Databases & Data Storage
Database Embedding
Definition
In machine learning, an embedding is a learned representation for text, images, or other data where items that have a similar meaning are positioned close to each other in a high-dimensional vector space.
Why It Matters
Embeddings are what allow computers to understand the semantic "meaning" and relationships between data. They convert complex data like words or images into a numerical format that machine learning models can process.
Contextual Example
A word embedding model might represent the words "king" and "queen" as vectors that are very close to each other. The relationship between them (the vector from "king" to "queen") might be similar to the vector from "man" to "woman."
Common Misunderstandings
- These high-dimensional vectors are what get stored and searched in a vector database.
- Embeddings are created by training a neural network on a massive dataset.