{"id":110134,"date":"2026-05-25T11:45:00","date_gmt":"2026-05-25T11:45:00","guid":{"rendered":"https:\/\/www.red-gate.com\/simple-talk\/?p=110134"},"modified":"2026-05-13T15:59:53","modified_gmt":"2026-05-13T15:59:53","slug":"how-to-automate-vector-embeddings-with-pgai-vectorizer-in-postgresql","status":"publish","type":"post","link":"https:\/\/www.red-gate.com\/simple-talk\/databases\/postgresql\/how-to-automate-vector-embeddings-with-pgai-vectorizer-in-postgresql\/","title":{"rendered":"How to automate vector embeddings with pgai Vectorizer in PostgreSQL"},"content":{"rendered":"\n

While pgvector enables powerful semantic search, it doesn\u2019t automatically keep embeddings in sync when your data changes, requiring manual updates. The pgai Vectorizer automatically keeps PostgreSQL vector embeddings in sync by generating and updating them whenever your data changes, removing the need for manual regeneration with pgvector. <\/strong><\/p>\n\n\n\n

It runs in the background using a worker that processes changes via queues, triggers, and embedding APIs. This makes it easy to build real-time semantic search in PostgreSQL using pgvector and TigerData’s pgai tools.<\/strong> Learn everything you need to know in this guide.<\/strong><\/p>\n\n\n\n

It’s no secret that PostgreSQL now stores vector embeddings of unstructured data using pgvector<\/a>, enabling both relational and semantic search. When it comes to keeping your source data and corresponding AI-generated embeddings in sync during changes, however, pgvector<\/a> falls short.<\/p>\n\n\n\n

Simply put, it requires you to manually regenerate the vector embeddings to mirror any changes made in your PostgreSQL database. It doesn’t happen automatically.<\/p>\n\n\n\n

Thankfully, the pgai Vectorizer<\/a> tool, created by Timescale (now TigerData), is here to save the day. With a SQL<\/a> command, it creates AI-generated vector embeddings<\/a> and regenerates them when your source data changes.<\/p>\n\n\n\n

Timescale also provides Docker<\/a> images to quickly set up a PostgreSQL environment that is ready for pgai Vectorizer. In this article, I’ll use these images to demonstrate exactly how the tool works.<\/p>\n\n\n\n

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Before you continue reading…<\/strong>
Are you new to using pgvector in PostgreSQL? If so, please first read this article<\/a>. It explains how pgvector works and how semantic search is handled in it.<\/p>\n\n<\/div>\n<\/div> \n\n\n

What is the pgai Vectorizer tool for PostgreSQL?<\/h2>\n\n\n\n

The pgai Vectorizer<\/a> tool uses pgvector<\/a> under the hood to store and manage vector embeddings in PostgreSQL<\/a>. It leverages pgai\u2019s SQL functions to define how embeddings are generated – specifying the embedding provider to use, the source table, the column to load raw data from to embed, formatting, and so on. It runs outside your database and is always on standby.<\/strong><\/p>\n\n\n\n

When you create a Vectorizer, it processes the embedding asynchronously, as follows:<\/p>\n\n\n

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