Embeddings are typically stored in vector indices using a data structure that allows for efficient retrieval and similarity search. These indices can be in various forms, but the most common ones are tree-based structures, hash tables, or specialized libraries optimized for high-dimensional spaces. The main goal is to store the high-dimensional vectors representing the embeddings in a way that enables quick access and comparison, particularly when dealing with large datasets.
A simple and effective way to store embeddings is through a flat array or matrix. For instance, if you have a set of text embeddings generated from a Natural Language Processing task, you can store them in a 2D NumPy array where each row represents an individual embedding vector. This method is straightforward and works well for smaller datasets but can be inefficient for larger sets when it comes to searching for nearest neighbors. To address this, developers often use more complex structures like KD-trees or Ball Trees. These structures partition the data space and allow for faster retrieval of similar vectors based on their distances.
Another popular method involves using approximate nearest neighbor (ANN) algorithms, which are especially useful when operating at scale. Libraries like FAISS (Facebook AI Similarity Search) or Annoy (Approximate Nearest Neighbors Oh Yeah) implement these techniques to enable fast searching through embeddings. For example, FAISS uses inverted file systems and quantization methods to compress storage and speed up similarity searches in high-dimensional spaces. By leveraging these advanced indexing techniques, developers can efficiently manage and query large sets of embeddings, making it easier to implement applications such as recommendation systems, image retrieval, or search functionalities.