A full-text search index using BM25 or similar may actually work a lot better for many RAG applications.
I wrote up some notes on building FTS-based RAG here: https://simonwillison.net/2024/Jun/21/search-based-rag/
The other problem is that embeddings search can miss things that a direct keyword match would have caught. If you have key terms that are specific to your corpus - product names for example - there's a risk that a vector match might not score those as highly as BM25 would have so you may miss the most relevant documents.
Finally, embeddings are much more black box and hard to debug and reason about. We have decades of experience tweaking and debugging and improving BM25-style FTS search - the whole field of "Information Retrieval". Throwing that all away in favour of weird new embedding vectors is suboptimal.
Why not have a similarity threshold? Say, if the distance is below 0.7, do not accept the search result.
That seems like a better stacking of the technologies even now
I did similar in 2019 but typically in reverse, FTS, and a dual tower model to rerank. Vector search was an additional capability but never augmented the FTS.
So vector search would reduce the space to like 10k documents and then we'd take the document ids and FTS acted as the final authority on the ranking.