969 lines
41 KiB
Python
969 lines
41 KiB
Python
import asyncio
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import json
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import re
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from datetime import datetime
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from typing import Tuple, Union, Dict, Any
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import aiohttp
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from aiohttp.client import ClientResponse
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from src.common.logger import get_logger
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import base64
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from PIL import Image
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import io
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import os
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from src.common.database.database import db # 确保 db 被导入用于 create_tables
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from src.common.database.database_model import LLMUsage # 导入 LLMUsage 模型
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from src.config.config import global_config
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from src.common.tcp_connector import get_tcp_connector
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from rich.traceback import install
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install(extra_lines=3)
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logger = get_logger("model_utils")
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class PayLoadTooLargeError(Exception):
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"""自定义异常类，用于处理请求体过大错误"""
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def __init__(self, message: str):
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super().__init__(message)
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self.message = message
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def __str__(self):
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return "请求体过大，请尝试压缩图片或减少输入内容。"
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class RequestAbortException(Exception):
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"""自定义异常类，用于处理请求中断异常"""
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def __init__(self, message: str, response: ClientResponse):
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super().__init__(message)
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self.message = message
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self.response = response
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def __str__(self):
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return self.message
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class PermissionDeniedException(Exception):
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"""自定义异常类，用于处理访问拒绝的异常"""
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def __init__(self, message: str):
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super().__init__(message)
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self.message = message
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def __str__(self):
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return self.message
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# 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/bot_config.toml和.env中的配置是否正确哦~",
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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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async def _safely_record(request_content: Dict[str, Any], payload: Dict[str, Any]):
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image_base64: str = request_content.get("image_base64")
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image_format: str = request_content.get("image_format")
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if (
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image_base64
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and payload
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and isinstance(payload, dict)
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and "messages" in payload
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and len(payload["messages"]) > 0
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):
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if isinstance(payload["messages"][0], dict) and "content" in payload["messages"][0]:
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content = payload["messages"][0]["content"]
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if isinstance(content, list) and len(content) > 1 and "image_url" in content[1]:
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payload["messages"][0]["content"][1]["image_url"]["url"] = (
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f"data:image/{image_format.lower() if image_format else 'jpeg'};base64,"
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f"{image_base64[:10]}...{image_base64[-10:]}"
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)
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return payload
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class LLMRequest:
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# 定义需要转换的模型列表，作为类变量避免重复
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MODELS_NEEDING_TRANSFORMATION = [
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"o1",
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"o1-2024-12-17",
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"o1-mini",
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"o1-mini-2024-09-12",
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"o1-preview",
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"o1-preview-2024-09-12",
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"o1-pro",
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"o1-pro-2025-03-19",
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"o3",
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"o3-2025-04-16",
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"o3-mini",
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"o3-mini-2025-01-31",
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"o4-mini",
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"o4-mini-2025-04-16",
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]
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def __init__(self, model: dict, **kwargs):
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# 将大写的配置键转换为小写并从config中获取实际值
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try:
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# print(f"model['provider']: {model['provider']}")
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self.api_key = os.environ[f"{model['provider']}_KEY"]
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self.base_url = os.environ[f"{model['provider']}_BASE_URL"]
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except AttributeError as e:
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logger.error(f"原始 model dict 信息：{model}")
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logger.error(f"配置错误：找不到对应的配置项 - {str(e)}")
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raise ValueError(f"配置错误：找不到对应的配置项 - {str(e)}") from e
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except KeyError:
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logger.warning(
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f"找不到{model['provider']}_KEY或{model['provider']}_BASE_URL环境变量，请检查配置文件或环境变量设置。"
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)
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self.model_name: str = model["name"]
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self.params = kwargs
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self.enable_thinking = model.get("enable_thinking", False)
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self.temp = model.get("temp", 0.7)
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self.thinking_budget = model.get("thinking_budget", 4096)
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self.stream = model.get("stream", False)
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self.pri_in = model.get("pri_in", 0)
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self.pri_out = model.get("pri_out", 0)
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self.max_tokens = model.get("max_tokens", global_config.model.model_max_output_length)
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# print(f"max_tokens: {self.max_tokens}")
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# 获取数据库实例
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self._init_database()
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# 从 kwargs 中提取 request_type，如果没有提供则默认为 "default"
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self.request_type = kwargs.pop("request_type", "default")
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@staticmethod
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def _init_database():
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"""初始化数据库集合"""
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try:
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# 使用 Peewee 创建表，safe=True 表示如果表已存在则不会抛出错误
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db.create_tables([LLMUsage], safe=True)
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# logger.debug("LLMUsage 表已初始化/确保存在。")
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except Exception as e:
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logger.error(f"创建 LLMUsage 表失败: {str(e)}")
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def _record_usage(
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self,
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prompt_tokens: int,
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completion_tokens: int,
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total_tokens: int,
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user_id: str = "system",
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request_type: str = None,
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endpoint: str = "/chat/completions",
