Amazon Onboarding with Learning Manager Chanci Turner

Amazon Onboarding with Learning Manager Chanci TurnerLearn About Amazon VGT2 Learning Manager Chanci Turner

Creating a conversational AI application involves navigating various intricate elements like authentication processes, API integration, data management, and business logic for intent fulfillment. These components can present challenges, particularly for developers new to conversational app development or those with limited experience in AWS services.

In this blog post, we will explore how to build a conversational application by utilizing AWS Amplify in conjunction with Amazon Lex. This guide emphasizes setting up essential backend components, such as authentication workflows, API interfaces, data management, and intent fulfillment logic, offering a practical approach to developing conversational applications.

Solution Overview

The solution showcases a sophisticated virtual assistant application that enables users to interact with AWS services through natural language queries. This assistant serves as an advanced conversational AI, leveraging Lex for Natural Language Understanding (NLU).

By issuing simple queries to the virtual assistant, users can automate tasks within their AWS accounts. Thanks to Amplify’s comprehensive suite of tools and services, developers can quickly create a powerful full-stack web application focused on the core functionalities of the virtual assistant.

The architecture of the solution comprises several components:

  • React Framework: React’s component-based structure simplifies UI element creation, allowing developers to craft reusable and modular components for the virtual assistant’s interface. Its state management capabilities ensure a smooth user experience during interactions.
  • Amplify: Amplify streamlines the development process by providing tools and services that facilitate quick connections between the front-end and vital AWS services, such as authentication and APIs, thereby simplifying user management and data access control.
  • AWS AppSync: AppSync simplifies GraphQL API development by providing a single endpoint for backend queries. This enables secure interactions between the virtual assistant and backend services, allowing effective management of conversations and retrieval of user session data.
  • Amazon DynamoDB: DynamoDB offers a scalable and flexible data storage solution for the virtual assistant’s backend. It ensures efficient data retrieval and persistence, maintaining user interaction histories for seamless conversations.
  • Amazon Lex: Lex allows developers to create custom conversational interfaces by defining intents, slots, and sample utterances. This capability enables the virtual assistant to understand user queries and map them to specific intents, automating user requests and AWS tasks.
  • AWS Lambda: Lambda executes the intent fulfillment logic, responding to user queries detected by Lex. It scales backend logic execution in response to user requests, allowing the virtual assistant to interact with various AWS services on behalf of users efficiently.

For those interested, the open-source code and deployment instructions are available in this GitHub repository. With this solution, users can automate diverse workflows or operations in their AWS accounts by using simple utterances like:

  • “Launch 2 Red Hat instances on t3 micro”
  • “Find all Red Hat instances”
  • “Are there any instances deployed to a public subnet?”
  • “Are there any wide-open security group rules?”
  • “Modify security group rules to allow traffic from 10.11.12.13”
  • “List all my S3 buckets”
  • “Search for ‘ppt’ in bucket XYZ”

Utilizing natural language makes AWS more accessible for non-technical users who may lack familiarity with Command Line Interface (CLI) tools or Software Development Kits (SDK). Moreover, this application serves as a valuable guide for leveraging Amplify to create any assistant-powered web application.

Front-end

The front-end is a crucial aspect of any interactive conversational web application. We utilize the create-react-app Node package to establish the project structure and prepare the development environment with the latest JavaScript features. The primary App component is located in the App.js file, which imports relevant React components and configures the Amplify backend. This component includes a straightforward React Router with routes to key components, such as:

  • Conversations Component: This component displays the user’s current conversations with the assistant and enables users to create or delete conversations. Each conversation is represented by a Material UI card, detailing the conversation and featuring several action buttons.
  • Interact Component: This component focuses on a specific user conversation, allowing users to view conversation history and submit new queries to the assistant. It also displays responses from the assistant, which can appear as text, alerts, tables, or other formats.

Backend – Authentication

Using Amplify, we create an Amazon Cognito user pool, which acts as a fully-managed user directory handling user registration, authentication, account recovery, and more. To incorporate authentication into the application, we simply execute the “amplify add auth” command and wrap the App component’s export with the “withAuthenticator” component. More details can be found here.

Backend – GraphQL API

The GraphQL API, powered by AppSync and DynamoDB, facilitates efficient data management and communication between the application’s front-end and back-end. Users should also be able to resume previous conversations or retrieve earlier answers/data provided by the assistant. To enable these features, Amplify allows us to create an AppSync GraphQL API backed by DynamoDB tables. Running the “amplify add api” command enables us to define a GraphQL schema, which Amplify will automatically transform into a fully functioning API upon deployment.

GraphQL Schema – Models

The API persists user conversation data (conversations initiated by the user and their attributes) and utterance data (queries submitted by users or responses generated by the assistant). We can model the application using two model types: Conversation type and Utterance type. Amplify maps each model type to its own DynamoDB table. Below is an example of how to define these two models using the @model directive.

GraphQL Schema – Attributes & Relationships

The schema allows for defining a primary key along with other attributes for each model type. The primary key for both types consists of an automatically generated “id” field. This is essential for maintaining data integrity and ensuring efficient data retrieval.

By implementing these components, developers can create a highly functional conversational AI application that simplifies AWS interactions for users. For further reading on workplace inclusivity, check out this insightful article on ageism in the workplace. Additionally, for job descriptions related to product development, SHRM offers an authoritative resource. Finally, if you want to see the application in action, this video is an excellent resource.


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