To evaluate retrieval performance in a vector database without exact ground-truth nearest neighbors, developers can use methods like human relevance judgments, approximate ground-truth generation, or downstream task metrics. These approaches rely on proxies to estimate retrieval quality when definitive labels are unavailable. Each method has trade-offs in accuracy, scalability, and practicality.
One approach is human relevance judgments, where annotators manually assess whether retrieved items match a query’s intent. For example, developers can sample a subset of queries and ask reviewers to label top-(k) results as relevant or irrelevant. Metrics like precision@(k) (proportion of relevant items in the top (k)) or mean average precision (MAP) can then be calculated. This method works well for small datasets or critical use cases (e.g., medical search systems) but becomes costly and time-consuming at scale. To ensure reliability, use multiple annotators and measure inter-rater agreement (e.g., Cohen’s kappa). However, human bias and subjectivity can skew results, especially for ambiguous queries. A hybrid approach might combine human evaluation for high-priority queries with automated methods for broader coverage.
Another strategy is approximate ground-truth generation using computationally expensive but accurate methods on a subset of data. For instance, run brute-force exact nearest neighbor search on a small, representative sample of the dataset and treat those results as pseudo-ground truth. Compare the vector database’s output against this subset using metrics like recall@(k) (percentage of true top-(k) items retrieved). This assumes the subset reflects the full dataset’s structure, which may not hold for skewed distributions. Alternatively, use cross-validation: split the dataset, index one subset, and test retrieval on the other. For embedding-based systems, leverage pretrained models (e.g., BERT for text) to compute semantic similarity scores between queries and results as a proxy for relevance. While efficient, this depends on the embedding model’s quality and may not align with human judgments.
Finally, downstream task evaluation ties retrieval performance to application-specific outcomes. For example, in a recommendation system, measure click-through rates (CTR) or conversion rates of retrieved items. In a question-answering pipeline, track answer accuracy when using retrieved context. This method directly measures real-world impact but conflates retrieval quality with other components (e.g., ranking algorithms). To isolate retrieval effectiveness, conduct A/B tests where only the database implementation varies. For unsupervised tasks like clustering, use intrinsic metrics like silhouette scores to assess coherence of retrieved clusters. While practical, this approach requires careful experimental design and may lack granular insights into retrieval errors.