If retrieval is slow, start by identifying the bottleneck through profiling. Measure query latency, CPU/GPU utilization, memory usage, and recall rates to determine where the problem lies. For example, if latency increases sharply with dataset size, the indexing method might be the issue. If GPU utilization is low during inference, hardware acceleration could help. If memory bandwidth is saturated, reducing vector dimensions or using quantization might be effective. Prioritize optimizations based on which component (computation, memory, or I/O) shows the highest resource contention.
Optimizing Indexing Techniques If measurements show high CPU usage during retrieval and linearly increasing latency with dataset growth, consider switching to a more efficient indexing method. Hierarchical Navigable Small World (HNSW) graphs are effective for high-recall scenarios but require more memory, while Inverted File (IVF) indexes trade some accuracy for faster searches by clustering vectors. For example, if brute-force search is used, moving to HNSW could reduce latency from O(n) to O(log n). Test this by comparing recall rates and query speed across indexes using a validation dataset. If recall remains acceptable (e.g., 95%+) with a faster index, adopt it. Tools like FAISS or Annoy provide built-in benchmarks to evaluate these trade-offs.
Hardware and Parallelization If profiling reveals underutilized GPUs or high CPU-bound computation (e.g., distance calculations consuming 90% of CPU time), offload work to accelerators. GPUs excel at batched vector operations: libraries like FAISS-GPU or CUDA-optimized code can process thousands of vectors in parallel. For smaller datasets, ensure batch sizes are large enough to amortize GPU overhead. Alternatively, optimize CPU usage with SIMD instructions (e.g., AVX-512 for faster dot products) or multithreading. Measure throughput (queries/second) before and after changes—if GPU utilization jumps from 20% to 80% with a 5x speedup, the bottleneck was hardware-related.
Reducing Vector Size When memory bandwidth is saturated (e.g., vectors exceed available RAM, forcing disk swaps) or distance calculations dominate latency, reduce vector dimensions. Techniques like PCA or autoencoder-based compression can shrink vectors by 50% with minimal accuracy loss. For example, reducing 768-dim BERT embeddings to 256-dim using PCA might retain 95% of variance. Quantization (e.g., 32-bit floats → 8-bit integers) can also cut memory usage and computation time by 4x. Validate this by testing recall on a subset of data: if reducing dimensions or precision drops recall from 98% to 94% but speeds up retrieval 3x, the trade-off may be acceptable. Use tools like scikit-learn for dimensionality reduction and libraries like FAISS for quantization support.