diff --git a/] b/]
new file mode 100644
index 0000000..bd63c3d
--- /dev/null
+++ b/]
@@ -0,0 +1,17 @@
+docs
+# Please enter the commit message for your changes. Lines starting
+# with '#' will be ignored, and an empty message aborts the commit.
+#
+# On branch master
+# Your branch is ahead of 'origin/master' by 5 commits.
+#   (use "git push" to publish your local commits)
+#
+# Changes to be committed:
+#	modified:   config/config.yaml
+#	modified:   docs/TODO.txt
+#	modified:   "docs/\346\220\234\347\264\242API\345\257\271\346\216\245\346\214\207\345\215\227-07-\345\276\256\346\234\215\345\212\241\346\216\245\345\217\243\357\274\210Embedding-Reranker-Translation\357\274\211.md"
+#	modified:   "docs/\347\233\270\345\205\263\346\200\247\346\243\200\347\264\242\344\274\230\345\214\226\350\257\264\346\230\216.md"
+#
+# Changes not staged for commit:
+#	modified:   third-party/clip-as-service (untracked content)
+#
diff --git a/frontend/index.html b/frontend/index.html
index ede46f0..77ac86f 100644
--- a/frontend/index.html
+++ b/frontend/index.html
@@ -199,7 +199,7 @@
     </footer>
 
     <script src="/static/js/tenant_facets_config.js?v=1.4"></script>
-    <script src="/static/js/app.js?v=1.0"></script>
+    <script src="/static/js/app.js?v=1.1"></script>
     <script>
         // 自动补全功能（使用后端 /search/suggestions 接口）
         const SUGGEST_API = API_BASE_URL + '/search/suggestions';
diff --git a/frontend/static/css/style.css b/frontend/static/css/style.css
index 73478aa..99be1a1 100644
--- a/frontend/static/css/style.css
+++ b/frontend/static/css/style.css
@@ -371,9 +371,31 @@ body {
     margin-bottom: 2px;
 }
 
+.product-debug-actions {
+    display: flex;
+    flex-wrap: wrap;
+    align-items: center;
+    gap: 10px;
+    margin-top: 8px;
+}
+
+.product-debug-btn-api-result {
+    font-family: inherit;
+    font-size: 12px;
+    padding: 4px 10px;
+    border: 1px solid #d35400;
+    border-radius: 4px;
+    background: #fff8f3;
+    color: #d35400;
+    cursor: pointer;
+}
+
+.product-debug-btn-api-result:hover {
+    background: #fdebd0;
+}
+
 .product-debug-link {
     display: inline-block;
-    margin-top: 6px;
     font-size: 12px;
     color: #e67e22;
     text-decoration: none;
@@ -383,6 +405,80 @@ body {
     text-decoration: underline;
 }
 
+.api-result-viewer-backdrop {
+    display: none;
+    position: fixed;
+    inset: 0;
+    z-index: 2000;
+    background: rgba(0, 0, 0, 0.45);
+    align-items: center;
+    justify-content: center;
+    padding: 24px;
+    box-sizing: border-box;
+}
+
+.api-result-viewer-panel {
+    background: #fff;
+    border-radius: 8px;
+    max-width: min(920px, 100%);
+    max-height: min(85vh, 100%);
+    display: flex;
+    flex-direction: column;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.2);
+    overflow: hidden;
+}
+
+.api-result-viewer-header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    gap: 12px;
+    padding: 12px 14px;
+    border-bottom: 1px solid #eee;
+    flex-shrink: 0;
+}
+
+.api-result-viewer-title {
+    font-size: 14px;
+    font-weight: 600;
+    color: #333;
+}
+
+.api-result-viewer-title code {
+    font-size: 12px;
+    font-weight: normal;
+    background: #f4f4f4;
+    padding: 1px 6px;
+    border-radius: 3px;
+}
+
+.api-result-viewer-close {
+    border: none;
+    background: transparent;
+    font-size: 22px;
+    line-height: 1;
+    color: #888;
+    cursor: pointer;
+    padding: 0 4px;
+}
+
+.api-result-viewer-close:hover {
+    color: #333;
+}
+
+.api-result-viewer-pre {
+    margin: 0;
+    padding: 14px;
+    overflow: auto;
+    flex: 1;
+    font-size: 12px;
+    line-height: 1.45;
+    background: #fafafa;
+    border: none;
+    font-family: Menlo, Consolas, "Courier New", monospace;
+    white-space: pre;
+}
+
 .product-card:hover {
     box-shadow: 0 4px 12px rgba(0,0,0,0.1);
     transform: translateY(-2px);
diff --git a/frontend/static/js/app.js b/frontend/static/js/app.js
index 7875a39..4c4d1ad 100644
--- a/frontend/static/js/app.js
+++ b/frontend/static/js/app.js
@@ -63,11 +63,81 @@ let state = {
     debug: true  // Always enable debug mode for test frontend
 };
 
