Embeddings transform text, images, and other media into dense numeric vectors
that capture their underlying meaning. This makes it possible for machines to
perform semantic search, similarity, recommendations, clustering, RAG, and
more.
How Text & Image Embeddings Work
Text embeddings turn sentences or documents into high-dimensional vectors that capture meaning.
Example:
“A young boy is playing soccer in a park.”
“A child is kicking a football on a playground.”
These sentences get mapped to similar vectors, letting your AI recognize their meaning, regardless of wording.Image embeddings use neural networks (like CNNs) or vision-language models to turn images into numeric vectors, capturing shapes, colors, and features. For example: A cat image → vector that is “close” to other cats and “far” from cars in vector space.