Installation 🤖

Install the nestjs-grpc-helper package with:

npm install @hodfords/nestjs-grpc-helper --save

Next, automatically generate the proto file and include it in main.ts before starting the application:

import { generateProtoService } from '@hodfords/nestjs-grpc-helper';

generateProtoService(camelCase(env.APP_NAME), env.ROOT_PATH + '/../');

Usage 🚀

Creating Microservices

Create microservices using the @RegisterGrpcMicroservice decorator, similar to how you would use a Controller. Ensure that the response adheres to the nestjs-response rules:

@Controller()
@RegisterGrpcMicroservice('User microservice')
export class UserMicroservice {
    constructor(private userService: UserService) {}

    @GrpcAction('Get user by id')
    @ResponseModel(UserResponse)
    findUserById(@GrpcValue() dto: GetUserByIdDto): Promise<UserEntity> {
        return this.userService.findUserById(dto.userId);
    }
}

Decorators

Service & Method Decorators

| Decorator | Description | |--------------------------------------------|-------------------------------------------------------------------| | @RegisterGrpcMicroservice(description?) | Marks a class as a gRPC microservice | | @GrpcAction(description?) | Marks a method as a gRPC action/endpoint |

Request Parameter Decorators

| Decorator | Description | |------------------------|-------------------------------------------------------------------------------| | @GrpcValue() | Marks a parameter as the main request body (used with DTO types) | | @GrpcParam(option) | Defines a direct request parameter with type, name, and validation | | @GrpcId(name) | Shortcut for a UUID string parameter (includes IsNotEmpty + IsUUID) | | @GrpcIds(name) | Shortcut for an array of UUID string parameters | | @GrpcEnum(option) | Defines an enum parameter with automatic validation | | @GrpcEnums(option) | Defines an array of enum parameters | | @GrpcPagination() | Built-in pagination parameter (page, perPage) | | @GrpcSort() | Built-in sort parameter (sortField, sortDirection) | | @SdkFlattenParams() | Flattens nested DTO properties into individual parameters in the generated SDK |

Metadata Decorators

| Decorator | Description | |------------------------------|----------------------------------------------------| | @GrpcMetadata(option) | Extracts values from gRPC metadata (like headers) | | @GrpcMetadataId(name) | Shortcut for UUID metadata fields |

Property & Type Decorators

| Decorator | Description | |-----------------------------|---------------------------------------------------------------------| | @Property(option) | Defines DTO/response properties with type, validation, and metadata | | @AnyType({ isDto? }) | Handles JSON serialization for dynamic any typed properties | | @ExtendType() | Preserves property metadata for composed types |

Example using @Property and @AnyType:

@Property({ type: String, format: 'any', required: false })
@AnyType()
data: any;

Type Helpers

Compose DTOs while preserving property metadata for proto generation and SDK creation:

// Pick only specific properties
export class UserSummary extends PickResponseType(UserResponse, ['id', 'name']) {}

// Omit specific properties
export class UserWithoutEmail extends OmitResponseType(UserResponse, ['email']) {}

// Make all properties optional
export class UpdateUserDto extends PartialResponseType(UserResponse) {}

// Combine multiple types
export class FullUser extends IntersectionResponseType(UserResponse, ProfileResponse) {}

Mock Response

To effectively generate and handle mock data in your application, you can use the @MockMethod, @MockSample, and @MockNested decorators.

Generate dynamic data with @MockMethod

Use @MockMethod to apply Faker methods for generating random values.

For example, to create a random string of 10 characters

@Property({ type: String, required: false })
@MockMethod('faker.datatype.string', [10])
@IsString()
name: string;

Set fixed values with @MockSample

If you need to set a fixed value for a property, use the @MockSample decorator. This is useful for enumerations or other predefined values.

For example, to set a fixed user type

@Property({
    type: String,
    enum: UserTypeEnum,
    enumName: 'UserTypeEnum'
})
@MockSample(UserTypeEnum.STANDARD)
@IsEnum(UserTypeEnum)
type: UserTypeEnum;

Generate nested data

Use @MockNested to generate mock data for nested objects or arrays of nested objects.

For example, to create an array of 5 nested objects

@Property({ type: UserResponse, isArray: true })
@IsArray()
@ValidateNested()
@Type(() => UserResponse)
@MockNested(5)
users: UserResponse[];

Generate mock instances programmatically

import { sample } from '@hodfords/nestjs-grpc-helper';

const mockUser = sample(UserResponse);

Create SDK

To generate a TypeScript SDK for your gRPC services, you can use the make-sdk command. This command will automatically generate the necessary proto files and package them into a JavaScript SDK. You also need the following configuration in your sdk-config.json file:

{
  "name": "sdkName",
  "packageName": "@hodfords/package-name",
  "format": true,
  "build": true,
  "output": "sdk",
  "outputBuild": "sdkBuild",
  "removeOutput": true,
  "addAllowDecorator": true,
  "tsconfig": {
    "extends": "./tsconfig.json",
    "compilerOptions": {
      "outDir": "sdkBuild"
    },
    "include": ["sdk"]
  }
}

Details of the configuration:

| Field | Description | |---------------------|-------------------------------------------------------| | name | Name of the SDK | | packageName | Name of the package | | format | Format the generated code | | build | Build the generated code | | output | Output directory for the generated code | | outputBuild | Output directory for the built code | | removeOutput | Remove the output directory | | addAllowDecorator | Add the allow decorator, need class-validator package | | tsconfig | TypeScript configuration | | aiSkill | AI skill configuration (optional) | | aiSkill.name | Name of the AI skill | | aiSkill.description | Description of the AI skill |

When aiSkill is configured, the SDK build will generate a SKILL.md file and a skill.json metadata file in the output directory. These files describe your gRPC services, models, and enums in a format that AI agents can consume.

To generate the SDK, run the following command:

npm run wz-command make-sdk

What this command does

This command will:

1. Collect all request and response types: It gathers all @GrpcValue request and response types from your project.
2. Generate proto file: Automatically generates the necessary proto files based on the collected types.
3. Create JavaScript Package: Packages the generated code into a JavaScript SDK. The SDK will be published using the name and version specified in your package.json, making it available for other services to import and use. The arguments, response structure, and method names remain consistent with the definitions in your gRPC service, ensuring seamless integration and functionality across services.

SDK Usage

After publishing the SDK, other services can easily integrate it. Here's an example of how to use the generated SDK:

1. Import the SDK package

2. Register the microservice module: Configure the microservice in AppModule with the appropriate gRPC URL and timeout settings.

   UserModule.register({
       url: env.GRPC_URL,
       timeout: 5000
   });

3. Use the SDK in another service: Import the SDK and use it to interact with your gRPC services.

   export class OtherService {
       constructor(private userMicroservice: UserMicroservice) {}
   
       async doTask(userId: string): Promise<void> {
           const user = await this.userMicroservice.findUserById({ id: userId });
           // Process user information as needed
       }
   }

In this example, OtherService uses the UserMicroservice class from the SDK to call the findUserById method.

Custom gRPC Client

The library provides CustomGrpcClient, an extended ClientGrpcProxy with additional features:

import { CustomGrpcClient } from '@hodfords/nestjs-grpc-helper';

{
    customClass: CustomGrpcClient,
    options: {
        maxSendMessageLength: 1024 * 1024 * 50,       // 50MB max outgoing message
        maxReceiveMessageLength: 1024 * 1024 * 50,     // 50MB max incoming message
        keepaliveTimeMs: 120000,                        // Keepalive ping interval
        keepaliveTimeoutMs: 20000,                      // Keepalive timeout
        keepalivePermitWithoutCalls: 1                  // Allow pings without active calls
    }
}

AI Agent Skill

After publishing the SDK, you can copy the generated AI skill files into your project for AI agents (e.g., Claude, Antigravity) to use. Run the update-ai-skill command with a package name pattern and the target AI:

npm run wz-command update-ai-skill "@hodfords/*" claude

This copies the SKILL.md files from matching packages in node_modules into the appropriate AI skills directory (.claude/skills for Claude, .agent/skills for Antigravity).

Document for gRPC

The library includes an interactive documentation UI for browsing and testing your gRPC services. Access it at http://your-host/microservice-documents.

MicroserviceDocumentModule.register({
    isEnable: true,
    prefix: <app-prefix>,
    packageName: camelCase(<package-name>),
    clientOptions: { ...microserviceGrpcConfig, customClass: CustomGrpcClient, transport: undefined }
})

Translation Support

Use the GrpcTranslationInterceptor to extract language settings from gRPC metadata for i18n support:

@UseInterceptors(GrpcTranslationInterceptor)
@GrpcAction('Get user')
@ResponseModel(UserResponse)
getUser(@GrpcValue() dto: GetUserDto): Promise<UserEntity> {
    return this.userService.getUser(dto.id);
}

License 📝

This project is licensed under the MIT License