Indexing significantly improves the speed of vector search by organizing data into structures optimized for similarity comparisons. Without indexing, a brute-force search would compare a query vector against every vector in the dataset, which becomes computationally expensive as the dataset grows. For example, searching through 1 million vectors with 768 dimensions each would require 1 million distance calculations per query. Indexing methods like hierarchical navigable small worlds (HNSW), inverted file (IVF), or product quantization (PQ) reduce this workload by grouping similar vectors or approximating distances. HNSW, for instance, builds a graph structure where nodes represent vectors, and edges connect to nearby neighbors. This allows the search algorithm to “hop” through the graph to find the closest matches in logarithmic time rather than linear time.
However, indexing involves trade-offs between speed, accuracy, and resource usage. For example, HNSW provides fast and accurate results but requires more memory to store the graph layers. In contrast, IVF partitions the dataset into clusters using algorithms like k-means, so queries only search within the nearest clusters. While this reduces computation, it risks missing relevant vectors if the query falls near cluster boundaries. Similarly, PQ compresses vectors into smaller codes, speeding up distance calculations at the cost of precision. Developers must choose an indexing method based on their priorities: a recommendation system needing high recall might prioritize HNSW, while a mobile app with limited memory might opt for PQ. The indexing process itself also takes time and resources, which can be a bottleneck for real-time updates.
Practical implementation requires balancing these factors. For instance, in a search engine using text embeddings, IVF with 1,024 clusters might reduce query time from 100ms to 10ms but require precomputing cluster centroids during indexing. Parameters like the number of clusters in IVF or the number of connections per node in HNSW can be tuned to optimize performance. Tools like FAISS or Annoy abstract some complexity but still require testing with real data. Additionally, indexes optimized for static datasets (e.g., IVF) may struggle with frequent updates, while graph-based methods like HNSW can handle dynamic data better. By profiling metrics like query latency, recall rate, and memory usage, developers can iteratively refine their indexing strategy to meet specific application needs.