598 lines
26 KiB
Python
598 lines
26 KiB
Python
import re
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import asyncio
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import time
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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
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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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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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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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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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# 模型选择
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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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# 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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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
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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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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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# 模型选择
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model_info, api_provider, client = self._select_model()
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# 🔥 内容混淆处理
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processed_prompt = self._apply_content_obfuscation(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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# 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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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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tool_options=tool_built,
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)
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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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model_usage=usage,
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user_id="system",
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request_type=self.request_type,
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endpoint="/chat/completions",
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)
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if not content:
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if raise_when_empty:
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logger.warning("生成的响应为空")
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raise RuntimeError("生成的响应为空")
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content = "生成的响应为空，请检查模型配置或输入内容是否正确"
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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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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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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.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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model_usage=usage,
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user_id="system",
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request_type=self.request_type,
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endpoint="/embeddings",
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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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"""
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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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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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)
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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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client = client_registry.get_client_class_instance(api_provider)
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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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return model_info, api_provider, client
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async def _execute_request(
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self,
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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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) -> 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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"""
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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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tool_options=tool_options,
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max_tokens=self.model_for_task.max_tokens if max_tokens is None else max_tokens,
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temperature=self.model_for_task.temperature if temperature is None else 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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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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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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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 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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total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
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self.model_usage[model_info.name] = (total_tokens, penalty + 1, usage_penalty)
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wait_interval, compressed_messages = self._default_exception_handler(
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e,
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self.task_name,
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model_name=model_info.name,
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remain_try=retry_remain,
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retry_interval=api_provider.retry_interval,
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messages=(message_list, compressed_messages is not None) if message_list else None,
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)
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if wait_interval == -1:
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retry_remain = 0 # 不再重试
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elif wait_interval > 0:
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logger.info(f"等待 {wait_interval} 秒后重试...")
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await asyncio.sleep(wait_interval)
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finally:
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# 放在finally防止死循环
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retry_remain -= 1
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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) # 使用结束，减少使用惩罚值
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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")
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raise RuntimeError("请求失败，已达到最大重试次数")
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def _default_exception_handler(
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self,
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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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Args:
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e (Exception): 异常对象
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task_name (str): 任务名称
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model_name (str): 模型名称
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remain_try (int): 剩余尝试次数
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retry_interval (int): 重试间隔
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messages (tuple[list[Message], bool] | None): (消息列表, 是否已压缩过)
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Returns:
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(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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return self._check_retry(
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remain_try,
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retry_interval,
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can_retry_msg=f"任务-'{task_name}' 模型-'{model_name}': 连接异常，将于{retry_interval}秒后重试",
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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",
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)
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elif isinstance(e, ReqAbortException):
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logger.warning(f"任务-'{task_name}' 模型-'{model_name}': 请求被中断，详细信息-{str(e.message)}")
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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 self._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(f"任务-'{task_name}' 模型-'{model_name}': 响应解析错误，错误信息-{e.message}")
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logger.debug(f"附加内容: {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(f"任务-'{task_name}' 模型-'{model_name}': 未知异常，错误信息-{str(e)}")
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return -1, None # 不再重试请求该模型
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def _check_retry(
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self,
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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, List[Message] | None]:
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"""辅助函数：检查是否可以重试
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Args:
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remain_try (int): 剩余尝试次数
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retry_interval (int): 重试间隔
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can_retry_msg (str): 可以重试时的提示信息
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cannot_retry_msg (str): 不可以重试时的提示信息
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can_retry_callable (Callable | None): 可以重试时调用的函数（如果有）
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**kwargs: 其他参数
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Returns:
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(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）)
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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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self,
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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,
|
||
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
|
||
|
||
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)
|