eb1feeeb0b
将工具缓存的实现从`ToolExecutor`的装饰器模式重构为直接集成。缓存逻辑被移出`cache_manager.py`并整合进`ToolExecutor.execute_tool_call`方法中，简化了代码结构并使其更易于维护。 主要变更： - 从`cache_manager.py`中移除了`wrap_tool_executor`函数。 - 在`tool_use.py`中，`execute_tool_call`现在包含完整的缓存检查和设置逻辑。 - 调整了`llm_models/utils_model.py`中的LLM请求逻辑，为模型生成的空回复或截断响应增加了内部重试机制，增强了稳定性。 - 清理了项目中未使用的导入和过时的文档文件，以保持代码库的整洁。
842 lines
37 KiB
Python
842 lines
37 KiB
Python
import re
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import asyncio
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import time
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import random
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from enum import Enum
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from rich.traceback import install
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from typing import Tuple, List, Dict, Optional, Callable, Any, Coroutine, Generator
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from src.common.logger import get_logger
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from src.config.config import model_config
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from src.config.api_ada_configs import APIProvider, ModelInfo, TaskConfig
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from .payload_content.message import MessageBuilder, Message
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from .payload_content.resp_format import RespFormat
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from .payload_content.tool_option import ToolOption, ToolCall, ToolOptionBuilder, ToolParamType
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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 NetworkConnectionError, ReqAbortException, RespNotOkException, RespParseException
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install(extra_lines=3)
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logger = get_logger("model_utils")
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# 常见Error Code Mapping
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error_code_mapping = {
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400: "参数不正确",
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401: "API key 错误，认证失败，请检查 config/model_config.toml 中的配置是否正确",
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402: "账号余额不足",
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403: "需要实名,或余额不足",
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404: "Not Found",
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429: "请求过于频繁，请稍后再试",
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500: "服务器内部故障",
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503: "服务器负载过高",
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}
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def _normalize_image_format(image_format: str) -> str:
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"""
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标准化图片格式名称，确保与各种API的兼容性
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Args:
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image_format (str): 原始图片格式
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Returns:
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str: 标准化后的图片格式
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"""
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format_mapping = {
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"jpg": "jpeg",
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"JPG": "jpeg",
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"JPEG": "jpeg",
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"jpeg": "jpeg",
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"png": "png",
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"PNG": "png",
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"webp": "webp",
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"WEBP": "webp",
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"gif": "gif",
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"GIF": "gif",
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"heic": "heic",
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"HEIC": "heic",
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"heif": "heif",
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"HEIF": "heif"
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}
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normalized = format_mapping.get(image_format, image_format.lower())
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logger.debug(f"图片格式标准化: {image_format} -> {normalized}")
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return normalized
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class RequestType(Enum):
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"""请求类型枚举"""
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RESPONSE = "response"
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EMBEDDING = "embedding"
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AUDIO = "audio"
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async def execute_concurrently(
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coro_callable: Callable[..., Coroutine[Any, Any, Any]],
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concurrency_count: int,
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*args,
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**kwargs,
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) -> Any:
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"""
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执行并发请求并从成功的结果中随机选择一个。
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Args:
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coro_callable (Callable): 要并发执行的协程函数。
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concurrency_count (int): 并发执行的次数。
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*args: 传递给协程函数的位置参数。
