Applications can reduce the impact of latency in vector retrieval by decoupling operations from user-facing workflows. Asynchronous queries allow the application to initiate a retrieval request without blocking other tasks, enabling parallel processing. For example, a recommendation system might send a vector search to a database while simultaneously rendering non-dependent UI elements or processing other user inputs. Similarly, splitting large queries into smaller, parallelizable sub-queries can reduce perceived latency. A video streaming platform could divide a user's watch history into segments, search each in parallel for related content, and combine results. However, this requires careful error handling and may increase infrastructure costs due to concurrent resource usage.
Prefetching and caching are effective for predictable or repetitive access patterns. By anticipating likely follow-up requests, an application can retrieve vectors before they’re explicitly needed. A search engine might prefetch vectors for trending topics while a user types a query, using partial input to narrow options. Caching frequently accessed vectors (e.g., popular product embeddings in an e-commerce app) or intermediate results (like precomputed similarity scores) minimizes redundant computations. For instance, a music app could cache playlist embeddings for users with similar tastes. The trade-off involves increased memory usage and potential staleness if cached data isn’t refreshed appropriately.
Hierarchical indexing and approximation techniques balance speed and accuracy. A two-stage approach might use a smaller, optimized index (like IVF or HNSW) for rapid candidate selection, followed by a precise search on a subset of data. A fraud detection system could first filter transactions using a coarse-grained index to identify high-risk candidates, then apply detailed analysis. Approximate Nearest Neighbor (ANN) algorithms like FAISS or ScaNN sacrifice marginal accuracy for faster retrieval—acceptable in scenarios like image search where near-matches are sufficient. These methods require tuning: overly aggressive approximation might miss relevant results, while overly large hierarchies reintroduce latency.