"""
  Embedding service (FastAPI).
  

  API (simple list-in, list-out; aligned by index):
  - POST /embed/text   body: ["text1", "text2", ...] -> [[...], ...]
  - POST /embed/image  body: ["url_or_path1", ...]  -> [[...], ...]

  """
  

  import logging

  import os

  import pathlib

  import threading

  import time

  import uuid

  from collections import deque
  from dataclasses import dataclass

  from logging.handlers import TimedRotatingFileHandler

  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_image_cache_key, build_text_cache_key

  from embeddings.config import CONFIG

  from embeddings.protocols import ImageEncoderProtocol

  from embeddings.redis_embedding_cache import RedisEmbeddingCache

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

  
  class _DefaultRequestIdFilter(logging.Filter):
      def filter(self, record: logging.LogRecord) -> bool:
          if not hasattr(record, "reqid"):
              record.reqid = "-1"
          return True
  
  
  def configure_embedding_logging() -> None:
      root_logger = logging.getLogger()
      if getattr(root_logger, "_embedding_logging_configured", False):
          return
  
      log_dir = pathlib.Path("logs")
      verbose_dir = log_dir / "verbose"
      log_dir.mkdir(exist_ok=True)
      verbose_dir.mkdir(parents=True, exist_ok=True)
  
      log_level = os.getenv("LOG_LEVEL", "INFO").upper()
      numeric_level = getattr(logging, log_level, logging.INFO)
      formatter = logging.Formatter(
          "%(asctime)s | reqid:%(reqid)s | %(name)s | %(levelname)s | %(message)s"
      )
      request_filter = _DefaultRequestIdFilter()
  
      root_logger.setLevel(numeric_level)
  
      file_handler = TimedRotatingFileHandler(
          filename=log_dir / "embedding_api.log",
          when="midnight",
          interval=1,
          backupCount=30,
          encoding="utf-8",
      )
      file_handler.setLevel(numeric_level)
      file_handler.setFormatter(formatter)
      file_handler.addFilter(request_filter)
      root_logger.addHandler(file_handler)
  
      error_handler = TimedRotatingFileHandler(
          filename=log_dir / "embedding_api_error.log",
          when="midnight",
          interval=1,
          backupCount=30,
          encoding="utf-8",
      )
      error_handler.setLevel(logging.ERROR)
      error_handler.setFormatter(formatter)
      error_handler.addFilter(request_filter)
      root_logger.addHandler(error_handler)
  
      verbose_logger = logging.getLogger("embedding.verbose")
      verbose_logger.setLevel(numeric_level)
      verbose_logger.handlers.clear()
      verbose_logger.propagate = False
  
      verbose_handler = TimedRotatingFileHandler(
          filename=verbose_dir / "embedding_verbose.log",
          when="midnight",
          interval=1,
          backupCount=30,
          encoding="utf-8",
      )
      verbose_handler.setLevel(numeric_level)
      verbose_handler.setFormatter(formatter)
      verbose_handler.addFilter(request_filter)
      verbose_logger.addHandler(verbose_handler)
  
      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", "1")))
  _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._sem = threading.BoundedSemaphore(self.limit)
          self._lock = threading.Lock()
          self._active = 0
          self._rejected = 0
          self._completed = 0
          self._failed = 0
          self._max_active = 0
  
      def try_acquire(self) -> tuple[bool, int]:
          if not self._sem.acquire(blocking=False):
              with self._lock:
                  self._rejected += 1
                  active = self._active
              return False, active
          with self._lock:
              self._active += 1
              self._max_active = max(self._max_active, self._active)
              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
          self._sem.release()
          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,
              }
  
  
  _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")

  @dataclass
  class _SingleTextTask:
      text: str
      normalize: bool
      created_at: float

      request_id: str

      done: threading.Event
      result: Optional[List[float]] = None
      error: Optional[Exception] = None
  
  
  _text_single_queue: "deque[_SingleTextTask]" = deque()
  _text_single_queue_cv = threading.Condition()
  _text_batch_worker: Optional[threading.Thread] = None
  _text_batch_worker_stop = False

  
  
  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 _request_log_extra(request_id: str) -> Dict[str, str]:
      return {"reqid": request_id}
  
