I Built a Vector Search Engine from Scratch — Here's What I Learned

I Built a Vector Search Engine from Scratch — Here's What I Learned

Implementing HNSW (Hierarchical Navigable Small World) graphs, hybrid BM25 + dense retrieval, HyDE query rewriting, and atomic index persistence — achieving recall@10 = 0.984.

Why Build Your Own Vector Search?

When I started building Vektr — a RAG (Retrieval-Augmented Generation) engine — I had a choice: use an existing vector database like Pinecone, Weaviate, or FAISS, or build my own.

I chose to build my own. Not because existing solutions are bad (they're excellent), but because you don't truly understand a system until you've built it.