Yes, embeddings can be precomputed and stored for later use, which is common in applications where embeddings are frequently reused. Precomputing embeddings involves training a model on a large dataset, generating the embeddings, and saving these embeddings to a database or file system for later retrieval. This is particularly useful in scenarios where the same data is processed repeatedly, such as word embeddings in NLP tasks or image embeddings in computer vision.
Precomputing embeddings helps save computational resources and time during inference, as the model does not need to generate embeddings on-the-fly for every input. For example, in search engines, embeddings for products, articles, or documents can be precomputed and stored, enabling fast similarity searches or recommendations based on precomputed vectors.
However, precomputing embeddings requires sufficient storage space, especially when working with high-dimensional embeddings for large datasets. Additionally, when embeddings need to be updated based on new data, they must be recomputed periodically. This can introduce challenges in terms of maintaining up-to-date embeddings while balancing computational efficiency.