diff --git a/memos/plugins/vlm/main.py b/memos/plugins/vlm/main.py
index 3276ea0..30286ae 100644
--- a/memos/plugins/vlm/main.py
+++ b/memos/plugins/vlm/main.py
@@ -7,6 +7,8 @@ from fastapi import APIRouter, FastAPI, Request, HTTPException
 from memos.schemas import Entity, MetadataType
 import logging
 import uvicorn
+import os
+
 
 PLUGIN_NAME = "vlm"
 PROMPT = "描述这张图片的内容"
@@ -44,19 +46,22 @@ def image2base64(img_path):
 
 async def fetch(endpoint: str, client, request_data, headers: Optional[dict] = None):
     async with semaphore:  # 使用信号量控制并发
-        response = await client.post(
-            f"{endpoint}/v1/chat/completions",
-            json=request_data,
-            timeout=60,
-            headers=headers,
-        )
-        if response.status_code != 200:
+        try:
+            response = await client.post(
+                f"{endpoint}/v1/chat/completions",
+                json=request_data,
+                timeout=60,
+                headers=headers,
+            )
+            response.raise_for_status()
+            result = response.json()
+            choices = result.get("choices", [])
+            if choices and "message" in choices[0] and "content" in choices[0]["message"]:
+                return choices[0]["message"]["content"]
+            return ""
+        except Exception as e:
+            logger.error(f"Exception occurred: {str(e)}")
             return None
-        result = response.json()
-        choices = result.get("choices", [])
-        if choices and "message" in choices[0] and "content" in choices[0]["message"]:
-            return choices[0]["message"]["content"]
-        return ""
 
 
 async def predict(
@@ -65,6 +70,19 @@ async def predict(
     img_base64 = image2base64(img_path)
     if not img_base64:
         return None
+    
+    # Get the file extension
+    _, file_extension = os.path.splitext(img_path)
+    file_extension = file_extension.lower()[1:]  # Remove the dot and convert to lowercase
+
+    # Determine the MIME type
+    mime_types = {
+        'png': 'image/png',
+        'jpg': 'image/jpeg',
+        'jpeg': 'image/jpeg',
+        'webp': 'image/webp'
+    }
+    mime_type = mime_types.get(file_extension, 'image/jpeg')
 
     request_data = {
         "model": modelname,
@@ -72,12 +90,11 @@ async def predict(
             {
                 "role": "user",
                 "content": [
-                    {"type": "text", "text": PROMPT},
                     {
                         "type": "image_url",
-                        "image_url": {"url": f"data:image/;base64,{img_base64}"},
-                        "detail": "high",
+                        "image_url": {"url": f"data:{mime_type};base64,{img_base64}"},
                     },
+                    {"type": "text", "text": PROMPT},
                 ],
             }
         ],
@@ -99,6 +116,7 @@ async def read_root():
     return {"healthy": True}
 
 
+@router.post("", include_in_schema=False)
 @router.post("/")
 async def vlm(entity: Entity, request: Request):
     global modelname, endpoint, token