文档更新，changelog更新
This commit is contained in:
UnCLAS-Prommer
@@ -1,380 +0,0 @@
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import asyncio
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from typing import Callable, Any
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from openai import AsyncStream
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from openai.types.chat import ChatCompletionChunk, ChatCompletion
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from .base_client import BaseClient, APIResponse
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from src.config.api_ada_configs import (
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ModelInfo,
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ModelUsageArgConfigItem,
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RequestConfig,
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ModuleConfig,
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)
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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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)
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from ..payload_content.message import Message
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from ..payload_content.resp_format import RespFormat
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from ..payload_content.tool_option import ToolOption
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from ..utils import compress_messages
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from src.common.logger import get_logger
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logger = get_logger("模型客户端")
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def _check_retry(
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remain_try: int,
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retry_interval: int,
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can_retry_msg: str,
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cannot_retry_msg: str,
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can_retry_callable: Callable | None = None,
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**kwargs,
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) -> tuple[int, Any | None]:
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"""
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辅助函数：检查是否可以重试
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:param remain_try: 剩余尝试次数
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:param retry_interval: 重试间隔
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:param can_retry_msg: 可以重试时的提示信息
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:param cannot_retry_msg: 不可以重试时的提示信息
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:return: (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）)
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"""
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if remain_try > 0:
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# 还有重试机会
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logger.warning(f"{can_retry_msg}")
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if can_retry_callable is not None:
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return retry_interval, can_retry_callable(**kwargs)
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else:
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return retry_interval, None
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else:
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# 达到最大重试次数
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logger.warning(f"{cannot_retry_msg}")
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return -1, None # 不再重试请求该模型
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def _handle_resp_not_ok(
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e: RespNotOkException,
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task_name: str,
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model_name: str,
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remain_try: int,
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retry_interval: int = 10,
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messages: tuple[list[Message], bool] | None = None,
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):
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"""
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处理响应错误异常
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:param e: 异常对象
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:param task_name: 任务名称
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:param model_name: 模型名称
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:param remain_try: 剩余尝试次数
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:param retry_interval: 重试间隔
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:param messages: (消息列表, 是否已压缩过)
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:return: (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）)
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"""
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# 响应错误
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if e.status_code in [401, 403]:
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# API Key认证错误 - 让多API Key机制处理，给一次重试机会
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if remain_try > 0:
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logger.warning(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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f"API Key认证失败（错误代码-{e.status_code}），多API Key机制会自动切换"
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)
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return 0, None # 立即重试，让底层客户端切换API Key
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else:
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logger.warning(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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f"所有API Key都认证失败，错误代码-{e.status_code}，错误信息-{e.message}"
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)
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return -1, None # 不再重试请求该模型
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elif e.status_code in [400, 402, 404]:
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# 其他客户端错误（不应该重试）
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logger.warning(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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f"请求失败，错误代码-{e.status_code}，错误信息-{e.message}"
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)
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return -1, None # 不再重试请求该模型
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elif e.status_code == 413:
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if messages and not messages[1]:
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# 消息列表不为空且未压缩，尝试压缩消息
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return _check_retry(
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remain_try,
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0,
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can_retry_msg=(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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"请求体过大，尝试压缩消息后重试"
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),
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cannot_retry_msg=(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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"请求体过大，压缩消息后仍然过大，放弃请求"
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),
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can_retry_callable=compress_messages,
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messages=messages[0],
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)
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# 没有消息可压缩
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logger.warning(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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"请求体过大，无法压缩消息，放弃请求。"
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)
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return -1, None
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elif e.status_code == 429:
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# 请求过于频繁 - 让多API Key机制处理，适当延迟后重试
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return _check_retry(
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remain_try,
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min(retry_interval, 5), # 限制最大延迟为5秒，让API Key切换更快生效
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can_retry_msg=(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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f"请求过于频繁，多API Key机制会自动切换，{min(retry_interval, 5)}秒后重试"
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),
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cannot_retry_msg=(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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"请求过于频繁，所有API Key都被限制，放弃请求"
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),
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)
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elif e.status_code >= 500:
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# 服务器错误
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return _check_retry(
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remain_try,
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retry_interval,
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can_retry_msg=(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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f"服务器错误，将于{retry_interval}秒后重试"
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),
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cannot_retry_msg=(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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"服务器错误，超过最大重试次数，请稍后再试"
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),
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)
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else:
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# 未知错误
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logger.warning(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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f"未知错误，错误代码-{e.status_code}，错误信息-{e.message}"
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)
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return -1, None
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def default_exception_handler(
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e: Exception,
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task_name: str,
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model_name: str,
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remain_try: int,
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retry_interval: int = 10,
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messages: tuple[list[Message], bool] | None = None,
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) -> tuple[int, list[Message] | None]:
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"""
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默认异常处理函数
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:param e: 异常对象
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:param task_name: 任务名称
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:param model_name: 模型名称
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:param remain_try: 剩余尝试次数
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:param retry_interval: 重试间隔
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:param messages: (消息列表, 是否已压缩过)
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:return (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）)
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"""
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if isinstance(e, NetworkConnectionError): # 网络连接错误
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# 网络错误可能是某个API Key的端点问题，给多API Key机制一次快速重试机会
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return _check_retry(
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remain_try,
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min(retry_interval, 3), # 网络错误时减少等待时间，让API Key切换更快
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can_retry_msg=(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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f"连接异常，多API Key机制会尝试其他Key，{min(retry_interval, 3)}秒后重试"
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),
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cannot_retry_msg=(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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f"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"
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),
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)
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elif isinstance(e, ReqAbortException):
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logger.warning(
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f"任务-'{task_name}' 模型-'{model_name}'\n请求被中断，详细信息-{str(e.message)}"
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)
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return -1, None # 不再重试请求该模型
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elif isinstance(e, RespNotOkException):
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return _handle_resp_not_ok(
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e,
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task_name,
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model_name,
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remain_try,
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retry_interval,
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messages,
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)
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elif isinstance(e, RespParseException):
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# 响应解析错误
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logger.error(
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f"任务-'{task_name}' 模型-'{model_name}'\n"
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f"响应解析错误，错误信息-{e.message}\n"
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)
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logger.debug(f"附加内容:\n{str(e.ext_info)}")
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return -1, None # 不再重试请求该模型
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else:
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logger.error(
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f"任务-'{task_name}' 模型-'{model_name}'\n未知异常，错误信息-{str(e)}"
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)
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return -1, None # 不再重试请求该模型
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class ModelRequestHandler:
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"""
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模型请求处理器
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"""
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def __init__(
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self,
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task_name: str,
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config: ModuleConfig,
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api_client_map: dict[str, BaseClient],
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):
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self.task_name: str = task_name
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"""任务名称"""
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self.client_map: dict[str, BaseClient] = {}
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"""API客户端列表"""
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self.configs: list[tuple[ModelInfo, ModelUsageArgConfigItem]] = []
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"""模型参数配置"""
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self.req_conf: RequestConfig = config.req_conf
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"""请求配置"""
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# 获取模型与使用配置
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for model_usage in config.task_model_arg_map[task_name].usage:
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if model_usage.name not in config.models:
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logger.error(f"Model '{model_usage.name}' not found in ModelManager")
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raise KeyError(f"Model '{model_usage.name}' not found in ModelManager")
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model_info = config.models[model_usage.name]
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if model_info.api_provider not in self.client_map:
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# 缓存API客户端
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self.client_map[model_info.api_provider] = api_client_map[
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model_info.api_provider
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]
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self.configs.append((model_info, model_usage)) # 添加模型与使用配置
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async def get_response(
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self,
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messages: list[Message],
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tool_options: list[ToolOption] | None = None,
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response_format: RespFormat | None = None, # 暂不启用
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stream_response_handler: Callable[
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[AsyncStream[ChatCompletionChunk], asyncio.Event | None], APIResponse
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]
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| None = None,
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async_response_parser: Callable[[ChatCompletion], APIResponse] | None = None,
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interrupt_flag: asyncio.Event | None = None,
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) -> APIResponse:
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"""
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获取对话响应
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:param messages: 消息列表
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:param tool_options: 工具选项列表
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:param response_format: 响应格式
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:param stream_response_handler: 流式响应处理函数（可选）
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:param async_response_parser: 响应解析函数（可选）
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:param interrupt_flag: 中断信号量（可选，默认为None）
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:return: APIResponse
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"""
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# 遍历可用模型，若获取响应失败，则使用下一个模型继续请求
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for config_item in self.configs:
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client = self.client_map[config_item[0].api_provider]
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model_info: ModelInfo = config_item[0]
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model_usage_config: ModelUsageArgConfigItem = config_item[1]
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remain_try = (
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model_usage_config.max_retry or self.req_conf.max_retry
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) + 1 # 初始化：剩余尝试次数 = 最大重试次数 + 1
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compressed_messages = None
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retry_interval = self.req_conf.retry_interval
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while remain_try > 0:
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try:
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return await client.get_response(
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model_info,
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message_list=(compressed_messages or messages),
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tool_options=tool_options,
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max_tokens=model_usage_config.max_tokens
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or self.req_conf.default_max_tokens,
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temperature=model_usage_config.temperature
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or self.req_conf.default_temperature,
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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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interrupt_flag=interrupt_flag,
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)
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except Exception as e:
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logger.debug(e)
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remain_try -= 1 # 剩余尝试次数减1
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# 处理异常
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handle_res = default_exception_handler(
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e,
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self.task_name,
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model_info.name,
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remain_try,
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retry_interval=self.req_conf.retry_interval,
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messages=(messages, compressed_messages is not None),
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)
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if handle_res[0] == -1:
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# 等待间隔为-1，表示不再请求该模型
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remain_try = 0
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elif handle_res[0] != 0:
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# 等待间隔不为0，表示需要等待
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await asyncio.sleep(handle_res[0])
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retry_interval *= 2
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if handle_res[1] is not None:
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# 压缩消息
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compressed_messages = handle_res[1]
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logger.error(f"任务-'{self.task_name}' 请求执行失败，所有模型均不可用")
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raise RuntimeError("请求失败，所有模型均不可用") # 所有请求尝试均失败
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async def get_embedding(
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self,
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embedding_input: str,
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) -> APIResponse:
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"""
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获取嵌入向量
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:param embedding_input: 嵌入输入
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:return: APIResponse
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"""
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for config in self.configs:
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client = self.client_map[config[0].api_provider]
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model_info: ModelInfo = config[0]
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model_usage_config: ModelUsageArgConfigItem = config[1]
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remain_try = (
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model_usage_config.max_retry or self.req_conf.max_retry
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) + 1 # 初始化：剩余尝试次数 = 最大重试次数 + 1
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while remain_try:
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try:
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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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)
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except Exception as e:
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logger.debug(e)
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remain_try -= 1 # 剩余尝试次数减1
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# 处理异常
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handle_res = default_exception_handler(
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e,
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self.task_name,
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model_info.name,
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remain_try,
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retry_interval=self.req_conf.retry_interval,
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)
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if handle_res[0] == -1:
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# 等待间隔为-1，表示不再请求该模型
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remain_try = 0
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elif handle_res[0] != 0:
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# 等待间隔不为0，表示需要等待
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await asyncio.sleep(handle_res[0])
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logger.error(f"任务-'{self.task_name}' 请求执行失败，所有模型均不可用")
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raise RuntimeError("请求失败，所有模型均不可用") # 所有请求尝试均失败
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