Document embeddings are numerical representations of text documents that capture their semantic meaning. By converting words and phrases into vectors in a high-dimensional space, these embeddings allow for easier comparison and analysis of documents. This technique is commonly used in natural language processing tasks, such as text classification and information retrieval. The process of creating document embeddings often involves algorithms like Word2Vec, GloVe, or BERT. These models learn to represent documents based on the context of words within them, enabling machines to understand and process human language more effectively. This approach enhances various applications, including search engines and recommendation systems.