"""
Main Searcher module - executes search queries against Elasticsearch.

Handles query parsing, ranking, and result formatting.
"""

from typing import Dict, Any, List, Optional, Union, Tuple
import os
import time, json
import logging
import hashlib
from string import Formatter
import numpy as np

from utils.es_client import ESClient
from query import QueryParser, ParsedQuery
from embeddings.image_encoder import CLIPImageEncoder
from .es_query_builder import ESQueryBuilder
from config import SearchConfig
from config.tenant_config_loader import get_tenant_config_loader
from context.request_context import RequestContext, RequestContextStage
from api.models import FacetResult, FacetValue, FacetConfig
from api.result_formatter import ResultFormatter
from indexer.mapping_generator import get_tenant_index_name

logger = logging.getLogger(__name__)
backend_verbose_logger = logging.getLogger("backend.verbose")


def _log_backend_verbose(payload: Dict[str, Any]) -> None:
    if not backend_verbose_logger.handlers:
        return
    backend_verbose_logger.info(
        json.dumps(payload, ensure_ascii=False, separators=(",", ":"))
    )


class SearchResult:
    """Container for search results (外部友好格式)."""

    def __init__(
        self,
        results: List[Any],  # List[SpuResult]
        total: int,
        max_score: float,
        took_ms: int,
        facets: Optional[List[FacetResult]] = None,
        query_info: Optional[Dict[str, Any]] = None,
        suggestions: Optional[List[str]] = None,
        related_searches: Optional[List[str]] = None,
        debug_info: Optional[Dict[str, Any]] = None
    ):
        self.results = results
        self.total = total
        self.max_score = max_score
        self.took_ms = took_ms
        self.facets = facets
        self.query_info = query_info or {}
        self.suggestions = suggestions or []
        self.related_searches = related_searches or []
        self.debug_info = debug_info

    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary representation."""
        result = {
            "results": [r.model_dump() if hasattr(r, 'model_dump') else r for r in self.results],
            "total": self.total,
            "max_score": self.max_score,
            "took_ms": self.took_ms,
            "facets": [f.model_dump() for f in self.facets] if self.facets else None,
            "query_info": self.query_info,
            "suggestions": self.suggestions,
            "related_searches": self.related_searches
        }
        if self.debug_info is not None:
            result["debug_info"] = self.debug_info
        return result


class Searcher:
    """
    Main search engine class.

    Handles:
    - Query parsing and translation
    - Dynamic multi-language text recall planning
    - ES query building
    - Result ranking and formatting
    """

    def __init__(
        self,
        es_client: ESClient,
        config: SearchConfig,
        query_parser: Optional[QueryParser] = None,
        image_encoder: Optional[CLIPImageEncoder] = None,
    ):
        """
        Initialize searcher.

        Args:
            es_client: Elasticsearch client
            config: SearchConfig instance
            query_parser: Query parser (created if not provided)
            image_encoder: Optional pre-initialized image encoder
        """
        self.es_client = es_client
        self.config = config
        # Index name is now generated dynamically per tenant, no longer stored here
        self.query_parser = query_parser or QueryParser(config)
        self.text_embedding_field = config.query_config.text_embedding_field or "title_embedding"
        self.image_embedding_field = config.query_config.image_embedding_field
        if self.image_embedding_field and image_encoder is None:
            self.image_encoder = CLIPImageEncoder()
        else:
            self.image_encoder = image_encoder
        self.source_fields = config.query_config.source_fields

        # Query builder - simplified single-layer architecture
        self.query_builder = ESQueryBuilder(
            match_fields=[],
            field_boosts=self.config.field_boosts,
            multilingual_fields=self.config.query_config.multilingual_fields,
            shared_fields=self.config.query_config.shared_fields,
            core_multilingual_fields=self.config.query_config.core_multilingual_fields,
            text_embedding_field=self.text_embedding_field,
            image_embedding_field=self.image_embedding_field,
            source_fields=self.source_fields,
            function_score_config=self.config.function_score,
            default_language=self.config.query_config.default_language,
            knn_boost=self.config.query_config.knn_boost,
            base_minimum_should_match=self.config.query_config.base_minimum_should_match,
            translation_minimum_should_match=self.config.query_config.translation_minimum_should_match,
            translation_boost=self.config.query_config.translation_boost,
            tie_breaker_base_query=self.config.query_config.tie_breaker_base_query,
        )

    def _apply_source_filter(self, es_query: Dict[str, Any]) -> None:
        """
        Apply tri-state _source semantics:
        - None: do not set _source (return full source)
        - []: _source=false (return no source fields)
        - [..]: _source.includes=[..]
        """
        if self.source_fields is None:
            return
        if not isinstance(self.source_fields, list):
            raise ValueError("query_config.source_fields must be null or list[str]")
        if len(self.source_fields) == 0:
            es_query["_source"] = False
            return
        es_query["_source"] = {"includes": self.source_fields}

    def _resolve_rerank_source_filter(self, doc_template: str) -> Dict[str, Any]:
        """
        Build a lightweight _source filter for rerank prefetch.

        Only fetch fields required by rerank doc template to reduce ES payload.
        """
        field_map = {
            "title": "title",
            "brief": "brief",
            "vendor": "vendor",
            "description": "description",
            "category_path": "category_path",
        }
        includes: set[str] = set()
        template = str(doc_template or "{title}")
        for _, field_name, _, _ in Formatter().parse(template):
            if not field_name:
                continue
            key = field_name.split(".", 1)[0].split("!", 1)[0].split(":", 1)[0]
            mapped = field_map.get(key)
            if mapped:
                includes.add(mapped)

        # Fallback to title-only to keep rerank docs usable.
        if not includes:
            includes.add("title")

        return {"includes": sorted(includes)}

    def _fetch_hits_by_ids(
        self,
        index_name: str,
        doc_ids: List[str],
        source_spec: Optional[Any],
    ) -> tuple[Dict[str, Dict[str, Any]], int]:
        """
        Fetch page documents by IDs for final response fill.

        Returns:
            (hits_by_id, es_took_ms)
        """
        if not doc_ids:
            return {}, 0

        body: Dict[str, Any] = {
            "query": {
                "ids": {
                    "values": doc_ids,
                }
            }
        }
        if source_spec is not None:
            body["_source"] = source_spec

        resp = self.es_client.search(
            index_name=index_name,
            body=body,
            size=len(doc_ids),
            from_=0,
        )
        hits = resp.get("hits", {}).get("hits") or []
        hits_by_id: Dict[str, Dict[str, Any]] = {}
        for hit in hits:
            hid = hit.get("_id")
            if hid is None:
                continue
            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())

    @staticmethod
    def _sku_option1_embedding_key(
        sku: Dict[str, Any],
        spu_option1_name: Optional[Any] = None,
    ) -> Optional[str]:
        """
        Text sent to the embedding service for option1 must be "name:value"
        (option name from SKU row or SPU-level option1_name).
        """
        value_raw = sku.get("option1_value")
        if value_raw is None:
            return None
        value = str(value_raw).strip()
        if not value:
            return None
        name = sku.get("option1_name")
        if name is None or not str(name).strip():
            name = spu_option1_name
        name_str = str(name).strip() if name is not None and str(name).strip() else ""
        if name_str:
            value = f"{name_str}:{value}"
        return value.casefold()

    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)

        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],
        spu_option1_name: Optional[Any] = None,
    ) -> Optional[int]:
        """Return the first SKU whose option1_value (or name: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
            embed_key = self._sku_option1_embedding_key(sku, spu_option1_name)
            if embed_key and embed_key != option1_value:
                composite_norm = self._normalize_sku_match_text(embed_key.replace(":", " "))
                if any(composite_norm in query_text for query_text in query_texts):
                    return index
                if any(embed_key.casefold() 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,
        spu_option1_name: Optional[Any] = None,
    ) -> Tuple[Optional[int], Optional[float]]:
        """Select the SKU whose option1 embedding key (name:value) is most similar to the query."""
        best_index: Optional[int] = None
        best_score: Optional[float] = None

        for index, sku in enumerate(skus):
            embed_key = self._sku_option1_embedding_key(sku, spu_option1_name)
            if not embed_key:
                continue
            option_vector = option1_vectors.get(embed_key)
            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

            spu_option1_name = source.get("option1_name")
            match_index = self._find_query_matching_sku_index(
                skus, query_texts, spu_option1_name=spu_option1_name
            )
            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:
                embed_key = self._sku_option1_embedding_key(sku, spu_option1_name)
                if not embed_key or embed_key in seen_option1_values:
                    continue
                seen_option1_values.add(embed_key)
                option1_values_to_encode.append(embed_key)

        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,
                spu_option1_name=source.get("option1_name"),
            )
            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,
        tenant_id: str,
        size: int = 10,
        from_: int = 0,
        filters: Optional[Dict[str, Any]] = None,
        range_filters: Optional[Dict[str, Any]] = None,
        facets: Optional[List[FacetConfig]] = None,
        min_score: Optional[float] = None,
        context: Optional[RequestContext] = None,
        sort_by: Optional[str] = None,
        sort_order: Optional[str] = "desc",
        debug: bool = False,
        language: str = "en",
        sku_filter_dimension: Optional[List[str]] = None,
        enable_rerank: Optional[bool] = None,
        rerank_query_template: Optional[str] = None,
        rerank_doc_template: Optional[str] = None,
    ) -> SearchResult:
        """
        Execute search query (外部友好格式).

        Args:
            query: Search query string
            tenant_id: Tenant ID (required for filtering)
            size: Number of results to return
            from_: Offset for pagination
            filters: Exact match filters
            range_filters: Range filters for numeric fields
            facets: Facet configurations for faceted search
            min_score: Minimum score threshold
            context: Request context for tracking (required)
            sort_by: Field name for sorting
            sort_order: Sort order: 'asc' or 'desc'
            debug: Enable debug information output
            language: Response / field selection language hint (e.g. zh, en)
            sku_filter_dimension: SKU grouping dimensions for per-SPU variant pick
            enable_rerank: If None, use ``config.rerank.enabled``; if set, overrides
                whether the rerank provider is invoked (subject to rerank window).
            rerank_query_template: Override for rerank query text template; None uses
                ``config.rerank.rerank_query_template`` (e.g. ``"{query}"``).
            rerank_doc_template: Override for per-hit document text passed to rerank;
                None uses ``config.rerank.rerank_doc_template``. Placeholders are
                resolved in ``search/rerank_client.py``.

        Returns:
            SearchResult object with formatted results
        """
        if context is None:
            raise ValueError("context is required")

        # 根据租户配置决定翻译开关（离线/在线统一）
        tenant_loader = get_tenant_config_loader()
        tenant_cfg = tenant_loader.get_tenant_config(tenant_id)
        index_langs = tenant_cfg.get("index_languages") or []
        enable_translation = len(index_langs) > 0
        enable_embedding = self.config.query_config.enable_text_embedding
        rc = self.config.rerank
        effective_query_template = rerank_query_template or rc.rerank_query_template
        effective_doc_template = rerank_doc_template or rc.rerank_doc_template
        # 重排开关优先级：请求参数显式传值 > 服务端配置（默认开启）
        rerank_enabled_by_config = bool(rc.enabled)
        do_rerank = rerank_enabled_by_config if enable_rerank is None else bool(enable_rerank)
        rerank_window = rc.rerank_window
        # 若开启重排且请求范围在窗口内：从 ES 取前 rerank_window 条、重排后再按 from/size 分页；否则不重排，按原 from/size 查 ES
        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

        # Start timing
        context.start_stage(RequestContextStage.TOTAL)

        context.logger.info(
            f"开始搜索请求 | 查询: '{query}' | 参数: size={size}, from_={from_}, "
            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"enable_translation={enable_translation}, enable_embedding={enable_embedding}, min_score={min_score}",
            extra={'reqid': context.reqid, 'uid': context.uid}
        )

