ParadeDB is AGPL so not generally available on the hyperscalars. However, you can use https://github.com/timescale/pg_textsearch on Azure HorizonDB (and likely soon Flex). Disclosure: I'm the pg_textsearch maintainer and now at Azure.
I didn't quite follow your comment about vector support, are you asking for something beyond what pgvector + diskann provide (both available on Azure)?
ParadeDB maintainer here :). We would happily make it available on Azure (and all other cloud providers!) if there were a way for us to earn a living in doing so.
Fyi, we are in discussion with some hyperscalers on making this possible.
> I didn't quite follow your comment about vector support, are you asking for something beyond what pgvector + diskann provide (both available on Azure)?
You dont support ultra-wide vectors from the largest embeddings models. We have to wierd stuff like chop up vectors across fields.
Some thing I've learned, but rarely seen explained anywhere: Storing the vectors is most likely not an issue, mostly likely you're having a problem with the indexes on top of them in which case you can use quantized vector indexes[0] (handled by pgvector) which will get past the limits imposed by PostgreSQL.
I had to switch off pgvecto.rs at some point and figured that out.
I don't have specific experience with the Azure environment here, but this probably applies if you have access to pgvector.
Comments
ParadeDB is AGPL so not generally available on the hyperscalars. However, you can use https://github.com/timescale/pg_textsearch on Azure HorizonDB (and likely soon Flex). Disclosure: I'm the pg_textsearch maintainer and now at Azure.
I didn't quite follow your comment about vector support, are you asking for something beyond what pgvector + diskann provide (both available on Azure)?
ParadeDB maintainer here :). We would happily make it available on Azure (and all other cloud providers!) if there were a way for us to earn a living in doing so.
Fyi, we are in discussion with some hyperscalers on making this possible.
You dont support ultra-wide vectors from the largest embeddings models. We have to wierd stuff like chop up vectors across fields.
Some thing I've learned, but rarely seen explained anywhere: Storing the vectors is most likely not an issue, mostly likely you're having a problem with the indexes on top of them in which case you can use quantized vector indexes[0] (handled by pgvector) which will get past the limits imposed by PostgreSQL.
I had to switch off pgvecto.rs at some point and figured that out.
I don't have specific experience with the Azure environment here, but this probably applies if you have access to pgvector.
[0]: Types of indexes + number of bits supported at bottom of this section: https://github.com/pgvector/pgvector#hnsw