Vector search tuning guide
Developers can improve search accuracy by 0.28 by adjusting candidate depth instead of randomly tuning parameters.
Qdrant tested vector search tuning parameters across five public datasets. Increasing candidate depth from 10 to 500 improved best achievable scores by up to 0.28. The final score improved by at most 0.01 because relevant documents were already retrieved but not ranked highly. Their article explains how to systematically tune retrieval without guessing.
Your search can be working perfectly and still be badly tuned. Vector search has a lot of tuning knobs: `hnsw_ef`, candidate depth, RRF `k`, quantization, reranking, and more. The problem is, when search quality isn't where you want it to be, which knob should you change first? We tested this across five public datasets and found that the answer depends on where the problem actually is. For example, increasing candidate depth from 10 → 500 improved the best achievable score by up to 0.28. But the final score improved by at most 0.01 because the relevant documents were already being retrieved, they just weren't ranking high enough. So instead of changing settings until the score improves, first identify what's failing, then tune the knob connected to that problem. That's what we explore in our latest article: how to systematically tune retrieval without guessing. qdrant.tech/blog/tuning-re… 💬 0 🔄 0 ❤️ 3 👀 132 📊 1 ⚡
