# 使用 vLLM 部署
from vllm import LLM

llm = LLM(model="meta-llama/Llama-2-7b")
output = llm.generate("Hello, world!")

# 基础提示
response = llm.generate("What is 2+2?")

# 带示例的提示（Few-shot）
prompt = """
Example: 2+2 = 4
Question: 3+3 = ?
Answer:"

# 标准提示
Q: 小明有5个苹果，丢了2个，又买了3个，现在有几个？
A: 6个  # 错误

# 思维链提示
Q: 小明有5个苹果，丢了2个，又买了3个，现在有几个？
A: 5-2=3，3+3=6，所以是6个。答案是6个。  # 正确

# 知识编辑的三个维度
metrics = {
    "fluency": "编辑后文本是否流畅",
    "proximity": "编辑后与原文的相似度",
    "specificity": "是否精准修改目标知识"
}

# 数学推理训练数据格式
{
    "question": "求1+2+...+100的和",
    "reasoning": "使用等差数列求和公式...",
    "answer": "5050"
}

# PPO 训练循环
for episode in range(1000):
    response = model.generate(prompt)
    reward = compute_math_reward(response)
    model.update(reward)  # PPO 更新

# 水印嵌入
watermarked_text = embed_watermark(
    original_text="Hello world",
    key="secret_key"
)

# 水印检测
is_ai = detect_watermark(watermarked_text, key="secret_key")

# 越狱检测
def detect_jailbreak(prompt: str) -> bool:
    # 检测对抗性模式
    patterns = [
        r"pretend to be",
        r"forget.*safety",
        r"ignore.*instruction"
    ]
    return any(re.search(p, prompt) for p in patterns)

# 隐写编码
hidden_message = "秘密信息"
cover_text = "今天的天气真不错！"

steganographic_text = encode(cover_text, hidden_message)
# 输出：今天的天气（真）不错（！）

# 隐写解码
decoded = decode(steganographic_text)
# 输出：秘密信息

class GUIAgent:
    def __init__(self):
        self.vision = VisionModel()      # 视觉理解
        self.planner = PlannerModel()    # 任务规划
        self.executor = Executor()       # 动作执行

    def run(self, task: str):
        # 1. 截图获取当前界面
        screenshot = self.capture_screen()

        # 2. 视觉理解界面
        ui_elements = self.vision.parse(screenshot)

        # 3. 规划下一步行动
        action = self.planner.decide(task, ui_elements)

        # 4. 执行动作
        self.executor.perform(action)

# 多层安全检查
class SafeAgent:
    def execute(self, action):
        # 1. 权限检查
        if not self.check_permission(action):
            return "拒绝：权限不足"

        # 2. 风险评估
        risk = self.assess_risk(action)
        if risk > self.threshold:
            return "拒绝：风险过高"

        # 3. 人工确认
        if action.is_destructive:
            return "等待用户确认"

        # 4. 执行
        return self.perform(action)

# PPO 核心循环
for epoch in range(num_epochs):
    # 1. 收集 rollout
    trajectories = collect_rollout(policy, env)

    # 2. 计算 advantage
    advantages = compute_advantage(rewards, values)

    # 3. PPO 更新
    for _ in range(ppo_epochs):
        # 重要性采样裁剪
        ratio = pi_new / pi_old
        clipped_ratio = torch.clamp(ratio, 1-eps, 1+eps)

        # 裁剪后的损失
        loss = -min(ratio * advantages, clipped_ratio * advantages)