search/rerank_client.py

"""
重排客户端：调用外部 BGE 重排服务，并对 ES 分数与重排分数进行融合。
流程：
1. 从 ES hits 构造用于重排的文档文本列表
2. POST 请求到重排服务 /rerank，获取每条文档的 relevance 分数
3. 提取 ES 文本/向量子句分数，与重排分数做乘法融合并重排序
"""
from typing import Dict, Any, List, Optional, Tuple
import logging
from config.schema import CoarseRankFusionConfig, RerankFusionConfig
from providers import create_rerank_provider
logger = logging.getLogger(__name__)
# 历史配置项，保留签名兼容；当前乘法融合公式不再使用线性权重。
DEFAULT_WEIGHT_ES = 0.4
DEFAULT_WEIGHT_AI = 0.6
# 重排服务默认超时（文档较多时需更大，建议 config 中 timeout_sec 调大）
DEFAULT_TIMEOUT_SEC = 15.0
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）。
    使用 doc_template 将文档字段组装为重排服务输入。
    支持占位符：{title} {brief} {vendor} {description} {category_path}
    Args:
        es_hits: ES 返回的 hits 列表，每项含 _source
        language: 语言代码，如 "zh"、"en"
    Returns:
        与 es_hits 等长的字符串列表，用于 POST /rerank 的 docs
    """
    lang = (language or "zh").strip().lower()
    if lang not in ("zh", "en"):
        lang = "zh"
    def pick_lang_text(obj: Any) -> str:
        if obj is None:
            return ""
        if isinstance(obj, dict):
            return str(obj.get(lang) or obj.get("zh") or obj.get("en") or "").strip()
        return str(obj).strip()
    class _SafeDict(dict):
        def __missing__(self, key: str) -> str:
            return ""
    docs: List[str] = []
    only_title = "{title}" == doc_template
    need_brief = "{brief}" in doc_template
    need_vendor = "{vendor}" in doc_template
    need_description = "{description}" in doc_template
    need_category_path = "{category_path}" in doc_template
    for hit in es_hits:
        src = hit.get("_source") or {}
        title_suffix = str(hit.get("_style_rerank_suffix") or "").strip()
        title_str=(
            f"{pick_lang_text(src.get('title'))} {title_suffix}".strip()
            if title_suffix
            else pick_lang_text(src.get("title"))
        )
        title_str = str(title_str).strip()
        if only_title:
            doc_text = title_str
            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": title_str,
                    },
                    "doc_preview": preview,
                    "doc_length": len(doc_text),
                })
        else:
            values = _SafeDict(
                title=title_str,
                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 "",
            )
            doc_text = str(doc_template).format_map(values)
            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": title_str,
                        "brief": values.get("brief") or None,
                        "vendor": values.get("vendor") or None,
                        "category_path": values.get("category_path") or None
                    },
                    "doc_preview": preview,
                    "doc_length": len(doc_text),
                })
        docs.append(doc_text)
    return docs
def call_rerank_service(
    query: str,
    docs: List[str],
    timeout_sec: float = DEFAULT_TIMEOUT_SEC,
    top_n: Optional[int] = None,
    service_profile: Optional[str] = None,
) -> Tuple[Optional[List[float]], Optional[Dict[str, Any]]]:
    """
    调用重排服务 POST /rerank，返回分数列表与 meta。
    Provider 和 URL 从 services_config 读取。
    """
    if not docs:
        return [], {}
    try:
        client = create_rerank_provider(service_profile=service_profile)
        return client.rerank(query=query, docs=docs, timeout_sec=timeout_sec, top_n=top_n)
    except Exception as e:
        logger.warning("Rerank request failed: %s", e, exc_info=True)
        return None, None
def _to_score(value: Any) -> float:
    try:
        if value is None:
            return 0.0
        return float(value)
    except (TypeError, ValueError):
        return 0.0
def _extract_named_query_score(matched_queries: Any, name: str) -> float:
    if isinstance(matched_queries, dict):
        return _to_score(matched_queries.get(name))
    if isinstance(matched_queries, list):
        return 1.0 if name in matched_queries else 0.0
    return 0.0
def _collect_knn_score_components(
    matched_queries: Any,
    fusion: RerankFusionConfig,
) -> Dict[str, float]:
    text_knn_score = _extract_named_query_score(matched_queries, "knn_query")
    image_knn_score = _extract_named_query_score(matched_queries, "image_knn_query")
    weighted_text_knn_score = text_knn_score * float(fusion.knn_text_weight)
    weighted_image_knn_score = image_knn_score * float(fusion.knn_image_weight)
    weighted_components = [weighted_text_knn_score, weighted_image_knn_score]
    primary_knn_score = max(weighted_components)
