Infrastructure
Vector database
A vector database is a type of database that stores data as high-dimensional vectors, which are numerical representations of data features. These vectors capture the semantic meaning of the data, enabling efficient similarity searches based on proximity in the vector space.
Explanation
Vector databases are designed to handle the unique demands of unstructured data like text, images, and audio. Unlike traditional databases that excel at structured data, vector databases index and store data points as vectors in a high-dimensional space. The vectors are typically generated by embedding models (e.g., Transformer models for text, CNNs for images) which convert raw data into numerical representations that capture semantic relationships. The key advantage of vector databases lies in their ability to perform similarity searches quickly and efficiently. By using algorithms like approximate nearest neighbor (ANN) search, these databases can find vectors that are closest to a query vector, effectively retrieving data points with similar meanings or features. This makes them invaluable for applications like recommendation systems, semantic search, image retrieval, and question answering, where understanding the underlying meaning of data is crucial. Vector databases are typically used alongside other database solutions, handling vector embeddings and similarity search, while the main database handles structured data and metadata.