mirror of https://github.com/Mai-with-u/MaiBot.git
fix: 清理无用异常，现用 RespNotOkException 加上状态码，且将 429 和 5xx 错误的处理逻辑从“硬失败”移回“可重试”
google-labs-jules[bot]
Ronifue
parent
3900684ffd
commit
01b06ed302
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@ -65,39 +65,6 @@ class RespParseException(Exception):
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return self.message or "解析响应内容时发生未知错误，请检查是否配置了正确的解析方法"
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class PayLoadTooLargeError(Exception):
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"""自定义异常类，用于处理请求体过大错误"""
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def __init__(self, message: str):
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super().__init__(message)
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self.message = message
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def __str__(self):
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return "请求体过大，请尝试压缩图片或减少输入内容。"
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class RequestAbortException(Exception):
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"""自定义异常类，用于处理请求中断异常"""
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def __init__(self, message: str):
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super().__init__(message)
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self.message = message
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def __str__(self):
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return self.message
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class PermissionDeniedException(Exception):
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"""自定义异常类，用于处理访问拒绝的异常"""
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def __init__(self, message: str):
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super().__init__(message)
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self.message = message
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def __str__(self):
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return self.message
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class EmptyResponseException(Exception):
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"""响应内容为空"""
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@ -107,3 +74,15 @@ class EmptyResponseException(Exception):
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def __str__(self):
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return self.message
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class ModelAttemptFailed(Exception):
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"""当在单个模型上的所有重试都失败后，由“执行者”函数抛出，以通知“调度器”切换模型。"""
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def __init__(self, message: str, original_exception: Exception | None = None):
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super().__init__(message)
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self.message = message
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self.original_exception = original_exception
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def __str__(self):
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return self.message
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@ -17,10 +17,9 @@ from .model_client.base_client import BaseClient, APIResponse, client_registry
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from .utils import compress_messages, llm_usage_recorder
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from .exceptions import (
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NetworkConnectionError,
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ReqAbortException,
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RespNotOkException,
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RespParseException,
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EmptyResponseException,
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ModelAttemptFailed,
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)
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install(extra_lines=3)
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@ -77,32 +76,27 @@ class LLMRequest:
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Returns:
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(Tuple[str, str, str, Optional[List[ToolCall]]]): 响应内容、推理内容、模型名称、工具调用列表
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"""
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# 模型选择
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start_time = time.time()
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model_info, api_provider, client = self._select_model()
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# 请求体构建
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message_builder = MessageBuilder()
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message_builder.add_text_content(prompt)
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message_builder.add_image_content(
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image_base64=image_base64, image_format=image_format, support_formats=client.get_support_image_formats()
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)
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messages = [message_builder.build()]
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def message_factory(client: BaseClient) -> List[Message]:
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message_builder = MessageBuilder()
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message_builder.add_text_content(prompt)
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message_builder.add_image_content(
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image_base64=image_base64,
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image_format=image_format,
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support_formats=client.get_support_image_formats()
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)
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return [message_builder.build()]
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# 请求并处理返回值
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response = await self._execute_request(
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api_provider=api_provider,
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client=client,
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response, model_info = await self._execute_request(
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request_type=RequestType.RESPONSE,
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model_info=model_info,
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message_list=messages,
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message_factory=message_factory,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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content = response.content or ""
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reasoning_content = response.reasoning_content or ""
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tool_calls = response.tool_calls
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# 从内容中提取<think>标签的推理内容（向后兼容）
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if not reasoning_content and content:
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content, extracted_reasoning = self._extract_reasoning(content)
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reasoning_content = extracted_reasoning
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@ -125,15 +119,8 @@ class LLMRequest:
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Returns:
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(Optional[str]): 生成的文本描述或None
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"""
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# 模型选择
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model_info, api_provider, client = self._select_model()
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# 请求并处理返回值
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response = await self._execute_request(
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api_provider=api_provider,
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client=client,
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response, _ = await self._execute_request(
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request_type=RequestType.AUDIO,
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model_info=model_info,
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audio_base64=voice_base64,
