[Feature]Optimization of Thinking Pattern Framework (#4302)

* add model status in vl

* add x1 parser

* add model_status

* fix parser

* fix parser

* fix parser

* fix parser

* Revert "fix parser"

This reverts commit 300f446d8a.

* fix parser

* fix

* fix

* fix

* fix

* fix parser

* fix unit test

* fix unit test

* add unit test

* fix

* fix

* add unit test

* fix unit test

* add unit test

* add unit test

* fix unit test

* fix unit test

* fix bug

* fix unit test

* x1 tool parser

* fix unit test

* fix unit test

* fix unit test

* fix n

* fix unit test

* add unit test

* add unit test

* remove pring

fastdeploy/entrypoints/openai/response_processors.py

@@ -72,13 +72,12 @@ class ChatResponseProcessor:
             else:
                 self._multipart_buffer.append({"decode_type": decode_type, "request_output": request_output})
 
-    async def process_response_chat(self, request_outputs, stream, enable_thinking, include_stop_str_in_output):
+    async def process_response_chat(self, request_outputs, stream, include_stop_str_in_output):
         """
         Process a list of responses into a generator that yields each processed response as it's generated.
         Args:
             request_outputs: The list of outputs to be processed.
             stream: Whether or not to stream the output.
-            enable_thinking: Whether or not to show thinking messages.
             include_stop_str_in_output: Whether or not to include stop strings in the output.
         """
         for request_output in request_outputs:
@@ -99,7 +98,6 @@ class ChatResponseProcessor:
                             response = await self.data_processor.process_response_dict(
                                 response_dict=request_output,
                                 stream=stream,
-                                enable_thinking=enable_thinking,
                                 include_stop_str_in_output=include_stop_str_in_output,
                                 audio_tokens=all_audio_tokens,
                                 tts=tts,
@@ -108,7 +106,6 @@ class ChatResponseProcessor:
                             response = self.data_processor.process_response_dict(
                                 response_dict=request_output,
                                 stream=stream,
-                                enable_thinking=enable_thinking,
                                 include_stop_str_in_output=include_stop_str_in_output,
                                 audio_tokens=all_audio_tokens,
                                 tts=tts,
@@ -127,7 +124,6 @@ class ChatResponseProcessor:
                     yield self.data_processor.process_response_dict(
                         response_dict=request_output,
                         stream=stream,
-                        enable_thinking=enable_thinking,
                         include_stop_str_in_output=include_stop_str_in_output,
                     )
             elif stream:
@@ -156,14 +152,12 @@ class ChatResponseProcessor:
                         await self.data_processor.process_response_dict(
                             response_dict=request_output,
                             stream=stream,
-                            enable_thinking=enable_thinking,
                             include_stop_str_in_output=include_stop_str_in_output,
                         )
                     else:
                         self.data_processor.process_response_dict(
                             response_dict=request_output,
                             stream=stream,
-                            enable_thinking=enable_thinking,
                             include_stop_str_in_output=include_stop_str_in_output,
                         )
                     text = {"type": "text", "text": request_output["outputs"]["text"]}
@@ -185,14 +179,12 @@ class ChatResponseProcessor:
                                 await self.data_processor.process_response_dict(
                                     response_dict=part["request_output"],
                                     stream=False,
-                                    enable_thinking=enable_thinking,
                                     include_stop_str_in_output=include_stop_str_in_output,
                                 )
                             else:
                                 self.data_processor.process_response_dict(
                                     response_dict=request_output,
                                     stream=stream,
-                                    enable_thinking=enable_thinking,
                                     include_stop_str_in_output=include_stop_str_in_output,
                                 )
                             text = {"type": "text", "text": part["request_output"]["outputs"]["text"]}

fastdeploy/entrypoints/openai/serving_chat.py

@@ -220,8 +220,6 @@ class OpenAIServingChat:
 
         max_streaming_response_tokens = max(1, max_streaming_response_tokens)
 
-        enable_thinking = self._get_thinking_status(request)
-
         include_stop_str_in_output = request.include_stop_str_in_output
 
         stream_options = request.stream_options
@@ -277,7 +275,6 @@ class OpenAIServingChat:
                 generator = response_processor.process_response_chat(
                     response,
                     stream=True,
-                    enable_thinking=enable_thinking,
                     include_stop_str_in_output=include_stop_str_in_output,
                 )
 
@@ -507,7 +504,6 @@ class OpenAIServingChat:
         """
         created_time = int(time.time())
         num_choices = 1 if request.n is None else request.n
-        enable_thinking = self._get_thinking_status(request)
 
         include_stop_str_in_output = request.include_stop_str_in_output
         try:
@@ -562,7 +558,6 @@ class OpenAIServingChat:
                 generator = response_processor.process_response_chat(
                     response,
                     stream=False,
-                    enable_thinking=enable_thinking,
                     include_stop_str_in_output=include_stop_str_in_output,
                 )
                 async for data in generator:
@@ -824,23 +819,6 @@ class OpenAIServingChat:
             api_server_logger.error(error_msg)
             return None
 
-    def _get_thinking_status(self, request: ChatCompletionRequest) -> bool:
-        """
-        Get the thinking status from the request.
-        """
-        enable_thinking = request.chat_template_kwargs.get("enable_thinking") if request.chat_template_kwargs else None
-        if enable_thinking is None:
-            enable_thinking = request.metadata.get("enable_thinking") if request.metadata else None
-        options = request.chat_template_kwargs.get("options") if request.chat_template_kwargs else None
-        if options:
-            thinking_mode = options.get("thinking_mode")
-            if thinking_mode:
-                if thinking_mode == "close" or thinking_mode == "false":
-                    enable_thinking = False
-                else:
-                    enable_thinking = True
-        return enable_thinking
-
     def _build_prompt_logprobs(
         self,
         prompt_logprobs_tensors: LogprobsTensors,

fastdeploy/input/ernie4_5_processor.py

@@ -61,6 +61,7 @@ class Ernie4_5Processor(BaseDataProcessor):
         self.decode_status = dict()
         self.tool_parser_dict = dict()
         self.thinking_parser_dict = dict()
+        self.model_status_dict = dict()
         self._load_tokenizer()
         data_processor_logger.info(
             f"tokenizer information: bos_token is {self.tokenizer.bos_token} \
@@ -148,8 +149,18 @@ class Ernie4_5Processor(BaseDataProcessor):
             request.set("temperature", 1)
         if request.get("top_p") < _SAMPLING_EPS:
             request.set("top_p", _SAMPLING_EPS)
-        if self.reasoning_parser and self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser":
-            request.enable_thinking = True
+        if self.reasoning_parser:
+            model_status = self.reasoning_parser.get_model_status(request.prompt_token_ids)
+            parts = request.request_id.split("_")
+            if len(parts) > 1:
+                real_req_id = parts[0]
+                index = int(parts[1])
+                n = request.get("n", 1)
+                for idx in range(index * n, (index + 1) * n):
+                    self.model_status_dict[f"{real_req_id}_{idx}"] = model_status
+            else:
+                self.model_status_dict[request.request_id] = model_status
+            request.enable_thinking = model_status == "think_start"
 
         data_processor_logger.info(f"Processed request: {request}")
         return request
@@ -222,9 +233,19 @@ class Ernie4_5Processor(BaseDataProcessor):
             request["temperature"] = 1
         if request.get("top_p") < _SAMPLING_EPS:
             request["top_p"] = _SAMPLING_EPS
-        if self.reasoning_parser and self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser":
-            request["enable_thinking"] = True
 
+        if self.reasoning_parser:
+            model_status = self.reasoning_parser.get_model_status(request["prompt_token_ids"])
+            parts = request["request_id"].split("_")
+            if len(parts) > 1:
+                real_req_id = parts[0]
+                index = int(parts[1])
+                n = request.get("n", 1)
+                for idx in range(index * n, (index + 1) * n):
+                    self.model_status_dict[f"{real_req_id}_{idx}"] = model_status
+            else:
+                self.model_status_dict[request["request_id"]] = model_status
+            request["enable_thinking"] = model_status == "think_start"
         data_processor_logger.info(f"Processed request dict: {request}")
         return request
 
@@ -246,7 +267,11 @@ class Ernie4_5Processor(BaseDataProcessor):
             token_ids = token_ids[:-1]
         full_text = self.tokenizer.decode(token_ids)
         if self.reasoning_parser:
-            reasoning_content, text = self.reasoning_parser.extract_reasoning_content(full_text, response_dict)
+            reasoning_content, text = self.reasoning_parser.extract_reasoning_content(
+                full_text,
+                response_dict,
+                self.model_status_dict[req_id],
+            )
             response_dict.outputs.text = text
             response_dict.outputs.reasoning_content = reasoning_content
         else:
@@ -257,6 +282,8 @@ class Ernie4_5Processor(BaseDataProcessor):
             if tool_call_info.tools_called:
                 response_dict.outputs.tool_calls = tool_call_info.tool_calls
                 response_dict.outputs.text = tool_call_info.content
+        if req_id in self.model_status_dict:
+            del self.model_status_dict[req_id]
         data_processor_logger.info(f"req_id:{req_id}, token_ids: {token_ids}")
         if response_dict.outputs.text == "" and response_dict.outputs.reasoning_content == "":
             return None
@@ -287,7 +314,6 @@ class Ernie4_5Processor(BaseDataProcessor):
         Returns:
             Dict: response contain text fields
         """
-        enable_thinking = kwargs.get("enable_thinking")
         token_ids = response_dict["outputs"]["token_ids"]
         is_end = response_dict["finished"]
         req_id = response_dict["request_id"]
@@ -297,16 +323,17 @@ class Ernie4_5Processor(BaseDataProcessor):
         delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
         if is_end:
             full_text = previous_texts + delta_text
-            if self.reasoning_parser and (
-                enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
-            ):
-                reasoning_content, text = self.reasoning_parser.extract_reasoning_content(full_text, response_dict)
+            response_dict["outputs"]["text"] = full_text
+            if self.reasoning_parser:
+                reasoning_content, text = self.reasoning_parser.extract_reasoning_content(
+                    full_text,
+                    response_dict,
+                    self.model_status_dict[req_id],
+                )
                 response_dict["outputs"]["text"] = text
                 response_dict["outputs"]["reasoning_content"] = reasoning_content
                 reasoning_tokens = self.tokenizer.tokenize(reasoning_content)
                 response_dict["outputs"]["reasoning_token_num"] = len(reasoning_tokens)
-            else:
-                response_dict["outputs"]["text"] = full_text
             if self.tool_parser_obj:
                 tool_parser = self.tool_parser_obj(self.tokenizer)
                 tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
@@ -316,6 +343,8 @@ class Ernie4_5Processor(BaseDataProcessor):
             response_dict["outputs"]["completion_tokens"] = full_text
             data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
             del self.decode_status[req_id]
+            if req_id in self.model_status_dict:
+                del self.model_status_dict[req_id]
         return response_dict
 
     def process_response_dict_streaming(self, response_dict, **kwargs):
@@ -328,7 +357,6 @@ class Ernie4_5Processor(BaseDataProcessor):
         Returns:
             Dict: response contain text fields
         """
-        enable_thinking = kwargs.get("enable_thinking")
         is_end = response_dict["finished"]
         req_id = response_dict["request_id"]
         token_ids = response_dict["outputs"]["token_ids"]
@@ -338,9 +366,7 @@ class Ernie4_5Processor(BaseDataProcessor):
                 token_ids = token_ids[:-1]
         delta_text, previous_token_ids, previous_texts = self.ids2tokens(token_ids, req_id)
         response_dict["outputs"]["completion_tokens"] = delta_text
-        if self.reasoning_parser and (
-            enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
-        ):
+        if self.reasoning_parser:
             reasoning_delta_message = self.reasoning_parser.extract_reasoning_content_streaming(
                 previous_texts,
                 previous_texts + delta_text,
@@ -348,6 +374,7 @@ class Ernie4_5Processor(BaseDataProcessor):
                 previous_token_ids,
                 previous_token_ids + token_ids,
                 token_ids,
+                self.model_status_dict[req_id],
             )
             response_dict["outputs"]["delta_message"] = reasoning_delta_message
             reasoning_content = reasoning_delta_message.reasoning_content if reasoning_delta_message else None
@@ -374,6 +401,8 @@ class Ernie4_5Processor(BaseDataProcessor):
             del self.decode_status[req_id]
             if req_id in self.tool_parser_dict:
                 del self.tool_parser_dict[req_id]
+            if req_id in self.model_status_dict:
+                del self.model_status_dict[req_id]
         return response_dict
 
     def messages2ids(self, request_or_messages, **kwargs):

fastdeploy/input/ernie4_5_vl_processor/ernie4_5_vl_processor.py

@@ -58,6 +58,7 @@ class Ernie4_5_VLProcessor(Ernie4_5Processor):
 
         self.tool_parser_dict = dict()
         self.decode_status = dict()
+        self.model_status_dict = dict()
         self._load_tokenizer()
 
