Embeddings
An embedding is a numerical representation (a vector of several hundred to a few thousand dimensions) of a piece of content — text, image, audio — computed by a machine-learning model in such a way that semantically sim…
An embedding is a numerical representation (a vector of several hundred to a few thousand dimensions) of a piece of content — text, image, audio — computed by a machine-learning model in such a way that semantically similar contents produce nearby vectors in that space.
Embeddings are the foundational building block of semantic search, clustering, classification — and most importantly RAG (Retrieval-Augmented Generation), where they allow you to retrieve passages from a corpus relevant to a question.
They are stored and queried efficiently in vector databases (Pinecone, Qdrant, Weaviate, pgvector). The main embedding model providers in 2026 are OpenAI, Voyage, Cohere, Mistral and open-source models like BGE and E5.