Optimizing query throughput in semantic search involves balancing speed, accuracy, and resource usage. Three key techniques include improving indexing efficiency, optimizing hardware and model performance, and implementing smart caching or scaling strategies. Each approach addresses different bottlenecks in the system to handle more queries per second without sacrificing relevance.
First, efficient indexing and approximate nearest neighbor (ANN) algorithms are foundational. Semantic search often relies on comparing vector embeddings, and brute-force similarity checks are too slow for large datasets. ANN libraries like FAISS, HNSW, or Annoy create optimized data structures to speed up searches. For example, HNSW (Hierarchical Navigable Small World) organizes vectors into a layered graph, allowing fast traversal with minimal accuracy loss. Additionally, pre-filtering using metadata (e.g., category or date ranges) reduces the subset of vectors needing comparison. If a query is limited to “articles published in 2023,” the system only searches that subset, cutting computation time. Dimensionality reduction techniques like PCA can also shrink vector sizes, making comparisons faster without significantly impacting result quality.
Second, hardware and model optimizations directly impact processing speed. Using GPUs for encoding queries or computing similarities leverages parallel processing, which is especially effective for batch operations. For instance, processing 100 queries at once on a GPU can be faster than handling them sequentially on a CPU. Quantization—converting 32-bit vectors to 8-bit integers—reduces memory bandwidth usage and speeds up calculations. Model choices matter too: smaller language models (e.g., DistilBERT or TinyBERT) trade slight accuracy gains for faster inference. Pruning unused layers from a neural network or using ONNX Runtime for optimized execution can further reduce latency. For example, switching from BERT-base to a distilled version might cut encoding time by 40% with minimal impact on search quality.
Finally, caching and distributed architectures prevent overloading the system. Caching frequent query results or precomputed embeddings avoids redundant processing. A hybrid approach might cache embeddings for top-100 trending search terms, allowing instant results for popular queries. Horizontal scaling, such as sharding the vector index across multiple servers, splits the workload. If a dataset is divided into four shards, each server handles 25% of the data, and results are combined post-search. Load balancers can also distribute incoming queries evenly across replicas of the search service. Asynchronous processing (e.g., using Python’s async/await or Kafka for queuing) lets the system handle more concurrent requests by avoiding thread-blocking operations. These strategies collectively increase throughput while maintaining responsiveness under high load.