OpenAI实际营收较此前预期差距高达200亿美元

import os
import asyncio
import httpx
from typing import Dict, Any, List, Optional

# 配置统一网关API端点（例如接入 n1n.ai 聚合节点）
API_BASE_URL = os.getenv("LLM_GATEWAY_URL", "https://api.n1n.ai/v1")
API_KEY = os.getenv("N1N_API_KEY", "your-api-key-here")

class EnterpriseModelRouter:
    def __init__(self, base_url: str, api_key: str):
        self.base_url = base_url
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
        # 路由优先级队列：优先高性价比节点，逐级向顶级模型容灾
        self.routing_ladder = [
            {"model": "deepseek-v3", "timeout": 8.0, "max_retry": 1},
            {"model": "claude-3-5-sonnet-20241022", "timeout": 12.0, "max_retry": 2},
            {"model": "gpt-4o", "timeout": 15.0, "max_retry": 1}
        ]

    async def dispatch_request(
        self,
        messages: List[Dict[str, str]],
        max_tokens: int = 1500,
        force_advanced: bool = False
    ) -> Dict[str, Any]:
        """
        执行模型推理，带有实时超时检测、状态码降级和健康重试逻辑
        """
        active_candidates = self.routing_ladder
        if force_advanced:
            # 若业务明确标记为高难度推理任务，直接切入高阶梯队
            active_candidates = [
                {"model": "claude-3-5-sonnet-20241022", "timeout": 15.0, "max_retry": 2},
                {"model": "gpt-4o", "timeout": 18.0, "max_retry": 1}
            ]

        async with httpx.AsyncClient(base_url=self.base_url, headers=self.headers) as client:
            for candidate in active_candidates:
                model_target = candidate["model"]
                timeout_budget = candidate["timeout"]
                
                request_body = {
                    "model": model_target,
                    "messages": messages,
                    "max_tokens": max_tokens,
                    "temperature": 0.1
                }
                
                try:
                    response = await client.post(
                        "/chat/completions",
                        json=request_body,
                        timeout=timeout_budget
                    )
                    
                    if response.status_code == 200:
                        payload = response.json()
                        payload["_actual_dispatched_model"] = model_target
                        return payload
                    
                    # 遭遇429限频或5xx服务端错误时，立即触发下一级故障转移
                    if response.status_code in [429, 500, 502, 503, 504]:
                        continue
                        
                except (httpx.TimeoutException, httpx.NetworkError):
                    # 网络连接超时或握手失败，平滑切换至后备模型
                    continue
                    
        raise RuntimeError("全链路模型调用耗尽，网关容灾未能恢复请求。")

# 运行测试用例
async def run_benchmark():
    router = EnterpriseModelRouter(base_url=API_BASE_URL, api_key=API_KEY)
    test_prompt = [
        {"role": "system", "content": "你是一名具备十年经验的高性能后端系统架构师。"},
        {"role": "user", "content": "请简述在处理海量并发请求时，如何设计基于分布式内存网格的二级缓存防击穿方案。"}
    ]
    
    try:
        result = await router.dispatch_request(messages=test_prompt, force_advanced=False)
        dispatched_node = result.get("_actual_dispatched_model")
        content = result["choices"][0]["message"]["content"]
        print(f"请求成功响应，实际承载端点: {dispatched_node}")
        print(f"返回内容截选: {content[:180]}...")
    except Exception as err:
        print(f"业务调度出现不可抗异常: {err}")

if __name__ == "__main__":
    asyncio.run(run_benchmark())