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
- Navigate to https://azure.microsoft.com/en-us/free/ai-services/ to sign up for a free account.
- Create a Translator service in the Azure Portal
- 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
Install dependencies
npm i --save-exact zod @nestjs/swagger @nestjs/throttler dotenv compression helmet
Generate a Translation Module
nest g mo translation
nest g co translation/http/translator --flat
nest g s translation/application/azureTranslator --flat
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
Copy .env.example
to .env
, 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,
});
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,
};
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,
},
]);
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);
}
}
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));
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>;
// 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>;
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 and 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;
}
// translate-result.interface.ts
import { Integration } from '~core/types/integration.type';
export interface TranslationResult {
text: string;
aiService: Integration;
}
// translator.interface.ts
export interface Translator {
translate(input: TranslateInput): Promise<TranslationResult>;
}
// 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$);
}
}
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;
}
}
}
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);
}
}
The TranslatorController
injects Translator
that 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
// integration.type.ts
export type Integration = 'azureOpenAI' | 'langchain_googleChatModel' | 'google_translate';
// 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,
};
}
}
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 {}
Test the endpoints
After launching the application, I can test the endpoints with cURL, Postman, or Swagger documentation.
npm run start:dev
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
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" ]
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
.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:
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:
- Github Repo: https://github.com/railsstudent/fullstack-genai-translation/tree/main/nestjs-genai-translation
- Azure OpenAI Translator Service: https://learn.microsoft.com/en-us/azure/ai-services/translator/text-translation-overview