"""
  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 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,

  ) -> 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()

          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_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 _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_without_style_boost)."""

      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 _has_selected_sku(hit: Dict[str, Any]) -> bool:
      return bool(str(hit.get("_style_rerank_suffix") or "").strip())
  
  

  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,

      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):

          es_score = _to_score(hit.get("_score"))

          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, text_factor, knn_factor, fused = _multiply_fusion_factors(
              rerank_score, text_score, knn_score, f
          )

          sku_selected = _has_selected_sku(hit)
          style_boost = style_intent_selected_sku_boost if sku_selected else 1.0
          fused *= style_boost

  
          hit["_original_score"] = hit.get("_score")

          hit["_rerank_score"] = rerank_score

          hit["_text_score"] = text_score
          hit["_knn_score"] = knn_score

          hit["_fused_score"] = fused

          hit["_style_intent_selected_sku_boost"] = style_boost

          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 = {
                  "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,

                  "style_intent_selected_sku": sku_selected,
                  "style_intent_selected_sku_boost": style_boost,

                  "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)

      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,

  ) -> 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,

      )

  
      if scores is None or len(scores) != len(hits):
          return es_response, None, []
  
      fused_debug = fuse_scores_and_resort(
          hits,
          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