I Built a Vector Search Engine from Scratch — Here's What I Learned
Sameer Ahmed shares his experience building a vector search engine from scratch, focusing on the HNSW algorithm. He emphasizes the importance of understanding a system by creating it rather than relying on existing solutions. His implementation achieved a high recall rate, demonstrating the effectiveness of approximate nearest neighbor search.
- ▪Sameer Ahmed built a vector search engine called Vektr using the HNSW algorithm.
- ▪The implementation achieved a recall@10 of 0.984, meaning 98.4% of queries returned all true nearest neighbors in the top 10 results.
- ▪HNSW organizes vectors into a hierarchical graph, allowing for efficient approximate nearest neighbor searches.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3965551) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Sameer Ahmed Posted on Jun 3 I Built a Vector Search Engine from Scratch — Here's What I Learned #java #machinelearning #algorithms #programming 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.
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