         # Generation config
@@ -242,15 +243,6 @@ class Ernie4_5_VLProcessor(Ernie4_5Processor):
                             request[k] = v
                 else:
                     raise ValueError("Invalid input: chat_template_kwargs must be a dict")
-                options = chat_template_kwargs.get("options")
-                if options:
-                    thinking_mode = options.get("thinking_mode")
-                    if thinking_mode:
-                        if thinking_mode == "close" or thinking_mode == "false":
-                            request["enable_thinking"] = False
-                        else:
-                            request["enable_thinking"] = True
-            request.setdefault("enable_thinking", True)
             outputs = self.ernie4_5_processor.request2ids(request)
         else:
             raise ValueError(f"Request must contain 'prompt', or 'messages': {request}")
@@ -279,6 +271,18 @@ class Ernie4_5_VLProcessor(Ernie4_5Processor):
             request["reasoning_max_tokens"] = max(int(request["max_tokens"] * 0.8), 1)
         data_processor_logger.info(f"Processed request {request}")
 
+        if self.reasoning_parser:
+            model_status = self.reasoning_parser.get_model_status(request["prompt_token_ids"])
+            parts = request["request_id"].split("_")
+            if len(parts) > 1:
+                real_req_id = parts[0]
+                index = int(parts[1])
+                n = request.get("n", 1)
+                for idx in range(index * n, (index + 1) * n):
+                    self.model_status_dict[f"{real_req_id}_{idx}"] = model_status
+            else:
+                self.model_status_dict[request["request_id"]] = model_status
+            request["enable_thinking"] = model_status == "think_start"
         if request.get("top_p") is not None and request.get("top_p") < _SAMPLING_EPS:
             request["top_p"] = _SAMPLING_EPS
 
@@ -314,21 +318,3 @@ class Ernie4_5_VLProcessor(Ernie4_5Processor):
         outs["position_ids"] = np.array(outs["position_ids"], dtype=np.int64)
 
         return outs
-
-    def process_response_dict(self, response_dict, stream, **kwargs):
-        """
-        Preprocess the response
-
-        Args:
-            response_dict (Dict): response for engine, contain ids fields
-
-        Returns:
-            Dict: response contain text fields
-        """
-        enable_thinking = kwargs.pop("enable_thinking", True)
-        if enable_thinking is None:
-            enable_thinking = True
-        if stream:
-            return self.process_response_dict_streaming(response_dict, enable_thinking=enable_thinking, **kwargs)
-        else:
-            return self.process_response_dict_normal(response_dict, enable_thinking=enable_thinking, **kwargs)

fastdeploy/input/paddleocr_vl_processor/paddleocr_vl_processor.py

@@ -254,6 +254,19 @@ class PaddleOCRVLProcessor(TextProcessor):
         if request.get("top_p") is not None and request.get("top_p") < _SAMPLING_EPS:
             request["top_p"] = _SAMPLING_EPS
 
+        if self.reasoning_parser:
+            model_status = self.reasoning_parser.get_model_status(request["prompt_token_ids"])
+            parts = request["request_id"].split("_")
+            if len(parts) > 1:
+                real_req_id = parts[0]
+                index = int(parts[1])
+                n = request.get("n", 1)
+                for idx in range(index * n, (index + 1) * n):
+                    self.model_status_dict[f"{real_req_id}_{idx}"] = model_status
+            else:
+                self.model_status_dict[request["request_id"]] = model_status
+            request["enable_thinking"] = model_status == "think_start"
+
         return request
 
     def append_generated_tokens(self, multimodal_inputs, generated_token_ids):

fastdeploy/input/qwen_vl_processor/qwen_vl_processor.py

@@ -267,6 +267,18 @@ class QwenVLProcessor(TextProcessor):
         # Set default max_tokens if not specified
         if request.get("max_tokens") is None:
             request["max_tokens"] = max(1, max_model_len - len(request["prompt_token_ids"]))  # Ensure at least 1 token
+        if self.reasoning_parser:
+            model_status = self.reasoning_parser.get_model_status(request["prompt_token_ids"])
+            parts = request["request_id"].split("_")
+            if len(parts) > 1:
+                real_req_id = parts[0]
+                index = int(parts[1])
+                n = request.get("n", 1)
+                for idx in range(index * n, (index + 1) * n):
+                    self.model_status_dict[f"{real_req_id}_{idx}"] = model_status
+            else:
+                self.model_status_dict[request["request_id"]] = model_status
+            request["enable_thinking"] = model_status == "think_start"
         data_processor_logger.info(f"Processed request {request}")
 
         return request

fastdeploy/input/text_processor.py

@@ -176,6 +176,7 @@ class DataProcessor(BaseDataProcessor):
             self.generation_config = None
 
         self.decode_status = dict()
+        self.model_status_dict = dict()
         self.tool_parser_dict = dict()
         self.tokenizer = self._load_tokenizer()
         data_processor_logger.info(
@@ -267,6 +268,18 @@ class DataProcessor(BaseDataProcessor):
             request.set("temperature", 1)
         if request.get("top_p") < _SAMPLING_EPS:
             request.set("top_p", _SAMPLING_EPS)
+        if self.reasoning_parser:
+            model_status = self.reasoning_parser.get_model_status(request.prompt_token_ids)
+            parts = request.request_id.split("_")
+            if len(parts) > 1:
+                real_req_id = parts[0]
+                index = int(parts[1])
+                n = request.get("n", 1)
+                for idx in range(index * n, (index + 1) * n):
+                    self.model_status_dict[f"{real_req_id}_{idx}"] = model_status
+            else:
+                self.model_status_dict[request.request_id] = model_status
+            request.enable_thinking = model_status == "think_start"
 
         data_processor_logger.info(f"Processed request: {request}")
         return request
@@ -340,6 +353,18 @@ class DataProcessor(BaseDataProcessor):
             request["temperature"] = 1
         if request.get("top_p") < _SAMPLING_EPS:
             request["top_p"] = _SAMPLING_EPS
+        if self.reasoning_parser:
+            model_status = self.reasoning_parser.get_model_status(request["prompt_token_ids"])
+            parts = request["request_id"].split("_")
+            if len(parts) > 1:
+                real_req_id = parts[0]
+                index = int(parts[1])
+                n = request.get("n", 1)
+                for idx in range(index * n, (index + 1) * n):
+                    self.model_status_dict[f"{real_req_id}_{idx}"] = model_status
+            else:
+                self.model_status_dict[request["request_id"]] = model_status
+            request["enable_thinking"] = model_status == "think_start"
 
         data_processor_logger.info(f"Processed request dict: {request}")
         return request
@@ -363,21 +388,21 @@ class DataProcessor(BaseDataProcessor):
         if token_ids[-1] == self.tokenizer.eos_token_id:
             token_ids = token_ids[:-1]
         full_text = self.tokenizer.decode(token_ids)
-
-        # 模型支持思考,并且支持思考
+        response_dict.outputs.text = full_text
         if self.reasoning_parser:
-            reasoning_content, text = self.reasoning_parser.extract_reasoning_content(full_text, response_dict)
+            reasoning_content, text = self.reasoning_parser.extract_reasoning_content(
+                full_text, response_dict, self.model_status_dict[req_id]
+            )
             response_dict.outputs.text = text
             response_dict.outputs.reasoning_content = reasoning_content
-        else:
-            # 模型不支持思考,并且没单独设置enable_thinking为false
-            response_dict.outputs.text = full_text
         if self.tool_parser_obj:
             tool_parser = self.tool_parser_obj(self.tokenizer)
             tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
             if tool_call_info.tools_called:
                 response_dict.outputs.tool_calls = tool_call_info.tool_calls
                 response_dict.outputs.text = tool_call_info.content
+        if req_id in self.model_status_dict:
+            del self.model_status_dict[req_id]
         data_processor_logger.info(f"req_id:{req_id}, token_ids: {token_ids}")
 
         return response_dict
@@ -392,7 +417,6 @@ class DataProcessor(BaseDataProcessor):
         Returns:
             Dict: response contain text fields
         """
-        enable_thinking = kwargs.get("enable_thinking")
         token_ids = response_dict["outputs"]["token_ids"]
         is_end = response_dict["finished"]
         req_id = response_dict["request_id"]
@@ -403,14 +427,17 @@ class DataProcessor(BaseDataProcessor):
         if is_end:
             full_text = previous_texts + delta_text
             response_dict["outputs"]["completion_tokens"] = full_text
-            if enable_thinking and self.reasoning_parser:
-                reasoning_content, text = self.reasoning_parser.extract_reasoning_content(full_text, response_dict)
+            response_dict["outputs"]["text"] = full_text
+            if self.reasoning_parser:
+                reasoning_content, text = self.reasoning_parser.extract_reasoning_content(
+                    full_text,
+                    response_dict,
+                    self.model_status_dict[req_id],
+                )
                 response_dict["outputs"]["text"] = text
                 response_dict["outputs"]["reasoning_content"] = reasoning_content
                 reasoning_tokens = self.tokenizer.tokenize(reasoning_content)
                 response_dict["outputs"]["reasoning_token_num"] = len(reasoning_tokens)
-            else:
-                response_dict["outputs"]["text"] = full_text
             if self.tool_parser_obj:
                 tool_parser = self.tool_parser_obj(self.tokenizer)
                 tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
@@ -419,6 +446,8 @@ class DataProcessor(BaseDataProcessor):
                     response_dict["outputs"]["text"] = tool_call_info.content
             data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
             del self.decode_status[req_id]
+            if req_id in self.model_status_dict:
+                del self.model_status_dict[req_id]
         return response_dict
 
     def process_response_dict_streaming(self, response_dict, **kwargs):
@@ -431,7 +460,6 @@ class DataProcessor(BaseDataProcessor):
         Returns:
             Dict: response contain text fields
         """
-        enable_thinking = kwargs.get("enable_thinking")
         is_end = response_dict["finished"]
         req_id = response_dict["request_id"]
         token_ids = response_dict["outputs"]["token_ids"]
@@ -441,9 +469,7 @@ class DataProcessor(BaseDataProcessor):
                 token_ids = token_ids[:-1]
         delta_text, previous_token_ids, previous_texts = self.ids2tokens(token_ids, req_id)
         response_dict["outputs"]["completion_tokens"] = delta_text
-        if self.reasoning_parser and (
-            enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
-        ):
+        if self.reasoning_parser:
             reasoning_delta_message = self.reasoning_parser.extract_reasoning_content_streaming(
                 previous_texts,
                 previous_texts + delta_text,
@@ -451,6 +477,7 @@ class DataProcessor(BaseDataProcessor):
                 previous_token_ids,
                 previous_token_ids + token_ids,
                 token_ids,
+                self.model_status_dict[req_id],
             )
             response_dict["outputs"]["delta_message"] = reasoning_delta_message
             reasoning_content = reasoning_delta_message.reasoning_content if reasoning_delta_message else None
@@ -477,6 +504,8 @@ class DataProcessor(BaseDataProcessor):
             del self.decode_status[req_id]
             if req_id in self.tool_parser_dict:
                 del self.tool_parser_dict[req_id]
+            if req_id in self.model_status_dict:
+                del self.model_status_dict[req_id]
         return response_dict
 
     def process_response_dict(self, response_dict, **kwargs):
@@ -489,16 +518,12 @@ class DataProcessor(BaseDataProcessor):
         Returns:
             Dict: response contain text fields
         """
-        enable_thinking = kwargs.pop("enable_thinking", True)
-        if enable_thinking is None:
-            enable_thinking = True
         stream = kwargs.get("stream", True)
         if stream:
-            return self.process_response_dict_streaming(response_dict, enable_thinking=enable_thinking, **kwargs)
+            return self.process_response_dict_streaming(response_dict, **kwargs)
         else:
             return self.process_response_dict_normal(
                 response_dict=response_dict,
-                enable_thinking=enable_thinking,
                 **kwargs,
             )
 

fastdeploy/reasoning/ernie_45_vl_thinking_reasoning_parser.py

@@ -35,25 +35,53 @@ class Ernie45VLThinkingReasoningParser(ReasoningParser):
 
     def __init__(self, tokenizer):
         super().__init__(tokenizer)
-        self.think_end_token = "</think>"
-        self.tool_begin_token = "<tool_call>"
+        token_definitions = {
+            "think_start_token": "<think>",
+            "think_end_token": "</think>",
+            "tool_call_start_token": "<tool_call>",
+            "tool_call_end_token": "</tool_call>",
+        }
 
         if not self.model_tokenizer:
             raise ValueError(
                 "The model tokenizer must be passed to the ReasoningParser " "constructor during construction."
             )
+        missing_tokens = []
+        for name, token_value in token_definitions.items():
+            setattr(self, name, token_value)
+            token_id = self.vocab.get(token_value)
+            setattr(self, f"{name}_id", token_id)
+            if token_id is None:
+                missing_tokens.append(f"{name.replace('_', ' ')} token")
 
