Embeddings are stored in vector indices using specialized data structures designed to efficiently handle high-dimensional vectors. When you generate an embedding—a numerical representation of data like text, images, or audio—it’s typically a dense array of floating-point numbers (e.g., 768 or 1536 dimensions). Vector indices organize these embeddings in a way that enables fast similarity searches, such as finding the nearest neighbors based on cosine similarity or Euclidean distance. Unlike traditional databases, which aren’t optimized for high-dimensional vector operations, vector indices use algorithms like hierarchical navigable small worlds (HNSW), inverted file (IVF) structures, or tree-based methods to partition and search the data efficiently.
For example, HNSW creates a layered graph where each layer represents a subset of the data, with higher layers containing fewer nodes. This allows the algorithm to quickly traverse the graph during a search by starting at the top layer and moving downward. Another approach, IVF, clusters embeddings into groups using techniques like k-means, creating an inverted index that maps clusters to their member vectors. During a search, the index first identifies the most relevant clusters, then compares the query vector only to the vectors within those clusters. Tools like FAISS (Facebook AI Similarity Search) or libraries such as Annoy (Approximate Nearest Neighbors Oh Yeah) implement these methods, enabling developers to trade off between search speed, accuracy, and memory usage based on their needs.
To optimize storage and performance, vector indices often employ techniques like quantization or compression. Product quantization, for instance, splits high-dimensional vectors into smaller subvectors and replaces each with a code from a precomputed codebook, reducing memory usage. Some systems also use sharding to distribute vectors across multiple machines, scaling horizontally. However, these optimizations come with trade-offs: aggressive compression might reduce search accuracy, while sharding adds complexity to query routing. Developers must choose an index type and configuration that aligns with their application’s requirements—such as real-time search latency, scalability, or precision—and test it against their specific dataset to ensure balanced performance.