Skip to main content

One post tagged with "Workflow"

View All Tags

· 阅读需要 1 分钟
Fog Dong

ChatGPT is taking the tech industry by storm, thanks to its unparalleled natural language processing capabilities. As a powerful AI language model, it has the ability to understand and generate human-like responses, revolutionizing communication in various industries. From streamlining customer service chatbots to enabling seamless language translation tools, ChatGPT has already proved its mettle in creating innovative solutions that improve efficiency and user experience.

Now the question is, can we leverage ChatGPT to transform the way we deliver applications? With the integration of ChatGPT into DevOps workflows, we are witnessing the possible emergence of a new era of automation called PromptOps. This advancement in AIOps technology is revolutionizing the way businesses operate, allowing for faster and more efficient application delivery.

In this article, we will explore how to integrate ChatGPT into your DevOps workflow to deliver applications.

Integrate ChatGPT into Your DevOps Workflow

When it comes to integrating ChatGPT into DevOps workflows, many developers are faced with the challenge of managing extra resources and writing complicated shells. However, there is a better way - KubeVela Workflow. This open-source cloud-native workflow project offers a streamlined solution that eliminates the need for pods or complex scripting.

In KubeVela Workflow, every step has a type that can be easily abstracted and reused. The step-type is programmed in CUE language, making it incredibly easy to customize and use atomic capabilities like a function call in every step. An important point to note is that with all these atomic capabilities, such as HTTP requests, it is possible to integrate ChatGPT in just 5 minutes by writing a new step.

Check out the Installation Guide to get started with KubeVela Workflow. The complete code of this chat-gpt step type is available at GitHub.

Now that we choose the right tool, let's see the capabilities of ChatGPT in delivery.

Case 1: Diagnose the resources

It's quite common in the DevOps world to encounter problems like "I don't know why the pod is not running" or "I don't know why the service is not available". In this case, we can use ChatGPT to diagnose the resource.

For example, In our workflow, we can apply a Deployment with an invalid image in the first step. Since the deployment will never be ready, we can add a timeout in the step to ensure the workflow is not stuck in this step. Then, passing the unhealthy resources deployed in the first step to the second step, we can use the chat-gpt step type to diagnose the resource to determine the issue. Note that the second step is only executed if the first one fails.

The process of diagnosing the resource in the workflow

The complete workflow is shown below:

apiVersion: core.oam.dev/v1alpha1
kind: WorkflowRun
metadata:
name: chat-gpt-diagnose
namespace: default
spec:
workflowSpec:
steps:
# Apply an invalid deployment with a timeout
- name: apply
type: apply-deployment
timeout: 3s
properties:
image: invalid
# output the resource to the next step
outputs:
- name: resource
valueFrom: output.value

# Use chat-gpt to diagnose the resource
- name: chat-diagnose
# only execute this step if the `apply` step fails
if: status.apply.failed
type: chat-gpt
# use the resource as inputs and pass it to prompt.content
inputs:
- from: resource
parameterKey: prompt.content
properties:
token:
value: <your token>
prompt:
type: diagnose

Apply this Workflow and check the result, the first step will fail because of timeout. Then the second step will be executed and the result of chat-gpt will be shown in the log:

vela workflow logs chat-gpt-diagnose

The logs of diagnose step

Visualize in the dashboard

If you want to visualize the process and the result in the dashboard, it's time to enable the [velaux](https://kubevela.io/docs/reference/addons/velaux#install) addon.

vela addon enable velaux

Copy all the steps in the above yaml to create a pipeline.

Create the pipeline in VelaUX

Run this pipeline, and you can check out the failed reason analyzed by ChatGPT in the logs of the second step.

Run the pipeline in VelaUX

Write the chat-gpt step from scratch

How to write this chat-gpt step type? Is it simple for you to write a step type like this? Let's see how to complete this step type.

We can first define what this step type need from the user. That is: the users' token for ChatGPT, and the resource to diagnose. For some other parameters like the model or the request timeout, we can set the default value with * like below:

parameter: {
token: value: string
// +usage=the model name
model: *"gpt-3.5-turbo" | string
// +usage=the prompt to use
prompt: {
type: *"diagnose" | string
lang: *"English" | string
content: {...}
}
timeout: *"30s" | string
}

Let's complete this step type by writing the logic of the step. We can first import vela/op package in which we can use the op.#HTTPDo capability to send a request to the ChatGPT API. If the request fails, the step should be failed with op.#Fail. We can also set this step's log data with ChatGPT's answer. The complete step type is shown below:

// import packages
import (
"vela/op"
"encoding/json"
)

// this is the name of the step type
"chat-gpt": {
description: "Send request to chat-gpt"
type: "workflow-step"
}

// this is the logic of the step type
template: {
// send http request to chat gpt
http: op.#HTTPDo & {
method: "POST"
url: "https://api.openai.com/v1/chat/completions"
request: {
timeout: parameter.timeout
body: json.Marshal({
model: parameter.model
messages: [{
if parameter.prompt.type == "diagnose" {
content: """
You are a professional kubernetes administrator.
Carefully read the provided information, being certain to spell out the diagnosis & reasoning, and don't skip any steps.
Answer in \(parameter.prompt.lang).
---
\(json.Marshal(parameter.prompt.content))
---
What is wrong with this object and how to fix it?
"""
}
role: "user"
}]
})
header: {
"Content-Type": "application/json"
"Authorization": "Bearer \(parameter.token.value)"
}
}
}

response: json.Unmarshal(http.response.body)

fail: op.#Steps & {
if http.response.statusCode >= 400 {
requestFail: op.#Fail & {
message: "\(http.response.statusCode): failed to request: \(response.error.message)"
}
}
}
result: response.choices[0].message.content
log: op.#Log & {
data: result
}
parameter: {
token: value: string
// +usage=the model name
model: *"gpt-3.5-turbo" | string
// +usage=the prompt to use
prompt: {
type: *"diagnose" | string
lang: *"English" | string
content: {...}
}
timeout: *"30s" | string
}
}

That's it! Apply this step type and we can use it in our Workflow like the above.

vela def apply chat-gpt.cue

Case 2: Audit the resource

Now the ChatGPT is our Kubernetes expert and can diagnose the resource. Can it also give us some security advice for the resource? Definitely! It's just prompt. Let's modify the step type that we wrote in the previous case to add the audit feature. We can add a new prompt type audit and pass the resource to the prompt. You can check out the whole step type in GitHub.

In the Workflow, we can apply a Deployment with nginx image and pass it to the second step. The second step will use the audit prompt to audit the resource. The process of auditing the resource in workflow The complete Workflow is shown below:

apiVersion: core.oam.dev/v1alpha1
kind: WorkflowRun
metadata:
name: chat-gpt-audit
namespace: default
spec:
workflowSpec:
steps:
- name: apply
type: apply-deployment
# output the resource to the next step
outputs:
- name: resource
valueFrom: output.value
properties:
image: nginx

- name: chat-audit
type: chat-gpt
# use the resource as inputs and pass it to prompt.content
inputs:
- from: resource
parameterKey: prompt.content
properties:
token:
value: <your token>
prompt:
type: audit

image.png

Use Diagnose & Audit in one Workflow

Now that we have the capability to diagnose and audit the resource, we can use them in one Workflow, and use the if condition to control the execution of the steps. For example, if the apply step fails, then diagnose the resource, if it succeeds, audit the resource.