  
  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 _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_queue and not _text_batch_worker_stop:
                  _text_single_queue_cv.wait()
              if _text_batch_worker_stop:
                  return
  
              batch: List[_SingleTextTask] = [_text_single_queue.popleft()]
              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_queue:
                      _text_single_queue_cv.wait(timeout=remaining)
                      continue
                  while _text_single_queue and len(batch) < max_batch:
                      batch.append(_text_single_queue.popleft())
  
          try:

              queue_wait_ms = [(time.perf_counter() - task.created_at) * 1000.0 for task in batch]
              reqids = [task.request_id for task in batch]
              logger.info(
                  "text microbatch dispatch | size=%d queue_wait_ms_min=%.2f queue_wait_ms_max=%.2f reqids=%s preview=%s",
                  len(batch),
                  min(queue_wait_ms) if queue_wait_ms else 0.0,
                  max(queue_wait_ms) if queue_wait_ms else 0.0,
                  reqids,
                  _preview_inputs(
                      [task.text for task in batch],
                      _LOG_PREVIEW_COUNT,
                      _LOG_TEXT_PREVIEW_CHARS,
                  ),
              )
              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 dim=%d backend_elapsed_ms=%.2f",
                  len(batch),
                  reqids,
                  len(batch[0].result) if batch and batch[0].result is not None else 0,
                  (time.perf_counter() - batch_t0) * 1000.0,
              )

          except Exception as exc:

              logger.error(
                  "text microbatch failed | size=%d reqids=%s error=%s",
                  len(batch),
                  [task.request_id for task in batch],
                  exc,
                  exc_info=True,
              )

              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) -> List[float]:

      task = _SingleTextTask(
          text=text,
          normalize=normalize,
          created_at=time.perf_counter(),

          request_id=request_id,

          done=threading.Event(),
      )
      with _text_single_queue_cv:
          _text_single_queue.append(task)
          _text_single_queue_cv.notify()
  
      if not task.done.wait(timeout=_TEXT_REQUEST_TIMEOUT_SEC):
          with _text_single_queue_cv:
              try:
                  _text_single_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,
                  )
              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"
                  )
              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()
  
  

  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_cache_hit(
      urls: List[str],
      effective_normalize: bool,
  ) -> Optional[_EmbedResult]:
      out: List[Optional[List[float]]] = []
      for url in urls:
          cached = _image_cache.get(build_image_cache_key(url, 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",
      )
  
  

  @app.get("/health")
  def health() -> Dict[str, Any]:

      """Health check endpoint. Returns status and current throttling stats."""

      ready = (not open_text_model or _text_model is not None) and (not open_image_model or _image_model is not None)

      return {

          "status": "ok" if ready else "degraded",
          "service_kind": _SERVICE_KIND,

          "text_model_loaded": _text_model is not None,

          "text_backend": _text_backend_name,

          "image_model_loaded": _image_model is not None,

          "cache_enabled": {
              "text": _text_cache.redis_client is not None,
              "image": _image_cache.redis_client is not None,
          },

          "limits": {
              "text": _text_request_limiter.snapshot(),
              "image": _image_request_limiter.snapshot(),
          },

          "stats": {
              "text": _text_stats.snapshot(),
              "image": _image_stats.snapshot(),
          },

          "text_microbatch": {
              "window_ms": round(_TEXT_MICROBATCH_WINDOW_SEC * 1000.0, 3),
              "queue_depth": len(_text_single_queue),
              "worker_alive": bool(_text_batch_worker is not None and _text_batch_worker.is_alive()),
              "request_timeout_sec": _TEXT_REQUEST_TIMEOUT_SEC,
          },

      }

  @app.get("/ready")
  def ready() -> Dict[str, Any]:
      text_ready = (not open_text_model) or (_text_model is not None)
      image_ready = (not open_image_model) or (_image_model is not None)
      if not (text_ready and image_ready):
          raise HTTPException(
              status_code=503,
              detail={
                  "service_kind": _SERVICE_KIND,
                  "text_ready": text_ready,
                  "image_ready": image_ready,
              },
          )
      return {
          "status": "ready",
          "service_kind": _SERVICE_KIND,
          "text_ready": text_ready,
          "image_ready": image_ready,
      }
  
  

  def _embed_text_impl(
      normalized: List[str],
      effective_normalize: bool,
      request_id: str,

