# 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.

---

## 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.

---

## Setup Guide

### 1. Install Dependencies
Create a virtual environment (optional but recommended) and install the required dependencies:

```bash
# 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:

```bash
gcloud auth application-default login
```

### 3. Set Environment Variables
Specify your Google Cloud project and region:

```bash
# 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"
```

---

## Running the Examples

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

```bash
python call_gemini.py
```

---

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