It's not like a simple embedding search takes that much longer to implement. Especially on short descriptions where you don't have to deal with chunking. And if you let an LLM write the code it's even less of a difference. Combine that with embedding search promising to solve all your search problems, and I understand why people often skip over full text search and go straight to embeddings

Even that is an oversimplification unless you are doing something very basic.

Volume of documents, size of documents, versioning, frequency of update, documents similar or overlapping information, how much or exactly what you need for the LLM to understand, AI friendly documents, who has access and at what level, blue teaming, red teaming, multi-lingual, does the LLM know the domain language of the user and documents.

I probably missed a few things even with that.

I think Bitwarden implemented some vector search in their password search feature ... totally annoying it gives me back all kinds of stuff that I don't care.

I want fuzzy search like 95% of time and then I might consider having additional list of things that can be suggested by vector search.

I wish everyone thought like you, in my experience unfortunately it's not the case