Text translation using Azure AI Translator in a NestJS application

Connie Leung - Mar 27 - - Dev Community

Introduction

I am a language learner who learns Mandarin and Spanish in my spare time. When I discovered that text translation using Azure AI is possible, I wanted to leverage the strength of the translation service of Azure Cognitive Service in my hobby. Therefore, I built a NestJS application to translate texts between two languages through Azure AI's translator service.

Sign up for Azure AI Service for free

  1. Navigate to https://azure.microsoft.com/en-us/free/ai-services/ to sign up for a free account.
  2. Create a Translator service in the Azure Portal
  3. Click APIs and Keys in the menu, and copy the API Key, location, and Translation URL

Create a new NestJS Project 

nest new nestjs-genai-translation
Enter fullscreen mode Exit fullscreen mode

Install dependencies 

npm i --save-exact  zod @nestjs/swagger @nestjs/throttler dotenv compression helmet
Enter fullscreen mode Exit fullscreen mode

Generate a Translation Module 

nest g mo translation
nest g co translation/presenters/http/translator --flat
nest g s translation/application/azureTranslator --flat
Enter fullscreen mode Exit fullscreen mode

Create a Translation module, a controller, and a service for the API.

Define Azure OpenAI environment variables 

// .env.example

PORT=3000
AZURE_OPENAI_TRANSLATOR_API_KEY=<translator api key>
AZURE_OPENAI_TRANSLATOR_URL=<translator url>/translate
AZURE_OPENAI_TRANSLATOR_API_VERSION="3.0"
AZURE_OPENAI_LOCATION=eastasia
AI_SERVICE=azureOpenAI
Enter fullscreen mode Exit fullscreen mode

Copy .env.example to .env, and replace AZURE_OPENAI_TRANSLATOR_API_KEY and AZURE_OPENAI_TRANSLATOR_URL with the actual API Key and the translator URL, respectively.

  • AZURE_OPENAI_TRANSLATOR_API_KEY - API key of Azure OpenAI translator service
  • AZURE_OPENAI_TRANSLATOR_URL - Translator URL of Azure OpenAI translator service
  • AZURE_OPENAI_TRANSLATOR_API_VERSION - API version of Azure OpenAI translator service
  • AZURE_OPENAI_LOCATION - Location of Azure OpenAI translator service
  • AI_SERVICE - Generative AI service to be used in the application

Add .env to the .gitignore file to ensure we don't accidentally commit the Azure OpenAI Translator API Key to the GitHub repo.

Add configuration files

The project has three configuration files. validate.config.ts validates the payload is valid before any request can route to the controller to execute

// validate.config.ts

import { ValidationPipe } from '@nestjs/common';

export const validateConfig = new ValidationPipe({
  whitelist: true,
  stopAtFirstError: true,
  forbidUnknownValues: false,
});
Enter fullscreen mode Exit fullscreen mode

env.config.ts extracts the environment variables from process.env and stores the values in the env object.

// env.config.ts

import dotenv from 'dotenv';
import { Integration } from '~core/types/integration.type';

dotenv.config();

export const env = {
  PORT: parseInt(process.env.PORT || '3000'),
  AZURE_OPENAI_TRANSLATOR: {
    KEY: process.env.AZURE_OPENAI_TRANSLATOR_API_KEY || '',
    URL: process.env.AZURE_OPENAI_TRANSLATOR_URL || '',
    LOCATION: process.env.AZURE_OPENAI_LOCATION || 'eastasia',
    VERSION: process.env.AZURE_OPENAI_TRANSLATOR_API_VERSION || '3.0',
  },
  AI_SERVICE: (process.env.AI_SERVICE || 'azureOpenAI') as Integration,
};
Enter fullscreen mode Exit fullscreen mode

throttler.config.ts defines the rate limit of the Translation API.

// throttler.config.ts

import { ThrottlerModule } from '@nestjs/throttler';

export const throttlerConfig = ThrottlerModule.forRoot([
  {
    ttl: 60000,
    limit: 10,
  },
]);
Enter fullscreen mode Exit fullscreen mode

Each route allows ten requests in 60,000 milliseconds or 1 minute.

Bootstrap the application 

// bootstrap.ts

export class Bootstrap {
  private app: NestExpressApplication;

  async initApp() {
    this.app = await NestFactory.create(AppModule);
  }

  enableCors() {
    this.app.enableCors();
  }

  setupMiddleware() {
    this.app.use(express.json({ limit: '1000kb' }));
    this.app.use(express.urlencoded({ extended: false }));
    this.app.use(compression());
    this.app.use(helmet());
  }

  setupGlobalPipe() {
    this.app.useGlobalPipes(validateConfig);
  }

  async startApp() {
    await this.app.listen(env.PORT);
  }

  setupSwagger() {
    const config = new DocumentBuilder()
      .setTitle('Generative AI Translator')
      .setDescription('Integrate with Generative AI to translate a text from one language to another language')
      .setVersion('1.0')
      .addTag('Azure OpenAI, Langchain Gemini AI Model, Google Translate Cloud API')
      .build();
    const document = SwaggerModule.createDocument(this.app, config);
    SwaggerModule.setup('api', this.app, document);
  }
}
Enter fullscreen mode Exit fullscreen mode

Add a Bootstrap class to set up Swagger, middleware, global validation, cors, and finally, application start.

// main.ts

import { Bootstrap } from '~core/bootstrap';

async function bootstrap() {
  const bootstrap = new Bootstrap();
  await bootstrap.initApp();
  bootstrap.enableCors();
  bootstrap.setupMiddleware();
  bootstrap.setupGlobalPipe();
  bootstrap.setupSwagger();
  await bootstrap.startApp();
}
bootstrap()
  .then(() => console.log('The application starts successfully'))
  .catch((error) => console.error(error));
Enter fullscreen mode Exit fullscreen mode

The bootstrap function enables CORS, registers middleware to the application, sets up Swagger documentation, and uses a global pipe to validate payloads.

I have laid down the groundwork, and the next step is to add routes to receive payload and translate texts between the source language and target language.

Define Translation DTO 

// languages.enum

import { z } from 'zod';

const LANGUAGE_CODES = {
  English: 'en',
  Spanish: 'es',
  'Simplified Chinese': 'zh-Hans',
  'Traditional Chinese': 'zh-Hant',
  Vietnamese: 'vi',
  Japanese: 'ja',
} as const;

export const ZOD_LANGUAGE_CODES = z.nativeEnum(LANGUAGE_CODES, {
  required_error: 'Language code is required',
  invalid_type_error: 'Language code is invalid',
});
export type LanguageCodeType = z.infer<typeof ZOD_LANGUAGE_CODES>;
Enter fullscreen mode Exit fullscreen mode 
// translate-text.dto.ts

import { z } from 'zod';
import { ZOD_LANGUAGE_CODES } from '~translation/application/enums/languages.enum';

export const translateTextSchema = z
  .object({
    text: z.string({
      required_error: 'Text is required',
    }),
    srcLanguageCode: ZOD_LANGUAGE_CODES,
    targetLanguageCode: ZOD_LANGUAGE_CODES,
  })
  .required();

export type TranslateTextDto = z.infer<typeof translateTextSchema>;
Enter fullscreen mode Exit fullscreen mode

translateTextSchema accepts a text, a source language code, and a target language code. Then, I use zod to infer the type of translateTextSchema and assign it to TranslateTextDto.

Implement Azure Translator Service

This application will also support langchain.js, Google Gemini, and Google Translate Cloud API to translate texts. Therefore, I created a Translator interface, and all services that implement the interface must fulfill the contract.

//  translator-input.interface.ts

import { LanguageCodeType } from '../enums/languages.enum';

export interface TranslateInput {
  text: string;
  srcLanguageCode: LanguageCodeType;
  targetLanguageCode: LanguageCodeType;
}
Enter fullscreen mode Exit fullscreen mode 
// translate-result.interface.ts

import { Integration } from '~core/types/integration.type';

export interface TranslationResult {
  text: string;
  aiService: Integration;
}
Enter fullscreen mode Exit fullscreen mode 
// translator.interface.ts

export interface Translator {
  translate(input: TranslateInput): Promise<TranslationResult>;
}
Enter fullscreen mode Exit fullscreen mode 
// azure-translator.service.ts

type AzureTranslateResponse = {
  translations: [
    {
      text: string;
      to: string;
    },
  ];
};

@Injectable()
export class AzureTranslatorService implements Translator {
  constructor(private httpService: HttpService) {}

  async translate({ text, srcLanguageCode, targetLanguageCode }: TranslateInput): Promise<TranslationResult> {
    const data = [{ text }];
    const result$ = this.httpService
      .post<AzureTranslateResponse[]>(env.AZURE_OPENAI_TRANSLATOR.URL, data, {
        headers: {
          'Ocp-Apim-Subscription-Key': env.AZURE_OPENAI_TRANSLATOR.KEY,
          'Ocp-Apim-Subscription-Region': env.AZURE_OPENAI_TRANSLATOR.LOCATION,
          'Content-type': 'application/json',
          'X-ClientTraceId': v4(),
        },
        params: {
           'api-version': env.AZURE_OPENAI_TRANSLATOR.VERSION,
           from: srcLanguageCode,
           to: targetLanguageCode,
        },
        responseType: 'json',
      })
      .pipe(
         map(({ data }) => data?.[0]?.translations?.[0].text || 'No result'),
         map((text) => ({
           text,
           aiService: <Integration>'azureOpenAI',
         })),
      );
    return firstValueFrom(result$);
  }
}
Enter fullscreen mode Exit fullscreen mode

The translate method of AzureTranslatorService makes a POST request to translate the text from the source language code to the target language code. The httpService returns an Observable; therefore, the Observable is passed to the firstValueFrom RxJS operator to convert into a Promise.

