I run a super niche academic database - there is often that expectation that "search should know what I mean", yet there isn't enough data (in my opinion) to make any semantic search meaningful. There are about 23k data objects that can interlink, 50% of those belong to one specific data type, the rest are split.

So we've stuck with simple keyword search with filters to drill into specific categories within results, all pretty vanilla on a relational database structure.

Just wondering if there is a "volume" heuristic to this - I'd like to explore this more but realistically sometimes the academic user-base has big dreams with severe practical limitations.

You can train a good sentence transformer on ~10K sentences using TSDAE (an unsupervised training approach), covered in Chapter 7 here: https://www.pinecone.io/learn/unsupervised-training-sentence...