A Review of Hybrid Search in Milvus

Milvus 2.4 introduces multi-vector columns within a single collection, enabling more advanced and flexible data searches by allowing simultaneous queries across multiple vector types and fields. This hybrid search feature supports multimodal search, hybrid sparse and dense search, and hybrid dense and full-text search. The results from each field are integrated and re-ranked using multiple reranking algorithms to deliver more accurate outcomes. Hybrid search is designed to handle complex and multimodal data representations, accommodating diverse facets of information and various types of vector embeddings. It also supports the fusion of multimodal vectors from different unstructured data types such as images, videos, audio, and text files. The latest Milvus releases support hybrid search to meet escalating demands for handling intricate datasets in AI-driven applications.