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):
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"""记录模型使用情况到数据库
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Args:
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prompt_tokens: 输入token数
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completion_tokens: 输出token数
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total_tokens: 总token数
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user_id: 用户ID，默认为system
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request_type: 请求类型
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endpoint: API端点
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"""
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# 如果 request_type 为 None，则使用实例变量中的值
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if request_type is None:
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request_type = self.request_type
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try:
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# 使用 Peewee 模型创建记录
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LLMUsage.create(
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model_name=self.model_name,
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user_id=user_id,
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request_type=request_type,
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endpoint=endpoint,
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=total_tokens,
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cost=self._calculate_cost(prompt_tokens, completion_tokens),
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status="success",
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timestamp=datetime.now(), # Peewee 会处理 DateTimeField
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)
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logger.debug(
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f"Token使用情况 - 模型: {self.model_name}, "
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f"用户: {user_id}, 类型: {request_type}, "
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f"提示词: {prompt_tokens}, 完成: {completion_tokens}, "
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f"总计: {total_tokens}"
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)
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except Exception as e:
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logger.error(f"记录token使用情况失败: {str(e)}")
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def _calculate_cost(self, prompt_tokens: int, completion_tokens: int) -> float:
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"""计算API调用成本
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使用模型的pri_in和pri_out价格计算输入和输出的成本
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Args:
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prompt_tokens: 输入token数量
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completion_tokens: 输出token数量
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Returns:
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float: 总成本（元）
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"""
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# 使用模型的pri_in和pri_out计算成本
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input_cost = (prompt_tokens / 1000000) * self.pri_in
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output_cost = (completion_tokens / 1000000) * self.pri_out
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return round(input_cost + output_cost, 6)
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async def _prepare_request(
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self,
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endpoint: str,
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prompt: str = None,
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image_base64: str = None,
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image_format: str = None,
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file_bytes: bytes = None,
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file_format: str = None,
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payload: dict = None,
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retry_policy: dict = None,
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) -> Dict[str, Any]:
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"""配置请求参数
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Args:
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endpoint: API端点路径 (如 "chat/completions")
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prompt: prompt文本
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image_base64: 图片的base64编码
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image_format: 图片格式
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file_bytes: 文件的二进制数据
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file_format: 文件格式
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payload: 请求体数据
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retry_policy: 自定义重试策略
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request_type: 请求类型
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"""
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# 合并重试策略
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default_retry = {
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"max_retries": 3,
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"base_wait": 10,
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"retry_codes": [429, 413, 500, 503],
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"abort_codes": [400, 401, 402, 403],
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}
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policy = {**default_retry, **(retry_policy or {})}
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api_url = f"{self.base_url.rstrip('/')}/{endpoint.lstrip('/')}"
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stream_mode = self.stream
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# 构建请求体
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if image_base64:
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payload = await self._build_payload(prompt, image_base64, image_format)
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elif file_bytes:
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payload = await self._build_formdata_payload(file_bytes, file_format)
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elif payload is None:
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payload = await self._build_payload(prompt)
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if not file_bytes:
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if stream_mode:
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payload["stream"] = stream_mode
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if self.temp != 0.7:
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payload["temperature"] = self.temp
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# 添加enable_thinking参数（如果不是默认值False）
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if not self.enable_thinking:
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payload["enable_thinking"] = False
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if self.thinking_budget != 4096:
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payload["thinking_budget"] = self.thinking_budget
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if self.max_tokens:
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payload["max_tokens"] = self.max_tokens
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# if "max_tokens" not in payload and "max_completion_tokens" not in payload:
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# payload["max_tokens"] = global_config.model.model_max_output_length
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# 如果 payload 中依然存在 max_tokens 且需要转换，在这里进行再次检查
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if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION and "max_tokens" in payload:
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payload["max_completion_tokens"] = payload.pop("max_tokens")
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return {
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"policy": policy,
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"payload": payload,
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"api_url": api_url,
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"stream_mode": stream_mode,
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"image_base64": image_base64, # 保留必要的exception处理所需的原始数据
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"image_format": image_format,
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"file_bytes": file_bytes,
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"file_format": file_format,
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"prompt": prompt,
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}
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async def _execute_request(
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self,
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endpoint: str,
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prompt: str = None,
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image_base64: str = None,
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image_format: str = None,
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file_bytes: bytes = None,
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file_format: str = None,
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payload: dict = None,
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retry_policy: dict = None,
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response_handler: callable = None,
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user_id: str = "system",
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request_type: str = None,
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):
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"""统一请求执行入口
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Args:
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endpoint: API端点路径 (如 "chat/completions")
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prompt: prompt文本
