Exact vector search and approximate vector search are two approaches used to handle similarity search tasks, each with its trade-offs in terms of accuracy and efficiency. Exact vector search guarantees finding the most similar items by exhaustively comparing all data points in the search space. This method ensures accurate results but can be computationally expensive, especially with large datasets.
In contrast, approximate vector search aims to provide a balance between speed and accuracy. It employs algorithms like the hierarchical navigable small world (HNSW) to quickly identify the nearest neighbors in the vector space. While this method might not always find the exact nearest neighbors, it significantly reduces computational costs and is often sufficient for practical applications where high recall is more critical than absolute precision.
Choosing between exact and approximate vector search depends on the specific requirements of the application, such as the size of the dataset, the need for real-time results, and the acceptable level of accuracy. In many cases, approximate vector search provides a practical solution, offering a good trade-off between performance and resource usage.