Integrating AI into web applications has become increasingly prevalent. AWS Bedrock offers a powerful platform to access and leverage foundation models (FMs) for building generative AI applications. This article will guide you through incorporating AI capabilities into your Angular application using AWS Bedrock.

Prerequisites

Basic understanding of Angular and TypeScript.
AWS account with necessary permissions.
Node.js and npm (or yarn) installed.
An Angular project set up.

Step-by-Step Guide

This article will guide you through incorporating AI capabilities into your Angular application using AWS Bedrock.

1. Setting Up AWS Bedrock

Create an AWS account: If you don’t have one, create an AWS account.
Set up IAM roles: Create IAM roles with necessary permissions to access AWS Bedrock and other required services.
Choose a foundation model: AWS Bedrock offers a variety of foundation models from different providers. Select the model that best suits your application’s requirements.

2. Creating an AWS Lambda Function

Create a new Lambda function: Use the AWS Management Console or the AWS CLI to create a new Lambda function.
Choose Node.js runtime: Select Node.js as the runtime for your function.
Write the Lambda function code: This code will interact with the AWS Bedrock API to send prompts and receive responses.

const AWS = require('aws-sdk');

const bedrockClient = new AWS.Bedrock({ region: 'us-east-1' }); // Replace with your region

exports.handler = async (event) => {
  const prompt = event.prompt;

  const params = {
    modelId: 'YOUR_MODEL_ID', // Replace with your model ID
    inputText: prompt
  };

  try {
    const response = await bedrockClient.generateText(params).promise();
    return response.text;
  } catch (error) {
    console.error(error);
    throw error;
  }
};

Configure the function: Set the appropriate IAM role and environment variables.

3. Creating an Angular Service

Generate a new Angular service: Use the Angular CLI to create a new service to handle interactions with the Lambda function.

ng generate service bedrock

Inject HttpClient: Inject the HttpClient to make HTTP requests to the Lambda function.
Create a method to call the Lambda function: This method will send the prompt to the Lambda function and return the response.

import { Injectable } from '@angular/core';
import { HttpClient } from '@angular/common/http';

@Injectable({
  providedIn: 'root'
})
export class BedrockService {
  constructor(private http: HttpClient) {}

  generateText(prompt: string) {
    return this.http.post<string>('https://your-lambda-function-endpoint', { prompt });
  }
}

4. Integrating AI into Your Angular Component

Import the Bedrock service: Import the Bedrock service into your component.
Create a form or input field: Allow users to input text as a prompt.
Call the Bedrock service: When a user submits the prompt, call the Bedrock service to generate text.
Display the generated text: Display the generated text in your component’s view.

import { Component } from '@angular/core';
import { BedrockService } from './bedrock.service';

@Component({
  selector: 'app-my-component',
  templateUrl: './my-component.component.html',
  styleUrls: ['./my-component.component.css']
})
export class MyComponent {
  prompt: string = '';
  generatedText: string = '';

  constructor(private bedrockService: BedrockService) {}

  generate() {
    this.bedrockService.generateText(this.prompt)
      .subscribe(text => {
        this.generatedText = text;
      });
  }
}

Conclusion:

By following these steps, you can successfully integrate AI capabilities into your Angular application using AWS Bedrock. This integration can enhance user experiences, automate tasks, and unlock new possibilities for your application.

Note: Replace placeholders like YOUR_MODEL_ID and https://your-lambda-function-endpoint with actual values.

Adding GenAI to Angular Application Using AWS Bedrock