This project allows users to use LLM model to create a learning path for themselves. Project contians both frontend and backend code which can be deployed to AWS services. Project contains cloudformation file which could be executed to create a stack and complete the deployment.

AWS Infrastructure

Compute

1. AWS EC2 - deploy frontend application

   1. create a simple instance with key pair
   2. add nvm "curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash"
   3. source ~/.bashrc
   4. nvm install --lts
   5. open ec2 instance using WinSCP
   6. transfer dist files to VM
   7. npm install -g serve
   8. nohup serve -l 3000 -s app-name

2. AWS Lambda - deploy backend api

   1. Create a lambda with Java 21 environment and upload jar, change timeout to 1 minute or more

Storage

1. Dynamo DB - for storing user data
   1. Create a table with id as partition key

Network

1. AWS API Gateway – Route API requests to lambdas
   1. Create a API gateway with REST type, and create a post endpoint with integration to lambda

General

1. AWS Secrets manager - used to store database credentials, API key for backend

   1. chatGptApiKey : **************
   2. tableName : cloud-term-assignment
   3. emailFrom : [email protected]
   4. servicesRegion : us-east-1

2. AWS SES - used to send email

   1. Add two personal email as verified identities, one will be used to send email and one will be used to receive email

Commands to create and delete stack

Following command will create a stack named learn-pro

aws cloudformation create-stack --stack-name learn-pro --template-body=file://cloudformation.yaml --capabilities=CAPABILITY_NAMED_IAM

Following command will delete a stack named learn-pro

aws cloudformation delete-stack --stack-name learn-pro