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re-style: 格式化代码

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This commit is contained in:
John Richard
2025-10-02 20:26:01 +08:00
parent ecb02cae31
commit 7923eafef3
263 changed files with 3103 additions and 3123 deletions
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83
src/llm_models/utils_model.py
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@@ -1,4 +1,3 @@
# -*- coding: utf-8 -*-
"""
@desc: 该模块封装了与大语言模型（LLM）交互的所有核心逻辑。
它被设计为一个高度容错和可扩展的系统，包含以下主要组件：
@@ -19,24 +18,26 @@
作为模块的统一入口（Facade），为上层业务逻辑提供了简洁的接口来发起文本、图像、语音等不同类型的LLM请求。
"""
import re
import asyncio
import time
import random
import re
import string
import time
from collections.abc import Callable, Coroutine
from enum import Enum
from typing import Any
from rich.traceback import install
from typing import Tuple, List, Dict, Optional, Callable, Any, Coroutine
from src.common.logger import get_logger
from src.config.config import model_config
from src.config.api_ada_configs import APIProvider, ModelInfo, TaskConfig
from .payload_content.message import MessageBuilder, Message
from .payload_content.tool_option import ToolOption, ToolCall, ToolOptionBuilder
from .model_client.base_client import BaseClient, APIResponse, client_registry, UsageRecord
from .utils import compress_messages, llm_usage_recorder
from src.config.config import model_config
from .exceptions import NetworkConnectionError, ReqAbortException, RespNotOkException, RespParseException
from .model_client.base_client import APIResponse, BaseClient, UsageRecord, client_registry
from .payload_content.message import Message, MessageBuilder
from .payload_content.tool_option import ToolCall, ToolOption, ToolOptionBuilder
from .utils import compress_messages, llm_usage_recorder
install(extra_lines=3)
@@ -139,7 +140,7 @@ class _ModelSelector:
CRITICAL_PENALTY_MULTIPLIER = 5 # 严重错误惩罚乘数
DEFAULT_PENALTY_INCREMENT = 1 # 默认惩罚增量
def __init__(self, model_list: List[str], model_usage: Dict[str, Tuple[int, int, int]]):
def __init__(self, model_list: list[str], model_usage: dict[str, tuple[int, int, int]]):
"""
初始化模型选择器。
@@ -153,7 +154,7 @@ class _ModelSelector:
def select_best_available_model(
self, failed_models_in_this_request: set, request_type: str
) -> Optional[Tuple[ModelInfo, APIProvider, BaseClient]]:
) -> tuple[ModelInfo, APIProvider, BaseClient] | None:
"""
从可用模型中选择负载均衡评分最低的模型，并排除当前请求中已失败的模型。
@@ -306,7 +307,7 @@ class _PromptProcessor:
return processed_prompt
def process_response(self, content: str, use_anti_truncation: bool) -> Tuple[str, str, bool]:
def process_response(self, content: str, use_anti_truncation: bool) -> tuple[str, str, bool]:
"""
处理响应内容，提取思维链并检查截断。
@@ -393,7 +394,7 @@ class _PromptProcessor:
return " ".join(result)
@staticmethod
def _extract_reasoning(content: str) -> Tuple[str, str]:
def _extract_reasoning(content: str) -> tuple[str, str]:
"""
从模型返回的完整内容中提取被<think>...</think>标签包裹的思考过程，
并返回清理后的内容和思考过程。
@@ -462,7 +463,7 @@ class _RequestExecutor:
RuntimeError: 如果达到最大重试次数。
"""
retry_remain = api_provider.max_retry
compressed_messages: Optional[List[Message]] = None
compressed_messages: list[Message] | None = None
while retry_remain > 0:
try:
@@ -487,7 +488,7 @@ class _RequestExecutor:
return await client.get_audio_transcriptions(model_info=model_info, **kwargs)
except Exception as e:
logger.debug(f"请求失败: {str(e)}")
logger.debug(f"请求失败: {e!s}")
# 记录失败并更新模型的惩罚值
self.model_selector.update_failure_penalty(model_info.name, e)
@@ -514,7 +515,7 @@ class _RequestExecutor:
def _handle_exception(
self, e: Exception, model_info: ModelInfo, api_provider: APIProvider, remain_try: int, messages_info
) -> Tuple[int, Optional[List[Message]]]:
) -> tuple[int, list[Message] | None]:
"""
默认异常处理函数，决定是否重试。
@@ -532,12 +533,12 @@ class _RequestExecutor:
logger.error(f"任务-'{self.task_name}' 模型-'{model_name}': 响应解析错误 - {e.message}")
return -1, None
else:
logger.error(f"任务-'{self.task_name}' 模型-'{model_name}': 未知异常 - {str(e)}")
logger.error(f"任务-'{self.task_name}' 模型-'{model_name}': 未知异常 - {e!s}")
return -1, None
def _handle_resp_not_ok(
self, e: RespNotOkException, model_info: ModelInfo, api_provider: APIProvider, remain_try: int, messages_info
) -> Tuple[int, Optional[List[Message]]]:
