fix: 이미지 태그 EASONING_EFFORT = "low" 지정

app/utils/autotag.py

@@ -6,6 +6,10 @@ from app.utils.prompts.schemas import SpaceType, Subject, Camera, MotionRecommen
 
 import asyncio
 
+# medium 추론은 출력 비용의 80%를 차지하고, minimal은 A/B 비교(4회)에서 narrative 점수가
+# welcome 단계로 편향되고 태그를 과다 선택하는 패턴이 반복돼 low로 고정한다.
+IMAGE_TAG_REASONING_EFFORT = "low"
+
 async def autotag_image(image_url : str, industry: str = "") -> list[str]: #tag_list
     chatgpt = ChatgptService(model_type="gpt")
     image_input_data = {
@@ -17,7 +21,7 @@ async def autotag_image(image_url : str, industry: str = "") -> list[str]: #tag_
         "motion_recommended" : list(MotionRecommended)
     }
 
-    image_result = await chatgpt.generate_structured_output(image_autotag_prompt, image_input_data, image_url, False)   
+    image_result = await chatgpt.generate_structured_output(image_autotag_prompt, image_input_data, image_url, True, reasoning_effort=IMAGE_TAG_REASONING_EFFORT)
     return image_result
 
 async def autotag_images(image_url_list : list[str], industry: str = "") -> list[dict]: #tag_list
@@ -31,7 +35,7 @@ async def autotag_images(image_url_list : list[str], industry: str = "") -> list
         "motion_recommended" : list(MotionRecommended)
     }for image_url in image_url_list]
     
-    image_result_tasks = [chatgpt.generate_structured_output(image_autotag_prompt, image_input_data, image_input_data['img_url'], False, silent = True) for image_input_data in image_input_data_list] 
+    image_result_tasks = [chatgpt.generate_structured_output(image_autotag_prompt, image_input_data, image_input_data['img_url'], True, silent = True, reasoning_effort=IMAGE_TAG_REASONING_EFFORT) for image_input_data in image_input_data_list]
     image_result_list: list[BaseModel | BaseException] = await asyncio.gather(*image_result_tasks, return_exceptions=True)
     MAX_RETRY = 2
     for _ in range(MAX_RETRY):
@@ -40,7 +44,7 @@ async def autotag_images(image_url_list : list[str], industry: str = "") -> list
         if not failed_idx:
             break
         retried = await asyncio.gather(
-            *[chatgpt.generate_structured_output(image_autotag_prompt, image_input_data_list[i], image_input_data_list[i]['img_url'], False, silent=True) for i in failed_idx],
+            *[chatgpt.generate_structured_output(image_autotag_prompt, image_input_data_list[i], image_input_data_list[i]['img_url'], True, silent=True, reasoning_effort=IMAGE_TAG_REASONING_EFFORT) for i in failed_idx],
             return_exceptions=True
         )
         for i, result in zip(failed_idx, retried):

app/utils/prompts/chatgpt_prompt.py

@@ -47,6 +47,19 @@ class ChatgptService:
             case _:
                 raise NotImplementedError(f"Unknown Provider : {model_type}")
 
+    def _log_usage(self, response, model: str, output_format: type[BaseModel]) -> None:
+        usage = getattr(response, "usage", None)
+        if usage is None:
+            return
+        # 토큰 소모량 로깅 (필요 시 주석 해제)
+        # cached = getattr(getattr(usage, "prompt_tokens_details", None), "cached_tokens", None) or 0
+        # reasoning = getattr(getattr(usage, "completion_tokens_details", None), "reasoning_tokens", None) or 0
+        # logger.info(
+        #     f"[ChatgptService({self.model_type})] usage model={model} output={output_format.__name__} "
+        #     f"prompt={usage.prompt_tokens} cached={cached} "
+        #     f"completion={usage.completion_tokens} reasoning={reasoning} total={usage.total_tokens}"
+        # )
+
     async def _call_pydantic_output(
             self, 
             prompt : str, 
@@ -115,7 +128,8 @@ class ChatgptService:
             output_format : BaseModel,  #입력 output_format의 경우 Pydantic BaseModel Class를 상속한 Class 자체임에 유의할 것
             model : str,
             img_url : str,
-            image_detail_high : bool) -> BaseModel: 
+            image_detail_high : bool,
+            reasoning_effort : Optional[str] = None) -> BaseModel:
         content = []
         if img_url:
             content.append({
@@ -129,13 +143,16 @@ class ChatgptService:
             "type": "text",
             "text": prompt
         })
+        # gpt-5.4 계열/Gemini 호환 엔드포인트는 허용 값이 다르거나 파라미터를 거부하므로 지정된 경우에만 전달
+        extra_kwargs = {"reasoning_effort": reasoning_effort} if reasoning_effort else {}
         last_error = None
         for attempt in range(self.max_retries + 1):
             try:
                 response = await self.client.beta.chat.completions.parse(
                     model=model,
                     messages=[{"role": "user", "content": content}],
-                    response_format=output_format
+                    response_format=output_format,
+                    **extra_kwargs,
                 )
             except (ValidationError, json.JSONDecodeError) as e:
                 # 모델이 스키마에 맞지 않는 JSON을 반환한 경우 (예: trailing characters).
@@ -148,6 +165,7 @@ class ChatgptService:
                 if attempt < self.max_retries:
                     logger.info(f"[ChatgptService({self.model_type})] Retrying request...")
                 continue
+            self._log_usage(response, model, output_format)
             # Response 디버그 로깅
             # logger.debug(f"[ChatgptService({self.model_type})] attempt: {attempt}")
             # logger.debug(f"[ChatgptService({self.model_type})] Response ID: {response.id}")
@@ -225,6 +243,7 @@ class ChatgptService:
                     continue
                 raise last_error
 
+            self._log_usage(response, model, output_format)
             choice = response.choices[0]
             if choice.finish_reason == "stop":
                 return choice.message.parsed
@@ -242,7 +261,8 @@ class ChatgptService:
         input_data : dict,
         img_url : Optional[str] = None,
         img_detail_high : bool = False,
-        silent : bool = True
+        silent : bool = True,
+        reasoning_effort : Optional[str] = None,
     ) -> BaseModel:
         prompt_text = prompt.build_prompt(input_data, silent)
     
@@ -253,5 +273,5 @@ class ChatgptService:
         # GPT API 호출
         #parsed = await self._call_structured_output_with_response_gpt_api(prompt_text, prompt.prompt_output, prompt.prompt_model) 
         # parsed = await self._call_pydantic_output(prompt_text, prompt.prompt_output_class, prompt.prompt_model, img_url, img_detail_high) 
-        parsed = await self._call_pydantic_output_chat_completion(prompt_text, prompt.prompt_output_class, prompt.prompt_model, img_url, img_detail_high) 
+        parsed = await self._call_pydantic_output_chat_completion(prompt_text, prompt.prompt_output_class, prompt.prompt_model, img_url, img_detail_high, reasoning_effort)
         return parsed