"""
  Main Searcher module - executes search queries against Elasticsearch.
  
  Handles query parsing, boolean expressions, ranking, and result formatting.
  """
  

  from typing import Dict, Any, List, Optional, Union

  import time
  
  from config import CustomerConfig
  from utils.es_client import ESClient
  from query import QueryParser, ParsedQuery
  from indexer import MappingGenerator
  from .boolean_parser import BooleanParser, QueryNode
  from .es_query_builder import ESQueryBuilder

  from .multilang_query_builder import MultiLanguageQueryBuilder

  from .rerank_engine import RerankEngine

  from context.request_context import RequestContext, RequestContextStage, create_request_context

  from api.models import FacetResult, FacetValue

  
  
  class SearchResult:

      """Container for search results (重构版)."""

  
      def __init__(
          self,
          hits: List[Dict[str, Any]],
          total: int,
          max_score: float,
          took_ms: int,

          facets: Optional[List[FacetResult]] = None,

          query_info: Optional[Dict[str, Any]] = None,
          debug_info: Optional[Dict[str, Any]] = None

      ):
          self.hits = hits
          self.total = total
          self.max_score = max_score
          self.took_ms = took_ms

          self.facets = facets

          self.query_info = query_info or {}

          self.debug_info = debug_info

      def to_dict(self) -> Dict[str, Any]:
          """Convert to dictionary representation."""

          result = {

              "hits": self.hits,
              "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
          }

          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
      - Boolean expression parsing
      - ES query building
      - Result ranking and formatting
      """
  
      def __init__(
          self,
          config: CustomerConfig,
          es_client: ESClient,
          query_parser: Optional[QueryParser] = None
      ):
          """
          Initialize searcher.
  
          Args:
              config: Customer configuration
              es_client: Elasticsearch client
              query_parser: Query parser (created if not provided)
          """
          self.config = config
          self.es_client = es_client
          self.query_parser = query_parser or QueryParser(config)
  
          # Initialize components
          self.boolean_parser = BooleanParser()

          self.rerank_engine = RerankEngine(config.ranking.expression, enabled=False)

  
          # Get mapping info
          mapping_gen = MappingGenerator(config)
          self.match_fields = mapping_gen.get_match_fields_for_domain("default")
          self.text_embedding_field = mapping_gen.get_text_embedding_field()
          self.image_embedding_field = mapping_gen.get_image_embedding_field()
  

          # Query builder - use multi-language version
          self.query_builder = MultiLanguageQueryBuilder(
              config=config,

              index_name=config.es_index_name,

              text_embedding_field=self.text_embedding_field,

              image_embedding_field=self.image_embedding_field,
              source_fields=config.query_config.source_fields

          )
  
      def search(
          self,
          query: str,
          size: int = 10,
          from_: int = 0,
          filters: Optional[Dict[str, Any]] = None,

          range_filters: Optional[Dict[str, Any]] = None,
          facets: Optional[List[Any]] = None,

          min_score: Optional[float] = None,

          context: Optional[RequestContext] = None,

          sort_by: Optional[str] = None,

          sort_order: Optional[str] = "desc",
          debug: bool = False

      ) -> SearchResult:
          """

          Execute search query (重构版).

  
          Args:
              query: Search query string
              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 (created if not provided)

              sort_by: Field name for sorting
              sort_order: Sort order: 'asc' or 'desc'

              debug: Enable debug information output

  
          Returns:
              SearchResult object
          """

          # Create context if not provided (backward compatibility)
          if context is None:
              context = create_request_context()
  
          # Always use config defaults (these are backend configuration, not user parameters)
          enable_translation = self.config.query_config.enable_translation
          enable_embedding = self.config.query_config.enable_text_embedding
          enable_rerank = True  # Always enable reranking as it's part of the search logic
  
          # 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_translation={enable_translation}, enable_embedding={enable_embedding}, "
              f"enable_rerank={enable_rerank}, min_score={min_score}",
              extra={'reqid': context.reqid, 'uid': context.uid}
          )
  
          # Store search parameters in context
          context.metadata['search_params'] = {
              'size': size,
              'from_': from_,
              'filters': filters,

              'range_filters': range_filters,
              'facets': facets,

              'enable_translation': enable_translation,
              'enable_embedding': enable_embedding,
              'enable_rerank': enable_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': enable_rerank
          }

  
          # Step 1: Parse query

          context.start_stage(RequestContextStage.QUERY_PARSING)
          try:
              parsed_query = self.query_parser.parse(
                  query,
                  generate_vector=enable_embedding,
                  context=context
              )
              # Store query analysis results in context
              context.store_query_analysis(
                  original_query=parsed_query.original_query,
                  normalized_query=parsed_query.normalized_query,
                  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=parsed_query.domain,
                  is_simple_query=self.boolean_parser.is_simple_query(parsed_query.rewritten_query)
              )

