To improve the efficiency of approximate nearest neighbor (ANN) search, focus on optimizing data structures, preprocessing, and search parameters. The core challenge is balancing speed, accuracy, and memory usage. Start by selecting an algorithm suited to your data characteristics—such as dimensionality, dataset size, and query patterns—and then fine-tune its implementation.
First, consider using specialized data structures like hierarchical navigable small world graphs (HNSW) or inverted index-based methods (e.g., IVF). HNSW organizes data into a layered graph, allowing fast traversal by prioritizing “shortcut” connections between distant nodes. For example, in a 100-dimensional vector dataset, HNSW can reduce search time from O(n) to O(log n) with minimal accuracy loss. Similarly, IVF partitions data into clusters using k-means, enabling searches to focus on the most relevant clusters. Combining IVF with product quantization (PQ)—which compresses vectors into compact codes—can further reduce memory overhead. For instance, FAISS, a popular ANN library, uses IVF-PQ to handle billion-scale datasets efficiently by trading off some precision for faster lookups.
Second, preprocess your data to reduce dimensionality or improve indexing. Techniques like PCA (Principal Component Analysis) can shrink high-dimensional vectors while preserving meaningful relationships. If your vectors have 512 dimensions, applying PCA to reduce them to 128 dimensions might speed up distance calculations without significantly harming recall. Additionally, ensure your vectors are normalized (e.g., L2-normalized) to simplify distance computations. For text embeddings, using models that produce unit-length vectors by default (like Sentence-BERT) avoids runtime normalization overhead. Preprocessing also includes tuning hyperparameters during index construction, such as adjusting the number of clusters in IVF or the number of graph connections in HNSW, to match your hardware constraints.
Finally, optimize search parameters for your use case. Most ANN algorithms expose knobs like the number of probes (clusters to search in IVF) or the search depth in HNSW. Increasing these values improves accuracy but slows down queries. For example, setting nprobe=10 in IVF might yield 90% recall with 5ms latency, while nprobe=50 could reach 95% recall at 20ms. Profile your system to find the right trade-off. Batch processing queries (instead of single-shot searches) can also leverage parallelization, especially on GPUs. Libraries like FAISS and Annoy support batch APIs to exploit hardware acceleration. Additionally, caching frequently queried items or using hybrid approaches (e.g., exact search for top candidates followed by ANN filtering) can further reduce latency for repetitive workloads.