Python（OpenAI SDK）

OpenAI 官方 Python SDK 只需修改 base_url 和 api_key 就能调用本站的全部 OpenAI 兼容接口，包括 Claude、Gemini 等非 OpenAI 模型。

安装与初始化

pip install -U openai

from openai import OpenAI

client = OpenAI(
    base_url="https://vip.zen-ai.top/v1",
    api_key="sk-xxxxxxxx",
)

也可以通过环境变量配置，代码中直接 OpenAI() 即可：

export OPENAI_BASE_URL="https://vip.zen-ai.top/v1"
export OPENAI_API_KEY="sk-xxxxxxxx"

基础对话

resp = client.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[
        {"role": "system", "content": "你是一个简洁的助手。"},
        {"role": "user", "content": "HTTP 和 HTTPS 有什么区别？"},
    ],
)
print(resp.choices[0].message.content)
print(resp.usage)  # token 用量

流式输出

stream = client.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[{"role": "user", "content": "讲一个 100 字的小故事"}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

多轮对话

把历史消息保存在列表中，每轮追加用户消息和模型回复：

messages = [{"role": "system", "content": "你是一个耐心的编程老师。"}]

while True:
    user_input = input("你：")
    if user_input in ("exit", "quit"):
        break
    messages.append({"role": "user", "content": user_input})
    resp = client.chat.completions.create(model="gpt-4.1-mini", messages=messages)
    answer = resp.choices[0].message.content
    messages.append({"role": "assistant", "content": answer})
    print("AI：", answer)

图片理解

网络图片

resp = client.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "图片里有什么？"},
            {"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}},
        ],
    }],
)
print(resp.choices[0].message.content)

本地图片

import base64

with open("photo.jpg", "rb") as f:
    b64 = base64.b64encode(f.read()).decode()

resp = client.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "图片里有什么？"},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64}"}},
        ],
    }],
)
print(resp.choices[0].message.content)

Claude、Gemini 等支持视觉的模型也可以用同样的格式传图。

结构化输出（JSON Schema）

import json

resp = client.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[{"role": "user", "content": "提取信息：李雷，32 岁，住在成都，职业是设计师。"}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "person",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "age": {"type": "integer"},
                    "city": {"type": "string"},
                    "job": {"type": "string"},
                },
                "required": ["name", "age", "city", "job"],
                "additionalProperties": False,
            },
        },
    },
)
data = json.loads(resp.choices[0].message.content)
print(data["name"], data["age"])

函数调用的完整两轮示例见 创建聊天补全 → 函数调用。

Responses API

resp = client.responses.create(
    model="gpt-5.5",
    reasoning={"effort": "low"},
    input="用一句话解释量子纠缠",
)
print(resp.output_text)

参数说明见 创建模型响应。

向量（Embeddings）

resp = client.embeddings.create(
    model="text-embedding-3-small",
    input=["今天天气真好", "The weather is nice today"],
)
vectors = [d.embedding for d in resp.data]
print(len(vectors), len(vectors[0]))  # 2 1536

图像生成

import base64

resp = client.images.generate(
    model="gpt-image-1.5",
    prompt="一只戴着宇航员头盔的柴犬，赛博朋克风格",
    size="1024x1024",
)
img = resp.data[0]
if img.b64_json:
    with open("dog.png", "wb") as f:
        f.write(base64.b64decode(img.b64_json))
else:
    print(img.url)

语音合成与识别

文本转语音

with client.audio.speech.with_streaming_response.create(
    model="gpt-4o-mini-tts",
    voice="alloy",
    input="你好，欢迎使用语音合成服务。",
) as response:
    response.stream_to_file("speech.mp3")

语音转文字

with open("speech.mp3", "rb") as f:
    result = client.audio.transcriptions.create(
        model="gpt-4o-mini-transcribe",
        file=f,
    )
print(result.text)

异步客户端

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(base_url="https://vip.zen-ai.top/v1", api_key="sk-xxxxxxxx")

async def main():
    tasks = [
        client.chat.completions.create(model="gpt-4.1-mini", messages=[{"role": "user", "content": q}])
        for q in ["1+1=?", "天空为什么是蓝色的？"]
    ]
    for r in await asyncio.gather(*tasks):
        print(r.choices[0].message.content)

asyncio.run(main())

超时与重试

client = OpenAI(
    base_url="https://vip.zen-ai.top/v1",
    api_key="sk-xxxxxxxx",
    timeout=300,     # 秒，推理模型或长输出建议调大，或改用流式
    max_retries=2,   # 对 429 / 5xx / 网络错误自动重试
)

不使用 SDK（requests）

import json
import requests

resp = requests.post(
    "https://vip.zen-ai.top/v1/chat/completions",
    headers={"Authorization": "Bearer sk-xxxxxxxx"},
    json={
        "model": "gpt-4.1-mini",
        "stream": True,
        "messages": [{"role": "user", "content": "你好"}],
    },
    stream=True,
    timeout=300,
)
for line in resp.iter_lines(decode_unicode=True):
    if not line or not line.startswith("data: "):
        continue
    data = line[len("data: "):]
    if data == "[DONE]":
        break
    chunk = json.loads(data)
    if chunk.get("choices"):
        print(chunk["choices"][0]["delta"].get("content") or "", end="", flush=True)