Can someone tell me if these vector search things have anything in common with postgres's text search vectors, which have been implemented in postgres for quite a while now?
A "vector" in vector search solutions is a dense vector generated by a transformer model. A sentence like 'a fat cat sat on a mat and ate a fat rat' put through a model becomes an array of floating-point numbers like [0.183, -0.774, ...], with hundreds of values (often 768). The point is that this vector is positioned in a 768-dimensional space in close proximity to semantically similar sentences. So then searching by semantic meaning is a simple (well...) exercise in measuring the distance between your query and the surrounding vectors.
We have a whole course on the topic, coincidentally also on the front page of HN today: https://www.pinecone.io/learn/nlp
At what point does it make sense to switch to using vector search solutions (compared to keyword searches)? Obviously Google et al need it, but for regular apps, maybe academic repositories and so on, is there a threshold in which we can start the discussion to switch?
Or phrased another way, when do we know we should be looking at vector search solutions to enhance the search in our application?
Either because your catalog has grown to the point where a traditional keyword search makes it harder to find relevant items, or because users are increasingly expecting their apps to just "know what they mean" (like Google, Spotify, Amazon, and Netflix do).
So we've stuck with simple keyword search with filters to drill into specific categories within results, all pretty vanilla on a relational database structure.
Just wondering if there is a "volume" heuristic to this - I'd like to explore this more but realistically sometimes the academic user-base has big dreams with severe practical limitations.
Just a clarification that vectors are much broader than text vectors generated by transformer models. A more common application has been recommender models built on matrix factorization and other similar approaches. Word 2 Vec was another popular way to generate vectors dating back to 2013. Vectors are a very general approach with many benefits regardless of how they were generated. That is what makes these vector search libraries so exciting.
I wrote an article about the power of matrix factorization vectors for music recommendations back in 2016: https://tech.iheart.com/mapping-the-world-of-music-using-mac...
We also discussed how we used convolutional neural networks (deep networks, but not transformers) to build vectors on the acoustic content of music: https://tech.iheart.com/mapping-the-world-of-music-using-mac...
This is for finding nearby N-dimensional points, where N is typically greater than 50 and the point is the output of an ML or NN process.