Gemini 3.5 Flash-Lite 文本生成

curl -X POST "https://api.apiyi.com/v1beta/models/gemini-3.5-flash-lite:generateContent" \
  -H "x-goog-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{"parts": [{"text": "把這句話翻譯成英文：今天天氣不錯"}]}]
  }'

from google import genai
from google.genai import types

client = genai.Client(
    api_key="YOUR_API_KEY",
    http_options=types.HttpOptions(base_url="https://api.apiyi.com")
)

response = client.models.generate_content(
    model="gemini-3.5-flash-lite",
    contents="一個水池兩根進水管，甲單獨8小時注滿，乙單獨12小時注滿，同時開啟需要幾小時？",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="high")
    )
)
print(response.text)

from google import genai
from google.genai import types

client = genai.Client(
    api_key="YOUR_API_KEY",
    http_options=types.HttpOptions(base_url="https://api.apiyi.com")
)

response = client.models.generate_content(
    model="gemini-3.5-flash-lite",
    contents=[
        types.Part.from_bytes(data=open("photo.png", "rb").read(), mime_type="image/png"),
        "描述這張圖片"
    ]
)
print(response.text)

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"
)

response = client.chat.completions.create(
    model="gemini-3.5-flash-lite",
    messages=[{"role": "user", "content": "把下面的評論分類為正面/負面/中性：物流很快但包裝一般"}]
)
print(response.choices[0].message.content)

import OpenAI from 'openai';

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

const stream = await client.chat.completions.create({
  model: 'gemini-3.5-flash-lite',
  messages: [{ role: 'user', content: '用三句話總結 RAG 的原理' }],
  stream: true
});

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