Alexa AI Assistant with AWS Bedrock

A complete Alexa skill that integrates with AWS Bedrock (Claude 3 Haiku) through a Spring Boot backend deployed on Elastic Beanstalk.

🎓 Learning Project: This entire project was created using Cursor IDE and AWS Bedrock to explore modern AI-powered development tools. Every line of code, configuration, test, and documentation was generated with AI assistance through Cursor, demonstrating the power of AI-driven development while learning AWS Bedrock integration patterns.

🎯 What This Project Does

• Voice Interaction: Ask questions through any Alexa device
• AI Processing: Powered by Claude 3 Haiku via AWS Bedrock
• Scalable Backend: Spring Boot on Elastic Beanstalk
• Cost Protection: Automatic service cutoff to prevent unexpected bills
• Security: API key authentication between components
• Enterprise Testing: Comprehensive test suite with CI/CD pipeline

🏗️ System Architecture

The following diagram shows the complete end-to-end flow from voice command to AI response:

graph TB
    subgraph "User Interaction"
        A[🗣️ Alexa Device] --> B["Voice: 'Alexa, ask my ai what is Database?'"]
    end
    
    subgraph "AWS Lambda Layer"
        B --> C[🔵 Alexa Skill Lambda Function]
        C --> |"HTTP POST + API Key"| D[Backend Request]
        D --> |"JSON: {message: 'what is Database'}"| E[Spring Boot Backend]
    end
    
    subgraph "Elastic Beanstalk Environment"
        E[🌐 Spring Boot Backend<br/>Port 5000<br/>Corretto 17] --> F[API Key Validation]
        F --> |"Valid Key"| G[Input Validation]
        F --> |"Invalid Key"| H[❌ 401 Unauthorized]
        G --> |"Valid Input"| I[Bedrock API Call]
        G --> |"Invalid Input"| J[❌ 400 Bad Request]
    end
    
    subgraph "AWS Bedrock"
        I --> K[🤖 Claude 3 Haiku<br/>anthropic.claude-3-haiku-20240307-v1:0]
        K --> L["AI Response:<br/>'A database is a structured collection<br/>of data stored electronically...'"]
    end
    
    subgraph "Response Flow"
        L --> M[JSON Response]
        M --> |"HTTP 200"| N[Lambda Processing]
        N --> O[Alexa Response]
        O --> P[🗣️ Voice Output to User]
    end
    
    subgraph "Cost Protection System"
        Q[💰 CloudWatch Alarms<br/>$10, $25, $40 thresholds] --> R[📧 SNS Notifications<br/>Email Alerts]
        R --> |"$40 Threshold"| S[🚨 Budget Cutoff Lambda<br/>Auto-terminate services]
        S --> T[🛑 Service Shutdown<br/>EB Environment + Lambda]
    end
    
    subgraph "Security & Configuration"
        U[🔐 API Key Authentication<br/>Alexa ↔ Backend] 
        V[🌍 Environment Variables<br/>AWS_REGION, BEDROCK_MODEL_ID]
        W[⚙️ IAM Roles<br/>EC2 → Bedrock permissions]
    end
    
    subgraph "Development & Testing"
        X[🧪 Comprehensive Test Suite<br/>Unit + Integration + E2E]
        Y[🚀 CI/CD Pipeline<br/>GitHub Actions]
        Z[📊 Monitoring<br/>CloudWatch + Health Checks]
    end
    
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style E fill:#e8f5e8
    style K fill:#fff3e0
    style Q fill:#ffebee
    style U fill:#f1f8e9
    style X fill:#e3f2fd

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Architecture Components

Component	Technology	Purpose
Alexa Device	Amazon Echo/Dot	Voice interface for user interaction
Alexa Skill	Node.js Lambda	Processes voice commands and responses
Backend API	Spring Boot	Handles business logic and Bedrock integration
AI Engine	AWS Bedrock (Claude 3 Haiku)	Generates intelligent responses
Infrastructure	Elastic Beanstalk	Scalable hosting for backend service
Cost Protection	CloudWatch + Lambda	Automatic cost control and service cutoff
Security	API Keys + IAM	Authentication and authorization
Testing	JUnit + Mocha + Integration	Comprehensive quality assurance

🚀 Quick Start

Prerequisites

• AWS Account with appropriate permissions
• AWS CLI configured
• Java 17+, Maven 3.6+, Node.js 18+
• EB CLI installed

1. Clone and Setup

git clone <your-repo>
cd alexa-chatgpt-java

# Install prerequisites
./install-prerequisites.sh

# Automated setup (recommended)
./setup-environment.sh

# OR Manual setup
cp environment-variables.template .env
cp alexa-skill-nodejs/lambda-env.template alexa-skill-nodejs/.env
cp java-backend-springboot/.ebextensions/01-port.config.template java-backend-springboot/.ebextensions/01-port.config
# Edit the files above with your specific AWS account details

2. Deploy Backend

cd java-backend-springboot
eb init    # Choose your region and platform
eb create  # Create environment
eb deploy  # Deploy application

3. Deploy Alexa Skill

cd alexa-skill-nodejs
npm install
./deploy-lambda.sh  # Follow the script instructions

4. Configure Alexa Developer Console

1. Go to Alexa Developer Console
2. Create new skill using skill-package/skill.json
3. Update Lambda ARN with your function ARN
4. Test and publish

💰 Cost Protection

Includes comprehensive cost protection:

• $10 Threshold: Early warning email
• $25 Threshold: Budget alert email
• $40 Threshold: Automatic service termination

Deploy cost protection:

# Update budget-cutoff-lambda.py with your service names
./deploy-cost-protection.sh

🔧 Configuration

Environment Variables Required

• AWS_REGION: Your AWS region (e.g., eu-central-1)
• AWS_ACCOUNT_ID: Your 12-digit AWS account ID
• ALEXA_API_KEY: Generated API key for authentication
• BACKEND_HOST: Your Elastic Beanstalk URL
• BEDROCK_MODEL_ID: anthropic.claude-3-haiku-20240307-v1:0

Template Files

• environment-variables.template → Copy to .env
• alexa-skill-nodejs/lambda-env.template → Copy to alexa-skill-nodejs/.env
• java-backend-springboot/.ebextensions/01-port.config.template → Copy and configure

🧪 Testing

Test Backend API

curl -X POST http://YOUR_EB_URL/chat \
  -H 'Content-Type: application/json' \
  -H 'x-api-key: YOUR_API_KEY' \
  -d '{"message": "Hello, what is AI?"}'

Test Alexa Skill

"Alexa, ask my ai what is artificial intelligence?"

📊 Expected Costs

• Elastic Beanstalk (t3.micro): ~$8-15/month
• Lambda: ~$0.50/month (1000 requests)
• Bedrock (Claude 3 Haiku): ~$0.25 per 1M tokens
• Total: ~$10-20/month for moderate usage

🔒 Security Features

• API key authentication between Alexa and backend
• IAM roles with minimal required permissions
• Input validation and sanitization
• VPC deployment ready (optional)

📚 Documentation

• SETUP.md - Detailed setup instructions
• SECURITY.md - Security considerations
• COST_OPTIMIZATION.md - Cost optimization strategies
• TESTING_GUIDE.md - Comprehensive testing guide
• AWS_USER_PERMISSIONS.md - Required AWS permissions

🆘 Troubleshooting

Common Issues

Issue	Solution
EB CLI not found	brew install aws-elasticbeanstalk
Bedrock access denied	Request access in AWS Console
Lambda timeout	Increase timeout in configuration
API key errors	Check environment variables

Useful Commands

# Check deployment status
eb status
eb logs

# Test Lambda function
aws lambda invoke --function-name YOUR_FUNCTION_NAME test-output.json

# Monitor costs
aws budgets describe-budgets --account-id YOUR_ACCOUNT_ID

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

⚠️ Important Security Notes

• Never commit sensitive information (API keys, account IDs, emails)
• Always use environment variables for configuration
• Test in development before deploying to production
• Monitor costs regularly using AWS Cost Explorer
• Keep dependencies updated for security patches

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🎉 Features

• ✅ Voice-activated AI assistant
• ✅ AWS Bedrock integration (Claude 3 Haiku)
• ✅ Scalable Spring Boot backend
• ✅ Automatic cost protection
• ✅ Security with API keys
• ✅ Comprehensive monitoring
• ✅ Easy deployment scripts
• ✅ Production-ready configuration

