You've built a vector database
You built a vector database. The tower, bottom to top: heaps → seeded levels → SEARCH-LAYER → SELECT-HEURISTIC → INSERT → K-NN-SEARCH → recall under a budget → payload filtering → persistence → int8 quantization → the full index at scale. You implemented the engine inside Qdrant, Pinecone, Weaviate, and FAISS — straight from Malkov & Yashunin's paper — matched its recall under a hard ops budget, and added the three things that make it a product.
recall@10 0.94 · 157 comparisons/query vs N brute force · 7.6x fewer ops · int8 4x smaller · segment reload exact — PASS. You did not import a vector DB. You are the vector DB.
What's next: rag-pipeline plugs this index into a real retrieval system. The stretch goals from here are the parts production systems add on top — filter-aware edges per payload value, multi-segment merges, and product quantization for even denser storage.