So many missing details...

Different vector indexes have very different recall and even different parameters for each dramatically impact this.

HNSW can have very good recall even at high vector counts.

There's also the embedding model, whether you're quantizing, if it's pure rag vs hybrid bm25 / static word embeddings vs graph connections, whether you're reranking etc etc