Gemini API
モデル選定とリージョン設計

GAS (JavaScript) const projectId = 'YOUR_PROJECT_ID'; const location = 'us-central1'; // 2.5系はここ const model = 'gemini-2.5-flash'; // OAuthトークンで自動認証（APIキー不要） const token = ScriptApp.getOAuthToken(); // エンドポイント URL（リージョンプレフィックスあり） const url = `https://${location}-aiplatform.googleapis.com` + `/v1/projects/${projectId}` + `/locations/${location}` + `/publishers/google/models/${model}:generateContent`; const res = UrlFetchApp.fetch(url, { method: 'POST', headers: { 'Authorization': `Bearer ${token}`, 'Content-Type': 'application/json' }, payload: JSON.stringify({ contents: [{ role: 'user', parts: [{ text: 'こんにちは' }] }] }) });

GAS (JavaScript) const projectId = 'YOUR_PROJECT_ID'; const location = 'global'; // 3.x系はここ const model = 'gemini-3-flash-preview'; const token = ScriptApp.getOAuthToken(); // 注意: globalの場合URLにリージョンプレフィックスがつかない const url = `https://aiplatform.googleapis.com` + `/v1/projects/${projectId}` + `/locations/${location}` + `/publishers/google/models/${model}:generateContent`; const res = UrlFetchApp.fetch(url, { method: 'POST', headers: { 'Authorization': `Bearer ${token}`, 'Content-Type': 'application/json' }, payload: JSON.stringify({ contents: [{ role: 'user', parts: [{ text: 'こんにちは' }] }] }) });

Python from google import genai # us-central1 で利用可能なモデル一覧 client_25 = genai.Client( vertexai=True, project="YOUR_PROJECT_ID", location="us-central1" ) print("=== us-central1 ===") for m in client_25.models.list(): if 'gemini' in m.name.lower(): print(m.name) # global で利用可能なモデル一覧 client_3x = genai.Client( vertexai=True, project="YOUR_PROJECT_ID", location="global" ) print("=== global ===") for m in client_3x.models.list(): if 'gemini' in m.name.lower(): print(m.name)

GAS function callGemini25Flash() { const projectId = 'YOUR_PROJECT_ID'; const location = 'us-central1'; const model = 'gemini-2.5-flash'; const token = ScriptApp.getOAuthToken(); const url = `https://${location}-aiplatform.googleapis.com/v1` + `/projects/${projectId}/locations/${location}` + `/publishers/google/models/${model}:generateContent`; const res = UrlFetchApp.fetch(url, { method: 'POST', headers: { 'Authorization': `Bearer ${token}`, 'Content-Type': 'application/json' }, payload: JSON.stringify({ contents: [{ role: 'user', parts: [{ text: '「Hello Vertex AI」と返してください' }] }] }) }); const result = JSON.parse(res.getContentText()); Logger.log(result.candidates[0].content.parts[0].text); }

Python（ローカル実行） from google import genai PROJECT_ID = "YOUR_PROJECT_ID" # us-central1 で利用可能なモデルを確認 client_25 = genai.Client( vertexai=True, project=PROJECT_ID, location="us-central1" ) print("--- us-central1 (2.5系) ---") for m in client_25.models.list(): if 'gemini' in m.name.lower(): print(" •", m.name) # global で利用可能なモデルを確認 client_3x = genai.Client( vertexai=True, project=PROJECT_ID, location="global" ) print("--- global (3.x系) ---") for m in client_3x.models.list(): if 'gemini' in m.name.lower(): print(" •", m.name)