        # Store search parameters in context
        context.metadata['search_params'] = {
            'size': size,
            'from_': from_,
            'es_fetch_from': es_fetch_from,
            'es_fetch_size': es_fetch_size,
            'in_rerank_window': in_rerank_window,
            'rerank_enabled_by_config': rerank_enabled_by_config,
            'enable_rerank_request': enable_rerank,
            'rerank_query_template': effective_query_template,
            'rerank_doc_template': effective_doc_template,
            'filters': filters,
            'range_filters': range_filters,
            'facets': facets,
            'enable_translation': enable_translation,
            'enable_embedding': enable_embedding,
            'enable_rerank': do_rerank,
            'min_score': min_score,
            'sort_by': sort_by,
            'sort_order': sort_order
        }

        context.metadata['feature_flags'] = {
            'translation_enabled': enable_translation,
            'embedding_enabled': enable_embedding,
            'rerank_enabled': do_rerank
        }

        # Step 1: Parse query
        context.start_stage(RequestContextStage.QUERY_PARSING)
        try:
            parsed_query = self.query_parser.parse(
                query,
                tenant_id=tenant_id,
                generate_vector=enable_embedding,
                context=context,
                target_languages=index_langs if enable_translation else [],
            )
            # Store query analysis results in context
            context.store_query_analysis(
                original_query=parsed_query.original_query,
                query_normalized=parsed_query.query_normalized,
                rewritten_query=parsed_query.rewritten_query,
                detected_language=parsed_query.detected_language,
                translations=parsed_query.translations,
                query_vector=parsed_query.query_vector.tolist() if parsed_query.query_vector is not None else None,
                domain="default",
                is_simple_query=True
            )

            context.logger.info(
                f"查询解析完成 | 原查询: '{parsed_query.original_query}' | "
                f"重写后: '{parsed_query.rewritten_query}' | "
                f"语言: {parsed_query.detected_language} | "
                f"向量: {'是' if parsed_query.query_vector is not None else '否'}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
        except Exception as e:
            context.set_error(e)
            context.logger.error(
                f"查询解析失败 | 错误: {str(e)}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
            raise
        finally:
            context.end_stage(RequestContextStage.QUERY_PARSING)

        # Step 2: Query building
        context.start_stage(RequestContextStage.QUERY_BUILDING)
        try:
            # Generate tenant-specific index name
            index_name = get_tenant_index_name(tenant_id)
            # index_name = "search_products"
            
            # No longer need to add tenant_id to filters since each tenant has its own index

            es_query = self.query_builder.build_query(
                query_text=parsed_query.rewritten_query or parsed_query.query_normalized,
                query_vector=parsed_query.query_vector if enable_embedding else None,
                filters=filters,
                range_filters=range_filters,
                facet_configs=facets,
                size=es_fetch_size,
                from_=es_fetch_from,
                enable_knn=enable_embedding and parsed_query.query_vector is not None,
                min_score=min_score,
                parsed_query=parsed_query,
                index_languages=index_langs,
            )

            # Add facets for faceted search
            if facets:
                facet_aggs = self.query_builder.build_facets(facets)
                if facet_aggs:
                    if "aggs" not in es_query:
                        es_query["aggs"] = {}
                    es_query["aggs"].update(facet_aggs)

            # Add sorting if specified
            if sort_by:
                es_query = self.query_builder.add_sorting(es_query, sort_by, sort_order)
                es_query["track_scores"] = True

            # Keep requested response _source semantics for the final response fill.
            response_source_spec = es_query.get("_source")

            # In rerank window, first pass only fetches minimal fields required by rerank template.
            es_query_for_fetch = es_query
            rerank_prefetch_source = None
            if in_rerank_window:
                rerank_prefetch_source = self._resolve_rerank_source_filter(effective_doc_template)
                es_query_for_fetch = dict(es_query)
                es_query_for_fetch["_source"] = rerank_prefetch_source

            # Extract size and from from body for ES client parameters
            body_for_es = {k: v for k, v in es_query_for_fetch.items() if k not in ['size', 'from']}

            # Store ES query in context
            context.store_intermediate_result('es_query', es_query)
            if in_rerank_window and rerank_prefetch_source is not None:
                context.store_intermediate_result('es_query_rerank_prefetch_source', rerank_prefetch_source)
            context.store_intermediate_result('es_body_for_search', body_for_es)