+// 弹层：展示 /search/ 返回的 results[] 单条元素（非 ES 原始文档）
+function openApiResultViewer(item) {
+    let backdrop = document.getElementById('apiResultViewerBackdrop');
+    if (!backdrop) {
+        backdrop = document.createElement('div');
+        backdrop.id = 'apiResultViewerBackdrop';
+        backdrop.className = 'api-result-viewer-backdrop';
+        backdrop.innerHTML = `
+            <div class="api-result-viewer-panel" role="dialog" aria-modal="true" aria-labelledby="apiResultViewerTitle">
+                <div class="api-result-viewer-header">
+                    <span id="apiResultViewerTitle" class="api-result-viewer-title">搜索结果项（API <code>results[]</code>）</span>
+                    <button type="button" class="api-result-viewer-close" aria-label="关闭">&times;</button>
+                </div>
+                <pre class="api-result-viewer-pre"></pre>
+            </div>
+        `;
+        document.body.appendChild(backdrop);
+        backdrop.addEventListener('click', (e) => {
+            if (e.target === backdrop) {
+                closeApiResultViewer();
+            }
+        });
+        backdrop.querySelector('.api-result-viewer-close').addEventListener('click', closeApiResultViewer);
+        document.addEventListener('keydown', (e) => {
+            if (e.key === 'Escape') {
+                closeApiResultViewer();
+            }
+        });
+    }
+    const pre = backdrop.querySelector('.api-result-viewer-pre');
+    try {
+        pre.textContent = JSON.stringify(item, null, 2);
+    } catch (err) {
+        pre.textContent = String(item);
+    }
+    backdrop.style.display = 'flex';
+}
+
+function closeApiResultViewer() {
+    const backdrop = document.getElementById('apiResultViewerBackdrop');
+    if (backdrop) {
+        backdrop.style.display = 'none';
+    }
+}
+
+function initProductGridResultViewer() {
+    const grid = document.getElementById('productGrid');
+    if (!grid || grid.dataset.apiResultViewerBound === '1') {
+        return;
+    }
+    grid.dataset.apiResultViewerBound = '1';
+    grid.addEventListener('click', (e) => {
+        const btn = e.target.closest('.product-debug-btn-api-result');
+        if (!btn) {
+            return;
+        }
+        e.preventDefault();
+        const idx = parseInt(btn.getAttribute('data-result-index'), 10);
+        if (Number.isNaN(idx)) {
+            return;
+        }
+        const results = state.lastSearchData && state.lastSearchData.results;
+        if (!results || idx < 0 || idx >= results.length) {
+            return;
+        }
+        openApiResultViewer(results[idx]);
+    });
+}
+
 // Initialize
 function initializeApp() {
     // 初始化租户下拉框和分面面板
     console.log('Initializing app...');
     initTenantSelect();
+    initProductGridResultViewer();
     const searchInput = document.getElementById('searchInput');
     if (searchInput) {
         searchInput.focus();
@@ -350,7 +420,7 @@ function displayResults(data) {
 
     const tenantId = getTenantId();
 
