What is Approximate Nearest Neighbor (ANN)?

Why does Approximate Nearest Neighbor (ANN) matter?

Approximation makes large vector indexes practical, but it introduces a measurable tradeoff between search speed, memory, and retrieval recall.

Approximate Nearest Neighbor (ANN) in practice

Tune index and search parameters against a held-out query set, then report latency together with Recall@K instead of assuming every true neighbor is found.

What is the common confusion about Approximate Nearest Neighbor (ANN)?

ANN describes a search objective and tradeoff, while HNSW is one particular index algorithm that can implement it.

Learn Approximate Nearest Neighbor (ANN) in the course

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Related terms

• Vector DatabaseA storage and indexing system that supports nearest-neighbor queries over vector representations, often with metadata filtering,…
• HNSWAn approximate-nearest-neighbor index that organizes vectors in layered proximity graphs and searches from coarse upper layers toward…
• Cosine SimilarityThe normalized dot product of two vectors. It compares their direction rather than their magnitude and ranges from -1 to 1 for real-valued…
• Recall@KFor one query, Recall@K is `|relevant items intersecting the top k| / |relevant items|`.

Sources

• Efficient and Robust Approximate Nearest Neighbor Search Using HNSW

More terms in Retrieval & generation

• BM25
• Chunking
• Dense Retrieval
• Grounding
• HNSW
• Hybrid Retrieval
• Maximum Marginal Relevance (MMR)
• RAG (Retrieval-Augmented Generation)
• Recall@K
• Reciprocal Rank Fusion (RRF)
• Reranker
• Semantic Search
• Vector Database

Open the Retrieval & generation list in the glossary