            # Serialize ES query to compute a compact size + stable digest for correlation
            es_query_compact = json.dumps(es_query_for_fetch, ensure_ascii=False, separators=(",", ":"))
            es_query_digest = hashlib.sha256(es_query_compact.encode("utf-8")).hexdigest()[:16]
            knn_enabled = bool(enable_embedding and parsed_query.query_vector is not None)
            vector_dims = int(len(parsed_query.query_vector)) if parsed_query.query_vector is not None else 0

            context.logger.info(
                "ES query built | size: %s chars | digest: %s | KNN: %s | vector_dims: %s | facets: %s | rerank_prefetch_source: %s",
                len(es_query_compact),
                es_query_digest,
                "yes" if knn_enabled else "no",
                vector_dims,
                "yes" if facets else "no",
                rerank_prefetch_source,
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
            _log_backend_verbose({
                "event": "es_query_built",
                "reqid": context.reqid,
                "uid": context.uid,
                "tenant_id": tenant_id,
                "size_chars": len(es_query_compact),
                "sha256_16": es_query_digest,
                "knn_enabled": knn_enabled,
                "vector_dims": vector_dims,
                "has_facets": bool(facets),
                "query": es_query_for_fetch,
            })
        except Exception as e:
            context.set_error(e)
            context.logger.error(
                f"ES查询构建失败 | 错误: {str(e)}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
            raise
        finally:
            context.end_stage(RequestContextStage.QUERY_BUILDING)

        # Step 4: Elasticsearch search (primary recall)
        context.start_stage(RequestContextStage.ELASTICSEARCH_SEARCH_PRIMARY)
        try:
            # Use tenant-specific index name（开启重排且在窗口内时已用 es_fetch_size/es_fetch_from）
            es_response = self.es_client.search(
                index_name=index_name,
                body=body_for_es,
                size=es_fetch_size,
                from_=es_fetch_from,
                include_named_queries_score=bool(do_rerank and in_rerank_window),
            )

            # Store ES response in context
            context.store_intermediate_result('es_response', es_response)

            # 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}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
        except Exception as e:
            context.set_error(e)
            context.logger.error(
                f"ES搜索执行失败 | 错误: {str(e)}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
            raise
        finally:
            context.end_stage(RequestContextStage.ELASTICSEARCH_SEARCH_PRIMARY)

        # Optional Step 4.5: AI reranking（仅当请求范围在重排窗口内时执行）
        if do_rerank and in_rerank_window:
            context.start_stage(RequestContextStage.RERANKING)
            try:
                from .rerank_client import run_rerank

                rerank_query = parsed_query.original_query if parsed_query else query
                es_response, rerank_meta, fused_debug = run_rerank(
                    query=rerank_query,
                    es_response=es_response,
                    language=language,
                    timeout_sec=rc.timeout_sec,
                    weight_es=rc.weight_es,
                    weight_ai=rc.weight_ai,
                    rerank_query_template=effective_query_template,
                    rerank_doc_template=effective_doc_template,
                    top_n=(from_ + size),
                )

                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)
                    context.logger.info(
                        f"重排完成 | docs={len(fused_debug)} | meta={rerank_meta}",
                        extra={'reqid': context.reqid, 'uid': context.uid}
                    )
            except Exception as e:
                context.add_warning(f"Rerank failed: {e}")
                context.logger.warning(
                    f"调用重排服务失败 | error: {e}",
                    extra={'reqid': context.reqid, 'uid': context.uid},
                    exc_info=True,
                )
            finally:
                context.end_stage(RequestContextStage.RERANKING)

        # 当本次请求在重排窗口内时：已从 ES 取了 rerank_window 条并可能已重排，需按请求的 from/size 做分页切片
        if in_rerank_window:
            hits = es_response.get("hits", {}).get("hits") or []
            sliced = hits[from_ : from_ + size]
            es_response.setdefault("hits", {})["hits"] = sliced
            if sliced:
                # 对于启用重排的结果，优先使用 _fused_score 计算 max_score；否则退回原始 _score
                slice_max = max(
                    (h.get("_fused_score", h.get("_score", 0.0)) for h in sliced),
                    default=0.0,
                )
                try:
                    es_response["hits"]["max_score"] = float(slice_max)
                except (TypeError, ValueError):
                    es_response["hits"]["max_score"] = 0.0
            else:
                es_response["hits"]["max_score"] = 0.0

