Semantic Search on AWS Open Search

Vector search is a core capability within OpenSearch, enabling the comparison of items based on their numerical representations (vectors or embeddings).
Machine learning models encode content (text, images, audio) into high-dimensional vectors, capturing their semantic meaning and contextual relationships. OpenSearch then indexes these vectors, allowing for efficient similarity searches to find the “nearest neighbors” in the vector space.