OpenAI API - 行业标准 AI API 服务

from openai import OpenAI

# 初始化客户端
client = OpenAI(
    api_key="YOUR_API_KEY"  # 替换为您的 API 密钥
)

# 发送请求
response = client.chat.completions.create(
    model="gpt-4o-mini",  # 使用经济实惠的模型
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "什么是机器学习？"}
    ]
)

# 打印响应
print(response.choices[0].message.content)

# 查看使用情况
print(f"\n使用 Tokens: {response.usage.total_tokens}")

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY")

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "你是一个专业的AI助手。"},
        {"role": "user", "content": "解释一下什么是深度学习"}
    ],
    max_tokens=500,
    temperature=0.7
)

print(response.choices[0].message.content)

# 流式输出（实时显示）
stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "写一篇关于人工智能的文章"}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

# 分析图片
response = client.chat.completions.create(
    model="gpt-4o",  # Vision 需要 gpt-4o
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "这张图片里有什么？"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/image.jpg"
                    }
                }
            ]
        }
    ]
)

print(response.choices[0].message.content)

# 保持对话历史
messages = [
    {"role": "system", "content": "你是一个Python编程助手"}
]

# 第一轮
messages.append({"role": "user", "content": "如何读取CSV文件？"})
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages
)
messages.append({"role": "assistant", "content": response.choices[0].message.content})

# 第二轮（带上下文）
messages.append({"role": "user", "content": "如何过滤数据？"})
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages
)

print(response.choices[0].message.content)

# 定义函数
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "获取指定城市的天气",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "城市名称"
                    }
                },
                "required": ["city"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "北京今天天气怎么样？"}
    ],
    tools=tools
)

# 检查是否需要调用函数
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"需要调用函数: {tool_call.function.name}")
    print(f"参数: {tool_call.function.arguments}")

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "你好，介绍一下你自己"
      }
    ],
    "max_tokens": 500,
    "temperature": 0.7
  }'

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

async function main() {
  const completion = await client.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: '什么是机器学习？' }
    ],
    max_tokens: 500,
    temperature: 0.7,
  });

  console.log(completion.choices[0].message.content);
  console.log(`\n使用 Tokens: ${completion.usage.total_tokens}`);
}

main();

def select_model(task_complexity):
    if task_complexity == "simple":
        return "gpt-3.5-turbo"  # 快速且便宜
    elif task_complexity == "medium":
        return "gpt-4o-mini"  # 最推荐
    elif task_complexity == "complex":
        return "gpt-4o"  # 最强大
    elif task_complexity == "reasoning":
        return "o1-mini"  # 推理任务

# 使用 Batch API 降低成本（50% 折扣）
# 适合非实时任务

import hashlib
import json

cache = {}

def cached_completion(messages):
    key = hashlib.md5(json.dumps(messages).encode()).hexdigest()
    
    if key in cache:
        return cache[key]
    
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=messages
    )
    
    cache[key] = response
    return response

def customer_service_bot(user_message, chat_history=[]):
    """智能客服示例"""
    
    messages = [
        {
            "role": "system",
            "content": """你是一个专业的客服助手。
            - 礼貌友好
            - 提供准确信息
            - 如不确定，建议联系人工客服"""
        }
    ]
    
    # 添加对话历史
    messages.extend(chat_history)
    
    # 添加用户消息
    messages.append({"role": "user", "content": user_message})
    
    # 调用 API
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=messages,
        temperature=0.7
    )
    
    return response.choices[0].message.content

# 使用示例
answer = customer_service_bot("我的订单什么时候发货？")
print(answer)

def document_analyzer(document_text):
    """文档分析和总结"""
    
    response = client.chat.completions.create(
        model="gpt-4o",  # 复杂任务用 GPT-4o
        messages=[
            {
                "role": "system",
                "content": "你是一个文档分析专家，擅长提取关键信息。"
            },
            {
                "role": "user",
                "content": f"""请分析以下文档并提供：
                1. 主要内容概述（3-5句话）
                2. 关键要点（列表形式）
                3. 重要数据和日期
                4. 建议的后续行动
                
                文档内容：
                {document_text}"""
            }
        ],
        temperature=0.3  # 较低温度，更准确
    )
    
    return response.choices[0].message.content

def code_reviewer(code, language="python"):
    """代码审查和优化建议"""
    
    response = client.chat.completions.create(
        model="o1-mini",  # 代码任务用 o1-mini
        messages=[
            {
                "role": "user",
                "content": f"""请审查以下{language}代码：

                {code}

                提供：
                1. 潜在问题和bug
                2. 性能优化建议
                3. 代码风格改进
                4. 安全性建议
                5. 重构后的代码"""
            }
        ]
    )
    
    return response.choices[0].message.content

def content_generator(topic, style="professional", length="medium"):
    """内容生成"""
    
    length_map = {
        "short": "300-500字",
        "medium": "800-1000字",
        "long": "1500-2000字"
    }
    
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": f"你是一个{style}风格的内容创作者。"
            },
            {
                "role": "user",
                "content": f"""请写一篇关于"{topic}"的文章：
                - 风格：{style}
                - 长度：{length_map[length]}
                - 包含引言、正文、结论
                - 使用小标题组织内容"""
            }
        ],
        temperature=0.8  # 较高温度，更有创意
    )
    
    return response.choices[0].message.content