Gemini 4 Argon 文本生成

curl -X POST "https://api.apiyi.com/v1beta/models/gemini-4-argon:generateContent" \
  -H "x-goog-api-key: sk-your-apiyi-key" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{"parts": [{"text": "用一句话介绍你自己"}]}],
    "generationConfig": {"thinkingConfig": {"thinkingLevel": "low"}}
  }'

from google import genai
from google.genai import types

client = genai.Client(
    api_key="sk-your-apiyi-key",
    http_options=types.HttpOptions(base_url="https://api.apiyi.com", timeout=1200_000)
)

response = client.models.generate_content(
    model="gemini-4-argon",
    contents="找出这段代码的并发问题并给出修复方案：...",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="high")
    )
)
print(response.text)

from openai import OpenAI

client = OpenAI(
    api_key="sk-your-apiyi-key",
    base_url="https://api.apiyi.com/v1",
    timeout=1200
)

response = client.chat.completions.create(
    model="gemini-4-argon",
    messages=[{"role": "user", "content": "设计一个支持百万并发的短链服务，给出架构与关键取舍"}]
)
print(response.choices[0].message.content)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'sk-your-apiyi-key',
  baseURL: 'https://api.apiyi.com/v1'
});

const stream = await client.chat.completions.create({
  model: 'gemini-4-argon',
  messages: [{ role: 'user', content: '写一份 Rust 迁移评估报告的大纲' }],
  stream: true
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? '');
}