When evaluating vector stores or approximate nearest neighbor (ANN) algorithms for retrieval-augmented generation (RAG), focus on three categories: performance metrics, accuracy metrics, and practical considerations. Each category addresses distinct aspects of how well a solution fits your use case, balancing speed, precision, and real-world constraints.
Performance Metrics measure efficiency. Query latency (time per search) and throughput (queries handled per second) are critical for real-time applications. For example, FAISS might offer lower latency on GPU but consume more memory, while HNSW prioritizes a balance of speed and memory efficiency. Indexing time—how long it takes to build the data structure—matters for large or frequently updated datasets. DiskANN, for instance, optimizes for disk-based storage, trading slower indexing for reduced memory usage. Memory footprint is also key: in-memory stores like FAISS require substantial RAM, whereas solutions like Annoy use less memory but may sacrifice speed. Scalability with dataset size (e.g., handling 1M vs. 100M vectors) and dimensionality (e.g., 768 vs. 1536 dimensions) should be tested, as performance often degrades with higher dimensions.
Accuracy Metrics determine retrieval quality. Recall@K (percentage of true top-K results retrieved) and precision@K (percentage of retrieved top-K results that are relevant) are foundational. For RAG, higher recall ensures critical context isn’t missed, while precision reduces noise. Mean Reciprocal Rank (MRR) evaluates how high the first relevant result appears in the list, which matters for tasks where the top result drives downstream processing. For example, if an ANN algorithm returns the correct answer at position 3 instead of 1, MRR would drop from 1.0 to 0.33. Testing with domain-specific data (e.g., medical texts vs. product descriptions) is essential, as embedding quality and ANN behavior vary by context. Tools like ann-benchmarks provide standardized comparisons but should be supplemented with custom datasets reflecting your application.
Practical Considerations tie metrics to real-world constraints. Trade-offs between speed and accuracy are unavoidable: HNSW may achieve 95% recall@10 with 2ms latency, while a brute-force approach guarantees 100% recall but takes 200ms. Dataset characteristics (sparse vs. dense vectors, update frequency) also influence choices—some algorithms handle dynamic data poorly. Hardware limits (CPU vs. GPU, available RAM) further narrow options. For example, FAISS with GPU acceleration suits high-throughput environments, while lighter libraries like Annoy work for resource-constrained setups. Finally, integration effort (custom APIs, language support) and maintenance costs (e.g., reindexing pipelines) should factor into the decision. A slower but easier-to-maintain solution might be preferable for small teams.
