What type of learning is most commonly used for creating embeddings in vector search?

Creating embeddings in vector search commonly relies on deep learning techniques, especially when dealing with complex data types like images, text, and audio. Deep learning models, such as neural networks, are particularly effective at capturing high-dimensional data representations, resulting in embeddings that encode semantic meanings and relationships within the data.

Deep learning frameworks are often used to train models on large datasets, allowing the model to learn intricate patterns and features that are then represented in the embeddings. These embeddings enable efficient similarity searches and retrieval processes in vector search systems.

In contrast, while unsupervised and supervised learning techniques can also be utilized in the context of generating embeddings, they are not as directly associated with the creation of embeddings specifically for vector search as deep learning is. Moreover, reinforcement learning, which involves learning optimal actions through trial and error, is less relevant to the embedding creation process for vector search purposes.