score_normalization_factor ?? ''))}</div>`;
         html += '</div>';
     }
     if (debugInfo.rerank) {
         html += '<div style="margin-bottom: 15px;"><strong style="font-size: 14px;">Rerank:</strong>';
-        html += `<div>requested: ${debugInfo.rerank.requested ? 'yes' : 'no'}</div>`;
-        html += `<div>executed: ${debugInfo.rerank.executed ? 'yes' : 'no'}</div>`;
-        html += `<div>in_rerank_window: ${debugInfo.rerank.in_rerank_window ? 'yes' : 'no'}</div>`;
-        html += `<div>top_n: ${escapeHtml(String(debugInfo.rerank.top_n ?? ''))}</div>`;
         html += `<div>query_template: ${escapeHtml(debugInfo.rerank.query_template || 'N/A')}</div>`;
         html += `<div>doc_template: ${escapeHtml(debugInfo.rerank.doc_template || 'N/A')}</div>`;
-        html += '</div>';
-    }
-
-    if (debugInfo.page_fill) {
-        html += '<div style="margin-bottom: 15px;"><strong style="font-size: 14px;">Page Fill:</strong>';
-        html += `<pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 220px;">${escapeHtml(customStringify(debugInfo.page_fill))}</pre>`;
+        html += `<div>query_text: ${escapeHtml(debugInfo.rerank.query_text || 'N/A')}</div>`;
+        html += `<div>docs: ${escapeHtml(String(debugInfo.rerank.docs ?? ''))}</div>`;
+        html += `<div>top_n: ${escapeHtml(String(debugInfo.rerank.top_n ?? ''))}</div>`;
+        if (debugInfo.rerank.fusion) {
+            html += '<div>fusion:</div>';
+            html += `<pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 160px;">${escapeHtml(customStringify(debugInfo.rerank.fusion))}</pre>`;
+        }
         html += '</div>';
     }
@@ -992,7 +993,7 @@ function displayDebugInfo(data) {
     if (debugInfo.es_query_context) {
         html += '<div style="margin-bottom: 15px;"><strong style="font-size: 14px;">ES Query Context:</strong>';
-        html += `<pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 300px;">${escapeHtml(customStringify(debugInfo.es_query_context))}</pre>`;
+        html += `<pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 240px;">${escapeHtml(customStringify(debugInfo.es_query_context))}</pre>`;
         html += '</div>';
     }
@@ -10,6 +10,7 @@
 from typing import Dict, Any, List, Optional, Tuple
 import logging
+from config.schema import RerankFusionConfig
 from providers import create_rerank_provider
 logger = logging.getLogger(__name__)
@@ -176,17 +177,34 @@ def _collect_text_score_components(matched_queries: Any, fallback_es_score: floa
     }
+def _multiply_fusion_factors(
+    rerank_score: float,
+    text_score: float,
+    knn_score: float,
+    fusion: RerankFusionConfig,
+) -> Tuple[float, float, float, float]:
+    """(rerank_factor, text_factor, knn_factor, fused)."""
+    r = (max(rerank_score, 0.0) + fusion.rerank_bias) ** fusion.rerank_exponent
+    t = (max(text_score, 0.0) + fusion.text_bias) ** fusion.text_exponent
+    k = (max(knn_score, 0.0) + fusion.knn_bias) ** fusion.knn_exponent
+    return r, t, k, r * t * k
+
+
 def fuse_scores_and_resort(
     es_hits: List[Dict[str, Any]],
     rerank_scores: List[float],
     weight_es: float = DEFAULT_WEIGHT_ES,
     weight_ai: float = DEFAULT_WEIGHT_AI,
+    fusion: Optional[RerankFusionConfig] = None,
