Definition in Simple Terms
A vector is a way to represent data as a list of numbers that describe both direction and magnitude (think: an arrow pointing somewhere). In tech, vectors often represent data points in a multi-dimensional space (e.g., a user profile turned into numbers like [0.2, 0.8, 0.5]).
Why It Matters
Vectors are the backbone of AI, search engines, and recommendation systems. They allow machines to understand relationships between things, like how similar two songs, images, or documents are.
How It Works (Step-by-Step)
Convert Data to Numbers
Text, images, or audio are transformed into numeric lists (e.g., “cat” → [0.12, 0.98, 0.33]).Place in Vector Space
Each vector lives in a multi-dimensional space where similar items are close together.Compare Using Math
Algorithms measure distance between vectors (e.g., cosine similarity) to find related items.
Real-World Example
Spotify uses vectors to recommend songs: if your favorite track is represented as a vector, the system finds other vectors nearby and those are your next favorite songs.
👍 Pros & 👎 Cons
👍 Pros
Enables powerful search & recommendations
Works across text, images, and audio
Scales well for big data
👎 Cons
Requires lots of computing power
Hard to interpret (black box)
Needs quality data to work well
Embedding: The actual numeric representation of data as a vector.
Example: The word “king” might be embedded as [0.21, 0.87, 0.65], and “queen” as [0.20, 0.85, 0.67]—close together because they’re related.Vector Database: A database optimized for storing and searching vectors (e.g., Azure Cosmos DB, Pinecone, Weaviate).
Cosine Similarity: A method to measure how close two vectors are.