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feat: improving readme
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Signed-off-by: Alex Jones <alexsimonjones@gmail.com>
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AlexsJones committed Apr 28, 2023
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Expand Up @@ -12,10 +12,10 @@ It has SRE experience codified into its analyzers and helps to pull out the most

<a href="https://www.producthunt.com/posts/k8sgpt?utm_source=badge-featured&utm_medium=badge&utm_souce=badge-k8sgpt" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=389489&theme=light" alt="K8sGPT - K8sGPT&#0032;gives&#0032;Kubernetes&#0032;Superpowers&#0032;to&#0032;everyone | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>

# Installation
# CLI Installation


## Linux/Mac via brew
### Linux/Mac via brew

```
brew tap k8sgpt-ai/k8sgpt
Expand Down Expand Up @@ -100,18 +100,18 @@ If you install gcc as suggested, the problem will persist. Therefore, you need t
</details>


## Windows
### Windows

* Download the latest Windows binaries of **k8sgpt** from the [Release](https://github.com/k8sgpt-ai/k8sgpt/releases)
tab based on your system architecture.
* Extract the downloaded package to your desired location. Configure the system *path* variable with the binary location

## Operator Installation

## Verify installation
To install within a Kubernetes cluster please use our `k8sgpt-operator` with installation instructions available [here](https://github.com/k8sgpt-ai/k8sgpt-operator)

* Run `k8sgpt version`
_This mode of operation is ideal for continuous monitoring of your cluster and can integrate with your existing monitoring such as Prometheus and Alertmanager._

<hr>

## Quick Start

Expand Down Expand Up @@ -151,63 +151,7 @@ you will be able to write your own analyzers.
- [x] pdbAnalyzer
- [x] networkPolicyAnalyzer

## Usage

```
Kubernetes debugging powered by AI
Usage:
k8sgpt [command]
Available Commands:
analyze This command will find problems within your Kubernetes cluster
auth Authenticate with your chosen backend
completion Generate the autocompletion script for the specified shell
filters Manage filters for analyzing Kubernetes resources
generate Generate Key for your chosen backend (opens browser)
help Help about any command
integration Intergrate another tool into K8sGPT
serve Runs k8sgpt as a server
version Print the version number of k8sgpt
Flags:
--config string config file (default is $HOME/.k8sgpt.yaml)
-h, --help help for k8sgpt
--kubeconfig string Path to a kubeconfig. Only required if out-of-cluster. (default "$HOME/.kube/config")
--kubecontext string Kubernetes context to use. Only required if out-of-cluster.
Use "k8sgpt [command] --help" for more information about a command.
```

_Manage filters_

_List filters_

```
k8sgpt filters list
```

_Add default filters_

```
k8sgpt filters add [filter(s)]
```

### Examples :

- Simple filter : `k8sgpt filters add Service`
- Multiple filters : `k8sgpt filters add Ingress,Pod`

_Add default filters_

```
k8sgpt filters remove [filter(s)]
```

### Examples :

- Simple filter : `k8sgpt filters remove Service`
- Multiple filters : `k8sgpt filters remove Ingress,Pod`
## Examples

_Run a scan with the default analyzers_

Expand Down Expand Up @@ -240,41 +184,44 @@ _Anonymize during explain_
k8sgpt analyze --explain --filter=Service --output=json --anonymize
```

### How does anonymization work?

With this option, the data is anonymized before being sent to the AI Backend. During the analysis execution, `k8sgpt` retrieves sensitive data (Kubernetes object names, labels, etc.). This data is masked when sent to the AI backend and replaced by a key that can be used to de-anonymize the data when the solution is returned to the user.

