Turning text or images into a list of numbers that captures meaning, so content with similar meaning ends up numerically close.
An embedding converts meaning into numbers. A specialized model takes a sentence or paragraph and turns it into a long list of numbers called a vector. Sentences with similar meaning, such as how much is delivery and what does shipping cost, get vectors that sit close together even when the words differ.
This is what makes smart search possible in apps. Instead of matching exact keywords only, your app can find the content closest in meaning. Embeddings are the foundation of RAG, recommendations and grouping similar customer messages together. Practical tip: if your users write in Arabic and regional dialects, test the embedding model on real examples from them before relying on it, because the quality of Arabic understanding varies between models.