Semab Tariq explains index options for Postgres’s vector database:
This blog is part of our pgvector blog series. If you haven’t checked out the first blog, I recommend going through it first, where I dive into important concepts of pgvector and AI applications in detail. I provided a real-world example illustrating how you can perform searches based on the meaning of words rather than the words themselves. You can find it on the link here
In this blog, We will explore additional details about the indexes supported in pgvector. We will discuss how indexes are built in the backend, and the various parameters associated with these indexes, and guide you on selecting the most suitable index based on your requirements. Finally, we will assess which index offers the best recall rate for our search query across our dataset of one million records sourced from Wikipedia. Let’s dive into that
Click through to learn more about the two index types available.