curl -X POST "https://api.tokenops.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <API-KEY>" \
  -d '{
    "model": "gpt-5-2025-08-07",
    "messages": [
      {
        "role": "system",
        "content": "你是一个数据分析助手，请以JSON格式返回结果。"
      },
      {
        "role": "user",
        "content": "分析苹果公司的基本信息，包括公司名称、成立年份、创始人、主要业务等，以JSON格式返回"
      }
    ],
    "response_format": {
      "type": "json_object"
    },
    "max_tokens": 8000
  }'

import requests
import json

# 配置API密钥和基础URL
API_KEY = "<API-KEY>"
BASE_URL = "https://api.tokenops.ai/v1"

def get_structured_response(prompt, schema_description=""):
    url = f"{BASE_URL}/chat/completions"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}"
    }
    
    system_prompt = f"""你是一个数据分析助手，请严格按照以下JSON格式返回结果：
{schema_description}

请确保返回的是有效的JSON格式，不要包含任何额外的文本。"""
    
    data = {
        "model": "gemini-2.5-flash",
        "messages": [
            {
                "role": "system",
                "content": system_prompt
            },
            {
                "role": "user",
                "content": prompt
            }
        ],
        "response_format": {
            "type": "json_object"
        },
        "max_tokens": 1000,
        "temperature": 0.1
    }
    
    response = requests.post(url, headers=headers, json=data)
    
    if response.status_code == 200:
        result = response.json()
        content = result['choices'][0]['message']['content']
        try:
            return json.loads(content)
        except json.JSONDecodeError:
            return {"error": "返回的内容不是有效的JSON格式", "content": content}
    else:
        return {"error": f"API错误: {response.status_code} - {response.text}"}

# 公司信息分析示例
def analyze_company_info():
    prompt = "分析以下公司信息：苹果公司，成立于1976年，总部位于加利福尼亚州库比蒂诺，主要产品包括iPhone、iPad、Mac等。"
    
    schema = """{
  "company_name": "公司名称",
  "founded_year": "成立年份(数字)",
  "headquarters": "总部位置",
  "main_products": ["主要产品列表"],
  "industry": "所属行业",
  "company_type": "公司类型"
}"""
    
    result = get_structured_response(prompt, schema)
    return result

# 情感分析示例
def sentiment_analysis():
    prompt = "分析以下评论的情感：这个产品真的很棒，质量很好，价格也合理，我会推荐给朋友们。"
    
    schema = """{
  "sentiment": "情感倾向 (positive/negative/neutral)",
  "confidence": "置信度 (0-1之间的数字)",
  "key_phrases": ["关键词组"],
  "overall_score": "总体评分 (1-10)",
  "analysis": "详细分析"
}"""
    
    result = get_structured_response(prompt, schema)
    return result

# 使用示例
if __name__ == "__main__":
    print("=== 公司信息分析 ===")
    company_result = analyze_company_info()
    print(json.dumps(company_result, ensure_ascii=False, indent=2))
    
    print("\n=== 情感分析 ===")
    sentiment_result = sentiment_analysis()
    print(json.dumps(sentiment_result, ensure_ascii=False, indent=2))

const axios = require('axios');

// 配置API密钥和基础URL
const API_KEY = '<API-KEY>';
const BASE_URL = 'https://api.tokenops.ai/v1';

async function getStructuredResponse(prompt, schemaDescription = '') {
    const url = `${BASE_URL}/chat/completions`;
    
    const systemPrompt = `你是一个数据分析助手，请严格按照以下JSON格式返回结果：
${schemaDescription}

请确保返回的是有效的JSON格式，不要包含任何额外的文本。`;
    
    const headers = {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${API_KEY}`
    };
    
    const data = {
        model: 'gpt-5-2025-08-07',
        messages: [
            {
                role: 'system',
                content: systemPrompt
            },
            {
                role: 'user',
                content: prompt
            }
        ],
        response_format: {
            type: 'json_object'
        },
        max_tokens: 1000,
        temperature: 0.1
    };
    
    try {
        const response = await axios.post(url, data, { headers });
        const content = response.data.choices[0].message.content;
        
        try {
            return JSON.parse(content);
        } catch (parseError) {
            return {
                error: '返回的内容不是有效的JSON格式',
                content: content
            };
        }
    } catch (error) {
        return {
            error: `API错误: ${error.response?.status} - ${error.response?.data || error.message}`
        };
    }
}

// 产品评价分析示例
async function analyzeProductReview() {
    const prompt = '分析这个产品评价：这款手机拍照效果不错，但是电池续航有点短，整体还可以。';
    
    const schema = `{
  "overall_rating": "整体评分 (1-5)",
  "pros": ["优点列表"],
  "cons": ["缺点列表"],
  "aspects": {
    "camera": "拍照评价",
    "battery": "电池评价",
    "performance": "性能评价"
  },
  "recommendation": "是否推荐 (true/false)",
  "summary": "总结"
}`;
    
    const result = await getStructuredResponse(prompt, schema);
    return result;
}

// 文本分类示例
async function classifyText() {
    const prompt = '分类以下文本：明天会下雨，记得带伞出门。';
    
    const schema = `{
  "category": "文本类别",
  "subcategory": "子类别",
  "intent": "用户意图",
  "entities": ["提取的实体"],
  "confidence": "分类置信度 (0-1)"
}`;
    
    const result = await getStructuredResponse(prompt, schema);
    return result;
}

// 使用示例
(async () => {
    try {
        console.log('=== 产品评价分析 ===');
        const reviewResult = await analyzeProductReview();
        console.log(JSON.stringify(reviewResult, null, 2));
        
        console.log('\n=== 文本分类 ===');
        const classifyResult = await classifyText();
        console.log(JSON.stringify(classifyResult, null, 2));
    } catch (error) {
        console.error('错误:', error.message);
    }
})();