How Embeddings Are Stored in a Vector Database
Embeddings are stored in vector databases as numerical arrays, typically represented as high-dimensional vectors. These vectors are generated by machine learning models (like transformers or CNNs) and capture semantic or contextual features of the input data (text, images, etc.). To store them efficiently, vector databases use specialized indexing structures optimized for fast similarity searches. For example, a database might use techniques like Hierarchical Navigable Small Worlds (HNSW) or Inverted File (IVF) indexing to organize vectors into clusters or hierarchical layers. This allows the database to quickly locate vectors that are “close” to a query vector based on distance metrics like cosine similarity or Euclidean distance.
The storage architecture typically involves two key components: the raw vector data and the index. The raw vectors are stored in a format that balances memory efficiency and accessibility, such as compressed binary blobs or arrays in memory-mapped files. The index, which is separate from the raw data, acts as a map to accelerate search operations. For instance, in HNSW, vectors are organized into layers of graphs, where higher layers enable coarse-grained navigation and lower layers refine the search. When a query is performed, the database traverses these layers to find approximate nearest neighbors. Systems like FAISS or proprietary databases (e.g., Pinecone, Milvus) handle this by splitting data into shards or partitions to scale horizontally, ensuring that even large datasets (billions of vectors) can be queried efficiently.
Developers working with vector databases must consider trade-offs between accuracy, speed, and resource usage. For example, approximate nearest neighbor (ANN) algorithms sacrifice exact matches for faster searches, which is acceptable in many applications like recommendation systems. Metadata associated with embeddings (e.g., IDs, timestamps, or source data) is often stored alongside vectors in hybrid setups—vectors in ANN indexes and metadata in traditional databases like PostgreSQL. Practical implementations might involve preprocessing steps (normalizing vectors to unit length for cosine similarity) or tuning index parameters (like the number of clusters in IVF). Maintenance tasks, such as reindexing after updates or handling out-of-memory data, also require careful planning to ensure performance remains consistent as the dataset grows.