# API的优势
api_advantages = {
    "易于使用": "简单的API调用即可使用",
    "无需部署": "不需要部署模型",
    "持续更新": "自动获得模型更新",
    "成本可控": "按使用量付费"
}

# OpenAI API基础使用
from openai import OpenAI

# 初始化客户端
client = OpenAI(api_key="your-api-key")

# 文本生成
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "你是一位专业的Python开发专家。"},
        {"role": "user", "content": "解释什么是装饰器"}
    ],
    temperature=0.7,
    max_tokens=500
)

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

# 流式响应
stream = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "写一首关于AI的诗"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="", flush=True)

# 函数调用
functions = [
    {
        "name": "get_weather",
        "description": "获取天气信息",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "城市名称"
                }
            },
            "required": ["location"]
        }
    }
]

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

# 处理函数调用
if response.choices[0].message.function_call:
    function_name = response.choices[0].message.function_call.name
    arguments = json.loads(response.choices[0].message.function_call.arguments)
    # 调用函数
    result = get_weather(arguments["location"])

# Anthropic API基础使用
from anthropic import Anthropic

client = Anthropic(api_key="your-api-key")

message = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "解释什么是大模型"}
    ]
)

print(message.content[0].text)

# 流式响应
with client.messages.stream(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    messages=[{"role": "user", "content": "写一个Python函数"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

# 错误处理
import time
from openai import OpenAI, RateLimitError, APIError

client = OpenAI(api_key="your-api-key")

def call_api_with_retry(messages, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model="gpt-4",
                messages=messages
            )
            return response
        except RateLimitError:
            wait_time = 2 ** attempt
            print(f"Rate limit hit, waiting {wait_time} seconds...")
            time.sleep(wait_time)
        except APIError as e:
            print(f"API error: {e}")
            if attempt == max_retries - 1:
                raise
            time.sleep(1)
    return None

# Gemini API使用
import google.generativeai as genai

genai.configure(api_key="your-api-key")

model = genai.GenerativeModel('gemini-pro')
response = model.generate_content("解释什么是大模型")
print(response.text)