diff --git a/embeddings/README.md b/embeddings/README.md
index 3a1deca..b6418f1 100644
--- a/embeddings/README.md
+++ b/embeddings/README.md
@@ -33,8 +33,3 @@
 - `TEXT_MODEL_DIR`, `TEXT_DEVICE`, `TEXT_BATCH_SIZE`
 - `IMAGE_MODEL_NAME`, `IMAGE_DEVICE`
 
-### 目录说明（旧文件）
-
-旧的 `vector_service/` 目录与 `*_encoder__local.py` 文件已经废弃，统一由本目录实现与维护。
-
-
diff --git a/embeddings/server.py b/embeddings/server.py
index d26c4c5..32bac6b 100644
--- a/embeddings/server.py
+++ b/embeddings/server.py
@@ -6,6 +6,7 @@ API (simple list-in, list-out; aligned by index; failures -> null):
 - POST /embed/image  body: ["url_or_path1", ...]  -> [[...], null, ...]
 """
 
+import logging
 import threading
 from typing import Any, Dict, List, Optional
 
@@ -16,38 +17,47 @@ from embeddings.config import CONFIG
 from embeddings.bge_model import BgeTextModel
 from embeddings.clip_model import ClipImageModel
 
+logger = logging.getLogger(__name__)
 
 app = FastAPI(title="SearchEngine Embedding Service", version="1.0.0")
 
-_text_model = None
-_image_model = None
-
-_text_init_lock = threading.Lock()
-_image_init_lock = threading.Lock()
+# Models are loaded at startup, not lazily
+_text_model: Optional[BgeTextModel] = None
+_image_model: Optional[ClipImageModel] = None
 
 _text_encode_lock = threading.Lock()
 _image_encode_lock = threading.Lock()
 
 
-def _get_text_model():
-    global _text_model
-    if _text_model is None:
-        with _text_init_lock:
-            if _text_model is None:
-                _text_model = BgeTextModel(model_dir=CONFIG.TEXT_MODEL_DIR)
-    return _text_model
+@app.on_event("startup")
+def load_models():
+    """Load models at service startup to avoid first-request latency."""
+    global _text_model, _image_model
 
+    logger.info("Loading embedding models at startup...")
 
-def _get_image_model():
-    global _image_model
-    if _image_model is None:
-        with _image_init_lock:
-            if _image_model is None:
-                _image_model = ClipImageModel(
-                    model_name=CONFIG.IMAGE_MODEL_NAME,
-                    device=CONFIG.IMAGE_DEVICE,
-                )
-    return _image_model
+    # Load text model
+    try:
+        logger.info(f"Loading text model: {CONFIG.TEXT_MODEL_DIR}")
+        _text_model = BgeTextModel(model_dir=CONFIG.TEXT_MODEL_DIR)
+        logger.info("Text model loaded successfully")
+    except Exception as e:
+        logger.error(f"Failed to load text model: {e}", exc_info=True)
+        raise
+
+    # Load image model
+    try:
+        logger.info(f"Loading image model: {CONFIG.IMAGE_MODEL_NAME} (device: {CONFIG.IMAGE_DEVICE})")
+        _image_model = ClipImageModel(
+            model_name=CONFIG.IMAGE_MODEL_NAME,
+            device=CONFIG.IMAGE_DEVICE,
+        )
+        logger.info("Image model loaded successfully")
+    except Exception as e:
+        logger.error(f"Failed to load image model: {e}", exc_info=True)
+        raise
+
+    logger.info("All embedding models loaded successfully, service ready")
 
 
 def _as_list(embedding: Optional[np.ndarray]) -> Optional[List[float]]:
@@ -62,12 +72,18 @@ def _as_list(embedding: Optional[np.ndarray]) -> Optional[List[float]]:
 
 @app.get("/health")
 def health() -> Dict[str, Any]:
-    return {"status": "ok"}
+    """Health check endpoint. Returns status and model loading state."""
+    return {
+        "status": "ok",
+        "text_model_loaded": _text_model is not None,
+        "image_model_loaded": _image_model is not None,
+    }
 
 
 @app.post("/embed/text")
 def embed_text(texts: List[str]) -> List[Optional[List[float]]]:
-    model = _get_text_model()
+    if _text_model is None:
+        raise RuntimeError("Text model not loaded")
     out: List[Optional[List[float]]] = [None] * len(texts)
 
     indexed_texts: List[tuple] = []
@@ -87,7 +103,7 @@ def embed_text(texts: List[str]) -> List[Optional[List[float]]]:
     batch_texts = [t for _, t in indexed_texts]
     try:
         with _text_encode_lock:
-            embs = model.encode_batch(
+            embs = _text_model.encode_batch(
                 batch_texts, batch_size=int(CONFIG.TEXT_BATCH_SIZE), device=CONFIG.TEXT_DEVICE
             )
         for j, (idx, _t) in enumerate(indexed_texts):
@@ -100,7 +116,8 @@ def embed_text(texts: List[str]) -> List[Optional[List[float]]]:
 
 @app.post("/embed/image")
 def embed_image(images: List[str]) -> List[Optional[List[float]]]:
-    model = _get_image_model()
+    if _image_model is None:
+        raise RuntimeError("Image model not loaded")
     out: List[Optional[List[float]]] = [None] * len(images)
 
     with _image_encode_lock:
@@ -113,7 +130,7 @@ def embed_image(images: List[str]) -> List[Optional[List[float]]]:
                 url_or_path = url_or_path.strip()
                 if not url_or_path:
                     continue
-                emb = model.encode_image_from_url(url_or_path)
+                emb = _image_model.encode_image_from_url(url_or_path)
                 out[i] = _as_list(emb)
             except Exception:
                 out[i] = None
--
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