Face similarity search

Identify the celebrities you looks most similar to using Supabase Vecs.

This guide will walk you through a "Face Similarity Search" example using Colab and Supabase Vecs. You'll identify the celebrities you (or any other person) looks most similar to. You will:

1. Launch a Postgres database that uses pgvector to store embeddings
2. Launch a notebook that connects to your database
3. Load the "ashraq/tmdb-people-image" celebrity dataset
4. Use the face_recognition model to create an embedding for every celebrity photo.
5. Search for similar faces inside the dataset.

Project setup#

Let's create a new Postgres database. This is as simple as starting a new Project in Supabase:

1. Create a new project in the Supabase dashboard.
2. Enter your project details. Remember to store your password somewhere safe.

Your database will be available in less than a minute.

Finding your credentials:

You can find your project credentials inside the project settings, including:

• Database credentials: connection strings and connection pooler details.
• API credentials: your serverless API URL and anon / service_role keys.

Launching a notebook#

Launch our semantic_text_deduplication notebook in Colab:

At the top of the notebook, you'll see a button Copy to Drive. Click this button to copy the notebook to your Google Drive.

Connecting to your database#

Inside the Notebook, find the cell which specifies the DB_CONNECTION. It will contain some code like this:

Replace the DB_CONNECTION with your own connection string for your database, which you set up in first step of this guide.

Stepping through the notebook#

Now all that's left is to step through the notebook. You can do this by clicking the "execute" button (ctrl+enter) at the top left of each code cell. The notebook guides you through the process of creating a collection, adding data to it, and querying it.

You can view the inserted items in the Table Editor, by selecting the vecs schema from the schema dropdown.

Next steps#

You can now start building your own applications with Vecs. Check our examples for ideas.