-    data.results.forEach((result) => {
+    data.results.forEach((result, resultIndex) => {
         const product = result;
         const title = product.title || product.name || 'N/A';
         const price = product.min_price || product.price || 'N/A';
@@ -401,9 +471,14 @@ function displayResults(data) {
                     <div class="product-debug-line">Rerank score: ${rerankScore}</div>
                     <div class="product-debug-line">Fused score: ${fusedScore}</div>
                     ${titleLines}
-                    <a class="product-debug-link" href="${rawUrl}" target="_blank" rel="noopener noreferrer">
-                        查看 ES 原始文档
-                    </a>
+                    <div class="product-debug-actions">
+                        <button type="button" class="product-debug-btn-api-result" data-result-index="${resultIndex}">
+                            查看 API 结果项
+                        </button>
+                        <a class="product-debug-link" href="${rawUrl}" target="_blank" rel="noopener noreferrer">
+                            查看 ES 原始文档
+                        </a>
+                    </div>
                 </div>
             `;
         }
diff --git a/search/searcher.py b/search/searcher.py
index 231ffeb..8b9cf79 100644
--- a/search/searcher.py
+++ b/search/searcher.py
@@ -4,12 +4,13 @@ Main Searcher module - executes search queries against Elasticsearch.
 Handles query parsing, ranking, and result formatting.
 """
 
-from typing import Dict, Any, List, Optional, Union
+from typing import Dict, Any, List, Optional, Union, Tuple
 import os
 import time, json
 import logging
 import hashlib
 from string import Formatter
+import numpy as np
 
 from utils.es_client import ESClient
 from query import QueryParser, ParsedQuery
@@ -224,6 +225,231 @@ class Searcher:
             hits_by_id[str(hid)] = hit
         return hits_by_id, int(resp.get("took", 0) or 0)
 
