Text semantic search retrieves documents or content based on the meaning of a query, rather than relying on exact keyword matches. It leverages embeddings created by machine learning models, which encode the semantic meaning of text into vectors. These vectors are compared to find the most relevant results.
For example, if a user searches for "ways to stay healthy," a semantic search system might retrieve articles about exercise, diet, and wellness tips, even if the exact words aren’t present. This approach improves accuracy and relevance, especially for complex or nuanced queries.
Text semantic search is widely used in customer support, enterprise knowledge systems, and AI-powered search engines, enabling more intuitive and effective information retrieval.