"""

  Text embedding encoder using network service.

  Generates embeddings via HTTP API service running on localhost:5001.

  """
  
  import sys

  import requests

  import time
  import threading

  import numpy as np

  import pickle
  import redis
  from datetime import timedelta
  from typing import List, Union, Dict, Any, Optional

  import logging

  
  logger = logging.getLogger(__name__)

  # Try to import REDIS_CONFIG, but allow import to fail
  try:
      from config.env_config import REDIS_CONFIG
  except ImportError:
      REDIS_CONFIG = {}
  

  
  class BgeEncoder:
      """

      Singleton text encoder using network service.

      Thread-safe singleton pattern ensures only one instance exists.

      """
      _instance = None
      _lock = threading.Lock()
  

      def __new__(cls, service_url='http://localhost:5001'):

          with cls._lock:
              if cls._instance is None:
                  cls._instance = super(BgeEncoder, cls).__new__(cls)

                  logger.info(f"Creating BgeEncoder instance with service URL: {service_url}")
                  cls._instance.service_url = service_url
                  cls._instance.endpoint = f"{service_url}/embedding/generate_embeddings"

                  
                  # Initialize Redis cache
                  try:
                      cls._instance.redis_client = redis.Redis(
                          host=REDIS_CONFIG.get('host', 'localhost'),
                          port=REDIS_CONFIG.get('port', 6479),
                          password=REDIS_CONFIG.get('password'),
                          decode_responses=False,  # Keep binary data as is
                          socket_timeout=REDIS_CONFIG.get('socket_timeout', 1),
                          socket_connect_timeout=REDIS_CONFIG.get('socket_connect_timeout', 1),
                          retry_on_timeout=REDIS_CONFIG.get('retry_on_timeout', False),
                          health_check_interval=10  # 避免复用坏连接
                      )
                      # Test connection
                      cls._instance.redis_client.ping()
                      cls._instance.expire_time = timedelta(days=REDIS_CONFIG.get('cache_expire_days', 180))
                      logger.info("Redis cache initialized for embeddings")
                  except Exception as e:
                      logger.warning(f"Failed to initialize Redis cache for embeddings: {e}, continuing without cache")
                      cls._instance.redis_client = None

          return cls._instance
  

      def _call_service(self, request_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
          """
          Call the embedding service API.
  
          Args:
              request_data: List of dictionaries with id and text fields
  
          Returns:
              List of dictionaries with id and embedding fields
          """
          try:
              response = requests.post(
                  self.endpoint,
                  json=request_data,
                  timeout=60
              )
              response.raise_for_status()
              return response.json()
          except requests.exceptions.RequestException as e:
              logger.error(f"BgeEncoder service request failed: {e}", exc_info=True)
              raise
  

      def encode(
          self,
          sentences: Union[str, List[str]],
          normalize_embeddings: bool = True,

          device: str = 'cpu',

          batch_size: int = 32
      ) -> np.ndarray:
          """

          Encode text into embeddings via network service with Redis caching.

  
          Args:
              sentences: Single string or list of strings to encode

              normalize_embeddings: Whether to normalize embeddings (ignored for service)
              device: Device parameter ignored for service compatibility
              batch_size: Batch size for processing (used for service requests)

  
          Returns:
              numpy array of shape (n, 1024) containing embeddings
          """

          # Convert single string to list
          if isinstance(sentences, str):
              sentences = [sentences]

          # Check cache first
          cached_embeddings = []
          uncached_indices = []
          uncached_texts = []
          

          for i, text in enumerate(sentences):

              cached = self._get_cached_embedding(text, 'en')  # Use 'en' as default language for title embedding
              if cached is not None:
                  cached_embeddings.append((i, cached))
              else:
                  uncached_indices.append(i)
                  uncached_texts.append(text)
          
          # Prepare request data for uncached texts
          request_data = []
          for i, text in enumerate(uncached_texts):

              request_item = {

                  "id": str(uncached_indices[i]),

                  "name_zh": text
              }

              # Add English and Russian fields as empty for now
              # Could be enhanced with language detection in the future
              request_item["name_en"] = None
              request_item["name_ru"] = None

              request_data.append(request_item)
  

