Free o4-mini API：免费渠道已失效，API 将于 2026 年 10 月 23 日停用

<!DOCTYPE html>
<html>
<head>
    <script src="https://js.puter.com/v2/"></script>
</head>
<body>
    <script>
        // 初始化Puter
        puter.ai.chat(
            "解释什么是量子计算，用简单的语言",
            { model: "o4-mini" }
        ).then(response => {
            console.log(response);
            document.body.innerHTML = `<pre>${response}</pre>`;
        });
    </script>
</body>
</html>

// 数学问题求解器
async function solveMathProblem(problem) {
    const prompt = `请解决以下数学问题，展示详细的解题步骤：
    
    问题：${problem}
    
    要求：
    1. 分析问题类型
    2. 列出解题步骤
    3. 进行计算
    4. 验证答案`;
    
    try {
        const response = await puter.ai.chat(prompt, {
            model: "o4-mini"  // 停用后换成 Puter 文档列出的可用模型
        });
        
        return response;
    } catch (error) {
        console.error("求解失败:", error);
        return "抱歉，无法解决这个问题。";
    }
}

// 使用示例
solveMathProblem("一个圆的半径增加20%，面积增加多少百分比？")
    .then(solution => {
        console.log(solution);
    });

import requests
import PyPDF2
import json

class ResearchPaperReviewer:
    def __init__(self, api_key):
        self.api_key = api_key
        self.api_url = "https://api.laozhang.ai/v1/chat/completions"
        
    def extract_pdf_content(self, pdf_path):
        """提取PDF内容"""
        with open(pdf_path, 'rb') as file:
            reader = PyPDF2.PdfReader(file)
            content = ""
            for page in reader.pages:
                content += page.extract_text()
        return content
    
    def analyze_paper(self, content):
        """分析论文质量"""
        prompt = f"""作为一个严格的学术审稿人，请分析以下研究论文：

{content[:5000]}  # 限制长度以节省成本

请从以下方面进行评估：
1. 研究问题的原创性和重要性
2. 方法论的严谨性
3. 实验设计和数据分析的合理性
4. 结论的可靠性
5. 写作质量和逻辑结构

对每个方面给出具体的改进建议。"""
        
        response = requests.post(
            self.api_url,
            headers={
                "Authorization": f"Bearer {self.api_key}",
                "Content-Type": "application/json"
            },
            json={
                "model": "gpt-5.6-terra",  # o4-mini 将于 2026-10-23 停用
                "messages": [
                    {"role": "system", "content": "你是一位经验丰富的学术审稿专家。"},
                    {"role": "user", "content": prompt}
                ],
                # 推理模型用 max_completion_tokens，且要为推理 token 留出余量
                "max_completion_tokens": 4000
            }
        )
        
        return response.json()['choices'][0]['message']['content']
    
    def generate_review_report(self, pdf_path):
        """生成完整的审稿报告"""
        content = self.extract_pdf_content(pdf_path)
        analysis = self.analyze_paper(content)
        
        report = {
            "paper_title": pdf_path.split('/')[-1],
            "review_date": "2026-10-08",
            "overall_assessment": analysis,
            "recommendation": self.get_recommendation(analysis)
        }
        
        return report
    
    def get_recommendation(self, analysis):
        """基于分析结果给出推荐"""
        # 这里可以加入更复杂的逻辑
        if "major issues" in analysis.lower():
            return "Major Revision"
        elif "minor" in analysis.lower():
            return "Minor Revision"
        else:
            return "Accept"

# 使用示例
reviewer = ResearchPaperReviewer(api_key="your_laozhang_api_key")
report = reviewer.generate_review_report("research_paper.pdf")
print(json.dumps(report, indent=2, ensure_ascii=False))

from openai import OpenAI
client = OpenAI()

# 简单任务使用低推理强度
response = client.chat.completions.create(
    model="o4-mini",  # 停用后换成 gpt-5.6-terra
    reasoning_effort="low",
    messages=[{"role": "user", "content": prompt}]
)

# 复杂推理使用高推理强度
response = client.chat.completions.create(
    model="o4-mini",
    reasoning_effort="high",
    messages=[{"role": "user", "content": prompt}]
)

def batch_process_questions(questions):
    combined_prompt = "请回答以下所有问题，每个问题独立作答：\n\n"
    for i, q in enumerate(questions, 1):
        combined_prompt += f"问题{i}: {q}\n"
    
    # 一次API调用处理所有问题
    response = call_o4_mini_api(combined_prompt)
    return parse_batch_response(response)

import hashlib
import redis

class O4MiniCache:
    def __init__(self):
        self.redis_client = redis.Redis()
        self.cache_ttl = 3600  # 1小时过期
    
    def get_or_generate(self, prompt, model_params):
        # 生成缓存键
        cache_key = hashlib.md5(
            f"{prompt}{json.dumps(model_params)}".encode()
        ).hexdigest()
        
        # 尝试从缓存获取
        cached = self.redis_client.get(cache_key)
        if cached:
            return json.loads(cached)
        
        # 生成新响应
        response = call_o4_mini_api(prompt, **model_params)
        
        # 存入缓存
        self.redis_client.setex(
            cache_key, 
            self.cache_ttl, 
            json.dumps(response)
        )
        
        return response

# 使用laozhang.ai访问推理模型（o4-mini 停用后换成替代模型）
import requests

def call_laozhang_o4mini(prompt):
    url = "https://api.laozhang.ai/v1/chat/completions"
    headers = {
        "Authorization": "Bearer YOUR_LAOZHANG_API_KEY",
        "Content-Type": "application/json"
    }
    
    data = {
        "model": "gpt-5.6-terra",  # 原为 o4-mini
        "messages": [
            {"role": "user", "content": prompt}
        ],
        "stream": False
    }
    
    response = requests.post(url, headers=headers, json=data)
    return response.json()['choices'][0]['message']['content']

# 使用示例
result = call_laozhang_o4mini("用Python实现快速排序算法")
print(result)