HNSW vs IVF indexing for vector databases | Principles and Architectures скачать в хорошем качестве

HNSW vs IVF indexing for vector databases | Principles and Architectures

These sources examine the primary indexing strategies used in vector databases to facilitate efficient Approximate Nearest Neighbor searches. The documentation focuses on comparing HNSW, a high-precision graph-based approach ideal for memory-intensive and dynamic datasets, against IVF, a cluster-based method optimized for massive scales and limited hardware resources. Additional techniques like Product Quantization and Hashing are introduced as ways to further compress data and enhance retrieval speed. Each technical guide highlights the critical trade-offs between accuracy, memory consumption, and search latency. Ultimately, the literature suggests that selecting the right index depends on specific dataset sizes and hardware constraints, while noting a modern shift toward hybrid architectures that combine the strengths of both systems.