最佳实践（自定义 API - 已过时）

import time
import math

def exponential_backoff_polling(client, task_id, initial_interval=5, max_interval=60):
    """
    指数退避轮询策略
    """
    interval = initial_interval
    attempt = 0
    
    while True:
        try:
            status_data = client.get_status(task_id)
            status = status_data.get('status')
            
            if status == 'completed':
                return status_data['result']
            elif status == 'failed':
                raise Exception(f"生成失败: {status_data.get('error')}")
            
            # 指数退避
            time.sleep(interval)
            attempt += 1
            interval = min(initial_interval * math.pow(1.5, attempt), max_interval)
            
        except Exception as e:
            print(f"轮询出错: {e}")
            time.sleep(interval)

def retry_with_backoff(func, max_retries=3, backoff_factor=2):
    """
    带退避的重试机制
    """
    for attempt in range(max_retries):
        try:
            return func()
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            
            wait_time = backoff_factor ** attempt
            print(f"失败，{wait_time}秒后重试...")
            time.sleep(wait_time)

try:
    result = client.submit_task(prompt)
except requests.exceptions.ConnectionError:
    print("网络连接失败，请检查网络")
except requests.exceptions.Timeout:
    print("请求超时，请稍后重试")

try:
    result = client.submit_task(prompt)
except Exception as e:
    if "QUOTA_EXCEEDED" in str(e):
        print("配额已用完")
    elif "INVALID_PROMPT" in str(e):
        print("提示词无效")

status = client.get_status(task_id)
if status['status'] == 'failed':
    error_info = status.get('error', {})
    print(f"任务失败: {error_info.get('message')}")
    # 可以尝试重新提交

async def batch_process_videos(prompts, max_concurrent=5):
    """
    批量处理视频生成
    """
    semaphore = asyncio.Semaphore(max_concurrent)
    
    async def process_one(prompt):
        async with semaphore:
            return await client.submit_and_wait(prompt)
    
    tasks = [process_one(prompt) for prompt in prompts]
    return await asyncio.gather(*tasks)

def choose_model(requirements):
    """
    根据需求智能选择模型
    """
    if requirements.get('need_fast'):
        return 'veo3-fast'
    elif requirements.get('high_quality'):
        return 'veo3-pro'
    elif requirements.get('precise_control'):
        return 'veo3-pro-frames'
    else:
        return 'veo3'  # 默认选择标准版

import logging
from datetime import datetime

class VEOLogger:
    def __init__(self):
        self.logger = logging.getLogger('veo_api')
        
    def log_task_submission(self, task_id, prompt, model):
        self.logger.info(f"Task submitted: {task_id}")
        self.logger.debug(f"Prompt: {prompt[:50]}...")
        self.logger.debug(f"Model: {model}")
        
    def log_task_completion(self, task_id, duration, video_url):
        self.logger.info(f"Task completed: {task_id}")
        self.logger.info(f"Duration: {duration}s")
        self.logger.debug(f"Video URL: {video_url}")