debug工具，每条结果的打分中间过程展示

The backend now exposes a structured debug_info that is much closer to the real ranking pipeline: query_analysis now includes index_languages, query_tokens, query-vector summary, translation/enrichment plan, and translation debug. query_build now explains the ES recall plan: base-language clause, translated clauses, filters vs post-filters, KNN settings, function-score config, and related inputs. es_request distinguishes the logical DSL from the actual body sent to ES, including rerank prefetch _source. es_response now includes the initial ES ranking window stats used for score interpretation. rerank now includes execution state, templates, rendered rerank query text, window/top_n, service/meta, and the fusion formula. pagination now shows rerank-window fetch vs requested page plus page-fill details. For each result in debug_info.per_result, ranking debug is now much richer: initial rank and final rank raw ES score es_score_normalized = raw score / initial ES window max es_score_norm = min-max normalization over the initial ES window explicit normalization notes explaining that fusion does not directly consume an ES-normalized score rerank input details: doc template, title suffix, template field values, doc preview/length fusion breakdown: rerank_factor, text_factor, knn_factor, constants, raw inputs, final fused score text subcomponents: source/translation/weighted/primary/support/fallback evidence via matched_queries richer style-intent SKU debug, including selected SKU summary and intent texts

debug工具，每条结果的打分中间过程展示
The backend now exposes a structured debug_info that is much closer to the real ranking pipeline: query_analysis now includes index_languages, query_tokens, query-vector summary, translation/enrichment plan, and translation debug. query_build now explains the ES recall plan: base-language clause, translated clauses, filters vs post-filters, KNN settings, function-score config, and related inputs. es_request distinguishes the logical DSL from the actual body sent to ES, including rerank prefetch _source. es_response now includes the initial ES ranking window stats used for score interpretation. rerank now includes execution state, templates, rendered rerank query text, window/top_n, service/meta, and the fusion formula. pagination now shows rerank-window fetch vs requested page plus page-fill details. For each result in debug_info.per_result, ranking debug is now much richer: initial rank and final rank raw ES score es_score_normalized = raw score / initial ES window max es_score_norm = min-max normalization over the initial ES window explicit normalization notes explaining that fusion does not directly consume an ES-normalized score rerank input details: doc template, title suffix, template field values, doc preview/length fusion breakdown: rerank_factor, text_factor, knn_factor, constants, raw inputs, final fused score text subcomponents: source/translation/weighted/primary/support/fallback evidence via matched_queries richer style-intent SKU debug, including selected SKU summary and intent texts
tangwang
1 parent 8ae95af0
Showing 5 changed files with 290 additions and 56 deletions Show diff stats
frontend/static/js/app.js
search/rerank_client.py
search/searcher.py
tests/test_rerank_client.py
tests/test_search_rerank_window.py
@@ -412,7 +412,7 @@ function displayResults(data) {
                 ? debug.es_score_normalized.toFixed(4)
                 : (debug.es_score_normalized == null ? '' : String(debug.es_score_normalized));
-            const esNormRerank = typeof debug.es_score_norm === 'number'
+            const esNormMinMax = typeof debug.es_score_norm === 'number'
                 ? debug.es_score_norm.toFixed(4)
                 : (debug.es_score_norm == null ? '' : String(debug.es_score_norm));
@@ -436,17 +436,37 @@ 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>`
+                : '';
+            const styleIntentHtml = debug.style_intent_sku
+                ? `<details><summary>Selected SKU</summary><pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 220px;">${escapeHtml(customStringify(debug.style_intent_sku))}</pre></details>`
+                : '';
+            const matchedQueriesHtml = debug.matched_queries
+                ? `<details><summary>matched_queries</summary><pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 220px;">${escapeHtml(customStringify(debug.matched_queries))}</pre></details>`
+                : '';
             debugHtml = `
                 <div class="product-debug">
                     <div class="product-debug-title">Ranking Debug</div>
                     <div class="product-debug-line">spu_id: ${escapeHtml(String(spuId || ''))}</div>
+                    <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: ${esNorm}</div>
-                    <div class="product-debug-line">ES norm (rerank input): ${esNormRerank}</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">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>
+                    <div class="product-debug-line">text_factor: ${escapeHtml(String(debug.text_factor ?? ''))}</div>
