Black Alert - Gemini AI-Powered Chat System

Overview

Black Alert is a serverless chat system that leverages Google's Gemini AI API through AWS Lambda. The system provides a scalable, efficient way to interact with Gemini AI's advanced language model, with infrastructure managed through Terraform and automated deployments via BitBucket Pipelines.

Cloud Architecture

Note: Replace the above image path with your actual cloud architecture diagram.

Key Features

• Serverless Architecture: AWS Lambda-based deployment for optimal scaling and cost efficiency
• Gemini AI Integration: Direct integration with Google's latest Gemini AI model
• Infrastructure as Code: Complete Terraform-managed infrastructure
• Automated CI/CD: Streamlined deployment pipeline using BitBucket Pipelines
• Error Handling: Comprehensive error management and logging
• CORS Support: Built-in CORS configuration for web integration

Technologies Used

• Python 3.12
• AWS Lambda
• Google Gemini AI API
• Terraform
• BitBucket Pipelines
• AWS API Gateway
• AWS S3

Prerequisites

• AWS Account with appropriate permissions
• Google Cloud Platform account with Gemini AI API access
• Terraform installed locally
• Python 3.12
• BitBucket account for CI/CD

Installation & Setup

Local Development Setup

1. Clone the repository:

   git clone https://github.com/Black-Alert/google-gemini-aws-ai-code.git
   cd black-alert-gemini-demo

2. Create and activate virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: .\venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   cp .env.example .env
   # Add your GEMINI_API_KEY and AWS credentials

Infrastructure Setup

1. Initialize Terraform:

   cd infra
   terraform init

2. Apply Terraform configuration:

   terraform plan
   terraform apply

Configuration

1. Configure AWS credentials
2. Set up Gemini AI API key
3. Update BitBucket pipeline variables:
   • AWS_ACCESS_KEY_ID
   • AWS_SECRET_ACCESS_KEY
   • AWS_DEFAULT_REGION
   • LAMBDA_NAME
   • ZIP_BUCKET

Usage

API Endpoint

POST request to your API Gateway endpoint:

{
  "message": "Your message here"
}

Response:

{
  "response": "AI generated response"
}

Project Structure

black-alert/
├── infra/                  # Terraform infrastructure code
│   ├── modules/           # Reusable Terraform modules
│   ├── main.tf           # Main Terraform configuration
│   ├── lambdas.tf        # Lambda function configuration
│   └── rest_api.tf       # API Gateway configuration
├── lambda_function.py     # Main Lambda function code
├── requirements.txt       # Python dependencies
├── bitbucket-pipelines.yml # CI/CD configuration
└── docs/                  # Additional documentation

Development

Deployment Process

1. Push changes to the dev branch
2. BitBucket Pipeline automatically:
   • Builds the deployment package
   • Uploads to S3
   • Updates Lambda function

Local Testing

Test the Lambda function locally using AWS SAM or direct Python execution:

python lambda_function.py

Monitoring

• CloudWatch Logs for Lambda function monitoring
• API Gateway metrics
• Terraform state monitoring
• BitBucket Pipeline logs

Security

• Environment variables for sensitive data
• IAM roles with least privilege
• API Gateway authentication (if configured)
• CORS policy implementation

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Support & Contact

For support and queries:

• Create an issue in the repository
• Contact the development team at [your-email@domain.com]

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Google Cloud Platform and Gemini AI team
• AWS Lambda and Serverless community
• Terraform and HashiCorp
• BitBucket team for CI/CD capabilities