Tokenizing Product Descriptions with OpenAI Embeddings and Supabase

This guide demonstrates how to tokenize product descriptions from a Supabase table for semantic meaning using OpenAI’s embedding model.

We will cover:

• how to set up Supabase,
• retrieve data,
• generate embeddings,
• store them efficiently in the database,
• and perform semantic searches using vector similarity.

Each step includes sample code and clear explanations. Are you ready?

Step 1: Setting Up Supabase and Retrieving Product Descriptions

Before generating embeddings, set up your Supabase database to store and handle vector data:

• Enable the PNG vector extension: Supabase is built on PostgreSQL, which allows storing and querying embedding vectors. Enable it via the Supabase web dashboard (under Database → Extensions) or by executing SQL in the SQL editor. For example:

-- Enable vector extension for vector embeddings
CREATE EXTENSION IF NOT EXISTS vector;

• Prepare your table: If you already have a product table with descriptions, add a new column to hold the embedding vector. The OpenAI text-embedding-ada-002 model returns 1536-dimensional vectors, so define the column with that size. For example: