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快速入门

在几分钟内完成您的第一次 API 调用。

本快速入门专为 OpenAI 兼容推理服务设计。请将下面的占位符值替换为您的 API 密钥和模型名称。


前提条件

在开始之前,请确保您具备以下条件:

  • 您的推理服务 API 密钥
  • 已安装 curl,或者如果您想使用 SDK,需安装 Python / Node.js

第 1 步:创建并导出 API 密钥

您可以在此处创建 API 密钥。请将 API 密钥存储在环境变量中,而不是硬编码在源文件中。

export INFERENCE_BASE_URL="https://api.luchentech.com/inference/v1"
export INFERENCE_API_KEY="your_api_key_here"
export INFERENCE_MODEL="minimax/minimax-m2.5"

第 2 步:发起第一次 API 调用

curl

curl --request POST \
--url "$INFERENCE_BASE_URL/chat/completions" \
--header "Authorization: Bearer $INFERENCE_API_KEY" \
--header "Content-Type: application/json" \
--data @- <<EOF
{
"model": "${INFERENCE_MODEL}",
"messages": [
{
"role": "user",
"content": "How are you?"
}
],
"temperature": 0.7,
"max_tokens": 128
}
EOF

成功的响应通常如下所示:

{
"id": "e63095aef9bc4d7292b769edb2cb6583",
"object": "chat.completion",
"created": 1773651537,
"model": "minimax/minimax-m2.5",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hi there! I'm doing well, thank you for asking. How about you? How's your day going so far? Is there anything I can help you with today?",
"reasoning_content": null,
"tool_calls": null
},
"logprobs": null,
"finish_reason": "stop",
"matched_stop": 248046
}
],
"usage": {
"prompt_tokens": 15,
"total_tokens": 692,
"completion_tokens": 677,
"prompt_tokens_details": null,
"reasoning_tokens": 0
},
"metadata": {
"weight_version": "default"
}
}

Python (OpenAI SDK)

安装 SDK:

pip install openai

然后调用您的服务:

import os
from openai import OpenAI

client = OpenAI(
api_key=os.environ["INFERENCE_API_KEY"],
base_url=os.environ["INFERENCE_BASE_URL"],
)

response = client.chat.completions.create(
model=os.environ["INFERENCE_MODEL"],
messages=[
{"role": "user", "content": "Say hello in Spanish."}
],
temperature=0.7,
max_tokens=128,
)

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

JavaScript / TypeScript (OpenAI SDK)

安装 SDK:

npm install openai

然后调用您的服务:

import OpenAI from "openai";

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

const response = await client.chat.completions.create({
model: process.env.INFERENCE_MODEL,
messages: [
{
role: "user",
content: "Say hello in Spanish.",
},
],
temperature: 0.7,
max_tokens: 128,
});

console.log(response.choices[0].message.content);