Nvidia 携手 Cloverleaf Infrastructure 解决 AI 数据中心电力瓶颈

import os
import httpx

# 初始化 n1n.ai API 客户端配置
N1N_API_KEY = os.environ.get("N1N_API_KEY")
N1N_BASE_URL = "https://api.n1n.ai/v1"

def estimate_complexity(prompt: str) -> str:
    """
    通过启发式规则判断 Prompt 是否需要高功耗的前沿模型，
    还是可以由高能效的小模型处理。
    """
    complex_keywords = ["analyze", "optimize", "refactor", "prove", "compile", "architect", "分析", "优化", "重构"]

    # 估算 Token 长度
    words = prompt.split()
    if len(words) > 150:
        return "high_complexity"

    # 检测复杂关键词
    if any(keyword in prompt.lower() for keyword in complex_keywords):
        return "high_complexity"

    return "low_complexity"

def route_llm_request(prompt: str):
    complexity = estimate_complexity(prompt)

    # 通过 n1n.ai 将不同复杂度的请求映射到合适的端点
    # 低复杂度 -> 高能效模型 (例如 Llama 3.1 8B 或 Claude 3.5 Haiku)
    # 高复杂度 -> 高性能前沿模型 (例如 DeepSeek-V3 或 Claude 3.5 Sonnet)
    if complexity == "low_complexity":
        model_target = "meta-llama/llama-3.1-8b-instruct"
        max_tokens = 150
        print(f"路由至高能效模型: {model_target}")
    else:
        model_target = "deepseek/deepseek-chat" # 通过 n1n.ai 调用 DeepSeek-V3
        max_tokens = 1000
        print(f"路由至高性能前沿模型: {model_target}")

    headers = {
        "Authorization": f"Bearer {N1N_API_KEY}",
        "Content-Type": "application/json"
    }

    payload = {
        "model": model_target,
        "messages": [
            {"role": "user", "content": prompt}
        ],
        "max_tokens": max_tokens,
        "temperature": 0.2
    }

    try:
        with httpx.Client() as client:
            response = client.post(
                f"{N1N_BASE_URL}/chat/completions",
                headers=headers,
                json=payload,
                timeout=30.0
            )

            if response.status_code == 200:
                data = response.json()
                return data["choices"][0]["message"]["content"]
            else:
                print(f"API 错误: {response.status_code} - {response.text}")
                return None
    except Exception as e:
        print(f"请求失败: {str(e)}")
        return None

# 使用示例
if __name__ == "__main__":
    simple_prompt = "提取这句话中的主要实体: Nvidia 宣布与 Cloverleaf 达成合作。"
    complex_prompt = """
    请提供一份详细的架构设计方案，说明如何设计一个无停机时间的数据库迁移方案，
    将数据从 PostgreSQL 迁移到 MongoDB，包括模式映射、增量同步和回滚策略。
    """

    print("--- 测试 1 (简单任务) ---")
    res1 = route_llm_request(simple_prompt)
    print(f"响应结果: {res1}\n")

    print("--- 测试 2 (复杂任务) ---")
    res2 = route_llm_request(complex_prompt)
    print(f"响应结果: {res2}")