How are embeddings stored in vector databases?

Embeddings are stored in vector databases as numerical arrays designed for efficient similarity searches. When you store an embedding, the database saves it as a high-dimensional vector—a list of floating-point numbers—that represents features extracted from your data (text, images, etc.). For example, a text embedding from a model like BERT might be a 768-dimensional vector. These vectors are indexed using data structures optimized for fast comparisons, such as approximate nearest neighbor (ANN) algorithms. Alongside the vector, metadata like unique identifiers, timestamps, or labels are often stored to provide context. The database’s core job is to retrieve vectors similar to a query vector, which requires balancing storage efficiency with search speed.

Vector databases use specialized indexing techniques to manage embeddings effectively. One common approach is hierarchical navigable small worlds (HNSW), which organizes vectors into layers of graphs to reduce search time. Another method is inverted file (IVF) indexing, which groups similar vectors into clusters and searches only relevant clusters during a query. For example, in a product recommendation system, embeddings of user preferences and product features might be indexed using IVF to quickly narrow down candidate matches. Some databases also apply quantization, compressing vectors into lower-bit representations (e.g., 8-bit integers) to save memory and speed up computations. These techniques trade some accuracy for performance, allowing queries to scale to billions of vectors without exhaustive comparisons.

Developers interact with vector databases through APIs that abstract the underlying storage mechanics. For instance, when using a database like Pinecone or Milvus, you’d typically upload embeddings via a client library, specifying parameters like dimensionality and distance metrics (e.g., cosine similarity). The database handles partitioning, replication, and updates behind the scenes. For example, adding a new embedding to an existing index might trigger a background process to update the ANN graph or recluster data. When querying, the database uses the index to find approximate matches and returns them with associated metadata. This setup allows applications like semantic search or fraud detection to run efficiently, even as datasets grow. Most databases also support filtering by metadata, letting you combine vector similarity with traditional query constraints.