+    @staticmethod
+    def _normalize_sku_match_text(value: Optional[str]) -> str:
+        """Normalize free text for lightweight SKU option matching."""
+        if value is None:
+            return ""
+        return " ".join(str(value).strip().casefold().split())
+
+    def _build_sku_query_texts(self, parsed_query: ParsedQuery) -> List[str]:
+        """Collect original and translated query texts for SKU option matching."""
+        candidates: List[str] = []
+        for text in (
+            getattr(parsed_query, "original_query", None),
+            getattr(parsed_query, "query_normalized", None),
+            getattr(parsed_query, "rewritten_query", None),
+        ):
+            normalized = self._normalize_sku_match_text(text)
+            if normalized:
+                candidates.append(normalized)
+
+        query_text_by_lang = getattr(parsed_query, "query_text_by_lang", {}) or {}
+        if isinstance(query_text_by_lang, dict):
+            for text in query_text_by_lang.values():
+                normalized = self._normalize_sku_match_text(text)
+                if normalized:
+                    candidates.append(normalized)
+
+        translations = getattr(parsed_query, "translations", {}) or {}
+        if isinstance(translations, dict):
+            for text in translations.values():
+                normalized = self._normalize_sku_match_text(text)
+                if normalized:
+                    candidates.append(normalized)
+
+        deduped: List[str] = []
+        seen = set()
+        for text in candidates:
+            if text in seen:
+                continue
+            seen.add(text)
+            deduped.append(text)
+        return deduped
+
+    def _find_query_matching_sku_index(
+        self,
+        skus: List[Dict[str, Any]],
+        query_texts: List[str],
+    ) -> Optional[int]:
+        """Return the first SKU whose option1_value appears in query texts."""
+        if not skus or not query_texts:
+            return None
+
+        for index, sku in enumerate(skus):
+            option1_value = self._normalize_sku_match_text(sku.get("option1_value"))
+            if not option1_value:
+                continue
+            if any(option1_value in query_text for query_text in query_texts):
+                return index
+        return None
+
+    def _encode_query_vector_for_sku_matching(
+        self,
+        parsed_query: ParsedQuery,
+        context: Optional[RequestContext] = None,
+    ) -> Optional[np.ndarray]:
+        """Best-effort fallback query embedding for final-page SKU matching."""
+        query_text = (
+            getattr(parsed_query, "rewritten_query", None)
+            or getattr(parsed_query, "query_normalized", None)
+            or getattr(parsed_query, "original_query", None)
+        )
+        if not query_text:
+            return None
+
+        text_encoder = getattr(self.query_parser, "text_encoder", None)
+        if text_encoder is None:
+            return None
+
+        try:
+            vectors = text_encoder.encode([query_text], priority=1)
+        except Exception as exc:
+            logger.warning("Failed to encode query vector for SKU matching: %s", exc, exc_info=True)
+            if context is not None:
+                context.add_warning(f"SKU query embedding failed: {exc}")
+            return None
+
+        if vectors is None or len(vectors) == 0:
+            return None
+
+        vector = vectors[0]
+        if vector is None:
+            return None
+        return np.asarray(vector, dtype=np.float32)
+
+    def _select_sku_by_embedding(
+        self,
+        skus: List[Dict[str, Any]],
+        option1_vectors: Dict[str, np.ndarray],
+        query_vector: np.ndarray,
+    ) -> Tuple[Optional[int], Optional[float]]:
+        """Select the SKU whose option1_value is most similar to the query."""
+        best_index: Optional[int] = None
+        best_score: Optional[float] = None
+
+        for index, sku in enumerate(skus):
+            option1_value_raw = sku.get("option1_value")
+            if option1_value_raw is None:
+                continue
+            option1_value = str(option1_value_raw).strip()
+            if not option1_value:
+                continue
+            option_vector = option1_vectors.get(option1_value)
+            if option_vector is None:
+                continue
+            score = float(np.inner(query_vector, option_vector))
+            if best_score is None or score > best_score:
