日志优化、日志串联（uid rqid）

tangwang
1 parent cda1cd62
Showing 13 changed files with 520 additions and 220 deletions Show diff stats
api/app.py
api/routes/search.py
context/request_context.py
embeddings/README.md
embeddings/config.py
embeddings/image_encoder.py
embeddings/server.py
embeddings/text_embedding_tei.py
embeddings/text_encoder.py
query/query_parser.py
request_log_context.py
tests/test_embedding_pipeline.py
utils/logger.py
@@ -27,6 +27,8 @@ from slowapi.errors import RateLimitExceeded
 # Configure backend logging
 import pathlib
+from request_log_context import LOG_LINE_FORMAT, RequestLogContextFilter
+
 def configure_backend_logging() -> None:
     log_dir = pathlib.Path("logs")
@@ -34,9 +36,8 @@ def configure_backend_logging() -&gt; None:
     log_level = os.getenv("LOG_LEVEL", "INFO").upper()
     numeric_level = getattr(logging, log_level, logging.INFO)
-    default_formatter = logging.Formatter(
-        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-    )
+    default_formatter = logging.Formatter(LOG_LINE_FORMAT)
+    request_filter = RequestLogContextFilter()
     root_logger = logging.getLogger()
     root_logger.setLevel(numeric_level)
@@ -45,6 +46,7 @@ def configure_backend_logging() -&gt; None:
     console_handler = logging.StreamHandler()
     console_handler.setLevel(numeric_level)
     console_handler.setFormatter(default_formatter)
+    console_handler.addFilter(request_filter)
     root_logger.addHandler(console_handler)
     backend_handler = TimedRotatingFileHandler(
@@ -56,6 +58,7 @@ def configure_backend_logging() -&gt; None:
     )
     backend_handler.setLevel(numeric_level)
     backend_handler.setFormatter(default_formatter)
+    backend_handler.addFilter(request_filter)
     root_logger.addHandler(backend_handler)
     verbose_logger = logging.getLogger("backend.verbose")
@@ -71,11 +74,16 @@ def configure_backend_logging() -&gt; None:
         encoding="utf-8",
     )
     verbose_handler.setLevel(numeric_level)
-    verbose_handler.setFormatter(
-        logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
-    )
+    verbose_handler.setFormatter(logging.Formatter(LOG_LINE_FORMAT))
+    verbose_handler.addFilter(request_filter)
     verbose_logger.addHandler(verbose_handler)
+    for logger_name in ("uvicorn", "uvicorn.error", "uvicorn.access"):
+        uvicorn_logger = logging.getLogger(logger_name)
+        uvicorn_logger.handlers.clear()
+        uvicorn_logger.setLevel(numeric_level)
+        uvicorn_logger.propagate = True
+
 configure_backend_logging()
 logger = logging.getLogger(__name__)
@@ -101,6 +109,16 @@ _suggestion_service: Optional[SuggestionService] = None
 _app_config = None
+def _request_log_extra_from_http(request: Request) -> dict:
+    reqid = getattr(getattr(request, "state", None), "reqid", None) or request.headers.get("X-Request-ID")
+    uid = (
+        getattr(getattr(request, "state", None), "uid", None)
+        or request.headers.get("X-User-ID")
+        or request.headers.get("User-ID")
+    )
+    return {"reqid": reqid or "-1", "uid": uid or "-1"}
+
+
 def init_service(es_host: str = "http://localhost:9200"):
     """
     Initialize search service with unified configuration.
@@ -261,7 +279,11 @@ async def shutdown_event():
 async def global_exception_handler(request: Request, exc: Exception):
     """Global exception handler with detailed logging."""
     client_ip = request.client.host if request.client else "unknown"
-    logger.error(f"Unhandled exception from {client_ip}: {exc}", exc_info=True)
+    logger.error(
+        f"Unhandled exception from {client_ip}: {exc}",
+        exc_info=True,
+        extra=_request_log_extra_from_http(request),
+    )
     return JSONResponse(
         status_code=500,
@@ -276,7 +298,10 @@ async def global_exception_handler(request: Request, exc: Exception):
 @app.exception_handler(HTTPException)
 async def http_exception_handler(request: Request, exc: HTTPException):
     """HTTP exception handler."""
-    logger.warning(f"HTTP exception from {request.client.host if request.client else 'unknown'}: {exc.status_code} - {exc.detail}")
+    logger.warning(
+        f"HTTP exception from {request.client.host if request.client else 'unknown'}: {exc.status_code} - {exc.detail}",
+        extra=_request_log_extra_from_http(request),
+    )
     return JSONResponse(
         status_code=exc.status_code,
@@ -59,6 +59,8 @@ async def search(request: SearchRequest, http_request: Request):
     Requires tenant_id in header (X-Tenant-ID) or query parameter (tenant_id).
     """
     reqid, uid = extract_request_info(http_request)
+    http_request.state.reqid = reqid
+    http_request.state.uid = uid
     # Extract tenant_id (required)
     tenant_id = http_request.headers.get('X-Tenant-ID')
@@ -213,6 +215,8 @@ async def search_by_image(request: ImageSearchRequest, http_request: Request):
     Requires tenant_id in header (X-Tenant-ID) or query parameter (tenant_id).
