HNSW
Hierarchical Navigable Small World, a graph-based nearest neighbor index that gives high recall at low latency by navigating layers of links, at the cost of more memory than IVF.
What is HNSW?
Hierarchical Navigable Small World, a graph-based nearest neighbor index that gives high recall at low latency by navigating layers of links, at the cost of more memory than IVF.
HNSW is a advanced concept that sits in the LLM and GenAI Ops area of system design. Engineers reach for it whenever they need to reason about real-world trade-offs in that space, not just for textbook correctness, but because real production systems at companies like Netflix, Amazon, and Google make these decisions every day.
If you want to go deeper than this definition, with diagrams, code, and a quiz to lock it in, work through the "Vector Databases and ANN" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
Learn HNSW in depth
Full interactive lesson with diagrams, code examples, real-world references, and a quiz.
Open the Vector Databases and ANN lessonRelated lessons
Lessons that touch on HNSW as part of a larger topic.
See also
Related glossary terms you might want to look up next.
ANN (Approximate Nearest Neighbor)
Search that finds vectors close to a query without scanning every item, trading a small chance of missing a true neighbor for a large gain in speed. HNSW and IVF are the common indexes.
Vector Database
A database optimized for storing and querying high-dimensional vectors. Powers AI applications like semantic search and recommendation systems.
RAG (Retrieval-Augmented Generation)
A pattern that retrieves relevant text from an external index and puts it in the model's prompt so the answer is grounded in current, verifiable facts rather than the model's memory.
Embedding
A list of numbers that represents the meaning of a piece of text, so that similar meanings sit close together in vector space. The same model must embed both the corpus and the query.
Reranking
A second retrieval stage where a cross-encoder scores each candidate chunk against the query directly. Slower than the first-stage retriever but far more precise, so it runs only over the top few dozen results.
Hybrid Retrieval
Running dense vector search and sparse keyword search together and fusing the results, so exact terms like an error code and paraphrased meaning both count.