diff --git a/app/services/providers/anthropic_adapter.py b/app/services/providers/anthropic_adapter.py
index f6f2580..bc051ae 100644
--- a/app/services/providers/anthropic_adapter.py
+++ b/app/services/providers/anthropic_adapter.py
@@ -12,6 +12,7 @@
     ProviderAPIException,
     InvalidCompletionRequestException,
 )
+from app.utils.translator import download_image_url
 
 from .base import ProviderAdapter
 
@@ -57,11 +58,22 @@ def format_anthropic_usage(usage_data: dict[str, Any]) -> dict[str, Any]:
         }
     
     @staticmethod
-    def convert_openai_image_content_to_anthropic(
-        msg: dict[str, Any],
+    async def convert_openai_image_content_to_anthropic(
+        msg: dict[str, Any], allow_url_download: bool = False
     ) -> dict[str, Any]:
         """Convert OpenAI image content to Anthropic image content"""
         data_url = msg["image_url"]["url"]
+        if allow_url_download:
+            try:
+                data_url = await download_image_url(logger, data_url)
+            except Exception as e:
+                logger.exception(f"Error downloading image: {e}")
+                raise ProviderAPIException(
+                    provider_name="anthropic",
+                    error_code=400,
+                    error_message=f"Error downloading image: {e}",
+                )
+
         if data_url.startswith("data:"):
             # Extract media type and base64 data
             parts = data_url.split(",", 1)
@@ -115,8 +127,9 @@ def translate_anthropic_content_to_openai(
         
 
     @staticmethod
-    def convert_openai_content_to_anthropic(
+    async def convert_openai_content_to_anthropic(
         content: list[dict[str, Any]] | str | None,
+        allow_url_download: bool = False,
     ) -> list[dict[str, Any]] | str:
         """Convert OpenAI content model to Anthropic content model"""
         if content is None:
@@ -138,7 +151,7 @@ def convert_openai_content_to_anthropic(
                 result.append({"type": "text", "text": msg.get("text", "")})
             elif _type == "image_url":
                 result.append(
-                    AnthropicAdapter.convert_openai_image_content_to_anthropic(msg)
+                    await AnthropicAdapter.convert_openai_image_content_to_anthropic(msg, allow_url_download=allow_url_download)
                 )
             else:
                 error_message = f"{_type} is not supported"
@@ -189,7 +202,7 @@ async def list_models(self, api_key: str) -> list[str]:
             return models
 
     @staticmethod
-    def convert_openai_payload_to_anthropic(payload: dict[str, Any]) -> dict[str, Any]:
+    async def convert_openai_payload_to_anthropic(payload: dict[str, Any], allow_url_download: bool = False) -> dict[str, Any]:
         """Convert OpenAI completion payload to Anthropic format"""
         anthropic_payload = {
             "model": payload["model"],
@@ -319,7 +332,7 @@ def convert_openai_payload_to_anthropic(payload: dict[str, Any]) -> dict[str, An
                     anthropic_content = content
                 else:
                     anthropic_content = (
-                        AnthropicAdapter.convert_openai_content_to_anthropic(content)
+                        await AnthropicAdapter.convert_openai_content_to_anthropic(content, allow_url_download=allow_url_download)
                     )
 
                 anthropic_message = {"role": role, "content": anthropic_content}
@@ -399,7 +412,7 @@ async def process_completion(
 
         streaming = payload.get("stream", False)
         # Convert OpenAI format to Anthropic format
-        anthropic_payload = self.convert_openai_payload_to_anthropic(payload)
+        anthropic_payload = await self.convert_openai_payload_to_anthropic(payload)
 
         # Choose the appropriate API endpoint - using ternary operator
         api_endpoint = "messages" if "messages" in anthropic_payload else "complete"
diff --git a/app/services/providers/gemini_openai_adapter.py b/app/services/providers/gemini_openai_adapter.py
index c7af314..7ddaf79 100644
--- a/app/services/providers/gemini_openai_adapter.py
+++ b/app/services/providers/gemini_openai_adapter.py
@@ -33,8 +33,6 @@ def __init__(
         if not base_url.endswith("/openai"):
             base_url = f"{base_url}/openai"
 