     """
     reqid, uid = extract_request_info(http_request)
+    http_request.state.reqid = reqid
+    http_request.state.uid = uid
     # Extract tenant_id (required)
     tenant_id = http_request.headers.get('X-Tenant-ID')
@@ -12,6 +12,8 @@ from typing import Dict, Any, Optional, List
 from dataclasses import dataclass, field
 import uuid
+from request_log_context import bind_request_log_context, reset_request_log_context
+
 class RequestContextStage(Enum):
     """搜索阶段枚举"""
@@ -375,9 +377,15 @@ def get_current_request_context() -&gt; Optional[RequestContext]:
 def set_current_request_context(context: RequestContext) -> None:
     """设置当前线程的请求上下文"""
     threading.current_thread().request_context = context
+    _, _, tokens = bind_request_log_context(context.reqid, context.uid)
+    threading.current_thread().request_log_tokens = tokens
 def clear_current_request_context() -> None:
     """清除当前线程的请求上下文"""
+    tokens = getattr(threading.current_thread(), 'request_log_tokens', None)
+    if tokens is not None:
+        reset_request_log_context(tokens)
+        delattr(threading.current_thread(), 'request_log_tokens')
     if hasattr(threading.current_thread(), 'request_context'):
         delattr(threading.current_thread(), 'request_context')
 \ No newline at end of file
@@ -5,6 +5,8 @@
 - `../docs/TEI_SERVICE说明文档.md`
 - `../docs/CNCLIP_SERVICE说明文档.md`
+**请求日志串联（reqid / uid）**：统一实现在仓库根目录的 `request_log_context.py`（勿放到 `utils/` 下，以免 `.venv-embedding` 因 `utils/__init__.py` 拉取数据库依赖）。Uvicorn 日志配置见 `config/uvicorn_embedding_logging.json`。
+
 ---
 这个目录是一个完整的“向量化模块”，包含：
@@ -2,6 +2,7 @@
 from __future__ import annotations
+import os
 from typing import Optional
 from config.loader import get_app_config
@@ -25,6 +26,11 @@ class EmbeddingConfig(object):
         self.TEXT_NORMALIZE_EMBEDDINGS = bool(text_backend.get("normalize_embeddings", True))
         self.TEI_BASE_URL = str(text_backend.get("base_url") or "http://127.0.0.1:8080")
         self.TEI_TIMEOUT_SEC = int(text_backend.get("timeout_sec", 60))
+        self.TEI_MAX_CLIENT_BATCH_SIZE = int(
+            os.getenv("TEI_MAX_CLIENT_BATCH_SIZE")
+            or text_backend.get("max_client_batch_size")
+            or 24
+        )
         self.USE_CLIP_AS_SERVICE = services.image_backend == "clip_as_service"
         self.CLIP_AS_SERVICE_SERVER = str(image_backend.get("server") or "grpc://127.0.0.1:51000")
@@ -13,6 +13,7 @@ from config.loader import get_app_config
 from config.services_config import get_embedding_image_base_url
 from embeddings.cache_keys import build_image_cache_key
 from embeddings.redis_embedding_cache import RedisEmbeddingCache
+from request_log_context import build_downstream_request_headers, build_request_log_extra
 class CLIPImageEncoder:
@@ -40,6 +41,8 @@ class CLIPImageEncoder:
         request_data: List[str],
         normalize_embeddings: bool = True,
         priority: int = 0,
+        request_id: Optional[str] = None,
+        user_id: Optional[str] = None,
     ) -> List[Any]:
         """
         Call the embedding service API.
@@ -50,6 +53,7 @@ class CLIPImageEncoder:
         Returns:
             List of embeddings (list[float]) or nulls (None), aligned to input order
         """
+        response = None
         try:
             response = requests.post(
                 self.endpoint,
@@ -58,12 +62,26 @@ class CLIPImageEncoder:
                     "priority": max(0, int(priority)),
                 },
                 json=request_data,
+                headers=build_downstream_request_headers(request_id=request_id, user_id=user_id),
                 timeout=60
             )
             response.raise_for_status()
             return response.json()
         except requests.exceptions.RequestException as e:
-            logger.error(f"CLIPImageEncoder service request failed: {e}", exc_info=True)
+            body_preview = ""
+            if response is not None:
+                try:
+                    body_preview = (response.text or "")[:300]
+                except Exception:
+                    body_preview = ""
+            logger.error(
+                "CLIPImageEncoder service request failed | status=%s body=%s error=%s",
+                getattr(response, "status_code", "n/a"),
+                body_preview,
+                e,
+                exc_info=True,
+                extra=build_request_log_extra(request_id=request_id, user_id=user_id),
+            )
             raise
     def encode_image(self, image: Image.Image) -> np.ndarray:
@@ -79,6 +97,8 @@ class CLIPImageEncoder:
         url: str,
         normalize_embeddings: bool = True,
         priority: int = 0,
+        request_id: Optional[str] = None,
+        user_id: Optional[str] = None,
     ) -> np.ndarray:
         """
         Generate image embedding via network service using URL.
@@ -98,6 +118,8 @@ class CLIPImageEncoder:
             [url],
             normalize_embeddings=normalize_embeddings,
             priority=priority,
+            request_id=request_id,
+            user_id=user_id,
         )
         if not response_data or len(response_data) != 1 or response_data[0] is None:
             raise RuntimeError(f"No image embedding returned for URL: {url}")
@@ -113,6 +135,8 @@ class CLIPImageEncoder:
         batch_size: int = 8,
         normalize_embeddings: bool = True,
         priority: int = 0,
+        request_id: Optional[str] = None,
+        user_id: Optional[str] = None,
     ) -> List[np.ndarray]:
         """
         Encode a batch of images efficiently via network service.