     debug: bool = False,
     rerank_debug_rows: Optional[List[Dict[str, Any]]] = None,
 ) -> List[Dict[str, Any]]:
     """
     将 ES 分数与重排分数按乘法公式融合（不修改原始 _score），并按融合分数降序重排。
+    融合形式（由 ``fusion`` 配置 bias / exponent）::
+        fused = (max(rerank,0)+b_r)^e_r * (max(text,0)+b_t)^e_t * (max(knn,0)+b_k)^e_k
+
     对每条 hit 会写入：
     - _original_score: 原始 ES 分数
     - _rerank_score: 重排服务返回的分数
@@ -199,40 +217,35 @@ def fuse_scores_and_resort(
         rerank_scores: 与 es_hits 等长的重排分数列表
         weight_es: 兼容保留，当前未使用
         weight_ai: 兼容保留，当前未使用
-
-    Returns:
-        每条文档的融合调试信息列表，用于 debug_info
     """
     n = len(es_hits)
     if n == 0 or len(rerank_scores) != n:
         return []
-    fused_debug: List[Dict[str, Any]] = []
+    f = fusion or RerankFusionConfig()
+    fused_debug: List[Dict[str, Any]] = [] if debug else []
     for idx, hit in enumerate(es_hits):
         es_score = _to_score(hit.get("_score"))
-
-        ai_score_raw = rerank_scores[idx]
-        rerank_score = _to_score(ai_score_raw)
-
+        rerank_score = _to_score(rerank_scores[idx])
         matched_queries = hit.get("matched_queries")
         knn_score = _extract_named_query_score(matched_queries, "knn_query")
         text_components = _collect_text_score_components(matched_queries, es_score)
         text_score = text_components["text_score"]
-        rerank_factor = max(rerank_score, 0.0) + 0.00001
-        text_factor = (max(text_score, 0.0) + 0.1) ** 0.35
-        knn_factor = (max(knn_score, 0.0) + 0.6) ** 0.2
-        fused = rerank_factor * text_factor * knn_factor
+        rerank_factor, text_factor, knn_factor, fused = _multiply_fusion_factors(
+            rerank_score, text_score, knn_score, f
+        )
         hit["_original_score"] = hit.get("_score")
         hit["_rerank_score"] = rerank_score
         hit["_text_score"] = text_score
         hit["_knn_score"] = knn_score
-        hit["_text_source_score"] = text_components["source_score"]
-        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["_fused_score"] = fused
+        if debug:
+            hit["_text_source_score"] = text_components["source_score"]
+            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"]
         if debug:
             debug_entry = {
@@ -262,7 +275,6 @@ def fuse_scores_and_resort(
                 debug_entry["rerank_input"] = rerank_debug_rows[idx]
             fused_debug.append(debug_entry)
-    # 按融合分数降序重排
     es_hits.sort(
         key=lambda h: h.get("_fused_score", h.get("_score", 0.0)),
         reverse=True,
@@ -281,6 +293,7 @@ def run_rerank(
     rerank_doc_template: str = "{title}",
     top_n: Optional[int] = None,
     debug: bool = False,
+    fusion: Optional[RerankFusionConfig] = None,
 ) -> Tuple[Dict[str, Any], Optional[Dict[str, Any]], List[Dict[str, Any]]]:
     """
     完整重排流程：从 es_response 取 hits -> 构造 docs -> 调服务 -> 融合分数并重排 -> 更新 max_score。
@@ -314,6 +327,7 @@ def run_rerank(
         scores,
         weight_es=weight_es,
         weight_ai=weight_ai,
+        fusion=fusion,
         debug=debug,
         rerank_debug_rows=rerank_debug_rows,
     )
@@ -4,9 +4,8 @@ Main Searcher module - executes search queries against Elasticsearch.
 Handles query parsing, ranking, and result formatting.
