Google Gemini Setup

Configure Google Gemini as your AI provider for language model services

Overview

Google Gemini provides language models through Google Cloud’s Vertex AI platform, including Gemini 2.5 Pro, Gemini 2.5 Flash, and Gemini 1.5 Pro. This guide walks you through setting up Google Gemini as an AI Provider in Elementum.

Prerequisites: You’ll need a Google Cloud account with billing enabled and access to Vertex AI APIs.

Step 1: Set Up Google Cloud Project

Create or Select a Project

1. Access Google Cloud Console

   • Go to console.cloud.google.com
   • Sign in with your Google account

2. Create a New Project (or select an existing one)

   • Click on the project selector at the top of the page
   • Click New Project
   • Enter a project name (e.g., “Elementum AI Integration”)
   • Select your billing account
   • Click Create

3. Enable Billing

   • Ensure your project has billing enabled
   • Navigate to Billing in the left sidebar
   • Link a billing account if not already configured

Enable Required APIs

Enable the following APIs for Vertex AI access:

1. In the Google Cloud Console, go to APIs & Services → Library
2. Search for Vertex AI API and click Enable — this may take a few minutes to complete
3. Search for Cloud Resource Manager API and click Enable — this is required for project access

Step 2: Create Service Account

Generate Service Account

1. Navigate to IAM & Admin

   In the Google Cloud Console, go to IAM & Admin → Service Accounts

2. Create Service Account

   Click Create Service Account

   Service Account Name: Enter a descriptive name (e.g., “elementum-ai-service”)

   Service Account ID: Will be auto-generated

   Description: Optional description for the service account

3. Grant Permissions

   Assign the following roles to your service account:

   Required Role:

   • Vertex AI User (roles/aiplatform.user) — Access to Vertex AI models including Gemini

   Optional Roles (for advanced features):

   • BigQuery User — If integrating with BigQuery
   • Storage Object Viewer — If accessing Cloud Storage
4. Complete Creation

   Click Continue and then Done to create the service account

Generate Service Account Key

1. Access Service Account

   In the Service Accounts list, click on your newly created service account

2. Create Key

   Go to the Keys tab

   Click Add Key → Create new key

3. Select Key Type

   Choose JSON as the key type

   Click Create

4. Download Key File

   The JSON key file will be automatically downloaded

   Critical: Copy and store this file securely — it contains credentials for your service account and cannot be downloaded again

Never share your service account key file or commit it to version control. Store it in a secure location like a password manager.

Step 3: Configure Gemini in Elementum

Add the Provider

1. In Elementum, go to Organization Settings and select the Providers tab
2. Click + Provider and select Gemini from the provider options
3. Configure the provider settings:

Basic Configuration

Provider Name: Enter a descriptive name (e.g., “Google Gemini Production”)

Location: Select your Google Cloud region (e.g., “us-central1”)

Project ID: Enter your Google Cloud project ID

CloudLink: Select which CloudLinks can access models from this provider. Leave as “All CloudLinks” unless you need to restrict access.

Service Account

Service Account Credentials: Upload or paste your JSON key file content

The JSON should look like this:

{
  "type": "service_account",
  "project_id": "your-project-id",
  "private_key_id": "...",
  "private_key": "...",
  "client_email": "[e-mail]",
  "client_id": "...",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token"
}

4. Click Save to create the provider. Elementum will automatically validate your credentials — look for a green checkmark indicating a successful connection.

Step 4: Review Available Models

Once your provider is connected, the following Gemini models are available for use in AI Services:

Language Models (LLMs)

Model	Primary Use Case	Speed	Intelligence	Best For
Gemini 2.5 Pro	Complex reasoning and large responses	Low	Very High	Advanced analysis, long-form content, complex problem-solving
Gemini 2.5 Flash	Fast, balanced performance	Very High	High	General-purpose tasks, customer support, content creation
Gemini 1.5 Pro	Established multimodal performance	Medium	High	Large document analysis, production workloads

Model Recommendations: Use Gemini 2.5 Flash for most daily tasks and general-purpose applications. Choose Gemini 2.5 Pro for complex reasoning that requires the highest intelligence. Gemini 1.5 Pro is reliable for established production workloads.

