image gen format fixes

tee_gateway/controllers/chat_controller.py

@@ -911,9 +911,7 @@ def _chat_request_to_dict(chat_request: CreateChatCompletionRequest) -> dict:
             messages.append(
                 {
                     "role": "user",
-                    "content": msg.content
-                    if isinstance(msg.content, str)
-                    else str(msg.content),
+                    "content": msg.content,
                 }
             )
         elif isinstance(msg, ChatCompletionRequestAssistantMessage):

tee_gateway/llm_backend.py

@@ -325,6 +325,17 @@ def generate_images(model: str, prompt: str, n: int = 1) -> tuple[list[str], int
     return images, len(images)
 
 
+def _normalize_user_content_parts(content: list) -> list:
+    """Preserve multimodal user content while tolerating primitive text parts."""
+    normalized = []
+    for part in content:
+        if isinstance(part, dict):
+            normalized.append(part)
+        else:
+            normalized.append({"type": "text", "text": str(part)})
+    return normalized
+
+
 def convert_messages(messages: list) -> List[Any]:
     """Convert OpenAI-format message objects or dicts to LangChain message objects."""
     langchain_messages: List[BaseMessage] = []
@@ -333,13 +344,17 @@ def convert_messages(messages: list) -> List[Any]:
         # Support both OpenAPI model objects and plain dicts
         if isinstance(msg, dict):
             role = msg.get("role", "").lower()
-            content = msg.get("content", "") or ""
+            content = msg.get("content", "")
+            if content is None:
+                content = ""
             tool_calls = msg.get("tool_calls")
             tool_call_id = msg.get("tool_call_id")
             name = msg.get("name")
         else:
             role = getattr(msg, "role", "").lower()
-            content = getattr(msg, "content", "") or ""
+            content = getattr(msg, "content", "")
+            if content is None:
+                content = ""
             tool_calls = getattr(msg, "tool_calls", None)
             tool_call_id = getattr(msg, "tool_call_id", None)
             name = getattr(msg, "name", None)
@@ -348,12 +363,8 @@ def convert_messages(messages: list) -> List[Any]:
             langchain_messages.append(SystemMessage(content=content))
 
         elif role == "user":
-            # content may be a string or a list of content parts; handle both
             if isinstance(content, list):
-                content = "".join(
-                    part.get("text", "") if isinstance(part, dict) else str(part)
-                    for part in content
-                )
+                content = _normalize_user_content_parts(content)
             langchain_messages.append(HumanMessage(content=content))
 
         elif role == "assistant":

tee_gateway/test/test_tee_core.py

@@ -569,20 +569,30 @@ def test_multi_turn_order_preserved(self):
         self.assertIsInstance(result[2], AIMessage)
 
     def test_user_content_as_list_of_parts(self):
-        """Multimodal content parts should be concatenated into a single string."""
+        """Multimodal content parts should be preserved for vision-capable models."""
+        content = [
+            {"type": "text", "text": "Hello world"},
+            {
+                "type": "image_url",
+                "image_url": {"url": "data:image/png;base64,abcd"},
+            },
+        ]
         result = convert_messages(
             [
                 {
                     "role": "user",
-                    "content": [
-                        {"type": "text", "text": "Hello "},
-                        {"type": "text", "text": "world"},
-                    ],
+                    "content": content,
                 }
             ]
         )
         self.assertIsInstance(result[0], HumanMessage)
-        self.assertEqual(result[0].content, "Hello world")
+        self.assertEqual(result[0].content, content)
+
+    def test_empty_user_content_list_is_preserved(self):
+        """Empty multimodal content lists should not be coerced to empty strings."""
+        result = convert_messages([{"role": "user", "content": []}])
+        self.assertIsInstance(result[0], HumanMessage)
+        self.assertEqual(result[0].content, [])
 
     def test_full_tool_call_conversation(self):
         """End-to-end multi-turn with tool use: user → assistant (tool call) → tool result."""

tests/test_structured_outputs.py

@@ -9,6 +9,9 @@
 from tee_gateway.models.create_chat_completion_request import (
     CreateChatCompletionRequest,
 )
+from tee_gateway.models.chat_completion_request_user_message import (
+    ChatCompletionRequestUserMessage,
+)
 
 
 class TestResponseFormatParsing(unittest.TestCase):
@@ -109,6 +112,26 @@ def test_hash_differs_with_and_without_response_format(self):
         h2 = json.dumps(chat_request_to_dict(req_json), sort_keys=True)
         self.assertNotEqual(h1, h2)
 
+    def test_hash_dict_preserves_multimodal_user_content(self):
+        content = [
+            {"type": "text", "text": "Describe this image"},
+            {
+                "type": "image_url",
+                "image_url": {"url": "data:image/png;base64,abcd"},
+            },
+        ]
+        req = CreateChatCompletionRequest(
+            model="gpt-4.1",
+            messages=[ChatCompletionRequestUserMessage(role="user", content=content)],
+            temperature=1.0,
+        )
+        request_dict = chat_request_to_dict(req)
+        self.assertEqual(request_dict["messages"][0]["content"], content)
+
+        dumped_once = json.dumps(request_dict, sort_keys=True)
+        dumped_twice = json.dumps(chat_request_to_dict(req), sort_keys=True)
+        self.assertEqual(dumped_once, dumped_twice)
+
 
 class TestResponseFormatModelBinding(unittest.TestCase):
     """Tests that response_format is bound to the model before invocation."""