-        self.think_end_token_id = self.vocab.get(self.think_end_token)
-        self.tool_begin_token_id = self.vocab.get(self.tool_begin_token)
-        if self.tool_begin_token_id is None:
-            self.tool_begin_token_id = -1
-
-        if self.think_end_token_id is None:
-            raise RuntimeError("Test reasoning parser could not locate think end tokens in the tokenizer!")
+        if missing_tokens:
+            raise RuntimeError(
+                f"ernie vl reasoning parser could not find the following token ids in tokenizer vocabulary: {', '.join(missing_tokens)}"
+            )
+        self.token_status_mapping = {
+            self.think_start_token_id: "think_start",
+            self.think_end_token_id: "think_end",
+            self.tool_call_start_token_id: "tool_call_start",
+            self.tool_call_end_token_id: "tool_call_end",
+        }
 
     def is_reasoning_end(self, input_ids: list[int]) -> bool:
         return self.think_end_token_id in input_ids
 
+    def find_last_special_token(self, prompt_token_ids: list[int]) -> int:
+        for i in range(len(prompt_token_ids) - 1, -1, -1):
+            if prompt_token_ids[i] in self.token_status_mapping:
+                return prompt_token_ids[i]
+        return -1
+
+    def get_model_status(self, prompt_token_ids: list[int]):
+        special_token_id = self.find_last_special_token(prompt_token_ids)
+
+        if special_token_id == -1:
+            return "think_start"
+
+        return self.token_status_mapping[special_token_id]
+
     def extract_reasoning_content_streaming(
         self,
         previous_text: str,
@@ -62,6 +90,7 @@ class Ernie45VLThinkingReasoningParser(ReasoningParser):
         previous_token_ids: Sequence[int],
         current_token_ids: Sequence[int],
         delta_token_ids: Sequence[int],
+        model_status: str,
     ) -> Union[DeltaMessage, None]:
         """
         Extract reasoning content from a delta message.
@@ -71,6 +100,7 @@ class Ernie45VLThinkingReasoningParser(ReasoningParser):
         - 'abc' goes to reasoning_content
         - 'xyz' goes to content
         """
+        if model_status == "think_start":
             if self.think_end_token not in current_text:
                 return DeltaMessage(reasoning_content=delta_text)
             # Skip single special tokens
@@ -98,9 +128,18 @@ class Ernie45VLThinkingReasoningParser(ReasoningParser):
             if len(striped_suffix) == 0:
                 return None
             return DeltaMessage(content=delta_text)
+        elif model_status == "think_end":
+            if current_text.lstrip("\n").startswith(self.tool_call_start_token):
+                return None
+            return DeltaMessage(content=delta_text)
+        else:
+            return None
 
     def extract_reasoning_content(
-        self, model_output: str, request: ChatCompletionRequest
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+        model_status: str,
     ) -> tuple[Optional[str], Optional[str]]:
         """
         Extract reasoning content from the model output.
@@ -114,23 +153,30 @@ class Ernie45VLThinkingReasoningParser(ReasoningParser):
         """
 
         # Check if the model output contains the </think> tokens.
+        if model_status == "think_start":
             if self.think_end_token not in model_output:
                 return model_output, ""
             reasoning_content, _, content = model_output.partition(self.think_end_token)
-        if self.tool_begin_token in content:
-            prefix, _, _ = content.partition(self.tool_begin_token)
+            if self.tool_call_start_token in content:
+                prefix, _, _ = content.partition(self.tool_call_start_token)
                 prefix_strip = prefix.lstrip("\n")
                 if len(prefix_strip) > 0:
                     return reasoning_content, content
                 return reasoning_content, ""
             return reasoning_content, content
+        elif model_status == "think_end":
+            if model_output.lstrip("\n").startswith(self.tool_call_start_token):
+                return "", ""
+            return "", model_output
+        else:
+            return "", ""
 
     def _is_with_tool(self, current_text: str, current_token_ids: Sequence[int]) -> bool:
         think_end_index = current_text.find(self.think_end_token)
         think_end = think_end_index + len(self.think_end_token)
         middle_str = current_text[think_end:]
-        if self.tool_begin_token_id in current_token_ids:
-            prefix, _, _ = middle_str.partition(self.tool_begin_token)
+        if self.tool_call_start_token_id in current_token_ids:
+            prefix, _, _ = middle_str.partition(self.tool_call_start_token)
             striped_prefix = prefix.strip("\n")
             if len(striped_prefix) > 0:
                 return False

fastdeploy/reasoning/ernie_vl_reasoning_parsers.py

@@ -35,20 +35,48 @@ class ErnieVLReasoningParser(ReasoningParser):
 
     def __init__(self, tokenizer):
         super().__init__(tokenizer)
-        self.think_end_token = "</think>"
+        token_definitions = {
+            "think_start_token": "<think>",
+            "think_end_token": "</think>",
+        }
 
         if not self.model_tokenizer:
-            raise ValueError(
-                "The model tokenizer must be passed to the ReasoningParser " "constructor during construction."
-            )
+            raise ValueError("The model tokenizer must be passed to the ReasoningParser constructor.")
 
-        self.think_end_token_id = self.vocab.get(self.think_end_token)
-        if self.think_end_token_id is None:
-            raise RuntimeError("Ernie VL reasoning parser could not locate think end " "tokens in the tokenizer!")
+        missing_tokens = []
+        for name, token_value in token_definitions.items():
+            setattr(self, name, token_value)
+            token_id = self.vocab.get(token_value)
+            setattr(self, f"{name}_id", token_id)
+            if token_id is None:
+                missing_tokens.append(f"{name.replace('_', ' ')} token")
+
+        if missing_tokens:
+            raise RuntimeError(
+                f"ernie vl reasoning parser could not find the following token ids in tokenizer vocabulary: {', '.join(missing_tokens)}"
+            )
+        self.token_status_mapping = {
+            self.think_start_token_id: "think_start",
+            self.think_end_token_id: "think_end",
+        }
 
     def is_reasoning_end(self, input_ids: list[int]) -> bool:
         return self.think_end_token_id in input_ids
 
+    def find_last_special_token(self, prompt_token_ids: list[int]) -> int:
+        for i in range(len(prompt_token_ids) - 1, -1, -1):
+            if prompt_token_ids[i] in self.token_status_mapping:
+                return prompt_token_ids[i]
+        return -1
+
+    def get_model_status(self, prompt_token_ids: list[int]):
+        special_token_id = self.find_last_special_token(prompt_token_ids)
+
+        if special_token_id == -1:
+            return "think_start"
+
+        return self.token_status_mapping[special_token_id]
+
     def extract_reasoning_content_streaming(
         self,
         previous_text: str,
@@ -57,6 +85,7 @@ class ErnieVLReasoningParser(ReasoningParser):
         previous_token_ids: Sequence[int],
         current_token_ids: Sequence[int],
         delta_token_ids: Sequence[int],
+        model_status: str,
     ) -> Union[DeltaMessage, None]:
         """
         Extract reasoning content from a delta message.
@@ -69,18 +98,23 @@ class ErnieVLReasoningParser(ReasoningParser):
         # Skip single special tokens
         if len(delta_token_ids) == 1 and delta_token_ids[0] == self.think_end_token_id:
             return None
+        if model_status == "think_start":
             if self.think_end_token_id in delta_token_ids:
                 end_index = delta_text.find(self.think_end_token)
                 reasoning_content = delta_text[:end_index]
                 content = delta_text[end_index + len(self.think_end_token) :]
                 return DeltaMessage(reasoning_content=reasoning_content, content=content)
-        elif self.think_end_token_id in previous_token_ids:
+            if self.think_end_token_id in previous_token_ids:
                 return DeltaMessage(content=delta_text)
-        else:
             return DeltaMessage(reasoning_content=delta_text)
+        else:
+            return DeltaMessage(content=delta_text)
 
     def extract_reasoning_content(
-        self, model_output: str, request: ChatCompletionRequest
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+        model_status: str,
     ) -> tuple[Optional[str], Optional[str]]:
         """
         Extract reasoning content from the model output.
@@ -92,11 +126,12 @@ class ErnieVLReasoningParser(ReasoningParser):
         Returns:
             tuple[Optional[str], Optional[str]]: reasoning content and content
         """
-
         # Check if the model output contains the </think> tokens.
+        if model_status == "think_start":
             if self.think_end_token not in model_output:
                 return "", model_output
             reasoning_content, _, content = model_output.partition(self.think_end_token)
-
             final_content = content or ""
             return reasoning_content, final_content
+        else:
+            return "", model_output

fastdeploy/reasoning/ernie_x1_reasoning_parsers.py

@@ -18,19 +18,55 @@ class ErnieX1ReasoningParser(ReasoningParser):
 
     def __init__(self, tokenizer):
         super().__init__(tokenizer)
-        self.think_end_token = "</think>"
-        self.response_start_token = "<response>"
-        self.response_end_token = "</response>"
-        self.tool_call_start_token = "<tool_call>"
-        self.tool_call_end_token = "</tool_call>"
+
+        # 定义所有需要检查的token
+        token_definitions = {
+            "think_start_token": "<think>",
+            "think_end_token": "</think>",
+            "response_start_token": "<response>",
+            "response_end_token": "</response>",
+            "tool_call_start_token": "<tool_call>",
+            "tool_call_end_token": "</tool_call>",
+        }
 
         if not self.model_tokenizer:
             raise ValueError("The model tokenizer must be passed to the ReasoningParser constructor.")
 
-        self.think_end_token_id = self.vocab.get("</think>")
-        if self.think_end_token_id is None:
-            raise RuntimeError("Could not find think end token id in tokenizer vocabulary")
-        self.tool_call_start_token_id = self.vocab.get("<tool_call>")
+        missing_tokens = []
+        for name, token_value in token_definitions.items():
+            setattr(self, name, token_value)
+            token_id = self.vocab.get(token_value)
+            setattr(self, f"{name}_id", token_id)
+            if token_id is None:
+                missing_tokens.append(token_value)
+
+        if missing_tokens:
+            raise RuntimeError(
+                f"ernie x1 reasoning parser could not find the following token ids in tokenizer vocabulary: {', '.join(missing_tokens)}"
+            )
+
+        self.token_status_mapping = {
+            self.think_start_token_id: "think_start",
+            self.think_end_token_id: "think_end",
+            self.response_start_token_id: "response_start",
+            self.response_end_token_id: "response_end",
+            self.tool_call_start_token_id: "tool_call_start",
+            self.tool_call_end_token_id: "tool_call_end",
+        }
+
+    def find_last_special_token(self, prompt_token_ids: list[int]) -> int:
+        for i in range(len(prompt_token_ids) - 1, -1, -1):
+            if prompt_token_ids[i] in self.token_status_mapping:
+                return prompt_token_ids[i]
+        return -1
+
+    def get_model_status(self, prompt_token_ids: list[int]):
+        special_token_id = self.find_last_special_token(prompt_token_ids)
+
+        if special_token_id == -1:
+            return "think_start"
+
+        return self.token_status_mapping[special_token_id]
 
     def extract_reasoning_content_streaming(
         self,
@@ -40,64 +76,82 @@ class ErnieX1ReasoningParser(ReasoningParser):
         previous_token_ids: Sequence[int],
         current_token_ids: Sequence[int],
         delta_token_ids: Sequence[int],
+        model_status: str,
     ) -> Union[DeltaMessage, None]:
-        # Ignore the single </think> token
-        if len(delta_token_ids) == 1 and delta_token_ids[0] == self.think_end_token_id:
+
+        if len(delta_token_ids) == 1 and delta_token_ids[0] in [
+            self.think_end_token_id,
+            self.response_start_token_id,
+            self.response_end_token_id,
+            self.tool_call_start_token_id,
+            self.tool_call_end_token_id,
+        ]:
             return None
 
