I'm currently building RAG for our product (using Lucene). What I've found is that embeddings alone don't help much. With hybrid search (BM25+HNSW) they gave me only like +10% boost compared to BM25 alone (on average). In my evaluation datasets, the only case where they helped tremendously was for cases like "a user asks a question in French but the documents are all in English", it went from 6% retrieval to 65% on some datasets.

I got a significant boost (from 65% on average to over 80%) by adding a proper reranker and query rewriting (3 additional phrases to search for).

I think embeddings are overrated in that blog posts often make you believe they are the end of the story. What I've found is that they should be rather treated as a lightweight filtering/screening tool to quickly find a pool of candidates as a first stage, before you do the actual stuff (apply a reranker). If BM25 already works as well as a pre-filtering tool, you don't even need embeddings (with all the indexing headaches).