Redis discusses the use of machine learning models like GPT to obtain vector embeddings from unstructured data. These embeddings can be stored and indexed using Redis search 2.4 and above, allowing for efficient searching at low latency. For example, in a customer call center scenario, thousands of calls can be stored as vector embeddings in Redis. By querying these embeddings, it becomes possible to identify calls that match specific tones or sentiments, providing insights into customer satisfaction without the need to manually listen to every call. This approach offers a more efficient and effective way to understand customer sentiments and improve customer service.