Custom Rest Backend

Overview

K8sGPT allows you to integrate custom backends using RESTful API, enabling you to use your own AI models or services. This tutorial will guide you through the process of creating and integrating a custom REST backend with K8sGPT.

Prerequisites

• K8sGPT CLI installed
• Basic understanding of RESTful APIs
• Access to your custom AI model or service
• Development environment with your preferred programming language

API Specification

Your custom backend must implement the following REST endpoints:

POST /v1/analyze
Content-Type: application/json

{
  "cluster": {
    "resources": [...],
    "state": "..."
  },
  "config": {
    "filters": [...],
    "options": {...}
  }
}

Implementation Steps

1. Set up your REST server: Create a new server that implements the required endpoints.
2. Implement the analysis logic: Process the incoming cluster data and return appropriate analysis results.
3. Configure K8sGPT: Update your K8sGPT configuration to use your custom backend.
4. Test the integration: Run K8sGPT with your custom backend to ensure everything works as expected.

Example Configuration

Add the following to your K8sGPT configuration file:

backend: custom
custom:
  url: "http://your-backend-url/v1/analyze"
  headers:
    Authorization: "Bearer your-api-key"

Next Steps

• Implement error handling and retry logic
• Add authentication and security measures
• Optimize performance and response times
• Consider implementing caching for frequently accessed data