) -> tuple[int, list[Message] | None]:
"""
处理非200的HTTP响应异常。
@@ -583,7 +584,7 @@ class _RequestExecutor:
logger.warning(f"任务-'{self.task_name}' 模型-'{model_name}': 未知响应错误 {e.status_code} - {e.message}")
return -1, None
def _check_retry(self, remain_try: int, interval: int, reason: str, model_name: str) -> Tuple[int, None]:
def _check_retry(self, remain_try: int, interval: int, reason: str, model_name: str) -> tuple[int, None]:
"""
辅助函数，根据剩余次数决定是否进行下一次重试。
@@ -620,7 +621,7 @@ class _RequestStrategy:
model_selector: _ModelSelector,
prompt_processor: _PromptProcessor,
executor: _RequestExecutor,
model_list: List[str],
model_list: list[str],
task_name: str,
):
"""
@@ -644,13 +645,13 @@ class _RequestStrategy:
request_type: RequestType,
raise_when_empty: bool = True,
**kwargs,
) -> Tuple[APIResponse, ModelInfo]:
) -> tuple[APIResponse, ModelInfo]:
"""
执行请求，动态选择最佳可用模型，并在模型失败时进行故障转移。
"""
failed_models_in_this_request = set()
max_attempts = len(self.model_list)
last_exception: Optional[Exception] = None
last_exception: Exception | None = None
for attempt in range(max_attempts):
selection_result = self.model_selector.select_best_available_model(
@@ -787,9 +788,7 @@ class LLMRequest:
"""
self.task_name = request_type
self.model_for_task = model_set
self.model_usage: Dict[str, Tuple[int, int, int]] = {
model: (0, 0, 0) for model in self.model_for_task.model_list
}
self.model_usage: dict[str, tuple[int, int, int]] = dict.fromkeys(self.model_for_task.model_list, (0, 0, 0))
"""模型使用量记录，(total_tokens, penalty, usage_penalty)"""
# 初始化辅助类
@@ -805,9 +804,9 @@ class LLMRequest:
prompt: str,
image_base64: str,
image_format: str,
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
) -> Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
temperature: float | None = None,
max_tokens: int | None = None,
) -> tuple[str, tuple[str, str, list[ToolCall] | None]]:
"""
为图像生成响应。
@@ -855,7 +854,7 @@ class LLMRequest:
return content, (reasoning, model_info.name, response.tool_calls)
async def generate_response_for_voice(self, voice_base64: str) -> Optional[str]:
async def generate_response_for_voice(self, voice_base64: str) -> str | None:
"""
为语音生成响应（语音转文字）。
使用故障转移策略来确保即使主模型失败也能获得结果。
@@ -872,11 +871,11 @@ class LLMRequest:
async def generate_response_async(
self,
prompt: str,
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
tools: Optional[List[Dict[str, Any]]] = None,
temperature: float | None = None,
max_tokens: int | None = None,
tools: list[dict[str, Any]] | None = None,
raise_when_empty: bool = True,
) -> Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
) -> tuple[str, tuple[str, str, list[ToolCall] | None]]:
"""
异步生成响应，支持并发请求。
@@ -914,11 +913,11 @@ class LLMRequest:
async def _execute_single_text_request(
self,
prompt: str,
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
tools: Optional[List[Dict[str, Any]]] = None,
temperature: float | None = None,
max_tokens: int | None = None,
tools: list[dict[str, Any]] | None = None,
raise_when_empty: bool = True,
) -> Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
) -> tuple[str, tuple[str, str, list[ToolCall] | None]]:
"""
执行单次文本生成请求的内部方法。
这是 `generate_response_async` 的核心实现，处理单个请求的完整生命周期，
@@ -956,7 +955,7 @@ class LLMRequest:
return response.content or "", (response.reasoning_content or "", model_info.name, response.tool_calls)
async def get_embedding(self, embedding_input: str) -> Tuple[List[float], str]:
async def get_embedding(self, embedding_input: str) -> tuple[list[float], str]:
"""
获取嵌入向量。
@@ -978,7 +977,7 @@ class LLMRequest:
return response.embedding, model_info.name
async def _record_usage(self, model_info: ModelInfo, usage: Optional[UsageRecord], time_cost: float, endpoint: str):
async def _record_usage(self, model_info: ModelInfo, usage: UsageRecord | None, time_cost: float, endpoint: str):
"""
记录模型使用情况。
@@ -1009,7 +1008,7 @@ class LLMRequest:
)
@staticmethod
def _build_tool_options(tools: Optional[List[Dict[str, Any]]]) -> Optional[List[ToolOption]]:
def _build_tool_options(tools: list[dict[str, Any]] | None) -> list[ToolOption] | None:
"""
根据输入的字典列表构建并验证 `ToolOption` 对象列表。
@@ -1028,7 +1027,7 @@ class LLMRequest:
if not tools:
return None
tool_options: List[ToolOption] = []
tool_options: list[ToolOption] = []
# 遍历每个工具定义
for tool in tools:
try: