curl --request POST \
--url https://xcompute.us/v1/chat/completions \
--header 'Authorization: Bearer YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-5.6-sol",
"messages": [
{
"role": "user",
"content": "请用一句话介绍你自己。"
}
]
}'
from openai import OpenAI
client = OpenAI(
base_url="https://xcompute.us/v1",
api_key="YOUR_API_KEY",
)
response = client.chat.completions.create(
model="gpt-5.6-sol",
messages=[
{"role": "user", "content": "请用一句话介绍你自己。"}
],
)
print(response.choices[0].message.content)
const response = await fetch("https://xcompute.us/v1/chat/completions", {
method: "POST",
headers: {
Authorization: "Bearer YOUR_API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "gpt-5.6-sol",
messages: [{ role: "user", content: "请用一句话介绍你自己。" }],
}),
});
if (!response.ok) throw new Error(await response.text());
const data = await response.json();
console.log(data.choices[0].message.content);
{
"id": "chatcmpl_xxx",
"object": "chat.completion",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "你好,我是一个 AI 助手。"
},
"finish_reason": "stop"
}
]
}
文本生成
文本对话
使用 OpenAI Chat Completions 兼容格式调用 Xcompute 文本模型。
POST
https://xcompute.us
/
v1
/
chat
/
completions
curl --request POST \
--url https://xcompute.us/v1/chat/completions \
--header 'Authorization: Bearer YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-5.6-sol",
"messages": [
{
"role": "user",
"content": "请用一句话介绍你自己。"
}
]
}'
from openai import OpenAI
client = OpenAI(
base_url="https://xcompute.us/v1",
api_key="YOUR_API_KEY",
)
response = client.chat.completions.create(
model="gpt-5.6-sol",
messages=[
{"role": "user", "content": "请用一句话介绍你自己。"}
],
)
print(response.choices[0].message.content)
const response = await fetch("https://xcompute.us/v1/chat/completions", {
method: "POST",
headers: {
Authorization: "Bearer YOUR_API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "gpt-5.6-sol",
messages: [{ role: "user", content: "请用一句话介绍你自己。" }],
}),
});
if (!response.ok) throw new Error(await response.text());
const data = await response.json();
console.log(data.choices[0].message.content);
{
"id": "chatcmpl_xxx",
"object": "chat.completion",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "你好,我是一个 AI 助手。"
},
"finish_reason": "stop"
}
]
}
使用 OpenAI 兼容的 Chat Completions 格式调用文本模型。模型 ID 以模型目录和模型广场当前显示的值为准。
读取
请求
curl --request POST \
--url https://xcompute.us/v1/chat/completions \
--header 'Authorization: Bearer YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-5.6-sol",
"messages": [
{
"role": "user",
"content": "请用一句话介绍你自己。"
}
]
}'
from openai import OpenAI
client = OpenAI(
base_url="https://xcompute.us/v1",
api_key="YOUR_API_KEY",
)
response = client.chat.completions.create(
model="gpt-5.6-sol",
messages=[
{"role": "user", "content": "请用一句话介绍你自己。"}
],
)
print(response.choices[0].message.content)
const response = await fetch("https://xcompute.us/v1/chat/completions", {
method: "POST",
headers: {
Authorization: "Bearer YOUR_API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "gpt-5.6-sol",
messages: [{ role: "user", content: "请用一句话介绍你自己。" }],
}),
});
if (!response.ok) throw new Error(await response.text());
const data = await response.json();
console.log(data.choices[0].message.content);
Header 参数
string
required
Bearer Token,例如
Authorization: Bearer YOUR_API_KEY。string
default:"application/json"
required
固定使用
application/json。Body 参数
array
required
对话消息数组。每条消息包含
role 和 content,常用角色包括 system、user 和 assistant。boolean
default:"false"
是否以流式方式返回结果。使用流式响应时,请按照 SSE 事件逐段读取内容。
成功响应
{
"id": "chatcmpl_xxx",
"object": "chat.completion",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "你好,我是一个 AI 助手。"
},
"finish_reason": "stop"
}
]
}
choices[0].message.content 获取非流式响应文本。生产环境请同时检查 HTTP 状态码和响应中的错误信息。
注意事项
- 模型名称会随渠道配置变化,请不要依赖过期的固定列表。
- API Key 只能放在服务端或本地安全配置中,不能写入浏览器前端代码。
- 需要长文本输出时,建议设置合理的客户端超时,并处理限流和网络重试。