              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"域: {parsed_query.domain} | "
                  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: Boolean parsing
          context.start_stage(RequestContextStage.BOOLEAN_PARSING)
          try:
              query_node = None
              if self.boolean_parser.is_simple_query(parsed_query.rewritten_query):
                  # Simple query
                  query_text = parsed_query.rewritten_query
                  context.logger.debug(
                      f"简单查询 | 无布尔表达式",
                      extra={'reqid': context.reqid, 'uid': context.uid}
                  )
              else:
                  # Complex boolean query
                  query_node = self.boolean_parser.parse(parsed_query.rewritten_query)
                  query_text = parsed_query.rewritten_query
                  context.store_intermediate_result('query_node', query_node)
                  context.store_intermediate_result('boolean_ast', str(query_node))
                  context.logger.info(
                      f"布尔表达式解析 | AST: {query_node}",
                      extra={'reqid': context.reqid, 'uid': context.uid}
                  )
          except Exception as e:
              context.set_error(e)
              context.logger.error(
                  f"布尔表达式解析失败 | 错误: {str(e)}",
                  extra={'reqid': context.reqid, 'uid': context.uid}
              )
              raise
          finally:
              context.end_stage(RequestContextStage.BOOLEAN_PARSING)
  
          # Step 3: Query building
          context.start_stage(RequestContextStage.QUERY_BUILDING)
          try:
              es_query = self.query_builder.build_multilang_query(
                  parsed_query=parsed_query,
                  query_vector=parsed_query.query_vector if enable_embedding else None,
                  query_node=query_node,
                  filters=filters,

                  range_filters=range_filters,

                  size=size,
                  from_=from_,
                  enable_knn=enable_embedding and parsed_query.query_vector is not None,
                  min_score=min_score
              )

              # Add SPU collapse if configured
              if self.config.spu_config.enabled:
                  es_query = self.query_builder.add_spu_collapse(
                      es_query,
                      self.config.spu_config.spu_field,
                      self.config.spu_config.inner_hits_size
                  )
  

              # 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)
  

              # Extract size and from from body for ES client parameters
              body_for_es = {k: v for k, v in es_query.items() if k not in ['size', 'from']}
  
              # Store ES query in context
              context.store_intermediate_result('es_query', es_query)
              context.store_intermediate_result('es_body_for_search', body_for_es)
  
              context.logger.info(
                  f"ES查询构建完成 | 大小: {len(str(es_query))}字符 | "
                  f"KNN: {'是' if enable_embedding and parsed_query.query_vector is not None else '否'} | "

                  f"分面: {'是' if facets else '否'}",

                  extra={'reqid': context.reqid, 'uid': context.uid}
              )
              context.logger.debug(
                  f"ES查询详情: {es_query}",
                  extra={'reqid': context.reqid, 'uid': context.uid}
              )
          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
          context.start_stage(RequestContextStage.ELASTICSEARCH_SEARCH)
          try:
              es_response = self.es_client.search(
                  index_name=self.config.es_index_name,
                  body=body_for_es,
                  size=size,
                  from_=from_

              )
  

              # 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)
  
          # Step 5: Result processing
          context.start_stage(RequestContextStage.RESULT_PROCESSING)
          try:
              hits = []
              raw_hits = []
  
              if 'hits' in es_response and 'hits' in es_response['hits']:
                  for hit in es_response['hits']['hits']:
                      raw_hits.append(hit)
  
                      result_doc = {
                          '_id': hit['_id'],

                          '_score': hit.get('_score') or 0.0,

                          '_source': hit['_source']
                      }
  

                      # 应用本地重排（仅当启用时）
                      if enable_rerank and self.rerank_engine.enabled:

                          base_score = hit.get('_score') or 0.0

                          knn_score = None
  

                          # 检查是否使用了KNN（新结构：在function_score内部）
                          query_section = es_query.get('query', {})
                          if 'function_score' in query_section:
                              fs_query = query_section['function_score'].get('query', {})
                              outer_bool = fs_query.get('bool', {})
                              inner_bool_list = outer_bool.get('must', [])
                              if inner_bool_list and 'bool' in inner_bool_list[0]:
                                  inner_should = inner_bool_list[0]['bool'].get('should', [])
                                  if any('knn' in clause for clause in inner_should):
                                      knn_score = base_score * 0.2
  
                          custom_score = self.rerank_engine.calculate_score(

                              hit,
                              base_score,
                              knn_score
                          )
                          result_doc['_custom_score'] = custom_score
                          result_doc['_original_score'] = base_score
  
                      hits.append(result_doc)
  

