An embedding is a numerical representation of data (text, images, audio) as a dense vector of floating-point numbers, typically with hundreds or thousands of dimensions. Embeddings capture semantic meaning, so similar concepts have vectors close together in the embedding space. Text embeddings are fundamental to semantic search, RAG systems, recommendation engines, and clustering. They are generated by specialized embedding models like OpenAI text-embedding-3, Cohere Embed, or open-source alternatives like BGE and E5. Embeddings enable machines to understand meaning rather than just matching keywords.
Frequently Asked Questions
What is an embedding in AI?
An embedding is a numerical vector that represents the meaning of text, images, or other data. Similar concepts have similar embeddings, enabling semantic search and comparison.
How are embeddings used?
Embeddings power semantic search, RAG systems, recommendation engines, duplicate detection, and clustering. They convert meaning into numbers that machines can compare.