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`TestCuVSRandomizedHNSWVectorSearch.testVectorSearch` required every hit of one query to be among the closest `3 * topK` vectors. The search is approximate: on this test's data (uniform random vectors with up to 2048 dimensions), a topK = 1 to 3 search sometimes returns a valid neighbor just outside that window, typically about 1% farther away than the nearest neighbor. The test now checks all the queries and allows 2 such misses in total. It keeps hard checks that every result is a distinct document with a vector, scored by its own vector, in score order, with `min(topK, vectors)` results. This reuses the `assertSearchFindsNearestNeighbors` helper from #2775, extended for indexes where some documents have no vector. `TOP_K_LIMIT` is raised to 128, since the search does not fail beyond 64. Measurements and the reproducer are in #2819. Part of #2783. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - Igor Motov (https://github.com/imotov) URL: #2824
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Reproducer for the
TestCuVSRandomizedHNSWVectorSearch.testVectorSearchflake ("Result returned was not in topK*3") found by the stress run in #2793. Workstream O of #2783.CI is reduced to the cpp, java and lucene builds plus 4 lucene test jobs (2x amd64 12.9, 1x amd64 13.x, 1x arm64 12.9). Each job replays the failing seed, runs a recall sweep that compares the accelerated format against Lucene's CPU HNSW on the same segments, and loops the test with random seeds.