    support_knn_score = sum(weighted_components) - primary_knn_score
    knn_score = primary_knn_score + float(fusion.knn_tie_breaker) * support_knn_score
    return {
        "text_knn_score": text_knn_score,
        "image_knn_score": image_knn_score,
        "weighted_text_knn_score": weighted_text_knn_score,
        "weighted_image_knn_score": weighted_image_knn_score,
        "primary_knn_score": primary_knn_score,
        "support_knn_score": support_knn_score,
        "knn_score": knn_score,
    }
"""
原始变量：
ES总分
source_score：从 ES 返回的 matched_queries 里取 base_query 这条 named query 的分（dict 用具体分数；list 形式则“匹配到名字就算 1.0”）。
translation_score：所有名字以 base_query_trans_ 开头的 named query 的分，在 dict 里取 最大值；在 list 里只要存在这类名字就记为 1.0。
中间变量：计算原始query得分和翻译query得分
weighted_source :
weighted_translation : 0.8 * translation_score
区分主信号和辅助信号：
合成primary_text_score和support_text_score，取 更强 的那一路（原文检索 vs 翻译检索）作为主信号
primary_text_score : max(weighted_source, weighted_translation)
support_text_score : weighted_source + weighted_translation - primary_text_score
主信号和辅助信号的融合：dismax融合公式
最终text_score：主信号 + 0.25 * 辅助信号
text_score : primary_text_score + 0.25 * support_text_score
"""
def _collect_text_score_components(matched_queries: Any, fallback_es_score: float) -> Dict[str, float]:
    source_score = _extract_named_query_score(matched_queries, "base_query")
    translation_score = 0.0
    if isinstance(matched_queries, dict):
        for query_name, score in matched_queries.items():
            if not isinstance(query_name, str):
                continue
            numeric_score = _to_score(score)
            if query_name.startswith("base_query_trans_"):
                translation_score = max(translation_score, numeric_score)
    elif isinstance(matched_queries, list):
        for query_name in matched_queries:
            if not isinstance(query_name, str):
                continue
            if query_name.startswith("base_query_trans_"):
                translation_score = 1.0
    weighted_source = source_score
    weighted_translation = 0.8 * translation_score
    weighted_components = [weighted_source, weighted_translation]
    primary_text_score = max(weighted_components)
    support_text_score = sum(weighted_components) - primary_text_score
    text_score = primary_text_score + 0.25 * support_text_score
    if text_score <= 0.0:
        text_score = fallback_es_score
        weighted_source = fallback_es_score
        primary_text_score = fallback_es_score
        support_text_score = 0.0
    return {
        "source_score": source_score,
        "translation_score": translation_score,
        "weighted_source_score": weighted_source,
        "weighted_translation_score": weighted_translation,
        "primary_text_score": primary_text_score,
        "support_text_score": support_text_score,
        "text_score": text_score,
    }
def _format_debug_float(value: float) -> str:
    return f"{float(value):.6g}"
def _build_hit_signal_bundle(
    hit: Dict[str, Any],
    fusion: CoarseRankFusionConfig | RerankFusionConfig,
) -> Dict[str, Any]:
    es_score = _to_score(hit.get("_score"))
    matched_queries = hit.get("matched_queries")
    text_components = _collect_text_score_components(matched_queries, es_score)
    knn_components = _collect_knn_score_components(matched_queries, fusion)
    return {
        "doc_id": hit.get("_id"),
        "es_score": es_score,
        "matched_queries": matched_queries,
        "text_components": text_components,
        "knn_components": knn_components,
        "text_score": text_components["text_score"],
        "knn_score": knn_components["knn_score"],
    }
def _build_formula_summary(
    term_rows: List[Dict[str, Any]],
    style_boost: float,
    final_score: float,
) -> str:
    segments = [
        (
            f"{row['name']}=("
            f"{_format_debug_float(row['raw_score'])}"
            f"+{_format_debug_float(row['bias'])})"
            f"^{_format_debug_float(row['exponent'])}"
            f"={_format_debug_float(row['factor'])}"
        )
        for row in term_rows
    ]
    if style_boost != 1.0:
        segments.append(f"style_boost={_format_debug_float(style_boost)}")
    segments.append(f"final={_format_debug_float(final_score)}")
    return " | ".join(segments)
def _compute_multiplicative_fusion(
    *,
    text_score: float,
    knn_score: float,
    fusion: RerankFusionConfig,
    rerank_score: Optional[float] = None,
    fine_score: Optional[float] = None,
    style_boost: float = 1.0,
) -> Dict[str, Any]:
    term_rows: List[Dict[str, Any]] = []
