Creating embeddings for PostgreSQL data can unlock various applications such as semantic search, recommendation systems, and generative AI. The process involves generating mathematical representations of data that machines can process, enabling machines to grasp the deeper intent behind user queries. A robust system for managing embeddings should maintain the original table without modification, update embeddings when rows change, ensure resilience against network failures, and automatically sync with applications that interact with the table. The Timescale Vector Python library provides a simple way to create and manage embeddings using the PgVectorizer class, which can be used to embed data stored in PostgreSQL tables and leverage semantic and hybrid search in applications.