+                    <div class="product-debug-line">knn_score: ${escapeHtml(String(debug.knn_score ?? ''))}</div>
+                    <div class="product-debug-line">knn_factor: ${escapeHtml(String(debug.knn_factor ?? ''))}</div>
                     <div class="product-debug-line">Fused score: ${fusedScore}</div>
                     ${titleLines}
+                    ${rerankInputHtml}
+                    ${styleIntentHtml}
+                    ${matchedQueriesHtml}
                     <div class="product-debug-actions">
                         <button type="button" class="product-debug-inline-result-btn"
                             data-action="toggle-result-inline-doc"
@@ -866,7 +886,7 @@ function displayDebugInfo(data) {
         if (data.query_info) {
             let html = '<div style="padding: 10px;">';
             html += `<div><strong>original_query:</strong> ${escapeHtml(data.query_info.original_query || 'N/A')}</div>`;
-            html += `<div><strong>detected_language:</strong> ${data.query_info.detected_languag}</div>`;
+            html += `<div><strong>detected_language:</strong> ${escapeHtml(data.query_info.detected_language || 'N/A')}</div>`;
             html += '</div>';
             debugInfoDiv.innerHTML = html;
         } else {
@@ -885,9 +905,14 @@ function displayDebugInfo(data) {
         html += `<div>original_query: ${escapeHtml(debugInfo.query_analysis.original_query || 'N/A')}</div>`;
         html += `<div>query_normalized: ${escapeHtml(debugInfo.query_analysis.query_normalized || 'N/A')}</div>`;
         html += `<div>rewritten_query: ${escapeHtml(debugInfo.query_analysis.rewritten_query || 'N/A')}</div>`;
-        html += `<div>detected_language: ${debugInfo.query_analysis.detected_language}</div>`;
+        html += `<div>detected_language: ${escapeHtml(debugInfo.query_analysis.detected_language || 'N/A')}</div>`;
+        html += `<div>index_languages: ${escapeHtml((debugInfo.query_analysis.index_languages || []).join(', ') || 'N/A')}</div>`;
+        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: ';
@@ -921,6 +946,25 @@ 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>';
+    }
+
+    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>';
     }
@@ -945,6 +989,12 @@ function displayDebugInfo(data) {
         html += `<pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 400px;">${escapeHtml(customStringify(debugInfo.es_query))}</pre>`;
         html += '</div>';
     }
+
+    if (debugInfo.es_query_context) {
+        html += '<div style="margin-bottom: 15px;"><strong style="font-size: 14px;">ES Query Context:</strong>';
+        html += `<pre style="background: #f5f5f5; padding: 10px; overflow: auto; max-height: 300px;">${escapeHtml(customStringify(debugInfo.es_query_context))}</pre>`;
+        html += '</div>';
+    }
     html += '</div>';
     debugInfoDiv.innerHTML = html;
@@ -25,6 +25,7 @@ def build_docs_from_hits(
     es_hits: List[Dict[str, Any]],
     language: str = "zh",
     doc_template: str = "{title}",
+    debug_rows: Optional[List[Dict[str, Any]]] = None,
 ) -> List[str]:
     """
     从 ES 命中结果构造重排服务所需的文档文本列表（与 hits 一一对应）。
@@ -63,24 +64,36 @@ def build_docs_from_hits(
     for hit in es_hits:
         src = hit.get("_source") or {}
         title_suffix = str(hit.get("_style_rerank_suffix") or "").strip()
+        values = _SafeDict(
+            title=(
+                f"{pick_lang_text(src.get('title'))} {title_suffix}".strip()
+                if title_suffix
+                else pick_lang_text(src.get("title"))
+            ),
+            brief=pick_lang_text(src.get("brief")) if need_brief else "",
+            vendor=pick_lang_text(src.get("vendor")) if need_vendor else "",
+            description=pick_lang_text(src.get("description")) if need_description else "",
+            category_path=pick_lang_text(src.get("category_path")) if need_category_path else "",
+        )
         if only_title:
-            title = pick_lang_text(src.get("title"))
-            if title_suffix:
-                title = f"{title} {title_suffix}".strip()
-            docs.append(title)
+            doc_text = values["title"]
         else:
-            values = _SafeDict(
-                title=(
-                    f"{pick_lang_text(src.get('title'))} {title_suffix}".strip()
-                    if title_suffix
-                    else pick_lang_text(src.get("title"))
-                ),
-                brief=pick_lang_text(src.get("brief")) if need_brief else "",
-                vendor=pick_lang_text(src.get("vendor")) if need_vendor else "",
-                description=pick_lang_text(src.get("description")) if need_description else "",
-                category_path=pick_lang_text(src.get("category_path")) if need_category_path else "",
-            )
-            docs.append(str(doc_template).format_map(values))
+            doc_text = str(doc_template).format_map(values)
+        docs.append(doc_text)
+        if debug_rows is not None:
+            preview = doc_text if len(doc_text) <= 300 else f"{doc_text[:300]}..."
