Gemini CLI Incident Agent Tutorial

This project demonstrates a Python microservices application (Frontend and Backend) deployed on Kubernetes with a complete observability stack including Prometheus, Grafana, and Loki.

Prerequisites

To run this project, you need the following tools installed:

1. Kubernetes Cluster

• Minikube (Recommended for local development) or Docker Desktop (with Kubernetes enabled).
• Ensure your cluster is running: minikube start or your equivalent.

2. Standard CLI Tools

• kubectl: The Kubernetes command-line tool.
• Docker: For building microservice images.
• Python 3.9+: For local testing of microservices.

3. Git

• Used for version control and cloning the repository.

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Getting Started

1. Build the Microservices

Navigate to the microservices directory and build the Docker images. If using Minikube, point your shell to the Minikube Docker daemon:

# If using Minikube:
eval $(minikube docker-env)

# Build Frontend
docker build -t frontend:v1 ./python-k8s-app/microservices/frontend

# Build Backend
docker build -t backend:v1 ./python-k8s-app/microservices/backend

2. Deploy the Application and Observability Stack

Apply the Kubernetes manifests in the k8s directory:

kubectl apply -f python-k8s-app/k8s/

3. Access the Services

Verify all pods are running:

kubectl get pods

• Frontend: Access via NodePort 30002 (if using Minikube: minikube service frontend-service --url).
• Grafana: Access via NodePort 30003 (if using Minikube: minikube service grafana-service --url).

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Project Structure

• python-k8s-app/microservices/: Source code and Dockerfiles for Frontend and Backend.
• python-k8s-app/k8s/: Kubernetes manifests for the application and observability stack (Loki, Prometheus, Grafana, Promtail).
• .gemini/: Context and configuration for the Gemini CLI agent.