pgvectorscale

ings based semantic search. The point of semantic search is not to match on exact keywords but to match on the /meaning/. If you want exact keyword matching, use full text search. This is something that can also be solved by hybrid search, combining full text search and semantic search and using a re-ranker. PostgreSQL has built in FTS with tsvector. > 3. Performant Filters and Order By’s The authors do not disclose any details about what index they use here or what kind of filtering they are trying to do. As other commenters point out, the StreamingDiskANN in the pgvectorscale extension [0] (complement to pgvector, you can use them together) improves on performance and accuracy of filtering vs pgvector HNSW (see details in [1]). >4. Support for sparse vectors, BM25, and other inverse document frequency search modes This is probably the most fair point in the post. But there exists projects like pg_search from ParadeDB which bring bm_25 to Postgres and help solve this [2] Lastly, I respect companies trying to provide real-world examples of the trade-offs of different systems, and so thank the authors for sharing their experience and spurring discussion. Disclaimer: I work at Timescale, where we offer pgvector, and also made other extensions for AI/ vector workloads on PostgreSQL, namely pgvectorscale, and pgai. I've tried to be as even in my analysis as possible but as with everything on the internet, you can make up your own mind and decide for yourself. [0]: https://github.com/timescale/pgvectorscale/ [1]: https://www.timescale.com/blog/how-we-made-postgresql-as-fas... [2] https://github.com/paradedb/paradedb