+            debug_rows.append({
+                "doc_template": doc_template,
+                "title_suffix": title_suffix or None,
+                "fields": {
+                    "title": values["title"] or None,
+                    "brief": values["brief"] or None,
+                    "vendor": values["vendor"] or None,
+                    "category_path": values["category_path"] or None,
+                },
+                "doc_preview": preview,
+                "doc_length": len(doc_text),
+            })
     return docs
@@ -163,18 +176,13 @@ def _collect_text_score_components(matched_queries: Any, fallback_es_score: floa
     }
-def _fuse_score(rerank_score: float, text_score: float, knn_score: float) -> float:
-    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
-    return rerank_factor * text_factor * knn_factor
-
-
 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,
+    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。
@@ -211,7 +219,10 @@ def fuse_scores_and_resort(
         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"]
-        fused = _fuse_score(rerank_score, text_score, knn_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
         hit["_original_score"] = hit.get("_score")
         hit["_rerank_score"] = rerank_score
@@ -223,19 +234,33 @@ def fuse_scores_and_resort(
         hit["_text_support_score"] = text_components["support_text_score"]
         hit["_fused_score"] = fused
-        fused_debug.append({
-            "doc_id": hit.get("_id"),
-            "es_score": es_score,
-            "rerank_score": rerank_score,
-            "text_score": text_score,
-            "text_source_score": text_components["source_score"],
-            "text_translation_score": text_components["translation_score"],
-            "text_primary_score": text_components["primary_text_score"],
-            "text_support_score": text_components["support_text_score"],
-            "knn_score": knn_score,
-            "matched_queries": matched_queries,
-            "fused_score": fused,
-        })
+        if debug:
+            debug_entry = {
+                "doc_id": hit.get("_id"),
+                "es_score": es_score,
+                "rerank_score": rerank_score,
+                "text_score": text_score,
+                "text_source_score": text_components["source_score"],
+                "text_translation_score": text_components["translation_score"],
+                "text_weighted_source_score": text_components["weighted_source_score"],
+                "text_weighted_translation_score": text_components["weighted_translation_score"],
+                "text_primary_score": text_components["primary_text_score"],
+                "text_support_score": text_components["support_text_score"],
+                "text_score_fallback_to_es": (
+                    text_score == es_score
+                    and text_components["source_score"] <= 0.0
+                    and text_components["translation_score"] <= 0.0
+                ),
+                "knn_score": knn_score,
+                "rerank_factor": rerank_factor,
+                "text_factor": text_factor,
+                "knn_factor": knn_factor,
+                "matched_queries": matched_queries,
+                "fused_score": fused,
+            }
+            if rerank_debug_rows is not None and idx < len(rerank_debug_rows):
+                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(
@@ -255,6 +280,7 @@ def run_rerank(
     rerank_query_template: str = "{query}",
     rerank_doc_template: str = "{title}",
     top_n: Optional[int] = None,
+    debug: bool = False,
 ) -> 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。
@@ -266,7 +292,13 @@ def run_rerank(
         return es_response, None, []
     query_text = str(rerank_query_template).format_map({"query": query})
-    docs = build_docs_from_hits(hits, language=language, doc_template=rerank_doc_template)
+    rerank_debug_rows: Optional[List[Dict[str, Any]]] = [] if debug else None
+    docs = build_docs_from_hits(
+        hits,
+        language=language,
+        doc_template=rerank_doc_template,
+        debug_rows=rerank_debug_rows,
+    )
     scores, meta = call_rerank_service(
         query_text,
         docs,
@@ -282,6 +314,8 @@ def run_rerank(
         scores,
         weight_es=weight_es,
         weight_ai=weight_ai,
+        debug=debug,
+        rerank_debug_rows=rerank_debug_rows,
     )
     # 更新 max_score 为融合后的最高分
@@ -339,6 +339,21 @@ 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
+        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)
@@ -348,6 +363,7 @@ class Searcher:
             f"enable_rerank(request)={enable_rerank}, enable_rerank(config)={rerank_enabled_by_config}, "
             f"enable_rerank(effective)={do_rerank}, in_rerank_window={in_rerank_window}, "
             f"es_fetch=({es_fetch_from},{es_fetch_size}) | "
+            f"index_languages={index_langs} | "
             f"enable_translation={enable_translation}, enable_embedding={enable_embedding}, min_score={min_score}",
             extra={'reqid': context.reqid, 'uid': context.uid}
         )
@@ -531,13 +547,36 @@ 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] = []
+                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')
+                try:
+                    initial_es_max_score = float(raw_max_score) if raw_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
             # 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"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())}",
                 extra={'reqid': context.reqid, 'uid': context.uid}
             )
         except Exception as e:
@@ -581,24 +620,31 @@ class Searcher:
                     rerank_query_template=effective_query_template,
                     rerank_doc_template=effective_doc_template,
                     top_n=(from_ + size),
+                    debug=debug,
                 )
                 if rerank_meta is not None:
                     from config.services_config import get_rerank_service_url
                     rerank_url = get_rerank_service_url()
-                    context.metadata.setdefault("rerank_info", {})
-                    context.metadata["rerank_info"].update({
-                        "service_url": rerank_url,
-                        "docs": len(es_response.get("hits", {}).get("hits") or []),
-                        "meta": rerank_meta,
-                    })
-                    context.store_intermediate_result("rerank_scores", fused_debug)
+                    if debug and rerank_debug_info is not None:
+                        rerank_debug_info.update({
+                            "executed": True,
+                            "service_url": rerank_url,
+                            "query_text": str(effective_query_template).format_map({"query": rerank_query}),
+                            "docs": len(es_response.get("hits", {}).get("hits") or []),
+                            "meta": rerank_meta,
+                        })
+                        context.store_intermediate_result("rerank_scores", fused_debug)
                     context.logger.info(
-                        f"重排完成 | docs={len(fused_debug)} | meta={rerank_meta}",
+                        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}",
                         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},
@@ -661,6 +707,13 @@ 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}
@@ -728,6 +781,11 @@ 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)
+                    for rank, hit in enumerate(es_hits, 1)
+                    if hit.get("_id") is not None
+                }
                 for hit, spu in zip(es_hits, formatted_results):
                     source = hit.get("_source", {}) or {}
                     doc_id = hit.get("_id")
@@ -746,9 +804,29 @@ class Searcher:
                     except (TypeError, ValueError):
                         es_score = 0.0
                     try:
-                        normalized = float(es_score) / float(max_score) if max_score else None
+                        normalized = (
+                            float(es_score) / float(initial_es_max_score)
+                            if initial_es_max_score
+                            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
@@ -758,6 +836,11 @@ 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,
                         "title_multilingual": title_multilingual,
                         "brief_multilingual": brief_multilingual,
                         "vendor_multilingual": vendor_multilingual,
@@ -771,10 +854,17 @@ class Searcher:
                         debug_entry["text_score"] = rerank_debug.get("text_score")
                         debug_entry["text_source_score"] = rerank_debug.get("text_source_score")
                         debug_entry["text_translation_score"] = rerank_debug.get("text_translation_score")
+                        debug_entry["text_weighted_source_score"] = rerank_debug.get("text_weighted_source_score")
+                        debug_entry["text_weighted_translation_score"] = rerank_debug.get("text_weighted_translation_score")
                         debug_entry["text_primary_score"] = rerank_debug.get("text_primary_score")
                         debug_entry["text_support_score"] = rerank_debug.get("text_support_score")
+                        debug_entry["text_score_fallback_to_es"] = rerank_debug.get("text_score_fallback_to_es")
