"""

  Reranking engine for post-processing search result scoring.
  
  本地重排引擎，用于ES返回结果后的二次排序。
  当前状态：已禁用，优先使用ES的function_score。

  
  Supports expression-based ranking with functions like:
  - bm25(): Base BM25 text relevance score
  - text_embedding_relevance(): KNN embedding similarity
  - field_value(field): Use field value in scoring
  - timeliness(date_field): Time decay function
  """
  
  import re
  from typing import Dict, Any, List, Optional
  import math
  
  

  class RerankEngine:
      """
      本地重排引擎（当前禁用）
      
      功能：对ES返回的结果进行二次打分和排序
      用途：复杂的自定义排序逻辑、实时个性化等
      """

      def __init__(self, ranking_expression: str, enabled: bool = False):

          """

          Initialize rerank engine.

  
          Args:
              ranking_expression: Ranking expression string
                                Example: "bm25() + 0.2*text_embedding_relevance() + general_score*2"

              enabled: Whether local reranking is enabled (default: False)

          """

          self.enabled = enabled

          self.expression = ranking_expression

          self.parsed_terms = []
          
          if enabled:
              self.parsed_terms = self._parse_expression(ranking_expression)

  
      def _parse_expression(self, expression: str) -> List[Dict[str, Any]]:
          """
          Parse ranking expression into terms.
  
          Args:
              expression: Ranking expression
  
          Returns:
              List of term dictionaries
          """
          terms = []
  
          # Pattern to match: coefficient * function() or field_name
          # Example: "0.2*text_embedding_relevance()" or "general_score*2"
          pattern = r'([+-]?\s*\d*\.?\d*)\s*\*?\s*([a-zA-Z_]\w*(?:\([^)]*\))?)'
  
          for match in re.finditer(pattern, expression):
              coef_str = match.group(1).strip()
              func_str = match.group(2).strip()
  
              # Parse coefficient
              if coef_str in ['', '+']:
                  coefficient = 1.0
              elif coef_str == '-':
                  coefficient = -1.0
              else:
                  try:
                      coefficient = float(coef_str)
                  except ValueError:
                      coefficient = 1.0
  
              # Check if function or field
              if '(' in func_str:
                  # Function call
                  func_name = func_str[:func_str.index('(')]
                  args_str = func_str[func_str.index('(') + 1:func_str.rindex(')')]
                  args = [arg.strip() for arg in args_str.split(',')] if args_str else []
  
                  terms.append({
                      'type': 'function',
                      'name': func_name,
                      'args': args,
                      'coefficient': coefficient
                  })
              else:
                  # Field reference
                  terms.append({
                      'type': 'field',
                      'name': func_str,
                      'coefficient': coefficient
                  })
  
          return terms
  
      def calculate_score(
          self,
          hit: Dict[str, Any],
          base_score: float,
          knn_score: Optional[float] = None
      ) -> float:
          """
          Calculate final score for a search result.
  
          Args:
              hit: ES hit document
              base_score: Base BM25 score
              knn_score: KNN similarity score (if available)
  
          Returns:
              Final calculated score
          """

          if not self.enabled:
              return base_score
          

          score = 0.0
          source = hit.get('_source', {})
  
          for term in self.parsed_terms:
              term_value = 0.0
  
              if term['type'] == 'function':
                  func_name = term['name']
  
                  if func_name == 'bm25':
                      term_value = base_score
  
                  elif func_name == 'text_embedding_relevance':
                      term_value = knn_score if knn_score is not None else 0.0
  
                  elif func_name == 'timeliness':
                      # Time decay function
                      if term['args']:
                          date_field = term['args'][0]
                          if date_field in source:
                              # Simple time decay (would need actual implementation)
                              term_value = 1.0
                      else:
                          term_value = 1.0
  
                  elif func_name == 'field_value':
                      # Get field value
                      if term['args'] and term['args'][0] in source:
                          field_value = source[term['args'][0]]
                          try:
                              term_value = float(field_value)
                          except (ValueError, TypeError):
                              term_value = 0.0
  
              elif term['type'] == 'field':
                  # Direct field reference
                  field_name = term['name']
                  if field_name in source:
                      try:
                          term_value = float(source[field_name])
                      except (ValueError, TypeError):
                          term_value = 0.0
  
              score += term['coefficient'] * term_value
  
          return score
  
      def get_expression(self) -> str:
          """Get ranking expression."""
          return self.expression
  
      def get_terms(self) -> List[Dict[str, Any]]:
          """Get parsed expression terms."""
          return self.parsed_terms