🔮 Future Enhancements

• Multi-turn conversations with context
• Different AI models based on query complexity
• Web interface for testing
• Integration with other voice assistants
• Advanced analytics and usage tracking

🎓 Learning Journey: Cursor IDE + AWS Bedrock

This project represents a comprehensive exploration of modern AI-powered development tools and cloud services:

🖱️ Cursor IDE Experience

This entire codebase was built using Cursor IDE, showcasing its capabilities for:

• 🤖 AI-Assisted Development: Leveraged Cursor's AI features for code generation, debugging, and optimization
• 🧠 Intelligent Code Completion: Used AI suggestions for complex AWS integrations and Spring Boot configurations
• 🔍 Context-Aware Assistance: Benefited from Cursor's understanding of the project structure for consistent code patterns
• ⚡ Rapid Prototyping: Accelerated development through AI-powered code generation and refactoring
• 🐛 Smart Debugging: Utilized AI assistance for troubleshooting deployment issues and configuration problems
• 📚 Documentation Generation: Leveraged AI for creating comprehensive documentation and README content

🧠 AWS Bedrock Integration Learning

The project served as a hands-on exploration of AWS Bedrock capabilities:

• 🤝 Claude 3 Haiku Integration: Implemented direct API calls to Anthropic's Claude model through Bedrock
• 🔧 SDK Configuration: Learned AWS SDK setup and configuration for Bedrock services
• 🌍 Regional Deployment: Configured services in eu-central-1 region for optimal performance
• 💰 Cost Management: Implemented comprehensive cost controls and monitoring for Bedrock usage
• 🔐 Security Best Practices: Applied proper IAM roles and API key management for Bedrock access
• ⚡ Performance Optimization: Optimized request patterns and response handling for Claude interactions

📈 Skills and Technologies Mastered

Through this learning project, the following technologies were explored:

Category	Technologies Learned
AI Development	Cursor IDE, AWS Bedrock, Claude 3 Haiku API
Backend Development	Spring Boot, Java 17, Maven, REST APIs
Cloud Infrastructure	AWS Elastic Beanstalk, Lambda, CloudWatch, SNS
Voice Technology	Amazon Alexa Skills Kit, Voice User Interface design
DevOps & Testing	GitHub Actions, JUnit, Integration testing, CI/CD
Security	API key management, IAM roles, Cost protection
Monitoring	CloudWatch alarms, SNS notifications, Health checks

🎯 Key Learning Outcomes

1. 🚀 Modern IDE Capabilities: Experienced the power of AI-assisted development with Cursor
2. ☁️ Cloud-Native Architecture: Built a production-ready, scalable cloud application
3. 🤖 AI Service Integration: Successfully integrated enterprise AI services (Bedrock) into applications
4. 🗣️ Voice Interface Development: Created a functional voice-activated AI assistant
5. 🔒 Security-First Approach: Implemented comprehensive security and cost controls
6. 🧪 Test-Driven Development: Built enterprise-grade testing and CI/CD pipelines
7. 📊 Production Monitoring: Set up comprehensive monitoring and alerting systems

💡 Development Insights

What Worked Well:

• Cursor's AI assistance accelerated development by ~3-4x
• AWS Bedrock provided reliable, high-quality AI responses
• Spring Boot offered excellent AWS SDK integration
• Comprehensive testing prevented deployment issues

Challenges Overcome:

• Complex AWS IAM permission configurations
• Alexa skill deployment and testing workflows
• Cost management and automatic service cutoffs
• Multi-service integration and error handling

Best Practices Discovered:

• Environment variable management for multi-service applications
• API key rotation and security patterns
• Cost monitoring and automatic protection mechanisms
• Comprehensive testing strategies for cloud applications

🌟 Project Impact

This learning project demonstrates:

• Production-Ready Code: Enterprise-grade application with full testing and CI/CD
• Modern Development Practices: AI-assisted development with proper DevOps workflows
• Cloud-Native Design: Scalable, secure, and cost-effective cloud architecture
• Real-World Application: Functional voice assistant that actually works on physical Alexa devices

Perfect example of how modern AI development tools can accelerate learning and building complex, production-ready applications! 🎉

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Ready to deploy your own AI-powered Alexa skill? Follow the setup guide and start building! 🚀