 """
-from typing import Dict, Any, List, Optional, Union, Tuple
-import os
-import time, json
+from typing import Dict, Any, List, Optional
+import json
 import logging
 import hashlib
 from string import Formatter
@@ -20,7 +19,7 @@ from .sku_intent_selector import SkuSelectionDecision, StyleSkuSelector
 from config import SearchConfig
 from config.tenant_config_loader import get_tenant_config_loader
 from context.request_context import RequestContext, RequestContextStage
-from api.models import FacetResult, FacetValue, FacetConfig
+from api.models import FacetResult, FacetConfig
 from api.result_formatter import ResultFormatter
 from indexer.mapping_generator import get_tenant_index_name
@@ -259,13 +258,7 @@ class Searcher:
         if context is not None:
             context.start_stage(RequestContextStage.STYLE_SKU_PREPARE_HITS)
         try:
-            decisions = self.style_sku_selector.prepare_hits(es_hits, parsed_query)
-            if decisions and context is not None:
-                context.store_intermediate_result(
-                    "style_intent_sku_decisions",
-                    {doc_id: decision.to_dict() for doc_id, decision in decisions.items()},
-                )
-            return decisions
+            return self.style_sku_selector.prepare_hits(es_hits, parsed_query)
         finally:
             if context is not None:
                 context.end_stage(RequestContextStage.STYLE_SKU_PREPARE_HITS)
@@ -339,21 +332,10 @@ class Searcher:
         in_rerank_window = do_rerank and (from_ + size) <= rerank_window
         es_fetch_from = 0 if in_rerank_window else from_
         es_fetch_size = rerank_window if in_rerank_window else size
-        initial_es_positions: Dict[str, int] = {}
-        initial_es_min_score: Optional[float] = None
-        initial_es_max_score: Optional[float] = None
-        page_fill_debug: Optional[Dict[str, Any]] = None
+
+        es_score_normalization_factor: Optional[float] = None
+        initial_ranks_by_doc: Dict[str, int] = {}
         rerank_debug_info: Optional[Dict[str, Any]] = None
-        if debug:
-            rerank_debug_info = {
-                "requested": do_rerank,
-                "executed": False,
-                "in_rerank_window": in_rerank_window,
-                "rerank_window": rerank_window,
-                "top_n": from_ + size,
-                "query_template": effective_query_template,
-                "doc_template": effective_doc_template,
-            }
         # Start timing
         context.start_stage(RequestContextStage.TOTAL)
@@ -402,8 +384,8 @@ class Searcher:
         try:
             parsed_query = self.query_parser.parse(
                 query,
-                tenant_id=tenant_id,
                 generate_vector=enable_embedding,
+                tenant_id=tenant_id,
                 context=context,
                 target_languages=index_langs if enable_translation else [],
             )
@@ -493,8 +475,6 @@ class Searcher:
             context.store_intermediate_result('es_query', es_query)
             if in_rerank_window and rerank_prefetch_source is not None:
                 context.store_intermediate_result('es_query_rerank_prefetch_source', rerank_prefetch_source)
-            context.store_intermediate_result('es_body_for_search', body_for_es)
-
             # Serialize ES query to compute a compact size + stable digest for correlation
             es_query_compact = json.dumps(es_query_for_fetch, ensure_ascii=False, separators=(",", ":"))
             es_query_digest = hashlib.sha256(es_query_compact.encode("utf-8")).hexdigest()[:16]
@@ -548,35 +528,29 @@ class Searcher:
             # Store ES response in context
             context.store_intermediate_result('es_response', es_response)
             if debug:
-                initial_hits = es_response.get('hits', {}).get('hits') or []
-                initial_scores: List[float] = []
+                initial_hits = es_response.get("hits", {}).get("hits") or []
                 for rank, hit in enumerate(initial_hits, 1):
                     doc_id = hit.get("_id")
                     if doc_id is not None:
-                        initial_es_positions[str(doc_id)] = rank
-                    raw_score = hit.get("_score")
-                    try:
-                        if raw_score is not None:
-                            initial_scores.append(float(raw_score))
-                    except (TypeError, ValueError):
-                        pass
-                raw_max_score = es_response.get('hits', {}).get('max_score')
+                        initial_ranks_by_doc[str(doc_id)] = rank
+                raw_initial_max_score = es_response.get("hits", {}).get("max_score")
                 try:
-                    initial_es_max_score = float(raw_max_score) if raw_max_score is not None else None
+                    es_score_normalization_factor = float(raw_initial_max_score) if raw_initial_max_score is not None else None
                 except (TypeError, ValueError):
-                    initial_es_max_score = None
-                if initial_es_max_score is None and initial_scores:
-                    initial_es_max_score = max(initial_scores)