@@ -151,6 +175,8 @@ class CLIPImageEncoder:
                 batch_urls,
                 normalize_embeddings=normalize_embeddings,
                 priority=priority,
+                request_id=request_id,
+                user_id=user_id,
             )
             if not response_data or len(response_data) != len(batch_urls):
                 raise RuntimeError(
@@ -176,6 +202,8 @@ class CLIPImageEncoder:
         batch_size: Optional[int] = None,
         normalize_embeddings: bool = True,
         priority: int = 0,
+        request_id: Optional[str] = None,
+        user_id: Optional[str] = None,
     ) -> List[np.ndarray]:
         """
         与 ClipImageModel / ClipAsServiceImageEncoder 一致的接口，供索引器 document_transformer 调用。
@@ -192,4 +220,6 @@ class CLIPImageEncoder:
             batch_size=batch_size or 8,
             normalize_embeddings=normalize_embeddings,
             priority=priority,
+            request_id=request_id,
+            user_id=user_id,
         )
@@ -26,17 +26,17 @@ 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
+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")
-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):
@@ -47,17 +47,15 @@ def configure_embedding_logging() -&gt; None:
     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()
+    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(request_filter)
+    stream_handler.addFilter(context_filter)
     root_logger.addHandler(stream_handler)
     verbose_logger = logging.getLogger("embedding.verbose")
@@ -231,6 +229,7 @@ class _TextDispatchTask:
     normalized: List[str]
     effective_normalize: bool
     request_id: str
+    user_id: str
     priority: int
     created_at: float
     done: threading.Event
@@ -321,12 +320,13 @@ def _text_dispatch_worker_loop(worker_idx: int) -&gt; None:
                 _priority_label(task.priority),
                 len(task.normalized),
                 queue_wait_ms,
-                extra=_request_log_extra(task.request_id),
+                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:
@@ -339,6 +339,7 @@ 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):
@@ -347,6 +348,7 @@ def _submit_text_dispatch_and_wait(
         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(),
@@ -380,6 +382,7 @@ class _SingleTextTask:
     priority: int
     created_at: float
     request_id: str
+    user_id: str
     done: threading.Event
     result: Optional[List[float]] = None
     error: Optional[Exception] = None
@@ -435,10 +438,6 @@ def _preview_vector(vec: Optional[List[float]], max_dims: int = _VECTOR_PREVIEW_
     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():
@@ -446,6 +445,13 @@ def _resolve_request_id(http_request: Request) -&gt; str:
     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)
@@ -522,18 +528,21 @@ def _text_batch_worker_loop() -&gt; None:
         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 preview=%s",
+                "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)
@@ -548,19 +557,23 @@ def _text_batch_worker_loop() -&gt; 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",
+                "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 error=%s",
+                "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
@@ -573,6 +586,7 @@ def _encode_single_text_with_microbatch(
     text: str,
     normalize: bool,
     request_id: str,
+    user_id: str,
     priority: int,
 ) -> List[float]:
     task = _SingleTextTask(
@@ -581,6 +595,7 @@ def _encode_single_text_with_microbatch(
         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:
@@ -632,6 +647,9 @@ def load_models():
                 _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
@@ -823,6 +841,7 @@ def _embed_text_impl(
     normalized: List[str],
     effective_normalize: bool,
     request_id: str,
+    user_id: str,
     priority: int = 0,
 ) -> _EmbedResult:
     if _text_model is None:
@@ -854,7 +873,7 @@ def _embed_text_impl(
             effective_normalize,
             len(out[0]) if out and out[0] is not None else 0,
             cache_hits,
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
         return _EmbedResult(
             vectors=out,
@@ -873,6 +892,7 @@ def _embed_text_impl(
                         missing_texts[0],
                         normalize=effective_normalize,
                         request_id=request_id,
+                        user_id=user_id,
                         priority=priority,
                     )
                 ]
@@ -905,7 +925,7 @@ def _embed_text_impl(
             "Text embedding backend failure: %s",
             e,
             exc_info=True,
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
         raise RuntimeError(f"Text embedding backend failure: {e}") from e
@@ -931,7 +951,7 @@ def _embed_text_impl(
         cache_hits,
         len(missing_texts),
         backend_elapsed_ms,
-        extra=_request_log_extra(request_id),
+        extra=build_request_log_extra(request_id, user_id),
     )
     return _EmbedResult(
         vectors=out,
@@ -954,75 +974,79 @@ async def embed_text(
         raise HTTPException(status_code=503, detail="Text embedding model not loaded in this service")
     request_id = _resolve_request_id(http_request)
+    user_id = _resolve_user_id(http_request)
+    _, _, log_tokens = bind_request_log_context(request_id, user_id)
     response.headers["X-Request-ID"] = request_id
-
-    if priority < 0:
-        raise HTTPException(status_code=400, detail="priority must be >= 0")
-    effective_priority = _effective_priority(priority)
-    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 priority=%s inputs=%d normalize=%s dim=%d cache_hits=%d cache_misses=0 first_vector=%s latency_ms=%.2f",
-            _text_backend_name,
-            _priority_label(effective_priority),
-            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(bypass_limit=effective_priority > 0)
-    if not accepted:
-        _text_stats.record_rejected()
-        logger.warning(
-            "embed_text rejected | client=%s backend=%s priority=%s inputs=%d normalize=%s active=%d limit=%d preview=%s",
-            _request_client(http_request),
-            _text_backend_name,
-            _priority_label(effective_priority),
-            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=(
-                "Text embedding service busy for priority=0 requests: "
-                f"active={active}, limit={_TEXT_MAX_INFLIGHT}"
-            ),
-        )
-
+    response.headers["X-User-ID"] = user_id
     request_started = time.perf_counter()
     success = False
     backend_elapsed_ms = 0.0
