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Frame

Deploying Frame

Frame deploys several key components in your AWS account using AWS CDK (Cloud Development Kit).

Full details of the infrastructure to be deployed for each individual stack can be found in our Github repository. Please ensure you review these documents before deploying the infrastructure.

Prerequisites

Deployment Process

Step 1: Get and Install the Code

Clone the repository and move into the top-level directory:

Terminal window
git clone https://github.com/Frame-Platform/Frame.git
cd Frame

Step 2: Build the Project Environment

Run the below command:

Terminal window
npm run build:all

This will run the build-all.js script to:

  • Set up the project environment by installing the required dependencies for the CLI and the CDK.
  • Build the CLI by compiling the TypeScript files into JavaScript.
  • Link the CLI globally on your system (to enable the use of the document-embedding commands referenced below).
  • Build the Lambda functions referenced in the CDK (packaging them up into zip files ready for deployment).

Step 3: Configure Your Settings

Run the below command:

Terminal window
document-embedding init

This command will guide you through the initialization process. In particular, you will be prompted to provide the following information:

  • AWS Access Key ID (can be obtained in the AWS console)
  • AWS Secret Access Key (can be obtained in the AWS console)
  • AWS Region
  • Database Credentials (used for setup)
    • PostgreSQL Username
    • PostgreSQL Password
    • PostgreSQL Database Name

Step 4: Deploy the Infrastructure to AWS

Run the below command to deploy Frame’s infrastructure to your AWS account:

Terminal window
document-embedding deploy

During the deployment:

  • The CDK will create all necessary AWS resources. This process can take 10-15 minutes, so please be patient.
  • Upon completion, you’ll receive confirmation of successful deployment along with important endpoints or access information for the created resources.
  • In particular, you will receive confirmation of the API Gateway URL and your specific API Access Key ID details. You will need to locate the API Key associated with the API Key ID (in the AWS Management Console) in order to connect to Frame’s API endpoints via the Frame SDK client.

Step 5: Set Up The Database

Run the below command:

Terminal window
npm run database:setup

This creates an optimized database table for storing vector embeddings.

Step 6: Enable Amazon Bedrock Access

For the AI models to work, you need to enable Bedrock access.

Go to the Amazon Bedrock console. Details of how to enable access can be found here.

At the time of writing, the steps are:

  1. In the left menu, select “Model access” under “Bedrock configurations”.
  2. Select “Enable Specific Models”.
  3. Select the check box next to “Amazon Titan Multimodal Embeddings G1” model.
  4. Click “Next” → “Submit”.
  5. Wait for “Access granted” status (may take a few minutes)

Step 7: Verify the Deployment

Upload a test image using our SDK client to verify everything works correctly. See our SDK documentation for further information.

Step 8: Destroying the Infrastructure

When you no longer need the Frame image-embedding pipeline, you can remove all deployed resources with:

Terminal window
document-embedding destroy

Important:

  • This action will permanently delete all resources created by the deployment
  • All stored data, including images and embeddings, will be lost
  • This operation cannot be undone, so proceed with caution

Usage

With your deployment complete, you can:

  • Upload images for automatic embedding
  • Search for similar images using text descriptions
  • Find visually similar images
  • Query, update, or delete your image data

Refer to the SDK documentation for integration with your applications.

Monitoring and Troubleshooting

Your deployment includes monitoring through AWS CloudWatch:

  • Check Lambda function logs for errors
  • Monitor database connections
  • Track S3 storage operations

To access logs:

  • Go to the AWS CloudWatch console
  • Select the log group for the component you’re investigating
  • Review logs for errors or performance issues