The AI Terraform Chatbot is an intelligent, developer-friendly application designed to provide real-time assistance in creating and troubleshooting AWS resources using Terraform. It leverages an embedded knowledge base, AI-powered query processing, and is hosted in a highly scalable and secure AWS environment.

• AI Assistance: Provides up-to-date Terraform information to assist developers with AWS resource creation.
• Preprocessing Pipeline: Converts markdown documentation into ChromaDB vector indexes for efficient query handling.
• Web Interface: Intuitive frontend with a Flask-based backend to handle user queries.
• Infrastructure-as-Code (IaC): Uses Terraform to provision and manage the entire AWS infrastructure.
• Scalable Hosting: Deployed via ECS Fargate for container orchestration.

• Frontend: HTML, CSS, and JavaScript for a responsive user interface.
• Backend: Flask application that handles:
  • User queries
  • AI processing via OpenAI APIs
  • Session and state management
• Preprocessing: Python scripts to:
  • Chunk and vectorise markdown documents
  • Store processed data in ChromaDB

• AWS ECS Fargate: Runs containerised frontend and backend services.
• ALB (Application Load Balancer): Distributes traffic between ECS tasks.
• CloudWatch: Monitors metrics such as CPU and memory utilisation, ALB requests, and custom alerts.

• Python 3.10 or higher
• Node.js 20.x
• Docker
• AWS CLI
• Terraform CLI
• OpenAI API key
• Snyk API token (optional for security scans)

git clone <repository-url>
cd <repository-directory>

python -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

npm install

Create a .env file and include:

OPENAI_API_KEY=<your-api-key>
AWS_ACCESS_KEY_ID=<your-aws-access-key>
AWS_SECRET_ACCESS_KEY=<your-aws-secret-key>

• Preprocessing:

  python preprocessing.py

• Backend:

  flask run

• Frontend: Serve static files using any HTTP server or the provided Docker setup.

1. Push changes to the main branch.
2. The CI/CD pipeline:
   • Runs unit tests for Python and JavaScript.
   • Scans for vulnerabilities using Snyk.
   • Deploys the infrastructure using Terraform.
   • Builds and pushes Docker images to Amazon ECR.
   • Updates the ECS Fargate tasks with the latest images.

1. Use Terraform to initialise and apply the infrastructure:

   cd infrastructure/environment/dev
   terraform init
   terraform apply

2. Build and push Docker images:

   docker build -t backend:latest -f Dockerfile.backend .
   docker build -t frontend:latest -f Dockerfile.frontend .

3. Update ECS services with the new images.

• CloudWatch Dashboard:
  • Metrics: ECS CPU/Memory utilisation, ALB request counts, 4xx/5xx errors.
  • Alarms: Low CPU utilisation, ECS task failure rates.

• Python: Run pytest for backend and preprocessing modules.
• JavaScript: Run npm test for frontend testing.

• Mock AWS S3 interactions and test data loading.

• Snyk Scans: Ensures secure dependencies in Python, JavaScript, and Terraform configurations.
• IAM Policies: Restrict ECS task roles to only necessary permissions.
• Secrets Management: Uses AWS Secrets Manager for API keys.

• Integrate more custom metrics for advanced monitoring.
• Implement load testing to simulate high-traffic scenarios.