     cache_hits = 0
     cache_misses = 0
+    limiter_acquired = False
+
     try:
+        if priority < 0:
+            raise HTTPException(status_code=400, detail="priority must be >= 0")
+        effective_priority = _effective_priority(priority)
+        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 priority=%s inputs=%d normalize=%s dim=%d cache_hits=%d cache_misses=0 first_vector=%s latency_ms=%.2f",
+                _text_backend_name,
+                _priority_label(effective_priority),
+                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=build_request_log_extra(request_id, user_id),
+            )
+            return cache_only.vectors
+
+        accepted, active = _text_request_limiter.try_acquire(bypass_limit=effective_priority > 0)
+        if not accepted:
+            _text_stats.record_rejected()
+            logger.warning(
+                "embed_text rejected | client=%s backend=%s priority=%s inputs=%d normalize=%s active=%d limit=%d preview=%s",
+                _request_client(http_request),
+                _text_backend_name,
+                _priority_label(effective_priority),
+                len(normalized),
+                effective_normalize,
+                active,
+                _TEXT_MAX_INFLIGHT,
+                _preview_inputs(normalized, _LOG_PREVIEW_COUNT, _LOG_TEXT_PREVIEW_CHARS),
+                extra=build_request_log_extra(request_id, user_id),
+            )
+            raise HTTPException(
+                status_code=_OVERLOAD_STATUS_CODE,
+                detail=(
+                    "Text embedding service busy for priority=0 requests: "
+                    f"active={active}, limit={_TEXT_MAX_INFLIGHT}"
+                ),
+            )
+        limiter_acquired = True
         logger.info(
             "embed_text request | client=%s backend=%s priority=%s inputs=%d normalize=%s active=%d limit=%d preview=%s",
             _request_client(http_request),
@@ -1033,7 +1057,7 @@ async def embed_text(
             active,
             _TEXT_MAX_INFLIGHT,
             _preview_inputs(normalized, _LOG_PREVIEW_COUNT, _LOG_TEXT_PREVIEW_CHARS),
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
         verbose_logger.info(
             "embed_text detail | payload=%s normalize=%s backend=%s priority=%s",
@@ -1041,13 +1065,14 @@ async def embed_text(
             effective_normalize,
             _text_backend_name,
             _priority_label(effective_priority),
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
         result = await run_in_threadpool(
             _submit_text_dispatch_and_wait,
             normalized,
             effective_normalize,
             request_id,
+            user_id,
             effective_priority,
         )
         success = True
@@ -1074,7 +1099,7 @@ async def embed_text(
             cache_misses,
             _preview_vector(result.vectors[0] if result.vectors else None),
             latency_ms,
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
         verbose_logger.info(
             "embed_text result detail | count=%d priority=%s first_vector=%s latency_ms=%.2f",
@@ -1084,7 +1109,7 @@ async def embed_text(
             if result.vectors and result.vectors[0] is not None
             else [],
             latency_ms,
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
         return result.vectors
     except HTTPException:
@@ -1107,24 +1132,27 @@ async def embed_text(
             latency_ms,
             e,
             exc_info=True,
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_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 priority=%s active_after=%d",
-            success,
-            _priority_label(effective_priority),
-            remaining,
-            extra=_request_log_extra(request_id),
-        )
+        if limiter_acquired:
+            remaining = _text_request_limiter.release(success=success)
+            logger.info(
+                "embed_text finalize | success=%s priority=%s active_after=%d",
+                success,
+                _priority_label(effective_priority),
+                remaining,
+                extra=build_request_log_extra(request_id, user_id),
+            )
+        reset_request_log_context(log_tokens)
 def _embed_image_impl(
     urls: List[str],
     effective_normalize: bool,
     request_id: str,
+    user_id: str,
 ) -> _EmbedResult:
     if _image_model is None:
         raise RuntimeError("Image model not loaded")
@@ -1154,7 +1182,7 @@ def _embed_image_impl(
             effective_normalize,
             len(out[0]) if out and out[0] is not None else 0,
             cache_hits,
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
         return _EmbedResult(
             vectors=out,
@@ -1194,7 +1222,7 @@ def _embed_image_impl(
         cache_hits,
         len(missing_urls),
         backend_elapsed_ms,
-        extra=_request_log_extra(request_id),
+        extra=build_request_log_extra(request_id, user_id),
     )
     return _EmbedResult(
         vectors=out,
@@ -1217,74 +1245,78 @@ async def embed_image(
         raise HTTPException(status_code=503, detail="Image embedding model not loaded in this service")
     request_id = _resolve_request_id(http_request)
+    user_id = _resolve_user_id(http_request)
+    _, _, log_tokens = bind_request_log_context(request_id, user_id)
     response.headers["X-Request-ID"] = request_id
-
-    if priority < 0:
-        raise HTTPException(status_code=400, detail="priority must be >= 0")
-    effective_priority = _effective_priority(priority)
-
-    effective_normalize = bool(CONFIG.IMAGE_NORMALIZE_EMBEDDINGS) if normalize is None else bool(normalize)
-    urls: List[str] = []
-    for i, url_or_path in enumerate(images):
-        if not isinstance(url_or_path, str):
-            raise HTTPException(status_code=400, detail=f"Invalid image at index {i}: must be string URL/path")
-        s = url_or_path.strip()
-        if not s:
-            raise HTTPException(status_code=400, detail=f"Invalid image at index {i}: empty URL/path")
-        urls.append(s)
-
-    cache_check_started = time.perf_counter()
-    cache_only = _try_full_image_cache_hit(urls, effective_normalize)
-    if cache_only is not None:
-        latency_ms = (time.perf_counter() - cache_check_started) * 1000.0
-        _image_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_image response | mode=cache-only priority=%s inputs=%d normalize=%s dim=%d cache_hits=%d cache_misses=0 first_vector=%s latency_ms=%.2f",
-            _priority_label(effective_priority),
-            len(urls),
