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AWS AI Stack – A ready-to-use, full-stack boilerplate project for building serverless AI applications on AWS
AWS AI Stack – A ready-to-use, full-stack boilerplate project for building serverless AI applications on AWS. A great fit for those seeking a trusted AWS foundation for AI apps and access to powerful LLM models via Bedrock that keep your app’s data separate from model providers.
View the Live Demo – awsaistack.com
Use this as a boilerplate project to create an AI Chat bot, authentication services, business logic, async workers, all on AWS Lambda, API Gateway, DynamoDB, and EventBridge.
This is a true serverless architecture, so you only pay for what you use, not for idle time. Some services, like DynamoDB, or AWS Bedrock trained models, may have additional storage costs.
serverless-domain-manager pluginInstall Serverless Framework
npm i -g serverless
Install NPM dependencies
This project is structured as a monorepo with multiple services. Each service
has its own package.json file, so you must install the dependencies for each
service. Running npm install in the root directory will install the
dependencies for all services.
npm install
Setup AWS Credentials
If you haven't already, setup your AWS Credentials. You can follow the AWS Credentials doc for step-by-step instructions.
This example requires the meta.llama3-70b-instruct-v1:0 AWS Bedrock
Model to be enabled. By default, AWS does not enable these models, you must go
to the AWS Console
and individually request access to the AI Models.
There is no cost to enable the models, but you must request access to use them.
Upon request, it may take a few minutes for AWS to enable the model. Once they are enabled, you will receive an email from AWS confirming the model is enabled.
Some users have reported issues with getting models enabled on AWS Bedrock. Make sure you have sufficient permissions in AWS to enable the models first. Often, AWS accounts that are new or have not historically had a monthly invoice over a few dollars may require contacting AWS to enable models.
Now you are ready to deploy the services. This will deploy all the services
to your AWS account. You can deploy the services to the default stage, which
is the default stage for development.
Deploy the services
serverless deploy
At this point the service is live. When running the serverless deploy command,
you will see the output of the services that were deployed. One of those
services is the web service, which is the website service. To view the app,
go to the URL in the endpoint: ANY - section for the web service.
Deploying "web" to stage "dev" (us-east-1)
endpoint: ANY - https://ps5s7dd634.execute-api.us-east-1.amazonaws.com
functions:
app: web-dev-app (991 kB)
Once you start developing it is easier to run the service locally for faster iteration. We recommend using Serverless Dev Mode. You can run Dev Mode for individual services. This emulates Lambda locally and proxies requests to the real service.
serverless auth dev
Once done, you can redeploy individual services using the serverless command
with the service name.
serverless auth deploy
The website service is a static website that is served from an AWS Lambda
function. As such, it can run locally without needing to use Dev Mode. However,
it has a dependency on the AI Chat service and the Auth service, so you must
configure environment variables locally.
# If you have the jq CLI command installed you can use that with the --json flag
# on serverless info to get the URLs from the deployed services. If you do not
# have jq installed, you can get the URLs by running "serverless auth info" and
# "serverless ai-chat info" and copying the URLs manually into the environment
# variables.
export VITE_CHAT_API_URL=$(serverless aiChatApi info --json | jq -r '.outputs[] | select(.OutputKey == "ChatApiUrl") | .OutputValue')
export VITE_AUTH_API_URL=$(serverless auth info --json | jq -r '.outputs[] | select(.OutputKey == "AuthApiUrl") | .OutputValue')
# now you can run the local development server
cd website/app
npm run build
Now that the app is up and running in a development environment, lets get it ready for production by setting up a custom domain name, and setting a new shared secret for JWT token authentication.
This project is configured to use custom domain names. For non prod
deployments this is disabled. Deployments to prod are designed to use a custom
domain name and require additional setup:
Register the domain name & create a Route53 hosted zone
If you haven't already, register a domain name, and create a Route53 hosted zone for the domain name.
https://us-east-1.console.aws.amazon.com/route53/v2/hostedzones?region=us-east-1#
Create a Certificate in AWS Certificate Manager
A Certificate is required in order to use SSL (https) with a custom domain
name. AWS Certificate Manager (ACM) provides free SSL certificates for use with
your custom domain name. A certificate must first be requested, which requires
verification, and may take a few minutes.
https://us-east-1.console.aws.amazon.com/acm/home?region=us-east-1#/certificates/list
After you have created the certificate, you must validate the certificate by following the instructions in the AWS Console. This may require adding a CNAME record to your DNS provider.
This example uses a Certificate with the following full qualified domain names:
awsaistack.com
\*.awsaistack.com
The base domain name, awsaistack.com is used for the website service
to host the static website. The wildcard domain name,
*.awsaistack.com is used for the API services,
api.awsaistack.com, and chat.awsaistack.com.
