快速开始
30 秒内完成第一次调用
先从 dashboard 拿到 API key,然后选择一种语言。QSP 相关的改动只有 base_url 和 key —— 其余完全是标准的 OpenAI SDK。
1. 获取 API key
登录 quicksilverpro.io/dashboard,在 "Connect your agent" 区域复制你的 key。
将其设为 QSP_KEY 环境变量,这样下面的代码片段就可以直接使用:
shell
export QSP_KEY="sk-..."2. 发起调用
Python
python
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.quicksilverpro.io/v1",
api_key=os.environ["QSP_KEY"],
)
resp = client.chat.completions.create(
model="deepseek-v4-flash",
messages=[{"role": "user", "content": "Hello!"}],
reasoning={"enabled": False},
)
print(resp.choices[0].message.content)Node.js / TypeScript
typescript
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.quicksilverpro.io/v1",
apiKey: process.env.QSP_KEY,
});
const resp = await client.chat.completions.create({
model: "deepseek-v4-flash",
messages: [{ role: "user", content: "Hello!" }],
reasoning: { enabled: false },
});
console.log(resp.choices[0].message.content);Swift
swift
import Foundation
import OpenAI
let openAI = OpenAI(
configuration: .init(
token: ProcessInfo.processInfo.environment["QSP_KEY"]!,
host: "api.quicksilverpro.io",
basePath: "/v1"
)
)
let query = ChatQuery(
messages: [.user(.init(content: .string("Hello!")))],
model: "deepseek-v4-flash"
)
let resp = try await openAI.chats(query: query)
print(resp.choices.first?.message.content?.string ?? "")curl
shell
curl https://api.quicksilverpro.io/v1/chat/completions \
-H "Authorization: Bearer $QSP_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-flash",
"messages": [{"role": "user", "content": "Hello!"}],
"reasoning": {"enabled": false}
}'3. 你会收到的响应
标准的 OpenAI chat-completions JSON。choices[0].message.content 是模型的回复。usage 字段汇报 prompt 与 completion token 数, 外加一个根据公开单价计算出的合成 cost 字段。
json
{
"id": "chatcmpl-...",
"object": "chat.completion",
"created": 1715800000,
"model": "deepseek-v4-flash",
"choices": [{
"index": 0,
"message": {"role": "assistant", "content": "Hello! How can I help?"},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 8,
"total_tokens": 17,
"cost": 0.00000275
}
}下一步
- 为你的工作负载挑选合适的模型 — 模型。
- 按 token 流式接收输出 — Streaming。
- 拿到强类型的 JSON 响应 — Structured output。