-            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 = _image_request_limiter.try_acquire(bypass_limit=effective_priority > 0)
-    if not accepted:
-        _image_stats.record_rejected()
-        logger.warning(
-            "embed_image rejected | client=%s priority=%s inputs=%d normalize=%s active=%d limit=%d preview=%s",
-            _request_client(http_request),
-            _priority_label(effective_priority),
-            len(urls),
-            effective_normalize,
-            active,
-            _IMAGE_MAX_INFLIGHT,
-            _preview_inputs(urls, _LOG_PREVIEW_COUNT, _LOG_IMAGE_PREVIEW_CHARS),
-            extra=_request_log_extra(request_id),
-        )
-        raise HTTPException(
-            status_code=_OVERLOAD_STATUS_CODE,
-            detail=(
-                "Image embedding service busy for priority=0 requests: "
-                f"active={active}, limit={_IMAGE_MAX_INFLIGHT}"
-            ),
-        )
-
+    response.headers["X-User-ID"] = user_id
     request_started = time.perf_counter()
     success = False
     backend_elapsed_ms = 0.0
     cache_hits = 0
     cache_misses = 0
+    limiter_acquired = False
+
     try:
+        if priority < 0:
+            raise HTTPException(status_code=400, detail="priority must be >= 0")
+        effective_priority = _effective_priority(priority)
+
+        effective_normalize = bool(CONFIG.IMAGE_NORMALIZE_EMBEDDINGS) if normalize is None else bool(normalize)
+        urls: List[str] = []
+        for i, url_or_path in enumerate(images):
+            if not isinstance(url_or_path, str):
+                raise HTTPException(status_code=400, detail=f"Invalid image at index {i}: must be string URL/path")
+            s = url_or_path.strip()
+            if not s:
+                raise HTTPException(status_code=400, detail=f"Invalid image at index {i}: empty URL/path")
+            urls.append(s)
+
+        cache_check_started = time.perf_counter()
+        cache_only = _try_full_image_cache_hit(urls, effective_normalize)
+        if cache_only is not None:
+            latency_ms = (time.perf_counter() - cache_check_started) * 1000.0
+            _image_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_image response | mode=cache-only priority=%s inputs=%d normalize=%s dim=%d cache_hits=%d cache_misses=0 first_vector=%s latency_ms=%.2f",
+                _priority_label(effective_priority),
+                len(urls),
+                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=build_request_log_extra(request_id, user_id),
+            )
+            return cache_only.vectors
+
+        accepted, active = _image_request_limiter.try_acquire(bypass_limit=effective_priority > 0)
+        if not accepted:
+            _image_stats.record_rejected()
+            logger.warning(
+                "embed_image rejected | client=%s priority=%s inputs=%d normalize=%s active=%d limit=%d preview=%s",
+                _request_client(http_request),
+                _priority_label(effective_priority),
+                len(urls),
+                effective_normalize,
+                active,
+                _IMAGE_MAX_INFLIGHT,
+                _preview_inputs(urls, _LOG_PREVIEW_COUNT, _LOG_IMAGE_PREVIEW_CHARS),
+                extra=build_request_log_extra(request_id, user_id),
+            )
+            raise HTTPException(
+                status_code=_OVERLOAD_STATUS_CODE,
+                detail=(
+                    "Image embedding service busy for priority=0 requests: "
+                    f"active={active}, limit={_IMAGE_MAX_INFLIGHT}"
+                ),
+            )
+        limiter_acquired = True
         logger.info(
             "embed_image request | client=%s priority=%s inputs=%d normalize=%s active=%d limit=%d preview=%s",
             _request_client(http_request),
@@ -1294,16 +1326,16 @@ async def embed_image(
             active,
             _IMAGE_MAX_INFLIGHT,
             _preview_inputs(urls, _LOG_PREVIEW_COUNT, _LOG_IMAGE_PREVIEW_CHARS),
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
         verbose_logger.info(
             "embed_image detail | payload=%s normalize=%s priority=%s",
             urls,
             effective_normalize,
             _priority_label(effective_priority),
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
-        result = await run_in_threadpool(_embed_image_impl, urls, effective_normalize, request_id)
+        result = await run_in_threadpool(_embed_image_impl, urls, effective_normalize, request_id, user_id)
         success = True
         backend_elapsed_ms = result.backend_elapsed_ms
         cache_hits = result.cache_hits
@@ -1327,7 +1359,7 @@ async def embed_image(
             cache_misses,
             _preview_vector(result.vectors[0] if result.vectors else None),
             latency_ms,
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
         verbose_logger.info(
             "embed_image result detail | count=%d first_vector=%s latency_ms=%.2f",
@@ -1336,7 +1368,7 @@ async def embed_image(
             if result.vectors and result.vectors[0] is not None
             else [],
             latency_ms,
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
         return result.vectors
     except HTTPException:
@@ -1358,15 +1390,17 @@ async def embed_image(
             latency_ms,
             e,
             exc_info=True,
-            extra=_request_log_extra(request_id),
+            extra=build_request_log_extra(request_id, user_id),
         )
         raise HTTPException(status_code=502, detail=f"Image embedding backend failure: {e}") from e
     finally:
-        remaining = _image_request_limiter.release(success=success)
-        logger.info(
-            "embed_image finalize | success=%s priority=%s active_after=%d",
-            success,
-            _priority_label(effective_priority),
-            remaining,
-            extra=_request_log_extra(request_id),
-        )
+        if limiter_acquired:
+            remaining = _image_request_limiter.release(success=success)
+            logger.info(
+                "embed_image finalize | success=%s priority=%s active_after=%d",
+                success,
+                _priority_label(effective_priority),
+                remaining,
+                extra=build_request_log_extra(request_id, user_id),
+            )
+        reset_request_log_context(log_tokens)
@@ -2,11 +2,14 @@
 from __future__ import annotations
+import logging
 from typing import Any, List, Union
 import numpy as np
 import requests
+logger = logging.getLogger(__name__)
+
 class TEITextModel:
     """
@@ -18,12 +21,13 @@ class TEITextModel:
       response: [[...], [...], ...]
