Embeddings are a way to represent the meaning of text, images, or information as a list of numbers. Through a simple comparison function, a similarity score can be calculated between two embeddings, thereby determining whether two pieces of information are talking about similar content. Common use cases for embeddings include semantic search, clustering, and classification.
Embedding concepts
Cohere Embed 5 converts content and queries into numeric vectors for semantic retrieval. Your application stores document vectors in a search index, compares a query vector against them, and passes selected material to a reader or generation step. It does not itself provide a chatbot, an answer-gene
