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Quick start with Google Cloud Vertex AI

plan support
  • Self-hosted: Business, Enterprise Plus
Supported regions

Ensure that your selected region supports the Google models you plan to use. Refer to the official Google model endpoint locations: Google model endpoint locations.

If you encounter any issues, feel free to contact us to discuss alternative model selections for your region.

Prerequisites

  1. Enable Vertex AI API Go to the Vertex AI console and enable the Vertex AI API.

    Enable Vertex AI API

  2. A service account private key (JSON) with the role "Vertex AI User"

    How to create a service account:

    1. Go to GCP Console → IAM & Admin → Service Accounts. Click "Create service account".

    Create service account

    1. Fill in name.

    Service account name

    1. Assign permissions — choose "Vertex AI User".

    Assign Vertex AI User role

    1. Open the service account you just created.

    Open service account

    1. In the "Keys" tab, click "Add key" → Create new key, then choose JSON key type to download the private key.

    Add key Create JSON key

Steps (Quick setup)

  1. During onboarding, select "Quick setup" and then "Google Gemini (Vertex AI)".

Google Gemini (Vertex AI) onboarding

  1. Enter Vertex location (region), Vertex project (your GCP project ID), then upload your service account JSON.

Google Gemini (Vertex AI) region and project

  1. Test embedding model connection.

Google Gemini (Vertex AI) test embedding model connection

  1. Update model configurations.

    Best Model Recommendation

    The following steps and screenshots use gemini-embedding-001 as the embedding model and gemini-2.5-pro as the LLM model for illustration purposes only, and may not reflect the current best model configuration. For the latest recommended model combination and configuration, please contact your account manager or sales representative.

    There are default configs of the pre-selected models on the UI.

    Default model configs

    You'll need to update the configurations for optimization. Click "Configure" for each listed model and set:

    {
    "kwargs": {
    "n": 1,
    "timeout": 60,
    "temperature": 0
    },
    "context_window_size": 1000000
    }
  2. Review the pipeline assignments.

    Best Model Recommendation

    The screenshot below shows pipelines assigned to gemini-2.5-pro for illustration purposes only, and may not reflect the current best model configuration. For the latest recommended pipeline assignments and model combinations, please contact your account manager or sales representative.

    Google Gemini (Vertex AI) pipeline assignments

    We provide default pipelines optimized for Gemini models. You could leave it as is.

  3. Complete the setup.

    Scroll to the bottom and click "Complete setup".