### Using filters
<details>

1. Error reported during analysis:
```bash
Error: HorizontalPodAutoscaler uses StatefulSet/fake-deployment as ScaleTargetRef which does not exist.
```
_List filters_

2. Payload sent to the AI backend:
```bash
Error: HorizontalPodAutoscaler uses StatefulSet/tGLcCRcHa1Ce5Rs as ScaleTargetRef which does not exist.
```
k8sgpt filters list
```

3. Payload returned by the AI:
```bash
The Kubernetes system is trying to scale a StatefulSet named tGLcCRcHa1Ce5Rs using the HorizontalPodAutoscaler, but it cannot find the StatefulSet. The solution is to verify that the StatefulSet name is spelled correctly and exists in the same namespace as the HorizontalPodAutoscaler.
_Add default filters_

```
k8sgpt filters add [filter(s)]
```

4. Payload returned to the user:
```bash
The Kubernetes system is trying to scale a StatefulSet named fake-deployment using the HorizontalPodAutoscaler, but it cannot find the StatefulSet. The solution is to verify that the StatefulSet name is spelled correctly and exists in the same namespace as the HorizontalPodAutoscaler.
### Examples :

- Simple filter : `k8sgpt filters add Service`
- Multiple filters : `k8sgpt filters add Ingress,Pod`

_Add default filters_

```
k8sgpt filters remove [filter(s)]
```

**Anonymization does not currently apply to events.**
### Examples :

- Simple filter : `k8sgpt filters remove Service`
- Multiple filters : `k8sgpt filters remove Ingress,Pod`

</details>


### Additional commands

<details>

_Manage integrations_

_List integrations_

Expand Down Expand Up @@ -314,6 +261,37 @@ curl -X GET "http://localhost:8080/analyze?namespace=k8sgpt&explain=false"

</details>

## How does anonymization work?

With this option, the data is anonymized before being sent to the AI Backend. During the analysis execution, `k8sgpt` retrieves sensitive data (Kubernetes object names, labels, etc.). This data is masked when sent to the AI backend and replaced by a key that can be used to de-anonymize the data when the solution is returned to the user.

<details>

1. Error reported during analysis:
```bash
Error: HorizontalPodAutoscaler uses StatefulSet/fake-deployment as ScaleTargetRef which does not exist.
```

2. Payload sent to the AI backend:
```bash
Error: HorizontalPodAutoscaler uses StatefulSet/tGLcCRcHa1Ce5Rs as ScaleTargetRef which does not exist.
```

3. Payload returned by the AI:
```bash
The Kubernetes system is trying to scale a StatefulSet named tGLcCRcHa1Ce5Rs using the HorizontalPodAutoscaler, but it cannot find the StatefulSet. The solution is to verify that the StatefulSet name is spelled correctly and exists in the same namespace as the HorizontalPodAutoscaler.
```

4. Payload returned to the user:
```bash
The Kubernetes system is trying to scale a StatefulSet named fake-deployment using the HorizontalPodAutoscaler, but it cannot find the StatefulSet. The solution is to verify that the StatefulSet name is spelled correctly and exists in the same namespace as the HorizontalPodAutoscaler.
```

**Anonymization does not currently apply to events.**

</details>


## Running local models

To run local models, it is possible to use OpenAI compatible APIs, for instance [LocalAI](https://github.com/go-skynet/LocalAI) which uses [llama.cpp](https://github.com/ggerganov/llama.cpp) and [ggml](https://github.com/ggerganov/ggml) to run inference on consumer-grade hardware. Models supported by LocalAI for instance are Vicuna, Alpaca, LLaMA, Cerebras, GPT4ALL, GPT4ALL-J and koala.
Expand Down Expand Up @@ -346,6 +324,7 @@ k8sgpt analyze --explain --backend localai

## Configuration

<details>
`k8sgpt` stores config data in the `$XDG_CONFIG_HOME/k8sgpt/k8sgpt.yaml` file. The data is stored in plain text, including your OpenAI key.

Config file locations:
Expand All @@ -354,7 +333,7 @@ Config file locations:
| MacOS | ~/Library/Application Support/k8sgpt/k8sgpt.yaml |
| Linux | ~/.config/k8sgpt/k8sgpt.yaml |
| Windows | %LOCALAPPDATA%/k8sgpt/k8sgpt.yaml |

</details>

## Contributing

Expand Down

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