Vector databases are specifically designed to handle high-dimensional vectors, making them ideal for real-time vector search. These databases store vector embeddings efficiently and allow for rapid retrieval of similar vectors. Real-time vector search involves quickly finding vectors in a database that are most similar to a given query vector. This is achieved by utilizing algorithms like the hierarchical navigable small world (HNSW) and approximate nearest neighbors (ANN), which reduce the computational cost and time needed to search through large datasets.
The process begins with data points being transformed into vector representations using machine learning models. These vectors are then indexed in the vector database, creating an embedding space where similar items are grouped closely. When a query vector is introduced, the database searches within this space to identify the nearest neighbors, based on vector similarity measures such as Euclidean distance.
Vector databases also support data partitioning, which optimizes the search space by dividing it into smaller, manageable sections. This allows for parallel processing, further enhancing the speed of real-time searches. Additionally, these databases can handle unstructured data, such as text, images, and audio, by converting them into vectors, thus broadening the scope of applications.