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:

The seed method generates embeddings for the images in the images folder and upserts them into a collection in Supabase Vector.
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/_/settings/database > Connection string > URI

README.md

Image Search with Amazon Bedrock and Supabase Vector

Setup

Run locally

Generate the embeddings and seed the collection

Perform a search

Run on hosted Supabase project

Attributions

Models

Images