"""
  Embedding service (FastAPI).
  

  API (simple list-in, list-out; aligned by index):

  - POST /embed/text       body: ["text1", "text2", ...] -> [[...], ...]   (TEI/BGE，语义检索 title_embedding)
  - POST /embed/image      body: ["url_or_path1", ...]  -> [[...], ...]   (CN-CLIP 图向量)
  - POST /embed/clip_text  body: ["短语1", "短语2", ...] -> [[...], ...] (CN-CLIP 文本塔，与 /embed/image 同空间)

  """
  

  import logging

  import os

  import pathlib

  import threading

  import time

  import uuid

  from collections import deque
  from dataclasses import dataclass

  from typing import Any, Dict, List, Optional
  
  import numpy as np

  from fastapi import FastAPI, HTTPException, Request, Response
  from fastapi.concurrency import run_in_threadpool

  from config.env_config import REDIS_CONFIG

  from config.services_config import get_embedding_backend_config

  from embeddings.cache_keys import (
      build_clip_text_cache_key as _mm_clip_text_cache_key,
      build_image_cache_key as _mm_image_cache_key,
      build_text_cache_key,
  )

  from embeddings.config import CONFIG

  from embeddings.protocols import ImageEncoderProtocol

  from embeddings.redis_embedding_cache import RedisEmbeddingCache

  from request_log_context import (
      LOG_LINE_FORMAT,
      RequestLogContextFilter,
      bind_request_log_context,
      build_request_log_extra,
      reset_request_log_context,
  )

  app = FastAPI(title="saas-search Embedding Service", version="1.0.0")

  def configure_embedding_logging() -> None:
      root_logger = logging.getLogger()
      if getattr(root_logger, "_embedding_logging_configured", False):
          return
  
      log_dir = pathlib.Path("logs")

      log_dir.mkdir(exist_ok=True)

  
      log_level = os.getenv("LOG_LEVEL", "INFO").upper()
      numeric_level = getattr(logging, log_level, logging.INFO)

      formatter = logging.Formatter(LOG_LINE_FORMAT)
      context_filter = RequestLogContextFilter()

  
      root_logger.setLevel(numeric_level)

      root_logger.handlers.clear()
      stream_handler = logging.StreamHandler()
      stream_handler.setLevel(numeric_level)
      stream_handler.setFormatter(formatter)

      stream_handler.addFilter(context_filter)

      root_logger.addHandler(stream_handler)

  
      verbose_logger = logging.getLogger("embedding.verbose")
      verbose_logger.setLevel(numeric_level)
      verbose_logger.handlers.clear()

      # Consolidate verbose logs into the main embedding log stream.
      verbose_logger.propagate = True

  
      root_logger._embedding_logging_configured = True  # type: ignore[attr-defined]
  
  
  configure_embedding_logging()
  logger = logging.getLogger(__name__)
  verbose_logger = logging.getLogger("embedding.verbose")
  

  # Models are loaded at startup, not lazily

  _text_model: Optional[Any] = None

  _image_model: Optional[ImageEncoderProtocol] = None

  _text_backend_name: str = ""

  _SERVICE_KIND = (os.getenv("EMBEDDING_SERVICE_KIND", "all") or "all").strip().lower()
  if _SERVICE_KIND not in {"all", "text", "image"}:
      raise RuntimeError(
          f"Invalid EMBEDDING_SERVICE_KIND={_SERVICE_KIND!r}; expected all, text, or image"
      )
  _TEXT_ENABLED_BY_ENV = os.getenv("EMBEDDING_ENABLE_TEXT_MODEL", "true").lower() in ("1", "true", "yes")
  _IMAGE_ENABLED_BY_ENV = os.getenv("EMBEDDING_ENABLE_IMAGE_MODEL", "true").lower() in ("1", "true", "yes")
  open_text_model = _TEXT_ENABLED_BY_ENV and _SERVICE_KIND in {"all", "text"}
  open_image_model = _IMAGE_ENABLED_BY_ENV and _SERVICE_KIND in {"all", "image"}

  
  _text_encode_lock = threading.Lock()
  _image_encode_lock = threading.Lock()
  

  _TEXT_MICROBATCH_WINDOW_SEC = max(
      0.0, float(os.getenv("TEXT_MICROBATCH_WINDOW_MS", "4")) / 1000.0
  )
  _TEXT_REQUEST_TIMEOUT_SEC = max(
      1.0, float(os.getenv("TEXT_REQUEST_TIMEOUT_SEC", "30"))
  )
  _TEXT_MAX_INFLIGHT = max(1, int(os.getenv("TEXT_MAX_INFLIGHT", "32")))

  _IMAGE_MAX_INFLIGHT = max(1, int(os.getenv("IMAGE_MAX_INFLIGHT", "20")))

  _OVERLOAD_STATUS_CODE = int(os.getenv("EMBEDDING_OVERLOAD_STATUS_CODE", "503"))
  _LOG_PREVIEW_COUNT = max(1, int(os.getenv("EMBEDDING_LOG_PREVIEW_COUNT", "3")))
  _LOG_TEXT_PREVIEW_CHARS = max(32, int(os.getenv("EMBEDDING_LOG_TEXT_PREVIEW_CHARS", "120")))
  _LOG_IMAGE_PREVIEW_CHARS = max(32, int(os.getenv("EMBEDDING_LOG_IMAGE_PREVIEW_CHARS", "180")))
  _VECTOR_PREVIEW_DIMS = max(1, int(os.getenv("EMBEDDING_VECTOR_PREVIEW_DIMS", "6")))

  _CACHE_PREFIX = str(REDIS_CONFIG.get("embedding_cache_prefix", "embedding")).strip() or "embedding"
  
  
  @dataclass
  class _EmbedResult:
      vectors: List[Optional[List[float]]]
      cache_hits: int
      cache_misses: int
      backend_elapsed_ms: float
      mode: str
  
  
  class _EndpointStats:
      def __init__(self, name: str):
          self.name = name
          self._lock = threading.Lock()
          self.request_total = 0
          self.success_total = 0
          self.failure_total = 0
          self.rejected_total = 0
          self.cache_hits = 0
          self.cache_misses = 0
          self.total_latency_ms = 0.0
          self.total_backend_latency_ms = 0.0
  
      def record_rejected(self) -> None:
          with self._lock:
              self.request_total += 1
              self.rejected_total += 1
  
      def record_completed(
          self,
          *,
          success: bool,
          latency_ms: float,
          backend_latency_ms: float,
          cache_hits: int,
          cache_misses: int,
      ) -> None:
          with self._lock:
              self.request_total += 1
              if success:
                  self.success_total += 1
              else:
                  self.failure_total += 1
              self.cache_hits += max(0, int(cache_hits))
              self.cache_misses += max(0, int(cache_misses))
              self.total_latency_ms += max(0.0, float(latency_ms))
              self.total_backend_latency_ms += max(0.0, float(backend_latency_ms))
  
      def snapshot(self) -> Dict[str, Any]:
          with self._lock:
              completed = self.success_total + self.failure_total
              return {
                  "request_total": self.request_total,
                  "success_total": self.success_total,
                  "failure_total": self.failure_total,
                  "rejected_total": self.rejected_total,
                  "cache_hits": self.cache_hits,
                  "cache_misses": self.cache_misses,
                  "avg_latency_ms": round(self.total_latency_ms / completed, 3) if completed else 0.0,
                  "avg_backend_latency_ms": round(self.total_backend_latency_ms / completed, 3)
                  if completed
                  else 0.0,
              }