Update serverless-compose.yml
stages.prod.params.customDomainName to your custom domain name.stages.prod.params.customDomainCertificateARN to the ARN of the
certificate you created in ACM.Authentication is implemented using JWT tokens. A shared secret is used to sign the JWT tokens when a user logs in. The secret is also used to verify the JWT tokens when a user makes a request to the API. It is important that this secret is kept secure and not shared.
In the serverless-compose.yml file, you'll see that the sharedTokenSecret is
set to "DEFAULT" in the stages.default.params section. This is a placeholder
value that is used when the secret is not provided in non-prod environments.
The prod stage uses the ${ssm} parameter to retrieve the secret from AWS
Systems Manager Parameter Store.
Generate a random secret and store it in the AWS Systems Manager Parameter Store
with a key like /serverless-ai-service/shared-token, and set it in the
stages.prod.params.sharedTokenSecret parameter in the serverless-compose.yml
file:
sharedTokenSecret: ${ssm:/serverless-ai-service/shared-token}
Once you've setup the custom domain name (optional), and created the secret, you are ready to deploy the service to prod.
serverless deploy --stage prod
Now you can use the service by visiting your domain name, or https://awsaistack.com. This uses the Auth service to login and register users, the AI Chat service to interact with the AI Chat bot.
This example uses serverless services like AWS Lambda, API Gateway, DynamoDB, EventBridge, and CloudFront. These services are designed to scale with usage, and you only pay for what you use. This means you do not pay for idle, and only pay for the resources you consume. If you have 0 usage, you will have $0 cost.
If you are using the custom domain names, it will require Route53 which has a fixed monthly cost.
This example uses Serverless Compose to share configuration across all services.
It defines the global parameters in the serverless-compose.yml file under
stages.default.params and stages.prod.params. These parameters are used
across all services to provide shared configuration.
It also uses CloudFormation from services to set parameters on other services.
For example, the auth service publishes the CloudFormation Output
AuthApiUrl, which is used by the website service.
web:
path: ./website
params:
authApiUrl: ${auth.AuthApiUrl}
Using Serverless Compose also allows you to deploy all services with a single
command, serverless deploy.
The auth service contains a shared client library that is used by the other
services to validate the JWT token. This library is defined as an NPM package
and is used by the ai-chat-api and business-api services and included using
relative paths in the package.json file.
The auth service is an Express.js-based API service that provides login and
registration endpoints. It uses a DynamoDB table to store user information and
uses JWT tokens for authentication.
Upon login or registration, the service returns a JWT token. These APIs are used
by the website service to authenticate users. The token is stored in
localstorage and is used to authenticate requests to the ai-chat-api and
business-api services.
The ai-chat-api service uses AWS Lambda Function URLs instead of API Gateway,
in order to support streaming responses. As such, it uses the Auth class from
auth-sdk to validate the JWT token, instead of using an API Gateway
authorizer.
The auth service also publishes the CloudFormation Output AuthApiUrl, which
is used by the website service to make requests to the auth service.
In most cases APIs on AWS Lambda use the API Gateway to expose the API. However,
the ai-chat-api service uses Lambda Function URLs instead of API Gateway, in
order to support streaming responses as streaming responses are not supported by
API Gateway.
Since the ai-chat-api service does not use API Gateway, it does not support
custom domain names natively. Instead, it uses a CloudFront Distribution to
support a custom domain name.
To provide the AI Chat functionality, the service uses the AWS Bedrock Models service to interact with the AI Chat bot. The requests from the frontend (via the API) are sent to the AWS Bedrock Models service, and the streaming response from Bedrock is sent back to the frontend via the streaming response.
The AWS Bedrock AI Model is selected using the modelId parameter in the
ai-chat-api/serverless.yml file.
stages:
default:
params:
modelId: meta.llama3-70b-instruct-v1:0
The AI Chat service also implements a simple throttling schema to limit cost exposure when using AWS Bedrock. It implements a monthly limit for the number of requests per user and a global monthly limit for all users. It uses a DynamoDB Table to persist the request counts and other AI usage metrics.
The inline comments provider more details on this mechanism as well as ways to customize it to use other metrics, like token usage.
stages:
default:
params:
throttleMonthlyLimitUser: 10
throttleMonthlyLimitGlobal: 100
The website service is a simple Lambda function which uses Express to serve
static assets. The service uses the serverless-plugin-scripts plugin to
run the npm run build command to build the website before deploying.
The build command uses the parameters to set the REACT_APP_* environment
variables, which are used in the React app to configure the API URLs.
The frontend website is built using React. It uses the auth service to
login and register uses, and uses the ai-chat-api to interact with the
AI Chat bot API.
This is an Express.js-based API service that provides a placeholder for your
business logic. It is configured to use the same custom domain name as the
auth service, but with a different base path (/business).
The endpoints are protected using the express-jwt middleware, which uses the
JWT token provided by the auth service to authenticate the user.
This is a placeholder function for your business logic for processing asynchronous events. It subscribes to events on the EventBridge and processes the events.
Currently this subscribes to the auth.register event, which is published by
the auth service when a user registers.
Both the Business Worker and the Auth service therefore depend on the
EventBridge which is provisioned in the event-bus service.
The services which use API Gateway use the serverless-domain-manager plugin to
setup the custom domain name. More details about the plugin can be found on the
serverless-domain-manager plugin page.
The api-ai-chat service uses Lambda Function URLs instead of API Gateway, so
custom domain name is supported by creating a CloudFront Distribution with the
custom domain name and the Lambda Function URL as the origin.
The business-api and auth APIs both use the same custom domain name. Instead
of sharing an API Gateway, they are configured to use the same domain name
with different base paths, one for each service.
Below are a few simple API requests using the curl command.
curl -X POST https://api.awsaistack.com/auth/register \
-H 'Content-Type: application/json' \
-d '{"email": "me@example.com", "password": "password"}'
curl -X POST https://api.awsaistack.com/auth/login \
-H 'Content-Type: application/json' \
-d '{"email": "me@example.com", "password": "password"}'
If you have jq installed, you can wrap the login request in a command to set
the token as an environment variable so you can use the token in subsequent
requests.
export SERVERLESS_EXAMPLE_TOKEN=$(curl -X POST https://api.awsaistack.com/auth/login \
-H 'Content-Type: application/json' \
-d '{"email": "me@example.com", "password": "password"}' \
| jq -r '.token')
You can also use the Chat API directly; however, the response payload is a a stream of JSON objects containing the response and other metadata. Each buffer may also contain multiple JSON objects.
This endpoint is authenticated and requires the JWT token from the login API.
curl -N -X POST https://chat.awsaistack.com/ \
-H 'Content-Type: application/json' \
-H "Authorization: Bearer $SERVERLESS_EXAMPLE_TOKEN" \
-d '[{"role":"user","content":[{"text":"What makes the serverless framework so great?"}]}]'
This endpoint is also authenticated and requires the JWT token from the login API. The response is a simple message.
curl -X GET https://api.awsaistack.com/business/ \
-H 'Content-Type: application/json' \
-H "Authorization: Bearer $SERVERLESS_EXAMPLE_TOKEN"
The Chat API uses CloudFront Distributions to add support for custom domain names to the AWS Lambda Function URL, as it is not natively supported. The Auth & Business APIs on the other hand use API Gateway which supports custom domain names natively. However, an API Gateway and a CloudFront Distribution do not support using the same hostname as they both require a CNAME record.
For these two services to share the same domain name, consider using the CloudFront distribution to proxy the API Gateway requests. This would allow both services to use the same domain name, and would also allow the Chat API to use the same domain name as the other services.
In this configuration, the Auth and Business APIs use the paths /auth and
/business respectively on api.awsaistack.com. The Custom Domain
Name Path Mapping was used in the Custom Domain Name support in API Gateway
to use the same domain name but shared across multiple API Gateway instances.
Alternatively, you can use a single API Gateway and map the paths to the respective services. This would allow you to use the same domain name for multiple services, and would also allow you to use the same authorizer for all the services. However, sharing an API Gateway instance may have performance implications at scale, which is why this example uses separate API Gateway instances for each service.
The auth, business-api, and chat-api all validate the user input, and in
the case of chat-api use Zod to validate the schema. Consider including
schema validation on all API requests using a library like Zod, and/or
Express.js middleware.
This example, for simplicity, hosts the static assets from an AWS Lambda Function. This is not recommended for production, and you should consider using a static website hosting service like S3 or CloudFront to host your website. Consider using one of the following plugins to deploy your website:
This example uses a custom authorization method using JWT tokens for the
ai-chat-api service, which doesn't use API Gateway.
The business-api is based on Express.js and uses the authMiddleware method
in the auth-sdk to validate the JWT token.
API Gateway supports Lambda Authorizers which can be used to validate JWT tokens
before the request is passed to the Lambda Function. This is a more robust
solution than the custom method used in this example, and should be considered
for production services. This method will not work for the ai-chat-api service
as it does not use API Gateway.
Using Github Actions this example deploys all the services using Serverless
Compose. This ensures that any changes to the individual services or the
serverless-compose.yml will reevaluate the interdependent parameters. However,
all services are redeployed on any change in the repo, which may not be
necessary.
Consider using a more fine-grained approach to deploying services, such as
only deploying the services that have changed by using the serverless <service> deploy command.
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