refac

backend/open_webui/utils/middleware.py

@@ -2165,7 +2165,27 @@ async def load_messages_from_db(chat_id: str, message_id: str) -> Optional[list[
     return [{k: v for k, v in msg.items() if k in ('role', 'content', 'output', 'files')} for msg in db_messages]
 
 
-def process_messages_with_output(messages: list[dict]) -> list[dict]:
+def get_reasoning_format(model: dict) -> str | None:
+    """
+    Determine how reasoning should be included in reconstructed messages.
+
+    Returns:
+        'think_tags': Ollama expects <think> tags in content.
+        'reasoning_content': llama.cpp supports reasoning_content as a top-level field.
+        None: skip reasoning (safe default for strict providers).
+    """
+    provider = model.get('provider', '')
+    if provider == 'ollama':
+        return 'think_tags'
+    if provider == 'llama.cpp':
+        return 'reasoning_content'
+    return None
+
+
+def process_messages_with_output(
+    messages: list[dict],
+    reasoning_format: str | None = None,
+) -> list[dict]:
     """
     Process messages with OR-aligned output items for LLM consumption.
 
@@ -2177,7 +2197,9 @@ def process_messages_with_output(messages: list[dict]) -> list[dict]:
     for message in messages:
         if message.get('role') == 'assistant' and message.get('output'):
             # Use output items for clean OpenAI-format messages
-            output_messages = convert_output_to_messages(message['output'], raw=True)
+            output_messages = convert_output_to_messages(
+                message['output'], raw=True, reasoning_format=reasoning_format,
+            )
             if output_messages:
                 processed.extend(output_messages)
                 continue
@@ -2315,7 +2337,10 @@ async def process_chat_payload(request, form_data, user, metadata, model):
         form_data['messages'].append({'role': 'user', 'content': regeneration_prompt})
 
     # Process messages with OR-aligned output items for clean LLM messages
-    form_data['messages'] = process_messages_with_output(form_data.get('messages', []))
+    form_data['messages'] = process_messages_with_output(
+        form_data.get('messages', []),
+        reasoning_format=get_reasoning_format(model),
+    )
 
     system_message = get_system_message(form_data.get('messages', []))
     if system_message:  # Chat Controls/User Settings
@@ -4741,10 +4766,10 @@ async def streaming_chat_response_handler(response, ctx):
                             system_message = get_system_message(form_data['messages'])
                             new_form_data['messages'] = (
                                 [system_message] if system_message else []
-                            ) + convert_output_to_messages(output, raw=True)
+                            ) + convert_output_to_messages(output, raw=True, reasoning_format=get_reasoning_format(model))
                             new_form_data['previous_response_id'] = last_response_id
                         else:
-                            tool_messages = convert_output_to_messages(output, raw=True)
+                            tool_messages = convert_output_to_messages(output, raw=True, reasoning_format=get_reasoning_format(model))
 
                             # Chat Completions providers don't support multimodal
                             # tool messages.  Extract images into a user message.
@@ -4964,7 +4989,7 @@ async def streaming_chat_response_handler(response, ctx):
                                 'metadata': metadata,
                                 'messages': [
                                     *form_data['messages'],
-                                    *convert_output_to_messages(output, raw=True),
+                                    *convert_output_to_messages(output, raw=True, reasoning_format=get_reasoning_format(model)),
                                 ],
                             }
 

backend/open_webui/utils/misc.py

@@ -129,7 +129,11 @@ def get_content_from_message(message: dict) -> Optional[str]:
     return None
 
 
-def convert_output_to_messages(output: list, raw: bool = False) -> list[dict]:
+def convert_output_to_messages(
+    output: list,
+    raw: bool = False,
+    reasoning_format: str | None = None,
+) -> list[dict]:
     """
     Convert OR-aligned output items to OpenAI Chat Completion-format messages.
 
@@ -139,8 +143,14 @@ def convert_output_to_messages(output: list, raw: bool = False) -> list[dict]:
 
     Args:
         output: List of OR-aligned output items (Responses API format).
-        raw: If True, include reasoning blocks (with original tags) and code
-             interpreter blocks for LLM re-processing follow-ups.
+        raw: If True, include code interpreter blocks for LLM re-processing
+             follow-ups.
+        reasoning_format: How to include reasoning blocks in the output:
+            - None: skip reasoning (default, safe for strict providers).
+            - ``'think_tags'``: wrap in ``<think>`` tags inside content
+              (for Ollama, which expects reasoning as tagged content).
+            - ``'reasoning_content'``: set as ``reasoning_content`` top-level field
+              (for llama.cpp, which routes it via the chat template).
     """
     if not output or not isinstance(output, list):
         return []
@@ -148,19 +158,26 @@ def convert_output_to_messages(output: list, raw: bool = False) -> list[dict]:
     messages = []
     pending_tool_calls = []
     pending_content = []
+    pending_reasoning = []  # Only populated when reasoning_format == 'reasoning_content'
 
     def flush_pending():
-        nonlocal pending_content, pending_tool_calls
-        if pending_content or pending_tool_calls:
-            messages.append(
-                {
-                    'role': 'assistant',
-                    'content': '\n'.join(pending_content) if pending_content else '',
-                    **({'tool_calls': pending_tool_calls} if pending_tool_calls else {}),
-                }
-            )
-            pending_content = []
-            pending_tool_calls = []
+        nonlocal pending_content, pending_tool_calls, pending_reasoning
+        if not pending_content and not pending_tool_calls and not pending_reasoning:
+            return
+
+        message = {
+            'role': 'assistant',
+            'content': '\n'.join(pending_content) if pending_content else '',
+            **({'tool_calls': pending_tool_calls} if pending_tool_calls else {}),
+        }
+
+        if pending_reasoning:
+            message['reasoning_content'] = '\n'.join(pending_reasoning)
+
+        messages.append(message)
+        pending_content = []
+        pending_tool_calls = []
+        pending_reasoning = []
 
     for item in output:
         item_type = item.get('type', '')
@@ -231,27 +248,26 @@ def convert_output_to_messages(output: list, raw: bool = False) -> list[dict]:
                 )
 
         elif item_type == 'reasoning':
-            if raw:
-                # Include reasoning with original tags for LLM re-processing
-                reasoning_text = ''
-                source_list = item.get('summary', []) or item.get('content', [])
-                for part in source_list:
-                    if part.get('type') == 'output_text':
-                        reasoning_text += part.get('text', '')
-                    elif 'text' in part:
-                        reasoning_text += part.get('text', '')
+            if not reasoning_format:
+                continue
 
-                if reasoning_text:
+            reasoning_text = ''
+            source_list = item.get('summary', []) or item.get('content', [])
+            for part in source_list:
+                if part.get('type') == 'output_text':
+                    reasoning_text += part.get('text', '')
+                elif 'text' in part:
+                    reasoning_text += part.get('text', '')
+
+            if reasoning_text:
+                if reasoning_format == 'think_tags':
+                    # Ollama: embed in content with the item's original tags
                     start_tag = item.get('start_tag', '<think>')
                     end_tag = item.get('end_tag', '</think>')
                     pending_content.append(f'{start_tag}{reasoning_text}{end_tag}')
-                    # NOTE: Some providers (e.g. Moonshot/Kimi K2.5) require
-                    # reasoning_content as a top-level field on assistant
-                    # messages.  This should be handled externally via a
-                    # pipeline filter or connection-level middleware, not
-                    # here — adding it universally breaks strict providers
-                    # (OpenAI, Vertex AI, Azure) that reject unknown fields.
-            # else: skip reasoning blocks for normal LLM messages
+                elif reasoning_format == 'reasoning_content':
+                    # llama.cpp: collect for reasoning_content field
+                    pending_reasoning.append(reasoning_text)
 
         elif item_type == 'open_webui:code_interpreter':
             # Always include code interpreter content so the LLM knows