     """
-    def __init__(self, base_url: str, timeout_sec: int = 60):
+    def __init__(self, base_url: str, timeout_sec: int = 60, max_client_batch_size: int = 24):
         if not base_url or not str(base_url).strip():
             raise ValueError("TEI base_url must not be empty")
         self.base_url = str(base_url).rstrip("/")
         self.endpoint = f"{self.base_url}/embed"
         self.timeout_sec = int(timeout_sec)
+        self.max_client_batch_size = max(1, int(max_client_batch_size))
         self._health_check()
     def _health_check(self) -> None:
@@ -72,16 +76,28 @@ class TEITextModel:
             if not isinstance(t, str) or not t.strip():
                 raise ValueError(f"Invalid input text at index {i}: {t!r}")
-        response = requests.post(
-            self.endpoint,
-            json={"inputs": texts},
-            timeout=self.timeout_sec,
-        )
-        response.raise_for_status()
-        payload = response.json()
-        vectors = self._parse_payload(payload, expected_len=len(texts))
-        if normalize_embeddings:
-            vectors = [self._normalize(vec) for vec in vectors]
+        if len(texts) > self.max_client_batch_size:
+            logger.info(
+                "TEI batch split | total_inputs=%d chunk_size=%d chunks=%d",
+                len(texts),
+                self.max_client_batch_size,
+                (len(texts) + self.max_client_batch_size - 1) // self.max_client_batch_size,
+            )
+
+        vectors: List[np.ndarray] = []
+        for start in range(0, len(texts), self.max_client_batch_size):
+            batch = texts[start : start + self.max_client_batch_size]
+            response = requests.post(
+                self.endpoint,
+                json={"inputs": batch},
+                timeout=self.timeout_sec,
+            )
+            response.raise_for_status()
+            payload = response.json()
+            parsed = self._parse_payload(payload, expected_len=len(batch))
+            if normalize_embeddings:
+                parsed = [self._normalize(vec) for vec in parsed]
+            vectors.extend(parsed)
         return np.array(vectors, dtype=object)
     def _parse_payload(self, payload: Any, expected_len: int) -> List[np.ndarray]:
@@ -13,6 +13,7 @@ from config.loader import get_app_config
 from config.services_config import get_embedding_text_base_url
 from embeddings.cache_keys import build_text_cache_key
 from embeddings.redis_embedding_cache import RedisEmbeddingCache
+from request_log_context import build_downstream_request_headers, build_request_log_extra
 class TextEmbeddingEncoder:
@@ -40,6 +41,8 @@ class TextEmbeddingEncoder:
         request_data: List[str],
         normalize_embeddings: bool = True,
         priority: int = 0,
+        request_id: Optional[str] = None,
+        user_id: Optional[str] = None,
     ) -> List[Any]:
         """
         Call the embedding service API.
@@ -50,6 +53,7 @@ class TextEmbeddingEncoder:
         Returns:
             List of embeddings (list[float]) or nulls (None), aligned to input order
         """
+        response = None
         try:
             response = requests.post(
                 self.endpoint,
@@ -58,12 +62,26 @@ class TextEmbeddingEncoder:
                     "priority": max(0, int(priority)),
                 },
                 json=request_data,
+                headers=build_downstream_request_headers(request_id=request_id, user_id=user_id),
                 timeout=60
             )
             response.raise_for_status()
             return response.json()
         except requests.exceptions.RequestException as e:
-            logger.error(f"TextEmbeddingEncoder service request failed: {e}", exc_info=True)
+            body_preview = ""
+            if response is not None:
+                try:
+                    body_preview = (response.text or "")[:300]
+                except Exception:
+                    body_preview = ""
+            logger.error(
+                "TextEmbeddingEncoder service request failed | status=%s body=%s error=%s",
+                getattr(response, "status_code", "n/a"),
+                body_preview,
+                e,
+                exc_info=True,
+                extra=build_request_log_extra(request_id=request_id, user_id=user_id),
+            )
             raise
     def encode(
@@ -72,7 +90,9 @@ class TextEmbeddingEncoder:
         normalize_embeddings: bool = True,
         priority: int = 0,
         device: str = 'cpu',
-        batch_size: int = 32
+        batch_size: int = 32,
+        request_id: Optional[str] = None,
+        user_id: Optional[str] = None,
     ) -> np.ndarray:
         """
         Encode text into embeddings via network service with Redis caching.
@@ -113,6 +133,8 @@ class TextEmbeddingEncoder:
                 request_data,
                 normalize_embeddings=normalize_embeddings,
                 priority=priority,
+                request_id=request_id,
+                user_id=user_id,
             )
             # Process response
@@ -301,7 +301,12 @@ class QueryParser:
                     log_debug("Submitting query vector generation")
                     def _encode_query_vector() -> Optional[np.ndarray]:
-                        arr = self.text_encoder.encode([query_text], priority=1)
+                        arr = self.text_encoder.encode(
+                            [query_text],
+                            priority=1,
+                            request_id=(context.reqid if context else None),
+                            user_id=(context.uid if context else None),
+                        )
                         if arr is None or len(arr) == 0:
                             return None
                         vec = arr[0]
@@ -0,0 +1,107 @@
+"""
+Request-scoped reqid/uid for logging and downstream HTTP headers.
+
+Kept as a **top-level module** (not under ``utils/``) because ``utils/__init__.py``
+pulls optional deps (e.g. sqlalchemy) that are not installed in ``.venv-embedding``.
+Uvicorn ``--log-config`` and the embedding service must be able to import this module
+without importing the full ``utils`` package.
+"""
+
+from __future__ import annotations
+
+import logging
+from contextvars import ContextVar, Token
+from typing import Dict, Optional, Tuple
+
+_DEFAULT_REQUEST_ID = "-1"
+_DEFAULT_USER_ID = "-1"
+
+_request_id_var: ContextVar[str] = ContextVar("request_log_reqid", default=_DEFAULT_REQUEST_ID)
+_user_id_var: ContextVar[str] = ContextVar("request_log_uid", default=_DEFAULT_USER_ID)
+
+LOG_LINE_FORMAT = (
+    "%(asctime)s | reqid:%(reqid)s | uid:%(uid)s | %(levelname)-8s | %(name)s | %(message)s"
+)
+
+
+def _normalize_value(value: Optional[str], *, fallback: str) -> str:
+    text = str(value or "").strip()
+    return text[:64] if text else fallback
+
+
+def bind_request_log_context(
+    request_id: Optional[str] = None,
+    user_id: Optional[str] = None,
+) -> Tuple[str, str, Tuple[Token[str], Token[str]]]:
+    """Bind reqid/uid to contextvars for the current execution context."""
+    normalized_reqid = _normalize_value(request_id, fallback=_DEFAULT_REQUEST_ID)
+    normalized_uid = _normalize_value(user_id, fallback=_DEFAULT_USER_ID)
+    req_token = _request_id_var.set(normalized_reqid)
+    uid_token = _user_id_var.set(normalized_uid)
+    return normalized_reqid, normalized_uid, (req_token, uid_token)
+
+
+def reset_request_log_context(tokens: Tuple[Token[str], Token[str]]) -> None:
+    """Reset reqid/uid contextvars back to their previous values."""
+    req_token, uid_token = tokens
+    _request_id_var.reset(req_token)
+    _user_id_var.reset(uid_token)
+
+
+def current_request_log_context() -> Tuple[str, str]:
+    """Return the currently bound reqid/uid pair."""
+    return _request_id_var.get(), _user_id_var.get()
+
+
+def build_request_log_extra(
+    request_id: Optional[str] = None,
+    user_id: Optional[str] = None,
+) -> Dict[str, str]:
+    """Build logging extras, defaulting to the current bound context."""
+    current_reqid, current_uid = current_request_log_context()
+    return {
+        "reqid": _normalize_value(request_id, fallback=current_reqid),
+        "uid": _normalize_value(user_id, fallback=current_uid),
+    }
+
+
+def build_downstream_request_headers(
+    request_id: Optional[str] = None,
+    user_id: Optional[str] = None,
+) -> Dict[str, str]:
+    """Build headers for downstream service calls when request context exists."""
+    extra = build_request_log_extra(request_id=request_id, user_id=user_id)
+    if extra["reqid"] == _DEFAULT_REQUEST_ID and extra["uid"] == _DEFAULT_USER_ID:
+        return {}
+    headers = {"X-Request-ID": extra["reqid"]}
+    if extra["uid"]:
+        headers["X-User-ID"] = extra["uid"]
+    return headers
+
+
+class RequestLogContextFilter(logging.Filter):
+    """Inject reqid/uid defaults into all log records."""