  
  
  class _InflightLimiter:
      def __init__(self, name: str, limit: int):
          self.name = name
          self.limit = max(1, int(limit))

          self._lock = threading.Lock()
          self._active = 0
          self._rejected = 0
          self._completed = 0
          self._failed = 0
          self._max_active = 0

          self._priority_bypass_total = 0

      def try_acquire(self, *, bypass_limit: bool = False) -> tuple[bool, int]:
          with self._lock:
              if not bypass_limit and self._active >= self.limit:

                  self._rejected += 1
                  active = self._active

                  return False, active

              self._active += 1
              self._max_active = max(self._max_active, self._active)

              if bypass_limit:
                  self._priority_bypass_total += 1

              active = self._active
          return True, active
  
      def release(self, *, success: bool) -> int:
          with self._lock:
              self._active = max(0, self._active - 1)
              if success:
                  self._completed += 1
              else:
                  self._failed += 1
              active = self._active

          return active
  
      def snapshot(self) -> Dict[str, int]:
          with self._lock:
              return {
                  "limit": self.limit,
                  "active": self._active,
                  "rejected_total": self._rejected,
                  "completed_total": self._completed,
                  "failed_total": self._failed,
                  "max_active": self._max_active,

                  "priority_bypass_total": self._priority_bypass_total,

              }
  
  

  def _effective_priority(priority: int) -> int:
      return 1 if int(priority) > 0 else 0
  
  
  def _priority_label(priority: int) -> str:
      return "high" if _effective_priority(priority) > 0 else "normal"
  
  
  @dataclass
  class _TextDispatchTask:
      normalized: List[str]
      effective_normalize: bool
      request_id: str

      user_id: str

      priority: int
      created_at: float
      done: threading.Event
      result: Optional[_EmbedResult] = None
      error: Optional[Exception] = None
  
  
  _text_dispatch_high_queue: "deque[_TextDispatchTask]" = deque()
  _text_dispatch_normal_queue: "deque[_TextDispatchTask]" = deque()
  _text_dispatch_cv = threading.Condition()
  _text_dispatch_workers: List[threading.Thread] = []
  _text_dispatch_worker_stop = False
  _text_dispatch_worker_count = 0
  
  
  def _text_dispatch_queue_depth() -> Dict[str, int]:
      with _text_dispatch_cv:
          return {
              "high": len(_text_dispatch_high_queue),
              "normal": len(_text_dispatch_normal_queue),
              "total": len(_text_dispatch_high_queue) + len(_text_dispatch_normal_queue),
          }
  
  
  def _pop_text_dispatch_task_locked() -> Optional["_TextDispatchTask"]:
      if _text_dispatch_high_queue:
          return _text_dispatch_high_queue.popleft()
      if _text_dispatch_normal_queue:
          return _text_dispatch_normal_queue.popleft()
      return None
  
  
  def _start_text_dispatch_workers() -> None:
      global _text_dispatch_workers, _text_dispatch_worker_stop, _text_dispatch_worker_count
      if _text_model is None:
          return
      target_worker_count = 1 if _text_backend_name == "local_st" else _TEXT_MAX_INFLIGHT
      alive_workers = [worker for worker in _text_dispatch_workers if worker.is_alive()]
      if len(alive_workers) == target_worker_count:
          _text_dispatch_workers = alive_workers
          _text_dispatch_worker_count = target_worker_count
          return
      _text_dispatch_worker_stop = False
      _text_dispatch_worker_count = target_worker_count
      _text_dispatch_workers = []
      for idx in range(target_worker_count):
          worker = threading.Thread(
              target=_text_dispatch_worker_loop,
              args=(idx,),
              name=f"embed-text-dispatch-{idx}",
              daemon=True,
          )
          worker.start()
          _text_dispatch_workers.append(worker)
      logger.info(
          "Started text dispatch workers | backend=%s workers=%d",
          _text_backend_name,
          target_worker_count,
      )
  
  
  def _stop_text_dispatch_workers() -> None:
      global _text_dispatch_worker_stop
      with _text_dispatch_cv:
          _text_dispatch_worker_stop = True
          _text_dispatch_cv.notify_all()
  
  
  def _text_dispatch_worker_loop(worker_idx: int) -> None:
      while True:
          with _text_dispatch_cv:
              while (
                  not _text_dispatch_high_queue
                  and not _text_dispatch_normal_queue
                  and not _text_dispatch_worker_stop
              ):
                  _text_dispatch_cv.wait()
              if _text_dispatch_worker_stop:
                  return
              task = _pop_text_dispatch_task_locked()
          if task is None:
              continue
          try:
              queue_wait_ms = (time.perf_counter() - task.created_at) * 1000.0
              logger.info(
                  "text dispatch start | worker=%d priority=%s inputs=%d queue_wait_ms=%.2f",
                  worker_idx,
                  _priority_label(task.priority),
                  len(task.normalized),
                  queue_wait_ms,

                  extra=build_request_log_extra(task.request_id, task.user_id),

              )
              task.result = _embed_text_impl(
                  task.normalized,
                  task.effective_normalize,
                  task.request_id,

                  task.user_id,

                  task.priority,
              )
          except Exception as exc:
              task.error = exc
          finally:
              task.done.set()
  
  
  def _submit_text_dispatch_and_wait(
      normalized: List[str],
      effective_normalize: bool,
      request_id: str,

      user_id: str,

      priority: int,
  ) -> _EmbedResult:
      if not any(worker.is_alive() for worker in _text_dispatch_workers):
          _start_text_dispatch_workers()
      task = _TextDispatchTask(
          normalized=normalized,
          effective_normalize=effective_normalize,
          request_id=request_id,

          user_id=user_id,

          priority=_effective_priority(priority),
          created_at=time.perf_counter(),
          done=threading.Event(),
      )
      with _text_dispatch_cv:
          if task.priority > 0:
              _text_dispatch_high_queue.append(task)
          else:
              _text_dispatch_normal_queue.append(task)
          _text_dispatch_cv.notify()
      task.done.wait()
      if task.error is not None:
          raise task.error
      if task.result is None:
          raise RuntimeError("Text dispatch worker returned empty result")
      return task.result
  
  

  _text_request_limiter = _InflightLimiter(name="text", limit=_TEXT_MAX_INFLIGHT)
  _image_request_limiter = _InflightLimiter(name="image", limit=_IMAGE_MAX_INFLIGHT)

  _text_stats = _EndpointStats(name="text")
  _image_stats = _EndpointStats(name="image")
  _text_cache = RedisEmbeddingCache(key_prefix=_CACHE_PREFIX, namespace="")
  _image_cache = RedisEmbeddingCache(key_prefix=_CACHE_PREFIX, namespace="image")

  _clip_text_cache = RedisEmbeddingCache(key_prefix=_CACHE_PREFIX, namespace="clip_text")

  @dataclass
  class _SingleTextTask:
      text: str
      normalize: bool

      priority: int

      created_at: float

      request_id: str

      user_id: str

      done: threading.Event
      result: Optional[List[float]] = None
      error: Optional[Exception] = None
  
  

  _text_single_high_queue: "deque[_SingleTextTask]" = deque()
  _text_single_normal_queue: "deque[_SingleTextTask]" = deque()

  _text_single_queue_cv = threading.Condition()
  _text_batch_worker: Optional[threading.Thread] = None
  _text_batch_worker_stop = False

  def _text_microbatch_queue_depth() -> Dict[str, int]:
      with _text_single_queue_cv:
          return {
              "high": len(_text_single_high_queue),
              "normal": len(_text_single_normal_queue),
              "total": len(_text_single_high_queue) + len(_text_single_normal_queue),
          }
  
  
  def _pop_single_text_task_locked() -> Optional["_SingleTextTask"]:
      if _text_single_high_queue:
          return _text_single_high_queue.popleft()
      if _text_single_normal_queue:
          return _text_single_normal_queue.popleft()
      return None
  
  

  def _compact_preview(text: str, max_chars: int) -> str:
      compact = " ".join((text or "").split())
      if len(compact) <= max_chars:
          return compact
      return compact[:max_chars] + "..."
  
  
  def _preview_inputs(items: List[str], max_items: int, max_chars: int) -> List[Dict[str, Any]]:
      previews: List[Dict[str, Any]] = []
      for idx, item in enumerate(items[:max_items]):
          previews.append(
              {
                  "idx": idx,
                  "len": len(item),
                  "preview": _compact_preview(item, max_chars),
              }
          )
      return previews

  def _preview_vector(vec: Optional[List[float]], max_dims: int = _VECTOR_PREVIEW_DIMS) -> List[float]:
      if not vec:
          return []
      return [round(float(v), 6) for v in vec[:max_dims]]
  
  

  def _resolve_request_id(http_request: Request) -> str:
      header_value = http_request.headers.get("X-Request-ID")
      if header_value and header_value.strip():
          return header_value.strip()[:32]
      return str(uuid.uuid4())[:8]
  
  

  def _resolve_user_id(http_request: Request) -> str:
      header_value = http_request.headers.get("X-User-ID") or http_request.headers.get("User-ID")
      if header_value and header_value.strip():
          return header_value.strip()[:64]
      return "-1"
  
  

  def _request_client(http_request: Request) -> str:
      client = getattr(http_request, "client", None)
      host = getattr(client, "host", None)
      return str(host or "-")
  
  

  def _encode_local_st(texts: List[str], normalize_embeddings: bool) -> Any:
      with _text_encode_lock:

          return _text_model.encode(

              texts,
              batch_size=int(CONFIG.TEXT_BATCH_SIZE),
              device=CONFIG.TEXT_DEVICE,
              normalize_embeddings=normalize_embeddings,
          )
  
  
  def _start_text_batch_worker() -> None:
      global _text_batch_worker, _text_batch_worker_stop
      if _text_batch_worker is not None and _text_batch_worker.is_alive():
          return
      _text_batch_worker_stop = False
      _text_batch_worker = threading.Thread(
          target=_text_batch_worker_loop,
          name="embed-text-microbatch-worker",
          daemon=True,
      )
      _text_batch_worker.start()
      logger.info(
          "Started local_st text micro-batch worker | window_ms=%.1f max_batch=%d",
          _TEXT_MICROBATCH_WINDOW_SEC * 1000.0,
          int(CONFIG.TEXT_BATCH_SIZE),
      )
  
  
  def _stop_text_batch_worker() -> None:
      global _text_batch_worker_stop
      with _text_single_queue_cv:
          _text_batch_worker_stop = True
          _text_single_queue_cv.notify_all()
  
  
  def _text_batch_worker_loop() -> None:
      max_batch = max(1, int(CONFIG.TEXT_BATCH_SIZE))
      while True:
          with _text_single_queue_cv:

              while (
                  not _text_single_high_queue
                  and not _text_single_normal_queue
                  and not _text_batch_worker_stop
              ):

                  _text_single_queue_cv.wait()
              if _text_batch_worker_stop:
                  return
  

              first_task = _pop_single_text_task_locked()
              if first_task is None:
                  continue
              batch: List[_SingleTextTask] = [first_task]

              deadline = time.perf_counter() + _TEXT_MICROBATCH_WINDOW_SEC
  
              while len(batch) < max_batch:
                  remaining = deadline - time.perf_counter()
                  if remaining <= 0:
                      break

                  if not _text_single_high_queue and not _text_single_normal_queue:

                      _text_single_queue_cv.wait(timeout=remaining)
                      continue

                  while len(batch) < max_batch:
                      next_task = _pop_single_text_task_locked()
                      if next_task is None:
                          break
                      batch.append(next_task)

  
          try:

              queue_wait_ms = [(time.perf_counter() - task.created_at) * 1000.0 for task in batch]
              reqids = [task.request_id for task in batch]

              uids = [task.user_id for task in batch]

              logger.info(

                  "text microbatch dispatch | size=%d priority=%s queue_wait_ms_min=%.2f queue_wait_ms_max=%.2f reqids=%s uids=%s preview=%s",

                  len(batch),

                  _priority_label(max(task.priority for task in batch)),

                  min(queue_wait_ms) if queue_wait_ms else 0.0,
                  max(queue_wait_ms) if queue_wait_ms else 0.0,
                  reqids,

                  uids,

                  _preview_inputs(
                      [task.text for task in batch],
                      _LOG_PREVIEW_COUNT,
                      _LOG_TEXT_PREVIEW_CHARS,
                  ),