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image_base64: 图片的base64编码
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image_format: 图片格式
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file_bytes: 文件的二进制数据
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file_format: 文件格式
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payload: 请求体数据
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retry_policy: 自定义重试策略
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response_handler: 自定义响应处理器
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user_id: 用户ID
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request_type: 请求类型
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"""
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# 获取请求配置
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request_content = await self._prepare_request(
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endpoint, prompt, image_base64, image_format, file_bytes, file_format, payload, retry_policy
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)
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if request_type is None:
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request_type = self.request_type
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for retry in range(request_content["policy"]["max_retries"]):
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try:
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# 使用上下文管理器处理会话
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if file_bytes:
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headers = await self._build_headers(is_formdata=True)
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else:
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headers = await self._build_headers(is_formdata=False)
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# 似乎是openai流式必须要的东西,不过阿里云的qwq-plus加了这个没有影响
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if request_content["stream_mode"]:
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headers["Accept"] = "text/event-stream"
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async with aiohttp.ClientSession(connector=await get_tcp_connector()) as session:
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post_kwargs = {"headers": headers}
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#form-data数据上传方式不同
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if file_bytes:
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post_kwargs["data"] = request_content["payload"]
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else:
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post_kwargs["json"] = request_content["payload"]
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async with session.post(
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request_content["api_url"], **post_kwargs
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) as response:
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handled_result = await self._handle_response(
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response, request_content, retry, response_handler, user_id, request_type, endpoint
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)
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return handled_result
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except Exception as e:
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handled_payload, count_delta = await self._handle_exception(e, retry, request_content)
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retry += count_delta # 降级不计入重试次数
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if handled_payload:
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# 如果降级成功，重新构建请求体
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request_content["payload"] = handled_payload
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continue
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logger.error(f"模型 {self.model_name} 达到最大重试次数，请求仍然失败")
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raise RuntimeError(f"模型 {self.model_name} 达到最大重试次数，API请求仍然失败")
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async def _handle_response(
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self,
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response: ClientResponse,
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request_content: Dict[str, Any],
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retry_count: int,
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response_handler: callable,
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user_id,
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request_type,
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endpoint,
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) -> Union[Dict[str, Any], None]:
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policy = request_content["policy"]
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stream_mode = request_content["stream_mode"]
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if response.status in policy["retry_codes"] or response.status in policy["abort_codes"]:
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await self._handle_error_response(response, retry_count, policy)
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return None
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response.raise_for_status()
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result = {}
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if stream_mode:
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# 将流式输出转化为非流式输出
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result = await self._handle_stream_output(response)
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else:
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result = await response.json()
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return (
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response_handler(result)
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if response_handler
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else self._default_response_handler(result, user_id, request_type, endpoint)
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)
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async def _handle_stream_output(self, response: ClientResponse) -> Dict[str, Any]:
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flag_delta_content_finished = False
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accumulated_content = ""
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usage = None # 初始化usage变量，避免未定义错误
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reasoning_content = ""
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content = ""
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tool_calls = None # 初始化工具调用变量
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async for line_bytes in response.content:
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try:
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line = line_bytes.decode("utf-8").strip()
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if not line:
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continue
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if line.startswith("data:"):
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data_str = line[5:].strip()
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if data_str == "[DONE]":
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break
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try:
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chunk = json.loads(data_str)
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if flag_delta_content_finished:
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chunk_usage = chunk.get("usage", None)
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if chunk_usage:
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usage = chunk_usage # 获取token用量
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else:
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delta = chunk["choices"][0]["delta"]
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delta_content = delta.get("content")
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if delta_content is None:
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delta_content = ""
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accumulated_content += delta_content
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# 提取工具调用信息
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if "tool_calls" in delta:
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if tool_calls is None:
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tool_calls = delta["tool_calls"]
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else:
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# 合并工具调用信息
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tool_calls.extend(delta["tool_calls"])
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# 检测流式输出文本是否结束
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finish_reason = chunk["choices"][0].get("finish_reason")
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if delta.get("reasoning_content", None):
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reasoning_content += delta["reasoning_content"]
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if finish_reason == "stop" or finish_reason == "tool_calls":
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chunk_usage = chunk.get("usage", None)
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if chunk_usage:
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usage = chunk_usage
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break
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# 部分平台在文本输出结束前不会返回token用量，此时需要再获取一次chunk
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flag_delta_content_finished = True
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except Exception as e:
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logger.exception(f"模型 {self.model_name} 解析流式输出错误: {str(e)}")
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except Exception as e:
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if isinstance(e, GeneratorExit):
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log_content = f"模型 {self.model_name} 流式输出被中断，正在清理资源..."
