Exercise - Generate vector embeddings with Azure OpenAI
The first step to implementing semantic search is to generate and store embeddings. In this exercise, you install the vector and azure_ai extensions in an Azure Database for PostgreSQL flexible server, then apply the extensions to store embedding vectors generated by Azure OpenAI. Finally, you get query string embedding vectors and run a vector similarity search.
Note
To complete this exercise, you will need an Azure subscription, and you need to be approved for Azure OpenAI access. If you need Azure OpenAI access, apply at the Azure OpenAI limited access page.
Launch the exercise and follow the instructions.