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)
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return response.content or None
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@ -152,43 +139,35 @@ class LLMRequest:
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prompt (str): 提示词
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temperature (float, optional): 温度参数
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max_tokens (int, optional): 最大token数
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tools (Optional[List[Dict[str, Any]]]): 工具列表
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raise_when_empty (bool): 当响应为空时是否抛出异常
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Returns:
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(Tuple[str, str, str, Optional[List[ToolCall]]]): 响应内容、推理内容、模型名称、工具调用列表
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"""
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# 请求体构建
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start_time = time.time()
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message_builder = MessageBuilder()
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message_builder.add_text_content(prompt)
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messages = [message_builder.build()]
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def message_factory(client: BaseClient) -> List[Message]:
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message_builder = MessageBuilder()
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message_builder.add_text_content(prompt)
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return [message_builder.build()]
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tool_built = self._build_tool_options(tools)
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# 模型选择
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model_info, api_provider, client = self._select_model()
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# 请求并处理返回值
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logger.debug(f"LLM选择耗时: {model_info.name} {time.time() - start_time}")
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response = await self._execute_request(
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api_provider=api_provider,
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client=client,
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response, model_info = await self._execute_request(
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request_type=RequestType.RESPONSE,
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model_info=model_info,
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message_list=messages,
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message_factory=message_factory,
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temperature=temperature,
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max_tokens=max_tokens,
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tool_options=tool_built,
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)
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logger.debug(f"LLM请求总耗时: {time.time() - start_time}")
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content = response.content
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reasoning_content = response.reasoning_content or ""
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tool_calls = response.tool_calls
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# 从内容中提取<think>标签的推理内容（向后兼容）
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if not reasoning_content and content:
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content, extracted_reasoning = self._extract_reasoning(content)
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reasoning_content = extracted_reasoning
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if usage := response.usage:
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llm_usage_recorder.record_usage_to_database(
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model_info=model_info,
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@ -198,31 +177,22 @@ class LLMRequest:
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endpoint="/chat/completions",
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time_cost=time.time() - start_time,
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)
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return content or "", (reasoning_content, model_info.name, tool_calls)
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return content, (reasoning_content, model_info.name, tool_calls)
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async def get_embedding(self, embedding_input: str) -> Tuple[List[float], str]:
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"""获取嵌入向量
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"""
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获取嵌入向量
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Args:
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embedding_input (str): 获取嵌入的目标
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Returns:
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(Tuple[List[float], str]): (嵌入向量，使用的模型名称)
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"""
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# 无需构建消息体，直接使用输入文本
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start_time = time.time()
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model_info, api_provider, client = self._select_model()
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# 请求并处理返回值
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response = await self._execute_request(
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api_provider=api_provider,
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client=client,
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response, model_info = await self._execute_request(
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request_type=RequestType.EMBEDDING,
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model_info=model_info,
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embedding_input=embedding_input,
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)
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embedding = response.embedding
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if usage := response.usage:
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llm_usage_recorder.record_usage_to_database(
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model_info=model_info,
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@ -232,59 +202,61 @@ class LLMRequest:
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endpoint="/embeddings",
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time_cost=time.time() - start_time,
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)
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if not embedding:
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raise RuntimeError("获取embedding失败")
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return embedding, model_info.name
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def _select_model(self) -> Tuple[ModelInfo, APIProvider, BaseClient]:
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def _select_model(self, exclude_models: set = None) -> Tuple[ModelInfo, APIProvider, BaseClient]:
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"""
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根据总tokens和惩罚值选择的模型
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"""
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available_models = {
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model: scores
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for model, scores in self.model_usage.items()
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if not exclude_models or model not in exclude_models
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}
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if not available_models:
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raise RuntimeError("没有可用的模型可供选择。所有模型均已尝试失败。")
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least_used_model_name = min(
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self.model_usage,
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key=lambda k: self.model_usage[k][0] + self.model_usage[k][1] * 300 + self.model_usage[k][2] * 1000,
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available_models,
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key=lambda k: available_models[k][0] + available_models[k][1] * 300 + available_models[k][2] * 1000,
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)
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model_info = model_config.get_model_info(least_used_model_name)
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api_provider = model_config.get_provider(model_info.api_provider)
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# 对于嵌入任务，强制创建新的客户端实例以避免事件循环问题
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force_new_client = self.request_type == "embedding"
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force_new_client = (self.request_type == "embedding")
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client = client_registry.get_client_class_instance(api_provider, force_new=force_new_client)
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logger.debug(f"选择请求模型: {model_info.name}")
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total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
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self.model_usage[model_info.name] = (total_tokens, penalty, usage_penalty + 1) # 增加使用惩罚值防止连续使用
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self.model_usage[model_info.name] = (total_tokens, penalty, usage_penalty + 1)
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return model_info, api_provider, client
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async def _execute_request(
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async def _attempt_request_on_model(
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self,
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model_info: ModelInfo,
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api_provider: APIProvider,
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client: BaseClient,
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request_type: RequestType,
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model_info: ModelInfo,
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message_list: List[Message] | None = None,
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tool_options: list[ToolOption] | None = None,
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response_format: RespFormat | None = None,
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stream_response_handler: Optional[Callable] = None,
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async_response_parser: Optional[Callable] = None,
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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embedding_input: str = "",
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audio_base64: str = "",
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message_list: List[Message],
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tool_options: list[ToolOption] | None,
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response_format: RespFormat | None,
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stream_response_handler: Optional[Callable],
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async_response_parser: Optional[Callable],
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temperature: Optional[float],
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max_tokens: Optional[int],
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embedding_input: str | None,
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audio_base64: str | None,
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compressed_messages: Optional[List[Message]] = None,
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) -> APIResponse:
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"""
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实际执行请求的方法
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包含了重试和异常处理逻辑
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在单个模型上执行请求，包含针对临时错误的重试逻辑。
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如果成功，返回APIResponse。如果失败（重试耗尽或硬错误），则抛出ModelAttemptFailed异常。
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"""
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retry_remain = api_provider.max_retry
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compressed_messages: Optional[List[Message]] = None
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while retry_remain > 0:
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try:
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if request_type == RequestType.RESPONSE:
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assert message_list is not None, "message_list cannot be None for response requests"
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return await client.get_response(
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model_info=model_info,
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message_list=(compressed_messages or message_list),
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@ -297,202 +269,123 @@ class LLMRequest:
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extra_params=model_info.extra_params,
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)
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elif request_type == RequestType.EMBEDDING:
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assert embedding_input, "embedding_input cannot be empty for embedding requests"
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assert embedding_input is not None
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return await client.get_embedding(
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model_info=model_info,
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embedding_input=embedding_input,
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extra_params=model_info.extra_params,
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)
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elif request_type == RequestType.AUDIO:
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assert audio_base64 is not None, "audio_base64 cannot be None for audio requests"
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assert audio_base64 is not None
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return await client.get_audio_transcriptions(
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model_info=model_info,
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audio_base64=audio_base64,
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extra_params=model_info.extra_params,
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)
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except (EmptyResponseException, NetworkConnectionError) as e:
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retry_remain -= 1
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if retry_remain <= 0:
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logger.error(f"模型 '{model_info.name}' 在用尽对临时错误的重试次数后仍然失败。")
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raise ModelAttemptFailed(f"模型 '{model_info.name}' 重试耗尽", original_exception=e) from e
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logger.warning(f"模型 '{model_info.name}' 遇到可重试错误: {str(e)}。剩余重试次数: {retry_remain}")
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await asyncio.sleep(api_provider.retry_interval)
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except RespNotOkException as e:
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# 可重试的HTTP错误
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if e.status_code == 429 or e.status_code >= 500:
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retry_remain -= 1
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if retry_remain <= 0:
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logger.error(f"模型 '{model_info.name}' 在遇到 {e.status_code} 错误并用尽重试次数后仍然失败。")
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raise ModelAttemptFailed(f"模型 '{model_info.name}' 重试耗尽", original_exception=e) from e
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logger.warning(f"模型 '{model_info.name}' 遇到可重试的HTTP错误: {str(e)}。剩余重试次数: {retry_remain}")
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await asyncio.sleep(api_provider.retry_interval)
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continue
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# 特殊处理413，尝试压缩
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if e.status_code == 413 and message_list and not compressed_messages:
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logger.warning(f"模型 '{model_info.name}' 返回413请求体过大，尝试压缩后重试...")