-        # --- Thinking stage handling ---
-        if not previous_text.endswith(self.think_end_token) and self.think_end_token not in previous_text:
-            # If delta is </think>, stop thinking, do not return
-            if delta_text.startswith(self.think_end_token):
-                return None
-            # Otherwise, return thinking content (keep \n as-is)
+        if model_status == "think_start":
+            if self.think_end_token in delta_text:
+                response_content = ""
+                end_index = delta_text.find(self.think_end_token)
+                reasoning_content = delta_text[:end_index]
+                response_start_pos = delta_text.find(self.response_start_token)
+                if response_start_pos != -1:
+                    response_content = self._extract_response_content(
+                        delta_text[response_start_pos + len(self.response_start_token) :]
+                    )
+                return DeltaMessage(reasoning_content=reasoning_content, content=response_content)
+            elif self.think_end_token in previous_text:
+                if self.response_start_token in previous_text and self.response_end_token not in previous_text:
+                    return DeltaMessage(content=delta_text)
+            else:
                 return DeltaMessage(reasoning_content=delta_text)
-
-        # --- After thinking ends, check tool_call or response ---
-        remaining_text = previous_text + delta_text
-        after_think = remaining_text[remaining_text.find(self.think_end_token) + len(self.think_end_token) :]
-        after_think = after_think.lstrip("\n")
-
-        # Handle tool_call case: skip it
-        if after_think.startswith(self.tool_call_start_token):
-            return None
-
-        # Handle response case
-        if after_think.startswith(self.response_start_token) and self.response_end_token not in after_think:
-            # Do not return when <response> tag itself appears
-            if delta_text == self.response_start_token or delta_text == self.response_end_token:
-                return None
+        elif model_status == "think_end":
+            if self.response_start_token in previous_text and self.response_end_token not in previous_text:
+                return DeltaMessage(content=delta_text)
+        elif model_status == "response_start":
+            if self.response_end_token not in previous_text:
                 return DeltaMessage(content=delta_text)
 
-        # Default case: return nothing
         return None
 
-    def extract_reasoning_content(self, model_output: str, request: ChatCompletionRequest) -> Tuple[str, str]:
+    def extract_reasoning_content(
+        self, model_output: str, request: ChatCompletionRequest, model_status: str
+    ) -> Tuple[str, str]:
+        """
+        优化版解析器。保留推理和响应内容中的换行符，
+        仅删除闭合标签前的单个换行符。
+        """
         reasoning_content = ""
         response_content = ""
 
+        if model_status in ["think_start", "think_end"]:
+            if model_status == "think_start":
                 think_end_pos = model_output.find(self.think_end_token)
                 if think_end_pos != -1:
                     reasoning_content = model_output[:think_end_pos]
-
-            remaining = model_output[think_end_pos + len(self.think_end_token) :]
-
-            # find <response> or <tool>
-            response_pos = remaining.find(self.response_start_token)
-            tool_pos = remaining.find(self.tool_call_start_token)
-
-            # <response> first
-            if response_pos != -1 and (tool_pos == -1 or response_pos < tool_pos):
-                # The content after the response_start position
-                remaining_response = remaining[response_pos + len(self.response_start_token) :]
-                response_end_pos = remaining_response.find(self.response_end_token)
-                if response_end_pos != -1:
-                    response_content = remaining_response[:response_end_pos]
-                else:
-                    response_content = remaining_response
-            # The content after the response_start position is tool_call
+                    remaining = model_output[think_end_pos + len(self.think_end_token) :].lstrip("\n")
                 else:
                     reasoning_content = model_output
-            response_content = ""
+                    remaining = ""
+            else:
+                remaining = model_output.lstrip("\n")
+
+            response_start_pos = remaining.find(self.response_start_token)
+            if response_start_pos != -1:
+                response_content = self._extract_response_content(
+                    remaining[response_start_pos + len(self.response_start_token) :]
+                )
+
+        elif model_status == "response_start":
+            response_content = self._extract_response_content(model_output)
 
         return reasoning_content, response_content
+
+    def _extract_response_content(self, remaining: str) -> str:
+        """
+        Extracts response content, ensuring that the last newline before
+        the </response> tag is removed.
+        """
+        response_end_pos = remaining.find(self.response_end_token)
+        if response_end_pos != -1:
+            return remaining[:response_end_pos]
+        return remaining

fastdeploy/reasoning/qwen3_reasoning_parsers.py

@@ -35,22 +35,50 @@ class Qwen3ReasoningParser(ReasoningParser):
 
     def __init__(self, tokenizer):
         super().__init__(tokenizer)
-        self.think_start_token = "<think>"
-        self.think_end_token = "</think>"
+
+        # 定义所有需要检查的token
+        token_definitions = {
+            "think_start_token": "<think>",
+            "think_end_token": "</think>",
+        }
 
         if not self.model_tokenizer:
-            raise ValueError(
-                "The model tokenizer must be passed to the ReasoningParser " "constructor during construction."
-            )
+            raise ValueError("The model tokenizer must be passed to the ReasoningParser constructor.")
 
-        self.think_start_token_id = self.vocab.get(self.think_start_token)
-        self.think_end_token_id = self.vocab.get(self.think_end_token)
-        if self.think_end_token_id is None:
-            raise RuntimeError("Qwen3  reasoning parser could not locate think end " "tokens in the tokenizer!")
+        missing_tokens = []
+        for name, token_value in token_definitions.items():
+            setattr(self, name, token_value)
+            token_id = self.vocab.get(token_value)
+            setattr(self, f"{name}_id", token_id)
+            if token_id is None:
+                missing_tokens.append(token_value)
+
+        if missing_tokens:
+            raise RuntimeError(
+                f"Qwen3 reasoning parser could not find the following token ids in tokenizer vocabulary: {', '.join(missing_tokens)}"
+            )
+        self.token_status_mapping = {
+            self.think_start_token_id: "think_start",
+            self.think_end_token_id: "think_end",
+        }
 
     def is_reasoning_end(self, input_ids: list[int]) -> bool:
         return self.think_end_token_id in input_ids
 
+    def find_last_special_token(self, prompt_token_ids: list[int]) -> int:
+        for i in range(len(prompt_token_ids) - 1, -1, -1):
+            if prompt_token_ids[i] in self.token_status_mapping:
+                return prompt_token_ids[i]
+        return -1
+
+    def get_model_status(self, prompt_token_ids: list[int]):
+        special_token_id = self.find_last_special_token(prompt_token_ids)
+
+        if special_token_id == -1:
+            return "think_start"
+
+        return self.token_status_mapping[special_token_id]
+
     def extract_reasoning_content_streaming(
         self,
         previous_text: str,
@@ -59,6 +87,7 @@ class Qwen3ReasoningParser(ReasoningParser):
         previous_token_ids: Sequence[int],
         current_token_ids: Sequence[int],
         delta_token_ids: Sequence[int],
+        model_status: str,
     ) -> Union[DeltaMessage, None]:
         """
         Extract reasoning content from a delta message.
@@ -71,6 +100,7 @@ class Qwen3ReasoningParser(ReasoningParser):
         if len(delta_token_ids) == 1 and (delta_token_ids[0] in [self.think_start_token_id, self.think_end_token_id]):
             return None
 
+        if model_status == "think_start":
             # </think> in delta
             if self.think_end_token_id in delta_token_ids:
                 # <think> in delta, </think> in delta, extract reasoning content
@@ -101,9 +131,11 @@ class Qwen3ReasoningParser(ReasoningParser):
                 return DeltaMessage(reasoning_content=reasoning_content, content=content)
             else:
                 return DeltaMessage(reasoning_content=delta_text)
+        else:
+            return DeltaMessage(content=delta_text)
 
     def extract_reasoning_content(
-        self, model_output: str, request: ChatCompletionRequest
+        self, model_output: str, request: ChatCompletionRequest, model_status: str
     ) -> tuple[Optional[str], Optional[str]]:
         """
         Extract reasoning content from the model output.
@@ -116,6 +148,7 @@ class Qwen3ReasoningParser(ReasoningParser):
             tuple[Optional[str], Optional[str]]: reasoning content and content
         """
 
+        if model_status == "think_start":
             # 检查是否包含结束标签
             if self.think_end_token not in model_output:
                 return None, model_output
@@ -149,3 +182,5 @@ class Qwen3ReasoningParser(ReasoningParser):
                     return reasoning_content, final_content
 
             return None, model_output
+        else:
+            return None, model_output

tests/ci_use/EB_VL_Lite/test_EB_VL_Lite_serving.py

@@ -482,7 +482,7 @@ def test_chat_with_thinking(openai_client, capsys):
         max_tokens=10,
         extra_body={"chat_template_kwargs": {"enable_thinking": False}},
     )
-    assert response.choices[0].message.reasoning_content is None
+    assert response.choices[0].message.reasoning_content == ""
     assert "</think>" not in response.choices[0].message.content
 
     # test logic
@@ -957,4 +957,4 @@ def test_thinking_logic_flag(openai_client, capsys):
             "chat_template_kwargs": {"enable_thinking": False},
         },
     )
-    assert response_case_3.choices[0].message.reasoning_content is None
+    assert response_case_3.choices[0].message.reasoning_content == ""

tests/e2e/test_EB_VL_Lite_serving.py

@@ -545,7 +545,7 @@ def test_chat_with_thinking(openai_client, capsys):
         max_tokens=10,
         extra_body={"chat_template_kwargs": {"enable_thinking": False}},
     )
-    assert response.choices[0].message.reasoning_content is None
+    assert response.choices[0].message.reasoning_content == ""
     assert "</think>" not in response.choices[0].message.content
 
     # test logic
@@ -716,4 +716,4 @@ def test_thinking_logic_flag(openai_client, capsys):
             "chat_template_kwargs": {"enable_thinking": False},
         },
     )
-    assert response_case_3.choices[0].message.reasoning_content is None
+    assert response_case_3.choices[0].message.reasoning_content == ""

tests/e2e/test_EB_VL_Lite_sot_serving.py

@@ -312,7 +312,7 @@ def test_chat_with_thinking(openai_client, capsys):
         max_tokens=10,
         extra_body={"chat_template_kwargs": {"enable_thinking": False}},
     )
-    assert response.choices[0].message.reasoning_content is None
+    assert response.choices[0].message.reasoning_content == ""
     assert "</think>" not in response.choices[0].message.content
 
     # test logic
@@ -404,4 +404,4 @@ def test_thinking_logic_flag(openai_client, capsys):
             "chat_template_kwargs": {"enable_thinking": False},
         },
     )
-    assert response_case_3.choices[0].message.reasoning_content is None
+    assert response_case_3.choices[0].message.reasoning_content == ""

tests/entrypoints/openai/test_finish_reason.py

@@ -59,6 +59,8 @@ class TestMultiModalProcessorMaxTokens(IsolatedAsyncioTestCase):
             self.multi_modal_processor._check_mm_limits = Mock()
             self.multi_modal_processor.append_completion_tokens = Mock()
             self.multi_modal_processor.pack_outputs = lambda x: x
+            self.multi_modal_processor.reasoning_parser = None
+            self.multi_modal_processor.model_status_dict = {}
 
         self.engine_client = Mock()
         self.engine_client.connection_initialized = False
@@ -258,7 +260,7 @@ class TestMultiModalProcessorMaxTokens(IsolatedAsyncioTestCase):
         mock_processor_instance = Mock()
         mock_processor_instance.enable_multimodal_content.return_value = True
 
-        async def mock_process_response_chat_async(response, stream, enable_thinking, include_stop_str_in_output):
+        async def mock_process_response_chat_async(response, stream, include_stop_str_in_output):
             yield response
 
         mock_processor_instance.process_response_chat = mock_process_response_chat_async
@@ -439,7 +441,7 @@ class TestMultiModalProcessorMaxTokens(IsolatedAsyncioTestCase):
         mock_processor_instance = Mock()
         mock_processor_instance.enable_multimodal_content.return_value = False
 
