I found that grep actually outperformed vector search for many queries. The only thing I was missing was when I didn't know how exactly to phrase something (the exact keyword to use).

Do keyword search systems have workarounds for this? My own idea was for each keyword to generate a list of neighbor keywords in semantic space. I figured with such a dataset, I'd get something approximating vector search for free.

I made some attempts at that (found neighbors by their proximity in text), but I ended up with a lot of noise (words that often go together without having the same meaning). So I'd probably have to use actual embeddings instead.

More generally, any suggestions for full-text indexing? Elasticsearch seems like overkill. I built my own keyword search in Python (simple tf-idf) which was surprisingly easy. (Long-term project is to have an offline copy of a useful/interesting subset of the internet. Acquiring the datasets is also an open question. Common Crawl is mostly random blogs and forum arguments...)