Moving from Full-Text search to Vector search in SurrealDB

The text discusses the limitations of Full-Text Search (FTS) in SurrealDB when searching for items like "baggy clothes," as it focuses on the presence or frequency of words rather than their meaning, resulting in unsatisfactory search results. It contrasts this with Vector Search, a semantic search approach that uses vector embeddings to understand the context and meaning of words, enabling the identification of conceptually similar items even when different terminology is used. The author explains how Vector Search operates by converting textual data into numerical vectors and employing mathematical techniques like cosine similarity to find items with similar vector representations, which successfully identifies items like hoodies as matches for "baggy clothes." The text concludes by highlighting that while FTS is suitable for searches where semantic meaning is irrelevant, Vector Search is more useful for e-commerce applications where users often search for products using terms not directly present in the product descriptions.