    def _add_term(name: str, raw_score: Optional[float], bias: float, exponent: float) -> None:
        if raw_score is None:
            return
        factor = (max(float(raw_score), 0.0) + bias) ** exponent
        term_rows.append(
            {
                "name": name,
                "raw_score": float(raw_score),
                "bias": float(bias),
                "exponent": float(exponent),
                "factor": factor,
            }
        )
    _add_term("rerank_score", rerank_score, fusion.rerank_bias, fusion.rerank_exponent)
    _add_term("fine_score", fine_score, fusion.fine_bias, fusion.fine_exponent)
    _add_term("text_score", text_score, fusion.text_bias, fusion.text_exponent)
    _add_term("knn_score", knn_score, fusion.knn_bias, fusion.knn_exponent)
    fused = 1.0
    factors: Dict[str, float] = {}
    inputs: Dict[str, float] = {}
    for row in term_rows:
        fused *= row["factor"]
        factors[row["name"]] = row["factor"]
        inputs[row["name"]] = row["raw_score"]
    fused *= style_boost
    factors["style_boost"] = style_boost
    return {
        "inputs": inputs,
        "factors": factors,
        "score": fused,
        "summary": _build_formula_summary(term_rows, style_boost, fused),
    }
def _multiply_coarse_fusion_factors(
    text_score: float,
    knn_score: float,
    fusion: CoarseRankFusionConfig,
) -> Tuple[float, float, float]:
    text_factor = (max(text_score, 0.0) + fusion.text_bias) ** fusion.text_exponent
    knn_factor = (max(knn_score, 0.0) + fusion.knn_bias) ** fusion.knn_exponent
    return text_factor, knn_factor, text_factor * knn_factor
def _has_selected_sku(hit: Dict[str, Any]) -> bool:
    return bool(str(hit.get("_style_rerank_suffix") or "").strip())
def coarse_resort_hits(
    es_hits: List[Dict[str, Any]],
    fusion: Optional[CoarseRankFusionConfig] = None,
    debug: bool = False,
) -> List[Dict[str, Any]]:
    """Coarse rank with text/knn fusion only."""
    if not es_hits:
        return []
    f = fusion or CoarseRankFusionConfig()
    coarse_debug: List[Dict[str, Any]] = [] if debug else []
    for hit in es_hits:
        signal_bundle = _build_hit_signal_bundle(hit, f)
        es_score = signal_bundle["es_score"]
        matched_queries = signal_bundle["matched_queries"]
        text_components = signal_bundle["text_components"]
        knn_components = signal_bundle["knn_components"]
        text_score = signal_bundle["text_score"]
        knn_score = signal_bundle["knn_score"]
        text_factor, knn_factor, coarse_score = _multiply_coarse_fusion_factors(
            text_score=text_score,
            knn_score=knn_score,
            fusion=f,
        )
        hit["_text_score"] = text_score
        hit["_knn_score"] = knn_score
        hit["_text_knn_score"] = knn_components["text_knn_score"]
        hit["_image_knn_score"] = knn_components["image_knn_score"]
        hit["_coarse_score"] = coarse_score
        if debug:
            coarse_debug.append(
                {
                    "doc_id": hit.get("_id"),
                    "es_score": es_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
                    ),
                    "text_knn_score": knn_components["text_knn_score"],
                    "image_knn_score": knn_components["image_knn_score"],
                    "weighted_text_knn_score": knn_components["weighted_text_knn_score"],
                    "weighted_image_knn_score": knn_components["weighted_image_knn_score"],
                    "knn_primary_score": knn_components["primary_knn_score"],
                    "knn_support_score": knn_components["support_knn_score"],
                    "knn_score": knn_score,
                    "coarse_text_factor": text_factor,
                    "coarse_knn_factor": knn_factor,
                    "coarse_score": coarse_score,
                    "matched_queries": matched_queries,
                }
            )
    es_hits.sort(key=lambda h: h.get("_coarse_score", h.get("_score", 0.0)), reverse=True)
    return coarse_debug
def fuse_scores_and_resort(
    es_hits: List[Dict[str, Any]],
    rerank_scores: List[float],
    fine_scores: Optional[List[float]] = None,
    weight_es: float = DEFAULT_WEIGHT_ES,
    weight_ai: float = DEFAULT_WEIGHT_AI,
    fusion: Optional[RerankFusionConfig] = None,
    style_intent_selected_sku_boost: float = 1.2,
    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 * sku_boost
    其中 sku_boost 仅在当前 hit 已选中 SKU 时生效，默认值为 1.2，可通过
    ``query.style_intent.selected_sku_boost`` 配置。
