Vector databases are designed to handle high-dimensional data, which is essential for vector search. They store vector representations of data points, enabling efficient similarity search. By organizing data in a way that facilitates quick retrieval, vector databases allow users to search for semantically similar items with ease. These databases use indexing methods like the HNSW algorithm to optimize the search process, ensuring accurate results with reduced computational cost.
The core function of a vector database is to transform unstructured data into structured vector embeddings. As these vectors represent the semantic meaning of the data, they enable the database to perform natural language processing tasks and semantic search. This is particularly useful for applications like recommendation systems, where understanding the context and meaning of user queries is crucial.
Vector databases support hybrid search methods, combining traditional keyword search with vector search to deliver a more comprehensive search experience. This approach allows for the retrieval of both exact matches and semantically similar items, improving the quality of search results.