nits + format

llms/mlx_lm/server.py

@@ -113,37 +113,33 @@ def convert_chat(messages: List[dict], role_mapping: Optional[dict] = None):
     prompt += role_mapping.get("assistant", "")
     return prompt.rstrip()
 
+
 def process_message_content(messages):
     """
-    The mlx-lm server currently only supports text content. OpenAI content may
-    support different types of content.
+    Convert message content to a format suitable for `apply_chat_template`.
+
+    The function operates on messages in place. It converts the 'content' field
+    to a string instead of a list of text fragments.
 
     Args:
-        message_list (list): A list of dictionaries, where each dictionary may have a
-            'content' key containing a list of dictionaries with 'type' and 'text' keys.
-
-    Returns:
-        list: A list of dictionaries similar to the input, but with the 'content'
-        field being a string instead of a list of text fragments.
+        message_list (list): A list of dictionaries, where each dictionary may
+          have a 'content' key containing a list of dictionaries with 'type' and
+          'text' keys.
 
     Raises:
         ValueError: If the 'content' type is not supported or if 'text' is missing.
 
     """
-    processed_messages = []
     for message in messages:
-        message_copy = message.copy()
-        if "content" in message_copy and isinstance(message_copy["content"], list):
+        content = message["content"]
+        if isinstance(content, list):
             text_fragments = [
-                fragment["text"]
-                for fragment in message_copy["content"]
-                if fragment.get("type") == "text"
+                fragment["text"] for fragment in content if fragment["type"] == "text"
             ]
-            if len(text_fragments) != len(message_copy["content"]):
+            if len(text_fragments) != len(content):
                 raise ValueError("Only 'text' content type is supported.")
-            message_copy["content"] = "".join(text_fragments)
-        processed_messages.append(message_copy)
-    return processed_messages
+            message["content"] = "".join(text_fragments)
+
 
 @dataclass
 class PromptCache:
@@ -623,11 +619,9 @@ class APIHandler(BaseHTTPRequestHandler):
         self.object_type = "chat.completion.chunk" if self.stream else "chat.completion"
         if self.tokenizer.chat_template:
             messages = body["messages"]
-            if isinstance(messages,list) and messages and isinstance(messages[0], dict) and "content" in messages[0] and isinstance(messages[0]["content"],list):
-               messages = process_message_content(messages)
-               body["messages"] = messages
+            process_message_content(messages)
             prompt = self.tokenizer.apply_chat_template(
-                body["messages"],
+                messages,
                 body.get("tools", None),
                 add_generation_prompt=True,
             )

llms/tests/test_server.py

@@ -91,13 +91,12 @@ class TestServer(unittest.TestCase):
             "messages": [
                 {
                     "role": "system",
-                    "content": [{"type": "text", "text": "You are a helpful assistant."}]
+                    "content": [
+                        {"type": "text", "text": "You are a helpful assistant."}
+                    ],
                 },
-                {
-                    "role": "user", 
-                    "content": [{"type": "text", "text": "Hello!"}]
-                }
-            ]
+                {"role": "user", "content": [{"type": "text", "text": "Hello!"}]},
+            ],
         }
         response = requests.post(url, json=chat_post_data)
         response_body = response.text