Enable semantic search in Azure Database for PostgreSQL

Module
9 Units

Intermediate

Developer

Azure Database for PostgreSQL

This module explores semantic search, embedding vectors, vector similarity, and using Azure Database for PostgreSQL - Flexible Server with PostgreSQL Vector and Azure AI extensions to generate, store, and query rows with semantic search.

Learning objectives

Examine the concepts of semantic search & embedding vectors.
Understand the difference between lexical search & semantic search.
Evaluate the capabilities provided by the azure_ai extension for PostgreSQL.
Install and explore the vector and azure_ai extensions in an Azure Database for PostgreSQL - Flexible Server database.

Prerequisites

Before starting this module, you should have experience working with PostgreSQL databases, writing SQL queries, and standard lexical search.

Introduction min
Understand semantic search min
Store vectors in Azure Database for PostgreSQL min
Create embeddings with the Azure AI extension min
Exercise - Generate vector embeddings with Azure OpenAI min
Explore semantic search use cases min
Exercise - Create a search function for a recommendation system min
Knowledge check min
Summary min