Vector databases are optimized for storing and retrieving high-dimensional data such as vector representations, in which each data point is mapped to a numerical vector. Vector databases allow efficient store of complex data, such as time series and embedded information. They have many common usages, from NLP and word embedding to information retrieval and recommendation systems.
NoSQL based similarity searches use databases are designed to handle large volumes of unstructured and semi-structured data, such as documents, key-value pairs, graphs, and time-series data. Unlike tabular SQL databases, NoSQL databases are schema-less by nature, allowing for greater flexibility in accommodating evolving data and to better handle dynamic and rapidly changing data requirements.
Hyperspace Hybrid Search
Hyper space offers a combinations of the vector search and similarity search. The hyper space index formulates data as key-value pairs, where dense and sparse vectors are stored under a designated key.