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**kwargs: 传递给协程函数的关键字参数。
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Returns:
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Any: 其中一个成功执行的结果。
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Raises:
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RuntimeError: 如果所有并发请求都失败。
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"""
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logger.info(f"启用并发请求模式，并发数: {concurrency_count}")
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tasks = [coro_callable(*args, **kwargs) for _ in range(concurrency_count)]
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results = await asyncio.gather(*tasks, return_exceptions=True)
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successful_results = [res for res in results if not isinstance(res, Exception)]
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if successful_results:
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selected = random.choice(successful_results)
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logger.info(f"并发请求完成，从{len(successful_results)}个成功结果中选择了一个")
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return selected
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# 如果所有请求都失败了，记录所有异常并抛出第一个
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for i, res in enumerate(results):
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if isinstance(res, Exception):
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logger.error(f"并发任务 {i+1}/{concurrency_count} 失败: {res}")
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first_exception = next((res for res in results if isinstance(res, Exception)), None)
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if first_exception:
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raise first_exception
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raise RuntimeError(f"所有 {concurrency_count} 个并发请求都失败了，但没有具体的异常信息")
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class LLMRequest:
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"""LLM请求类"""
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def __init__(self, model_set: TaskConfig, request_type: str = "") -> None:
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self.task_name = request_type
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self.model_for_task = model_set
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self.request_type = request_type
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self.model_usage: Dict[str, Tuple[int, int, int]] = {
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model: (0, 0, 0) for model in self.model_for_task.model_list
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}
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"""模型使用量记录，用于进行负载均衡，对应为(total_tokens, penalty, usage_penalty)，惩罚值是为了能在某个模型请求不给力或正在被使用的时候进行调整"""
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# 内容混淆过滤指令
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self.noise_instruction = """**【核心过滤规则】**
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在交互过程中,你会在用户的输入内容中看到一些无意义的、由字母和数字组成的随机字符串。这些字符串是API传输过程中的技术伪装信息,【不是】用户真实意图的一部分。
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你的任务是【完全并彻底地忽略】这些随机字符串。
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**【严禁】** 将这些字符串写入你的回复、对它们进行任何形式的解读、或将它们作为故事的一部分进行模拟。当看到这些字符串时,请将其视为空白或直接跳过,并根据用户其他有意义的输入(如有)或当前故事情节来继续你的叙事。此规则为最高优先级。
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"""
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# 反截断指令
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self.anti_truncation_instruction = """
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**【输出完成信令】**
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这是一个非常重要的指令,请务必遵守。在你的回复内容完全结束后,请务必在最后另起一行,只写 `[done]` 作为结束标志。
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例如:
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<你的回复内容>
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[done]
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这有助于我判断你的输出是否被截断。请不要在 `[done]` 前后添加任何其他文字或标点。
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"""
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async def generate_response_for_image(
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self,
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prompt: str,
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image_base64: str,
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image_format: str,
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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) -> Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
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"""
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为图像生成响应
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Args:
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prompt (str): 提示词
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image_base64 (str): 图像的Base64编码字符串
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image_format (str): 图像格式（如 'png', 'jpeg' 等）
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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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# 标准化图片格式以确保API兼容性
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normalized_format = _normalize_image_format(image_format)
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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=normalized_format, support_formats=client.get_support_image_formats()
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)
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messages = [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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request_type=RequestType.RESPONSE,
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model_info=model_info,
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message_list=messages,
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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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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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model_usage=usage,
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user_id="system",
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time_cost=time.time() - start_time,
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request_type=self.request_type,
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endpoint="/chat/completions",
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)
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return content, (reasoning_content, model_info.name, tool_calls)
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async def generate_response_for_voice(self, voice_base64: str) -> Optional[str]:
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"""
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为语音生成响应
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Args:
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voice_base64 (str): 语音的Base64编码字符串
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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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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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async def generate_response_async(
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self,
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prompt: str,
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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raise_when_empty: bool = True,
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) -> Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
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"""
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异步生成响应，支持并发请求
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Args:
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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: 工具配置
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raise_when_empty: 是否在空回复时抛出异常
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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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# 检查是否需要并发请求
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concurrency_count = getattr(self.model_for_task, "concurrency_count", 1)
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if concurrency_count <= 1:
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# 单次请求
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return await self._execute_single_request(prompt, temperature, max_tokens, tools, raise_when_empty)
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# 并发请求
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try:
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# 为 _execute_single_request 传递参数时，将 raise_when_empty 设为 False,
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# 这样单个请求失败时不会立即抛出异常，而是由 gather 统一处理
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return await execute_concurrently(
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self._execute_single_request,
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concurrency_count,
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prompt,
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temperature,
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max_tokens,
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tools,
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raise_when_empty=False,
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)
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except Exception as e:
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logger.error(f"所有 {concurrency_count} 个并发请求都失败了: {e}")
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if raise_when_empty:
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raise e
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return "所有并发请求都失败了", ("", "unknown", None)
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async def _execute_single_request(
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self,
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prompt: str,
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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raise_when_empty: bool = True,
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) -> Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
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"""
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执行单次请求，并在模型失败时按顺序切换到下一个可用模型。
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"""
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failed_models = set()
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last_exception: Optional[Exception] = None
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model_scheduler = self._model_scheduler(failed_models)
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for model_info, api_provider, client in model_scheduler:
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start_time = time.time()
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model_name = model_info.name
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logger.info(f"正在尝试使用模型: {model_name}")
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try:
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# 检查是否启用反截断
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use_anti_truncation = getattr(api_provider, "anti_truncation", False)
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processed_prompt = prompt
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if use_anti_truncation:
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processed_prompt += self.anti_truncation_instruction
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logger.info(f"'{model_name}' for task '{self.task_name}' 已启用反截断功能")
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processed_prompt = self._apply_content_obfuscation(processed_prompt, api_provider)
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message_builder = MessageBuilder()
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message_builder.add_text_content(processed_prompt)
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messages = [message_builder.build()]
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tool_built = self._build_tool_options(tools)
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# 针对当前模型的空回复/截断重试逻辑
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empty_retry_count = 0
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max_empty_retry = api_provider.max_retry
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empty_retry_interval = api_provider.retry_interval
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while empty_retry_count <= max_empty_retry:
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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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request_type=RequestType.RESPONSE,
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model_info=model_info,
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message_list=messages,
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tool_options=tool_built,
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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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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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is_empty_reply = not tool_calls and (not content or content.strip() == "")
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is_truncated = False
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if use_anti_truncation:
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if content.endswith("[done]"):
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content = content[:-6].strip()
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else:
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is_truncated = True
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if is_empty_reply or is_truncated:
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empty_retry_count += 1
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if empty_retry_count <= max_empty_retry:
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reason = "空回复" if is_empty_reply else "截断"
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logger.warning(f"模型 '{model_name}' 检测到{reason}，正在进行第 {empty_retry_count}/{max_empty_retry} 次重新生成...")
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if empty_retry_interval > 0:
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await asyncio.sleep(empty_retry_interval)
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continue # 继续使用当前模型重试
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else:
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# 当前模型重试次数用尽，跳出内层循环，触发外层循环切换模型
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reason = "空回复" if is_empty_reply else "截断"
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logger.error(f"模型 '{model_name}' 经过 {max_empty_retry} 次重试后仍然是{reason}的回复。")
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raise RuntimeError(f"模型 '{model_name}' 达到最大空回复/截断重试次数")
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# 成功获取响应
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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, model_usage=usage, time_cost=time.time() - start_time,
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user_id="system", request_type=self.request_type, endpoint="/chat/completions",
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)
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if not content and not tool_calls:
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if raise_when_empty:
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raise RuntimeError("生成空回复")
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content = "生成的响应为空"
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logger.info(f"模型 '{model_name}' 成功生成回复。")
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return content, (reasoning_content, model_name, tool_calls)
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except RespNotOkException as e:
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if e.status_code in [401, 403]:
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logger.error(f"模型 '{model_name}' 遇到认证/权限错误 (Code: {e.status_code})，将尝试下一个模型。")
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failed_models.add(model_name)
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last_exception = e
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continue # 切换到下一个模型
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else:
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logger.error(f"模型 '{model_name}' 请求失败，HTTP状态码: {e.status_code}")
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if raise_when_empty:
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raise
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# 对于其他HTTP错误，直接抛出，不再尝试其他模型
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return f"请求失败: {e}", ("", model_name, None)
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except RuntimeError as e:
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# 捕获所有重试失败（包括空回复和网络问题）
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logger.error(f"模型 '{model_name}' 在所有重试后仍然失败: {e}，将尝试下一个模型。")
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failed_models.add(model_name)
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last_exception = e