-        logger.debug(
-            "Initialised GeminiOpenAIAdapter with base_url=%s", base_url
-        )
+        logger.debug(f"Initialised GeminiOpenAIAdapter with base_url={base_url}")
 
         super().__init__(provider_name, base_url, config=config or {}) 
\ No newline at end of file
diff --git a/app/services/providers/google_adapter.py b/app/services/providers/google_adapter.py
index 2bcdb92..503278d 100644
--- a/app/services/providers/google_adapter.py
+++ b/app/services/providers/google_adapter.py
@@ -8,7 +8,6 @@
 and for reference while migrating any bespoke features that haven’t yet been
 replicated in the new adapter. **It will be removed in a future release.**
 """
-import asyncio
 import json
 import os
 import time
diff --git a/app/services/providers/vertex_adapter.py b/app/services/providers/vertex_adapter.py
index de49695..202d20e 100644
--- a/app/services/providers/vertex_adapter.py
+++ b/app/services/providers/vertex_adapter.py
@@ -217,7 +217,7 @@ async def process_completion(self, endpoint: str, payload: dict[str, Any], api_k
 
         streaming = payload.get("stream", False)
         model_name = payload["model"]
-        anthropic_payload = AnthropicAdapter.convert_openai_payload_to_anthropic(payload)
+        anthropic_payload = await AnthropicAdapter.convert_openai_payload_to_anthropic(payload, allow_url_download=True)
 
         # vertex specific payload
         anthropic_payload["anthropic_version"] = "vertex-2023-10-16"
@@ -226,13 +226,14 @@ async def process_completion(self, endpoint: str, payload: dict[str, Any], api_k
         logger.debug(f"Vertex API request - model: {model_name}, streaming: {streaming}, publisher: {self.publisher}, location: {self.location}")
 
         def error_handler(error_text: str, http_status: int):
+            logger.error(f"Vertex API error - code: {http_status}, message: {error_text}")
             try:
                 error_json = json.loads(error_text)
                 error_message = error_json.get("error", {}).get("message", "Unknown error")
                 error_code = error_json.get("error", {}).get("code", http_status)
-                raise ProviderAPIException("Vertex", error_code, error_message)
             except Exception:
                 raise ProviderAPIException("Vertex", http_status, error_text)
+            raise ProviderAPIException("Vertex", error_code, error_message)
 
         if streaming:
             # https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.endpoints/streamRawPredict
diff --git a/app/utils/translator.py b/app/utils/translator.py
new file mode 100644
index 0000000..ddc857b
--- /dev/null
+++ b/app/utils/translator.py
@@ -0,0 +1,31 @@
+import base64
+import aiohttp
+from http import HTTPStatus
+
+async def download_image_url(logger, image_url: str) -> str:
+    """
+    Download an image from a URL and return the base64 encoded string
+    """
+
+    # if the image url is a data url, return it as is
+    if image_url.startswith("data:"):
+        return image_url
+    
+    async with aiohttp.ClientSession() as session:
+        async with session.head(image_url) as response:
+            if response.status != HTTPStatus.OK:
+                error_text = await response.text()
+                log_error_msg = f"Failed to fetch file metadata from URL: {error_text}"
+                logger.error(log_error_msg)
+                raise RuntimeError(log_error_msg)
+
+            mime_type = response.headers.get("Content-Type", "")
+            file_size = int(response.headers.get("Content-Length", 0))
+            if file_size > 10 * 1024 * 1024:
+                log_error_msg = f"Image file size is too large: {file_size} bytes"
+                logger.error(log_error_msg)
+                raise RuntimeError(log_error_msg)
+
+        async with session.get(image_url) as response:
+            # return format is data:mime_type;base64,base64_data
+            return f"data:{mime_type};base64,{base64.b64encode(await response.read()).decode('utf-8')}"