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else:
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log_content = f"模型 {self.model_name} 处理流式输出时发生错误: {str(e)}"
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logger.warning(log_content)
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# 确保资源被正确清理
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try:
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await response.release()
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except Exception as cleanup_error:
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logger.error(f"清理资源时发生错误: {cleanup_error}")
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# 返回已经累积的内容
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content = accumulated_content
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if not content:
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content = accumulated_content
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think_match = re.search(r"<think>(.*?)</think>", content, re.DOTALL)
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if think_match:
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reasoning_content = think_match.group(1).strip()
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content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL).strip()
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# 构建消息对象
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message = {
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"content": content,
|
||
"reasoning_content": reasoning_content,
|
||
}
|
||
|
||
# 如果有工具调用，添加到消息中
|
||
if tool_calls:
|
||
message["tool_calls"] = tool_calls
|
||
|
||
result = {
|
||
"choices": [{"message": message}],
|
||
"usage": usage,
|
||
}
|
||
return result
|
||
|
||
async def _handle_error_response(
|
||
self, response: ClientResponse, retry_count: int, policy: Dict[str, Any]
|
||
) -> Union[Dict[str, any]]:
|
||
if response.status in policy["retry_codes"]:
|
||
wait_time = policy["base_wait"] * (2**retry_count)
|
||
logger.warning(f"模型 {self.model_name} 错误码: {response.status}, 等待 {wait_time}秒后重试")
|
||
if response.status == 413:
|
||
logger.warning("请求体过大，尝试压缩...")
|
||
raise PayLoadTooLargeError("请求体过大")
|
||
elif response.status in [500, 503]:
|
||
logger.error(
|
||
f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
|
||
)
|
||
raise RuntimeError("服务器负载过高，模型回复失败QAQ")
|
||
else:
|
||
logger.warning(f"模型 {self.model_name} 请求限制(429)，等待{wait_time}秒后重试...")
|
||
raise RuntimeError("请求限制(429)")
|
||
elif response.status in policy["abort_codes"]:
|
||
if response.status != 403:
|
||
raise RequestAbortException("请求出现错误，中断处理", response)
|
||
else:
|
||
raise PermissionDeniedException("模型禁止访问")
|
||
|
||
async def _handle_exception(
|
||
self, exception, retry_count: int, request_content: Dict[str, Any]
|
||
) -> Union[Tuple[Dict[str, Any], int], Tuple[None, int]]:
|
||
policy = request_content["policy"]
|
||
payload = request_content["payload"]
|
||
wait_time = policy["base_wait"] * (2**retry_count)
|
||
keep_request = False
|
||
if retry_count < policy["max_retries"] - 1:
|
||
keep_request = True
|
||
if isinstance(exception, RequestAbortException):
|
||
response = exception.response
|
||
logger.error(
|
||
f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
|
||
)
|
||
# print(request_content)
|
||
# print(response)
|
||
# 尝试获取并记录服务器返回的详细错误信息
|
||
try:
|
||
error_json = await response.json()
|
||
if error_json and isinstance(error_json, list) and len(error_json) > 0:
|
||
# 处理多个错误的情况
|
||
for error_item in error_json:
|
||
if "error" in error_item and isinstance(error_item["error"], dict):
|
||
error_obj: dict = error_item["error"]
|
||
error_code = error_obj.get("code")
|
||
error_message = error_obj.get("message")
|
||
error_status = error_obj.get("status")
|
||
logger.error(
|
||
f"服务器错误详情: 代码={error_code}, 状态={error_status}, 消息={error_message}"
|
||
)
|
||
elif isinstance(error_json, dict) and "error" in error_json:
|
||
# 处理单个错误对象的情况
|
||
error_obj = error_json.get("error", {})
|
||
error_code = error_obj.get("code")
|
||
error_message = error_obj.get("message")
|
||
error_status = error_obj.get("status")
|
||
logger.error(f"服务器错误详情: 代码={error_code}, 状态={error_status}, 消息={error_message}")
|
||
else:
|
||
# 记录原始错误响应内容
|
||
logger.error(f"服务器错误响应: {error_json}")
|
||
except Exception as e:
|
||
logger.warning(f"无法解析服务器错误响应: {str(e)}")
|
||
raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(response.status)}")
|
||
|
||
elif isinstance(exception, PermissionDeniedException):
|
||
# 只针对硅基流动的V3和R1进行降级处理
|
||
if self.model_name.startswith("Pro/deepseek-ai") and self.base_url == "https://api.siliconflow.cn/v1/":
|
||
old_model_name = self.model_name
|
||
self.model_name = self.model_name[4:] # 移除"Pro/"前缀
|
||
logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
|
||
|
||
# 对全局配置进行更新
|
||
if global_config.model.replyer_2.get("name") == old_model_name:
|
||
global_config.model.replyer_2["name"] = self.model_name
|
||
logger.warning(f"将全局配置中的 llm_normal 模型临时降级至{self.model_name}")
|
||
if global_config.model.replyer_1.get("name") == old_model_name:
|
||
global_config.model.replyer_1["name"] = self.model_name
|
||
logger.warning(f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}")
|
||
|
||
if payload and "model" in payload:
|
||
payload["model"] = self.model_name
|
||
|
||
await asyncio.sleep(wait_time)
|
||
return payload, -1
|
||
raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(403)}")
|
||
|
||
elif isinstance(exception, PayLoadTooLargeError):
|
||
if keep_request:
|
||
image_base64 = request_content["image_base64"]
|
||
compressed_image_base64 = compress_base64_image_by_scale(image_base64)
|
||
new_payload = await self._build_payload(
|
||
request_content["prompt"], compressed_image_base64, request_content["image_format"]
|
||
)
|
||
return new_payload, 0
|
||
else:
|
||
return None, 0
|
||
|
||
elif isinstance(exception, aiohttp.ClientError) or isinstance(exception, asyncio.TimeoutError):
|
||
if keep_request:
|
||
logger.error(f"模型 {self.model_name} 网络错误，等待{wait_time}秒后重试... 错误: {str(exception)}")
|
||
await asyncio.sleep(wait_time)
|
||
return None, 0
|
||
else:
|
||
logger.critical(f"模型 {self.model_name} 网络错误达到最大重试次数: {str(exception)}")
|
||
raise RuntimeError(f"网络请求失败: {str(exception)}")
|
||
|
||
elif isinstance(exception, aiohttp.ClientResponseError):
|
||
# 处理aiohttp抛出的，除了policy中的status的响应错误
|
||
if keep_request:
|
||
logger.error(
|
||
f"模型 {self.model_name} HTTP响应错误，等待{wait_time}秒后重试... 状态码: {exception.status}, 错误: {exception.message}"
|
||
)
|
||
try:
|
||
error_text = await exception.response.text()
|
||
error_json = json.loads(error_text)
|
||
if isinstance(error_json, list) and len(error_json) > 0:
|
||
# 处理多个错误的情况
|
||
for error_item in error_json:
|
||
if "error" in error_item and isinstance(error_item["error"], dict):
|
||
error_obj = error_item["error"]
|
||
logger.error(
|
||
f"模型 {self.model_name} 服务器错误详情: 代码={error_obj.get('code')}, "
|
||
f"状态={error_obj.get('status')}, "
|
||
f"消息={error_obj.get('message')}"
|
||
)
|
||
elif isinstance(error_json, dict) and "error" in error_json:
|
||
error_obj = error_json.get("error", {})
|
||
logger.error(
|
||
f"模型 {self.model_name} 服务器错误详情: 代码={error_obj.get('code')}, "
|
||
f"状态={error_obj.get('status')}, "
|
||
f"消息={error_obj.get('message')}"
|
||
)
|
||
else:
|
||
logger.error(f"模型 {self.model_name} 服务器错误响应: {error_json}")
|
||
except (json.JSONDecodeError, TypeError) as json_err:
|
||
logger.warning(
|
||
f"模型 {self.model_name} 响应不是有效的JSON: {str(json_err)}, 原始内容: {error_text[:200]}"
|
||
)
|
||
except Exception as parse_err:
|
||
logger.warning(f"模型 {self.model_name} 无法解析响应错误内容: {str(parse_err)}")
|
||
|
||
await asyncio.sleep(wait_time)
|
||
return None, 0
|
||
else:
|
||
logger.critical(
|
||
f"模型 {self.model_name} HTTP响应错误达到最大重试次数: 状态码: {exception.status}, 错误: {exception.message}"
|
||
)
|
||
# 安全地检查和记录请求详情
|
||
handled_payload = await _safely_record(request_content, payload)
|
||
logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {str(handled_payload)[:100]}")
|
||
raise RuntimeError(
|
||
f"模型 {self.model_name} API请求失败: 状态码 {exception.status}, {exception.message}"
|
||
)
|
||
|
||
else:
|
||
if keep_request:
|
||
logger.error(f"模型 {self.model_name} 请求失败，等待{wait_time}秒后重试... 错误: {str(exception)}")
|
||
await asyncio.sleep(wait_time)
|
||
return None, 0
|
||
else:
|
||
logger.critical(f"模型 {self.model_name} 请求失败: {str(exception)}")
|
||
# 安全地检查和记录请求详情
|
||
handled_payload = await _safely_record(request_content, payload)
|
||
logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {str(handled_payload)[:100]}")
|
||
raise RuntimeError(f"模型 {self.model_name} API请求失败: {str(exception)}")
|
||
|
||
async def _transform_parameters(self, params: dict) -> dict:
|
||
"""
|
||
根据模型名称转换参数：
|
||
- 对于需要转换的OpenAI CoT系列模型（例如 "o3-mini"），删除 'temperature' 参数，
|
||
并将 'max_tokens' 重命名为 'max_completion_tokens'
|
||
"""
|
||
# 复制一份参数，避免直接修改原始数据
|
||
new_params = dict(params)
|
||
|
||
if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION:
|
||
# 删除 'temperature' 参数（如果存在），但避免删除我们在_build_payload中添加的自定义温度
|
||
if "temperature" in new_params and new_params["temperature"] == 0.7:
|
||
new_params.pop("temperature")
|
||
# 如果存在 'max_tokens'，则重命名为 'max_completion_tokens'
|
||
if "max_tokens" in new_params:
|
||
new_params["max_completion_tokens"] = new_params.pop("max_tokens")
|
||
return new_params
|
||
|
||
async def _build_formdata_payload(self, file_bytes: bytes, file_format: str) -> aiohttp.FormData:
|
||
"""构建form-data请求体"""
|
||
# 目前只适配了音频文件
|
||
# 如果后续要支持其他类型的文件，可以在这里添加更多的处理逻辑
|
||
data = aiohttp.FormData()
|
||
content_type_list = {
|
||
"wav": "audio/wav",
|
||
"mp3": "audio/mpeg",
|
||
"ogg": "audio/ogg",
|
||
"flac": "audio/flac",
|
||
"aac": "audio/aac",
|
||
}
|
||
|
||
content_type = content_type_list.get(file_format)
|
||
if not content_type:
|
||
logger.warning(f"暂不支持的文件类型: {file_format}")
|
||
|
||
data.add_field(
|
||
"file",io.BytesIO(file_bytes),
|
||
filename=f"file.{file_format}",
|
||
content_type=f'{content_type}' # 根据实际文件类型设置
|
||
)
|
||
data.add_field(
|
||
"model", self.model_name
|
||
)
|
||
return data
|
||
|
||
async def _build_payload(self, prompt: str, image_base64: str = None, image_format: str = None) -> dict:
|
||
"""构建请求体"""
|
||
# 复制一份参数，避免直接修改 self.params
|
||
params_copy = await self._transform_parameters(self.params)
|
||
if image_base64:
|
||
messages = [
|
||
{
|
||
"role": "user",
|
||
"content": [
|
||
{"type": "text", "text": prompt},
|
||
{
|
||
"type": "image_url",
|
||
"image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"},
|
||
},
|
||
],
|
||
}
|
||
]
|
||
else:
|
||
messages = [{"role": "user", "content": prompt}]
|
||
|
||
payload = {
|
||
"model": self.model_name,
|
||
"messages": messages,
|
||
**params_copy,
|
||
}
|
||
|
||
# 添加temp参数（如果不是默认值0.7）
|
||
if self.temp != 0.7:
|
||
payload["temperature"] = self.temp
|
||
|
||
# 添加enable_thinking参数（如果不是默认值False）
|
||
if not self.enable_thinking:
|
||
payload["enable_thinking"] = False
|
||
|
||
if self.thinking_budget != 4096:
|
||
payload["thinking_budget"] = self.thinking_budget
|
||
|
||
if self.max_tokens:
|
||
payload["max_tokens"] = self.max_tokens
|
||
|
||
# if "max_tokens" not in payload and "max_completion_tokens" not in payload:
|
||
# payload["max_tokens"] = global_config.model.model_max_output_length
|
||
# 如果 payload 中依然存在 max_tokens 且需要转换，在这里进行再次检查
|
||
if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION and "max_tokens" in payload:
