KubeAIWhisperer

Welcome to KubeAIWhisperer, your ultimate Kubernetes deployment monitoring tool empowered by AzureOpenAI.

Overview

KubeAIWhisperer is designed to provide real-time insights and recommendations for optimizing your Kubernetes deployments. It leverages the latest advancements in AI to analyze your clusters and offer suggestions on security configurations, scaling strategies, and cost management. With KubeAIWhisperer, you can ensure your Kubernetes infrastructure is always performing at its best while minimizing risks and costs.

Features

• Automatic Monitoring: KubeAIWhisperer continuously monitors your Kubernetes deployments, detecting any changes or new deployments instantly.

• AI-driven Recommendations: Powered by AzureOpenAI, KubeAIWhisperer offers actionable insights and best practices for security, scalability, and cost optimization.

• Customizable Alerts: Set up alerts based on specific criteria to stay informed about critical changes in your Kubernetes environment.

• Events List Feature for Kubernetes: Now includes a feature to list events within your Kubernetes cluster, providing additional visibility into cluster activity.

• Kube Chat: Introducing Kube Chat, a chatbot that interacts with users to execute kubectl commands based on user input using GPT models, enhancing the ease of managing your Kubernetes environment.

## Getting Started

Prerequisites

• Kubernetes cluster up and running.
• Access to Kubernetes API server.
• Python 3.x installed.
• AzureOpenAI API key (Sign up here if you don't have one).

Deployment

To deploy KubeAIWhisperer, follow these steps:

1. Update the deployment.yaml file with the necessary values under the configmap section. Ensure to replace placeholders with actual values:

      ---
      apiVersion: v1
         kind: ConfigMap
         metadata:
            name: kubeaiwhisperer-config
         data:
            # AzureOpenAI endpoint URL
            AZURE_OPENAI_ENDPOINT: ""  
            # AzureOpenAI API key
            AZURE_OPENAI_API_KEY: ""
            # AzureOpenAI version
            AZURE_OPENAI_VERSION: ""
            # AzureOpenAI deployment name
            AZURE_OPENAI_DEPLOYMENT_NAME: ""
            # Name of the model to be used
            MODEL_NAME: ""
            # Maximum tokens to generate
            MAX_TOKENS: ""
            # MongoDB connection URI
            MONGODB_URI: ""
            # Interval in seconds to scan for changes
            SCAN_INTERVAL_SECONDS: ""
            # Namespaces to list for monitoring
            LIST_NAMESPACE: ""
            # Environment type (incluster or external)
            ENVIRONMENT: ""
      ---
   

2. Install the deployment using:

   kubectl apply -f deployment.yaml