Embeddings are stored in vector databases as numerical vectors in a high-dimensional space. Each embedding represents an object, such as a document, image, or user profile, and is indexed to enable fast similarity search and retrieval.
Vector databases, such as Milvus, FAISS, or Pinecone, are optimized for storing and querying embeddings using techniques like Approximate Nearest Neighbor (ANN) search. These databases often use specialized indexing structures like HNSW or IVF to efficiently find embeddings that are close to a query vector, based on distance metrics like cosine similarity or Euclidean distance.
Embeddings in vector databases are typically associated with metadata (e.g., document titles, categories, or timestamps) to provide context for retrieved results. These databases are widely used in applications like recommendation systems, search engines, and anomaly detection, where efficient retrieval of semantically similar objects is essential.