Vector Search
Search powered by embedding similarity instead of keyword matching.
Full definition
Vector search retrieves results by computing the distance between the query's embedding and each document's embedding in vector space. It powers semantic search, recommendation, and the retrieval step in RAG systems.
Vector search is why AI engines can find your page even when it doesn't contain the user's exact keywords. Writing for meaning — clear, single-concept paragraphs — outperforms keyword stuffing in vector-search-driven ranking.
Example
Pinecone, Weaviate, and pgvector are common vector search backends.
Related terms
Put it into practice
Run a free OptimAIze scan to see how your site handles Vector Search and the rest of the GEO checklist.
Run free scanFrequently asked questions
Is Vector Search the same as SEO?
No. Vector Search is one piece of the broader GEO (Generative Engine Optimization) program that sits on top of classical SEO. The two work together — classical SEO gets you crawled and indexed; Vector Search is part of what gets you cited by AI engines.
Do I need a tool to implement Vector Search?
For most teams, a free scanner like OptimAIze is enough to identify what's missing. Implementation is usually a copy-paste of generated markup or a small code change — no specialist tool required.