            # Page fill: fetch detailed fields only for final page hits.
            if sliced:
                if response_source_spec is False:
                    for hit in sliced:
                        hit.pop("_source", None)
                    context.logger.info(
                        "分页详情回填跳过 | 原查询 _source=false",
                        extra={'reqid': context.reqid, 'uid': context.uid}
                    )
                else:
                    context.start_stage(RequestContextStage.ELASTICSEARCH_PAGE_FILL)
                    try:
                        page_ids = [str(h.get("_id")) for h in sliced if h.get("_id") is not None]
                        details_by_id, fill_took = self._fetch_hits_by_ids(
                            index_name=index_name,
                            doc_ids=page_ids,
                            source_spec=response_source_spec,
                        )
                        filled = 0
                        for hit in sliced:
                            hid = hit.get("_id")
                            if hid is None:
                                continue
                            detail_hit = details_by_id.get(str(hid))
                            if detail_hit is None:
                                continue
                            if "_source" in detail_hit:
                                hit["_source"] = detail_hit.get("_source") or {}
                                filled += 1
                        if fill_took:
                            es_response["took"] = int((es_response.get("took", 0) or 0) + fill_took)
                        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}
                        )
                    finally:
                        context.end_stage(RequestContextStage.ELASTICSEARCH_PAGE_FILL)

            context.logger.info(
                f"重排分页切片 | from={from_}, size={size}, 返回={len(sliced)}条",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )

        # Step 5: Result processing
        context.start_stage(RequestContextStage.RESULT_PROCESSING)
        try:
            # Extract ES hits
            es_hits = []
            if 'hits' in es_response and 'hits' in es_response['hits']:
                es_hits = es_response['hits']['hits']
            # Extract total and max_score
            total = es_response.get('hits', {}).get('total', {})
            if isinstance(total, dict):
                total_value = total.get('value', 0)
            else:
                total_value = total
            # max_score 会在启用 AI 搜索时被更新为融合分数的最大值
            max_score = es_response.get('hits', {}).get('max_score') or 0.0

            # 从上下文中取出重排调试信息（若有）
            rerank_debug_raw = context.get_intermediate_result('rerank_scores', None)
            rerank_debug_by_doc: Dict[str, Dict[str, Any]] = {}
            if isinstance(rerank_debug_raw, list):
                for item in rerank_debug_raw:
                    if not isinstance(item, dict):
                        continue
                    doc_id = item.get("doc_id")
                    if doc_id is None:
                        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,
                max_score,
                language=language,
                sku_filter_dimension=sku_filter_dimension
            )

            # Build per-result debug info (per SPU) when debug mode is enabled
            per_result_debug = []
            if debug and es_hits and formatted_results:
                for hit, spu in zip(es_hits, formatted_results):
                    source = hit.get("_source", {}) or {}
                    doc_id = hit.get("_id")
                    rerank_debug = None
                    if doc_id is not None:
                        rerank_debug = rerank_debug_by_doc.get(str(doc_id))

                    raw_score = hit.get("_score")
                    try:
                        es_score = float(raw_score) if raw_score is not None else 0.0
                    except (TypeError, ValueError):
                        es_score = 0.0
                    try:
                        normalized = float(es_score) / float(max_score) if max_score else None
                    except (TypeError, ValueError, ZeroDivisionError):
                        normalized = 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
                    vendor_multilingual = source.get("vendor") if isinstance(source.get("vendor"), dict) else None

                    debug_entry: Dict[str, Any] = {
                        "spu_id": spu.spu_id,
                        "es_score": es_score,
                        "es_score_normalized": normalized,
                        "title_multilingual": title_multilingual,
                        "brief_multilingual": brief_multilingual,
                        "vendor_multilingual": vendor_multilingual,
                    }

                    # 若存在重排调试信息，则补充 doc 级别的融合分数信息
                    if rerank_debug:
                        debug_entry["doc_id"] = rerank_debug.get("doc_id")
                        # 与 rerank_client 中字段保持一致，便于前端直接使用
                        debug_entry["rerank_score"] = rerank_debug.get("rerank_score")
                        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_primary_score"] = rerank_debug.get("text_primary_score")
                        debug_entry["text_support_score"] = rerank_debug.get("text_support_score")
                        debug_entry["knn_score"] = rerank_debug.get("knn_score")
                        debug_entry["fused_score"] = rerank_debug.get("fused_score")
                        debug_entry["matched_queries"] = rerank_debug.get("matched_queries")

                    per_result_debug.append(debug_entry)

            # Format facets
            standardized_facets = None
            if facets:
                standardized_facets = ResultFormatter.format_facets(
                    es_response.get('aggregations', {}),
                    facets,
                    filters
                )

            # Generate suggestions and related searches
            query_text = parsed_query.original_query if parsed_query else query
            suggestions = ResultFormatter.generate_suggestions(query_text, formatted_results)
            related_searches = ResultFormatter.generate_related_searches(query_text, formatted_results)

            context.logger.info(
                f"结果处理完成 | 返回: {len(formatted_results)}条 | 总计: {total_value}条",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )

        except Exception as e:
            context.set_error(e)
            context.logger.error(
                f"结果处理失败 | 错误: {str(e)}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
            raise
        finally:
            context.end_stage(RequestContextStage.RESULT_PROCESSING)

        # End total timing and build result
        total_duration = context.end_stage(RequestContextStage.TOTAL)
        context.performance_metrics.total_duration = total_duration

        # Collect debug information if requested
        debug_info = None
        if debug:
            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,
                    "translations": context.query_analysis.translations,
                    "has_vector": context.query_analysis.query_vector is not None,
                    "is_simple_query": context.query_analysis.is_simple_query,
                    "domain": context.query_analysis.domain
                },
                "es_query": context.get_intermediate_result('es_query', {}),
                "es_response": {
                    "took_ms": es_response.get('took', 0),
                    "total_hits": total_value,
                    "max_score": max_score,
                    "shards": es_response.get('_shards', {})
                },
                "feature_flags": context.metadata.get('feature_flags', {}),
                "stage_timings": {
                    k: round(v, 2) for k, v in context.performance_metrics.stage_timings.items()
                },
                "search_params": context.metadata.get('search_params', {})
            }
            if per_result_debug:
                debug_info["per_result"] = per_result_debug

        # Build result
        result = SearchResult(
            results=formatted_results,
            total=total_value,
            max_score=max_score,
            took_ms=int(total_duration),
            facets=standardized_facets,
            query_info=parsed_query.to_dict(),
            suggestions=suggestions,
            related_searches=related_searches,
            debug_info=debug_info
        )

        # Log complete performance summary
        context.log_performance_summary()

        return result

    def search_by_image(
        self,
        image_url: str,
        tenant_id: str,
        size: int = 10,
        filters: Optional[Dict[str, Any]] = None,
        range_filters: Optional[Dict[str, Any]] = None
    ) -> SearchResult:
        """
        Search by image similarity (外部友好格式).

        Args:
            image_url: URL of query image
            tenant_id: Tenant ID (required for filtering)
            size: Number of results
            filters: Exact match filters
            range_filters: Range filters for numeric fields

        Returns:
            SearchResult object with formatted results
        """
        if not self.image_embedding_field:
            raise ValueError("Image embedding field not configured")

        # Generate image embedding
        if self.image_encoder is None:
            raise RuntimeError("Image encoder is not initialized at startup")
        image_vector = self.image_encoder.encode_image_from_url(image_url, priority=1)

        if image_vector is None:
            raise ValueError(f"Failed to encode image: {image_url}")

        # Generate tenant-specific index name
        index_name = get_tenant_index_name(tenant_id)
        
        # No longer need to add tenant_id to filters since each tenant has its own index

        # Build KNN query
        es_query = {
            "size": size,
            "knn": {
                "field": self.image_embedding_field,
                "query_vector": image_vector.tolist(),
                "k": size,
                "num_candidates": size * 10
            }
        }

        # Apply source filtering semantics (None / [] / list)
        self._apply_source_filter(es_query)

        if filters or range_filters:
            filter_clauses = self.query_builder._build_filters(filters, range_filters)
            if filter_clauses:
                if len(filter_clauses) == 1:
                    es_query["knn"]["filter"] = filter_clauses[0]
                else:
                    es_query["knn"]["filter"] = {
                        "bool": {
                            "filter": filter_clauses
                        }
                    }

