開発の過程で、複雑なオブジェクトを処理しなければならないケースに遭遇したことがありますか?おそらく、パラメーターが多すぎてネストされる可能性があるか、多くの構築手順と複雑なロジックを構築する必要があるためです。
おそらく、複雑なオブジェクトの作成コードを毎回分散したり考えたりする必要がなく、すっきりとした簡単なインターフェイスを備えたモジュールを設計したいと考えているでしょう。
そこでビルダー設計パターンの出番です!
このチュートリアル全体を通じて、ビルダー設計パターンについてすべて説明し、その後、ビルダー設計パターンを使用して DALL-E 3 に最適化されたイメージ生成プロンプトを生成するための CLI Node.js アプリケーションを構築します。 .
最終的なコードは、この Github リポジトリで入手できます。
Builder は 創造的なデザイン パターン であり、新しいオブジェクトを使用してオブジェクトを作成するネイティブな方法に伴うさまざまな問題に対処するデザイン パターンのカテゴリです。 キーワードまたは演算子。
ビルダー設計パターンは、次の問題の解決に焦点を当てています:
複雑なオブジェクトを作成するための簡単なインターフェイスを提供します : 多くの初期化手順が必要な、深くネストされたオブジェクトを想像してください。
オブジェクト自体から構築コードを分離し、同じオブジェクトから複数の表現または構成を作成できるようにします。
ビルダー デザイン パターンは、オブジェクト作成の責任をビルダーと呼ばれる特別なオブジェクトに委任することで、これら 2 つの問題を解決します。
ビルダー オブジェクトは元のオブジェクトを構成し、作成プロセスを複数の段階またはステップに分割します。
各ステップは、ビジネス ロジックに基づいてオブジェクト属性のサブセットを初期化するビルダー オブジェクト内のメソッドによって定義されます。
class PromptBuilder {
private prompt: Prompt
constructor() {
this.reset()
}
reset() {
this.prompt = new Prompt()
}
buildStep1() {
this.prompt.subject = "A cheese eating a burger"
//initialization code...
return this
}
buildStep2() {
//initialization code...
return this
}
buildStep3() {
//initialization code...
return this
}
build() {
const result = structuredClone(this.prompt) // deep clone
this.reset()
return result
}
}
クライアント コード: ビルダーを使用して個々のステップを呼び出すだけです
const promptBuilder = new PromptBuilder() const prompt1 = promptBuilder .buildStep1() // optional .buildStep2() // optional .buildStep3() // optional .build() // we've got a prompt const prompt2 = promptBuilder .buildStep1() // optional .buildStep3() // optional .build() // we've got a prompt
典型的なビルダー設計パターンは 4 つの主要なクラスで構成されます。
Builder : ビルダー インターフェイスは、作成されたエンティティを返す役割を担う build() メソッドを使用せずに構築メソッドのみを定義する必要があります。
コンクリート ビルダー クラス : 各コンクリート ビルダーは、オブジェクトの独自のバリアント (ビルダー インターフェイス メソッドの独自の実装を提供します。 >製品 1 または 製品 2 ).
Client : You can think of the client as the top-level consumer of our objects, the user who is importing library modules or the entry point of our application.
Director : Even the same builder object can produce many variants of the object.
const promptBuilder = new PromptBuilder() const prompt1 = promptBuilder.buildStep1().buildStep2().build() const prompt2 = promptBuilder.buildStep1().buildStep3().build()
As you can see from the code above, there is a big need for some entity to take the responsibility of directing or orchestrating the different possible combination sequences of calls to the builder methods, as each sequence may produce a different resulting object.
So can we further abstract the process and provide an even simpler interface for the client code?
That's where the Director class comes in. The director takes more responsibilities from the client and allows us to factor all of those builder sequence calls and reuse them as needed.
class Director {
private builder: PromptBuilder
constructor() {}
setBuilder(builder: PromptBuilder) {
this.builder = builder
}
makePrompt1() {
return this.builder.buildStep1().buildStep2().build()
}
makePrompt2() {
return this.builder.buildStep1().buildStep3().build()
}
}
Client code
const director = new Director() const builder = new PromptBuilder() director.setBuilder(builder) const prompt1 = director.makePrompt1() const prompt2 = director.makePrompt2()
As you can see from the code above, the client code doesn't need to know about the details for creating prompt1 or prompt2. It just calls the director, sets the correct builder object, and then calls the makePrompt methods.
To further demonstrate the builder design pattern's usefulness, let's build a prompt engineering image generation AI CLI tool from scratch.
The source code for this CLI app is available here.
The CLI tool will work as follows:
The realistic prompt will need all of the following configuration attributes to be constructed.
file: prompts.ts
class RealisticPhotoPrompt {
constructor(
public subject: string,
public location: string,
public timeOfDay: string,
public weather: string,
public camera: CameraType,
public lens: LensType,
public focalLength: number,
public aperture: string,
public iso: number,
public shutterSpeed: string,
public lighting: LightingCondition,
public composition: CompositionRule,
public perspective: string,
public foregroundElements: string[],
public backgroundElements: string[],
public colorScheme: ColorScheme,
public resolution: ImageResolution,
public postProcessing: string[]
) {}
}
file: prompts.ts
class DigitalArtPrompt {
constructor(
public subject: string,
public artStyle: ArtStyle,
public colorPalette: string[],
public brushTechnique: BrushTechnique,
public canvas: {
width: number
height: number
resolution: ImageResolution
},
public layers: number,
public composition: CompositionRule,
public perspective: string,
public lightingEffect: string,
public textureDetails: string[],
public backgroundTheme: string,
public foregroundElements: string[],
public moodKeywords: string[],
public artisticInfluences: string[],
public digitalEffects: string[]
) {}
}
As you can see here, each prompt type requires many complex attributes to be constructed, like artStyle , colorPalette , lightingEffect , perspective , cameraType , etc.
Feel free to explore all of the attribute details, which are defined in the enums.ts file of our project.
enums.ts
// Shared Enums
export enum ImageResolution {
Low = "512x512",
Medium = "1024x1024",
High = "2048x2048",
}
export enum ColorScheme {
Vibrant = "Vibrant",
Pastel = "Pastel",
Monochrome = "Monochrome",
Sepia = "Sepia",
}
// Realistic Photo Prompt enums
export enum CameraType {
DSLR = "DSLR",
Mirrorless = "Mirrorless",
Smartphone = "Smartphone",
Drone = "Drone",
}
export enum LensType {
WideAngle = "Wide Angle",
Telephoto = "Telephoto",
Macro = "Macro",
FishEye = "Fish Eye",
}
export enum LightingCondition {
Natural = "Natural",
Studio = "Studio",
LowLight = "Low Light",
HighContrast = "High Contrast",
}
// Digital Art Prompt enums
export enum ArtStyle {
Impressionist = "Impressionist",
Surrealist = "Surrealist",
PixelArt = "Pixel Art",
Cyberpunk = "Cyberpunk",
}
export enum BrushTechnique {
Impasto = "Impasto",
Watercolor = "Watercolor",
Airbrush = "Airbrush",
DigitalPen = "Digital Pen",
}
export enum CompositionRule {
RuleOfThirds = "Rule of Thirds",
GoldenRatio = "Golden Ratio",
Symmetry = "Symmetry",
LeadingLines = "Leading Lines",
}
The user of our CLI app may not be aware of all these configurations; they may just want to generate an image based on a specific subject like cheese eating burger and style (Realistic or Digital Art).
After cloning the Github repository, install the dependencies using the following command:
npm install
After installing the dependencies, run the following command:
npm start
You'll be prompted to choose a prompt type: Realistic or Digital Art. 
Then you will have to enter the subject of your prompt. Let's stick with cheese eating burger.
Depending on your choice, you will get the following text prompts as a result:
Realistic Style Prompt :
Create a realistic photo of a cheese eating a burger in the Swiss Alps during golden hour. The weather should be partly cloudy. Use a DSLR camera with a Wide Angle lens at 16mm. Set the aperture to f/11, ISO to 100, and shutter speed to 1/60. The lighting should be Natural with a Rule of Thirds composition. Capture the scene from a low angle view. Include rocky terrain, alpine flowers in the foreground, and snow-capped peaks, dramatic clouds in the background. Use a Vibrant color scheme and render at 2048x2048 resolution. In post-processing, apply HDR tone mapping and clarity enhancement.
Digital Art Style Prompt :
Create a digital art piece featuring a cheese eating a burger in a Cyberpunk style. Use a color palette of neon blue, electric purple, acid green, deep black. Apply the Digital Pen technique on a canvas of 1920x1080 at 2048x2048 resolution. Use 15 layers and follow the Leading Lines composition rule. Render the scene from a bird's eye view with volumetric fog with light shafts lighting. Include texture details like grungy surfaces and holographic reflections. The background should depict a dystopian megacity. In the foreground, feature flying vehicles, towering skyscrapers, neon signs. The overall mood should be gritty, high-tech, atmospheric. Draw inspiration from Blade Runner and Ghost in the Shell. Finally, apply digital effects including bloom effect, chromatic aberration, film grain.
Copy the previous commands and then paste them into ChatGPT. ChatGPT will use the DALL-E 3 model to generate the images.
Realistic Image Prompt Result
Digital Art Image Prompt Result
Remember the prompt parameters' complexity and the expertise needed to construct each type of prompt, not to mention the ugly constructor calls which are needed.
this.prompt = new RealisticPhotoPrompt( "", "", "", "", CameraType.DSLR, LensType.WideAngle, 24, "f/8", 100, "1/125", LightingCondition.Natural, CompositionRule.RuleOfThirds, "", [], [], ColorScheme.Vibrant, ImageResolution.Medium, [] )
Disclaimer: This ugly constructor call is not a big issue in JavaScript because we can pass a configuration object with all the properties being nullable.
To abstract the process of building the prompt and make our code open for extension and closed for modification (O in SOLID), and to make using our prompt generation library seamless or easier for our library clients, we will be opting to implement the builder design pattern.
Let's start by declaring the generic prompt builder interface.
The interface declares a bunch of methods:
builders.ts
interface PromptBuilder {
buildBaseProperties(): this
buildTechnicalDetails(): this
buildArtisticElements(): this
setSubject(subject: string): this
}
class RealisticPhotoPromptBuilder implements PromptBuilder {
private prompt: RealisticPhotoPrompt
constructor() {
this.reset()
}
private reset(): void {
this.prompt = new RealisticPhotoPrompt(
"",
"",
"",
"",
CameraType.DSLR,
LensType.WideAngle,
24,
"f/8",
100,
"1/125",
LightingCondition.Natural,
CompositionRule.RuleOfThirds,
"",
[],
[],
ColorScheme.Vibrant,
ImageResolution.Medium,
[]
)
}
setSubject(subject: string): this {
this.prompt.subject = subject
return this
}
buildBaseProperties(): this {
this.prompt.location = "Swiss Alps"
this.prompt.timeOfDay = "golden hour"
this.prompt.weather = "partly cloudy"
return this
}
buildTechnicalDetails(): this {
this.prompt.camera = CameraType.DSLR
this.prompt.lens = LensType.WideAngle
this.prompt.focalLength = 16
this.prompt.aperture = "f/11"
this.prompt.iso = 100
this.prompt.shutterSpeed = "1/60"
this.prompt.lighting = LightingCondition.Natural
this.prompt.resolution = ImageResolution.High
return this
}
buildArtisticElements(): this {
this.prompt.composition = CompositionRule.RuleOfThirds
this.prompt.perspective = "low angle view"
this.prompt.foregroundElements = ["rocky terrain", "alpine flowers"]
this.prompt.backgroundElements = ["snow-capped peaks", "dramatic clouds"]
this.prompt.colorScheme = ColorScheme.Vibrant
this.prompt.postProcessing = ["HDR tone mapping", "clarity enhancement"]
return this
}
build(): RealisticPhotoPrompt {
const result = Object.assign({}, this.prompt)
this.reset()
return result
}
}
builders.ts
class DigitalArtPromptBuilder implements PromptBuilder {
private prompt: DigitalArtPrompt
constructor() {
this.reset()
}
private reset(): void {
this.prompt = new DigitalArtPrompt(
"",
ArtStyle.Impressionist,
[],
BrushTechnique.Impasto,
{ width: 1920, height: 1080, resolution: ImageResolution.Medium },
10,
CompositionRule.GoldenRatio,
"",
"",
[],
"",
[],
[],
[],
[]
)
}
setSubject(subject: string): this {
this.prompt.subject = subject
return this
}
buildBaseProperties(): this {
this.prompt.artStyle = ArtStyle.Cyberpunk
this.prompt.colorPalette = [
"neon blue",
"electric purple",
"acid green",
"deep black",
]
this.prompt.canvas.resolution = ImageResolution.High
return this
}
buildTechnicalDetails(): this {
this.prompt.brushTechnique = BrushTechnique.DigitalPen
this.prompt.layers = 15
this.prompt.composition = CompositionRule.LeadingLines
this.prompt.perspective = "bird's eye view"
this.prompt.lightingEffect = "volumetric fog with light shafts"
return this
}
buildArtisticElements(): this {
this.prompt.textureDetails = ["grungy surfaces", "holographic reflections"]
this.prompt.backgroundTheme = "dystopian megacity"
this.prompt.foregroundElements = [
"flying vehicles",
"towering skyscrapers",
"neon signs",
]
this.prompt.moodKeywords = ["gritty", "high-tech", "atmospheric"]
this.prompt.artisticInfluences = ["Blade Runner", "Ghost in the Shell"]
this.prompt.digitalEffects = [
"bloom effect",
"chromatic aberration",
"film grain",
]
return this
}
build(): DigitalArtPrompt {
const result = Object.assign({}, this.prompt)
this.reset()
return result
}
}
As you can see from the implementations above, each builder chooses to build its own kind of prompt (the final prompt shapes are different) while sticking to the same building steps defined by the PromptBuilder contract!
Now, let's move on to our Director class definition.
director.ts
import { PromptBuilder } from "./builders"
export class PromptDirector {
private builder: PromptBuilder
setBuilder(builder: PromptBuilder): void {
this.builder = builder
}
makePrompt(subject: string): void {
this.builder
.setSubject(subject)
.buildBaseProperties()
.buildTechnicalDetails()
.buildArtisticElements()
}
}
The Director class wraps a PromptBuilder and allows us to create a prompt configuration which consists of calling all the builder methods starting from setSubject to buildArtisticElements.
This will simplify our client code in the index.ts file, which we will see in the next section.
serializers.ts
import { DigitalArtPrompt, RealisticPhotoPrompt } from "./prompts"
// Serialization functions
export function serializeRealisticPhotoPrompt(
prompt: RealisticPhotoPrompt
): string {
return `Create a realistic photo of ${prompt.subject} in the ${prompt.location} during ${prompt.timeOfDay}.
The weather should be ${prompt.weather}. Use a ${prompt.camera} camera with a ${prompt.lens} lens at ${prompt.focalLength}mm.
Set the aperture to ${prompt.aperture}, ISO to ${prompt.iso}, and shutter speed to ${prompt.shutterSpeed}.
The lighting should be ${prompt.lighting} with a ${prompt.composition} composition.
Capture the scene from a ${prompt.perspective}. Include ${prompt.foregroundElements.join(", ")} in the foreground,
and ${prompt.backgroundElements.join(", ")} in the background.
Use a ${prompt.colorScheme} color scheme and render at ${prompt.resolution} resolution.
In post-processing, apply ${prompt.postProcessing.join(" and ")}.`
}
export function serializeDigitalArtPrompt(prompt: DigitalArtPrompt): string {
return `Create a digital art piece featuring ${prompt.subject} in a ${prompt.artStyle} style.
Use a color palette of ${prompt.colorPalette.join(", ")}. Apply the ${prompt.brushTechnique} technique
on a canvas of ${prompt.canvas.width}x${prompt.canvas.height} at ${prompt.canvas.resolution} resolution.
Use ${prompt.layers} layers and follow the ${prompt.composition} composition rule.
Render the scene from a ${prompt.perspective} with ${prompt.lightingEffect} lighting.
Include texture details like ${prompt.textureDetails.join(" and ")}.
The background should depict a ${prompt.backgroundTheme}.
In the foreground, feature ${prompt.foregroundElements.join(", ")}.
The overall mood should be ${prompt.moodKeywords.join(", ")}.
Draw inspiration from ${prompt.artisticInfluences.join(" and ")}.
Finally, apply digital effects including ${prompt.digitalEffects.join(", ")}.`
}
To print the final prompt text to the terminal console, I've implemented some utility serialization functions.
Now our prompt library generation code is ready. Let's make use of it in the index.ts file.
index.ts
import inquirer from "inquirer"
import {
DigitalArtPromptBuilder,
PromptBuilder,
RealisticPhotoPromptBuilder,
} from "./builders"
import { PromptDirector } from "./director"
import { DigitalArtPrompt, RealisticPhotoPrompt } from "./prompts"
import {
serializeDigitalArtPrompt,
serializeRealisticPhotoPrompt,
} from "./serializers"
async function main() {
console.log("=====================")
console.log("Image Prompt Builder")
console.log("=====================")
const director = new PromptDirector()
let builder: PromptBuilder
let prompt: RealisticPhotoPrompt | DigitalArtPrompt
const { choice, subject } = await getUserInput()
if (choice === "Realistic Photo") {
builder = new RealisticPhotoPromptBuilder()
director.setBuilder(builder)
director.makePrompt(subject)
prompt = builder.build() as RealisticPhotoPrompt
console.log("\nGenerated Prompt:")
console.log(serializeRealisticPhotoPrompt(prompt))
} else {
builder = new DigitalArtPromptBuilder()
director.setBuilder(builder)
director.makePrompt(subject)
prompt = builder.build() as DigitalArtPrompt
console.log("\nGenerated Prompt:")
console.log(serializeDigitalArtPrompt(prompt))
}
}
main().catch(console.error)
// get user input function
async function getUserInput() {
const input = await inquirer.prompt([
{
type: "list",
name: "choice",
message: "Choose prompt type:",
choices: ["Realistic Photo", "Digital Art"],
},
{
type: "input",
name: "subject",
message: "Enter the subject for your image:",
},
])
return input
}
The code above performs the following actions:
Remember: it's not possible to get the prompt from the director because the shape of the prompt produced by each builder type is different.
The Builder design pattern proves to be an excellent solution for creating complex objects with multiple configurations, as demonstrated in our AI image prompt generation CLI application. Here's why the Builder pattern was beneficial in this scenario:
Simplified Object Creation : The pattern allowed us to create intricate RealisticPhotoPrompt and DigitalArtPrompt objects without exposing their complex construction process to the client code.
Flexibility : By using separate builder classes for each prompt type, we could easily add new prompt types or modify existing ones without changing the client code.
Code Organization : The pattern helped separate the construction logic from the representation, making the code more modular and easier to maintain.
Reusability : The PromptDirector class allowed us to reuse the same construction process for different types of prompts, enhancing code reusability.
抽象 ：index.ts 中的客户端代码保持简单并专注于高级逻辑，而提示构造的复杂性在构建器类中被抽象化。
如果您有任何疑问或想进一步讨论，请随时与我联系。
编码愉快！
以上是掌握构建器模式：创建动态 AI 提示生成器 CLI的详细内容。更多信息请关注PHP中文网其他相关文章！
