This Operator is designed to enable K8sGPT within a Kubernetes cluster. It will allow you to create a custom resource that defines the behaviour and scope of a managed K8sGPT workload. Analysis and outputs will also be configurable to enable integration into existing workflows.
helm repo add k8sgpt https://charts.k8sgpt.ai/
helm repo update
helm install release k8sgpt/k8sgpt-operator -n k8sgpt-operator-system --create-namespace
-
Install the operator from the Installation section.
-
Create secret:
kubectl create secret generic k8sgpt-sample-secret --from-literal=openai-api-key=$OPENAI_TOKEN -n k8sgpt-operator-system- Apply the K8sGPT configuration object:
kubectl apply -f - << EOF
apiVersion: core.k8sgpt.ai/v1alpha1
kind: K8sGPT
metadata:
name: k8sgpt-sample
namespace: k8sgpt-operator-system
spec:
ai:
enabled: true
model: gpt-3.5-turbo
backend: openai
secret:
name: k8sgpt-sample-secret
key: openai-api-key
# anonymized: false
# language: english
noCache: false
repository: ghcr.io/k8sgpt-ai/k8sgpt
version: v0.3.8
#integrations:
# trivy:
# enabled: true
# namespace: trivy-system
# filters:
# - Ingress
# sink:
# type: slack
# webhook: <webhook-url> # use the sink secret if you want to keep your webhook url private
# secret:
# name: slack-webhook
# key: url
#extraOptions:
# backstage:
# enabled: true
EOF- Once the custom resource has been applied the K8sGPT-deployment will be installed and you will be able to see the Results objects of the analysis after some minutes (if there are any issues in your cluster):
❯ kubectl get results -o json | jq .
{
"apiVersion": "v1",
"items": [
{
"apiVersion": "core.k8sgpt.ai/v1alpha1",
"kind": "Result",
"spec": {
"details": "The error message means that the service in Kubernetes doesn't have any associated endpoints, which should have been labeled with \"control-plane=controller-manager\". \n\nTo solve this issue, you need to add the \"control-plane=controller-manager\" label to the endpoint that matches the service. Once the endpoint is labeled correctly, Kubernetes can associate it with the service, and the error should be resolved.",Azure Blob storage
-
Install the operator from the Installation section.
-
Create secret:
kubectl create secret generic k8sgpt-sample-cache-secret --from-literal=azure_client_id=<AZURE_CLIENT_ID> --from-literal=azure_tenant_id=<AZURE_TENANT_ID> --from-literal=azure_client_secret=<AZURE_CLIENT_SECRET> -n k8sgpt-
operator-system- Apply the K8sGPT configuration object:
kubectl apply -f - << EOF
apiVersion: core.k8sgpt.ai/v1alpha1
kind: K8sGPT
metadata:
name: k8sgpt-sample
namespace: k8sgpt-operator-system
spec:
ai:
model: gpt-3.5-turbo
backend: openai
enabled: true
secret:
name: k8sgpt-sample-secret
key: openai-api-key
noCache: false
repository: ghcr.io/k8sgpt-ai/k8sgpt
version: v0.3.8
remoteCache:
credentials:
name: k8sgpt-sample-cache-secret
azure:
# Storage account must already exist
storageAccount: "account_name"
containerName: "container_name"
EOF
S3
-
Install the operator from the Installation section.
-
Create secret:
kubectl create secret generic k8sgpt-sample-cache-secret --from-literal=aws_access_key_id=<AWS_ACCESS_KEY_ID> --from-literal=aws_secret_access_key=<AWS_SECRET_ACCESS_KEY> -n k8sgpt-
operator-system- Apply the K8sGPT configuration object:
kubectl apply -f - << EOF
apiVersion: core.k8sgpt.ai/v1alpha1
kind: K8sGPT
metadata:
name: k8sgpt-sample
namespace: k8sgpt-operator-system
spec:
ai:
model: gpt-3.5-turbo
backend: openai
enabled: true
secret:
name: k8sgpt-sample-secret
key: openai-api-key
noCache: false
repository: ghcr.io/k8sgpt-ai/k8sgpt
version: v0.3.8
remoteCache:
credentials:
name: k8sgpt-sample-cache-secret
s3:
bucketName: foo
region: us-west-1
EOF
AzureOpenAI
-
Install the operator from the Installation section.
-
Create secret:
kubectl create secret generic k8sgpt-sample-secret --from-literal=azure-api-key=$AZURE_TOKEN -n k8sgpt-operator-system- Apply the K8sGPT configuration object:
kubectl apply -f - << EOF
apiVersion: core.k8sgpt.ai/v1alpha1
kind: K8sGPT
metadata:
name: k8sgpt-sample
namespace: k8sgpt-operator-system
spec:
ai:
enabled: true
secret:
name: k8sgpt-sample-secret
key: azure-api-key
model: gpt-35-turbo
backend: azureopenai
baseUrl: https://k8sgpt.openai.azure.com/
engine: llm
noCache: false
repository: ghcr.io/k8sgpt-ai/k8sgpt
version: v0.3.8
EOF
LocalAI
-
Install the operator from the Installation section.
-
Follow the LocalAI installation guide to install LocalAI. (No OpenAI secret is required when using LocalAI).
-
Apply the K8sGPT configuration object:
kubectl apply -f - << EOF
apiVersion: core.k8sgpt.ai/v1alpha1
kind: K8sGPT
metadata:
name: k8sgpt-local-ai
namespace: default
spec:
ai:
enabled: true
model: ggml-gpt4all-j
backend: localai
baseUrl: http://local-ai.local-ai.svc.cluster.local:8080/v1
noCache: false
repository: ghcr.io/k8sgpt-ai/k8sgpt
version: v0.3.8
EOFNote: ensure that the value of baseUrl is a properly constructed DNS name for the LocalAI Service. It should take the form: http://local-ai.<namespace_local_ai_was_installed_in>.svc.cluster.local:8080/v1.
- Same as step 4. in the example above.
ImagePullSecrets
You can use custom k8sgpt image by modifying `repository`, `version`, `imagePullSecrets`. `version` actually works as image tag.kubectl apply -f - << EOF
apiVersion: core.k8sgpt.ai/v1alpha1
kind: K8sGPT
metadata:
name: k8sgpt-sample
namespace: k8sgpt-operator-system
spec:
ai:
enabled: true
model: gpt-3.5-turbo
backend: openai
secret:
name: k8sgpt-sample-secret
key: openai-api-key
noCache: false
repository: sample.repository/k8sgpt
version: sample-tag
imagePullSecrets:
- name: sample-secret
EOFsink (integrations)
Optional parameters available for sink.
('type', 'webhook' are required parameters.)
| tool | channel | icon_url | username |
|---|---|---|---|
| Slack | |||
| Mattermost | ✔️ | ✔️ | ✔️ |
For details please see here
