向量搜索调优指南：精准定位问题
Your search can be working perfectly and still be badly tuned. Vector search has a lot of tuning kn...
Qdrant分享向量搜索调优经验，教你如何系统化定位问题，避免盲目调整参数。
Qdrant 研究了五个公共数据集，发现向量搜索调优应先识别问题再调整参数。增加候选深度从10到500可将最佳分数提高0.28，但最终分数仅提高0.01，因为相关文档已被检索，只是排序不够高。系统化调优需要先确定问题所在，再针对性调整参数。
Your search can be working perfectly and still be badly tuned. Vector search has a lot of tuning kn...
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 ⚡
