323 lines
14 KiB
Python
323 lines
14 KiB
Python
import aiohttp
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import asyncio
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import requests
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import time
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from typing import Tuple, Union
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from nonebot import get_driver
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from loguru import logger
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from ..chat.config import global_config
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driver = get_driver()
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config = driver.config
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class LLM_request:
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def __init__(self, model, **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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self.api_key = getattr(config, model["key"])
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self.base_url = getattr(config, model["base_url"])
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except AttributeError as e:
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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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self.model_name = model["name"]
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self.params = kwargs
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async def generate_response(self, prompt: str) -> Tuple[str, str]:
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"""根据输入的提示生成模型的异步响应"""
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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# 构建请求体
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data = {
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"model": self.model_name,
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"messages": [{"role": "user", "content": prompt}],
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**self.params
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}
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# 发送请求到完整的chat/completions端点
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api_url = f"{self.base_url.rstrip('/')}/chat/completions"
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logger.info(f"发送请求到URL: {api_url}") # 记录请求的URL
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max_retries = 3
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base_wait_time = 15
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for retry in range(max_retries):
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try:
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async with aiohttp.ClientSession() as session:
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async with session.post(api_url, headers=headers, json=data) as response:
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if response.status == 429:
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wait_time = base_wait_time * (2 ** retry) # 指数退避
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logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
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await asyncio.sleep(wait_time)
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continue
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response.raise_for_status() # 检查其他响应状态
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result = await response.json()
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if "choices" in result and len(result["choices"]) > 0:
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content = result["choices"][0]["message"]["content"]
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reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
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return content, reasoning_content
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return "没有返回结果", ""
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except Exception as e:
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if retry < max_retries - 1: # 如果还有重试机会
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wait_time = base_wait_time * (2 ** retry)
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logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True)
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await asyncio.sleep(wait_time)
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else:
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logger.critical(f"请求失败: {str(e)}", exc_info=True)
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raise RuntimeError(f"API请求失败: {str(e)}")
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logger.error("达到最大重试次数，请求仍然失败")
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raise RuntimeError("达到最大重试次数，API请求仍然失败")
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async def generate_response_for_image(self, prompt: str, image_base64: str) -> Tuple[str, str]:
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"""根据输入的提示和图片生成模型的异步响应"""
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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# 构建请求体
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data = {
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"model": self.model_name,
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": prompt
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{image_base64}"
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}
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}
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]
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}
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],
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**self.params
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}
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# 发送请求到完整的chat/completions端点
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api_url = f"{self.base_url.rstrip('/')}/chat/completions"
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logger.info(f"发送请求到URL: {api_url}") # 记录请求的URL
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max_retries = 3
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base_wait_time = 15
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for retry in range(max_retries):
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try:
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async with aiohttp.ClientSession() as session:
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async with session.post(api_url, headers=headers, json=data) as response:
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if response.status == 429:
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wait_time = base_wait_time * (2 ** retry) # 指数退避
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logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
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await asyncio.sleep(wait_time)
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continue
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response.raise_for_status() # 检查其他响应状态
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result = await response.json()
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if "choices" in result and len(result["choices"]) > 0:
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content = result["choices"][0]["message"]["content"]
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reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
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return content, reasoning_content
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return "没有返回结果", ""
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except Exception as e:
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if retry < max_retries - 1: # 如果还有重试机会
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wait_time = base_wait_time * (2 ** retry)
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logger.error(f"[image回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True)
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await asyncio.sleep(wait_time)
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else:
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logger.critical(f"请求失败: {str(e)}", exc_info=True)
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raise RuntimeError(f"API请求失败: {str(e)}")
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logger.error("达到最大重试次数，请求仍然失败")
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raise RuntimeError("达到最大重试次数，API请求仍然失败")
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def generate_response_for_image_sync(self, prompt: str, image_base64: str) -> Tuple[str, str]:
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"""同步方法：根据输入的提示和图片生成模型的响应"""
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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# 构建请求体
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data = {
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"model": self.model_name,
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": prompt
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{image_base64}"
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}
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}
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]
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}