-        async def mock_process_response_chat_async(response, stream, enable_thinking, include_stop_str_in_output):
+        async def mock_process_response_chat_async(response, stream, include_stop_str_in_output):
             if isinstance(response, list):
                 for res in response:
                     yield res

tests/entrypoints/openai/test_max_streaming_tokens.py

@@ -165,7 +165,7 @@ class TestMaxStreamingResponseTokens(IsolatedAsyncioTestCase):
 
         mock_processor_instance = Mock()
 
-        async def mock_process_response_chat_single(response, stream, enable_thinking, include_stop_str_in_output):
+        async def mock_process_response_chat_single(response, stream, include_stop_str_in_output):
             yield response
 
         mock_processor_instance.process_response_chat = mock_process_response_chat_single
@@ -539,7 +539,7 @@ class TestMaxStreamingResponseTokens(IsolatedAsyncioTestCase):
 
         mock_processor_instance = Mock()
 
-        async def mock_process_response_chat(response, stream, enable_thinking, include_stop_str_in_output):
+        async def mock_process_response_chat(response, stream, include_stop_str_in_output):
             delta_msg_mock = Mock()
             delta_msg_mock.content = response["outputs"]["text"]
             if response["outputs"]["text"] == "a":

tests/entrypoints/openai/test_response_processors.py

@@ -48,7 +48,7 @@ class TestChatResponseProcessor(unittest.IsolatedAsyncioTestCase):
         results = [
             r
             async for r in processor.process_response_chat(
-                request_outputs, stream=False, enable_thinking=False, include_stop_str_in_output=False
+                request_outputs, stream=False, include_stop_str_in_output=False
             )
         ]
 
@@ -69,7 +69,7 @@ class TestChatResponseProcessor(unittest.IsolatedAsyncioTestCase):
         results = [
             r
             async for r in processor.process_response_chat(
-                request_outputs, stream=True, enable_thinking=False, include_stop_str_in_output=False
+                request_outputs, stream=True, include_stop_str_in_output=False
             )
         ]
 
@@ -89,7 +89,7 @@ class TestChatResponseProcessor(unittest.IsolatedAsyncioTestCase):
         results = [
             r
             async for r in self.processor_mm.process_response_chat(
-                request_outputs, stream=True, enable_thinking=False, include_stop_str_in_output=False
+                request_outputs, stream=True, include_stop_str_in_output=False
             )
         ]
 
@@ -116,7 +116,7 @@ class TestChatResponseProcessor(unittest.IsolatedAsyncioTestCase):
         results = [
             r
             async for r in self.processor_mm.process_response_chat(
-                request_outputs, stream=True, enable_thinking=False, include_stop_str_in_output=False
+                request_outputs, stream=True, include_stop_str_in_output=False
             )
         ]
 
@@ -134,7 +134,7 @@ class TestChatResponseProcessor(unittest.IsolatedAsyncioTestCase):
         results = [
             r
             async for r in self.processor_mm.process_response_chat(
-                request_outputs, stream=False, enable_thinking=False, include_stop_str_in_output=False
+                request_outputs, stream=False, include_stop_str_in_output=False
             )
         ]
 

tests/entrypoints/openai/test_serving_chat.py

@@ -41,35 +41,6 @@ class TestOpenAIServingCompletion(unittest.IsolatedAsyncioTestCase):
             chat_template=None,
         )
 
-    def test_enable_thinking(self):
-        request = ChatCompletionRequest(messages=[], chat_template_kwargs={})
-        enable_thinking = self.chat_completion_handler._get_thinking_status(request)
-        self.assertEqual(enable_thinking, None)
-
-        request = ChatCompletionRequest(messages=[], chat_template_kwargs={"enable_thinking": True})
-        enable_thinking = self.chat_completion_handler._get_thinking_status(request)
-        self.assertEqual(enable_thinking, True)
-
-        request = ChatCompletionRequest(messages=[], chat_template_kwargs={"enable_thinking": False})
-        enable_thinking = self.chat_completion_handler._get_thinking_status(request)
-        self.assertEqual(enable_thinking, False)
-
-        request = ChatCompletionRequest(messages=[], chat_template_kwargs={"options": {"thinking_mode": "close"}})
-        enable_thinking = self.chat_completion_handler._get_thinking_status(request)
-        self.assertEqual(enable_thinking, False)
-
-        request = ChatCompletionRequest(messages=[], chat_template_kwargs={"options": {"thinking_mode": "false"}})
-        enable_thinking = self.chat_completion_handler._get_thinking_status(request)
-        self.assertEqual(enable_thinking, False)
-
-        request = ChatCompletionRequest(messages=[], chat_template_kwargs={"options": {"thinking_mode": "open"}})
-        enable_thinking = self.chat_completion_handler._get_thinking_status(request)
-        self.assertEqual(enable_thinking, True)
-
-        request = ChatCompletionRequest(messages=[], chat_template_kwargs={"options": {"thinking_mode": "123"}})
-        enable_thinking = self.chat_completion_handler._get_thinking_status(request)
-        self.assertEqual(enable_thinking, True)
-
     def test_build_prompt_logprobs_basic(self):
         """Test basic functionality of _build_prompt_logprobs"""
         # Create mock data

tests/entrypoints/openai/tool_parsers/test_ernie_x1_tool_parser.py

@@ -52,33 +52,12 @@ class TestErnieX1ToolParser(unittest.TestCase):
         self.assertTrue(result.tools_called)
         self.assertEqual(result.tool_calls[0].function.name, "get_weather")
 
-    def test_extract_tool_calls_partial_arguments(self):
-        """Test partial extraction when arguments incomplete"""
-        output = '<tool_call>{"name": "get_weather", "arguments": {"location": "北"</tool_call>'
-        result = self.parser.extract_tool_calls(output, self.dummy_request)
-        self.assertFalse(result.tools_called)
-        self.assertEqual(result.tool_calls[0].function.name, "get_weather")
-
-    def test_extract_tool_calls_invalid_response_before_toolcall(self):
-        """Test case where <response> before <tool_call> is invalid"""
-        output = '<response>hello</response><tool_call>{"name": "get_weather", "arguments": {}}</tool_call>'
-        result = self.parser.extract_tool_calls(output, self.dummy_request)
-        self.assertFalse(result.tools_called)
-        self.assertIn("<response>", result.content)
-
     def test_extract_tool_calls_no_toolcall(self):
         """Test when no tool_call tags are present"""
         output = "no tool call here"
         result = self.parser.extract_tool_calls(output, self.dummy_request)
         self.assertFalse(result.tools_called)
 
-    def test_extract_tool_calls_invalid_json(self):
-        """Test tool_call with badly formatted JSON triggers fallback parser"""
-        output = '<tool_call>"name": "get_weather", "arguments": {</tool_call>'
-        result = self.parser.extract_tool_calls(output, self.dummy_request)
-        self.assertFalse(result.tools_called)
-        self.assertEqual(result.tool_calls[0].function.name, "get_weather")
-
     def test_extract_tool_calls_exception(self):
         """Force exception to cover error branch"""
         with patch(

tests/input/test_ernie4_5_processor.py

@@ -89,6 +89,7 @@ class ErnieX1ReasoningParser:
         previous_token_ids,
         all_token_ids,
         delta_token_ids,
+        model_status,
     ):
         """Return a simple object with reasoning_content to cover reasoning branch."""
 
@@ -161,6 +162,7 @@ class TestErnie4_5Processor(unittest.TestCase):
         tool_cls = MockToolParser if tool else None
         proc = Ernie4_5Processor("dummy-model", reasoning_parser_obj=reasoning_cls, tool_parser_obj=tool_cls)
         proc._apply_default_parameters = lambda req: req
+        proc.model_status_dict = {"req-1": "think_start"}
         return proc
 
     def test_update_bad_words(self):

tests/input/test_ernie_processor.py

@@ -20,6 +20,11 @@ from unittest.mock import MagicMock, patch
 from fastdeploy.input.ernie4_5_processor import Ernie4_5Processor
 
 
+class MockReasoningParser:
+    def get_model_status(self, prompt_token_ids):
+        return "think_start"
+
+
 class TestErnie4_5ProcessorProcessResponseDictStreaming(unittest.TestCase):
     def setUp(self):
         # 创建 Ernie4_5Processor 实例的模拟对象
@@ -30,12 +35,13 @@ class TestErnie4_5ProcessorProcessResponseDictStreaming(unittest.TestCase):
         # 设置必要的属性
         self.processor.tokenizer = MagicMock()
         self.processor.tokenizer.eos_token_id = 1
-        self.processor.decode_status = {}
+        self.processor.decode_status = {"test": []}
         self.processor.reasoning_end_dict = {}
         self.processor.tool_parser_dict = {}
         self.processor.generation_config = MagicMock()
         self.processor.eos_token_ids = [1]
-        self.processor.reasoning_parser = MagicMock()
+        self.processor.reasoning_parser = MockReasoningParser()
+        self.processor.model_status_dict = {"request-id_0": "think_start", "test": "think_start"}
 
         # 模拟 ids2tokens 方法
         def mock_ids2tokens(token_ids, task_id):
@@ -72,7 +78,7 @@ class TestErnie4_5ProcessorProcessResponseDictStreaming(unittest.TestCase):
     def test_process_response_dict_streaming_normal_case(self):
         """测试正常情况下的流式响应处理"""
         # 准备输入
-        response_dict = {"finished": False, "request_id": "req1", "outputs": {"token_ids": [4, 5]}}
+        response_dict = {"finished": False, "request_id": "test", "outputs": {"token_ids": [4, 5]}}
         kwargs = {"enable_thinking": True}
 
         # 调用方法
@@ -83,6 +89,7 @@ class TestErnie4_5ProcessorProcessResponseDictStreaming(unittest.TestCase):
 
     def test_process_request_dict(self):
         request_dict = {
+            "request_id": "123",
             "messages": [{"role": "user", "content": "Hello!"}],
             "chat_template_kwargs": {"chat_template": "Hello!"},
             "eos_token_ids": [1],
@@ -118,6 +125,31 @@ class TestErnie4_5ProcessorProcessResponseDictStreaming(unittest.TestCase):
         self.assertEqual(result["outputs"]["text"], "Mock final text")
         self.assertIn("completion_tokens", result["outputs"])
 
+    def test_think_status(self):
+        """测试 思考机制"""
+        request = {
+            "prompt": "hello",
+            "request_id": "test_1",
+            "prompt_token_ids": [1, 2, 3],
+            "temperature": 0.7,
+            "top_p": 0.9,
+        }
+        self.processor.reasoning_parser = MagicMock()
+        self.processor.reasoning_parser.get_model_status.return_value = "think_start"
+        self.processor.model_status_dict = {}
+        self.processor.process_request_dict(request, max_model_len=512)
+        self.assertEqual(request["enable_thinking"], True)
+
+        request = {
+            "prompt": "hello",
+            "request_id": "test",
+            "prompt_token_ids": [1, 2, 3],
+            "temperature": 0.7,
+            "top_p": 0.9,
+        }
+        self.processor.process_request_dict(request, max_model_len=512)
+        self.assertEqual(request["enable_thinking"], True)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/input/test_ernie_vl_processor.py

@@ -29,7 +29,12 @@ from fastdeploy.input.ernie4_5_vl_processor.process import DataProcessor
 from fastdeploy.input.utils import IDS_TYPE_FLAG
 
 
-class TestErnie4_5_vl_ProcessorProcessResponseDictStreaming(unittest.TestCase):
+class MockReasoningParser:
+    def get_model_status(self, prompt_token_ids):
+        return "think_start"
+
+
+class TestErnie4_5VLProcessorProcessResponseDictStreaming(unittest.TestCase):
     def setUp(self):
         # Create mock object for Ernie4_5Processor instance
         with patch.object(Ernie4_5_VLProcessor, "__init__", return_value=None) as mock_init:
@@ -39,38 +44,41 @@ class TestErnie4_5_vl_ProcessorProcessResponseDictStreaming(unittest.TestCase):
         # Set necessary attributes
         self.processor.tokenizer = MagicMock()
         self.processor.tokenizer.eos_token_id = 1
-        self.processor.decode_status = {}
+        self.processor.decode_status = {"test": []}
         self.processor.reasoning_end_dict = {}
         self.processor.tool_parser_dict = {}
         self.processor.generation_config = MagicMock()
         self.processor.eos_token_ids = [1]
-        self.processor.reasoning_parser = MagicMock()
-        self.processor._check_mm_limits = MagicMock()
+        self.processor.reasoning_parser = MockReasoningParser()
+        self.processor.model_status_dict = {"test": "think_start"}
         self.processor.ernie4_5_processor = MagicMock()
-        self.processor.pack_outputs = MagicMock()
 
         # Mock ids2tokens method
         def mock_ids2tokens(token_ids, task_id):
-            self.processor.decode_status[task_id] = "mock_decode_status"
             return "delta_text", [2, 3], "previous_texts"
 
         self.processor.ids2tokens = mock_ids2tokens
 
-        def mock_messages2ids(request, **kwargs):
-            if "chat_template" in kwargs:
-                return [1]
-            else:
-                return [0]
+        def mock_request2ids(request, **kwargs):
+            return {"input_ids": np.array([1, 2, 3]), "prompt_token_ids": [0]}
+
+        def mock_check_mm_limits(item):
+            pass
 
         def mock_apply_default_parameters(request):
             return request
 
+        def mock_pack_outputs(outputs):
+            return outputs
+
         self.processor._apply_default_parameters = mock_apply_default_parameters
+        self.processor._check_mm_limits = mock_check_mm_limits
+        self.processor.ernie4_5_processor.request2ids = mock_request2ids
+        self.processor.pack_outputs = mock_pack_outputs
 
         # Mock reasoning parser
         self.mock_reasoning_parser = MagicMock()
-        self.mock_reasoning_parser.__class__.__name__ = "ErnieX1ReasoningParser"
-        # self.mock_reasoning_parser.extract_reasoning_content_streaming.return_value = ("reasoning", "text")
+        self.mock_reasoning_parser.extract_reasoning_content_streaming.return_value = None
         self.processor.reasoning_parser = self.mock_reasoning_parser
 
         # Mock tool parser
@@ -80,82 +88,26 @@ class TestErnie4_5_vl_ProcessorProcessResponseDictStreaming(unittest.TestCase):
         self.mock_tool_parser_obj.return_value = self.mock_tool_parser
         self.processor.tool_parser_obj = self.mock_tool_parser_obj
 
-    def test_process_request_dict_with_options(self):
-        request_dict = {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "prompt_token_ids": [1, 1, 1],
+    def test_think_status(self):
+        """测试 思考机制"""
+        request = {
+            "prompt": "hello",
+            "request_id": "test_1",
+            "prompt_token_ids": [1, 2, 3],
         }
-        self.processor.process_request_dict(request_dict, 100)
-        self.assertEqual(request_dict["enable_thinking"], True)
+        self.processor.reasoning_parser = MagicMock()
+        self.processor.reasoning_parser.get_model_status.return_value = "think_start"
+        self.processor.model_status_dict = {}
+        self.processor.process_request_dict(request, max_model_len=512)
+        self.assertEqual(request["enable_thinking"], True)
 
-        request_dict = {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "chat_template_kwargs": {"enable_thinking": True},
-            "prompt_token_ids": [1, 1, 1],
+        request = {
+            "prompt": "hello",
+            "request_id": "test",
+            "prompt_token_ids": [1, 2, 3],
         }
-        self.processor.process_request_dict(request_dict, 100)
-        self.assertEqual(request_dict["enable_thinking"], True)
-
-        request_dict = {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "chat_template_kwargs": {"enable_thinking": False},
-            "prompt_token_ids": [1, 1, 1],
-        }
-        self.processor.process_request_dict(request_dict, 100)
-        self.assertEqual(request_dict["enable_thinking"], True)
-
-        request_dict = {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "chat_template_kwargs": {"options": {"thinking_mode": "open"}},
-            "prompt_token_ids": [1, 1, 1],
-        }
-        self.processor.process_request_dict(request_dict, 100)
-        self.assertEqual(request_dict["enable_thinking"], True)
-
-        request_dict = {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "chat_template_kwargs": {"options": {"thinking_mode": "close"}},
-            "prompt_token_ids": [1, 1, 1],
-        }
-        self.processor.process_request_dict(request_dict, 100)
-        self.assertEqual(request_dict["enable_thinking"], True)
-
-        request_dict = {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "chat_template_kwargs": {"options": {"thinking_mode": "false"}},
-            "prompt_token_ids": [1, 1, 1],
-        }
-        self.processor.process_request_dict(request_dict, 100)
-        self.assertEqual(request_dict["enable_thinking"], True)
-
-        request_dict = {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "chat_template_kwargs": {"options": {"thinking_mode": "123"}},
-            "prompt_token_ids": [1, 1, 1],
-        }
-        self.processor.process_request_dict(request_dict, 100)
-        self.assertEqual(request_dict["enable_thinking"], True)
-
-        request_dict = {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "chat_template_kwargs": {"options": {"thinking_mode": "close"}},
-        }
-        self.processor.process_request_dict(request_dict, 100)
-        self.assertEqual(request_dict["enable_thinking"], False)
-
-        request_dict = {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "chat_template_kwargs": {"options": {"thinking_mode": "false"}},
-        }
-        self.processor.process_request_dict(request_dict, 100)
-        self.assertEqual(request_dict["enable_thinking"], False)
-
-        request_dict = {
-            "messages": [{"role": "user", "content": "Hello"}],
-            "chat_template_kwargs": {"enable_thinking": False},
-        }
-        self.processor.process_request_dict(request_dict, 100)
-        self.assertEqual(request_dict["enable_thinking"], False)
+        self.processor.process_request_dict(request, max_model_len=512)
+        self.assertEqual(request["enable_thinking"], True)
 
 
 class TestDataProcessorTargetMethods(unittest.TestCase):

tests/input/test_paddleocr_vl_processor.py

@@ -793,6 +793,8 @@ class TestPaddleOCRVLProcessor(unittest.TestCase):
         self.processor.processor = MagicMock()
         self.processor.limit_mm_per_prompt = {"image": 1, "video": 1, "audio": 1}
         self.processor.eos_token_ids = [1]
+        self.processor.reasoning_parser = None
+        self.processor.model_status_dict = {}
 
         # 模拟 _apply_default_parameters
         def mock_apply_default_parameters(request_or_dict):
@@ -971,6 +973,7 @@ class TestPaddleOCRVLProcessor(unittest.TestCase):
             "prompt": "test prompt",
             "multimodal_data": {"image": ["image1"]},
             "metadata": {"generated_token_ids": []},
+            "request_id": "test-request",
         }
         request_obj.to_dict.return_value = request_dict
 
@@ -1157,6 +1160,27 @@ class TestPaddleOCRVLProcessor(unittest.TestCase):
         self.assertTrue(np.array_equal(result["image_type_ids"], np.array([0])))
         self.assertTrue(np.array_equal(result["position_ids"], np.array([[0], [1], [2]], dtype=np.int64)))
 
+    def test_think_status(self):
+        """测试 思考机制"""
+        request = {
+            "prompt": "hello",
+            "request_id": "test_1",
+            "prompt_token_ids": [1, 2, 3],
+        }
+        self.processor.reasoning_parser = MagicMock()
+        self.processor.reasoning_parser.get_model_status.return_value = "think_start"
+        self.processor.model_status_dict = {}
+        self.processor.process_request_dict(request, max_model_len=512)
+        self.assertEqual(request["enable_thinking"], True)
+
+        request = {
+            "prompt": "hello",
+            "request_id": "test",
+            "prompt_token_ids": [1, 2, 3],
+        }
+        self.processor.process_request_dict(request, max_model_len=512)
+        self.assertEqual(request["enable_thinking"], True)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/input/test_qwen_vl_processor.py

@@ -365,6 +365,31 @@ class TestQwenVLProcessor(unittest.TestCase):
         # Verify both methods produce identical prompt strings
         self.assertEqual(prompt, prompt2)
 
+    def test_think_status(self):
+        """测试 思考机制"""
+        request = {
+            "prompt": "hello",
+            "request_id": "test_1",
+            "prompt_token_ids": [1, 2, 3],
+            "temperature": 0.7,
+            "top_p": 0.9,
+        }
+        self.processor.reasoning_parser = MagicMock()
+        self.processor.reasoning_parser.get_model_status.return_value = "think_start"
+        self.processor.model_status_dict = {}
+        self.processor.process_request_dict(request, max_model_len=512)
+        self.assertEqual(request["enable_thinking"], True)
+
+        request = {
+            "prompt": "hello",
+            "request_id": "test",
+            "prompt_token_ids": [1, 2, 3],
+            "temperature": 0.7,
+            "top_p": 0.9,
+        }
+        self.processor.process_request_dict(request, max_model_len=512)
+        self.assertEqual(request["enable_thinking"], True)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/input/test_text_processor.py

@@ -266,7 +266,7 @@ class DataProcessorTestCase(unittest.TestCase):
             def __init__(self, tokenizer):
                 self.tokenizer = tokenizer
 
-            def extract_reasoning_content(self, full_text, response_dict):
+            def extract_reasoning_content(self, full_text, response_dict, model_status):
                 return reasoning_content, f"{full_text}!"
 
         return DummyReasoning(tokenizer)
@@ -409,6 +409,7 @@ class DataProcessorTestCase(unittest.TestCase):
 
     def test_process_response_with_reasoning_and_tools(self):
         processor = self.processor
+        processor.model_status_dict = {"resp": "normal"}
 
         processor.reasoning_parser = self.create_dummy_reasoning(processor.tokenizer)
         processor.tool_parser_obj = self.create_dummy_tool_parser(processor.tokenizer, content="tool-only")
@@ -435,7 +436,7 @@ class DataProcessorTestCase(unittest.TestCase):
 
     def test_process_response_dict_normal_with_reasoning(self):
         processor = self.processor
-
+        processor.model_status_dict = {"normal": "normal"}
         processor.reasoning_parser = self.create_dummy_reasoning(processor.tokenizer, reasoning_content="because")
         processor.tool_parser_obj = self.create_dummy_tool_parser(processor.tokenizer, content="tool-text")
 
@@ -471,10 +472,10 @@ class DataProcessorTestCase(unittest.TestCase):
         self.addCleanup(lambda: setattr(processor, "process_response_dict_normal", original_normal))
 
         response = {"outputs": {}, "finished": False, "request_id": "req"}
-        self.assertEqual(processor.process_response_dict(response), "stream")
+        self.assertEqual(processor.process_response_dict(response, stream=True, enable_thinking=True), "stream")
         self.assertTrue(calls["stream"]["enable_thinking"])
         self.assertEqual(
-            processor.process_response_dict(response, stream=False, enable_thinking=None),
+            processor.process_response_dict(response, stream=False, enable_thinking=True),
             "normal",
         )
         self.assertTrue(calls["normal"]["enable_thinking"])