                         debug_entry["knn_score"] = rerank_debug.get("knn_score")
+                        debug_entry["rerank_factor"] = rerank_debug.get("rerank_factor")
+                        debug_entry["text_factor"] = rerank_debug.get("text_factor")
+                        debug_entry["knn_factor"] = rerank_debug.get("knn_factor")
                         debug_entry["fused_score"] = rerank_debug.get("fused_score")
+                        debug_entry["rerank_input"] = rerank_debug.get("rerank_input")
                         debug_entry["matched_queries"] = rerank_debug.get("matched_queries")
                     if style_intent_debug:
@@ -818,25 +908,51 @@ 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,
                     "query_normalized": context.query_analysis.query_normalized,
                     "rewritten_query": context.query_analysis.rewritten_query,
                     "detected_language": context.query_analysis.detected_language,
+                    "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,
                     "style_intent_profile": context.get_intermediate_result("style_intent_profile"),
                 },
                 "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,
+                    "rerank_prefetch_source": context.get_intermediate_result('es_query_rerank_prefetch_source'),
+                    "include_named_queries_score": bool(do_rerank and in_rerank_window),
+                },
                 "es_response": {
                     "took_ms": es_response.get('took', 0),
                     "total_hits": total_value,
                     "max_score": max_score,
-                    "shards": es_response.get('_shards', {})
+                    "shards": es_response.get('_shards', {}),
+                    "initial_es_max_score": initial_es_max_score,
+                    "initial_es_min_score": initial_es_min_score,
                 },
+                "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()
@@ -24,7 +24,7 @@ def test_fuse_scores_and_resort_aggregates_text_components_and_keeps_rerank_prim
         },
     ]
-    debug = fuse_scores_and_resort(hits, [0.9, 0.7])
+    debug = fuse_scores_and_resort(hits, [0.9, 0.7], debug=True)
     expected_text_1 = 2.4 + 0.25 * (0.8 * 1.8)
     expected_fused_1 = (0.9 + 0.00001) * ((expected_text_1 + 0.1) ** 0.35) * ((0.8 + 0.6) ** 0.2)
@@ -37,7 +37,9 @@ def test_fuse_scores_and_resort_aggregates_text_components_and_keeps_rerank_prim
     assert isclose(by_id["2"]["_fused_score"], expected_fused_2, rel_tol=1e-9)
     assert debug[0]["text_source_score"] == 2.4
     assert debug[0]["text_translation_score"] == 1.8
+    assert isclose(debug[0]["text_weighted_translation_score"], 1.44, rel_tol=1e-9)
     assert debug[0]["knn_score"] == 0.8
+    assert isclose(debug[0]["rerank_factor"], 0.90001, rel_tol=1e-9)
     assert [hit["_id"] for hit in hits] == ["2", "1"]
@@ -47,7 +49,7 @@ def test_fuse_scores_and_resort_falls_back_when_matched_queries_missing():
         {"_id": "2", "_score": 2.0},
     ]
-    fuse_scores_and_resort(hits, [0.4, 0.3])
+    debug = fuse_scores_and_resort(hits, [0.4, 0.3], debug=True)
     expected_1 = (0.4 + 0.00001) * ((0.5 + 0.1) ** 0.35) * ((0.0 + 0.6) ** 0.2)
     expected_2 = (0.3 + 0.00001) * ((2.0 + 0.1) ** 0.35) * ((0.0 + 0.6) ** 0.2)
@@ -58,6 +60,8 @@ def test_fuse_scores_and_resort_falls_back_when_matched_queries_missing():
     assert isclose(by_id["1"]["_fused_score"], expected_1, rel_tol=1e-9)
     assert isclose(by_id["2"]["_text_score"], 2.0, rel_tol=1e-9)
     assert isclose(by_id["2"]["_fused_score"], expected_2, rel_tol=1e-9)
+    assert debug[0]["text_score_fallback_to_es"] is True
+    assert debug[1]["text_score_fallback_to_es"] is True
     assert [hit["_id"] for hit in hits] == ["2", "1"]
@@ -612,3 +612,33 @@ def test_searcher_promotes_sku_by_embedding_when_query_has_no_direct_option_matc
     assert len(result.results) == 1
     assert result.results[0].skus[0].sku_id == "sku-blue"
     assert result.results[0].image_url == "https://img/blue.jpg"
+
+
+def test_searcher_debug_info_includes_es_positions_and_context(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")
+
+    monkeypatch.setattr(
+        "search.searcher.get_tenant_config_loader",
+        lambda: SimpleNamespace(get_tenant_config=lambda tenant_id: {"index_languages": ["en", "zh"]}),
+    )
+
+    result = searcher.search(
+        query="toy",
+        tenant_id="162",
+        from_=0,
+        size=2,
+        context=context,
+        enable_rerank=False,
+        debug=True,
+    )
+
+    assert result.debug_info["query_analysis"]["index_languages"] == ["en", "zh"]
+    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["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