Embeddings are generated using algorithms like Word2Vec, FastText, or transformer-based models like BERT, which convert words or documents into high-dimensional vectors. In vector databases, these embeddings enable efficient similarity searches by allowing queries to retrieve the nearest vectors based on a defined distance metric, such as cosine similarity.