What is it?
A vector database is like a specialized library for AI memories. Instead of storing books on shelves, it stores mathematical representations (vectors) of data - text, images, or audio converted into numerical lists. These databases excel at finding similar items quickly, like finding all photos that look similar to a query image.
How it works?
Data is first converted into vectors using AI models - a process called embedding. These vectors capture the meaning or features of the original data. The database then organizes these vectors using special indexing methods that allow rapid similarity searches, typically using techniques like approximate nearest neighbor search.
Example
Spotify uses vector databases to power music recommendations. Each song becomes a vector representing its musical features. When you like a song, the system quickly finds vectors (songs) that are mathematically similar, suggesting music you'll probably enjoy. Similarly, ChatGPT uses vector databases to find relevant context for your questions.
Why it matters
Vector databases are the backbone of modern AI applications. They enable semantic search (searching by meaning, not just keywords), recommendation systems, RAG applications, and similarity matching. As AI systems become more sophisticated, efficient vector storage and retrieval becomes critical for performance.
Key takeaways
- Stores and searches mathematical representations of data
- Enables fast similarity searches across large datasets
- Essential for AI applications like search and recommendations
- Bridges the gap between AI models and traditional databases