Gemini 3.6 Flash 文本生成

curl -X POST "https://api.apiyi.com/v1beta/models/gemini-3.6-flash:generateContent" \
  -H "x-goog-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{"parts": [{"text": "用一句话介绍你自己"}]}],
    "generationConfig": {"thinkingConfig": {"thinkingLevel": "minimal"}}
  }'

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-3.6-flash",
    contents="2026年7月 AI 领域最重要的发布是什么？",
    config=types.GenerateContentConfig(
        tools=[types.Tool(google_search=types.GoogleSearch())]
    )
)
print(response.text)

import base64
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")
)

pdf = base64.standard_b64encode(open("report.pdf", "rb").read()).decode()
response = client.models.generate_content(
    model="gemini-3.6-flash",
    contents=[
        types.Part.from_bytes(data=base64.b64decode(pdf), mime_type="application/pdf"),
        "总结这份文档的核心结论"
    ]
)
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-3.6-flash",
    messages=[{"role": "user", "content": "分析这段代码的时间复杂度"}],
    reasoning_effort="high",
    max_tokens=4000
)
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-3.6-flash',
  messages: [{ role: 'user', content: '写一首关于夏天的短诗' }],
  stream: true
});

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