To simulate worst-case scenarios for a vector store in a RAG system, start by isolating components and systematically stressing them. For cache misses, design tests that bypass or invalidate cached results. Use unique query variations (e.g., altering phrasing, parameters like temperature or seed) to prevent cache hits. Disable caching explicitly in test configurations to measure raw retrieval performance. For example, send 1,000 distinct semantic searches with slight wording changes and track latency increases. Compare response times and error rates between cached and non-cached scenarios to quantify cache dependency.
For large index sizes, generate synthetic datasets that exceed typical production scales. Use tools like Faker to create mock text embeddings with realistic metadata, or replicate existing data until hitting target sizes (e.g., 10M+ vectors). Test incremental scaling by loading data in batches and monitoring metrics like query latency, memory usage, and indexing time. For instance, measure how retrieval time degrades when querying a 100GB index versus a 1GB index. Validate whether sharding or partitioning strategies maintain acceptable performance, and test edge cases like querying across multiple shards with complex joins.
To stress-test complex filters, create queries combining multiple metadata conditions (e.g., date >= X AND (category = Y OR author IN (Z)) and nested logical operations. Validate if the vector store correctly applies filters before or after semantic search, as this impacts performance. For example, a filter excluding 99% of documents before vector search should execute faster than one applied post-search. Test high-cardinality metadata fields (e.g., unique user IDs) to expose indexing inefficiencies. Additionally, simulate "empty result" scenarios by applying contradictory filters (e.g., price < 10 AND price > 20) to ensure graceful handling rather than timeouts or crashes.
Use load-testing tools like Locust or k6 to combine these scenarios under concurrent user traffic. For instance, simulate 100 users simultaneously querying a large index with unique, uncached requests and multi-clause filters. Monitor hardware metrics (CPU, memory, disk I/O) to identify bottlenecks like memory leaks during long-running queries. Finally, integrate these tests into CI/CD pipelines with performance thresholds (e.g., <500ms latency at 99th percentile) to enforce robustness during deployment.