-                initial_es_min_score = min(initial_scores) if initial_scores else None
+                    es_score_normalization_factor = None
+                if es_score_normalization_factor is None and initial_hits:
+                    first_score = initial_hits[0].get("_score")
+                    try:
+                        es_score_normalization_factor = float(first_score) if first_score is not None else None
+                    except (TypeError, ValueError):
+                        es_score_normalization_factor = None
             # Extract timing from ES response
             es_took = es_response.get('took', 0)
             context.logger.info(
                 f"ES搜索完成 | 耗时: {es_took}ms | "
                 f"命中数: {es_response.get('hits', {}).get('total', {}).get('value', 0)} | "
-                f"最高分: {(es_response.get('hits', {}).get('max_score') or 0):.3f} | "
-                f"detected_language={parsed_query.detected_language} | "
-                f"translations={list((parsed_query.translations or {}).keys())}",
+                f"最高分: {(es_response.get('hits', {}).get('max_score') or 0):.3f}",
                 extra={'reqid': context.reqid, 'uid': context.uid}
             )
         except Exception as e:
@@ -621,30 +595,31 @@ class Searcher:
                     rerank_doc_template=effective_doc_template,
                     top_n=(from_ + size),
                     debug=debug,
+                    fusion=rc.fusion,
                 )
                 if rerank_meta is not None:
-                    from config.services_config import get_rerank_service_url
-                    rerank_url = get_rerank_service_url()
-                    if debug and rerank_debug_info is not None:
-                        rerank_debug_info.update({
-                            "executed": True,
-                            "service_url": rerank_url,
+                    if debug:
+                        from dataclasses import asdict
+                        from config.services_config import get_rerank_service_url
+                        rerank_debug_info = {
+                            "service_url": get_rerank_service_url(),
+                            "query_template": effective_query_template,
+                            "doc_template": effective_doc_template,
                             "query_text": str(effective_query_template).format_map({"query": rerank_query}),
                             "docs": len(es_response.get("hits", {}).get("hits") or []),
+                            "top_n": from_ + size,
                             "meta": rerank_meta,
-                        })
+                            "fusion": asdict(rc.fusion),
+                        }
                         context.store_intermediate_result("rerank_scores", fused_debug)
                     context.logger.info(
                         f"重排完成 | docs={len(es_response.get('hits', {}).get('hits') or [])} | "
-                        f"top_n={from_ + size} | query_template={effective_query_template} | "
-                        f"doc_template={effective_doc_template} | meta={rerank_meta}",
+                        f"top_n={from_ + size} | meta={rerank_meta}",
                         extra={'reqid': context.reqid, 'uid': context.uid}
                     )
             except Exception as e:
                 context.add_warning(f"Rerank failed: {e}")
-                if debug and rerank_debug_info is not None:
-                    rerank_debug_info["error"] = str(e)
                 context.logger.warning(
                     f"调用重排服务失败 | error: {e}",
                     extra={'reqid': context.reqid, 'uid': context.uid},
@@ -707,13 +682,6 @@ class Searcher:
                             )
                         if fill_took:
                             es_response["took"] = int((es_response.get("took", 0) or 0) + fill_took)
-                        if debug:
-                            page_fill_debug = {
-                                "requested_ids": page_ids,
-                                "filled": filled,
-                                "fill_took_ms": fill_took,
-                                "response_source_spec": response_source_spec,
-                            }
                         context.logger.info(
                             f"分页详情回填 | ids={len(page_ids)} | filled={filled} | took={fill_took}ms",
                             extra={'reqid': context.reqid, 'uid': context.uid}
@@ -781,8 +749,8 @@ class Searcher:
             # Build per-result debug info (per SPU) when debug mode is enabled
             per_result_debug = []
             if debug and es_hits and formatted_results:
-                final_positions_by_doc = {
-                    str(hit.get("_id")): (from_ + rank)
+                final_ranks_by_doc = {
+                    str(hit.get("_id")): from_ + rank
                     for rank, hit in enumerate(es_hits, 1)
                     if hit.get("_id") is not None
                 }
@@ -805,28 +773,11 @@ class Searcher:
                         es_score = 0.0
                     try:
                         normalized = (
-                            float(es_score) / float(initial_es_max_score)
