Nice article. I am interested to see this stuff blended with the GPU/ML powered databases rather than the TF-IDF of decades ago - as well as it works
These two features would allow you to do visual search, semantic text search, recommendations, learning to rank and etc.
We've frequently had the same dream of adding more native support for nearest-neighbor type queries, since that is the workhorse of so many useful techniques in the modern NLP stack.
Right now, we have lots of dense vectors stored in massive toast tables in PG. It's faster to fetch them rather than recompute them, especially since there are a number of preprocessing steps that limit what we pay attention to.
The discussion here about full text search versus semantic search is interesting. In our experience, both are highly relevant. Sometimes it's most useful for our customers to segment their conversation data by exact text matches, and other times semantic clustering is most effective. I think there's plenty of reason to offer both kinds of capabilities.
https://www.elastic.co/guide/en/elasticsearch/reference/curr...
The vectors are only used for scoring, not matching, but they are working on a ANN model for that.