+                best_index = index
+                best_score = score
+
+        return best_index, best_score
+
+    @staticmethod
+    def _promote_matching_sku(source: Dict[str, Any], match_index: int) -> Optional[Dict[str, Any]]:
+        """Move the matched SKU to the front and swap the SPU image."""
+        skus = source.get("skus")
+        if not isinstance(skus, list) or match_index < 0 or match_index >= len(skus):
+            return None
+
+        matched_sku = skus.pop(match_index)
+        skus.insert(0, matched_sku)
+
+        image_src = matched_sku.get("image_src") or matched_sku.get("imageSrc")
+        if image_src:
+            source["image_url"] = image_src
+        return matched_sku
+
+    def _apply_sku_sorting_for_page_hits(
+        self,
+        es_hits: List[Dict[str, Any]],
+        parsed_query: ParsedQuery,
+        context: Optional[RequestContext] = None,
+    ) -> None:
+        """Sort each page hit's SKUs so the best-matching SKU is first."""
+        if not es_hits:
+            return
+
+        query_texts = self._build_sku_query_texts(parsed_query)
+        unmatched_hits: List[Dict[str, Any]] = []
+        option1_values_to_encode: List[str] = []
+        seen_option1_values = set()
+        text_matched = 0
+        embedding_matched = 0
+
+        for hit in es_hits:
+            source = hit.get("_source")
+            if not isinstance(source, dict):
+                continue
+            skus = source.get("skus")
+            if not isinstance(skus, list) or not skus:
+                continue
+
+            match_index = self._find_query_matching_sku_index(skus, query_texts)
+            if match_index is not None:
+                self._promote_matching_sku(source, match_index)
+                text_matched += 1
+                continue
+
+            unmatched_hits.append(hit)
+            for sku in skus:
+                option1_value_raw = sku.get("option1_value")
+                if option1_value_raw is None:
+                    continue
+                option1_value = str(option1_value_raw).strip()
+                if not option1_value or option1_value in seen_option1_values:
+                    continue
+                seen_option1_values.add(option1_value)
+                option1_values_to_encode.append(option1_value)
+
+        if not unmatched_hits or not option1_values_to_encode:
+            return
+
+        query_vector = getattr(parsed_query, "query_vector", None)
+        if query_vector is None:
+            query_vector = self._encode_query_vector_for_sku_matching(parsed_query, context=context)
+        if query_vector is None:
+            return
+
+        text_encoder = getattr(self.query_parser, "text_encoder", None)
+        if text_encoder is None:
+            return
+
+        try:
+            encoded_option_vectors = text_encoder.encode(option1_values_to_encode, priority=1)
+        except Exception as exc:
+            logger.warning("Failed to encode SKU option1 values for final-page sorting: %s", exc, exc_info=True)
+            if context is not None:
+                context.add_warning(f"SKU option embedding failed: {exc}")
+            return
+
+        option1_vectors: Dict[str, np.ndarray] = {}
+        for option1_value, vector in zip(option1_values_to_encode, encoded_option_vectors):
+            if vector is None:
+                continue
+            option1_vectors[option1_value] = np.asarray(vector, dtype=np.float32)
+
+        query_vector_array = np.asarray(query_vector, dtype=np.float32)
+        for hit in unmatched_hits:
+            source = hit.get("_source")
+            if not isinstance(source, dict):
+                continue
+            skus = source.get("skus")
+            if not isinstance(skus, list) or not skus:
+                continue
+            match_index, _ = self._select_sku_by_embedding(skus, option1_vectors, query_vector_array)
+            if match_index is None:
+                continue
+            self._promote_matching_sku(source, match_index)
+            embedding_matched += 1
+
+        if text_matched or embedding_matched:
+            logger.info(
+                "Final-page SKU sorting completed | text_matched=%s | embedding_matched=%s",
+                text_matched,
+                embedding_matched,
+            )
+
     def search(
         self,
         query: str,
@@ -622,6 +848,8 @@ class Searcher:
                         continue
                     rerank_debug_by_doc[str(doc_id)] = item
 