-                            if initial_es_max_score
-                            else None
+                            float(es_score) / float(es_score_normalization_factor)
+                            if es_score_normalization_factor else None
                         )
                     except (TypeError, ValueError, ZeroDivisionError):
                         normalized = None
-                    try:
-                        es_score_norm = (
-                            (float(es_score) - float(initial_es_min_score))
-                            / (float(initial_es_max_score) - float(initial_es_min_score))
-                            if initial_es_min_score is not None
-                            and initial_es_max_score is not None
-                            and float(initial_es_max_score) > float(initial_es_min_score)
-                            else (
-                                1.0
-                                if initial_es_min_score is not None
-                                and initial_es_max_score is not None
-                                else None
-                            )
-                        )
-                    except (TypeError, ValueError, ZeroDivisionError):
-                        es_score_norm = None
                     title_multilingual = source.get("title") if isinstance(source.get("title"), dict) else None
                     brief_multilingual = source.get("brief") if isinstance(source.get("brief"), dict) else None
@@ -836,11 +787,8 @@ class Searcher:
                         "spu_id": spu.spu_id,
                         "es_score": es_score,
                         "es_score_normalized": normalized,
-                        "es_score_norm": es_score_norm,
-                        "es_score_min": initial_es_min_score,
-                        "es_score_max": initial_es_max_score,
-                        "initial_rank": initial_es_positions.get(str(doc_id)) if doc_id is not None else None,
-                        "final_rank": final_positions_by_doc.get(str(doc_id)) if doc_id is not None else None,
+                        "initial_rank": initial_ranks_by_doc.get(str(doc_id)) if doc_id is not None else None,
+                        "final_rank": final_ranks_by_doc.get(str(doc_id)) if doc_id is not None else None,
                         "title_multilingual": title_multilingual,
                         "brief_multilingual": brief_multilingual,
                         "vendor_multilingual": vendor_multilingual,
@@ -908,12 +856,6 @@ class Searcher:
         # Collect debug information if requested
         debug_info = None
         if debug:
-            query_vector_summary = None
-            if parsed_query.query_vector is not None:
-                query_vector_summary = {
-                    "dims": int(len(parsed_query.query_vector)),
-                    "preview": [round(float(v), 6) for v in parsed_query.query_vector[:8].tolist()],
-                }
             debug_info = {
                 "query_analysis": {
                     "original_query": context.query_analysis.original_query,
@@ -923,7 +865,6 @@ class Searcher:
                     "index_languages": index_langs,
                     "translations": context.query_analysis.translations,
                     "has_vector": context.query_analysis.query_vector is not None,
-                    "query_vector_summary": query_vector_summary,
                     "query_tokens": getattr(parsed_query, "query_tokens", []),
                     "is_simple_query": context.query_analysis.is_simple_query,
                     "domain": context.query_analysis.domain,
@@ -931,12 +872,6 @@ class Searcher:
                 },
                 "es_query": context.get_intermediate_result('es_query', {}),
                 "es_query_context": {
-                    "filters": filters,
-                    "range_filters": range_filters,
-                    "facets": [getattr(facet, "field", str(facet)) for facet in facets] if facets else [],
-                    "sort_by": sort_by,
-                    "sort_order": sort_order,
-                    "min_score": min_score,
                     "es_fetch_from": es_fetch_from,
                     "es_fetch_size": es_fetch_size,
                     "in_rerank_window": in_rerank_window,
@@ -948,11 +883,9 @@ class Searcher:
                     "total_hits": total_value,
                     "max_score": max_score,
                     "shards": es_response.get('_shards', {}),
-                    "initial_es_max_score": initial_es_max_score,
-                    "initial_es_min_score": initial_es_min_score,
+                    "es_score_normalization_factor": es_score_normalization_factor,
                 },
                 "rerank": rerank_debug_info,
-                "page_fill": page_fill_debug,
                 "feature_flags": context.metadata.get('feature_flags', {}),
                 "stage_timings": {
                     k: round(v, 2) for k, v in context.performance_metrics.stage_timings.items()
@@ -1126,76 +1059,3 @@ class Searcher:
             logger.error(f"Failed to get document {doc_id} from tenant {tenant_id}: {e}", exc_info=True)
             return None
-    def _standardize_facets(
