Task 3: Build a Serverless Backend - Build a Serverless Web Application using Generative AI
OverviewWhat you will accomplishImplementationConclusion

Task 3: Build a Serverless Backend

Time to complete

10 minutes

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Troubleshooting Lambda Functions

Overview

In this task, you will configure a serverless function using AWS Amplify and AWS Lambda. This function takes an input parameter i.e. ingredients to generate a prompt. It then sends this prompt to Amazon Bedrock via an HTTP POST request to the Claude 3 Sonnet model. The body of the request includes the prompt string within a messages array.

What you will accomplish

In this tutorial, you will:

  • Add Amazon Bedrock as a data source

  • Configure custom business logic handler code

Implementation

  1. Create a Lambda function

    On your local machine, navigate to the ai-recipe-generator/amplify/data folder, and create a file named bedrock.js.

    A file directory for the ai-recipe-generator project, showing folders amplify, auth, data, and files such as resource.ts, backend.ts, package.json. The file bedrock.js is highlighted in the data folder.

  2. Add the function code

    Then, update the file with the following code: 

    export function request(ctx) {
    const { ingredients = [] } = ctx.args;
  
    // Construct the prompt with the provided ingredients
    const prompt = `Suggest a recipe idea using these ingredients: ${ingredients.join(", ")}.`;
  
    // Return the request configuration
    return {
      resourcePath: `/model/anthropic.claude-3-sonnet-20240229-v1:0/invoke`,
      method: "POST",
      params: {
        headers: {
          "Content-Type": "application/json",
        },
        body: JSON.stringify({
          anthropic_version: "bedrock-2023-05-31",
          max_tokens: 1000,
          messages: [
            {
              role: "user",
              content: [
                {
                  type: "text",
                  text: `\n\nHuman: ${prompt}\n\nAssistant:`,
                },
              ],
            },
          ],
        }),
      },
    };
  }
  
  export function response(ctx) {
    // Parse the response body
    const parsedBody = JSON.parse(ctx.result.body);
    // Extract the text content from the response
    const res = {
      body: parsedBody.content[0].text,
    };
    // Return the response
    return res;
  }

    This code defines a request function that constructs the HTTP request to invoke the Claude 3 Sonnet foundation model in Amazon Bedrock. The response function parses the response and returns the generated recipe.

  • Update the backend file

    Update the amplify/backend.ts file with the following code. Then, save the file. 

    import { defineBackend } from "@aws-amplify/backend";
import { data } from "./data/resource";
import { PolicyStatement } from "aws-cdk-lib/aws-iam";
import { auth } from "./auth/resource";

const backend = defineBackend({
  auth,
  data,
});

const bedrockDataSource = backend.data.resources.graphqlApi.addHttpDataSource(
  "bedrockDS",
  "https://bedrock-runtime.us-east-1.amazonaws.com",
  {
    authorizationConfig: {
      signingRegion: "us-east-1",
      signingServiceName: "bedrock",
    },
  }
);

bedrockDataSource.grantPrincipal.addToPrincipalPolicy(
  new PolicyStatement({
    resources: [
      "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0",
    ],
    actions: ["bedrock:InvokeModel"],
    
  })
);

    • The code adds an HTTP data source for Amazon Bedrock to your API and grant it permissions to invoke the Claude model.

    The 'ai-recipe-generator' project directory structure, highlighting the backend.ts file located in the amplify/auth folder.

Conclusion

You have defined a Lambda function using Amplify, and added Amazon Bedrock as an HTTP data source.