The name "Kubenstein" is a play of words on Kubernetes and Frankenstein.
The goal is to explore the effectiveness of an artificial system administrator in dealing with open-ended system problems, by mediating communications between a generative AI and a Kubernetes cluster.
The idea is to restrict to focus the application on framing the problem and defer all troubleshooting, and eventually, mitigations, to the AI.
- Adaptive assessment of cluster health
- Adaptative troubleshooting of cluster problems
- Recommendations for next steps of troubleshooting for problems without clear resolutions.
- A Python 3.10 (or later) runtime environment
- The
kubectlCLI installed with a version compatible with the Kubernetes cluster and logged in to a Kubernetes cluster kubectllogged in to a Kubernetes cluster- An API Key to ChatGPT (only supported AI currently)
If you want to iterate through development using a terminal, you can consult the following reference on creating the Python environment: https://docs.python.org/3/library/venv.html
VIRTUAL_ENV_DIR=/path/to/new/virtual/environment
python -m venv ${VIRTUAL_ENV_DIR:?}Create a configuration file in a location in your filesystem, such as ${HOME}/etc/k8s-chat.env
cat << EOF > "${HOME}/etc/k8s-chat.env"
OPENAI_API_KEY=sk-...
LOGGING_LEVEL=debug
EOFActivate the virtual environment:
${VIRTUAL_ENV_DIR}/bin/activate
pip install -r requirements.txtLaunch the application:
python --env-file $HOME/etc/k8s-chat.env app.py- The application opens a chat session with the application, initially asking it to act as a system administrator following an assessment script.
- The AI is asked to provide one command at a time, waiting for the application to execute the command and return the output.
- If the AI response does not contain a single command, the AI is prodded to stick to the request of one command at a time (see the prompt.)
- If the command output returns too much content, the application asks the AI to provide a suggestion using more filtering or alternatives (see the prompt.)
- The AI is asked to create a list of errors found during the assessment and repeat the troubleshooting steps for each error.
- Once the AI completes the assessment script and the troubleshooting of specific errors, it is asked to generate a readable report of all findings and suggestions for troubleshooting next steps.
(under construction)
- Common issues and how to resolve them.
- Troubleshooting tips for AI integration.
(under construction)
- Guidelines for contributing to the project.
- Mention coding standards and pull request process.
MIT License
- I want to acknowledge Andre Tost for his 3-part blog series on Using ChatGPT (And Other Tools) To Troubleshoot OpenShift, who inspired me to create this application.
(under construction)
- Allow human interaction on each prompt to confirm whether the application can send the response to the AI.
- Branch out the troubleshooting prompts into more specific areas, such as prompts specific to troubleshooting Networking aspects versus Storage aspects.
- Add more initial assessments about what is installed to broaden the troubleshooting into those areas and consider the presence of these other components when troubleshooting other areas.
- Add corrective actions to the scope of assistance, asking the AI to fix the cluster.
- The AI should be treated as a third-party
- The AI is unconstrained in the range of commands it will suggest
- This application is a prototype, so DO NOT give it access to a production server.
- Consider creating a user credential with a narrow Cluster Role that only allows it to read specific resources in the cluster, excluding Secret resources, lest the AI inadvertently asks to see secrets in a namespace.
You can access the Contributor Covenant here.
- 2023-09-17 First draft
(under construction)
- Include screenshots or diagrams for visual representation.