Embeddings are stored in vector databases as multi-dimensional numerical representations of data points. Each embedding is typically represented as a high-dimensional vector, where each dimension corresponds to a feature of the data. For instance, in natural language processing, word embeddings like Word2Vec or GloVe represent words in a continuous vector space, allowing similar words to be close together in that space. These vectors are stored in the vector database along with any associated metadata, such as identifiers or types, which helps in efficiently retrieving and managing the embeddings.
When storing embeddings, vector databases often use specialized data structures, such as KD-trees, ball trees, or HNSW (Hierarchical Navigable Small World) graphs. These structures are designed for fast similarity searches, allowing the database to quickly retrieve the closest embeddings to a given input vector. For example, when a developer queries the database for similar items, the database can use these spatial data structures to perform nearest neighbor searches efficiently, even with large datasets. This capability is essential for applications like recommendation systems, where finding similar products or content is crucial for user engagement.
Moreover, vector databases usually provide mechanisms for updating and scaling the stored embeddings. As new data becomes available, embeddings can be added or updated in the database. Some databases also support operations like batch inserts or updates, enabling developers to manage embeddings efficiently. Additionally, features such as version control for embeddings can be valuable, ensuring that applications can refer to specific versions of data as they evolve. This flexibility and scalability make vector databases a powerful tool for developers working with applications that rely on embeddings for similarity and classification tasks.