-        self,
-        es_aggregations: Dict[str, Any],
-        facet_configs: Optional[List[Union[str, Any]]],
-        current_filters: Optional[Dict[str, Any]]
-    ) -> Optional[List[FacetResult]]:
-        """
-        将 ES 聚合结果转换为标准化的分面格式（返回 Pydantic 模型）。
-        
-        Args:
-            es_aggregations: ES 原始聚合结果
-            facet_configs: 分面配置列表（str 或 FacetConfig）
-            current_filters: 当前应用的过滤器
-        
-        Returns:
-            标准化的分面结果列表（FacetResult 对象）
-        """
-        if not es_aggregations or not facet_configs:
-            return None
-        
-        standardized_facets: List[FacetResult] = []
-        
-        for config in facet_configs:
-            # 解析配置
-            if isinstance(config, str):
-                field = config
-                facet_type = "terms"
-            else:
-                # FacetConfig 对象
-                field = config.field
-                facet_type = config.type
-            
-            agg_name = f"{field}_facet"
-            
-            if agg_name not in es_aggregations:
-                continue
-            
-            agg_result = es_aggregations[agg_name]
-            
-            # 获取当前字段的选中值
-            selected_values = set()
-            if current_filters and field in current_filters:
-                filter_value = current_filters[field]
-                if isinstance(filter_value, list):
-                    selected_values = set(filter_value)
-                else:
-                    selected_values = {filter_value}
-            
-            # 转换 buckets 为 FacetValue 对象
-            facet_values: List[FacetValue] = []
-            if 'buckets' in agg_result:
-                for bucket in agg_result['buckets']:
-                    value = bucket.get('key')
-                    count = bucket.get('doc_count', 0)
-                    
-                    facet_values.append(FacetValue(
-                        value=value,
-                        label=str(value),
-                        count=count,
-                        selected=value in selected_values
-                    ))
-            
-            # 构建 FacetResult 对象
-            facet_result = FacetResult(
-                field=field,
-                label=field,
-                type=facet_type,
-                values=facet_values
-            )
-            
-            standardized_facets.append(facet_result)
-        
-        return standardized_facets if standardized_facets else None
 from math import isclose
+from config.schema import RerankFusionConfig
 from search.rerank_client import fuse_scores_and_resort
@@ -88,3 +89,32 @@ def test_fuse_scores_and_resort_downweights_text_only_advantage():
     fuse_scores_and_resort(hits, [0.72, 0.98])
     assert [hit["_id"] for hit in hits] == ["rerank-better", "lexical-heavy"]
+
+
+def test_fuse_scores_and_resort_uses_configurable_fusion_params():
+    hits = [
+        {
+            "_id": "a",
+            "_score": 1.0,
+            "matched_queries": {"base_query": 2.0, "knn_query": 0.5},
+        },
+        {
+            "_id": "b",
+            "_score": 1.0,
+            "matched_queries": {"base_query": 3.0, "knn_query": 0.0},
+        },
+    ]
+    fusion = RerankFusionConfig(
+        rerank_bias=0.0,
+        rerank_exponent=1.0,
+        text_bias=0.0,
+        text_exponent=1.0,
+        knn_bias=0.0,
+        knn_exponent=1.0,
+    )
+    fuse_scores_and_resort(hits, [1.0, 1.0], fusion=fusion)
+    # b 的 knn 为 0 -> 融合为 0；a 为 1 * 2 * 0.5
+    assert [h["_id"] for h in hits] == ["a", "b"]
+    by_id = {h["_id"]: h for h in hits}
+    assert isclose(by_id["a"]["_fused_score"], 1.0, rel_tol=1e-9)
+    assert isclose(by_id["b"]["_fused_score"], 0.0, rel_tol=1e-9)
@@ -614,7 +614,7 @@ def test_searcher_promotes_sku_by_embedding_when_query_has_no_direct_option_matc
     assert result.results[0].image_url == "https://img/blue.jpg"
-def test_searcher_debug_info_includes_es_positions_and_context(monkeypatch):
+def test_searcher_debug_info_uses_initial_es_max_score_for_normalization(monkeypatch):
     es_client = _FakeESClient(total_hits=3)
     searcher = _build_searcher(_build_search_config(rerank_enabled=False), es_client)
     context = create_request_context(reqid="dbg", uid="u-dbg")
@@ -635,10 +635,10 @@ def test_searcher_debug_info_includes_es_positions_and_context(monkeypatch):
     )
     assert result.debug_info["query_analysis"]["index_languages"] == ["en", "zh"]
+    assert result.debug_info["query_analysis"]["query_tokens"] == []
     assert result.debug_info["es_query_context"]["es_fetch_size"] == 2
-    assert result.debug_info["es_response"]["initial_es_max_score"] == 3.0
-    assert result.debug_info["es_response"]["initial_es_min_score"] == 2.0
+    assert result.debug_info["es_response"]["es_score_normalization_factor"] == 3.0
     assert result.debug_info["per_result"][0]["initial_rank"] == 1
     assert result.debug_info["per_result"][0]["final_rank"] == 1
     assert result.debug_info["per_result"][0]["es_score_normalized"] == 1.0
-    assert result.debug_info["per_result"][1]["es_score_norm"] == 0.0
+    assert result.debug_info["per_result"][1]["es_score_normalized"] == 2.0 / 3.0