Vector Database
Specialized databases that store and search high-dimensional vectors, enabling semantic search and AI applications.
Definition
A vector database is a specialized database designed to store, index, and search high-dimensional vectors—numerical representations of data like text, images, or audio. Unlike traditional databases that match exact values, vector databases enable similarity search, finding content that is semantically related.
Vector databases power RAG systems and semantic search. When you embed text as vectors, similar meanings cluster together, enabling AI to find relevant content based on meaning rather than keywords. Popular options include Pinecone, Weaviate, and Chroma.
Why It Matters
Vector databases enable the AI applications transforming search and discovery. Understanding this technology helps marketers grasp how AI search works and why semantic content quality matters for GEO.
As more search and discovery tools use vector similarity, content that's semantically rich and clearly structured becomes more discoverable.
Examples in Practice
Perplexity uses vector search to find relevant web pages based on semantic similarity to user queries, not just keyword matching.
A company builds a product recommendation engine using vector embeddings—similar products cluster together, enabling "customers also viewed" features based on semantic similarity.