Top-p（核采样）

V_p = smallest V ⊆ V such that Σ P(x) ≥ p
         x∈V

import openai

response = openai.ChatCompletion.create(
    model="gpt-4-turbo",
    messages=[{"role": "user", "content": "写一个创意故事"}],
    temperature=0.8,
    top_p=0.9,  # 核采样参数
    max_tokens=500
)

import anthropic

client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-3-opus-20240229",
    messages=[{"role": "user", "content": "分析这段代码"}],
    temperature=0.5,
    top_p=0.85,  # 更保守的设置
    max_tokens=1000
)

def get_optimal_sampling_params(task_type):
    """根据任务类型返回最优采样参数组合"""
    params = {
        "factual": {
            "temperature": 0.3,
            "top_p": 0.85,
            "description": "事实准确性优先"
        },
        "balanced": {
            "temperature": 0.7,
            "top_p": 0.9,
            "description": "平衡准确性和创造性"
        },
        "creative": {
            "temperature": 0.9,
            "top_p": 0.95,
            "description": "创造性优先"
        },
        "experimental": {
            "temperature": 1.0,
            "top_p": 0.98,
            "description": "探索性生成"
        }
    }
    return params.get(task_type, params["balanced"])

import numpy as np

def adaptive_top_p(token_probs, base_p=0.9):
    """根据概率分布的熵动态调整p值"""
    # 计算熵
    entropy = -np.sum(token_probs * np.log(token_probs + 1e-10))
    
    # 高熵（不确定）时增加p值
    # 低熵（确定）时减少p值
    if entropy > 3.0:  # 高不确定性
        return min(base_p + 0.05, 0.99)
    elif entropy < 1.0:  # 高确定性
        return max(base_p - 0.1, 0.7)
    else:
        return base_p

class SamplingOptimizer:
    def __init__(self):
        self.task_patterns = {
            "code": {"keywords": ["function", "class", "def"], "top_p": 0.85},
            "math": {"keywords": ["calculate", "solve", "equation"], "top_p": 0.8},
            "story": {"keywords": ["story", "imagine", "creative"], "top_p": 0.95}
        }
    
    def optimize_params(self, prompt):
        """根据提示词内容优化采样参数"""
        prompt_lower = prompt.lower()
        
        for task, config in self.task_patterns.items():
            if any(kw in prompt_lower for kw in config["keywords"]):
                return {"top_p": config["top_p"], "task": task}
        
        return {"top_p": 0.9, "task": "general"}