  ) -> _EmbedResult:

      if _text_model is None:
          raise RuntimeError("Text model not loaded")

      out: List[Optional[List[float]]] = [None] * len(normalized)
      missing_indices: List[int] = []
      missing_texts: List[str] = []
      missing_cache_keys: List[str] = []
      cache_hits = 0
      for idx, text in enumerate(normalized):
          cache_key = build_text_cache_key(text, normalize=effective_normalize)
          cached = _text_cache.get(cache_key)
          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_texts.append(text)
          missing_cache_keys.append(cache_key)
  
      if not missing_texts:
          logger.info(
              "text backend done | backend=%s mode=cache-only inputs=%d normalize=%s dim=%d cache_hits=%d cache_misses=0 backend_elapsed_ms=0.00",
              _text_backend_name,
              len(normalized),
              effective_normalize,
              len(out[0]) if out and out[0] is not None else 0,
              cache_hits,
              extra=_request_log_extra(request_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()

      try:

          if _text_backend_name == "local_st":

              if len(missing_texts) == 1 and _text_batch_worker is not None:
                  computed = [

                      _encode_single_text_with_microbatch(

                          missing_texts[0],

                          normalize=effective_normalize,
                          request_id=request_id,
                      )
                  ]

                  mode = "microbatch-single"
              else:
                  embs = _encode_local_st(missing_texts, normalize_embeddings=False)
                  computed = []
                  for i, emb in enumerate(embs):
                      vec = _as_list(emb, normalize=effective_normalize)
                      if vec is None:
                          raise RuntimeError(f"Text model returned empty embedding for missing index {i}")
                      computed.append(vec)
                  mode = "direct-batch"

          else:

              embs = _text_model.encode(

                  missing_texts,

                  batch_size=int(CONFIG.TEXT_BATCH_SIZE),
                  device=CONFIG.TEXT_DEVICE,

                  normalize_embeddings=effective_normalize,

              )

              computed = []
              for i, emb in enumerate(embs):
                  vec = _as_list(emb, normalize=False)
                  if vec is None:
                      raise RuntimeError(f"Text model returned empty embedding for missing index {i}")
                  computed.append(vec)

              mode = "backend-batch"

      except Exception as e:

          logger.error(
              "Text embedding backend failure: %s",
              e,
              exc_info=True,
              extra=_request_log_extra(request_id),
          )
          raise RuntimeError(f"Text embedding backend failure: {e}") from e
  

      if len(computed) != len(missing_texts):

          raise RuntimeError(

              f"Text model response length mismatch: expected {len(missing_texts)}, "
              f"got {len(computed)}"

          )

      for pos, cache_key, vec in zip(missing_indices, missing_cache_keys, computed):
          out[pos] = vec
          _text_cache.set(cache_key, np.asarray(vec, dtype=np.float32))
  
      backend_elapsed_ms = (time.perf_counter() - backend_t0) * 1000.0

      logger.info(

          "text backend done | backend=%s mode=%s inputs=%d normalize=%s dim=%d cache_hits=%d cache_misses=%d backend_elapsed_ms=%.2f",

          _text_backend_name,

          mode,

          len(normalized),
          effective_normalize,

          len(out[0]) if out and out[0] is not None else 0,

          cache_hits,
          len(missing_texts),
          backend_elapsed_ms,

          extra=_request_log_extra(request_id),

      )

      return _EmbedResult(
          vectors=out,
          cache_hits=cache_hits,
          cache_misses=len(missing_texts),
          backend_elapsed_ms=backend_elapsed_ms,
          mode=mode,
      )

  @app.post("/embed/text")
  async def embed_text(
      texts: List[str],
      http_request: Request,
      response: Response,
      normalize: Optional[bool] = None,
  ) -> List[Optional[List[float]]]:

      if _text_model is None:
          raise HTTPException(status_code=503, detail="Text embedding model not loaded in this service")
  

      request_id = _resolve_request_id(http_request)
      response.headers["X-Request-ID"] = request_id
  
      effective_normalize = bool(CONFIG.TEXT_NORMALIZE_EMBEDDINGS) if normalize is None else bool(normalize)
      normalized: List[str] = []
      for i, t in enumerate(texts):
          if not isinstance(t, str):
              raise HTTPException(status_code=400, detail=f"Invalid text at index {i}: must be string")
          s = t.strip()

          if not s:

              raise HTTPException(status_code=400, detail=f"Invalid text at index {i}: empty string")
          normalized.append(s)

      cache_check_started = time.perf_counter()
      cache_only = _try_full_text_cache_hit(normalized, effective_normalize)
      if cache_only is not None:
          latency_ms = (time.perf_counter() - cache_check_started) * 1000.0
          _text_stats.record_completed(
              success=True,
              latency_ms=latency_ms,
              backend_latency_ms=0.0,
              cache_hits=cache_only.cache_hits,
              cache_misses=0,
          )
          logger.info(
              "embed_text response | backend=%s mode=cache-only inputs=%d normalize=%s dim=%d cache_hits=%d cache_misses=0 first_vector=%s latency_ms=%.2f",
              _text_backend_name,
              len(normalized),
              effective_normalize,
              len(cache_only.vectors[0]) if cache_only.vectors and cache_only.vectors[0] is not None else 0,
              cache_only.cache_hits,
              _preview_vector(cache_only.vectors[0] if cache_only.vectors else None),
              latency_ms,
              extra=_request_log_extra(request_id),
          )
          return cache_only.vectors
  

      accepted, active = _text_request_limiter.try_acquire()
      if not accepted:

