Image Search with Amazon Bedrock and Supabase Vector

In this example we're implementing image search using the Amazon Titan Multimodal Embeddings G1, a set of pre-trained high-performing image, multimodal, and text model, accessible via a fully managed API.

We're implementing two methods in the /image_search/main.py file:

1. The seed method generates embeddings for the images in the images folder and upserts them into a collection in Supabase Vector.
2. The search method generates an embedding from the search query and performs a vector similarity search query.

Setup

• Install poetry: pip install poetry
• Activate the virtual environment: poetry shell
  • (to leave the venv just run exit)
• Install app dependencies: poetry install

Run locally

Generate the embeddings and seed the collection

• supabase start
• poetry run seed
• Check the embeddings stored in the local Supabase Dashboard: http://localhost:54323/project/default/editor > schema: vecs

Perform a search

• poetry run search "bike in front of red brick wall"

Run on hosted Supabase project

• Set DB_CONNECTION with the connection string from your hosted Supabase Dashboard: https://supabase.com/dashboard/project/_/database/settings > Connection string > URI

Attributions

Models

Amazon Titan Multimodal Embeddings G1

Images

Images from https://unsplash.com/license via https://picsum.photos/