          # Process response
          embeddings = [None] * len(sentences)
          
          # Fill in cached embeddings
          for idx, cached_emb in cached_embeddings:
              embeddings[idx] = cached_emb
          
          # If there are uncached texts, call service
          if uncached_texts:
              try:
                  # Call service
                  response_data = self._call_service(request_data)
  
                  # Process response
                  for i, text in enumerate(uncached_texts):
                      original_idx = uncached_indices[i]
                      # Find corresponding response by ID
                      response_item = None
                      for item in response_data:
                          if str(item.get("id")) == str(original_idx):
                              response_item = item

                              break
  

                      if response_item:
                          # Try Chinese embedding first, then English, then Russian
                          embedding = None
                          for lang in ["embedding_zh", "embedding_en", "embedding_ru"]:
                              if lang in response_item and response_item[lang] is not None:
                                  embedding = response_item[lang]
                                  break
  
                          if embedding is not None:
                              embedding_array = np.array(embedding, dtype=np.float32)
                              embeddings[original_idx] = embedding_array
                              # Cache the embedding
                              self._set_cached_embedding(text, 'en', embedding_array)
                          else:
                              logger.warning(f"No embedding found for text {original_idx}: {text[:50]}...")
                              embeddings[original_idx] = np.zeros(1024, dtype=np.float32)

                      else:

                          logger.warning(f"No response found for text {original_idx}")
                          embeddings[original_idx] = np.zeros(1024, dtype=np.float32)
  
              except Exception as e:
                  logger.error(f"Failed to encode texts: {e}", exc_info=True)
                  # Fill missing embeddings with zeros
                  for idx in uncached_indices:
                      if embeddings[idx] is None:
                          embeddings[idx] = np.zeros(1024, dtype=np.float32)
          
          # Convert to numpy array
          return np.array(embeddings, dtype=np.float32)

  
      def encode_batch(
          self,
          texts: List[str],
          batch_size: int = 32,

          device: str = 'cpu'

      ) -> np.ndarray:
          """

          Encode a batch of texts efficiently via network service.

  
          Args:
              texts: List of texts to encode
              batch_size: Batch size for processing

              device: Device parameter ignored for service compatibility

  
          Returns:
              numpy array of embeddings
          """
          return self.encode(texts, batch_size=batch_size, device=device)

      
      def _get_cache_key(self, query: str, language: str) -> str:
          """Generate a cache key for the query"""
          return f"embedding:{language}:{query}"
      
      def _get_cached_embedding(self, query: str, language: str) -> Optional[np.ndarray]:
          """Get embedding from cache if exists (with sliding expiration)"""
          if not self.redis_client:
              return None
              
          try:
              cache_key = self._get_cache_key(query, language)
              cached_data = self.redis_client.get(cache_key)
              if cached_data:
                  logger.debug(f"Cache hit for embedding: {query}")
                  # Update expiration time on access (sliding expiration)
                  self.redis_client.expire(cache_key, self.expire_time)
                  return pickle.loads(cached_data)
              return None
          except Exception as e:
              logger.error(f"Error retrieving embedding from cache: {e}")
              return None
      
      def _set_cached_embedding(self, query: str, language: str, embedding: np.ndarray) -> bool:
          """Store embedding in cache"""
          if not self.redis_client:
              return False
              
          try:
              cache_key = self._get_cache_key(query, language)
              serialized_data = pickle.dumps(embedding)
              self.redis_client.setex(
                  cache_key,
                  self.expire_time,
                  serialized_data
              )
              logger.debug(f"Successfully cached embedding for query: {query}")
              return True
          except (redis.exceptions.BusyLoadingError, redis.exceptions.ConnectionError, 
                  redis.exceptions.TimeoutError, redis.exceptions.RedisError) as e:
              logger.warning(f"Redis error storing embedding in cache: {e}")
              return False
          except Exception as e:
              logger.error(f"Error storing embedding in cache: {e}")
              return False