[VoVAllen]

Hi, I'm the author of the article. In our actual product, VectorChord, we adopted a new quantization algorithm called RaBitQ. The accuracy has not been compromised by the quantization process. We’ve provided recall-QPS comparison curves against HNSW, which you can find in our blog: https://blog.pgvecto.rs/vectorchord-store-400k-vectors-for-1....

Many users choose PostgreSQL because they want to query their data across multiple dimensions, including leveraging time indexes, inverted indexes, geographic indexes, and more, while also being able to reuse their existing operational experiences. From my perspective, vector search in PostgreSQL does not have any disadvantages compared to specialized vector databases so fat.

[nostrebored]

But why are you benchmarking against pgvector HNSW, which is known to struggle with recall and performance at large numbers of vectors?

Why is the graph measuring precision and not recall?

The feature dump is entirely a subset of Vespa features.

This is just an odd benchmark. I can tell you in the wild, for revenue attached use cases, I saw _zero_ companies choose pg for embedding retrieval.