# Vector search

Category: [AI Search & GEO](https://seo-geo-glossary.com/en/glossary/category/ai-search-geo/) › [Retrieval & grounding](https://seo-geo-glossary.com/en/glossary/category/ai-search-geo/retrieval-grounding/)

**Vector search** finds similar items by representing text, images or audio as vectors, called embeddings, and comparing the distance between them. It matches on meaning instead of on exact words.

## How does vector search work?

An embedding model converts the content into vectors, which are stored in a vector database or a search index. The query is converted in the same way. The system returns the items whose vectors lie closest to the query vector.

IBM names Euclidean distance and cosine similarity as common measures of closeness, with cosine similarity ranging from -1 to 1. Exact comparison against millions of vectors is slow, so most systems use approximate nearest neighbor (ANN) algorithms. A widely used one is HNSW, introduced by Malkov and Yashunin in 2016, which builds a layered graph of vectors. Elastic notes that ANN gives up perfect accuracy for speed. Many systems combine vector scores with classic keyword scoring such as BM25, which is called hybrid search.

## How is it different from semantic search?

Tiger Data describes vector search as a building block of semantic search. Vector search is the technique that compares embeddings. Semantic search is the broader goal of returning results that fit the intent of a query, which can use further methods such as knowledge graphs (assessment based on that source).

## What does it mean for SEO?

Retrieval-augmented generation (RAG) uses vector search to find passages that fit a question. Clearly structured pages with self-contained sections are easier to retrieve this way (assessment). No documentation confirms vector search as a ranking method in Google Search.

## Example

A search index holds a page called "Washing a wool sweater by hand". The query "stop a cardigan from shrinking" contains none of its words but sits close to it in vector space. Vector search returns the page, a pure keyword search would not.

## FAQ

**What is the difference between vector search and keyword search?**
Keyword search compares terms. Vector search compares meaning through embeddings.

**Do you need a vector database for it?**
Not always. Several search engines offer vector features inside their own index.

*Diagram: Meaning as closeness in space.* Made-up example. According to Google, points that sit close together in embedding space are more closely related than distant ones. Real embeddings often have 256, 512 or 1024 dimensions.

*Diagram: Retrieval-augmented generation.* In retrieval-augmented generation, the system first retrieves matching passages and passes them to the language model as context. Google calls the link between model output and verifiable sources grounding. The answer can only cite what was retrieved first (an inference from the method, not a Google statement).

## Related terms

- [AI search](https://seo-geo-glossary.com/en/glossary/ai-search/index.md)
- [Vector database](https://seo-geo-glossary.com/en/glossary/vector-database/index.md)
- [Embeddings](https://seo-geo-glossary.com/en/glossary/embeddings/index.md)
- [Semantic search](https://seo-geo-glossary.com/en/glossary/semantic-search/index.md)
- [Retrieval-augmented generation (RAG)](https://seo-geo-glossary.com/en/glossary/retrieval-augmented-generation/index.md)
- [Information retrieval](https://seo-geo-glossary.com/en/glossary/information-retrieval/index.md)

## Sources

- [What is vector search? - Elastic](https://www.elastic.co/what-is/vector-search)
- [What is vector search? - IBM Think](https://www.ibm.com/think/topics/vector-search)
- [Efficient and robust approximate nearest neighbor search using HNSW graphs - arXiv:1603.09320 (Malkov, Yashunin)](https://arxiv.org/abs/1603.09320)
- [Vector search vs semantic search - Tiger Data (secondary source)](https://www.tigerdata.com/learn/vector-search-vs-semantic-search)

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Last updated: October 8, 2026
URL: https://seo-geo-glossary.com/en/glossary/vector-search/