Note: Embeddings for AI Search are handled exclusively through Snowflake Cortex. Gemini models are used for LLM services only.

Step 5: Create Your First AI Service

With your Gemini provider configured, create an AI Service:

1. In Organization Settings, go to the Services tab
2. Click + Service and select LLM (Language Model service). Configure the service name, select from available Gemini models, and optionally set cost per million tokens for tracking.
3. Use the built-in testing interface to verify your service works correctly

Usage Guidelines

Cost Management

Google Cloud charges for Vertex AI usage. To manage costs:

Monitor Usage

• Monitor usage in the Google Cloud Console
• Set up billing alerts for cost control
• Review and adjust API quotas as needed
• Regularly review usage patterns

Optimize Usage

• Choose appropriate models for each task
• Use Gemini 2.5 Flash for speed-critical applications
• Cache responses when possible
• Minimize unnecessary API calls

Best Practices

Model Selection

• Use Gemini 2.5 Flash for most general-purpose tasks and customer support
• Use Gemini 2.5 Pro for complex reasoning, advanced analysis, and large responses
• Use Gemini 1.5 Pro for established production workloads requiring reliable performance

Prompt Engineering

• Be specific and clear in your prompts
• Use system messages for consistent behavior
• Provide examples for better results
• For Gemini 2.5 Pro, structure complex problems step-by-step

Performance Optimization

• Select Google Cloud regions closest to your users
• Choose Gemini 2.5 Pro for tasks requiring detailed analysis
• Use Gemini 2.5 Flash for high-volume, simple tasks
• Implement retry logic for transient errors

Troubleshooting

Authentication Errors

Symptoms: Service account authentication failures

Common Causes:

• Invalid service account key
• Insufficient permissions
• Disabled APIs

Solutions:

1. Verify service account key is valid JSON
2. Check service account roles and permissions
3. Ensure required APIs are enabled
4. Regenerate service account key if needed

API Access Issues

Symptoms: Cannot access Vertex AI APIs

Common Causes:

• APIs not enabled
• Billing not configured
• Regional restrictions

Solutions:

1. Enable Vertex AI API in Google Cloud Console
2. Verify billing is enabled and active
3. Check regional availability of services
4. Review project quotas and limits

Rate Limit Issues

Symptoms: Requests being throttled or rejected

Common Causes:

• Exceeding Vertex AI quotas
• High concurrent usage
• Regional quota limitations

Solutions:

1. Implement exponential backoff
2. Reduce request frequency
3. Review and adjust quotas in Google Cloud Console
4. Distribute load across multiple regions

Model Unavailable

Symptoms: Expected models don’t appear in service creation

Common Causes:

• Regional model availability
• Account access restrictions
• Model deprecation

Solutions:

1. Check model availability in your region
2. Review account access and permissions
3. Contact Google Cloud support for access issues
4. Consider alternative models

Security Considerations

Service Account Security

• Regularly rotate service account keys
• Use IAM roles for fine-grained access control
• Monitor service account usage for anomalies
• Enable audit logging for security tracking

Data Privacy

• Review Google’s current data handling and privacy policies
• Consider data sensitivity when crafting prompts
• Data is encrypted in transit and at rest
• Monitor data access patterns

Advanced Configuration

Multi-Region Setup

For global deployments, consider the following when selecting regions:

• Region Selection: Choose regions closest to your users for lower latency
• Data Residency: Ensure your region choices meet data residency requirements
• Failover: Implement failover strategies across regions for high availability
• Compliance: Verify regional compliance with applicable regulations

Custom Model Access

If you need access to specialized or private models in Vertex AI:

• Model Registration: Register custom models in Vertex AI
• Access Control: Configure proper IAM permissions for model access
• Monitoring: Set up custom monitoring and alerting for model performance

Next Steps

With Google Gemini configured as your AI Provider:

Create AI Services

Set up specific LLM and embedding services for your workflows

Configure Snowflake Cortex

Set up Snowflake Cortex for AI Search and embeddings

Build Agents

Create conversational AI assistants using Gemini models

Use AI Actions

Add AI capabilities to your automation workflows