Implement Translator Controller 

// zod-validation.pipe.ts

export class ZodValidationPipe implements PipeTransform {
  constructor(private schema: ZodSchema) {}

  transform(value: unknown) {
    try {
      const parsedValue = this.schema.parse(value);
      return parsedValue;
    } catch (error) {
      console.error(error);
      if (error instanceof ZodError) {
        throw new BadRequestException(error.errors?.[0]?.message || 'Validation failed');
      } else if (error instanceof Error) {
        throw new BadRequestException(error.message);
      }
      throw error;
    }
  }
}
Enter fullscreen mode Exit fullscreen mode

ZodValidationPipe is a pipe that validates the payload against the Zod schema. When the validation is successful, the payload will be parsed and returned. When the validation fails, the pipe intercepts the ZodError and returns an instance of BadRequestException.

// translator.controller.ts

// omit the import statements to save space

@ApiTags('Translator')
@Controller('translator')
export class TranslatorController {
  constructor(@Inject(TRANSLATOR) private translatorService: Translator) {}

  @ApiBody({
    description: 'An intance of TranslatTextDto',
    required: true,
    schema: {
      type: 'object',
      properties: {
        text: {
          type: 'string',
          description: 'text to be translated',
        },
        srcLanguageCode: {
          type: 'string',
          description: 'source language code',
          enum: ['en', 'es', 'zh-Hans', 'zh-Hant', 'vi', 'ja'],
        },
        targetLanguageCode: {
          type: 'string',
          description: 'target language code',
          enum: ['en', 'es', 'zh-Hans', 'zh-Hant', 'vi', 'ja'],
        },
      },
    },
    examples: {
      greeting: {
        value: {
          text: 'Good morning, good afternoon, good evening.',
          srcLanguageCode: 'en',
          targetLanguageCode: 'es',
        },
      },
    },
  })
  @ApiResponse({
    description: 'The translated text',
    schema: {
      type: 'object',
      properties: {
        text: { type: 'string', description: 'translated text' },
        aiService: { type: 'string', description: 'AI service' },
      },
    },
    status: 200,
  })
  @HttpCode(200)
  @Post()
  @UsePipes(new ZodValidationPipe(translateTextSchema))
  translate(@Body() dto: TranslateTextDto): Promise<TranslationResult> {
    return this.translatorService.translate(dto);
  }
}
Enter fullscreen mode Exit fullscreen mode

The TranslatorController injects Translator, which is an instance of AzureTranslatorService. The endpoint invokes the translate method to perform text translation using Azure OpenAI.

Dynamic registration

This application registers the translation service based on the AI_SERVICE environment variable. The value of the environment variable is one of azureOpenAI, langchain_googleChatModel, and google_translate.

// .env.example

AI_SERVICE=azureOpenAI
Enter fullscreen mode Exit fullscreen mode 
// integration.type.ts

export type Integration = 'azureOpenAI' | 'langchain_googleChatModel' | 'google_translate';
Enter fullscreen mode Exit fullscreen mode 
// translator.module.ts

@Module({
  imports: [HttpModule],
  controllers: [TranslatorController],
})
export class TranslationModule {
  static register(type: Integration = 'azureOpenAI'): DynamicModule {
    const serviceMap = new Map<Integration, any>();
    serviceMap.set('azureOpenAI', AzureTranslatorService);
    const translatorService = serviceMap.get(type) || AzureTranslatorService;

    const providers: Provider[] = [
      {
        provide: TRANSLATOR,
        useClass: translatorService,
      },
    ];

    return {
      module: TranslationModule,
      providers,
    };
  }
}
Enter fullscreen mode Exit fullscreen mode

In TranslationModule, I define a register method that returns a DynamicModule. When type is azureOpenAI, the TRANSLATOR token provides AzureTranslatorService. Next, TranslationModule.register(env.AI_SERVICE) creates a TranslationModule that I import in the AppModule.

// app.module.ts

@Module({
  imports: [throttlerConfig, TranslationModule.register(env.AI_SERVICE)],
  controllers: [AppController],
  providers: [
    AppService,
    {
      provide: APP_GUARD,
      useClass: ThrottlerGuard,
    },
  ],
})
export class AppModule {}
Enter fullscreen mode Exit fullscreen mode

Test the endpoints

After launching the application, I can test the endpoints with cURL, Postman, or Swagger documentation.

npm run start:dev
Enter fullscreen mode Exit fullscreen mode

The URL of the Swagger documentation is http://localhost:3000/api.

Dockerize the application 

// .dockerignore

.git
.gitignore
node_modules/
dist/
Dockerfile
.dockerignore
npm-debug.log
Enter fullscreen mode Exit fullscreen mode

Create a .dockerignore file for Docker to ignore some files and directories.

// Dockerfile

# Use an official Node.js runtime as the base image
FROM node:20-alpine

# Set the working directory in the container
WORKDIR /app

# Copy package.json and package-lock.json to the working directory
COPY package*.json ./

# Install the dependencies
RUN npm install

RUN npm run build

# Copy the rest of the application code to the working directory
COPY . .

# Expose a port (if your application listens on a specific port)
EXPOSE 3000

# Define the command to run your application
CMD [ "npm", "start" ]
Enter fullscreen mode Exit fullscreen mode

I added the Dockerfile that installs the dependencies, builds the NestJS application, and starts it.

//  .env.docker.example

PORT=3000
AZURE_OPENAI_TRANSLATOR_API_KEY=<translator api key>
AZURE_OPENAI_TRANSLATOR_URL=<translator url>/translate
AZURE_OPENAI_TRANSLATOR_API_VERSION="3.0"
AZURE_OPENAI_LOCATION=eastasia
GOOGLE_GEMINI_API_KEY=<google gemini api key>
GOOGLE_GEMINI_MODEL=gemini-pro
AI_SERVICE=langchain_googleChatModel
Enter fullscreen mode Exit fullscreen mode

.env.docker.example stores the relevant environment variables that I copied from the NestJS application.

// docker-compose.yaml

version: '3.8'

services:
  backend:
    build:
      context: ./nestjs-genai-translation
      dockerfile: Dockerfile
    environment:
      - PORT=${PORT}
      - AZURE_OPENAI_TRANSLATOR_API_KEY=${AZURE_OPENAI_TRANSLATOR_API_KEY}
      - AZURE_OPENAI_TRANSLATOR_URL=${AZURE_OPENAI_TRANSLATOR_URL}
      - AZURE_OPENAI_TRANSLATOR_API_VERSION=${AZURE_OPENAI_TRANSLATOR_API_VERSION}
      - AZURE_OPENAI_LOCATION=${AZURE_OPENAI_LOCATION}
      - GOOGLE_GEMINI_API_KEY=${GOOGLE_GEMINI_API_KEY}
      - GOOGLE_GEMINI_MODEL=${GOOGLE_GEMINI_MODEL}
    ports:
      - "${PORT}:${PORT}"
    networks:
      - ai
    restart: always

networks:
  ai:
Enter fullscreen mode Exit fullscreen mode

I added the docker-compose.yaml in the root folder, which was responsible for creating the NestJS application container.

This concludes my blog post about using Azure OpenAI Translator Service to solve a real-world problem. I only scratched the surface of Azure OpenAI, and Microsoft offers many services. I hope you like the content and continue to follow my learning experience in Angular, NestJS, and other technologies.

Resources:

Image
Mastering "use client" in Next.js

webdev, javascript, programming, beginners
Image
How Apple Intelligence Transforms Your Devices

appleintelligence, smarttechnology, userprivacy
Image
Hati-hati Dengan Modus Agen Judi Online Penipu
Image
Cara dan Metode Buat Account Judi Online
Image
4. OAuth 2.0: Managing Secure Access in Modern Apps
Image
Teknik Mengetahui Judi Online Sah
Image
Data science courses in Noida

javascript, datasciencecoursesinnoida
Image
model negosiasi komplet dalam games slots online
Image
Understanding the Chain of Responsibility Design Pattern in Backend Development

java, backend, designpatterns, backenddevelopment
Image
Wat is krachtstroom? Wat is Draaistroom? Kosten en Aanvragen
Image
The Art of Planning: When Excitement Meets Reality 🎨
Image
Accessible Tech Solutions for Mental Health: Bridging the Gap

mentalhealth, accessibletech, ai
Image
langkah memastikan situs slots online terpilih
Image
Revolutionizing User Interaction with Context-Aware AI Assistants

ai, chatgpt, voicebot, voiceui
Image
The 6 Stages of the Node.js Event Loop

javascript, node, tutorial, beginners
Image
Cisza w Mieszkaniu: Jak Wyciszyć Swój Kąt i Żyć w Harmonii
Image
Permainan Judi Online yang Bagus Buat Pemula
Image
APAC hackathon! 🚀
Image
Introduction to DevOps for Developers

coding, programming, devops, beginners
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Terabox Video Player