Optimizing an Open Source Vector Database with Andrey Vasnetsov - Qdrant

This post details Qdrant's strategy for in-place filtering during graph traversal in vector search, which optimizes precision and exactness when scaling to billions of vectors. It introduces techniques like using subgraphs with overlapping intervals and geo hash regions for enhanced precision and connectivity. The post also discusses controlling search precision and speed by adjusting the beam size in HNSW indices and highlights the challenges of maintaining graph connectivity during filtering. Additionally, it touches upon vector compression methods.