    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
    - _fused_score: 融合分数
    - _text_score: 文本相关性分数（优先取 named queries 的 base_query 分数）
    - _knn_score: KNN 分数（优先取 named queries 的 knn_query 分数）
    Args:
        es_hits: ES hits 列表（会被原地修改）
        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
    """
    n = len(es_hits)
    if n == 0 or len(rerank_scores) != n:
        return []
    f = fusion or RerankFusionConfig()
    fused_debug: List[Dict[str, Any]] = [] if debug else []
    for idx, hit in enumerate(es_hits):
        signal_bundle = _build_hit_signal_bundle(hit, f)
        text_components = signal_bundle["text_components"]
        knn_components = signal_bundle["knn_components"]
        text_score = signal_bundle["text_score"]
        knn_score = signal_bundle["knn_score"]
        rerank_score = _to_score(rerank_scores[idx])
        fine_score_raw = (
            _to_score(fine_scores[idx])
            if fine_scores is not None and len(fine_scores) == n
            else _to_score(hit.get("_fine_score"))
        )
        fine_score = fine_score_raw if (fine_scores is not None and len(fine_scores) == n) or "_fine_score" in hit else None
        sku_selected = _has_selected_sku(hit)
        style_boost = style_intent_selected_sku_boost if sku_selected else 1.0
        fusion_result = _compute_multiplicative_fusion(
            rerank_score=rerank_score,
            fine_score=fine_score,
            text_score=text_score,
            knn_score=knn_score,
            fusion=f,
            style_boost=style_boost,
        )
        fused = fusion_result["score"]
        hit["_original_score"] = hit.get("_score")
        hit["_rerank_score"] = rerank_score
        if fine_score is not None:
            hit["_fine_score"] = fine_score
        hit["_text_score"] = text_score
        hit["_knn_score"] = knn_score
        hit["_text_knn_score"] = knn_components["text_knn_score"]
        hit["_image_knn_score"] = knn_components["image_knn_score"]
        hit["_fused_score"] = fused
        hit["_style_intent_selected_sku_boost"] = style_boost
        if debug:
            debug_entry = {
                "doc_id": hit.get("_id"),
                "score": fused,
                "es_score": signal_bundle["es_score"],
                "rerank_score": rerank_score,
                "fine_score": fine_score,
                "text_score": text_score,
                "knn_score": knn_score,
                "fusion_inputs": fusion_result["inputs"],
                "fusion_factors": fusion_result["factors"],
                "fusion_summary": fusion_result["summary"],
                "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_knn_score": knn_components["text_knn_score"],
                "image_knn_score": knn_components["image_knn_score"],
                "weighted_text_knn_score": knn_components["weighted_text_knn_score"],
                "weighted_image_knn_score": knn_components["weighted_image_knn_score"],
                "knn_primary_score": knn_components["primary_knn_score"],
                "knn_support_score": knn_components["support_knn_score"],
                "text_score_fallback_to_es": (
                    text_score == signal_bundle["es_score"]
                    and text_components["source_score"] <= 0.0
                    and text_components["translation_score"] <= 0.0
                ),
                "rerank_factor": fusion_result["factors"].get("rerank_score"),
                "fine_factor": fusion_result["factors"].get("fine_score"),
                "text_factor": fusion_result["factors"].get("text_score"),
                "knn_factor": fusion_result["factors"].get("knn_score"),
                "style_intent_selected_sku": sku_selected,
                "style_intent_selected_sku_boost": style_boost,
                "matched_queries": signal_bundle["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)
    es_hits.sort(
        key=lambda h: h.get("_fused_score", h.get("_score", 0.0)),
        reverse=True,
    )
    return fused_debug
def run_rerank(
    query: str,
    es_response: Dict[str, Any],
    language: str = "zh",
    timeout_sec: float = DEFAULT_TIMEOUT_SEC,
    weight_es: float = DEFAULT_WEIGHT_ES,
    weight_ai: float = DEFAULT_WEIGHT_AI,
    rerank_query_template: str = "{query}",
    rerank_doc_template: str = "{title}",
    top_n: Optional[int] = None,
    debug: bool = False,
    fusion: Optional[RerankFusionConfig] = None,
    style_intent_selected_sku_boost: float = 1.2,
    fine_scores: Optional[List[float]] = None,
    service_profile: Optional[str] = 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。