                  extra=build_request_log_extra(),

              )
              batch_t0 = time.perf_counter()

              embs = _encode_local_st([task.text for task in batch], normalize_embeddings=False)
              if embs is None or len(embs) != len(batch):
                  raise RuntimeError(
                      f"Text model response length mismatch in micro-batch: "
                      f"expected {len(batch)}, got {0 if embs is None else len(embs)}"
                  )
              for task, emb in zip(batch, embs):
                  vec = _as_list(emb, normalize=task.normalize)
                  if vec is None:
                      raise RuntimeError("Text model returned empty embedding in micro-batch")
                  task.result = vec

              logger.info(

                  "text microbatch done | size=%d reqids=%s uids=%s dim=%d backend_elapsed_ms=%.2f",

                  len(batch),
                  reqids,

                  uids,

                  len(batch[0].result) if batch and batch[0].result is not None else 0,
                  (time.perf_counter() - batch_t0) * 1000.0,

                  extra=build_request_log_extra(),

              )

          except Exception as exc:

              logger.error(

                  "text microbatch failed | size=%d reqids=%s uids=%s error=%s",

                  len(batch),
                  [task.request_id for task in batch],

                  [task.user_id for task in batch],

                  exc,
                  exc_info=True,

                  extra=build_request_log_extra(),

              )

              for task in batch:
                  task.error = exc
          finally:
              for task in batch:
                  task.done.set()
  
  

  def _encode_single_text_with_microbatch(
      text: str,
      normalize: bool,
      request_id: str,

      user_id: str,

      priority: int,
  ) -> List[float]:

      task = _SingleTextTask(
          text=text,
          normalize=normalize,

          priority=_effective_priority(priority),

          created_at=time.perf_counter(),

          request_id=request_id,

          user_id=user_id,

          done=threading.Event(),
      )
      with _text_single_queue_cv:

          if task.priority > 0:
              _text_single_high_queue.append(task)
          else:
              _text_single_normal_queue.append(task)

          _text_single_queue_cv.notify()
  
      if not task.done.wait(timeout=_TEXT_REQUEST_TIMEOUT_SEC):
          with _text_single_queue_cv:

              queue = _text_single_high_queue if task.priority > 0 else _text_single_normal_queue

              try:

                  queue.remove(task)

              except ValueError:
                  pass
          raise RuntimeError(
              f"Timed out waiting for text micro-batch worker ({_TEXT_REQUEST_TIMEOUT_SEC:.1f}s)"
          )
      if task.error is not None:
          raise task.error
      if task.result is None:
          raise RuntimeError("Text micro-batch worker returned empty result")
      return task.result
  
  

  @app.on_event("startup")
  def load_models():
      """Load models at service startup to avoid first-request latency."""

      global _text_model, _image_model, _text_backend_name

      logger.info(
          "Loading embedding models at startup | service_kind=%s text_enabled=%s image_enabled=%s",
          _SERVICE_KIND,
          open_text_model,
          open_image_model,
      )

      if open_text_model:
          try:

              backend_name, backend_cfg = get_embedding_backend_config()
              _text_backend_name = backend_name
              if backend_name == "tei":

                  from embeddings.text_embedding_tei import TEITextModel

                  base_url = backend_cfg.get("base_url") or CONFIG.TEI_BASE_URL
                  timeout_sec = int(backend_cfg.get("timeout_sec") or CONFIG.TEI_TIMEOUT_SEC)

                  logger.info("Loading text backend: tei (base_url=%s)", base_url)
                  _text_model = TEITextModel(
                      base_url=str(base_url),
                      timeout_sec=timeout_sec,

                      max_client_batch_size=int(
                          backend_cfg.get("max_client_batch_size") or CONFIG.TEI_MAX_CLIENT_BATCH_SIZE
                      ),

                  )
              elif backend_name == "local_st":

                  from embeddings.text_embedding_sentence_transformers import Qwen3TextModel

                  model_id = backend_cfg.get("model_id") or CONFIG.TEXT_MODEL_ID

                  logger.info("Loading text backend: local_st (model=%s)", model_id)
                  _text_model = Qwen3TextModel(model_id=str(model_id))

                  _start_text_batch_worker()

              else:
                  raise ValueError(
                      f"Unsupported embedding backend: {backend_name}. "
                      "Supported: tei, local_st"
                  )

              _start_text_dispatch_workers()

              logger.info("Text backend loaded successfully: %s", _text_backend_name)

          except Exception as e:

              logger.error("Failed to load text model: %s", e, exc_info=True)

              raise

      if open_image_model:
          try:

              if CONFIG.USE_CLIP_AS_SERVICE:

                  from embeddings.clip_as_service_encoder import ClipAsServiceImageEncoder
  

                  logger.info(
                      "Loading image encoder via clip-as-service: %s (configured model: %s)",
                      CONFIG.CLIP_AS_SERVICE_SERVER,
                      CONFIG.CLIP_AS_SERVICE_MODEL_NAME,
                  )

                  _image_model = ClipAsServiceImageEncoder(
                      server=CONFIG.CLIP_AS_SERVICE_SERVER,
                      batch_size=CONFIG.IMAGE_BATCH_SIZE,
                  )
                  logger.info("Image model (clip-as-service) loaded successfully")
              else:

                  from embeddings.clip_model import ClipImageModel
  

                  logger.info(
                      "Loading local image model: %s (device: %s)",
                      CONFIG.IMAGE_MODEL_NAME,
                      CONFIG.IMAGE_DEVICE,
                  )

                  _image_model = ClipImageModel(
                      model_name=CONFIG.IMAGE_MODEL_NAME,
                      device=CONFIG.IMAGE_DEVICE,
                  )
                  logger.info("Image model (local CN-CLIP) loaded successfully")

          except Exception as e:

              logger.error("Failed to load image model: %s", e, exc_info=True)
              raise

  
      logger.info("All embedding models loaded successfully, service ready")

  @app.on_event("shutdown")
  def stop_workers() -> None:
      _stop_text_batch_worker()

      _stop_text_dispatch_workers()

  def _normalize_vector(vec: np.ndarray) -> np.ndarray:
      norm = float(np.linalg.norm(vec))
      if not np.isfinite(norm) or norm <= 0.0:
          raise RuntimeError("Embedding vector has invalid norm (must be > 0)")
      return vec / norm
  
  
  def _as_list(embedding: Optional[np.ndarray], normalize: bool = False) -> Optional[List[float]]:

      if embedding is None:
          return None
      if not isinstance(embedding, np.ndarray):
          embedding = np.array(embedding, dtype=np.float32)
      if embedding.ndim != 1:
          embedding = embedding.reshape(-1)

      embedding = embedding.astype(np.float32, copy=False)
      if normalize:
          embedding = _normalize_vector(embedding).astype(np.float32, copy=False)
      return embedding.tolist()

  def _try_full_text_cache_hit(
      normalized: List[str],
      effective_normalize: bool,
  ) -> Optional[_EmbedResult]:
      out: List[Optional[List[float]]] = []
      for text in normalized:
          cached = _text_cache.get(build_text_cache_key(text, normalize=effective_normalize))
          if cached is None:
              return None
          vec = _as_list(cached, normalize=False)
          if vec is None:
              return None
          out.append(vec)
      return _EmbedResult(
          vectors=out,
          cache_hits=len(out),
          cache_misses=0,
          backend_elapsed_ms=0.0,
          mode="cache-only",
      )
  
  

  def _try_full_image_lane_cache_hit(
      items: List[str],

      effective_normalize: bool,

      *,
      lane: str,

  ) -> Optional[_EmbedResult]:
      out: List[Optional[List[float]]] = []

      for item in items:
          if lane == "image":

              ck = _mm_image_cache_key(
                  item, normalize=effective_normalize, model_name=CONFIG.MULTIMODAL_MODEL_NAME
              )

              cached = _image_cache.get(ck)
          else:

              ck = _mm_clip_text_cache_key(
                  item, normalize=effective_normalize, model_name=CONFIG.MULTIMODAL_MODEL_NAME
              )

              cached = _clip_text_cache.get(ck)

          if cached is None:
              return None
          vec = _as_list(cached, normalize=False)
          if vec is None:
              return None
          out.append(vec)
      return _EmbedResult(
          vectors=out,
          cache_hits=len(out),
          cache_misses=0,
          backend_elapsed_ms=0.0,
          mode="cache-only",
      )
  