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# 压缩消息本身不消耗重试次数
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compressed_messages = compress_messages(message_list)
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continue
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# 不可重试的HTTP错误
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logger.warning(f"模型 '{model_info.name}' 遇到不可重试的HTTP错误: {str(e)}")
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raise ModelAttemptFailed(f"模型 '{model_info.name}' 遇到硬错误", original_exception=e) from e
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except Exception as e:
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logger.debug(f"请求失败: {str(e)}")
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# 处理异常
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logger.error(traceback.format_exc())
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logger.warning(f"模型 '{model_info.name}' 遇到未知的不可重试错误: {str(e)}")
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raise ModelAttemptFailed(f"模型 '{model_info.name}' 遇到硬错误", original_exception=e) from e
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raise ModelAttemptFailed(f"模型 '{model_info.name}' 未被尝试，因为重试次数已配置为0或更少。")
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async def _execute_request(
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self,
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request_type: RequestType,
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message_factory: Optional[Callable[[BaseClient], List[Message]]] = None,
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tool_options: list[ToolOption] | None = None,
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response_format: RespFormat | None = None,
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stream_response_handler: Optional[Callable] = None,
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async_response_parser: Optional[Callable] = None,
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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embedding_input: str | None = None,
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audio_base64: str | None = None,
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) -> Tuple[APIResponse, ModelInfo]:
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"""
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调度器函数，负责模型选择、故障切换。
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"""
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failed_models_this_request = set()
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max_attempts = len(self.model_for_task.model_list)
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last_exception: Optional[Exception] = None
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compressed_messages: Optional[List[Message]] = None
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for _attempt in range(max_attempts):
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model_info, api_provider, client = self._select_model(exclude_models=failed_models_this_request)
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message_list = []
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if message_factory:
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message_list = message_factory(client)
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try:
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response = await self._attempt_request_on_model(
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model_info, api_provider, client, request_type,
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message_list=message_list,
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tool_options=tool_options,
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response_format=response_format,
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stream_response_handler=stream_response_handler,
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async_response_parser=async_response_parser,
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temperature=temperature,
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max_tokens=max_tokens,
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embedding_input=embedding_input,
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audio_base64=audio_base64,
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compressed_messages=compressed_messages,
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)
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return response, model_info
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except ModelAttemptFailed as e:
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last_exception = e.original_exception or e
|
||||
logger.warning(f"模型 '{model_info.name}' 尝试失败，切换到下一个模型。原因: {e}")
|
||||
total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
|
||||
self.model_usage[model_info.name] = (total_tokens, penalty + 1, usage_penalty)
|
||||
failed_models_this_request.add(model_info.name)
|
||||
|
||||
wait_interval, compressed_messages = self._default_exception_handler(
|
||||
e,
|
||||
self.task_name,
|
||||
model_name=model_info.name,
|
||||
remain_try=retry_remain,
|
||||
retry_interval=api_provider.retry_interval,
|
||||
messages=(message_list, compressed_messages is not None) if message_list else None,
|
||||
)
|
||||
if isinstance(last_exception, RespNotOkException) and last_exception.status_code == 400:
|
||||
logger.error("收到不可恢复的客户端错误 (400)，中止所有尝试。")
|
||||
raise last_exception from e
|
||||
|
||||
if wait_interval == -1:
|
||||
retry_remain = 0 # 不再重试
|
||||
elif wait_interval > 0:
|
||||
logger.info(f"等待 {wait_interval} 秒后重试...")