    Provider 和 URL 从 services_config 读取。
    top_n 可选；若传入，会透传给 /rerank（供云后端按 page+size 做部分重排）。
    """
    hits = es_response.get("hits", {}).get("hits") or []
    if not hits:
        return es_response, None, []
    query_text = str(rerank_query_template).format_map({"query": query})
    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,
        timeout_sec=timeout_sec,
        top_n=top_n,
        service_profile=service_profile,
    )
    if scores is None or len(scores) != len(hits):
        return es_response, None, []
    fused_debug = fuse_scores_and_resort(
        hits,
        scores,
        fine_scores=fine_scores,
        weight_es=weight_es,
        weight_ai=weight_ai,
        fusion=fusion,
        style_intent_selected_sku_boost=style_intent_selected_sku_boost,
        debug=debug,
        rerank_debug_rows=rerank_debug_rows,
    )
    # 更新 max_score 为融合后的最高分
    if hits:
        top = hits[0].get("_fused_score", hits[0].get("_score", 0.0)) or 0.0
        if "hits" in es_response:
            es_response["hits"]["max_score"] = top
    return es_response, meta, fused_debug
def run_lightweight_rerank(
    query: str,
    es_hits: List[Dict[str, Any]],
    language: str = "zh",
    timeout_sec: float = DEFAULT_TIMEOUT_SEC,
    rerank_query_template: str = "{query}",
    rerank_doc_template: str = "{title}",
    top_n: Optional[int] = None,
    debug: bool = False,
    fusion: Optional[RerankFusionConfig] = None,
    style_intent_selected_sku_boost: float = 1.2,
    service_profile: Optional[str] = "fine",
) -> Tuple[Optional[List[float]], Optional[Dict[str, Any]], List[Dict[str, Any]]]:
    """Call lightweight reranker and rank by lightweight-model fusion."""
    if not es_hits:
        return [], {}, []
    query_text = str(rerank_query_template).format_map({"query": query})
    rerank_debug_rows: Optional[List[Dict[str, Any]]] = [] if debug else None
    docs = build_docs_from_hits(
        es_hits,
        language=language,
        doc_template=rerank_doc_template,
        debug_rows=rerank_debug_rows,
    )
    scores, meta = call_rerank_service(
        query_text,
        docs,
        timeout_sec=timeout_sec,
        top_n=top_n,
        service_profile=service_profile,
    )
    if scores is None or len(scores) != len(es_hits):
        return None, None, []
    f = fusion or RerankFusionConfig()
    debug_rows: List[Dict[str, Any]] = [] if debug else []
    for idx, hit in enumerate(es_hits):
        signal_bundle = _build_hit_signal_bundle(hit, f)
        text_score = signal_bundle["text_score"]
        knn_score = signal_bundle["knn_score"]
        fine_score = _to_score(scores[idx])
        sku_selected = _has_selected_sku(hit)
        style_boost = style_intent_selected_sku_boost if sku_selected else 1.0
        fusion_result = _compute_multiplicative_fusion(
            fine_score=fine_score,
            text_score=text_score,
            knn_score=knn_score,
            fusion=f,
            style_boost=style_boost,
        )
        hit["_fine_score"] = fine_score
        hit["_fine_fused_score"] = fusion_result["score"]
        hit["_text_score"] = text_score
        hit["_knn_score"] = knn_score
        hit["_text_knn_score"] = signal_bundle["knn_components"]["text_knn_score"]
        hit["_image_knn_score"] = signal_bundle["knn_components"]["image_knn_score"]
        hit["_style_intent_selected_sku_boost"] = style_boost
        if debug:
            row: Dict[str, Any] = {
                "doc_id": hit.get("_id"),
                "score": fusion_result["score"],
                "fine_score": fine_score,
                "text_score": text_score,
                "knn_score": knn_score,
                "fusion_inputs": fusion_result["inputs"],
                "fusion_factors": fusion_result["factors"],
                "fusion_summary": fusion_result["summary"],
                "fine_factor": fusion_result["factors"].get("fine_score"),
                "text_factor": fusion_result["factors"].get("text_score"),
                "knn_factor": fusion_result["factors"].get("knn_score"),
                "style_intent_selected_sku": sku_selected,
                "style_intent_selected_sku_boost": style_boost,
            }
            if rerank_debug_rows is not None and idx < len(rerank_debug_rows):
                row["rerank_input"] = rerank_debug_rows[idx]
            debug_rows.append(row)
    es_hits.sort(key=lambda h: h.get("_fine_fused_score", h.get("_fine_score", 0.0)), reverse=True)
    return scores, meta, debug_rows