Evaluating the performance of vector-based search involves measuring accuracy, speed, and consistency across different scenarios. The goal is to ensure the system returns relevant results quickly, scales with data size, and behaves predictably under varying conditions. Key metrics include precision/recall for accuracy, query latency for speed, and robustness tests for handling edge cases. These evaluations often rely on labeled datasets, benchmarking tools, and stress-testing with realistic workloads.
First, accuracy is measured using metrics like precision (percentage of relevant results in the top-K retrieved items) and recall (percentage of all relevant results captured in the top-K). For ranked results, Normalized Discounted Cumulative Gain (NDCG) evaluates how well the order matches ideal rankings. For example, if a user searches for “sci-fi movies,” a vector search system might return 20 results, 15 of which are correctly labeled sci-fi (precision = 75%). If the dataset contains 100 relevant sci-fi movies, recall would be 15%. Tools like MS MARCO or custom-labeled datasets provide ground truth for these calculations. Additionally, exact vs. approximate search trade-offs matter: brute-force methods guarantee accuracy but are slow, while approximate nearest neighbor (ANN) algorithms like HNSW prioritize speed but may sacrifice some precision.
Second, speed and scalability are tested by measuring query latency (time per search) and throughput (queries per second). For instance, a system handling 1,000 queries per second with 10ms latency is more efficient than one managing 100 queries at 50ms. Scalability tests involve increasing the dataset size (e.g., from 1M to 100M vectors) to ensure latency remains stable. Tools like FAISS or Annoy optimize vector search for large datasets by using indexing techniques. Resource usage—such as memory consumption and CPU/GPU load—is also tracked. For example, a GPU-accelerated index might reduce latency but require expensive hardware, while a memory-efficient ANN implementation could trade slight accuracy losses for lower costs.
Finally, consistency and robustness are evaluated by testing diverse query types, noisy inputs, and data distributions. A robust system should handle misspelled or ambiguous queries (e.g., searching for “jaguar” returning both animal and car-related vectors) without significant performance drops. Stress tests might involve adding random noise to vectors or varying vector dimensions to simulate imperfect data. Consistency checks ensure the system performs reliably across hardware configurations or software versions. For example, if an update to the embedding model changes vector semantics, the search quality should be re-evaluated to detect regressions. Balancing these factors—accuracy, speed, and robustness—requires iterative testing and clear benchmarks aligned with the application’s needs.