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],
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**self.params
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}
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# 发送请求到完整的chat/completions端点
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api_url = f"{self.base_url.rstrip('/')}/chat/completions"
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logger.info(f"发送请求到URL: {api_url}") # 记录请求的URL
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max_retries = 2
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base_wait_time = 6
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for retry in range(max_retries):
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try:
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response = requests.post(api_url, headers=headers, json=data, timeout=30)
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if response.status_code == 429:
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wait_time = base_wait_time * (2 ** retry)
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logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
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time.sleep(wait_time)
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continue
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response.raise_for_status() # 检查其他响应状态
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result = response.json()
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if "choices" in result and len(result["choices"]) > 0:
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content = result["choices"][0]["message"]["content"]
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reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
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return content, reasoning_content
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return "没有返回结果", ""
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except Exception as e:
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if retry < max_retries - 1: # 如果还有重试机会
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wait_time = base_wait_time * (2 ** retry)
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logger.error(f"[image_sync回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True)
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time.sleep(wait_time)
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else:
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logger.critical(f"请求失败: {str(e)}", exc_info=True)
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raise RuntimeError(f"API请求失败: {str(e)}")
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logger.error("达到最大重试次数，请求仍然失败")
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raise RuntimeError("达到最大重试次数，API请求仍然失败")
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def get_embedding_sync(self, text: str, model: str = "BAAI/bge-m3") -> Union[list, None]:
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"""同步方法：获取文本的embedding向量
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Args:
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text: 需要获取embedding的文本
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model: 使用的模型名称，默认为"BAAI/bge-m3"
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Returns:
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list: embedding向量，如果失败则返回None
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"""
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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data = {
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"model": model,
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"input": text,
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"encoding_format": "float"
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}
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api_url = f"{self.base_url.rstrip('/')}/embeddings"
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logger.info(f"发送请求到URL: {api_url}") # 记录请求的URL
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max_retries = 2
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base_wait_time = 6
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for retry in range(max_retries):
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try:
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response = requests.post(api_url, headers=headers, json=data, timeout=30)
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if response.status_code == 429:
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wait_time = base_wait_time * (2 ** retry)
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logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
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time.sleep(wait_time)
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continue
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response.raise_for_status()
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result = response.json()
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if 'data' in result and len(result['data']) > 0:
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return result['data'][0]['embedding']
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return None
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except Exception as e:
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if retry < max_retries - 1:
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wait_time = base_wait_time * (2 ** retry)
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logger.error(f"[embedding_sync]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True)
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time.sleep(wait_time)
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else:
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logger.critical(f"embedding请求失败: {str(e)}", exc_info=True)
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return None
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logger.error("达到最大重试次数，embedding请求仍然失败")
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return None
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async def get_embedding(self, text: str, model: str = "BAAI/bge-m3") -> Union[list, None]:
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"""异步方法：获取文本的embedding向量
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Args:
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text: 需要获取embedding的文本
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model: 使用的模型名称，默认为"BAAI/bge-m3"
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Returns:
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list: embedding向量，如果失败则返回None
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"""
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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data = {
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"model": model,
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"input": text,
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"encoding_format": "float"
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}
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api_url = f"{self.base_url.rstrip('/')}/embeddings"
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logger.info(f"发送请求到URL: {api_url}") # 记录请求的URL
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max_retries = 3
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base_wait_time = 15
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for retry in range(max_retries):
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try:
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async with aiohttp.ClientSession() as session:
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async with session.post(api_url, headers=headers, json=data) as response:
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if response.status == 429:
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wait_time = base_wait_time * (2 ** retry)
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logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
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await asyncio.sleep(wait_time)
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continue
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response.raise_for_status()
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result = await response.json()
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if 'data' in result and len(result['data']) > 0:
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return result['data'][0]['embedding']
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return None
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except Exception as e:
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if retry < max_retries - 1:
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wait_time = base_wait_time * (2 ** retry)
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logger.error(f"[embedding]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True)
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await asyncio.sleep(wait_time)
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else:
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logger.critical(f"embedding请求失败: {str(e)}", exc_info=True)
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return None
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logger.error("达到最大重试次数，embedding请求仍然失败")
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return None |