  

  def _embed_image_lane_impl(
      items: List[str],
      effective_normalize: bool,
      request_id: str,
      user_id: str,
      *,
      lane: str,
  ) -> _EmbedResult:
      if _image_model is None:
          raise RuntimeError("Image model not loaded")
  
      out: List[Optional[List[float]]] = [None] * len(items)
      missing_indices: List[int] = []
      missing_items: List[str] = []
      missing_keys: List[str] = []
      cache_hits = 0
      for idx, item in enumerate(items):
          if lane == "image":

              ck = _mm_image_cache_key(
                  item, normalize=effective_normalize, model_name=CONFIG.MULTIMODAL_MODEL_NAME
              )

              cached = _image_cache.get(ck)
          else:

              ck = _mm_clip_text_cache_key(
                  item, normalize=effective_normalize, model_name=CONFIG.MULTIMODAL_MODEL_NAME
              )

              cached = _clip_text_cache.get(ck)
          if cached is not None:
              vec = _as_list(cached, normalize=False)
              if vec is not None:
                  out[idx] = vec
                  cache_hits += 1
                  continue
          missing_indices.append(idx)
          missing_items.append(item)
          missing_keys.append(ck)
  
      if not missing_items:
          logger.info(
              "%s lane cache-only | inputs=%d normalize=%s dim=%d cache_hits=%d",
              lane,
              len(items),
              effective_normalize,
              len(out[0]) if out and out[0] is not None else 0,
              cache_hits,
              extra=build_request_log_extra(request_id=request_id, user_id=user_id),
          )
          return _EmbedResult(
              vectors=out,
              cache_hits=cache_hits,
              cache_misses=0,
              backend_elapsed_ms=0.0,
              mode="cache-only",
          )
  
      backend_t0 = time.perf_counter()
      with _image_encode_lock:
          if lane == "image":
              vectors = _image_model.encode_image_urls(
                  missing_items,
                  batch_size=CONFIG.IMAGE_BATCH_SIZE,
                  normalize_embeddings=effective_normalize,
              )
          else:
              vectors = _image_model.encode_clip_texts(
                  missing_items,
                  batch_size=CONFIG.IMAGE_BATCH_SIZE,
                  normalize_embeddings=effective_normalize,
              )
      if vectors is None or len(vectors) != len(missing_items):
          raise RuntimeError(
              f"{lane} lane length mismatch: expected {len(missing_items)}, "
              f"got {0 if vectors is None else len(vectors)}"
          )
  
      for pos, ck, vec in zip(missing_indices, missing_keys, vectors):
          out_vec = _as_list(vec, normalize=effective_normalize)
          if out_vec is None:
              raise RuntimeError(f"{lane} lane empty embedding at position {pos}")
          out[pos] = out_vec
          if lane == "image":
              _image_cache.set(ck, np.asarray(out_vec, dtype=np.float32))
          else:
              _clip_text_cache.set(ck, np.asarray(out_vec, dtype=np.float32))
  
      backend_elapsed_ms = (time.perf_counter() - backend_t0) * 1000.0
      logger.info(
          "%s lane backend-batch | inputs=%d normalize=%s dim=%d cache_hits=%d cache_misses=%d backend_elapsed_ms=%.2f",
          lane,
          len(items),
          effective_normalize,
          len(out[0]) if out and out[0] is not None else 0,
          cache_hits,
          len(missing_items),
          backend_elapsed_ms,
          extra=build_request_log_extra(request_id=request_id, user_id=user_id),
      )
      return _EmbedResult(
          vectors=out,
          cache_hits=cache_hits,
          cache_misses=len(missing_items),
          backend_elapsed_ms=backend_elapsed_ms,
          mode="backend-batch",
      )