+            self._apply_sku_sorting_for_page_hits(es_hits, parsed_query, context=context)
+
             # Format results using ResultFormatter
             formatted_results = ResultFormatter.format_search_results(
                 es_hits,
diff --git a/tests/test_search_rerank_window.py b/tests/test_search_rerank_window.py
index bf23c9d..ef093c9 100644
--- a/tests/test_search_rerank_window.py
+++ b/tests/test_search_rerank_window.py
@@ -5,6 +5,7 @@ from pathlib import Path
 from types import SimpleNamespace
 from typing import Any, Dict, List
 
+import numpy as np
 import yaml
 
 from config import (
@@ -157,9 +158,7 @@ def _build_search_config(*, rerank_enabled: bool = True, rerank_window: int = 38
         rerank=RerankConfig(enabled=rerank_enabled, rerank_window=rerank_window),
         spu_config=SPUConfig(enabled=False),
         es_index_name="test_products",
-        tenant_config={},
         es_settings={},
-        services={},
     )
 
 
@@ -173,6 +172,19 @@ def _build_searcher(config: SearchConfig, es_client: _FakeESClient) -> Searcher:
     return searcher
 
 
+class _FakeTextEncoder:
+    def __init__(self, vectors: Dict[str, List[float]]):
+        self.vectors = {
+            key: np.array(value, dtype=np.float32)
+            for key, value in vectors.items()
+        }
+
+    def encode(self, sentences, priority: int = 0, **kwargs):
+        if isinstance(sentences, str):
+            sentences = [sentences]
+        return np.array([self.vectors[text] for text in sentences], dtype=object)
+
+
 def test_config_loader_rerank_enabled_defaults_true(tmp_path: Path):
     config_data = {
         "es_index_name": "test_products",
@@ -327,3 +339,116 @@ def test_searcher_skips_rerank_when_page_exceeds_window(monkeypatch):
     assert es_client.calls[0]["size"] == 10
     assert es_client.calls[0]["include_named_queries_score"] is False
     assert len(es_client.calls) == 1
+
+
+def test_searcher_promotes_sku_when_option1_matches_translated_query(monkeypatch):
+    es_client = _FakeESClient(total_hits=1)
+    searcher = _build_searcher(_build_search_config(rerank_enabled=False), es_client)
+    context = create_request_context(reqid="sku-text", uid="u-sku-text")
+
+    monkeypatch.setattr(
+        "search.searcher.get_tenant_config_loader",
+        lambda: SimpleNamespace(get_tenant_config=lambda tenant_id: {"index_languages": ["en", "zh"]}),
+    )
+
+    class _TranslatedQueryParser:
+        text_encoder = None
+
+        def parse(self, query: str, tenant_id: str, generate_vector: bool, context: Any):
+            return _FakeParsedQuery(
+                original_query=query,
+                query_normalized=query,
+                rewritten_query=query,
+                translations={"en": "black dress"},
+            )
+
+    searcher.query_parser = _TranslatedQueryParser()
+
+    def _full_source_with_skus(doc_id: str) -> Dict[str, Any]:
+        return {
+            "spu_id": doc_id,
+            "title": {"en": f"product-{doc_id}"},
+            "brief": {"en": f"brief-{doc_id}"},
+            "vendor": {"en": f"vendor-{doc_id}"},
+            "image_url": "https://img/default.jpg",
+            "skus": [
+                {"sku_id": "sku-red", "option1_value": "Red", "image_src": "https://img/red.jpg"},
+                {"sku_id": "sku-black", "option1_value": "Black", "image_src": "https://img/black.jpg"},
+            ],
+        }
+
+    monkeypatch.setattr(_FakeESClient, "_full_source", staticmethod(_full_source_with_skus))
+
+    result = searcher.search(
+        query="黑色 连衣裙",
+        tenant_id="162",
+        from_=0,
+        size=1,
+        context=context,
+        enable_rerank=False,
+    )
+
+    assert len(result.results) == 1
+    assert result.results[0].skus[0].sku_id == "sku-black"
+    assert result.results[0].image_url == "https://img/black.jpg"
+
+
+def test_searcher_promotes_sku_by_embedding_when_query_has_no_direct_option_match(monkeypatch):
+    es_client = _FakeESClient(total_hits=1)
+    searcher = _build_searcher(_build_search_config(rerank_enabled=False), es_client)
+    context = create_request_context(reqid="sku-embed", uid="u-sku-embed")
+
+    monkeypatch.setattr(
+        "search.searcher.get_tenant_config_loader",
+        lambda: SimpleNamespace(get_tenant_config=lambda tenant_id: {"index_languages": ["en"]}),
+    )
+
+    encoder = _FakeTextEncoder(
+        {
+            "linen summer dress": [0.8, 0.2],
+            "Red": [1.0, 0.0],
+            "Blue": [0.0, 1.0],
+        }
+    )
+
+    class _EmbeddingQueryParser:
+        text_encoder = encoder
+
+        def parse(self, query: str, tenant_id: str, generate_vector: bool, context: Any):
+            return _FakeParsedQuery(
+                original_query=query,
+                query_normalized=query,
+                rewritten_query=query,
+                translations={},
+                query_vector=np.array([0.0, 1.0], dtype=np.float32),
+            )
+
+    searcher.query_parser = _EmbeddingQueryParser()
+
+    def _full_source_with_skus(doc_id: str) -> Dict[str, Any]:
+        return {
+            "spu_id": doc_id,
+            "title": {"en": f"product-{doc_id}"},
+            "brief": {"en": f"brief-{doc_id}"},
+            "vendor": {"en": f"vendor-{doc_id}"},
+            "image_url": "https://img/default.jpg",
+            "skus": [
+                {"sku_id": "sku-red", "option1_value": "Red", "image_src": "https://img/red.jpg"},
+                {"sku_id": "sku-blue", "option1_value": "Blue", "image_src": "https://img/blue.jpg"},
+            ],
+        }
+
+    monkeypatch.setattr(_FakeESClient, "_full_source", staticmethod(_full_source_with_skus))
+
+    result = searcher.search(
+        query="linen summer dress",
+        tenant_id="162",
+        from_=0,
+        size=1,
+        context=context,
+        enable_rerank=False,
+    )
+
+    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"
--
libgit2 0.21.2