What is the purpose of indexing in a vector database, and how does having an index affect search performance and accuracy?

Purpose of Indexing in Vector Databases Indexing in vector databases serves to organize high-dimensional data (like embeddings from machine learning models) to enable efficient similarity searches. Without an index, searching for the closest vectors to a query would require comparing the query against every vector in the database—a process called “brute-force” search. This becomes impractical as datasets grow, since the time complexity scales linearly with the dataset size. An index structures the data to reduce the number of comparisons needed, allowing queries to return results faster. For example, a database storing image embeddings for a visual search app would use an index to avoid scanning millions of vectors for every user query.

Impact on Search Performance Indexing significantly improves search speed by narrowing the search space. Techniques like Hierarchical Navigable Small World (HNSW), Inverted File (IVF), or Product Quantization (PQ) create structures that group similar vectors or approximate distances to prioritize likely matches. For instance, HNSW builds a graph where each node represents a vector, and edges connect to nearby neighbors. During a search, the algorithm traverses this graph, skipping irrelevant regions. This reduces search time from O(n) (brute-force) to O(log n) or better. However, indexing involves trade-offs: precomputing structures adds overhead during data insertion, and some methods sacrifice exact results for speed. For example, IVF partitions data into clusters and only searches a subset, which speeds up queries but might miss optimal matches if the query falls near cluster boundaries.

Accuracy Considerations The choice of index directly affects accuracy. Exact indexes (like brute-force) guarantee correct results but are too slow for large datasets. Approximate Nearest Neighbor (ANN) indexes prioritize speed by accepting minor inaccuracies. For example, PQ compresses vectors into smaller codes, reducing memory usage and speeding up comparisons, but introduces quantization errors. Developers must balance these factors based on use cases: a recommendation system might tolerate slightly less accurate results for real-time responses, while a medical imaging tool might prioritize precision with a slower, more exact index. Tuning parameters (e.g., the number of clusters in IVF or graph layers in HNSW) allows further optimization. In practice, benchmarks on representative data are critical to validate that an index meets both speed and accuracy requirements.