Task 4: Deploy the Backend API - Build a Serverless Web Application using Generative AI
OverviewWhat you will accomplishImplementationConclusion

Task 4: Deploy the Backend API

Overview

In this task, you will configure a custom query that will reference the data source and the resolver you defined the previous model to produce a recipe based on a list of ingredients. This query will use a custom type to structure the response from Amazon Bedrock. 

What you will accomplish

In this tutorial, you will:

  • Define a GraphQL Query that takes an array of strings

  • Define a custom type to be used to structure the response from the query

Implementation

Time to complete

5 minutes

Get help

Troubleshooting Amplify

Learn about Data

  1. Update the resource.ts file

    On your local machine, navigate to the ai-recipe-generator/amplify/data/resource.ts file, and update it with the following code. Then, save the file.

    • The following code defines the askBedrock query that takes an array of strings called ingredients and returns a BedrockResponse. The .handler(a.handler.custom({ entry: "./bedrock.js", dataSource: "bedrockDS" })) line sets up a custom handler for this query, defined in bedrock.js, using bedrockDS as its data source. 

    import { type ClientSchema, a, defineData } from "@aws-amplify/backend";

const schema = a.schema({
  BedrockResponse: a.customType({
    body: a.string(),
    error: a.string(),
  }),
  askBedrock: a
    .query()
    .arguments({ ingredients: a.string().array() })
    .returns(a.ref("BedrockResponse"))
    .authorization((allow) => [allow.authenticated()])
    .handler(
      a.handler.custom({
        entry: "./bedrock.js",
        dataSource: "bedrockDS"
      })
    ),
});

export type Schema = ClientSchema<typeof schema>;

export const data = defineData({
  schema,
  authorizationModes: {
    defaultAuthorizationMode: "apiKey",
    apiKeyAuthorizationMode: {
      expiresInDays: 30,
    },
  },
});
    The folder structure of the 'ai-recipe-generator' project, with the 'resource.ts' file highlighted in the data directory.

  2. Deploy resources

    Open a new terminal window, navigate to your apps project folder (ai-recipe-generator), and run the following command to deploy cloud resources into an isolated development space so you can iterate fast. 

    npx ampx sandbox
    Result of running the npx ampx sandbox command.

  3. View confirmation message

    After the cloud sandbox has been fully deployed, your terminal will display a confirmation message.

    The confirmation message.

  4. Verify outputs file creation

    Verify that the amplify_outputs.json file was generated and added to your project.

    Showing the amplify_outputs.json file in Finder.

Conclusion

You have configured a GraphQL API to define a custom query to connect to Amazon Bedrock and generate recipes based on a list of ingredients.