|
||
payload["max_completion_tokens"] = payload.pop("max_tokens")
|
||
return payload
|
||
|
||
def _default_response_handler(
|
||
self, result: dict, user_id: str = "system", request_type: str = None, endpoint: str = "/chat/completions"
|
||
) -> Tuple:
|
||
"""默认响应解析"""
|
||
if "choices" in result and result["choices"]:
|
||
message = result["choices"][0]["message"]
|
||
content = message.get("content", "")
|
||
content, reasoning = self._extract_reasoning(content)
|
||
reasoning_content = message.get("model_extra", {}).get("reasoning_content", "")
|
||
if not reasoning_content:
|
||
reasoning_content = message.get("reasoning_content", "")
|
||
if not reasoning_content:
|
||
reasoning_content = reasoning
|
||
|
||
# 提取工具调用信息
|
||
tool_calls = message.get("tool_calls", None)
|
||
|
||
# 记录token使用情况
|
||
usage = result.get("usage", {})
|
||
if usage:
|
||
prompt_tokens = usage.get("prompt_tokens", 0)
|
||
completion_tokens = usage.get("completion_tokens", 0)
|
||
total_tokens = usage.get("total_tokens", 0)
|
||
self._record_usage(
|
||
prompt_tokens=prompt_tokens,
|
||
completion_tokens=completion_tokens,
|
||
total_tokens=total_tokens,
|
||
user_id=user_id,
|
||
request_type=request_type if request_type is not None else self.request_type,
|
||
endpoint=endpoint,
|
||
)
|
||
|
||
# 只有当tool_calls存在且不为空时才返回
|
||
if tool_calls:
|
||
logger.debug(f"检测到工具调用: {tool_calls}")
|
||
return content, reasoning_content, tool_calls
|
||
else:
|
||
return content, reasoning_content
|
||
elif "text" in result and result["text"]:
|
||
return result["text"]
|
||
return "没有返回结果", ""
|
||
|
||
@staticmethod
|
||
def _extract_reasoning(content: str) -> Tuple[str, str]:
|
||
"""CoT思维链提取"""
|
||
match = re.search(r"(?:<think>)?(.*?)</think>", content, re.DOTALL)
|
||
content = re.sub(r"(?:<think>)?.*?</think>", "", content, flags=re.DOTALL, count=1).strip()
|
||
if match:
|
||
reasoning = match.group(1).strip()
|
||
else:
|
||
reasoning = ""
|
||
return content, reasoning
|
||
|
||
async def _build_headers(self, no_key: bool = False, is_formdata: bool = False) -> dict:
|
||
"""构建请求头"""
|
||
if no_key:
|
||
if is_formdata:
|
||
return {"Authorization": "Bearer **********"}
|
||
return {"Authorization": "Bearer **********", "Content-Type": "application/json"}
|
||
else:
|
||
if is_formdata:
|
||
return {"Authorization": f"Bearer {self.api_key}"}
|
||
return {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
|
||
# 防止小朋友们截图自己的key
|
||
|
||
async def generate_response_for_image(self, prompt: str, image_base64: str, image_format: str) -> Tuple:
|
||
"""根据输入的提示和图片生成模型的异步响应"""
|
||
|
||
response = await self._execute_request(
|
||
endpoint="/chat/completions", prompt=prompt, image_base64=image_base64, image_format=image_format
|
||
)
|
||
# 根据返回值的长度决定怎么处理
|
||
if len(response) == 3:
|
||
content, reasoning_content, tool_calls = response
|
||
return content, reasoning_content, tool_calls
|
||
else:
|
||
content, reasoning_content = response
|
||
return content, reasoning_content
|
||
|
||
async def generate_response_for_voice(self, voice_bytes: bytes) -> Tuple:
|
||
"""根据输入的语音文件生成模型的异步响应"""
|
||
response = await self._execute_request(endpoint="/audio/transcriptions",file_bytes=voice_bytes, file_format='wav')
|
||
return response
|
||
|
||
async def generate_response_async(self, prompt: str, **kwargs) -> Union[str, Tuple]:
|
||
"""异步方式根据输入的提示生成模型的响应"""
|
||
# 构建请求体，不硬编码max_tokens
|
||
data = {
|
||
"model": self.model_name,
|
||
"messages": [{"role": "user", "content": prompt}],
|
||
**self.params,
|
||
**kwargs,
|
||
}
|
||
|
||
response = await self._execute_request(endpoint="/chat/completions", payload=data, prompt=prompt)
|
||
# 原样返回响应，不做处理
|
||
|
||
if len(response) == 3:
|
||
content, reasoning_content, tool_calls = response
|
||
return content, (reasoning_content, self.model_name, tool_calls)
