This post explores how to solve challenges in vector search using NVIDIA cuVS with the Meta Faiss library. It covers the benefits of integration, performance improvements, benchmarks, and code examples.
This article explains the internal workings of vector databases, highlighting that they don't perform a brute-force search as commonly described. It details algorithms like HNSW, IVF, and PQ, the tradeoffs between recall, speed, and memory, and how different RAG patterns impact vector database usage. It also discusses production challenges like filtering, updates, and sharding.