          _text_stats.record_rejected()

          logger.warning(
              "embed_text rejected | client=%s backend=%s inputs=%d normalize=%s active=%d limit=%d preview=%s",
              _request_client(http_request),
              _text_backend_name,
              len(normalized),
              effective_normalize,
              active,
              _TEXT_MAX_INFLIGHT,
              _preview_inputs(normalized, _LOG_PREVIEW_COUNT, _LOG_TEXT_PREVIEW_CHARS),
              extra=_request_log_extra(request_id),
          )
          raise HTTPException(
              status_code=_OVERLOAD_STATUS_CODE,
              detail=f"Text embedding service busy: active={active}, limit={_TEXT_MAX_INFLIGHT}",
          )
  
      request_started = time.perf_counter()
      success = False

      backend_elapsed_ms = 0.0
      cache_hits = 0
      cache_misses = 0

      try:
          logger.info(
              "embed_text request | client=%s backend=%s inputs=%d normalize=%s active=%d limit=%d preview=%s",
              _request_client(http_request),
              _text_backend_name,
              len(normalized),
              effective_normalize,
              active,
              _TEXT_MAX_INFLIGHT,
              _preview_inputs(normalized, _LOG_PREVIEW_COUNT, _LOG_TEXT_PREVIEW_CHARS),
              extra=_request_log_extra(request_id),
          )
          verbose_logger.info(
              "embed_text detail | payload=%s normalize=%s backend=%s",
              normalized,
              effective_normalize,
              _text_backend_name,
              extra=_request_log_extra(request_id),
          )

          result = await run_in_threadpool(_embed_text_impl, normalized, effective_normalize, request_id)

          success = True

          backend_elapsed_ms = result.backend_elapsed_ms
          cache_hits = result.cache_hits
          cache_misses = result.cache_misses

          latency_ms = (time.perf_counter() - request_started) * 1000.0

          _text_stats.record_completed(
              success=True,
              latency_ms=latency_ms,
              backend_latency_ms=backend_elapsed_ms,
              cache_hits=cache_hits,
              cache_misses=cache_misses,
          )

          logger.info(

              "embed_text response | backend=%s mode=%s inputs=%d normalize=%s dim=%d cache_hits=%d cache_misses=%d first_vector=%s latency_ms=%.2f",

              _text_backend_name,

              result.mode,

              len(normalized),
              effective_normalize,

              len(result.vectors[0]) if result.vectors and result.vectors[0] is not None else 0,
              cache_hits,
              cache_misses,
              _preview_vector(result.vectors[0] if result.vectors else None),

              latency_ms,
              extra=_request_log_extra(request_id),
          )
          verbose_logger.info(
              "embed_text result detail | count=%d first_vector=%s latency_ms=%.2f",

              len(result.vectors),
              result.vectors[0][: _VECTOR_PREVIEW_DIMS]
              if result.vectors and result.vectors[0] is not None
              else [],

              latency_ms,
              extra=_request_log_extra(request_id),
          )

          return result.vectors

      except HTTPException:
          raise
      except Exception as e:
          latency_ms = (time.perf_counter() - request_started) * 1000.0

          _text_stats.record_completed(
              success=False,
              latency_ms=latency_ms,
              backend_latency_ms=backend_elapsed_ms,
              cache_hits=cache_hits,
              cache_misses=cache_misses,
          )

          logger.error(
              "embed_text failed | backend=%s inputs=%d normalize=%s latency_ms=%.2f error=%s",
              _text_backend_name,
              len(normalized),
              effective_normalize,
              latency_ms,
              e,
              exc_info=True,
              extra=_request_log_extra(request_id),
          )
          raise HTTPException(status_code=502, detail=str(e)) from e
      finally:
          remaining = _text_request_limiter.release(success=success)
          logger.info(
              "embed_text finalize | success=%s active_after=%d",
              success,
              remaining,
              extra=_request_log_extra(request_id),
          )
  
  
  def _embed_image_impl(
      urls: List[str],
      effective_normalize: bool,
      request_id: str,