Vector Database
A vector database is a specialized database that stores and retrieves embeddings (high-dimensional vectors). AI search engines use vector databases to find semantically relevant content for a given query.
What it is
Vector databases store embeddings — numerical representations of text — and support fast nearest-neighbor search to find vectors semantically similar to a query vector. Common vector database systems include Pinecone, Weaviate, Chroma, and pgvector. AI engines use vector databases (or equivalent indexes) to perform the retrieval step in retrieval-augmented generation. Brands don't usually interact with vector databases directly, but understanding how they work helps explain why semantic clarity matters more than keyword density for GEO.
Why it matters for GEO
Vector retrieval rewards conceptually clear content. If your page is about one well-defined thing, it has a clean vector signature and ranks well in retrieval.
- Embeddings — Embeddings are numerical vector representations of text, used by AI systems to measure semantic similarity.
- Retrieval-Augmented Generation (RAG) — Retrieval-Augmented Generation (RAG) is an AI architecture where a model retrieves relevant external content at query time and uses it to generate an answer.
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