Source: https://aiprimetech.io/zh/docs/sdks/python/

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# Python 

使用 anthropic 和 openai 包的可运行示例，涵盖流式响应和异步调用。 

 复制页面 [ 以 Markdown 查看](/zh/docs/sdks/python.md) [ 向 ChatGPT 提问](https://chatgpt.com/?q=Read%20https%3A//aiprimetech.io/docs/sdks/python.md%20and%20answer%20questions%20about%20it.%20Start%20with%20a%20short%20summary.) [ 在 Claude 中提问](https://claude.ai/new?q=Read%20https%3A//aiprimetech.io/docs/sdks/python.md%20and%20answer%20questions%20about%20it.%20Start%20with%20a%20short%20summary.)   

## Anthropic SDK 

```
Copypip install anthropic
```

```
Copyimport os
from anthropic import Anthropic

client = Anthropic(
    api_key=os.environ[class="s">"CLAUDEAPIKEY"],
    base_url=class="s">"https://aiprimetech.io",          class=class="s">"c"># no /v1
)

msg = client.messages.create(
    model=class="s">"claude-sonnet-4-6",
    max_tokens=1024,
    system=class="s">"You are a concise assistant.",
    messages=[{class="s">"role": class="s">"user", class="s">"content": class="s">"Explain HTTP caching in three sentences."}],
)
print(msg.content[0].text)
print(msg.usage.input_tokens, msg.usage.output_tokens)
```

## 流式传输 

```
Copywith client.messages.stream(
    model=class="s">"claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{class="s">"role": class="s">"user", class="s">"content": class="s">"Write a haiku about latency."}],
) as stream:
    for text in stream.text_stream:
        print(text, end=class="s">"", flush=True)
    final = stream.get_final_message()
print(class="s">"\n", final.usage.output_tokens, class="s">"output tokens")
```

## 异步 

```
Copyimport asyncio
from anthropic import AsyncAnthropic

client = AsyncAnthropic(api_key=class="s">"sk-your-key", base_url=class="s">"https://aiprimetech.io")

async def ask(q):
    m = await client.messages.create(
        model=class="s">"claude-haiku-4-5", max_tokens=256,
        messages=[{class="s">"role": class="s">"user", class="s">"content": q}],
    )
    return m.content[0].text

async def main():
    sem = asyncio.Semaphore(8)                 class=class="s">"c"># respect rate limits
    async def guarded(q):
        async with sem:
            return await ask(q)
    print(await asyncio.gather(*(guarded(q) for q in [class="s">"1+1?", class="s">"2+2?", class="s">"3+3?"])))

asyncio.run(main())
```

## OpenAI SDK 

```
Copyfrom openai import OpenAI

client = OpenAI(api_key=class="s">"sk-your-key", base_url=class="s">"https://aiprimetech.io/v1")   class=class="s">"c"># with /v1

resp = client.chat.completions.create(
    model=class="s">"claude-sonnet-4-6",
    messages=[{class="s">"role": class="s">"user", class="s">"content": class="s">"Hello"}],
)
print(resp.choices[0].message.content)
```

两个代码片段以相同价格调用同一个模型。使用您的项目已经依赖的库即可。 

[消息 API字段参考](/zh/docs/api-reference/messages/)[流式传输事件结构](/zh/docs/api-reference/streaming/)[最佳实践超时与重试](/zh/docs/guides/best-practices/) 

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