From e9bd3196ba1f6f393d6eb97ebd6d6df5c27cb388 Mon Sep 17 00:00:00 2001 From: UnCLAS-Prommer Date: Thu, 20 Mar 2025 16:47:50 +0800 Subject: [PATCH] =?UTF-8?q?=E6=AD=A3=E7=A1=AE=E4=BF=9D=E5=AD=98=E6=A8=A1?= =?UTF-8?q?=E5=9E=8B=E5=90=8D=E7=A7=B0=E5=88=B0Database?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/plugins/chat/llm_generator.py | 5 +++-- src/plugins/models/utils_model.py | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py index bcd0b9e87..73cd12ed7 100644 --- a/src/plugins/chat/llm_generator.py +++ b/src/plugins/chat/llm_generator.py @@ -37,6 +37,7 @@ class ResponseGenerator: self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7, max_tokens=3000) self.model_v25 = LLM_request(model=global_config.llm_normal_minor, temperature=0.7, max_tokens=3000) self.current_model_type = "r1" # 默认使用 R1 + self.current_model_name = "unknown model" async def generate_response(self, message: MessageThinking) -> Optional[Union[str, List[str]]]: """根据当前模型类型选择对应的生成函数""" @@ -107,7 +108,7 @@ class ResponseGenerator: # 生成回复 try: - content, reasoning_content = await model.generate_response(prompt) + content, reasoning_content, self.current_model_name = await model.generate_response(prompt) except Exception: logger.exception("生成回复时出错") return None @@ -144,7 +145,7 @@ class ResponseGenerator: "chat_id": message.chat_stream.stream_id, "user": sender_name, "message": message.processed_plain_text, - "model": self.current_model_type, + "model": self.current_model_name, # 'reasoning_check': reasoning_content_check, # 'response_check': content_check, "reasoning": reasoning_content, diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py index d915b3759..ba85a1bd2 100644 --- a/src/plugins/models/utils_model.py +++ b/src/plugins/models/utils_model.py @@ -526,7 +526,7 @@ class LLM_request: """根据输入的提示生成模型的异步响应""" content, reasoning_content = await self._execute_request(endpoint="/chat/completions", prompt=prompt) - return content, reasoning_content + return content, reasoning_content, self.model_name async def generate_response_for_image(self, prompt: str, image_base64: str, image_format: str) -> Tuple[str, str]: """根据输入的提示和图片生成模型的异步响应"""