What Are Embeddings?

Embeddings are dense numerical representations of data, where similar items are closer together in a multi-dimensional space. Unlike traditional keyword-based search, embeddings allow models to understand context and meaning.

Example: Representing Text as Numbers

Consider the words "king", "queen", "man", and "woman".

Traditional search treats these as separate words, but embeddings represent them as vectors in a multi-dimensional space:

king   → [0.9, 1.2, 0.8, 0.7, 1.0]
queen  → [0.8, 1.3, 0.7, 0.6, 0.9]
man    → [0.5, 0.2, 0.6, 0.4, 0.1]
woman  → [0.6, 0.3, 0.5, 0.3, 0.2]

By analyzing these vectors, AI can infer relationships like:

king - man + woman ≈ queen

This mathematical property allows AI to understand relationships between words, enabling smarter search, translations, and AI-generated responses.

How Do Embeddings Work?

  1. Convert Data to Embeddings → Text, images, or audio are transformed into vector representations.
  2. Store Embeddings in a Database → A vector database efficiently stores and retrieves embeddings.
  3. Perform Similarity Search → When a query comes in, its vector representation is compared with stored embeddings to find the closest matches.

Types of Embeddings

Type Description Common Models
Text Embeddings Represent words/sentences as vectors BERT, OpenAI, Sentence Transformers
Image Embeddings Convert images into vector space CLIP, ResNet, ViT
Audio Embeddings Represent speech/audio as vectors Whisper, Wav2Vec
Multimodal Embeddings Combine different types of data OpenAI CLIP, Vision-Language Models

How Do We Use Embeddings in Search?

Traditional search engines use exact keyword matching, which often fails to capture meaning.