gemini-ent/examples/README.md

Gemini API Python Examples (Gemini Enterprise Agent Platform (Vertex AI) / ADC Authentication)

This directory contains examples for interacting with the Gemini API using the google-genai Python SDK and the gemini-3.5-flash model.

To comply with enterprise security policies that disable static API keys, these examples use the Gemini Enterprise Agent Platform (Vertex AI) authenticated via secure Application Default Credentials (ADC).

Core Features

• Gemini 3.5 Flash: Offers frontier-level intelligence, advanced reasoning, and pro-level coding capability inside an ultra-fast, cost-efficient Flash tier.
• Agentic Capabilities: Optimized out of the box for handling complex, multi-step agentic workflows and sustained reasoning tasks.
• Gemini Enterprise Agent Platform (Vertex AI) Integration: Uses native Google Cloud IAM authorization instead of long-lived API keys.

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Prerequisites

1. Google Cloud Project: You need an active Google Cloud Project.
2. Gemini Enterprise Agent Platform (Vertex AI) API Enabled: Make sure the API (aiplatform.googleapis.com) is enabled in your project.
3. IAM Permissions: Your Google account (or service account) must have the Gemini Enterprise Agent Platform User (Vertex AI User) (roles/aiplatform.user) role.
4. Google Cloud SDK (gcloud): Installed and configured on your machine.

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Setup Guide

1. Install Dependencies

Create a virtual environment (optional but recommended) and install the required dependencies:

# Create and activate a virtual environment
python3 -m venv venv
source venv/bin/activate

# Install the modern google-genai library
pip install -r requirements.txt

2. Authenticate locally with Application Default Credentials (ADC)

Generate local credentials that the SDK will automatically detect:

gcloud auth application-default login

3. Set Environment Variables

Specify your Google Cloud project and region:

# Set your GCP Project ID (Required)
export GOOGLE_CLOUD_PROJECT="your-gcp-project-id"

# Set your preferred location (Optional, defaults to us-central1)
export GOOGLE_CLOUD_LOCATION="us-central1"

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Running the Examples

Run the script to verify the connection and see both standard content generation and config-based content generation in action:

python call_gemini.py

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Troubleshooting

"Gemini Enterprise Agent Platform (Vertex AI) API has not been used in project..."

Enable the API on your Google Cloud Console or use the gcloud CLI:

gcloud services enable aiplatform.googleapis.com --project="your-gcp-project-id"

"Permission denied..." or "Lack of IAM permissions..."

Ensure your authenticated user or active service account has the Gemini Enterprise Agent Platform User (Vertex AI User) role on your project:

gcloud projects add-iam-policy-binding your-gcp-project-id \
  --member="user:your-email@example.com" \
  --role="roles/aiplatform.user"