|
||
else:
|
||
content, reasoning_content = response
|
||
return content, (reasoning_content, self.model_name)
|
||
|
||
async def get_embedding(self, text: str) -> Union[list, None]:
|
||
"""异步方法：获取文本的embedding向量
|
||
|
||
Args:
|
||
text: 需要获取embedding的文本
|
||
|
||
Returns:
|
||
list: embedding向量，如果失败则返回None
|
||
"""
|
||
|
||
if len(text) < 1:
|
||
logger.debug("该消息没有长度，不再发送获取embedding向量的请求")
|
||
return None
|
||
|
||
def embedding_handler(result):
|
||
"""处理响应"""
|
||
if "data" in result and len(result["data"]) > 0:
|
||
# 提取 token 使用信息
|
||
usage = result.get("usage", {})
|
||
if usage:
|
||
prompt_tokens = usage.get("prompt_tokens", 0)
|
||
completion_tokens = usage.get("completion_tokens", 0)
|
||
total_tokens = usage.get("total_tokens", 0)
|
||
# 记录 token 使用情况
|
||
self._record_usage(
|
||
prompt_tokens=prompt_tokens,
|
||
completion_tokens=completion_tokens,
|
||
total_tokens=total_tokens,
|
||
user_id="system", # 可以根据需要修改 user_id
|
||
# request_type="embedding", # 请求类型为 embedding
|
||
request_type=self.request_type, # 请求类型为 text
|
||
endpoint="/embeddings", # API 端点
|
||
)
|
||
return result["data"][0].get("embedding", None)
|
||
return result["data"][0].get("embedding", None)
|
||
return None
|
||
|
||
embedding = await self._execute_request(
|
||
endpoint="/embeddings",
|
||
prompt=text,
|
||
payload={"model": self.model_name, "input": text, "encoding_format": "float"},
|
||
retry_policy={"max_retries": 2, "base_wait": 6},
|
||
response_handler=embedding_handler,
|
||
)
|
||
return embedding
|
||
|
||
|
||
def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str:
|
||
"""压缩base64格式的图片到指定大小
|
||
Args:
|
||
base64_data: base64编码的图片数据
|
||
target_size: 目标文件大小（字节），默认0.8MB
|
||
Returns:
|
||
str: 压缩后的base64图片数据
|
||
"""
|
||
try:
|
||
# 将base64转换为字节数据
|
||
# 确保base64字符串只包含ASCII字符
|
||
if isinstance(base64_data, str):
|
||
base64_data = base64_data.encode("ascii", errors="ignore").decode("ascii")
|
||
image_data = base64.b64decode(base64_data)
|
||
|
||
# 如果已经小于目标大小，直接返回原图
|
||
if len(image_data) <= 2 * 1024 * 1024:
|
||
return base64_data
|
||
|
||
# 将字节数据转换为图片对象
|
||
img = Image.open(io.BytesIO(image_data))
|
||
|
||
# 获取原始尺寸
|
||
original_width, original_height = img.size
|
||
|
||
# 计算缩放比例
|
||
scale = min(1.0, (target_size / len(image_data)) ** 0.5)
|
||
|
||
# 计算新的尺寸
|
||
new_width = int(original_width * scale)
|
||
new_height = int(original_height * scale)
|
||
|
||
# 创建内存缓冲区
|
||
output_buffer = io.BytesIO()
|
||
|
||
# 如果是GIF，处理所有帧
|
||
if getattr(img, "is_animated", False):
|
||
frames = []
|
||
for frame_idx in range(img.n_frames):
|
||
img.seek(frame_idx)
|
||
new_frame = img.copy()
|
||
new_frame = new_frame.resize((new_width // 2, new_height // 2), Image.Resampling.LANCZOS) # 动图折上折
|
||
frames.append(new_frame)
|
||
|
||
# 保存到缓冲区
|
||
frames[0].save(
|
||
output_buffer,
|
||
format="GIF",
|
||
save_all=True,
|
||
append_images=frames[1:],
|
||
optimize=True,
|
||
duration=img.info.get("duration", 100),
|
||
loop=img.info.get("loop", 0),
|
||
)
|
||
else:
|
||
# 处理静态图片
|
||
resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
|
||
|
||
# 保存到缓冲区，保持原始格式
|
||
if img.format == "PNG" and img.mode in ("RGBA", "LA"):
|
||
resized_img.save(output_buffer, format="PNG", optimize=True)
|
||
else:
|
||
resized_img.save(output_buffer, format="JPEG", quality=95, optimize=True)
|
||
|
||
# 获取压缩后的数据并转换为base64
|
||
compressed_data = output_buffer.getvalue()
|
||
logger.info(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}")
|
||
logger.info(f"压缩前大小: {len(image_data) / 1024:.1f}KB, 压缩后大小: {len(compressed_data) / 1024:.1f}KB")
|
||
|
||
return base64.b64encode(compressed_data).decode("utf-8")
|
||
|
||
except Exception as e:
|
||
logger.error(f"压缩图片失败: {str(e)}")
|
||
import traceback
|
||
|
||
logger.error(traceback.format_exc())
|
||
return base64_data
|