Embeddings

Embeddings are mathematical representations that convert text, images, or other data into dense numerical vectors, capturing semantic meaning in a format that machines can compare and process. Two pieces of content with similar meaning will have vectors that are close together in the embedding space, even if they share no words in common. This property makes embeddings the foundation of semantic search, recommendation systems, and retrieval-augmented generation.

In practice, an embedding model takes an input like a sentence or paragraph and outputs a fixed-length array of floating-point numbers, typically 768 to 1536 dimensions. These vectors are stored in vector databases for efficient similarity search. When a user submits a query, it is embedded using the same model and compared against stored vectors to find the most semantically relevant matches. For web developers, embeddings enable features that keyword search cannot, such as finding conceptually related content, clustering similar documents, or powering AI assistants that retrieve relevant context from a knowledge base before generating responses.