Exercise - Implement vector search on Azure Database for PostgreSQL
In this exercise, you build a product similarity search application using Azure Database for PostgreSQL and the pgvector extension. You enable vector storage capabilities, create a database schema for products with embeddings, load sample data through a Flask web application, and perform similarity searches to find related products. This pattern provides a foundation for building recommendation systems, semantic search features, and other AI-powered applications.
Tasks performed in this exercise:
- Download project starter files and configure the deployment script
- Deploy an Azure Database for PostgreSQL Flexible Server with Microsoft Entra authentication
- Complete the Flask application code while the server deploys
- Enable the pgvector extension and create the products table schema
- Run the Flask application to load products and perform similarity searches
- Add new products and observe how similarity results change
This exercise takes approximately 30 minutes to complete.
Before you start
To complete the exercise, you need:
- An Azure subscription with the permissions to deploy the necessary Azure services. If you don't already have one, you can sign up for one.
- Visual Studio Code on one of the supported platforms.
- The latest version of the Azure CLI.
- Python 3.12 or greater.
- PostgreSQL command-line tools (psql)
Get started
Select the Launch Exercise button to open the exercise instructions in a new browser window. When you're finished with the exercise, return here to:
- Complete the module
- Earn a badge for completing this module
