A serverless function example built for DigitalOcean Functions that retrieves your Cluster's information from the DigitalOcean API. This function demonstrates how to create API-connected serverless functions that can be integrated with GenAI platforms for function calling capabilities. Built using Python 3.11, it leverages the kubernetes client to interact with DOKS along with boto3 for interacting with Spaces..

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Table of Contents

• Table of Contents
• Introduction
  • GenAI Integration Use Cases
• Features
• Directory Structure
• Setup & Installation
  • Environment Variables
  • Python Dependencies
• Deployment
  • Deploying to DigitalOcean Functions
• Sample Package

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Introduction

The do-api function is a practical example of creating serverless functions on DigitalOcean Functions. It retrieves a Cluster Pod information from the DigitalOcean API and is structured to be easily integrated as a function calling endpoint for GenAI platforms and Large Language Models (LLMs).

This function supports fetching details for a specific cluster by supplying a kubeconfig stored in Spaces. The function returns key details such as pod name, the namespace, pod status and its IP.

GenAI Integration Use Cases

This function can be used as:

• A function calling endpoint for LLMs to retrieve real-time infrastructure information
• An API connector for AI agents managing cloud resources
• A data source for AI-powered cloud management dashboards

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Features

• Retrieve Specific Pod: Get information on a single pod.
• List Pods: Retrieve a list all pods on your cluster.
• List Namespaces: Retrieve a list all namespaces on your cluster.
• List Pod Status and Ips: Retrieve the pod Status and IPs
• Web-Enabled Function: Configured to be deployed as a web-accessible function with an associated web secure token.

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Directory Structure

do-api/
├── project.yml                         # Project configuration file for deployment
└── packages/
    ── kube/
      └── list/
         ├── __main__.py             # Main API function script
         ├── build.sh                # Build script for dependency installation
         └── requirements.txt        # Python dependencies (kubernetes==32.0.1)

• project.yml Defines package parameters, function configuration (runtime, web settings, resource limits, etc.) including the secure token for web access.

• packages/do-api/api/main.py Contains the API logic to connect to DigitalOcean, retrieve droplet information, and format the API response.

• packages/do-api/api/build.sh A shell script to set up a virtual environment and install required Python packages targeting the correct runtime directory.

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Setup & Installation

Environment Variables

Before running the API function, you need to set the "SPACES_ACCESS_KEY" and "SPACES_SECRET_KEY" environment variables.

Python Dependencies

The function uses Python 3.11. The required dependency is specified in packages/do-api/api/requirements.txt:

kubernetes==32.0.1

To install the dependencies in your local environment, you can use the provided build script.

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Code Changes

Update the values for SPACES_REGION, SPACES_BUCKET and KUBECONFIG_FILE as needed.

SPACES_REGION = "nyc3"  #The region of your Spaces bucket
SPACES_BUCKET = "kubefiles"  # Spaces bucket name
KUBECONFIG_FILE = kubeconfig # The name of the kubeconfig file in Spaces BUcket 

Deployment

The project is configured via project.yml for deployment on DigitalOcean Functions. Key points from the configuration include:

• Runtime: Python 3.11 (supported by DigitalOcean Functions)
• Function Name: api
• Resource Limits: Memory 512 MB, Timeout 5000 ms (within DigitalOcean Functions limits)

Make sure to integrate your deployment pipeline with the project.yml configuration. The environment variables are injected automatically into the function runtime.

Deploying to DigitalOcean Functions

1. Install the DigitalOcean CLI (doctl)
2. Configure your DigitalOcean API token
3. Deploy the function:

   doctl serverless deploy .

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Happy coding!