tests/reasoning/test_qwen3_reasoning_parser.py

@@ -0,0 +1,197 @@
+import unittest
+
+from fastdeploy.entrypoints.openai.protocol import ChatCompletionRequest
+from fastdeploy.reasoning.qwen3_reasoning_parsers import Qwen3ReasoningParser
+
+
+class MockTokenizer:
+    """Minimal tokenizer with vocab for testing."""
+
+    def __init__(self):
+        self.vocab = {
+            "<think>": 100,
+            "</think>": 101,
+        }
+
+    def get_vocab(self):
+        """Return vocab dict for testing."""
+        return self.vocab
+
+
+class MissingTokenTokenizer:
+    def __init__(self):
+        self.vocab = {
+            "</think>": 100,
+        }
+
+    def get_vocab(self):
+        """Return vocab dict for testing."""
+        return self.vocab
+
+
+class TestQwen3ReasoningParser(unittest.TestCase):
+    def setUp(self):
+        self.parser = Qwen3ReasoningParser(MockTokenizer())
+        self.request = ChatCompletionRequest(model="test", messages=[{"role": "user", "content": "test message"}])
+        self.tokenizer = MockTokenizer()
+
+    def test_missing_token(self):
+        with self.assertRaises(RuntimeError) as context:
+            Qwen3ReasoningParser(MissingTokenTokenizer())
+        exception_message = str(context.exception)
+        expected_message_part = "Qwen3 reasoning parser could not find the following token ids"
+        self.assertIn(expected_message_part, exception_message)
+
+    def test_get_model_status(self):
+        status = self.parser.get_model_status([1, 2, 100])
+        self.assertEqual(status, "think_start")
+        status = self.parser.get_model_status([1, 2, 101])
+        self.assertEqual(status, "think_end")
+        status = self.parser.get_model_status([1])
+        self.assertEqual(status, "think_start")
+
+    def test_streaming_thinking_content(self):
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="",
+            current_text="a",
+            delta_text="a",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[200],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.reasoning_content, "a")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="",
+            current_text="a</think>b",
+            delta_text="a</think>b",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[99, 101, 102],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.reasoning_content, "a")
+        self.assertEqual(msg.content, "b")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="a</think>",
+            current_text="a</think>b",
+            delta_text="b",
+            previous_token_ids=[1, 101],
+            current_token_ids=[],
+            delta_token_ids=[102],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.content, "b")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="",
+            current_text="a",
+            delta_text="a",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.reasoning_content, "a")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="",
+            current_text="a",
+            delta_text="a",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[200],
+            model_status="think_end",
+        )
+        self.assertEqual(msg.content, "a")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="hello",
+            current_text="hello</think>hi",
+            delta_text="</think>hi",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[101, 200],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.content, "hi")
+        self.assertEqual(msg.reasoning_content, "")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="hello",
+            current_text="hello</think>hi",
+            delta_text="hi",
+            previous_token_ids=[100],
+            current_token_ids=[],
+            delta_token_ids=[],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.content, None)
+        self.assertEqual(msg.reasoning_content, "hi")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="hello",
+            current_text="hello</think>hi",
+            delta_text="<think>hi",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[100, 200],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.content, "")
+        self.assertEqual(msg.reasoning_content, "hi")
+
+    def test_none_streaming_thinking_content(self):
+        reasoning_content, content = self.parser.extract_reasoning_content(
+            model_output="a",
+            request={},
+            model_status="think_start",
+        )
+        self.assertEqual(reasoning_content, None)
+        self.assertEqual(content, "a")
+
+        reasoning_content, content = self.parser.extract_reasoning_content(
+            model_output="a</think>b",
+            request={},
+            model_status="think_start",
+        )
+        self.assertEqual(reasoning_content, "a")
+        self.assertEqual(content, "b")
+
+        reasoning_content, content = self.parser.extract_reasoning_content(
+            model_output="a",
+            request={},
+            model_status="think_end",
+        )
+        self.assertEqual(reasoning_content, None)
+        self.assertEqual(content, "a")
+
+        reasoning_content, content = self.parser.extract_reasoning_content(
+            model_output="<think>a",
+            request={},
+            model_status="think_start",
+        )
+        self.assertEqual(reasoning_content, None)
+        self.assertEqual(content, "<think>a")
+
+        reasoning_content, content = self.parser.extract_reasoning_content(
+            model_output="<think>a</think>b",
+            request={},
+            model_status="think_start",
+        )
+        self.assertEqual(reasoning_content, "a")
+        self.assertEqual(content, "b")
+
+        reasoning_content, content = self.parser.extract_reasoning_content(
+            model_output="</think>b",
+            request={},
+            model_status="think_start",
+        )
+        self.assertEqual(reasoning_content, "")
+        self.assertEqual(content, "b")
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/reasoning/test_reasoning_parser.py

@@ -31,10 +31,25 @@ class DummyTokenizer:
     def __init__(self):
         self.vocab = {
             "</think>": 100,
-            "<tool_call>": 101,
-            "</tool_call>": 102,
-            "<response>": 103,
-            "</response>": 104,
+            "<think>": 101,
+            "<tool_call>": 102,
+            "</tool_call>": 103,
+            "<response>": 104,
+            "</response>": 105,
+        }
+
+    def get_vocab(self):
+        """Return vocab dict for testing."""
+        return self.vocab
+
+
+class MissingTokenTokenizer:
+    def __init__(self):
+        self.vocab = {
+            "</think>": 100,
+            "<think>": 101,
+            "<tool_call>": 102,
+            "</tool_call>": 103,
         }
 
     def get_vocab(self):
@@ -132,6 +147,17 @@ class TestErnieX1ReasoningParser(unittest.TestCase):
         self.request = ChatCompletionRequest(model="test", messages=[{"role": "user", "content": "test message"}])
         self.tokenizer = DummyTokenizer()
 
+    def test_missing_token(self):
+        with self.assertRaises(RuntimeError) as context:
+            ErnieX1ReasoningParser(MissingTokenTokenizer())
+        exception_message = str(context.exception)
+        expected_message_part = "ernie x1 reasoning parser could not find the following token ids"
+        self.assertIn(expected_message_part, exception_message)
+
+    def test_get_model_status(self):
+        model_status = self.parser.get_model_status([88, 99, 104])
+        self.assertEqual(model_status, "response_start")
+
     # ---- Streaming parsing ----
     def test_streaming_thinking_content(self):
         msg = self.parser.extract_reasoning_content_streaming(
@@ -141,6 +167,7 @@ class TestErnieX1ReasoningParser(unittest.TestCase):
             previous_token_ids=[],
             current_token_ids=[],
             delta_token_ids=[200],
+            model_status="think_start",
         )
         self.assertEqual(msg.reasoning_content, "a")
 
@@ -152,6 +179,7 @@ class TestErnieX1ReasoningParser(unittest.TestCase):
             previous_token_ids=[],
             current_token_ids=[],
             delta_token_ids=[201],
+            model_status="think_start",
         )
         self.assertEqual(msg.reasoning_content, "\n")
 
@@ -163,6 +191,7 @@ class TestErnieX1ReasoningParser(unittest.TestCase):
             previous_token_ids=[],
             current_token_ids=[],
             delta_token_ids=[self.parser.think_end_token_id],
+            model_status="think_start",
         )
         self.assertIsNone(msg)
 
@@ -174,6 +203,7 @@ class TestErnieX1ReasoningParser(unittest.TestCase):
             previous_token_ids=[],
             current_token_ids=[],
             delta_token_ids=[202],
+            model_status="think_start",
         )
         self.assertEqual(msg.content, "h")
 
@@ -185,6 +215,7 @@ class TestErnieX1ReasoningParser(unittest.TestCase):
             previous_token_ids=[],
             current_token_ids=[],
             delta_token_ids=[203],
+            model_status="think_start",
         )
         self.assertEqual(msg.content, "\n")
 
@@ -197,6 +228,7 @@ class TestErnieX1ReasoningParser(unittest.TestCase):
                 previous_token_ids=[],
                 current_token_ids=[],
                 delta_token_ids=[self.parser.vocab["<response>"]],
+                model_status="think_start",
             )
         )
 
@@ -207,6 +239,7 @@ class TestErnieX1ReasoningParser(unittest.TestCase):
             previous_token_ids=[],
             current_token_ids=[],
             delta_token_ids=[204],
+            model_status="think_start",
         )
         self.assertIsInstance(msg, DeltaMessage)
         self.assertEqual(msg.content, "\n")
@@ -219,9 +252,82 @@ class TestErnieX1ReasoningParser(unittest.TestCase):
                 previous_token_ids=[],
                 current_token_ids=[],
                 delta_token_ids=[self.parser.vocab["</response>"]],
+                model_status="think_start",
             )
         )
 
+    def test_extract_reasoning_content_streaming(self):
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="hello</think>",
+            current_text="hello</think><response>",
+            delta_text="</think><response>",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[100, 200],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.content, "")
+        self.assertEqual(msg.reasoning_content, "")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="hello</think>",
+            current_text="hello</think><response>hi",
+            delta_text="</think><response>hi",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[100, 200],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.content, "hi")
+        self.assertEqual(msg.reasoning_content, "")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="",
+            current_text="hello</think><response>hi",
+            delta_text="hello</think><response>hi",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[100, 200],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.content, "hi")
+        self.assertEqual(msg.reasoning_content, "hello")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="hello</think><response>",
+            current_text="hello</think><response>hi",
+            delta_text="hi",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[100, 200],
+            model_status="think_end",
+        )
+        self.assertEqual(msg.content, "hi")
+        self.assertEqual(msg.reasoning_content, None)
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="hello</think><response>",
+            current_text="hello</think><response>hi",
+            delta_text="hi",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[100, 200],
+            model_status="response_start",
+        )
+        self.assertEqual(msg.content, "hi")
+        self.assertEqual(msg.reasoning_content, None)
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="hello</think><response>hi</response>",
+            current_text="hello</think><response>hi</response>end",
+            delta_text="end",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[100, 200],
+            model_status="response_start",
+        )
+        self.assertEqual(msg, None)
+
     def test_streaming_tool_call(self):
         msg = self.parser.extract_reasoning_content_streaming(
             previous_text="</think>",
@@ -230,40 +336,48 @@ class TestErnieX1ReasoningParser(unittest.TestCase):
             previous_token_ids=[],
             current_token_ids=[],
             delta_token_ids=[self.parser.vocab["<tool_call>"]],
+            model_status="think_start",
         )
         self.assertIsNone(msg)
 
     # ---- Batch parsing ----
     def test_batch_reasoning_and_response(self):
         text = "abc\n</think>\n<response>hello\nworld</response>"
-        reasoning, response = self.parser.extract_reasoning_content(text, self.request)
+        reasoning, response = self.parser.extract_reasoning_content(text, self.request, "think_start")
         self.assertEqual(reasoning, "abc\n")
         self.assertEqual(response, "hello\nworld")
 
     def test_batch_reasoning_and_tool_call(self):
         text = "abc</think><tool_call>call_here"
-        reasoning, response = self.parser.extract_reasoning_content(text, self.request)
+        reasoning, response = self.parser.extract_reasoning_content(text, self.request, "think_start")
         self.assertEqual(reasoning, "abc")
         self.assertEqual(response, "")
 
     def test_batch_no_thinking_tag(self):
         text = "no_thinking_here"
-        reasoning, response = self.parser.extract_reasoning_content(text, self.request)
+        reasoning, response = self.parser.extract_reasoning_content(text, self.request, "think_start")
         self.assertEqual(reasoning, "no_thinking_here")
         self.assertEqual(response, "")
 
     def test_batch_response_without_end_tag(self):
         text = "abc</think><response>partial response"
-        reasoning, response = self.parser.extract_reasoning_content(text, self.request)
+        reasoning, response = self.parser.extract_reasoning_content(text, self.request, "think_start")
         self.assertEqual(reasoning, "abc")
         self.assertEqual(response, "partial response")
 
     def test_batch_preserve_all_newlines(self):
         text = "abc\n</think>\n<response>line1\nline2\n</response>"
-        reasoning, response = self.parser.extract_reasoning_content(text, self.request)
+        reasoning, response = self.parser.extract_reasoning_content(text, self.request, "think_start")
         self.assertEqual(reasoning, "abc\n")
         self.assertEqual(response, "line1\nline2\n")
 
+    def test_extract_reasoning_content(self):
+        reasoning_content, response_content = self.parser.extract_reasoning_content(
+            model_output="hello", request=self.request, model_status="response_start"
+        )
+        self.assertEqual(reasoning_content, "")
+        self.assertEqual(response_content, "hello")
+
 
 class TestErnie45VLThinkingReasoningParser(unittest.TestCase):
     def setUp(self):
@@ -272,6 +386,9 @@ class TestErnie45VLThinkingReasoningParser(unittest.TestCase):
         self.test_request = ChatCompletionRequest(
             model="ernie-test", messages=[{"role": "user", "content": "test prompt"}]
         )
+        self.parser.token_status_mapping = {
+            100: "think_start",
+        }
 
     def test_streaming_non_reasoning(self):
         result = self.parser.extract_reasoning_content_streaming(
@@ -281,6 +398,7 @@ class TestErnie45VLThinkingReasoningParser(unittest.TestCase):
             previous_token_ids=[],
             current_token_ids=[200],
             delta_token_ids=[200],
+            model_status="think_start",
         )
         self.assertIsInstance(result, DeltaMessage)
         self.assertEqual(result.reasoning_content, "a")
@@ -294,6 +412,7 @@ class TestErnie45VLThinkingReasoningParser(unittest.TestCase):
             previous_token_ids=[200, 201],
             current_token_ids=[200, 201, 100],
             delta_token_ids=[100],
+            model_status="think_start",
         )
         self.assertIsNone(result)
 
@@ -305,6 +424,7 @@ class TestErnie45VLThinkingReasoningParser(unittest.TestCase):
             previous_token_ids=[200, 201],
             current_token_ids=[200, 201, 100, 300, 400],
             delta_token_ids=[100, 300, 400],
+            model_status="think_start",
         )
         self.assertIsInstance(result, DeltaMessage)
         self.assertIsNone(result.reasoning_content)
@@ -318,6 +438,7 @@ class TestErnie45VLThinkingReasoningParser(unittest.TestCase):
             previous_token_ids=[200, 201, 202],
             current_token_ids=[200, 201, 202, 100],
             delta_token_ids=[100],
+            model_status="think_start",
         )
         self.assertIsNone(result)
 
