ruff reformatted
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春河晴
@@ -179,7 +179,6 @@ class LLM_request:
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# logger.debug(f"{logger_msg}发送请求到URL: {api_url}")
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# logger.info(f"使用模型: {self.model_name}")
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# 构建请求体
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if image_base64:
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payload = await self._build_payload(prompt, image_base64, image_format)
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@@ -205,13 +204,17 @@ class LLM_request:
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# 处理需要重试的状态码
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if response.status in policy["retry_codes"]:
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wait_time = policy["base_wait"] * (2**retry)
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logger.warning(f"模型 {self.model_name} 错误码: {response.status}, 等待 {wait_time}秒后重试")
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logger.warning(
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f"模型 {self.model_name} 错误码: {response.status}, 等待 {wait_time}秒后重试"
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)
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if response.status == 413:
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logger.warning("请求体过大，尝试压缩...")
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image_base64 = compress_base64_image_by_scale(image_base64)
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payload = await self._build_payload(prompt, image_base64, image_format)
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elif response.status in [500, 503]:
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logger.error(f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}")
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logger.error(
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f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
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)
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raise RuntimeError("服务器负载过高，模型恢复失败QAQ")
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else:
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logger.warning(f"模型 {self.model_name} 请求限制(429)，等待{wait_time}秒后重试...")
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@@ -219,7 +222,9 @@ class LLM_request:
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await asyncio.sleep(wait_time)
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continue
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elif response.status in policy["abort_codes"]:
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logger.error(f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}")
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logger.error(
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f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
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)
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# 尝试获取并记录服务器返回的详细错误信息
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try:
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error_json = await response.json()
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@@ -257,7 +262,9 @@ class LLM_request:
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):
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old_model_name = self.model_name
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self.model_name = self.model_name[4:] # 移除"Pro/"前缀
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logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
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logger.warning(
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f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}"
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)
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# 对全局配置进行更新
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if global_config.llm_normal.get("name") == old_model_name:
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@@ -266,7 +273,9 @@ class LLM_request:
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if global_config.llm_reasoning.get("name") == old_model_name:
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global_config.llm_reasoning["name"] = self.model_name
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logger.warning(f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}")
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logger.warning(
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f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}"
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)
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# 更新payload中的模型名
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if payload and "model" in payload:
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@@ -328,7 +337,14 @@ class LLM_request:
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await response.release()
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# 返回已经累积的内容
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result = {
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"choices": [{"message": {"content": accumulated_content, "reasoning_content": reasoning_content}}],
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"choices": [
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{
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"message": {
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"content": accumulated_content,
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"reasoning_content": reasoning_content,
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}
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}
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],
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"usage": usage,
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}
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return (
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@@ -345,7 +361,14 @@ class LLM_request:
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logger.error(f"清理资源时发生错误: {cleanup_error}")
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# 返回已经累积的内容
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result = {
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"choices": [{"message": {"content": accumulated_content, "reasoning_content": reasoning_content}}],
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"choices": [
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{
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"message": {
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"content": accumulated_content,
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"reasoning_content": reasoning_content,
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}
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}
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],
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"usage": usage,
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}
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return (
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@@ -360,7 +383,9 @@ class LLM_request:
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content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL).strip()
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# 构造一个伪result以便调用自定义响应处理器或默认处理器
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result = {
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"choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}],
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"choices": [
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{"message": {"content": content, "reasoning_content": reasoning_content}}
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],
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"usage": usage,
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}
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return (
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@@ -394,7 +419,9 @@ class LLM_request:
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# 处理aiohttp抛出的响应错误
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if retry < policy["max_retries"] - 1:
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wait_time = policy["base_wait"] * (2**retry)
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logger.error(f"模型 {self.model_name} HTTP响应错误，等待{wait_time}秒后重试... 状态码: {e.status}, 错误: {e.message}")
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logger.error(
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f"模型 {self.model_name} HTTP响应错误，等待{wait_time}秒后重试... 状态码: {e.status}, 错误: {e.message}"
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)
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try:
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if hasattr(e, "response") and e.response and hasattr(e.response, "text"):
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error_text = await e.response.text()
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@@ -419,13 +446,17 @@ class LLM_request:
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else:
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logger.error(f"模型 {self.model_name} 服务器错误响应: {error_json}")
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except (json.JSONDecodeError, TypeError) as json_err:
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logger.warning(f"模型 {self.model_name} 响应不是有效的JSON: {str(json_err)}, 原始内容: {error_text[:200]}")
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logger.warning(
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f"模型 {self.model_name} 响应不是有效的JSON: {str(json_err)}, 原始内容: {error_text[:200]}"
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)
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except (AttributeError, TypeError, ValueError) as parse_err:
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logger.warning(f"模型 {self.model_name} 无法解析响应错误内容: {str(parse_err)}")
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await asyncio.sleep(wait_time)
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else:
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logger.critical(f"模型 {self.model_name} HTTP响应错误达到最大重试次数: 状态码: {e.status}, 错误: {e.message}")
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logger.critical(
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f"模型 {self.model_name} HTTP响应错误达到最大重试次数: 状态码: {e.status}, 错误: {e.message}"
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)
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# 安全地检查和记录请求详情
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if (
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image_base64
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