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.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/sameer_ahmed_/i-built-a-vector-search-engine-from-scratch-heres-what-i-learned-4lh5 |
| Publication time | Wed, 03 Jun 2026 11:01:02 +0000 |
| Retrieval time | 2026-06-03T11:12:03.049Z |
| Last seen | 2026-06-03T11:12:03.049Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | RlTvHBawz3KT |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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