Google Gemini API Setup

Self-Hosting Documentation Access

This section requires a password to access. Interested in self-hosting? Contact sales to learn more.

Password:

Access Documentation

Self-Hosting Documentation Access Granted Logout

LlamaCloud supports Google Gemini API for direct access to Google’s AI models with simple API key authentication. This provides a straightforward alternative to Google Vertex AI when you don’t need enterprise Google Cloud Platform features.

Prerequisites

• A valid Google account
• Google Gemini API key from Google AI Studio
• Access and quota for supported models:
  • Gemini 3.1 Pro
  • Gemini 3.0 Flash
  • Gemini 2.5 Flash
  • Gemini 2.5 Pro
  • Gemini 2.0 Flash
  • Gemini 2.0 Flash Lite

Environment Variables

The Google Gemini API integration uses the following environment variables:

• GOOGLE_GEMINI_API_KEY - Your Google Gemini API key (required)
• GOOGLE_GEMINI_BASE_URL - Override the Gemini API endpoint (optional; defaults to https://generativelanguage.googleapis.com)

Configuration

Follow these steps to configure Google Gemini API integration:

Step 1: Get Google Gemini API Key

Get your API key from Google AI Studio.

Step 2: Create Kubernetes Secret

Create a secret with your Google Gemini API key:

apiVersion: v1
kind: Secret
metadata:
  name: gemini-credentials
type: Opaque
stringData:
  GOOGLE_GEMINI_API_KEY: "your-api-key-here"

Apply the secret:

kubectl apply -f gemini-secret.yaml

Step 3: Configure Helm Values

Reference the secret in your Helm configuration:

# External Secret
config:
  llms:
    gemini:
      secret: "gemini-credentials"
      baseUrl: "https://generativelanguage.googleapis.com"  # Optional — override endpoint

# or direct configuration
config:
  llms:
    gemini:
      apiKey: "your-api-key-here"
      baseUrl: "https://generativelanguage.googleapis.com"  # Optional — override endpoint

The optional baseUrl / GOOGLE_GEMINI_BASE_URL override sends Gemini API traffic through a proxy or gateway (e.g. a self-hosted LLM gateway) instead of Google’s public endpoint. Leave unset to use the default https://generativelanguage.googleapis.com.

Verification

After configuration, verify your Google Gemini integration:

1. Verify in Admin UI: Check available Google models in LlamaCloud admin interface

2. Test functionality: Upload a document to confirm Gemini models are working

Troubleshooting

Common Issues

API Key Invalid

Error: API key not valid

Solution:

• Ensure your API key is correctly set and hasn’t expired
• Verify the key is from Google AI Studio
• Check that the API key has proper permissions

Quota Exceeded

Error: Quota exceeded

Solution:

• Check your Google AI Studio quotas and usage limits
• Consider upgrading your plan or requesting quota increases
• Monitor API usage to avoid rate limiting

Model Access Issues

Error: Model not found or access denied

Solution:

• Verify the model is available in your region
• Check if you have access to the specific model
• Ensure your API key has model access permissions

Debug Steps

1. Test Gemini API directly:

   curl -H "Content-Type: application/json" \
        -H "x-goog-api-key: YOUR_API_KEY" \
        -d '{"contents":[{"parts":[{"text":"Hello"}]}]}' \
        "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent"

2. Verify secret mounting:

   kubectl get secret gemini-credentials -o yaml
   kubectl describe pod <pod-name> | grep -A 20 Environment

3. Check network connectivity: Ensure your cluster can reach generativelanguage.googleapis.com