@@ -329,9 +450,10 @@ class TestErnie45VLThinkingReasoningParser(unittest.TestCase):
             previous_token_ids=[200, 201, 202],
             current_token_ids=[200, 201, 202, 100, 200, 101],
             delta_token_ids=[100, 200, 101],
+            model_status="think_start",
         )
         self.assertIsInstance(result, DeltaMessage)
-        self.assertEqual(result.reasoning_content, "")
+        self.assertEqual(result.reasoning_content, None)
 
     def test_streaming_with_reasoning_and_illegal_tool(self):
         result = self.parser.extract_reasoning_content_streaming(
@@ -341,6 +463,7 @@ class TestErnie45VLThinkingReasoningParser(unittest.TestCase):
             previous_token_ids=[200, 201, 202],
             current_token_ids=[200, 201, 202, 100, 200, 101],
             delta_token_ids=[109, 200, 101],
+            model_status="think_start",
         )
         self.assertIsInstance(result, DeltaMessage)
         self.assertEqual(result.content, "\n\nhello<tool_call>")
@@ -353,6 +476,7 @@ class TestErnie45VLThinkingReasoningParser(unittest.TestCase):
             previous_token_ids=[200, 201, 202],
             current_token_ids=[200, 201, 202, 100, 200, 110],
             delta_token_ids=[100, 200, 110],
+            model_status="think_start",
         )
         self.assertIsInstance(result, DeltaMessage)
         self.assertEqual(result.reasoning_content, "hello")
@@ -366,6 +490,7 @@ class TestErnie45VLThinkingReasoningParser(unittest.TestCase):
             previous_token_ids=[100],
             current_token_ids=[100, 110, 111],
             delta_token_ids=[110, 111],
+            model_status="think_start",
         )
         self.assertIsInstance(result, DeltaMessage)
         self.assertIsNone(result.reasoning_content)
@@ -379,52 +504,140 @@ class TestErnie45VLThinkingReasoningParser(unittest.TestCase):
             previous_token_ids=[101],
             current_token_ids=[101, 110],
             delta_token_ids=[110],
+            model_status="think_start",
         )
         self.assertIsInstance(result, DeltaMessage)
         self.assertEqual(result.reasoning_content, "hello")
 
+    def test_think_end_status_streaming(self):
+        result = self.parser.extract_reasoning_content_streaming(
+            previous_text="<tool_call>",
+            current_text="<tool_call>hello",
+            delta_text="hello",
+            previous_token_ids=[101],
+            current_token_ids=[101, 110],
+            delta_token_ids=[110],
+            model_status="think_end",
+        )
+        self.assertIs(result, None)
+
+        result = self.parser.extract_reasoning_content_streaming(
+            previous_text="hello, ",
+            current_text="hello, hi",
+            delta_text="hi",
+            previous_token_ids=[101],
+            current_token_ids=[101, 110],
+            delta_token_ids=[110],
+            model_status="think_end",
+        )
+        self.assertIsInstance(result, DeltaMessage)
+        self.assertEqual(result.content, "hi")
+
+    def test_other_status_streaming(self):
+        result = self.parser.extract_reasoning_content_streaming(
+            previous_text="hello, ",
+            current_text="hello, hi",
+            delta_text="hi",
+            previous_token_ids=[101],
+            current_token_ids=[101, 110],
+            delta_token_ids=[110],
+            model_status="tool_call_start",
+        )
+        self.assertIs(result, None)
+
     def test_batch_no_think_end(self):
         reasoning, content = self.parser.extract_reasoning_content(
-            model_output="direct response", request=self.test_request
+            model_output="direct response", request=self.test_request, model_status="think_start"
         )
         self.assertEqual(reasoning, "direct response")
         self.assertEqual(content, "")
 
     def test_batch_no_think_end_with_tool(self):
         reasoning, content = self.parser.extract_reasoning_content(
-            model_output="direct response<tool_call>abc", request=self.test_request
+            model_output="direct response<tool_call>abc", request=self.test_request, model_status="think_start"
         )
         self.assertEqual(reasoning, "direct response<tool_call>abc")
         self.assertEqual(content, "")
 
     def test_batch_think_end_normal_content(self):
         reasoning, content = self.parser.extract_reasoning_content(
-            model_output="reasoning</think>\nresponse", request=self.test_request
+            model_output="reasoning</think>\nresponse", request=self.test_request, model_status="think_start"
         )
         self.assertEqual(reasoning, "reasoning")
         self.assertEqual(content, "\nresponse")
 
     def test_batch_think_end_with_tool(self):
         reasoning, content = self.parser.extract_reasoning_content(
-            model_output="reasoning</think>\n<tool_call>tool params</tool_call>", request=self.test_request
+            model_output="reasoning</think>\n<tool_call>tool params</tool_call>",
+            request=self.test_request,
+            model_status="think_start",
         )
         self.assertEqual(reasoning, "reasoning")
         self.assertEqual(content, "")
 
     def test_batch_think_end_with_illegal_tool(self):
         reasoning, content = self.parser.extract_reasoning_content(
-            model_output="reasoning</think>\nABC\n<tool_call>tool params</tool_call>", request=self.test_request
+            model_output="reasoning</think>\nABC\n<tool_call>tool params</tool_call>",
+            request=self.test_request,
+            model_status="think_start",
         )
         self.assertEqual(reasoning, "reasoning")
         self.assertEqual(content, "\nABC\n<tool_call>tool params</tool_call>")
 
     def test_batch_think_end_content_with_newline(self):
         reasoning, content = self.parser.extract_reasoning_content(
-            model_output="reasoning</think>\n\n  actual response", request=self.test_request
+            model_output="reasoning</think>\n\n  actual response",
+            request=self.test_request,
+            model_status="think_start",
         )
         self.assertEqual(reasoning, "reasoning")
         self.assertEqual(content, "\n\n  actual response")
 
+    def test_think_end_status_non_streaming(self):
+        reasoning, content = self.parser.extract_reasoning_content(
+            model_output="response", request=self.test_request, model_status="think_end"
+        )
+        self.assertEqual(reasoning, "")
+        self.assertEqual(content, "response")
+
+        reasoning, content = self.parser.extract_reasoning_content(
+            model_output="<tool_call>response", request=self.test_request, model_status="think_end"
+        )
+        self.assertEqual(reasoning, "")
+        self.assertEqual(content, "")
+
+        reasoning, content = self.parser.extract_reasoning_content(
+            model_output="\n 1<tool_call>response", request=self.test_request, model_status="think_end"
+        )
+        self.assertEqual(reasoning, "")
+        self.assertEqual(content, "\n 1<tool_call>response")
+
+    def test_other_status_non_streaming(self):
+        reasoning, content = self.parser.extract_reasoning_content(
+            model_output="response", request=self.test_request, model_status="tool_call_start"
+        )
+        self.assertEqual(reasoning, "")
+        self.assertEqual(content, "")
+
+        reasoning, content = self.parser.extract_reasoning_content(
+            model_output="response", request=self.test_request, model_status="tool_call_end"
+        )
+        self.assertEqual(reasoning, "")
+        self.assertEqual(content, "")
+
+    def test_find_last_special_token(self):
+        result = self.parser.find_last_special_token([100, 110, 120, 130])
+        self.assertEqual(result, 100)
+        result = self.parser.find_last_special_token([0])
+        self.assertEqual(result, -1)
+
+    def test_get_model_status(self):
+        result = self.parser.get_model_status([100, 110, 120, 130])
+        self.assertEqual(result, "think_start")
+
+        result = self.parser.get_model_status([0])
+        self.assertEqual(result, "think_start")
+
 
 class TestErnieVLReasoningParser(unittest.TestCase):
     def setUp(self):
@@ -442,6 +655,7 @@ class TestErnieVLReasoningParser(unittest.TestCase):
             previous_token_ids=[200, 201, 202],
             current_token_ids=[200, 201, 202, 100, 110, 120, 130],
             delta_token_ids=[100, 110, 120, 130],
+            model_status="think_start",
         )
         self.assertIsInstance(result, DeltaMessage)
         self.assertEqual(result.reasoning_content, "")
@@ -455,6 +669,7 @@ class TestErnieVLReasoningParser(unittest.TestCase):
             previous_token_ids=[200, 201, 202, 100],
             current_token_ids=[200, 201, 202, 100, 110, 120, 130],
             delta_token_ids=[110, 120, 130],
+            model_status="think_start",
         )
         self.assertIsInstance(result, DeltaMessage)
         self.assertIsNone(result.reasoning_content)
@@ -468,6 +683,7 @@ class TestErnieVLReasoningParser(unittest.TestCase):
             previous_token_ids=[200, 201, 202],
             current_token_ids=[200, 201, 202, 110, 120, 130],
             delta_token_ids=[110, 120, 130],
+            model_status="think_start",
         )
         self.assertIsInstance(result, DeltaMessage)
         self.assertIsNone(result.content)
@@ -475,7 +691,7 @@ class TestErnieVLReasoningParser(unittest.TestCase):
 
     def test_extract_reasoning_content(self):
         reasoning, content = self.parser.extract_reasoning_content(
-            model_output="reasoning</think>\nactual response", request=self.test_request
+            model_output="reasoning</think>\nactual response", request=self.test_request, model_status="think_start"
         )
         self.assertEqual(reasoning, "reasoning")
         self.assertEqual(content, "\nactual response")

tests/reasoning/test_vl_reasoning_parser.py

@@ -0,0 +1,135 @@
+"""
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+import unittest
+
+from fastdeploy.entrypoints.openai.protocol import ChatCompletionRequest
+from fastdeploy.reasoning.ernie_vl_reasoning_parsers import ErnieVLReasoningParser
+
+
+class MockTokenizer:
+    """Minimal tokenizer with vocab for testing."""
+
+    def __init__(self):
+        self.vocab = {
+            "<think>": 100,
+            "</think>": 101,
+        }
+
+    def get_vocab(self):
+        """Return vocab dict for testing."""
+        return self.vocab
+
+
+class TestErnieVLReasoningParser(unittest.TestCase):
+    def setUp(self):
+        self.parser = ErnieVLReasoningParser(MockTokenizer())
+        self.request = ChatCompletionRequest(model="test", messages=[{"role": "user", "content": "test message"}])
+        self.tokenizer = MockTokenizer()
+
+    def test_get_model_status(self):
+        status = self.parser.get_model_status([1, 2, 100])
+        self.assertEqual(status, "think_start")
+        status = self.parser.get_model_status([1, 2, 101])
+        self.assertEqual(status, "think_end")
+        status = self.parser.get_model_status([1])
+        self.assertEqual(status, "think_start")
+
+    def test_streaming_thinking_content(self):
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="",
+            current_text="a",
+            delta_text="a",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[200],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.reasoning_content, "a")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="",
+            current_text="a</think>b",
+            delta_text="a</think>b",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[100, 101, 102],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.reasoning_content, "a")
+        self.assertEqual(msg.content, "b")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="a</think>",
+            current_text="a</think>b",
+            delta_text="b",
+            previous_token_ids=[1, 101],
+            current_token_ids=[],
+            delta_token_ids=[102],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.content, "b")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="",
+            current_text="a",
+            delta_text="a",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[],
+            model_status="think_start",
+        )
+        self.assertEqual(msg.reasoning_content, "a")
+
+        msg = self.parser.extract_reasoning_content_streaming(
+            previous_text="",
+            current_text="a",
+            delta_text="a",
+            previous_token_ids=[],
+            current_token_ids=[],
+            delta_token_ids=[200],
+            model_status="think_end",
+        )
+        self.assertEqual(msg.content, "a")
+
+    def test_none_streaming_thinking_content(self):
+        reasoning_content, content = self.parser.extract_reasoning_content(
+            model_output="a",
+            request={},
+            model_status="think_start",
+        )
+        self.assertEqual(reasoning_content, "")
+        self.assertEqual(content, "a")
+
+        reasoning_content, content = self.parser.extract_reasoning_content(
+            model_output="a</think>b",
+            request={},
+            model_status="think_start",
+        )
+        self.assertEqual(reasoning_content, "a")
+        self.assertEqual(content, "b")
+
+        reasoning_content, content = self.parser.extract_reasoning_content(
+            model_output="a",
+            request={},
+            model_status="think_end",
+        )
+        self.assertEqual(reasoning_content, "")
+        self.assertEqual(content, "a")
+
+
+if __name__ == "__main__":
+    unittest.main()