From 50ce8cf521b648beec6d9de2ac35f706d8e59e22 Mon Sep 17 00:00:00 2001 From: KawaiiYusora Date: Fri, 14 Mar 2025 00:09:07 +0800 Subject: [PATCH] =?UTF-8?q?fix:=20=E4=BC=98=E5=8C=96=E5=8F=91=E9=80=81?= =?UTF-8?q?=E9=94=99=E8=AF=AF=E6=97=B6=E5=9B=BE=E7=89=87=E5=A4=A7=E5=96=B7?= =?UTF-8?q?=E5=B0=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/plugins/models/utils_model.py | 55 +++++++++++++++++-------------- 1 file changed, 30 insertions(+), 25 deletions(-) diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py index 0f5bb335c..49c335eb2 100644 --- a/src/plugins/models/utils_model.py +++ b/src/plugins/models/utils_model.py @@ -185,9 +185,9 @@ class LLM_request: elif response.status in policy["abort_codes"]: logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}") if response.status == 403: - #只针对硅基流动的V3和R1进行降级处理 - if self.model_name.startswith( - "Pro/deepseek-ai") and self.base_url == "https://api.siliconflow.cn/v1/": + # 只针对硅基流动的V3和R1进行降级处理 + if self.model_name.startswith( + "Pro/deepseek-ai") and self.base_url == "https://api.siliconflow.cn/v1/": old_model_name = self.model_name self.model_name = self.model_name[4:] # 移除"Pro/"前缀 logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}") @@ -228,7 +228,7 @@ class LLM_request: try: chunk = json.loads(data_str) if flag_delta_content_finished: - usage = chunk.get("usage", None) # 获取tokn用量 + usage = chunk.get("usage", None) # 获取tokn用量 else: delta = chunk["choices"][0]["delta"] delta_content = delta.get("content") @@ -236,14 +236,14 @@ class LLM_request: delta_content = "" accumulated_content += delta_content # 检测流式输出文本是否结束 - finish_reason = chunk["choices"][0].get("finish_reason") + finish_reason = chunk["choices"][0].get("finish_reason") if finish_reason == "stop": usage = chunk.get("usage", None) if usage: break # 部分平台在文本输出结束前不会返回token用量，此时需要再获取一次chunk flag_delta_content_finished = True - + except Exception: logger.exception("解析流式输出错误") content = accumulated_content @@ -254,7 +254,8 @@ class LLM_request: content = re.sub(r'.*?', '', content, flags=re.DOTALL).strip() # 构造一个伪result以便调用自定义响应处理器或默认处理器 result = { - "choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}], "usage": usage} + "choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}], + "usage": usage} return response_handler(result) if response_handler else self._default_response_handler( result, user_id, request_type, endpoint) else: @@ -270,6 +271,9 @@ class LLM_request: await asyncio.sleep(wait_time) else: logger.critical(f"请求失败: {str(e)}") + if image_base64: + payload["messages"][0]["content"][1]["image_url"][ + "url"] = f"data:image/{image_format.lower()};base64,{image_base64[:10]}...{image_base64[-10:]}" logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {payload}") raise RuntimeError(f"API请求失败: {str(e)}") @@ -307,7 +311,8 @@ class LLM_request: "role": "user", "content": [ {"type": "text", "text": prompt}, - {"type": "image_url", "image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"}} + {"type": "image_url", + "image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"}} ] } ], @@ -452,6 +457,7 @@ class LLM_request: ) return embedding + def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str: """压缩base64格式的图片到指定大小 Args: @@ -463,36 +469,36 @@ def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 10 try: # 将base64转换为字节数据 image_data = base64.b64decode(base64_data) - + # 如果已经小于目标大小，直接返回原图 - if len(image_data) <= 2*1024*1024: + if len(image_data) <= 2 * 1024 * 1024: return base64_data - + # 将字节数据转换为图片对象 img = Image.open(io.BytesIO(image_data)) - + # 获取原始尺寸 original_width, original_height = img.size - + # 计算缩放比例 scale = min(1.0, (target_size / len(image_data)) ** 0.5) - + # 计算新的尺寸 new_width = int(original_width * scale) new_height = int(original_height * scale) - + # 创建内存缓冲区 output_buffer = io.BytesIO() - + # 如果是GIF，处理所有帧 if getattr(img, "is_animated", False): frames = [] for frame_idx in range(img.n_frames): img.seek(frame_idx) new_frame = img.copy() - new_frame = new_frame.resize((new_width//2, new_height//2), Image.Resampling.LANCZOS) # 动图折上折 + new_frame = new_frame.resize((new_width // 2, new_height // 2), Image.Resampling.LANCZOS) # 动图折上折 frames.append(new_frame) - + # 保存到缓冲区 frames[0].save( output_buffer, @@ -506,23 +512,22 @@ def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 10 else: # 处理静态图片 resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) - + # 保存到缓冲区，保持原始格式 if img.format == 'PNG' and img.mode in ('RGBA', 'LA'): resized_img.save(output_buffer, format='PNG', optimize=True) else: resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True) - + # 获取压缩后的数据并转换为base64 compressed_data = output_buffer.getvalue() logger.success(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}") - logger.info(f"压缩前大小: {len(image_data)/1024:.1f}KB, 压缩后大小: {len(compressed_data)/1024:.1f}KB") - + logger.info(f"压缩前大小: {len(image_data) / 1024:.1f}KB, 压缩后大小: {len(compressed_data) / 1024:.1f}KB") + return base64.b64encode(compressed_data).decode('utf-8') - + except Exception as e: logger.error(f"压缩图片失败: {str(e)}") import traceback logger.error(traceback.format_exc()) - return base64_data - + return base64_data