+
+    def filter(self, record: logging.LogRecord) -> bool:
+        reqid = getattr(record, "reqid", None)
+        uid = getattr(record, "uid", None)
+
+        if reqid is None or uid is None:
+            bound_reqid, bound_uid = current_request_log_context()
+            reqid = reqid if reqid is not None else bound_reqid
+            uid = uid if uid is not None else bound_uid
+
+        if reqid == _DEFAULT_REQUEST_ID and uid == _DEFAULT_USER_ID:
+            try:
+                from context.request_context import get_current_request_context
+
+                context = get_current_request_context()
+            except Exception:
+                context = None
+            if context is not None:
+                reqid = getattr(context, "reqid", None) or reqid
+                uid = getattr(context, "uid", None) or uid
+
+        record.reqid = _normalize_value(reqid, fallback=_DEFAULT_REQUEST_ID)
+        record.uid = _normalize_value(uid, fallback=_DEFAULT_USER_ID)
+        return True
@@ -13,9 +13,11 @@ from config import (
 )
 from embeddings.text_encoder import TextEmbeddingEncoder
 from embeddings.image_encoder import CLIPImageEncoder
+from embeddings.text_embedding_tei import TEITextModel
 from embeddings.bf16 import encode_embedding_for_redis
 from embeddings.cache_keys import build_image_cache_key, build_text_cache_key
 from query import QueryParser
+from context.request_context import create_request_context, set_current_request_context, clear_current_request_context
 class _FakeRedis:
@@ -168,6 +170,30 @@ def test_text_embedding_encoder_cache_hit(monkeypatch):
     assert np.allclose(out[1], np.array([0.3, 0.4], dtype=np.float32))
+def test_text_embedding_encoder_forwards_request_headers(monkeypatch):
+    fake_cache = _FakeEmbeddingCache()
+    monkeypatch.setattr("embeddings.text_encoder.RedisEmbeddingCache", lambda **kwargs: fake_cache)
+
+    captured = {}
+
+    def _fake_post(url, json, timeout, **kwargs):
+        captured["headers"] = dict(kwargs.get("headers") or {})
+        return _FakeResponse([[0.1, 0.2]])
+
+    monkeypatch.setattr("embeddings.text_encoder.requests.post", _fake_post)
+
+    context = create_request_context(reqid="req-ctx-1", uid="user-ctx-1")
+    set_current_request_context(context)
+    try:
+        encoder = TextEmbeddingEncoder(service_url="http://127.0.0.1:6005")
+        encoder.encode(["hello"])
+    finally:
+        clear_current_request_context()
+
+    assert captured["headers"]["X-Request-ID"] == "req-ctx-1"
+    assert captured["headers"]["X-User-ID"] == "user-ctx-1"
+
+
 def test_image_embedding_encoder_cache_hit(monkeypatch):
     fake_cache = _FakeEmbeddingCache()
     cached = np.array([0.5, 0.6], dtype=np.float32)
@@ -234,3 +260,37 @@ def test_query_parser_skips_query_vector_when_disabled():
     parsed = parser.parse("red dress", tenant_id="162", generate_vector=False)
     assert parsed.query_vector is None
+
+
+def test_tei_text_model_splits_batches_over_client_limit(monkeypatch):
+    monkeypatch.setattr(TEITextModel, "_health_check", lambda self: None)
+    calls = []
+
+    class _Response:
+        def __init__(self, payload):
+            self._payload = payload
+
+        def raise_for_status(self):
+            return None
+
+        def json(self):
+            return self._payload
+
+    def _fake_post(url, json, timeout):
+        inputs = list(json["inputs"])
+        calls.append(inputs)
+        return _Response([[float(idx)] for idx, _ in enumerate(inputs, start=1)])
+
+    monkeypatch.setattr("embeddings.text_embedding_tei.requests.post", _fake_post)
+
+    model = TEITextModel(
+        base_url="http://127.0.0.1:8080",
+        timeout_sec=20,
+        max_client_batch_size=24,
+    )
+    vectors = model.encode([f"text-{idx}" for idx in range(25)], normalize_embeddings=False)
+
+    assert len(calls) == 2
+    assert len(calls[0]) == 24
+    assert len(calls[1]) == 1
+    assert len(vectors) == 25
@@ -14,6 +14,8 @@ from datetime import datetime
 from typing import Any, Dict, Optional
 from pathlib import Path
+from request_log_context import LOG_LINE_FORMAT, RequestLogContextFilter
+
 class StructuredFormatter(logging.Formatter):
     """Structured JSON formatter with request context support"""
@@ -89,25 +91,6 @@ def _log_with_context(logger: logging.Logger, level: int, msg: str, **kwargs):
             logging.setLogRecordFactory(old_factory)
-class RequestContextFilter(logging.Filter):
-    """Filter that automatically injects request context from thread-local storage"""
-
-    def filter(self, record: logging.LogRecord) -> bool:
-        """Inject request context from thread-local storage"""
-        try:
-            # Import here to avoid circular imports
-            from context.request_context import get_current_request_context
-            context = get_current_request_context()
-            if context:
-                # Ensure every request-scoped log record carries reqid/uid.
-                # If they are missing in the context, fall back to "-1".
-                record.reqid = getattr(context, "reqid", None) or "-1"
-                record.uid = getattr(context, "uid", None) or "-1"
-        except (ImportError, AttributeError):
-            pass
-        return True
-
-
 class ContextAwareConsoleFormatter(logging.Formatter):
     """
     Console formatter that injects reqid/uid into the log line.
@@ -156,9 +139,7 @@ def setup_logging(
     # Create formatters
     structured_formatter = StructuredFormatter()
-    console_formatter = ContextAwareConsoleFormatter(
-        '%(asctime)s | reqid:%(reqid)s | uid:%(uid)s | %(levelname)-8s | %(name)-15s | %(message)s'
-    )
+    console_formatter = ContextAwareConsoleFormatter(LOG_LINE_FORMAT)
     # Add console handler
     if enable_console: