Deploy Chatbot Bedrock models using the AWS CDK

The seeds of a machine learning (ML)

Prerequisites

You must have the following prerequisites:

• An AWS account
• The AWS CLI v2
• Python 3.6 or later
• node.js 14.x or later
• The AWS CDK v2
• Docker v20.10 or later

You can deploy the infrastructure in this tutorial from your local computer or you can use AWS Cloud9 as your deployment workstation. AWS Cloud9 comes pre-loaded with AWS CLI, AWS CDK and Docker. If you opt for AWS Cloud9, create the environment from the AWS console.

The estimated cost to complete this post is $50, assuming you leave the resources running for 8 hours. Make sure you delete the resources you create in this post to avoid ongoing charges.

Install the AWS CLI and AWS CDK on your local machine

If you don’t already have the AWS CLI on your local machine, refer to Installing or updating the latest version of the AWS CLI and Configuring the AWS CLI.

Install the AWS CDK Toolkit globally using the following node package manager command:

npm install -g aws-cdk-lib@latest

Run the following command to verify the correct installation and print the version number of the AWS CDK:

cdk --version

Make sure you have Docker installed on your local machine. Issue the following command to verify the version:

docker --version

Clone and set up the AWS CDK application

On your local machine, clone the AWS CDK application with the following command:

git clone https://github.com/caguida/cdk_bedrock_tchat.git

Navigate to the project folder:

cd chatbotPack

Before we deploy the application, let's review the directory structure:

The stack folder contains the code for each stack in the AWS CDK application.

Setup a virtual environment

This project is set up like a standard Python project. Create a Python virtual environment using the following code:

python3 -m venv .venv

Use the following command to activate the virtual environment:

source .venv/bin/activate

If you’re on a Windows platform, activate the virtual environment as follows:

.venv\Scripts\activate.bat

After the virtual environment is activated, upgrade pip to the latest version:

python3 -m pip install --upgrade pip

Install the required dependencies:

pip install -r requirements.txt

Before you deploy any AWS CDK application, you need to bootstrap a space in your account and the Region you’re deploying into. To bootstrap in your default Region, issue the following command:

cdk bootstrap

If you want to deploy into a specific account and Region, issue the following command:

cdk bootstrap aws://ACCOUNT-NUMBER/REGION

For more information about this setup, visit Getting started with the AWS CDK.

AWS CDK application stack structure

The AWS CDK application contains multiple stacks as shown in the following diagram.

You can list stacks in your CDK application with the following command:

$ cdk list

Other useful AWS CDK commands:

• cdk ls - Lists all stacks in the app
• cdk synth - Emits the synthesized AWS CloudFormation template
• cdk deploy - Deploys this stack to your default AWS account and Region
• cdk diff - Compares the deployed stack with current state
• cdk docs - Opens the AWS CDK documentation

The next section shows you how to deploy the AWS CDK application.

Deploy the AWS CDK application

The AWS CDK application will be deployed to the default Region based on your workstation configuration. If you want to force the deployment in a specific Region, set your AWS_DEFAULT_REGION environment variable accordingly.

If necessary, change the arn role to access bedrock role_arn in app.py.

At this point, you can deploy the AWS CDK application. First, launch the kendra stack:

cdk deploy KendraS3StackID

If you are prompted, enter y to proceed with the deployment. You should see a list of AWS resources that are being provisioned in the stack.

In stack KendraS3StackID, we create a kendra stack whose index name is KendraIndex . We also create an S3 bucket named KendraS3Bucket, and then a datasource named KendraDataSource. Please load the S3 bucket with your documents to make them accessible to Kendra.

Now, launch the VPC Network stack:

cdk deploy VpcNetworkStack

After, launch your local docker application first and run the following command to build and deploy the lambda function image, and the link of API Gateway

cdk deploy ChatUserStack

You can collect the address of your API Gateway and test it with insomnia.

To finish, launch the streamlit web application stack:

cdk deploy WepAppStack

After execution, the link to your web application would be available.

You can delete the entire stack

cdk destroy --all 

or

cdk destroy WepAppStack
cdk destroy ChatUserStack
cdk destroy VpcNetworkStack
cdk destroy KendraS3StackID

Pay attention:

Please note that bedrock may not have access to the file you've uploaded to the S3 bucket. This is due to the datasource. In this case, via the aws console, access Kendra, choose the kendra index KendraIndex and recreate a new datasource. You can use the same bucket KendraS3Bucket

Note:

I've added a folder called draft which contains some draft functions for future.