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

Back-End Deployment - Data Source and Configuration Management Deployment with CDK

Deploy the back-end infrastructure for a Data Analyst Assistant for Video Game Sales using AWS Cloud Development Kit (CDK).

Overview

This tutorial deploys the foundational AWS services required for the video game sales data analyst agent with the following key components:

• IAM AgentCore Execution Role: Provides necessary permissions for Amazon Bedrock AgentCore execution
• VPC and Private Subnet: Network isolation and security for database resources
• Amazon Aurora Serverless PostgreSQL: Stores the video game sales data with RDS Data API integration
• Amazon DynamoDB: Tracks raw query results and agent interactions
• Parameter Store Configuration Management: Securely manages application configuration

Important

Remember to clean up resources after testing to avoid unnecessary costs by following the clean-up steps provided.

Prerequisites

Before you begin, ensure you have:

• AWS Account and appropriate IAM permissions for services deployment

• Development Environment:

  • Python 3.10 or later installed
  • AWS CDK Installed

• Run this command to create a service-linked role for RDS:

aws iam create-service-linked-role --aws-service-name rds.amazonaws.com

AWS Deployment

Navigate to the CDK project folder and deploy the infrastructure:

cdk deploy

Default Parameters:

• ProjectId: "agentcore-data-analyst-assistant" - Project identifier used for naming resources
• DatabaseName: "video_games_sales" - Name of the database

Deployed Resources:

• Aurora PostgreSQL Cluster

• S3 bucket for data import

• Secrets Manager secret for database credentials

• DynamoDB Tables for tracking questions query details and agent interactions

• Parameter Store for application configuration management:

  • /<projectId>/AGENT_INTERACTIONS_TABLE_NAME: DynamoDB agent interactions table name
  • /<projectId>/AWS_REGION: AWS region
  • /<projectId>/SECRET_ARN: Database secret ARN
  • /<projectId>/AURORA_RESOURCE_ARN: Aurora cluster ARN
  • /<projectId>/DATABASE_NAME: Database name
  • /<projectId>/QUESTION_ANSWERS_TABLE: DynamoDB question answers table name
  • /<projectId>/MAX_RESPONSE_SIZE_BYTES: Maximum response size in bytes (1MB)
  • /<projectId>/MEMORY_ID: AgentCore Memory ID for the Agent

  These parameters are automatically retrieved by the Strands Agent to establish database connections, track interactions, and configure agent behavior.

Important

Enhance AI safety and compliance by implementing Amazon Bedrock Guardrails for your AI applications with the seamless integration offered by Strands Agents SDK.

Load Sample Data into PostgreSQL Database

1. Set up the required environment variables:

# Set the stack name environment variable
export STACK_NAME=CdkAgentCoreStrandsDataAnalystAssistantStack

# Retrieve the output values and store them in environment variables
export SECRET_ARN=$(aws cloudformation describe-stacks --stack-name "$STACK_NAME" --query "Stacks[0].Outputs[?OutputKey=='SecretARN'].OutputValue" --output text)
export DATA_SOURCE_BUCKET_NAME=$(aws cloudformation describe-stacks --stack-name "$STACK_NAME" --query "Stacks[0].Outputs[?OutputKey=='DataSourceBucketName'].OutputValue" --output text)
export AURORA_SERVERLESS_DB_CLUSTER_ARN=$(aws cloudformation describe-stacks --stack-name "$STACK_NAME" --query "Stacks[0].Outputs[?OutputKey=='AuroraServerlessDBClusterARN'].OutputValue" --output text)
export AGENT_CORE_ROLE_EXECUTION=$(aws cloudformation describe-stacks --stack-name "$STACK_NAME" --query "Stacks[0].Outputs[?OutputKey=='AgentCoreMyRoleARN'].OutputValue" --output text)
export MEMORY_ID_SSM_PARAMETER=$(aws cloudformation describe-stacks --stack-name "$STACK_NAME" --query "Stacks[0].Outputs[?OutputKey=='MemoryIdSSMParameter'].OutputValue" --output text)
cat << EOF
STACK_NAME: ${STACK_NAME}
SECRET_ARN: ${SECRET_ARN}
DATA_SOURCE_BUCKET_NAME: ${DATA_SOURCE_BUCKET_NAME}
AURORA_SERVERLESS_DB_CLUSTER_ARN: ${AURORA_SERVERLESS_DB_CLUSTER_ARN}
AGENT_CORE_ROLE_EXECUTION: ${AGENT_CORE_ROLE_EXECUTION}
MEMORY_ID_SSM_PARAMETER: ${MEMORY_ID_SSM_PARAMETER}
EOF

2. Load sample data into PostgreSQL:

python3 resources/create-sales-database.py

The script uses the video_games_sales_no_headers.csv as the data source.

Note

The data source provided contains information from Video Game Sales which is made available under the ODC Attribution License.

Next Step

You can now proceed to the Agent Deployment - Strands Agent Infrastructure Deployment with AgentCore).

Cleaning-up Resources (Optional)

To avoid unnecessary charges, delete the CDK stack:

cdk destroy

Thank You

License

This project is licensed under the Apache-2.0 License.