An Embedding is a relatively low-dimensional vector into which you can translate high-dimensional vectors. Ideally, an embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space.
Google Developers, 2019
In other words, embeddings represent the semantic similarities between texts. It can be used for classification and analytics. RAG is among the most popular and impactful use cases of embeddings.
References
- Google Developers. (2019). Embeddings | Machine Learning Crash Course | Google Developers. [online] Available at: https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture.