Add reasoning/thinking extraction and forced reasoning support

- Add --force-reasoning CLI flag to force thinking mode for models like qwen3 coder
- Add check_force_reasoning() function to determine if reasoning should be forced
- Modify QwenParser to extract thinking/reasoning content instead of stripping it
- Add reasoning field to response message in non-streaming chat completions
- Prepend reasoning content to generated text in streaming responses
- Update OpenAIFormatter to include reasoning in response when available

codai/models/parser.py

@@ -117,6 +117,10 @@ class BaseParser:
 # 1. QWEN PARSER (Instruct & Coder Style)
 # 1. QWEN PARSER (Instruct & Coder Style)
 class QwenParser(BaseParser):
+    def __init__(self, tools: Dict[str, Any] = None):
+        super().__init__(tools)
+        self.reasoning_content = ""
+        
     @validate_tool_output
     def parse(self, text: str) -> List[Dict]:
         # 1. IMMEDIATE REPETITION GUARD
@@ -129,8 +133,32 @@ class QwenParser(BaseParser):
 
         results = []
 
-        # 2. Pre-cleaning (Think tags, system markers, and special tokens)
-        clean_text = re.sub(r'<\|.*?\|>|<(?:thought|think)>.*?((?:</(?:thought|think)>)|$)', '', text, flags=re.DOTALL | re.IGNORECASE)
+        # 2. Pre-cleaning (Extract thinking/reasoning content, then remove for tool parsing)
+        # Extract thinking content before removing it
+        # Enhanced pattern to capture various thinking tag formats
+        thinking_pattern = r'<\|.*?\|>|<(?:thought|think)>.*?((?:</(?:thought|think)>)|$)|<\|begin.*?\|><\|end.*?\|>'
+        thinking_matches = re.findall(thinking_pattern, text, flags=re.DOTALL | re.IGNORECASE)
+        if thinking_matches:
+            # Extract the actual thinking content from matches
+            thinking_content_parts = []
+            for match in thinking_matches:
+                if isinstance(match, tuple):
+                    # Handle tuple matches from regex groups
+                    if match[0]:  # First group contains the content
+                        thinking_content_parts.append(match[0])
+                    elif len(match) > 1 and match[1]:  # Second group might contain content
+                        thinking_content_parts.append(match[1])
+                else:
+                    # Direct string match
+                    thinking_content_parts.append(match)
+            
+            # Join all thinking matches with newlines, filtering out empty strings
+            self.reasoning_content = '\n'.join([part.strip() for part in thinking_content_parts if part and part.strip()])
+        else:
+            self.reasoning_content = ""
+            
+        # Remove thinking tags from text for tool call parsing
+        clean_text = re.sub(thinking_pattern, '', text, flags=re.DOTALL | re.IGNORECASE)
 
         # 3. FLEXIBLE TAG MATCHING
         # Matches <tool>, <tool_call>, or even just { "name": ... } if tags are missing
@@ -480,7 +508,7 @@ class OpenAIFormatter:
         self.model_name = model_name
         self.id = f"chatcmpl-{uuid.uuid4()}"
 
-    def format_full(self, text, prompt_tokens, completion_tokens, tool_calls=None):
+    def format_full(self, text, prompt_tokens, completion_tokens, tool_calls=None, reasoning=None):
         """Standard Response (Non-Streaming)"""
         if LITELLM_AVAILABLE and all([ModelResponse, Choices, Message, Usage]):
             try:
@@ -508,6 +536,8 @@ class OpenAIFormatter:
             "role": "assistant",
             "content": text if not tool_calls else None,
         }
+        if reasoning:
+            message["reasoning"] = reasoning
         if tool_calls:
             message["tool_calls"] = tool_calls
         

coderai

@@ -3501,6 +3501,51 @@ model_manager = ModelManager()
 # Global args for access in endpoints
 global_args = None
 
+# Global force reasoning list - models to force thinking mode for
+global_force_reasoning = []
+
+
+def check_force_reasoning(model_name: str = None) -> bool:
+    """
+    Check if thinking/reasoning should be forced for a specific model.
+    
+    Args:
+        model_name: The model name to check (optional - uses global_args if not provided)
+    
+    Returns:
+        True if reasoning should be forced for this model
+    
+    Usage:
+        # From CLI: --force-reasoning qwen3 qwq deepseek-r1
+        # Will match if model name contains any of these strings (case-insensitive)
+    """
+    global global_force_reasoning, global_args
+    
+    # Get force_reasoning list from args if not already stored globally
+    if not global_force_reasoning and global_args:
+        global_force_reasoning = getattr(global_args, 'force_reasoning', []) or []
+    
+    if not global_force_reasoning:
+        return False
+    
+    # If model_name not provided, try to get from global_args
+    if model_name is None and global_args:
+        model_name = getattr(global_args, 'model', None)
+        if isinstance(model_name, list) and model_name:
+            model_name = model_name[0]
+    
+    if not model_name:
+        return False
+    
+    model_name_lower = model_name.lower()
+    
+    # Check if any force_reasoning pattern matches the model name
+    for pattern in global_force_reasoning:
+        if pattern.lower() in model_name_lower:
+            return True
+    
+    return False
+
 
 def check_reply_filter(filter_type: str, model_type: str = "text", model_name: str = None) -> bool:
     """
@@ -5692,6 +5737,15 @@ async def stream_chat_response(
              print(f"--- END ESCAPED CONTENT ---")
              print(f"{'='*80}\n")
          
+         # Extract reasoning content if using QwenParser and reasoning is forced
+         reasoning_content = ""
+         if check_force_reasoning(model_name) and hasattr(tool_parser, 'reasoning_content'):
+             reasoning_content = tool_parser.reasoning_content
+             # Prepend reasoning content to generated text if we have reasoning content
+             if reasoning_content:
+                 generated_text = reasoning_content + "\n\n" + generated_text
+                 print(f"DEBUG: Prepended reasoning content (length: {len(reasoning_content)}) to generated text")
+        
         # Check for tool calls in complete output (for API response format)
         if tools:
             # Convert tools back to Tool objects for parsing
@@ -5854,9 +5908,14 @@ async def generate_chat_response(
         
         response_message = {
             "role": "assistant",
-            "content": generated_text,
         }
         
+        # Add reasoning content if available
+        if check_force_reasoning(model_name) and hasattr(tool_parser, 'reasoning_content'):
+            reasoning_content = tool_parser.reasoning_content
+            if reasoning_content:
+                response_message["reasoning"] = reasoning_content
+        
         finish_reason = "stop"
         
         # Check for tool calls
@@ -6437,6 +6496,13 @@ def parse_args():
         default=None,
         help="Path to store generated files (images, audio). If specified, files will be saved here and served over web.",
     )
+    parser.add_argument(
+        "--force-reasoning",
+        type=str,
+        nargs="+",
+        default=[],
+        help="Force thinking/reasoning mode for specific models. Usage: --force-reasoning qwen3 qwq deepseek-r1"
+    )
     parser.add_argument(
         "--parser",
         type=str,