I agree too. My impression is that almost all RAG tutorials _only_ talk about vector DBs, when these are not strictly required for Retrieval Augmented Generation. I'm guessing vector DBs are useful when you have massive amounts of documents on diverse topics.

Some gotchas I experienced (but I might be using the wrong embedding/vector DB: spaCy/FAISS):

- Short user questions might result a low signal query vector, e. g. user : "Who is Keanu Reeves?" -> false positives on Wikipedia articles which only contain "Who is"

- Typos and formatting affects the vectorization, a small difference might lead to a miss, e.g. "Who is Keanu Reeves?" -> match, "Who is keanu Reeves?" -> no match, no match with any other capitalization.

If there's only a single document, a simple keyword search might lead to better results.

In my experience, false positives (retrieving an irrelevant text and generating completely wrong answer) are a bigger problem than negatives (not retrieving text, possibly can't answer question).

Has somebody experience with Apache Lucene / Solr or Elasticsearch?