🪣 Switching to S3 Storage

This guide provides instructions on how to switch the default local storage in HridaAI config to Amazon S3.

Prerequisites

In order to follow this tutorial, you must have the following:

• An active AWS account
• An active AWS Access Key and Secret Key
• IAM permissions in AWS to create and put objects in S3
• Docker installed on your system

What is Amazon S3

Direct from AWS' website:

"Amazon S3 is an object storage service that offers industry-leading scalability, data availability, security, and performance. Store and protect any amount of data for a range of use cases, such as data lakes, websites, cloud-native applications, backups, archive, machine learning, and analytics. Amazon S3 is designed for 99.999999999% (11 9's) of durability, and stores data for millions of customers all around the world."

To learn more about S3, visit: Amazon S3's Official Page

How to Set-Up

1. Required environment variables

In order to configure this option, you need to gather the following environment variables:

hrida-ai Environment Variable	Example Value
S3_ACCESS_KEY_ID	ABC123
S3_SECRET_ACCESS_KEY	SuperSecret
S3_ENDPOINT_URL	https://s3.us-east-1.amazonaws.com
S3_REGION_NAME	us-east-1
S3_BUCKET_NAME	my-awesome-bucket-name

• S3_ACCESS_KEY_ID: This is an identifier for your AWS account's access key. You get this from the AWS Management Console or AWS CLI when creating an access key.
• S3_SECRET_ACCESS_KEY: This is the secret part of your AWS access key pair. It's provided when you create an access key in AWS and should be stored securely.
• S3_ENDPOINT_URL: This URL directs to your S3 service endpoint and can typically be found in AWS service documentation or account settings.
• S3_REGION_NAME: This is the AWS region where your S3 bucket resides, like "us-east-1". You can identify this from the AWS Management Console under your S3 bucket details.
• S3_BUCKET_NAME: This is the unique name of your S3 bucket, which you specified when creating the bucket in AWS.

For a complete list of the available S3 endpoint URLs, see: Amazon S3 Regular Endpoints

See all the Cloud Storage configuration options in the hrida-ai Cloud Storage Config documentation.

2. Run hrida-ai

Before we launch our instance of hrida-ai, there is one final environment variable called STORAGE_PROVIDER we need to set. This variable tells hrida-ai which provider you want to use. By default, STORAGE_PROVIDER is empty which means hrida-ai uses local storage.

Storage Provider	Type	Description	Default
local	str	Defaults to local storage if an empty string (' ') is provided	Yes
s3	str	Uses S3 client library and related environment variables mentioned in Amazon S3 Storage	No
gcs	str	Uses GCS client library and related environment variables mentioned in Google Cloud Storage	No

To use Amazon S3, we need to set STORAGE_PROVIDER to "S3" along with all the environment variables we gathered in Step 1 (S3_ACCESS_KEY_ID, S3_SECRET_ACCESS_KEY, S3_ENDPOINT_URL, S3_REGION_NAME, S3_BUCKET_NAME).

Here, I'm also setting the ENV to "dev", which will allow us to see the hrida-ai Swagger docs so we can further test and confirm the S3 storage set-up is working as expected.

docker run -d \
  -p 3000:8080 \
  -v hrida-ai:/app/backend/data \
  -e STORAGE_PROVIDER="s3" \
  -e S3_ACCESS_KEY_ID="ABC123" \
  -e S3_SECRET_ACCESS_KEY="SuperSecret" \
  -e S3_ENDPOINT_URL="https://s3.us-east-1.amazonaws.com" \
  -e S3_REGION_NAME="us-east-1" \
  -e S3_BUCKET_NAME="my-awesome-bucket-name" \
  -e ENV="dev" \
  --name hrida-ai \
  ghcr.io/hrida-ai/hrida-ai-studio:main

3. Test the set-up

Now that we have hrida-ai running, let's upload a simple Hello, World text file and test our set-up.

And confirm that we're getting a response from the selected LLM.

Great! Looks like everything is worked as expected in hrida-ai. Now let's verify that the text file was indeed uploaded and stored in the specified S3 bucket. Using the AWS Management Console, we can see that there is now a file in the S3 bucket. In addition to the name of the file we uploaded (hello.txt) you can see the object's name was appended with a unique ID. This is how hrida-ai tracks all the files uploaded.

Using hrida-ai's swagger docs, we can get all the information related to this file using the /api/v1/files/{id} endpoint and passing in the unique ID (4405fabb-603e-4919-972b-2b39d6ad7f5b).