It would be nice to have a page that actually loads. This one doesn't. RIP.

I find very little reason to use a pure vector database for enterprise retrieval. We built an enterprise retrieval engine on top of a SQL database with native vector support, and the flexibility is something we cannot ignore. Vector similarity is just one query primitive alongside full text search, filters, joins, ordering and normal relational predicates. Tenant/app/collection isolation becomes part of the query itself. ACLs, document versions, categories, metadata constraints and temporal filters are ordinary predicates rather than something you have to bolt onto a vector store. SQL is already going to be part of almost any enterprise system. Adding a separate vector database introduces another moving part and syncing two system whenever you update your data is the most difficult thing to get right.

This sounds like the Postgres vs. InnoDB argument 10 years later. Postings pointed at physical location (the ANN slot), so every SPFresh rebalance rewrote every index touching that doc. InnoDB solved this by pointing secondary indexes at the PK and eating an extra lookup on read. Curious what that extra lookup costs you when it's an S3 GET instead of a B-tree hop.

"Updating one vector can move hundreds of attributes and their indexes" is basically Uber's 2016 Postgres write amplification post, but for search. Same fix too: stop pointing indexes at where the row lives.

So ANN becomes a secondary index that points at a doc ID, and vector search now needs a hop to complete. Do clusters keep their own copy of the vectors so the search itself stays local, and only result fetch pays the indirection? Otherwise cold p99 seems like it gets worse.

> This write amplification is large enough that our efforts to tune indexing throughput have started to hit diminishing returns.

> don't key on the ANN address. That is precisely the change turbopuffer v3 makes. As you can imagine, it is not a trivial change.

This is a direct parallel to how Postgres and Mysql built indexes.

Your design choice went from a Postgres design pattern to a Mysql one. The difference is the reindexing cost vs the lookup cost - Postgres optimized for lookup and Mysql does for indexing on writes. Or more accurately, Postgres was better with good schema design using joins & mysql was optimized for a bad design with less normalization where many indexes exist for the same table.

Postgres always points an index to a row-id within postgres which is an arbitrary value which changes on each update.

Mysql, always assuming the storage engine is pluggable, points to the primary index entry and adds an extra indirection to the lookup.

This means that you point the mysql index to a stable id, so unless you go update the primary key for a row, you won't have to update the indexes for all the attribute lookups you might have made to data.

I don't do databases any more that much, but the design for NIMBLE file format has a lot of quirks which are relevant to this specific idea (wide tables).

But the old Uber post about switching from Postgres to Mysql to prevent index amplification[1] is a direct mirror to this post.

[1] - https://www.uber.com/us/en/blog/postgres-to-mysql-migration/

the multi-vector duplication thing makes sense, copying every attribute once per vector explodes quickly. what's the new primary index?

Vector databases were always more about retrieval than either vectors or data storage. But the term stuck all too well and companies held on to it a tad too long. Sorry :)

Anyone using pure vector dbs at this day and age is shooting themselves in the foot, but there is more to it than just deprecating vectors.

I built a corrective memory layer for our agents which is using filtering, hybrid/ranked fusion search and strongly typed predicates to provide the LLMs context to correct themselves in case of errors.

Small plug, if anyone wants to try it out- https://polign.com/recall

I struggled quite a bit relying on pure vector DBs, so this is a welcome change. You still need vectors to reach close enough areas to fetch the context though.

Im not full read up on RAG pipelines, but has anyone ever tried to make the database a neural net itself?

I see something like taking an auto encoder, cutting it in half to get the discrete latent space, and then mapping documents across that latent space in terms of threshold values.

When storing documents, you just compute their latent space representation, and for each value in the latent space, you have a map of threshold -> document.

When doing a query, you simply map the query into the space, and then filter each value on the activation threshold.

Then when you do a query, that gets mapped to latent space. Then you sequentially check every v

Meanwile do

That way to index documents, you just have to compute their latent space threshold values.