                  # 重排序（仅当启用时）
                  if enable_rerank and self.rerank_engine.enabled:

                      hits.sort(key=lambda x: x.get('_custom_score', x['_score']), reverse=True)
                      context.logger.info(

                          f"本地重排完成 | 使用RerankEngine",

                          extra={'reqid': context.reqid, 'uid': context.uid}

                      )

              # Store intermediate results in context
              context.store_intermediate_result('raw_hits', raw_hits)
              context.store_intermediate_result('processed_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

              # Standardize facets
              standardized_facets = self._standardize_facets(
                  es_response.get('aggregations', {}),
                  facets,
                  filters
              )

              context.logger.info(
                  f"结果处理完成 | 返回: {len(hits)}条 | 总计: {total_value}条 | "
                  f"重排序: {'是' if enable_rerank else '否'}",
                  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,
                      "normalized_query": context.query_analysis.normalized_query,
                      "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,
                      "boolean_ast": context.get_intermediate_result('boolean_ast'),
                      "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', {})
              }
  

          # Build result
          result = SearchResult(
              hits=hits,
              total=total_value,
              max_score=max_score,

              took_ms=int(total_duration),

              facets=standardized_facets,

              query_info=parsed_query.to_dict(),
              debug_info=debug_info

          )
  

          # Log complete performance summary
          context.log_performance_summary()

  
          return result
  
      def search_by_image(
          self,
          image_url: str,
          size: int = 10,

          filters: Optional[Dict[str, Any]] = None,
          range_filters: Optional[Dict[str, Any]] = None

      ) -> SearchResult:
          """

          Search by image similarity (重构版).

  
          Args:
              image_url: URL of query image
              size: Number of results

              filters: Exact match filters
              range_filters: Range filters for numeric fields

  
          Returns:
              SearchResult object
          """
          if not self.image_embedding_field:
              raise ValueError("Image embedding field not configured")
  
          # Generate image embedding
          from embeddings import CLIPImageEncoder
          image_encoder = CLIPImageEncoder()
          image_vector = image_encoder.encode_image_from_url(image_url)
  
          if image_vector is None:
              raise ValueError(f"Failed to encode image: {image_url}")
  
          # Build KNN query
          es_query = {
              "size": size,
              "knn": {
                  "field": self.image_embedding_field,
                  "query_vector": image_vector.tolist(),
                  "k": size,
                  "num_candidates": size * 10
              }
          }
  

          # Add _source filtering if source_fields are configured
          if self.config.query_config.source_fields:
              es_query["_source"] = {
                  "includes": self.config.query_config.source_fields
              }
  

          if filters or range_filters:
              filter_clauses = self.query_builder._build_filters(filters, range_filters)
              if filter_clauses:
                  es_query["query"] = {
                      "bool": {
                          "filter": filter_clauses
                      }

                  }

  
          # Execute search
          es_response = self.es_client.search(
              index_name=self.config.es_index_name,
              body=es_query,
              size=size
          )
  
          # Process results (similar to text search)
          hits = []
          if 'hits' in es_response and 'hits' in es_response['hits']:
              for hit in es_response['hits']['hits']:
                  hits.append({
                      '_id': hit['_id'],
                      '_score': hit['_score'],
                      '_source': hit['_source']
                  })
  
          total = es_response.get('hits', {}).get('total', {})
          if isinstance(total, dict):
              total_value = total.get('value', 0)
          else:
              total_value = total
  
          return SearchResult(
              hits=hits,
              total=total_value,

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

              took_ms=es_response.get('took', 0),
              query_info={'image_url': image_url, 'search_type': 'image_similarity'}
          )
  

      def get_domain_summary(self) -> Dict[str, Any]:
          """
          Get summary of all configured domains.
  
          Returns:
              Dictionary with domain information
          """
          return self.query_builder.get_domain_summary()
  

      def get_document(self, doc_id: str) -> Optional[Dict[str, Any]]:
          """
          Get single document by ID.
  
          Args:
              doc_id: Document ID
  
          Returns:
              Document or None if not found
          """
          try:
              response = self.es_client.client.get(
                  index=self.config.es_index_name,
                  id=doc_id
              )
              return response.get('_source')
          except Exception as e:
              print(f"[Searcher] Failed to get document {doc_id}: {e}")
              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=self._get_field_label(field),
                  type=facet_type,
                  values=facet_values
              )
              
              standardized_facets.append(facet_result)
          
          return standardized_facets if standardized_facets else None
  
      def _get_field_label(self, field: str) -> str:
          """获取字段的显示标签"""
          # 从配置中获取字段标签
          for field_config in self.config.fields:
              if field_config.name == field:
                  # 尝试获取 label 属性
                  return getattr(field_config, 'label', field)
          # 如果没有配置，返回字段名
          return field