Exercise - Optimize vector search performance in Azure Database for PostgreSQL
In this exercise, you deploy an Azure Database for PostgreSQL instance and optimize it for vector search workloads. You create test data with vector embeddings, analyze baseline performance, build and compare IVFFlat and HNSW indexes, and tune search parameters. These techniques are essential for production AI applications that require fast similarity search across large datasets.
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
- Create a test dataset with vector embeddings
- Analyze baseline vector search performance without indexes
- Create and compare IVFFlat and HNSW vector indexes
- Tune index parameters to balance speed and recall
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.
- 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
