Vector databases are specialized databases designed for storing and retrieving
high-dimensional vectors. They are becoming increasingly important in the
world of machine learning and AI applications.

Unlike traditional databases that work with structured data, vector databases
are optimized for similarity search operations on embedding vectors.

Key features of vector databases include:
1. Efficient similarity search algorithms
2. Support for high-dimensional vectors
3. Specialized indexing for fast retrieval
4. Scalability for large vector collections

Common applications include semantic search, recommendation systems,
image retrieval, and natural language processing tasks.
