Exercise - Build a semantic search application with Azure Cosmos DB for NoSQL
In this exercise, you implement vector similarity search using Azure Cosmos DB for NoSQL. Vector search enables semantic matching by comparing high-dimensional vector representations of text, finding relevant results even when exact terms don't match. You configure a container with vector embedding and indexing policies, load support tickets with pre-computed embeddings, and execute similarity queries using the VectorDistance function. This pattern provides a foundation for building AI applications that perform semantic search, such as finding similar support cases to help resolve customer issues faster.
Tasks performed in this exercise:
- Download project starter files and configure the deployment script
- Deploy an Azure Cosmos DB for NoSQL account with vector search capability
- Build Python functions for vector similarity search
- Create a container with vector embedding and indexing policies
- Test vector search using a Flask web application
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.
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
