[jkb79]

Disclaimer, I'm a developer working on the Vespa.ai project. One reason is that we simply don't have DevRel teams or marketing teams, but we still have decent interest, from large companies like Spotify using Vespa in production for semantic search (at scale) https://engineering.atspotify.com/2022/03/introducing-natura...

On topic: IMHO a better title of the blog post would be:

Do you actually need to enable approximate vector search?

Which is an excellent question, because, introducing approximation also introduces accuracy degradation, plus that you need an ANN algorithm, which also has tradeoffs.

In Vespa, you can choose between exact and approximate, with related tradeoffs such as recall (accuracy), search speed, indexing throughput and resource footprint. Just start easy with exact search, then it's just change the schema definition of the tensor field to include `index` and Vespa will build the necessary data structures for enabling approximation.