Key Features of Vector Databases
High-Dimensional Search: Vector databases allow Approximate Nearest Neighbor (ANN) search, a technique to find vectors that are closest to a given query vector. Unlike traditional SQL-based queries, which work well with structured data, ANN efficiently handles similarity searches across high-dimensional data spaces.
Scalability: A major advantage of vector databases is their ability to handle large datasets. As the number of vectors grows, traditional databases become inefficient. Vector databases are optimized to perform real-time searches across millions or even billions of vectors.
Real-Time Search: Many AI-powered applications need to serve results in real time, whether it’s a recommendation system or semantic search engine. Vector databases are optimized to deliver fast search results by utilizing indexing techniques like LSH (Locality Sensitive Hashing) or HNSW (Hierarchical
Navigable Small World), enabling near-instantaneous retrieval.
Similarity Search: Traditional databases use exact matching for search queries, which is unsuitable for many AI tasks. Vector databases, however, specialize in similarity search — retrieving vectors based on their distance from the query vector using metrics such as Euclidean distance, cosine similarity, or Manhattan distance.