amazon-bedrock-agentcore-samples/02-use-cases/video-games-sales-assistant/amplify-video-games-sales-assistant-agentcore-strands/README.md

Front-End Implementation - Integrating AgentCore with a Ready-to-Use Data Analyst Assistant Application

A powerful React-based web application that integrates with AWS Bedrock AgentCore to provide an intelligent data analysis interface for video game sales data.

Overview

This application creates a conversational AI assistant that can analyze video game sales data through natural language queries. The integration utilizes Amazon Bedrock AgentCore to deploy and operate AI agents securely at scale using any framework and model.

The application uses the InvokeAgentRuntimeCommand from the AWS JavaScript SDK v3 to interact with the deployed AgentCore infrastructure.

Key Features

• Conversational AI Interface: Chat with an intelligent assistant powered by Claude 3.5 Sonnet
• Tabular Data Display: View raw query results in organized tables
• Dynamic Data Visualization: Automatically generates chart visualizations

Important

This sample application is for demonstration purposes only and is not production-ready. Please validate the code against your organization's security best practices.

Prerequisites

Ensure you have the following installed and configured:

• Agent Deployment - Strands Agent Infrastructure Deployment with AgentCore
• Node.js 18+
• React Scripts - Install with: npm install react-scripts

Set Up the Front-End Application

Install the React application dependencies:

npm install

Configure IAM User Access for Front-End Permissions

• Create an IAM user
• Create Access key and Secret access key for programmatic access
• Add an inline policy to this user with the following JSON (replace placeholder values with your actual ARNs).

Update the values with your <account_id> and <question_answers_table_arn>, which you can find in the outputs from the CDK project, and the <agent_runtime_arn> that was created previously.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "InvokeBedrockModel",
            "Effect": "Allow",
            "Action": [
                "bedrock:InvokeModel"
            ],
            "Resource": [
                "arn:aws:bedrock:*:<account_id>:inference-profile/us.anthropic.claude-3-5-sonnet-20241022-v2:0",
                "arn:aws:bedrock:us-east-2::foundation-model/anthropic.claude-3-5-sonnet-20241022-v2:0",
                "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-5-sonnet-20241022-v2:0",
                "arn:aws:bedrock:us-west-2::foundation-model/anthropic.claude-3-5-sonnet-20241022-v2:0"
            ]
        },
        {
            "Sid": "DynamoDB",
            "Effect": "Allow",
            "Action": [
                "dynamodb:Query"
            ],
            "Resource": "<question_answers_table_arn>"
        },
        {
            "Sid": "BedrockAgentCorePermissions",
            "Effect": "Allow",
            "Action": "bedrock-agentcore:InvokeAgentRuntime",
            "Resource": [
                "<agent_runtime_arn>",
                "<agent_runtime_arn>/runtime-endpoint/*"
            ]
        }
    ]
}

Configure Environment Variables

• Rename the file src/sample.env.js to src/env.js and update the following environment variables:

  • AWS Credentials and Region:

    • ACCESS_KEY_ID
    • SECRET_ACCESS_KEY
    • AWS_REGION

  • You can find the DynamoDB table name in the CloudFormation Outputs from the CDK project:

    • QUESTION_ANSWERS_TABLE_NAME

  • Also, you can update the general application description:

    • APP_NAME
    • APP_SUBJECT
    • WELCOME_MESSAGE

  • Amazon Bedrock AgentCore Runtime information you can find in the AWS Console

    • AGENT_RUNTIME_ARN
    • AGENT_ENDPOINT_NAME
    • LAST_K_TURNS AgentCore Memory value to retrieve the last K conversation turns for context memory

Test Your Data Analyst Assistant

Start the application locally:

npm start

Try these sample questions to test the assistant:

• Hello!
• How can you help me?
• What is the structure of the data?
• Which developers tend to get the best reviews?
• What were the total sales for each region between 2000 and 2010? Give me the data in percentages.
• What were the best-selling games in the last 10 years?
• What are the best-selling video game genres?
• Give me the top 3 game publishers.
• Give me the top 3 video games with the best reviews and the best sales.
• Which is the year with the highest number of games released?
• Which are the most popular consoles and why?
• Give me a short summary and conclusion of our conversation.

Tip

🚀 For production deployment, consider using AWS Amplify Hosting and integrate Amazon Cognito or another identity provider for proper authentication and authorization instead of using IAM user credentials.

Application Features

Congratulations! Your Data Analyst Assistant can provide you with the following conversational experience:

• Conversational interface with an agent responding to user questions

• Raw query results displayed in tabular format

• Chart visualization generated from the agent's answer and the data query results (created using Apexcharts).

• Summary and conclusion derived from the data analysis conversation

Thank You

License

This project is licensed under the Apache-2.0 License.