Embeddings improve approximate nearest neighbor (ANN) search by providing a way to represent complex data in a lower-dimensional space while preserving essential relationships between data points. In simple terms, embeddings take high-dimensional data—such as images, text, or audio—and convert them into fixed-length vectors in a way that similar items are closer together in this new space. This property makes it easier and faster to locate nearby data points when performing searches, as the vector representations can simplify the computations involved in finding nearest neighbors.
For instance, consider a scenario where you want to find similar images in a large database. Each image can be converted into an embedding using a model like a convolutional neural network (CNN). Once you have embeddings for all images, you can use ANN algorithms and data structures, such as KD-trees or locality-sensitive hashing, which are designed to efficiently retrieve nearest neighbors in multi-dimensional spaces. The embeddings reduce the complexity of this search by allowing the algorithm to work in a more manageable space compared to the original images, thereby speeding up the query process without sacrificing accuracy significantly.
Furthermore, using embeddings facilitates a level of flexibility that traditional methods may not provide. For example, in text search, word embeddings like Word2Vec or GloVe capture semantic meaning and enable searches that consider context, such as finding documents that are conceptually similar even if they don't share exact keywords. This extensibility is beneficial for applications like recommendation systems, where the goal is to find items that align well with user preferences. By leveraging embeddings, developers can create systems that respond more effectively to user queries, enhancing the overall user experience.