The purpose of indexing in a vector database is to enable efficient similarity searches over high-dimensional data. Without indexing, searching for the nearest neighbors of a query vector would require comparing it against every vector in the database (a brute-force approach), which becomes computationally impractical as the dataset grows. Indexes organize vectors into structures that allow the database to quickly narrow down candidates, reducing the number of comparisons needed. For example, techniques like HNSW (Hierarchical Navigable Small World) build graph-based hierarchies to traverse vectors efficiently, while IVF (Inverted File) partitions data into clusters to limit search scope. This organization is critical for scaling to large datasets.
Indexing significantly improves search performance by reducing latency and computational overhead. Methods like HNSW optimize for speed by creating shortcuts between vectors, enabling logarithmic-time search complexity. Similarly, IVF accelerates queries by focusing on a subset of clusters closest to the query vector. However, these optimizations often trade exact accuracy for speed. For instance, IVF might miss nearest neighbors if the query falls near cluster boundaries, while HNSW’s graph connections can sometimes prioritize speed over precision. Performance gains depend on the index type and parameters: adjusting the number of clusters in IVF or the graph’s connectivity in HNSW allows developers to balance speed against resource usage.
Accuracy impacts vary by indexing strategy. Exact indexes (like flat indexing) guarantee perfect accuracy but are too slow for large datasets. Approximate Nearest Neighbor (ANN) indexes, such as HNSW or PQ (Product Quantization), sacrifice some precision for scalability. For example, PQ compresses vectors into smaller representations, speeding up comparisons but introducing approximation errors. The choice of index depends on the use case: recommendation systems might prioritize speed with HNSW, while medical imaging could require higher accuracy with techniques like IVF-PQ. Developers must tune parameters (e.g., probe counts for IVF) to align with their accuracy and latency requirements, making indexing a balance between practical performance and tolerable error margins.