In Natural Language Processing (NLP), text data is often represented as high-dimensional vectors using techniques like word embeddings or transformer-based models. These vectors capture the semantic meaning of the text, with the distance between vectors indicating the semantic similarity between the corresponding pieces of text. Vector databases can store these text vectors and provide efficient similarity search capabilities, which is particularly useful in applications like document retrieval.