Accept OpenAI content as string or array to fix 422 from Cline/Roo

app.py

@@ -28,7 +28,7 @@ import logging
 from fastapi import FastAPI, HTTPException, Header, UploadFile, File
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
-from typing import List, Optional, Dict, Any
+from typing import List, Optional, Dict, Any, Union
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger("moltbot-engine")
@@ -453,7 +453,7 @@ async def extract_date(filename: str, x_api_key: str = Header(None)):
 
 class ChatMessage(BaseModel):
     role: str
-    content: str
+    content: Union[str, List[Dict[str, Any]]]  # OpenAI: string or array of parts (vision/R1)
 
 class ChatCompletionRequest(BaseModel):
     model: str = "moltbot-legal"
@@ -479,25 +479,39 @@ MODEL_ROUTING = {
 }
 
 
+def _message_content_to_str(content: Union[str, List[Dict[str, Any]]]) -> str:
+    """Normalize message content to string (OpenAI can send string or array of parts)."""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        return " ".join(
+            p.get("text", "") if isinstance(p.get("text"), str) else str(p)
+            for p in content
+            if isinstance(p, dict)
+        )
+    return str(content)
+
+
 def _build_prompt_from_messages(messages: List[ChatMessage]) -> str:
     """Build a full prompt from the chat message history.
     Preserves conversation context, not just last message.
     """
     parts = []
     for msg in messages:
+        text = _message_content_to_str(msg.content)
         if msg.role == "system":
-            parts.append(f"System: {msg.content}")
+            parts.append(f"System: {text}")
         elif msg.role == "user":
-            parts.append(f"User: {msg.content}")
+            parts.append(f"User: {text}")
         elif msg.role == "assistant":
-            parts.append(f"Assistant: {msg.content}")
+            parts.append(f"Assistant: {text}")
     parts.append("Assistant:")
     return "\n\n".join(parts)
 
 
 def _build_hf_chat_messages(messages: List[ChatMessage]) -> List[Dict[str, str]]:
     """Build HF chat_completion message list from OpenAI messages."""
-    return [{"role": msg.role, "content": msg.content} for msg in messages]
+    return [{"role": msg.role, "content": _message_content_to_str(msg.content)} for msg in messages]
 
 
 def _generate_for_model(model_name: str, messages: List[ChatMessage], temperature: float = 0.7, max_tokens: int = 2048) -> Optional[str]: