// ⌕ RAG & Vector Retrieval
HNSW (Hierarchical Navigable Small World)
A layered small-world graph for millisecond approximate nearest-neighbor search.
HNSW 图索引 2016 Intermediate core
IN PLAIN WORDS
Sparse upper layers allow long jumps, denser lower layers refine. High recall and speed; the default index in most vector databases.
Used in: Milvus, Qdrant, pgvector, Elasticsearch
TIMELINE · prerequisites → this → what builds on it
01Original paper
Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
Yu. A. Malkov, D. A. Yashunin · 2016
arXiv ↗ ✓ Verified · arXiv API
02Where it sits
Learn the left column first; the right column builds on this one.
Learn first
Then you can learn
Related:Product Quantization (PQ)
Left: prerequisites · right: builds on this · dashed: related. Full map →
03Learning resources
Official docs, explainers, courses and reference code. All links checked.
04Where the course teaches it
On learning paths:AI Application Engineer
For AI / data
- Verification: verified, checked 2026-10-21.
- JSON: /api/learn/algorithms.json (id:
hnsw)