OpenAI 兼容 API

通过一个 OpenAI 风格的接口使用 Claude 和 GPT 模型。

BASE URLhttps://api.apivai.com/v1

APIVAI 使用 OpenAI 的 HTTP 请求格式，凡是支持自定义 base URL 的工具和 SDK 都能直接接入，无需额外开发。只需替换 base URL 和 Key，其余代码保持不变。

接口列表

方法与路径	用途	说明
GET /v1/models	获取模型列表	写死模型名之前先调用它确认。
POST /v1/chat/completions	对话补全	接收 OpenAI 风格的 messages 数组。
POST /v1/chat/completions	流式对话	加上 "stream": true 即以 SSE 方式逐段返回。
POST /v1/responses	Responses 接口	Codex CLI 使用该接口。

请求头

Authorization: Bearer YOUR_APIVAI_API_KEY
Content-Type: application/json

用 cURL 快速验证

# 1) List the models available to your key
curl https://api.apivai.com/v1/models \
  -H "Authorization: Bearer $APIVAI_API_KEY"

# 2) Send a minimal chat request
curl https://api.apivai.com/v1/chat/completions \
  -H "Authorization: Bearer $APIVAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "YOUR_MODEL_NAME",
    "messages": [{"role": "user", "content": "Return one short sentence."}]
  }'

OpenAI SDK

Python

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_APIVAI_API_KEY",
    base_url="https://api.apivai.com/v1",
)

resp = client.chat.completions.create(
    model="YOUR_MODEL_NAME",
    messages=[
        {"role": "system", "content": "You are a concise technical assistant."},
        {"role": "user", "content": "Explain API gateways in 2 sentences."},
    ],
    temperature=0.2,
)
print(resp.choices[0].message.content)

Node.js

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.APIVAI_API_KEY,
  baseURL: "https://api.apivai.com/v1",
});

const resp = await client.chat.completions.create({
  model: "YOUR_MODEL_NAME",
  messages: [{ role: "user", content: "Explain API gateways in 2 sentences." }],
});
console.log(resp.choices[0]?.message?.content);

流式输出

将 stream 设为 true，并逐段读取响应体。每段数据以 "data:" 开头，收到 [DONE] 或连接关闭即表示结束。

stream = client.chat.completions.create(
    model="YOUR_MODEL_NAME",
    messages=[{"role": "user", "content": "Write a short deployment checklist."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

函数调用（工具调用）

支持标准的两步流程：模型返回工具调用，你执行工具，再把结果发回去获取最终回答。

import json

TOOLS = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather for a city.",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"],
        },
    },
}]

messages = [{"role": "user", "content": "What's the weather in Tokyo?"}]
first = client.chat.completions.create(model=MODEL, messages=messages, tools=TOOLS)
choice = first.choices[0].message

messages.append(choice)
for call in choice.tool_calls or []:
    args = json.loads(call.function.arguments)
    messages.append({
        "role": "tool",
        "tool_call_id": call.id,
        "content": json.dumps({"city": args["city"], "temp_c": 21}),
    })

final = client.chat.completions.create(model=MODEL, messages=messages, tools=TOOLS)
print(final.choices[0].message.content)

常用框架

LangChain

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.apivai.com/v1",
    api_key="YOUR_APIVAI_API_KEY",
    model="YOUR_MODEL_NAME",
)
print(llm.invoke("Explain API gateways in 2 sentences.").content)

LlamaIndex

from llama_index.llms.openai_like import OpenAILike

llm = OpenAILike(
    model="YOUR_MODEL_NAME",
    api_base="https://api.apivai.com/v1",
    api_key="YOUR_APIVAI_API_KEY",
    is_chat_model=True,
)
print(llm.complete("Explain API gateways in 2 sentences."))

如何选择模型

用你的 API Key 调用 GET /v1/models。
从返回结果中选一个模型 ID。
用它发一条简短的对话请求。
确认可用后再写进应用配置。

「兼容」的含义

请求格式与 OpenAI 保持一致，但并非所有 OpenAI 接口的全部参数在任何场景下都受支持。迁移现有项目时，建议先用最小请求体跑通，确认模型可用，再逐个添加可选参数。