Semantic Search with Vector Database: A Comprehensive Review of Models, Indexing and Applications

The use of semantic search with the help of vector databases has become an impressive paradigm of retrieving the pertinent information by offering the contextual and conceptual sense of the information searching more than using the conventional methods of keyword searching. This paper provides an in-depth overview of the models of vector representation, transformer-based semantic encoders, and technologies of vectors database that jointly allow efficient and error-free semantic search. Classical distributional semantics, word-level embeddings, and transformer architectures are presented as background methods of making designed generating meaningful vectors representations. The paper also looks at the contemporary databases of vectors and indexing mechanisms which enable scalable similarity search in high-dimensional data. Moreover, different distance measures, hash algorithms and indexing strategies based on graphs are evaluated to determine how they can be used to maximize retrieval. Lastly, the paper presents practical examples of semantic searching with the use of the vector databases with text, image, audio and conversational applications, outlining both the main challenges and research opportunities.