Vector Search

Vector search uses embeddings to compare the meaning of queries and documents in high-dimensional space. Rather than looking only for literal keyword matches, it finds content that is conceptually related. AI systems rely on vector search for retrieval, recommendations, and question answering, which means clearly written, semantically rich content is more likely to be surfaced even when users phrase questions differently.