Use diagnose &amp; audit in one workflow

The complete Workflow is shown below:

apiVersion: core.oam.dev/v1alpha1
kind: WorkflowRun
metadata:
name: chat-gpt
namespace: default
spec:
workflowSpec:
steps:
- name: apply
type: apply-deployment
outputs:
- name: resource
valueFrom: output.value
properties:
image: nginx

# if the apply step fails, then diagnose the resource
- name: chat-diagnose
if: status.apply.failed
type: chat-gpt
inputs:
- from: resource
parameterKey: prompt.content
properties:
token:
value: <your token>
prompt:
type: diagnose

# if the apply step succeeds, then audit the resource
- name: chat-audit
if: status.apply.succeeded
type: chat-gpt
inputs:
- from: resource
parameterKey: prompt.content
properties:
token:
value: <your token>
prompt:
type: audit

Case 3: Use ChatGPT as a quality gate

If we want to apply the resources to a production environment, can we let ChatGPT rate the quality of the resource first, only if the quality is high enough, then apply the resource to the production environment? Absolutely!

Note that to make the score evaluated by chat-gpt more convincing, it's better to pass metrics than the resource in this case.

Let's write our Workflow. KubeVela Workflow has the capability to apply resources to multi clusters. The first step is to apply the Deployment to the test environment. The second step is to use the ChatGPT to rate the quality of the resource. If the quality is high enough, then apply the resource to the production environment.

The process of using quality gate in workflow

The complete Workflow is shown below:

apiVersion: core.oam.dev/v1alpha1
kind: WorkflowRun
metadata:
name: chat-gpt-quality-gate
namespace: default
spec:
workflowSpec:
steps:
# apply the resource to the test environment
- name: apply
type: apply-deployment
# output the resource to the next step
outputs:
- name: resource
valueFrom: output.value
properties:
image: nginx
cluster: test

- name: chat-quality-check
# this step will always be executed
if: always
type: chat-gpt
# get the inputs from resource and pass it to the prompt.content
inputs:
- from: resource
parameterKey: prompt.content
# output the score of ChatGPT and use strconv.Atoi to convert the score string to int
outputs:
- name: chat-result
valueFrom: |
import "strconv"
strconv.Atoi(result)
properties:
token:
value: <your token>
prompt:
type: quality-gate

# if the score is higher than 60, then apply the resource to the production environment
- name: apply-production
type: apply-deployment
# get the score from chat-result
inputs:
- from: chat-result
# check if the score is higher than 60
if: inputs["chat-result"] > 60
properties:
image: nginx
cluster: prod

Apply this Workflow and we can see that if the score is higher than 60, then the resource will be applied to the production environment.

In the End

ChatGPT brings imagination to the world of Kubernetes. Diagnose, audit, rate is just the beginning. In the new AI era, the most precious thing is idea. What do you want to do with ChatGPT? Share your insights with us in the KubeVela Community.

· 阅读需要 1 分钟
董天欣

Serverless 应用引擎(SAE) 是一款底层基于 Kubernetes,实现了 Serverless 架构与微服务架构结合的云产品。作为一款不断迭代的云产品,在快速发展的过程中也遇到了许多挑战。如何在蓬勃发展的云原生时代中解决这些挑战,并进行可靠快速的云架构升级?SAE 团队和 KubeVela 社区针对这些挑战开展了紧密合作,并给出了云原生下的开源可复制解决方案——KubeVela Workflow。

本文将详细介绍 SAE 使用 KubeVela Workflow 进行架构升级的解决方案,并对多个实践场景进行一一解读。

Serverless 时代下的挑战

Serverless 应用引擎(SAE)是面向业务应用架构、微服务架构的一站式应用托管平台,是一款底层基于 Kubernetes,实现了 Serverless 架构与微服务架构结合的云产品。

image.png

如上架构图,SAE 的用户可以将多种不同类型的业务应用托管在 SAE 之上。而在 SAE 底层,则会通过 JAVA 业务层处理相关的业务逻辑,以及与 Kubernetes 资源进行交互。在最底层,则依靠高可用,免运维,按需付费的弹性资源池。

在这个架构下,SAE 主要依托其 JAVA 业务层为用户提供功能。这样的架构在帮助用户一键式部署应用的同时,也带来了不少挑战。