As enterprise knowledge bases scale into billions of high-dimensional embeddings, naive brute-force cosine distance calculations become computationally untenable. Production Retrieval-Augmented Generation (RAG) engines demand sub-50ms latency with high recall guarantees.
Hierarchical Navigable Small World (HNSW)
HNSW constructs multi-layer proximity graphs providing logarithmic search scaling. While memory footprint is higher than quantized representations, HNSW delivers unparalleled recall accuracy for dense scientific corpora.
Inverted File Product Quantization (IVF-PQ)
IVF-PQ strikes a balance between compressed RAM consumption and query throughput by clustering vector partitions into Voronoi cells and quantizing vector sub-spaces. Choosing between them requires profiling indexing latency versus memory constraints.