        # Execute search
        es_response = self.es_client.search(
            index_name=index_name,
            body=es_query,
            size=size
        )

        # Extract ES hits
        es_hits = []
        if 'hits' in es_response and 'hits' in es_response['hits']:
            es_hits = es_response['hits']['hits']

        # Extract total and max_score
        total = es_response.get('hits', {}).get('total', {})
        if isinstance(total, dict):
            total_value = total.get('value', 0)
        else:
            total_value = total

        max_score = es_response.get('hits', {}).get('max_score') or 0.0

        # Format results using ResultFormatter
        formatted_results = ResultFormatter.format_search_results(
            es_hits, 
            max_score,
            language="en",  # Default language for image search
            sku_filter_dimension=None  # Image search doesn't support SKU filtering
        )

        return SearchResult(
            results=formatted_results,
            total=total_value,
            max_score=max_score,
            took_ms=es_response.get('took', 0),
            facets=None,
            query_info={'image_url': image_url, 'search_type': 'image_similarity'},
            suggestions=[],
            related_searches=[]
        )

    def get_domain_summary(self) -> Dict[str, Any]:
        """
        Get summary of dynamic text retrieval configuration.

        Returns:
            Dictionary with language-aware field information
        """
        return {
            "mode": "dynamic_language_fields",
            "multilingual_fields": self.config.query_config.multilingual_fields,
            "shared_fields": self.config.query_config.shared_fields,
            "core_multilingual_fields": self.config.query_config.core_multilingual_fields,
            "field_boosts": self.config.field_boosts,
        }

    def get_document(self, tenant_id: str, doc_id: str) -> Optional[Dict[str, Any]]:
        """
        Get single document by ID.

        Args:
            tenant_id: Tenant ID (required to determine which index to query)
            doc_id: Document ID

        Returns:
            Document or None if not found
        """
        try:
            index_name = get_tenant_index_name(tenant_id)
            response = self.es_client.client.get(
                index=index_name,
                id=doc_id
            )
            return response.get('_source')
        except Exception as e:
            logger.error(f"Failed to get document {doc_id} from tenant {tenant_id}: {e}", exc_info=True)
            return None

    def _standardize_facets(
        self,
        es_aggregations: Dict[str, Any],
        facet_configs: Optional[List[Union[str, Any]]],
        current_filters: Optional[Dict[str, Any]]
    ) -> Optional[List[FacetResult]]:
        """
        将 ES 聚合结果转换为标准化的分面格式（返回 Pydantic 模型）。
        
        Args:
            es_aggregations: ES 原始聚合结果
            facet_configs: 分面配置列表（str 或 FacetConfig）
            current_filters: 当前应用的过滤器
        
        Returns:
            标准化的分面结果列表（FacetResult 对象）
        """
        if not es_aggregations or not facet_configs:
            return None
        
        standardized_facets: List[FacetResult] = []
        
        for config in facet_configs:
            # 解析配置
            if isinstance(config, str):
                field = config
                facet_type = "terms"
            else:
                # FacetConfig 对象
                field = config.field
                facet_type = config.type
            
            agg_name = f"{field}_facet"
            
            if agg_name not in es_aggregations:
                continue
            
            agg_result = es_aggregations[agg_name]
            
            # 获取当前字段的选中值
            selected_values = set()
            if current_filters and field in current_filters:
                filter_value = current_filters[field]
                if isinstance(filter_value, list):
                    selected_values = set(filter_value)
                else:
                    selected_values = {filter_value}
            
            # 转换 buckets 为 FacetValue 对象
            facet_values: List[FacetValue] = []
            if 'buckets' in agg_result:
                for bucket in agg_result['buckets']:
                    value = bucket.get('key')
                    count = bucket.get('doc_count', 0)
                    
                    facet_values.append(FacetValue(
                        value=value,
                        label=str(value),
                        count=count,
                        selected=value in selected_values
                    ))
            
            # 构建 FacetResult 对象
            facet_result = FacetResult(
                field=field,
                label=field,
                type=facet_type,
                values=facet_values
            )
            
            standardized_facets.append(facet_result)
        
        return standardized_facets if standardized_facets else None