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continue # 切换到下一个模型
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except Exception as e:
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logger.error(f"使用模型 '{model_name}' 时发生未知异常: {e}")
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failed_models.add(model_name)
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last_exception = e
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continue # 切换到下一个模型
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# 所有模型都尝试失败
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logger.error("所有可用模型都已尝试失败。")
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if raise_when_empty:
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if last_exception:
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raise RuntimeError("所有模型都请求失败") from last_exception
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raise RuntimeError("所有模型都请求失败，且没有具体的异常信息")
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return "所有模型都请求失败", ("", "unknown", None)
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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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Args:
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embedding_input (str): 获取嵌入的目标
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Returns:
|
||
(Tuple[List[float], str]): (嵌入向量，使用的模型名称)
|
||
"""
|
||
# 无需构建消息体，直接使用输入文本
|
||
start_time = time.time()
|
||
model_info, api_provider, client = self._select_model()
|
||
|
||
# 请求并处理返回值
|
||
response = await self._execute_request(
|
||
api_provider=api_provider,
|
||
client=client,
|
||
request_type=RequestType.EMBEDDING,
|
||
model_info=model_info,
|
||
embedding_input=embedding_input,
|
||
)
|
||
|
||
embedding = response.embedding
|
||
|
||
if usage := response.usage:
|
||
llm_usage_recorder.record_usage_to_database(
|
||
model_info=model_info,
|
||
time_cost=time.time() - start_time,
|
||
model_usage=usage,
|
||
user_id="system",
|
||
request_type=self.request_type,
|
||
endpoint="/embeddings",
|
||
)
|
||
|
||
if not embedding:
|
||
raise RuntimeError("获取embedding失败")
|
||
|
||
return embedding, model_info.name
|
||
|
||
def _model_scheduler(self, failed_models: set) -> Generator[Tuple[ModelInfo, APIProvider, BaseClient], None, None]:
|
||
"""
|
||
一个模型调度器，按顺序提供模型，并跳过已失败的模型。
|
||
"""
|
||
for model_name in self.model_for_task.model_list:
|
||
if model_name in failed_models:
|
||
continue
|
||
|
||
model_info = model_config.get_model_info(model_name)
|
||
api_provider = model_config.get_provider(model_info.api_provider)
|
||
force_new_client = (self.request_type == "embedding")
|
||
client = client_registry.get_client_class_instance(api_provider, force_new=force_new_client)
|
||
|
||
yield model_info, api_provider, client
|
||
|
||
def _select_model(self) -> Tuple[ModelInfo, APIProvider, BaseClient]:
|
||
"""
|
||
根据总tokens和惩罚值选择的模型 (负载均衡)
|
||
"""
|
||
least_used_model_name = min(
|
||
self.model_usage,
|
||
key=lambda k: self.model_usage[k][0] + self.model_usage[k][1] * 300 + self.model_usage[k][2] * 1000,
|
||
)
|
||
model_info = model_config.get_model_info(least_used_model_name)
|
||
api_provider = model_config.get_provider(model_info.api_provider)
|
||
|
||
# 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
|
||
force_new_client = (self.request_type == "embedding")
|
||
client = client_registry.get_client_class_instance(api_provider, force_new=force_new_client)
|
||
logger.debug(f"选择请求模型: {model_info.name}")
|
||
total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
|
||
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
|
||
return model_info, api_provider, client
|
||
|
||
async def _execute_request(
|
||
self,
|
||
api_provider: APIProvider,
|
||
client: BaseClient,
|
||
request_type: RequestType,
|
||
model_info: ModelInfo,
|
||
message_list: List[Message] | None = None,
|
||
tool_options: list[ToolOption] | None = None,
|
||
response_format: RespFormat | None = None,
|
||
stream_response_handler: Optional[Callable] = None,
|
||
async_response_parser: Optional[Callable] = None,
|
||
temperature: Optional[float] = None,
|
||
max_tokens: Optional[int] = None,
|
||
embedding_input: str = "",
|
||
audio_base64: str = "",
|
||
) -> APIResponse:
|
||
"""
|
||
实际执行请求的方法
|
||
|
||
包含了重试和异常处理逻辑
|
||
"""
|
||
retry_remain = api_provider.max_retry
|
||
compressed_messages: Optional[List[Message]] = None
|
||
while retry_remain > 0:
|
||
try:
|
||
if request_type == RequestType.RESPONSE:
|
||
assert message_list is not None, "message_list cannot be None for response requests"
|
||
return await client.get_response(
|
||
model_info=model_info,
|
||
message_list=(compressed_messages or message_list),
|
||
tool_options=tool_options,
|
||
max_tokens=self.model_for_task.max_tokens if max_tokens is None else max_tokens,
|
||
temperature=self.model_for_task.temperature if temperature is None else temperature,
|
||
response_format=response_format,
|
||
stream_response_handler=stream_response_handler,
|
||
async_response_parser=async_response_parser,
|
||
extra_params=model_info.extra_params,
|
||
)
|
||
elif request_type == RequestType.EMBEDDING:
|
||
assert embedding_input, "embedding_input cannot be empty for embedding requests"
|
||
return await client.get_embedding(
|
||
model_info=model_info,
|
||
embedding_input=embedding_input,
|
||
extra_params=model_info.extra_params,
|
||
)
|
||
elif request_type == RequestType.AUDIO:
|
||
assert audio_base64 is not None, "audio_base64 cannot be None for audio requests"
|
||
return await client.get_audio_transcriptions(
|
||
model_info=model_info,
|
||
audio_base64=audio_base64,
|
||
extra_params=model_info.extra_params,
|
||
)
|
||
except Exception as e:
|
||
logger.debug(f"请求失败: {str(e)}")
|
||
# 处理异常
|
||
total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
|
||
self.model_usage[model_info.name] = (total_tokens, penalty + 1, usage_penalty)