|
||||
await asyncio.sleep(wait_interval)
|
||||
finally:
|
||||
# 放在finally防止死循环
|
||||
retry_remain -= 1
|
||||
total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
|
||||
self.model_usage[model_info.name] = (total_tokens, penalty, usage_penalty - 1) # 使用结束，减少使用惩罚值
|
||||
logger.error(f"模型 '{model_info.name}' 请求失败，达到最大重试次数 {api_provider.max_retry} 次")
|
||||
raise RuntimeError("请求失败，已达到最大重试次数")
|
||||
total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
|
||||
if usage_penalty > 0:
|
||||
self.model_usage[model_info.name] = (total_tokens, penalty, usage_penalty - 1)
|
||||
|
||||
def _default_exception_handler(
|
||||
self,
|
||||
e: Exception,
|
||||
task_name: str,
|
||||
model_name: str,
|
||||
remain_try: int,
|
||||
retry_interval: int = 10,
|
||||
messages: Tuple[List[Message], bool] | None = None,
|
||||
) -> Tuple[int, List[Message] | None]:
|
||||
"""
|
||||
默认异常处理函数
|
||||
Args:
|
||||
e (Exception): 异常对象
|
||||
task_name (str): 任务名称
|
||||
model_name (str): 模型名称
|
||||
remain_try (int): 剩余尝试次数
|
||||
retry_interval (int): 重试间隔
|
||||
messages (tuple[list[Message], bool] | None): (消息列表, 是否已压缩过)
|
||||
Returns:
|
||||
(等待间隔（如果为0则不等待，为-1则不再请求该模型）, 新的消息列表（适用于压缩消息）)
|
||||
"""
|
||||
|
||||
if isinstance(e, NetworkConnectionError): # 网络连接错误
|
||||
return self._check_retry(
|
||||
remain_try,
|
||||
retry_interval,
|
||||
can_retry_msg=f"任务-'{task_name}' 模型-'{model_name}': 连接异常，将于{retry_interval}秒后重试",
|
||||
cannot_retry_msg=f"任务-'{task_name}' 模型-'{model_name}': 连接异常，超过最大重试次数，请检查网络连接状态或URL是否正确",
|
||||
)
|
||||
elif isinstance(e, EmptyResponseException): # 空响应错误
|
||||
return self._check_retry(
|
||||
remain_try,
|
||||
retry_interval,
|
||||
can_retry_msg=f"任务-'{task_name}' 模型-'{model_name}': 收到空响应，将于{retry_interval}秒后重试。原因: {e}",
|
||||
cannot_retry_msg=f"任务-'{task_name}' 模型-'{model_name}': 收到空响应，超过最大重试次数，放弃请求",
|
||||
)
|
||||
elif isinstance(e, ReqAbortException):
|
||||
logger.warning(f"任务-'{task_name}' 模型-'{model_name}': 请求被中断，详细信息-{str(e.message)}")
|
||||
return -1, None # 不再重试请求该模型
|
||||
elif isinstance(e, RespNotOkException):
|
||||
return self._handle_resp_not_ok(
|
||||
e,
|
||||
task_name,
|
||||
model_name,
|
||||
remain_try,
|
||||
retry_interval,
|
||||
messages,
|
||||
)
|
||||
elif isinstance(e, RespParseException):
|
||||
# 响应解析错误
|
||||
logger.error(f"任务-'{task_name}' 模型-'{model_name}': 响应解析错误，错误信息-{e.message}")
|
||||
logger.debug(f"附加内容: {str(e.ext_info)}")
|
||||
return -1, None # 不再重试请求该模型
|
||||
else:
|
||||
print(traceback.format_exc())
|
||||
logger.error(f"任务-'{task_name}' 模型-'{model_name}': 未知异常，错误信息-{str(e)}")
|
||||
return -1, None # 不再重试请求该模型
|
||||
|
||||
def _check_retry(
|
||||
self,
|
||||
remain_try: int,
|
||||
retry_interval: int,
|
||||
can_retry_msg: str,
|
||||
cannot_retry_msg: str,
|
||||
can_retry_callable: Callable | None = None,
|
||||
**kwargs,
|
||||
) -> Tuple[int, List[Message] | None]:
|
||||
"""辅助函数：检查是否可以重试
|
||||
Args:
|
||||
remain_try (int): 剩余尝试次数
|
||||
retry_interval (int): 重试间隔
|
||||
can_retry_msg (str): 可以重试时的提示信息
|
||||
cannot_retry_msg (str): 不可以重试时的提示信息
|
||||
can_retry_callable (Callable | None): 可以重试时调用的函数（如果有）
|
||||
**kwargs: 其他参数
|
||||
|
||||
Returns:
|
||||
