How is indexing done in a vector database?

Indexing in a vector database is the process of organizing high-dimensional vector data to enable efficient similarity searches. Instead of comparing a query vector to every stored vector—a method that becomes computationally expensive as the dataset grows—indexing creates structured shortcuts. These shortcuts group vectors by similarity or reduce the complexity of comparisons, allowing the database to quickly narrow down candidates that are likely to match the query. Common techniques include tree-based structures, graph-based methods, or clustering algorithms, each tailored to balance speed, accuracy, and memory usage.

One widely used indexing method is the Hierarchical Navigable Small World (HNSW) graph. HNSW constructs layers of interconnected nodes, where higher layers represent coarse-grained relationships and lower layers capture finer details. During a search, the algorithm starts at the top layer, navigating toward the query vector’s approximate location, then refines the search in lower layers. Another approach is Inverted File Index (IVF), which partitions vectors into clusters using algorithms like k-means. Each cluster is represented by a centroid, and queries compare against centroids first to identify the most relevant clusters. For example, in an image retrieval system, IVF might group similar image embeddings into clusters, reducing the search scope from millions to thousands of vectors. Techniques like Product Quantization (PQ) further compress vectors into smaller codes, enabling faster distance calculations by approximating similarities.

The choice of indexing method depends on the use case’s requirements. HNSW is favored for high accuracy and scalability, making it suitable for applications like recommendation systems. However, it requires more memory. IVF with PQ, on the other hand, optimizes for memory efficiency and speed, which is useful in resource-constrained environments. Developers often tune parameters like the number of clusters in IVF or the graph’s connectivity in HNSW to balance performance. For instance, increasing the number of clusters in IVF reduces the search space but risks missing relevant vectors if the centroids aren’t representative. Indexing is typically a preprocessing step, and libraries like FAISS or databases like Milvus abstract much of the complexity, allowing developers to focus on configuring these parameters based on their data size, dimensionality, and latency needs.