gemini-2.5-pro-latest API 実装 — モデル指定・パラメータ調整・運用パターン

config = {
    "temperature": 0.2,       # 0.0 〜 2.0、低いほど決定論的
    "top_p": 0.9,             # 上位 p% の確率質量で切る
    "top_k": 40,              # 上位 k 候補から選ぶ
    "max_output_tokens": 4096 # 出力の上限
}
 
response = client.models.generate_content(
    model="gemini-2.5-pro-latest",
    contents="...",
    config=config,
)

import asyncio
import random
from google import genai
from google.genai import errors
 
client = genai.Client(api_key="YOUR_GEMINI_API_KEY")
 
async def generate_with_retry(prompt, max_retries=5):
    for attempt in range(max_retries):
        try:
            return client.models.generate_content(
                model="gemini-2.5-pro-latest",
                contents=prompt,
            )
        except errors.APIError as e:
            if e.code in (429, 503, 504) and attempt < max_retries - 1:
                wait = (2 ** attempt) + random.random()
                await asyncio.sleep(wait)
                continue
            raise

from google.genai import types
 
response = client.models.generate_content(
    model="gemini-2.5-pro-latest",
    contents=prompt,
    config=types.GenerateContentConfig(
        http_options=types.HttpOptions(timeout=30_000),  # 30 秒
    ),
)

stream = client.models.generate_content_stream(
    model="gemini-2.5-pro-latest",
    contents="長めの記事を書いてください。",
)
 
for chunk in stream:
    print(chunk.text, end="", flush=True)

from google.genai import types
from pydantic import BaseModel
 
class Article(BaseModel):
    title: str
    tags: list[str]
    summary: str
 
response = client.models.generate_content(
    model="gemini-2.5-pro-latest",
    contents="Claude Code の記事を 1 つ作成してください。",
    config=types.GenerateContentConfig(
        response_mime_type="application/json",
        response_schema=Article,
    ),
)
 
article = Article.model_validate_json(response.text)

import os
from google import genai
 
# 本番: 固定バージョンIDを環境変数で管理
PROD_MODEL = os.getenv("GEMINI_MODEL", "gemini-2.5-pro-002")
 
# ステージング: -latest で次バージョンの早期検証
STAGING_MODEL = "gemini-2.5-pro-latest"
 
client = genai.Client(api_key="YOUR_GEMINI_API_KEY")
 
def generate(prompt: str, env: str = "prod") -> str:
    model_id = PROD_MODEL if env == "prod" else STAGING_MODEL
    response = client.models.generate_content(
        model=model_id,
        contents=prompt
    )
    return response.text

import pytest
from google import genai
 
client = genai.Client(api_key="YOUR_GEMINI_API_KEY")
 
@pytest.mark.parametrize("model_id", [
    "gemini-2.5-pro-002",   # 現在の本番
    "gemini-2.5-pro-latest", # 次の候補
])
def test_json_output_structure(model_id):
    response = client.models.generate_content(
        model=model_id,
        contents="""ユーザー名 'taro' のプロフィールを以下のJSON形式で返してください:
        {"name": "...", "age": <int>, "tags": [...]}
        他のテキストは含めないでください。"""
    )
    import json
    data = json.loads(response.text.strip())
    assert "name" in data
    assert isinstance(data["age"], int)
    assert isinstance(data["tags"], list)

from google import genai
from google.api_core import exceptions
import logging
 
class GeminiWithFallback:
    def __init__(self, api_key: str):
        self.client = genai.Client(api_key=api_key)
        self.primary = "gemini-2.5-pro-latest"
        self.fallback = "gemini-2.5-pro-002"  # 既知の安定版を固定
    
    def generate(self, prompt: str, validator=None):
        try:
            response = self.client.models.generate_content(
                model=self.primary,
                contents=prompt
            )
            # 出力検証（任意）
            if validator and not validator(response.text):
                logging.warning(f"primary model output failed validation, falling back")
                raise ValueError("Primary output invalid")
            return response.text
        except (exceptions.GoogleAPIError, ValueError) as e:
            logging.warning(f"Primary {self.primary} failed: {e}, retrying with {self.fallback}")
            response = self.client.models.generate_content(
                model=self.fallback,
                contents=prompt
            )
            return response.text