(Tuple[int, List[Message] | None]): (等待间隔（如果为0则不等待，为-1则不再请求该模型）, 新的消息列表（适用于压缩消息）)
|
||||
"""
|
||||
if remain_try > 0:
|
||||
# 还有重试机会
|
||||
logger.warning(f"{can_retry_msg}")
|
||||
if can_retry_callable is not None:
|
||||
return retry_interval, can_retry_callable(**kwargs)
|
||||
else:
|
||||
return retry_interval, None
|
||||
else:
|
||||
# 达到最大重试次数
|
||||
logger.warning(f"{cannot_retry_msg}")
|
||||
return -1, None # 不再重试请求该模型
|
||||
|
||||
def _handle_resp_not_ok(
|
||||
self,
|
||||
e: RespNotOkException,
|
||||
task_name: str,
|
||||
model_name: str,
|
||||
remain_try: int,
|
||||
retry_interval: int = 10,
|
||||
messages: tuple[list[Message], bool] | None = None,
|
||||
):
|
||||
"""
|
||||
处理响应错误异常
|
||||
Args:
|
||||
e (RespNotOkException): 响应错误异常对象
|
||||
task_name (str): 任务名称
|
||||
model_name (str): 模型名称
|
||||
remain_try (int): 剩余尝试次数
|
||||
retry_interval (int): 重试间隔
|
||||
messages (tuple[list[Message], bool] | None): (消息列表, 是否已压缩过)
|
||||
Returns:
|
||||
(等待间隔（如果为0则不等待，为-1则不再请求该模型）, 新的消息列表（适用于压缩消息）)
|
||||
"""
|
||||
# 响应错误
|
||||
if e.status_code in [400, 401, 402, 403, 404]:
|
||||
# 客户端错误
|
||||
logger.warning(
|
||||
f"任务-'{task_name}' 模型-'{model_name}': 请求失败，错误代码-{e.status_code}，错误信息-{e.message}"
|
||||
)
|
||||
return -1, None # 不再重试请求该模型
|
||||
elif e.status_code == 413:
|
||||
if messages and not messages[1]:
|
||||
# 消息列表不为空且未压缩，尝试压缩消息
|
||||
return self._check_retry(
|
||||
remain_try,
|
||||
0,
|
||||
can_retry_msg=f"任务-'{task_name}' 模型-'{model_name}': 请求体过大，尝试压缩消息后重试",
|
||||
cannot_retry_msg=f"任务-'{task_name}' 模型-'{model_name}': 请求体过大，压缩消息后仍然过大，放弃请求",
|
||||
can_retry_callable=compress_messages,
|
||||
messages=messages[0],
|
||||
)
|
||||
# 没有消息可压缩
|
||||
logger.warning(f"任务-'{task_name}' 模型-'{model_name}': 请求体过大，无法压缩消息，放弃请求。")
|
||||
return -1, None
|
||||
elif e.status_code == 429:
|
||||
# 请求过于频繁
|
||||
return self._check_retry(
|
||||
remain_try,
|
||||
retry_interval,
|
||||
can_retry_msg=f"任务-'{task_name}' 模型-'{model_name}': 请求过于频繁，将于{retry_interval}秒后重试",
|
||||
cannot_retry_msg=f"任务-'{task_name}' 模型-'{model_name}': 请求过于频繁，超过最大重试次数，放弃请求",
|
||||
)
|
||||
elif e.status_code >= 500:
|
||||
# 服务器错误
|
||||
return self._check_retry(
|
||||
remain_try,
|
||||
retry_interval,
|
||||
can_retry_msg=f"任务-'{task_name}' 模型-'{model_name}': 服务器错误，将于{retry_interval}秒后重试",
|
||||
cannot_retry_msg=f"任务-'{task_name}' 模型-'{model_name}': 服务器错误，超过最大重试次数，请稍后再试",
|
||||
)
|
||||
else:
|
||||
# 未知错误
|
||||
logger.warning(
|
||||
f"任务-'{task_name}' 模型-'{model_name}': 未知错误，错误代码-{e.status_code}，错误信息-{e.message}"
|
||||
)
|
||||
return -1, None
|
||||
logger.error(f"所有 {max_attempts} 个模型均尝试失败。")
|
||||
if last_exception:
|
||||
raise last_exception
|
||||
raise RuntimeError("请求失败，所有可用模型均已尝试失败。")
|
||||
|
||||
def _build_tool_options(self, tools: Optional[List[Dict[str, Any]]]) -> Optional[List[ToolOption]]:
|
||||
# sourcery skip: extract-method
|
||||
|
|
@ -537,4 +430,4 @@ class LLMRequest:
|
|||
match = re.search(r"(?:<think>)?(.*?)</think>", content, re.DOTALL)
|
||||
content = re.sub(r"(?:<think>)?.*?</think>", "", content, flags=re.DOTALL, count=1).strip()
|
||||
reasoning = match[1].strip() if match else ""
|
||||
return content, reasoning
|
||||
return content, reasoning
|
||||
Loading…
Reference in New Issue