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# Deploy the Project on CamelX Platform # Google Vertex AI Sink
Deploy on CamelX Platform in three steps Send data to Google Vertex AI for generating content with generative AI models.
## Step 1: Create a release ## Metadata
From the project space, click on **"Create a release"**
The new version is automatically available in the list | Property | Value |
|----------|-------|
| Type | sink |
| Group | Google Vertex AI |
| Namespace | GCP |
| Support Level | Preview |
| Provider | Apache Software Foundation |
## Step 2: Deploy ## Properties
Click on **"Deploy"**
- **Version:** Select the desired release | Name | Type | Required | Default | Description |
- **Environment:** Choose `Development`, `Staging`, or `Production` |------|------|----------|---------|-------------|
- **Configuration:** Select the configuration source | `projectId` | string | ✓ | | The Google Cloud Project ID. |
- **Resources:** Set CPU and Memory | `location` | string | ✓ | | The Google Cloud region (e.g., us-central1). |
| `modelId` | string | ✓ | | The Model ID to use for predictions (e.g., gemini-2.5-pro). |
| `serviceAccountKey` | binary | | | The service account key to use as credentials for the Vertex AI client. You must encode this value in base64. |
| `operation` | string | | `generateText` | The operation to perform. |
| `temperature` | string | | | Controls randomness in generation. Lower values make output more deterministic. Range 0.0 to 1.0. |
| `maxOutputTokens` | string | | | Maximum number of tokens to generate in the response. |
| `topP` | string | | | Nucleus sampling parameter. Considers tokens with top_p probability mass. Range 0.0 to 1.0. |
| `topK` | string | | | Only sample from the top K options for each subsequent token. |
## Step 3: Expose ## Dependencies
Enable **"Expose"**
Choose an **API Gateway** (Internal, Public, etc.) - `camel:core`
- `camel:kamelet`
- `camel:google-vertexai`