Vector Database

A vector database is a specialised database management system designed to store, manage and efficiently search high-dimensional vector embeddings, which are numerical representations of data like text, images, or audio.

Vector databases use sophisticated indexing methods to organise and retrieve vectors based on their similarity rather than exact matching. They excel at performing nearest neighbour searches, allowing them to quickly find the most similar vectors to a query vector amongst millions or billions of entries.

These databases are optimised for machine learning applications and artificial intelligence systems where traditional relational databases fall short. They employ specialised algorithms like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) to enable fast similarity searches at scale, making them crucial for modern AI applications.

Examples

• Semantic search engines
• Recommendation systems
• Face recognition databases
• Content-based image retrieval
• Natural language processing applications