example(k8sgpt): Add k8sgpt example (#631)

Signed-off-by: mudler <mudler@localai.io>
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@ -24,6 +24,14 @@ This integration shows how to use LocalAI with [mckaywrigley/chatbot-ui](https:/
There is also a separate example to show how to manually setup a model: [example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui-manual/)
### K8sGPT
_by [@mudler](https://github.com/mudler)_
This example show how to use LocalAI inside Kubernetes with [k8sgpt](https://k8sgpt.ai).
![Screenshot from 2023-06-19 23-58-47](https://github.com/go-skynet/go-ggml-transformers.cpp/assets/2420543/cab87409-ee68-44ae-8d53-41627fb49509)
### Flowise
_by [@mudler](https://github.com/mudler)_

70
examples/k8sgpt/README.md Normal file
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# k8sgpt example
This example show how to use LocalAI with k8sgpt
![Screenshot from 2023-06-19 23-58-47](https://github.com/go-skynet/go-ggml-transformers.cpp/assets/2420543/cab87409-ee68-44ae-8d53-41627fb49509)
## Create the cluster locally with Kind (optional)
If you want to test this locally without a remote Kubernetes cluster, you can use kind.
Install [kind](https://kind.sigs.k8s.io/) and create a cluster:
```
kind create cluster
```
## Setup LocalAI
We will use [helm](https://helm.sh/docs/intro/install/):
```
helm repo add go-skynet https://go-skynet.github.io/helm-charts/
helm repo update
# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI
cd LocalAI/examples/k8sgpt
# modify values.yaml preload_models with the models you want to install.
# CHANGE the URL to a model in huggingface.
helm install local-ai go-skynet/local-ai --create-namespace --namespace local-ai --values values.yaml
```
## Setup K8sGPT
```
# Install k8sgpt
helm repo add k8sgpt https://charts.k8sgpt.ai/
helm repo update
helm install release k8sgpt/k8sgpt-operator -n k8sgpt-operator-system --create-namespace
```
Apply the k8sgpt-operator configuration:
```
kubectl apply -f - << EOF
apiVersion: core.k8sgpt.ai/v1alpha1
kind: K8sGPT
metadata:
name: k8sgpt-local-ai
namespace: default
spec:
backend: localai
baseUrl: http://local-ai.local-ai.svc.cluster.local:8080/v1
noCache: false
model: gpt-3.5-turbo
noCache: false
version: v0.3.0
enableAI: true
EOF
```
## Test
Apply a broken pod:
```
kubectl apply -f broken-pod.yaml
```

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apiVersion: v1
kind: Pod
metadata:
name: broken-pod
spec:
containers:
- name: broken-pod
image: nginx:1.a.b.c
livenessProbe:
httpGet:
path: /
port: 90
initialDelaySeconds: 3
periodSeconds: 3

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replicaCount: 1
deployment:
# https://quay.io/repository/go-skynet/local-ai?tab=tags
image: quay.io/go-skynet/local-ai:latest
env:
threads: 4
debug: "true"
context_size: 512
preload_models: '[{ "url": "github:go-skynet/model-gallery/wizard.yaml", "name": "gpt-3.5-turbo", "overrides": { "parameters": { "model": "WizardLM-7B-uncensored.ggmlv3.q5_1" }},"files": [ { "uri": "https://huggingface.co//WizardLM-7B-uncensored-GGML/resolve/main/WizardLM-7B-uncensored.ggmlv3.q5_1.bin", "sha256": "d92a509d83a8ea5e08ba4c2dbaf08f29015932dc2accd627ce0665ac72c2bb2b", "filename": "WizardLM-7B-uncensored.ggmlv3.q5_1" }]}]'
modelsPath: "/models"
resources:
{}
# We usually recommend not to specify default resources and to leave this as a conscious
# choice for the user. This also increases chances charts run on environments with little
# resources, such as Minikube. If you do want to specify resources, uncomment the following
# lines, adjust them as necessary, and remove the curly braces after 'resources:'.
# limits:
# cpu: 100m
# memory: 128Mi
# requests:
# cpu: 100m
# memory: 128Mi
# Prompt templates to include
# Note: the keys of this map will be the names of the prompt template files
promptTemplates:
{}
# ggml-gpt4all-j.tmpl: |
# The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response.
# ### Prompt:
# {{.Input}}
# ### Response:
# Models to download at runtime
models:
# Whether to force download models even if they already exist
forceDownload: false
# The list of URLs to download models from
# Note: the name of the file will be the name of the loaded model
list:
#- url: "https://gpt4all.io/models/ggml-gpt4all-j.bin"
# basicAuth: base64EncodedCredentials
# Persistent storage for models and prompt templates.
# PVC and HostPath are mutually exclusive. If both are enabled,
# PVC configuration takes precedence. If neither are enabled, ephemeral
# storage is used.
persistence:
pvc:
enabled: false
size: 6Gi
accessModes:
- ReadWriteOnce
annotations: {}
# Optional
storageClass: ~
hostPath:
enabled: false
path: "/models"
service:
type: ClusterIP
port: 8080
annotations: {}
# If using an AWS load balancer, you'll need to override the default 60s load balancer idle timeout
# service.beta.kubernetes.io/aws-load-balancer-connection-idle-timeout: "1200"
ingress:
enabled: false
className: ""
annotations:
{}
# kubernetes.io/ingress.class: nginx
# kubernetes.io/tls-acme: "true"
hosts:
- host: chart-example.local
paths:
- path: /
pathType: ImplementationSpecific
tls: []
# - secretName: chart-example-tls
# hosts:
# - chart-example.local
nodeSelector: {}
tolerations: []
affinity: {}