IBM Article: https://www.ibm.com/topics/vector-database
A vector database stores, manages and indexes high-dimensional vector data. Data points are stored as arrays of numbers called vectors which are clustered based on similarity. This design enables low-latency queries making it ideal for AI applications
Vector DB vs Traditional DB
- Data points in a vector database are represented by vectors with a fixed number of dimensions
- They use high dimensional vector embeddings so they are better able to handle unstructured datasets
- Relational Databases excel at managing structured and semistructured datasets in specific formats
- Vector Embeddings are numerical representations of data points that convert types of data (words, audio, or images) into arrays of numbers that ML models can process
Use cases
- Vector Storage
- Vector indexing
- Similarity search based on querying or prompting