Part 4/12:
Semantic or vector search represents a paradigm shift by interpreting the underlying meaning behind queries, rather than relying solely on exact text matches. This is enabled through embeddings, which convert content into mathematical vectors capturing contextual semantics.
For example, searching for a "blue T-shirt" with vector search might also retrieve "turquoise" or "cyan" T-shirts because their embeddings are close in the semantic space, even if textual matches are absent.
Metrics like precision (relevance of results) and recall (comprehensiveness) help evaluate the quality of vector search results, which often require specialized indexes like partitioned nearest-neighbor data structures.