SDK 示例

export MIROBODY_API_BASE="https://api.mirobody.cn/v1"  # 中国区账户
# export MIROBODY_API_BASE="https://api.mirobody.ai/v1" # 全球区账户：改用这一行

BASE="$MIROBODY_API_BASE"
KEY="$MIROBODY_API_KEY"

# 1) 有据可循的回答（Answers API）
curl $BASE/chat/completions \
  -H "Authorization: Bearer $KEY" -H "Content-Type: application/json" \
  -d '{"model":"mirobody-flash","messages":[{"role":"user","content":"How is my fasting glucose trending?"}],"user":"alice"}'

# 2) 写入一条记录（retention 必填），再问一次
curl $BASE/data \
  -H "Authorization: Bearer $KEY" -H "Content-Type: application/json" \
  -d '{"user":"alice","retention":"1h","records":[{"indicator":"fasting_glucose","value":97,"unit":"mg/dL","time":"2026-06-16T07:30:00Z"}]}'

# 3) 上传报告。原件立即保存，抽取的文本稍后即可用。
curl $BASE/files -H "Authorization: Bearer $KEY" -F "user=alice" -F "file=@report.pdf"

# 4) Agent API（Responses）
curl $BASE/responses \
  -H "Authorization: Bearer $KEY" -H "Content-Type: application/json" \
  -d '{"model":"mirobody-flash","input":"How is my fasting glucose trending?","user":"alice"}'

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["MIROBODY_API_KEY"], base_url=os.environ["MIROBODY_API_BASE"])

# Answers API —— 非流式
resp = client.chat.completions.create(
    model="mirobody-flash",
    messages=[{"role": "user", "content": "How is my fasting glucose trending?"}],
    user="alice",
)
print(resp.choices[0].message.content)

# Answers API —— 流式
stream = client.chat.completions.create(
    model="mirobody-flash",
    messages=[{"role": "user", "content": "Summarize my last checkup."}],
    user="alice",
    stream=True,
)
for chunk in stream:
    delta = chunk.choices[0].delta
    if getattr(delta, "content", None):
        print(delta.content, end="", flush=True)

# Agent API（Responses）—— 默认即存储、可链式续聊
r1 = client.responses.create(model="mirobody-flash",
                             input="How is my fasting glucose trending?", user="alice")
print(r1.output_text)
r2 = client.responses.create(model="mirobody-flash",
                             input="And compared with last quarter?",
                             previous_response_id=r1.id, user="alice")
print(r2.output_text)

# 文件上传是 multipart，直接调 REST 端点。
# status="processed" 表示原件已接收，不代表文本抽取已经完成。
import os
import requests

up = requests.post(
    f'{os.environ["MIROBODY_API_BASE"]}/files',
    headers={"Authorization": f'Bearer {os.environ["MIROBODY_API_KEY"]}'},
    data={"user": "alice"},
    files={"file": open("report.pdf", "rb")},
)
print(up.json())   # {"object": "file", "id": "...", "filename": "report.pdf",
                   #  "bytes": 20544, "status": "processed", "created_at": 1782924296,
                   #  "subject": "alice"}

import os
from agents import Agent, ModelSettings, Runner, function_tool, set_default_openai_client, set_tracing_disabled
from openai import AsyncOpenAI

set_default_openai_client(AsyncOpenAI(
    base_url=os.environ["MIROBODY_API_BASE"], api_key=os.environ["MIROBODY_API_KEY"]))
set_tracing_disabled(True)   # tracing 会请求 api.openai.com

@function_tool
def book_appointment(date: str) -> str:
    """Book a clinic appointment for the end user (ISO date)."""
    return f"Booked: {date} 09:30, Dr. Chen"

agent = Agent(name="Health assistant", model="mirobody-flash", tools=[book_appointment],
              model_settings=ModelSettings(extra_body={"user": "alice"}))
result = Runner.run_sync(agent, "Check my recent glucose and book a follow-up if it's trending up.")
print(result.final_output)

import OpenAI from "openai";

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

// Answers API —— 非流式
const resp = await client.chat.completions.create({
  model: "mirobody-flash",
  messages: [{ role: "user", content: "How is my fasting glucose trending?" }],
  user: "alice",
});
console.log(resp.choices[0].message.content);

// Answers API —— 流式
const stream = await client.chat.completions.create({
  model: "mirobody-flash",
  messages: [{ role: "user", content: "Summarize my last checkup." }],
  user: "alice",
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

// Agent API（Responses）
const r = await client.responses.create({
  model: "mirobody-flash",
  input: "How is my fasting glucose trending?",
  user: "alice",
});
console.log(r.output_text);