You built semantic search
Recap the journey: dot → l2norm → cosine → scoreAll → topK → search → recall@10-vs-exact-NN = 1.000 (correctness) → nDCG@10 and MRR vs qrels (quality) → a CLI that answers questions over a corpus. You can now read any vector-DB's API docs and know exactly what each call does, and you have the metric vocabulary to evaluate any retriever on earth — including the discipline of never reporting a self-consistency recall as if it were retrieval quality.
correctness: recall@10 vs exact-NN = 1.000 — PASS
quality: nDCG@10 ≈ 0.77 · MRR ≈ 0.77 — PASS
(real all-MiniLM on SciFact: nDCG@10 ≈ 0.645)
Where this goes next
vector-db (make this scale with HNSW — hold nDCG, slash latency, measure recall vs the exact NN you just built) → rag-pipeline (chunk + retrieve + assemble context) → reranker (a cross-encoder that boosts nDCG further) → embedding-server (serve embedQuery as a batched API). Every one of them stands on what you built here.