|
||
|
||
wait_interval, compressed_messages = self._default_exception_handler(
|
||
e,
|
||
self.task_name,
|
||
model_info=model_info,
|
||
api_provider=api_provider,
|
||
remain_try=retry_remain,
|
||
retry_interval=api_provider.retry_interval,
|
||
messages=(message_list, compressed_messages is not None) if message_list else None,
|
||
)
|
||
|
||
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")
|
||
|
||
def _default_exception_handler(
|
||
self,
|
||
e: Exception,
|
||
task_name: str,
|
||
model_info: ModelInfo,
|
||
api_provider: APIProvider,
|
||
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_info (ModelInfo): 模型信息
|
||
api_provider (APIProvider): API提供商
|
||
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）)
|
||
"""
|
||
model_name = model_info.name if model_info else "unknown"
|
||
|
||
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, 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_info,
|
||
api_provider,
|
||
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:
|
||
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_info: ModelInfo,
|
||
api_provider: APIProvider,
|
||
remain_try: int,
|
||
retry_interval: int = 10,
|
||
messages: tuple[list[Message], bool] | None = None,
|
||
):
|
||
model_name = model_info.name
|
||
"""
|
||
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_info (ModelInfo): 模型信息
|
||
api_provider (APIProvider): API提供商
|
||
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]:
|
||
model_name = model_info.name
|
||
if (
|
||
e.status_code == 403
|
||
and model_name.startswith("Pro/deepseek-ai")
|
||
and api_provider.base_url == "https://api.siliconflow.cn/v1/"
|
||
):
|
||
old_model_name = model_name
|
||
new_model_name = model_name[4:]
|
||
model_info.name = new_model_name
|
||
logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {new_model_name}")
|
||
# 更新任务配置中的模型列表
|
||
for i, m_name in enumerate(self.model_for_task.model_list):
|
||
if m_name == old_model_name:
|
||
self.model_for_task.model_list[i] = new_model_name
|
||
logger.warning(f"将任务 {self.task_name} 的模型列表中的 {old_model_name} 临时降级至 {new_model_name}")
|
||
break
|
||
return 0, None # 立即重试
|
||
# 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
|
||
|
||
def _build_tool_options(self, tools: Optional[List[Dict[str, Any]]]) -> Optional[List[ToolOption]]:
|
||
# sourcery skip: extract-method
|
||
"""构建工具选项列表"""
|
||
if not tools:
|
||
return None
|
||
tool_options: List[ToolOption] = []
|
||
for tool in tools:
|
||
tool_legal = True
|
||
tool_options_builder = ToolOptionBuilder()
|
||
tool_options_builder.set_name(tool.get("name", ""))
|
||
tool_options_builder.set_description(tool.get("description", ""))
|
||
parameters: List[Tuple[str, str, str, bool, List[str] | None]] = tool.get("parameters", [])
|
||
for param in parameters:
|
||
try:
|
||
assert isinstance(param, tuple) and len(param) == 5, "参数必须是包含5个元素的元组"
|
||
assert isinstance(param[0], str), "参数名称必须是字符串"
|
||
assert isinstance(param[1], ToolParamType), "参数类型必须是ToolParamType枚举"
|
||
assert isinstance(param[2], str), "参数描述必须是字符串"
|
||
assert isinstance(param[3], bool), "参数是否必填必须是布尔值"
|
||
assert isinstance(param[4], list) or param[4] is None, "参数枚举值必须是列表或None"
|
||
tool_options_builder.add_param(
|
||
name=param[0],
|
||
param_type=param[1],
|
||
description=param[2],
|
||
required=param[3],
|
||
enum_values=param[4],
|
||
)
|
||
except AssertionError as ae:
|
||
tool_legal = False
|
||
logger.error(f"{param[0]} 参数定义错误: {str(ae)}")
|
||
except Exception as e:
|
||
tool_legal = False
|
||
logger.error(f"构建工具参数失败: {str(e)}")
|
||
if tool_legal:
|
||
tool_options.append(tool_options_builder.build())
|
||
return tool_options or None
|
||
|
||
@staticmethod
|
||
def _extract_reasoning(content: str) -> Tuple[str, str]:
|
||
"""CoT思维链提取，向后兼容"""
|
||
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
|
||
|
||
def _apply_content_obfuscation(self, text: str, api_provider) -> str:
|
||
"""根据API提供商配置对文本进行混淆处理"""
|
||
if not hasattr(api_provider, 'enable_content_obfuscation') or not api_provider.enable_content_obfuscation:
|
||
logger.debug(f"API提供商 '{api_provider.name}' 未启用内容混淆")
|
||
return text
|
||
|
||
intensity = getattr(api_provider, 'obfuscation_intensity', 1)
|
||
logger.info(f"为API提供商 '{api_provider.name}' 启用内容混淆，强度级别: {intensity}")
|
||
|
||
# 在开头加入过滤规则指令
|
||
processed_text = self.noise_instruction + "\n\n" + text
|
||
logger.debug(f"已添加过滤规则指令，文本长度: {len(text)} -> {len(processed_text)}")
|
||
|
||
# 添加随机乱码
|
||
final_text = self._inject_random_noise(processed_text, intensity)
|
||
logger.debug(f"乱码注入完成，最终文本长度: {len(final_text)}")
|
||
|
||
return final_text
|
||
|
||
def _inject_random_noise(self, text: str, intensity: int) -> str:
|
||
"""在文本中注入随机乱码"""
|
||
import random
|
||
import string
|
||
|
||
def generate_noise(length: int) -> str:
|
||
"""生成指定长度的随机乱码字符"""
|
||
chars = (
|
||
string.ascii_letters + # a-z, A-Z
|
||
string.digits + # 0-9
|
||
'!@#$%^&*()_+-=[]{}|;:,.<>?' + # 特殊符号
|
||
'一二三四五六七八九零壹贰叁' + # 中文字符
|
||
'αβγδεζηθικλμνξοπρστυφχψω' + # 希腊字母
|
||
'∀∃∈∉∪∩⊂⊃∧∨¬→↔∴∵' # 数学符号
|
||
)
|
||
return ''.join(random.choice(chars) for _ in range(length))
|
||
|
||
# 强度参数映射
|
||
params = {
|
||
1: {"probability": 15, "length": (3, 6)}, # 低强度：15%概率，3-6个字符
|
||
2: {"probability": 25, "length": (5, 10)}, # 中强度：25%概率，5-10个字符
|
||
3: {"probability": 35, "length": (8, 15)} # 高强度：35%概率，8-15个字符
|
||
}
|
||
|
||
config = params.get(intensity, params[1])
|
||
logger.debug(f"乱码注入参数: 概率={config['probability']}%, 长度范围={config['length']}")
|
||
|
||
# 按词分割处理
|
||
words = text.split()
|
||
result = []
|
||
noise_count = 0
|
||
|
||
for word in words:
|
||
result.append(word)
|
||
# 根据概率插入乱码
|
||
if random.randint(1, 100) <= config["probability"]:
|
||
noise_length = random.randint(*config["length"])
|
||
noise = generate_noise(noise_length)
|
||
result.append(noise)
|
||
noise_count += 1
|
||
|
||
logger.debug(f"共注入 {noise_count} 个乱码片段，原词数: {len(words)}")
|
||
return ' '.join(result)
|