Evaluating vector search performance requires measuring accuracy, speed, and scalability. Start by defining metrics that align with your use case. For accuracy, common measures include recall@k (how many true matches appear in the top k results) and precision@k (how many of the top k results are relevant). For example, if you search for similar images, recall@10 would check if at least 8 of the 10 returned images are correct matches. Speed metrics include query latency (time to return results) and throughput (queries handled per second). Scalability tests how performance degrades as the dataset grows—like measuring latency when your index scales from 1 million to 10 million vectors.
Next, benchmark different algorithms and parameters. Vector search often uses approximate nearest neighbor (ANN) algorithms like HNSW, IVF, or LSH, which trade some accuracy for speed. Compare their performance using your metrics. For instance, HNSW might offer 95% recall@10 with 5ms latency, while IVF achieves 90% recall@10 but at 2ms latency. Test indexing time too—some methods build indexes faster but require more memory. Use datasets representative of your data (e.g., glove-100-angular for text embeddings or SIFT1M for generic vectors). Tools like FAISS, Annoy, or Milvus provide built-in evaluation utilities, letting you run repeatable tests across hardware configurations.
Finally, validate results in real-world scenarios. Synthetic benchmarks might not capture edge cases. For example, if your application searches for medical images, test with a subset of labeled data to ensure results align with expert judgments. Monitor memory usage, especially for large-scale deployments—some algorithms consume significant RAM, which impacts cost. Also, consider distance metrics: cosine similarity for text, Euclidean for spatial data. If your system uses hybrid filters (e.g., metadata constraints), measure how filtering affects performance. Iterate by adjusting parameters like HNSW’s “efConstruction” or IVF’s “nprobe” to balance speed and accuracy. Document trade-offs to inform future optimizations, ensuring your evaluation reflects both technical limits and user needs.