Vertex AI REST API

API Endpoints

Code Examples

curl -X POST \
  https://us-central1-aiplatform.googleapis.com/v1/projects/my-project/locations/us-central1/publishers/google/models/gemini-pro:generateContent \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{
      "role": "user",
      "parts": [{
        "text": "Explain quantum computing in simple terms"
      }]
    }],
    "generationConfig": {
      "temperature": 0.7,
      "maxOutputTokens": 1024
    }
  }'

const { GoogleAuth } = require('google-auth-library');

async function generateContent() {
  const auth = new GoogleAuth({
    scopes: 'https://www.googleapis.com/auth/cloud-platform'
  });
  const client = await auth.getClient();
  const projectId = 'my-project';
  const location = 'us-central1';
  const model = 'gemini-pro';
  
  const url = `https://${location}-aiplatform.googleapis.com/v1/projects/${projectId}/locations/${location}/publishers/google/models/${model}:generateContent`;
  
  const response = await client.request({
    url,
    method: 'POST',
    data: {
      contents: [{
        role: 'user',
        parts: [{ text: 'Explain quantum computing in simple terms' }]
      }],
      generationConfig: {
        temperature: 0.7,
        maxOutputTokens: 1024
      }
    }
  });
  
  console.log(response.data.candidates[0].content);
}

generateContent();

from google.cloud import aiplatform
from vertexai.preview.generative_models import GenerativeModel

aiplatform.init(project='my-project', location='us-central1')

model = GenerativeModel('gemini-pro')

response = model.generate_content(
    'Explain quantum computing in simple terms',
    generation_config={
        'temperature': 0.7,
        'max_output_tokens': 1024
    }
)

print(response.text)

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