Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1176 (573db77327*) started at 2025-09-21T19:06:35.312


################################################################################
# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 10.05s


################################################################################
# Installation
#

Installing SimilaritySearch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.13/Project.toml`
  [053f045d] + SimilaritySearch v0.13.0
    Updating `~/.julia/environments/v1.13/Manifest.toml`
  [7d9f7c33] + Accessors v0.1.42
  [79e6a3ab] + Adapt v4.3.0
  [4fba245c] + ArrayInterface v7.20.0
  [62783981] + BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] + CPUSummary v0.2.7
  [fb6a15b2] + CloseOpenIntervals v0.1.13
  [da1fd8a2] + CodeTracking v2.0.1
  [f70d9fcc] + CommonWorldInvalidations v1.0.0
  [34da2185] + Compat v4.18.0
  [807dbc54] + Compiler v0.1.1
  [a33af91c] + CompositionsBase v0.1.2
  [187b0558] + ConstructionBase v1.6.0
  [adafc99b] + CpuId v0.3.1
  [9a962f9c] + DataAPI v1.16.0
⌅ [864edb3b] + DataStructures v0.18.22
  [b4f34e82] + Distances v0.10.12
  [ffbed154] + DocStringExtensions v0.9.5
  [5789e2e9] + FileIO v1.17.0
  [076d061b] + HashArrayMappedTries v0.2.0
  [615f187c] + IfElse v0.1.1
  [3587e190] + InverseFunctions v0.1.17
  [92d709cd] + IrrationalConstants v0.2.4
  [c3a54625] + JET v0.10.7
⌅ [033835bb] + JLD2 v0.5.15
  [aa1ae85d] + JuliaInterpreter v0.10.5
  [70703baa] + JuliaSyntax v1.0.2
  [10f19ff3] + LayoutPointers v0.1.17
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [6f1432cf] + LoweredCodeUtils v3.4.3
  [1914dd2f] + MacroTools v0.5.16
  [d125e4d3] + ManualMemory v0.1.8
  [e1d29d7a] + Missings v1.2.0
  [bac558e1] + OrderedCollections v1.8.1
  [d96e819e] + Parameters v0.12.3
  [f517fe37] + Polyester v0.7.18
  [1d0040c9] + PolyesterWeave v0.2.2
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.0
  [92933f4c] + ProgressMeter v1.11.0
  [ae029012] + Requires v1.3.1
  [94e857df] + SIMDTypes v0.1.0
  [7e506255] + ScopedValues v1.5.0
  [0e966ebe] + SearchModels v0.4.1
  [053f045d] + SimilaritySearch v0.13.0
  [a2af1166] + SortingAlgorithms v1.2.2
  [aedffcd0] + Static v1.2.0
  [0d7ed370] + StaticArrayInterface v1.8.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.1
⌅ [2913bbd2] + StatsBase v0.33.21
  [7792a7ef] + StrideArraysCore v0.5.8
  [8290d209] + ThreadingUtilities v0.5.5
  [3bb67fe8] + TranscodingStreams v0.11.3
  [3a884ed6] + UnPack v1.0.2
  [0dad84c5] + ArgTools v1.1.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8ba89e20] + Distributed v1.11.0
  [f43a241f] + Downloads v1.7.0
  [7b1f6079] + FileWatching v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0
  [b27032c2] + LibCURL v0.6.4
  [76f85450] + LibGit2 v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [56ddb016] + Logging v1.11.0
  [d6f4376e] + Markdown v1.11.0
  [a63ad114] + Mmap v1.11.0
  [ca575930] + NetworkOptions v1.3.0
  [44cfe95a] + Pkg v1.13.0
  [de0858da] + Printf v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v0.7.0
  [9e88b42a] + Serialization v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [f489334b] + StyledStrings v1.11.0
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [8dfed614] + Test v1.11.0
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] + LibCURL_jll v8.16.0+0
  [e37daf67] + LibGit2_jll v1.9.1+0
  [29816b5a] + LibSSH2_jll v1.11.3+1
  [14a3606d] + MozillaCACerts_jll v2025.9.9
  [4536629a] + OpenBLAS_jll v0.3.29+0
  [458c3c95] + OpenSSL_jll v3.5.2+0
  [efcefdf7] + PCRE2_jll v10.46.0+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [83775a58] + Zlib_jll v1.3.1+2
  [3161d3a3] + Zstd_jll v1.5.7+1
  [8e850b90] + libblastrampoline_jll v5.13.1+0
  [8e850ede] + nghttp2_jll v1.67.1+0
  [3f19e933] + p7zip_jll v17.6.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

Installation completed after 3.99s


################################################################################
# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 107.07s


################################################################################
# Testing
#

     Testing SimilaritySearch
      Status `/tmp/jl_TkTb5y/Project.toml`
  [7d9f7c33] Accessors v0.1.42
  [4c88cf16] Aqua v0.8.14
  [b4f34e82] Distances v0.10.12
  [c3a54625] JET v0.10.7
⌅ [033835bb] JLD2 v0.5.15
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.18
  [92933f4c] ProgressMeter v1.11.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.0
  [10745b16] Statistics v1.11.1
⌅ [2913bbd2] StatsBase v0.33.21
  [7792a7ef] StrideArraysCore v0.5.8
  [ade2ca70] Dates v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [9a3f8284] Random v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_TkTb5y/Manifest.toml`
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.3.0
  [4c88cf16] Aqua v0.8.14
  [4fba245c] ArrayInterface v7.20.0
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [2a0fbf3d] CPUSummary v0.2.7
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [da1fd8a2] CodeTracking v2.0.1
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.18.0
  [807dbc54] Compiler v0.1.1
  [a33af91c] CompositionsBase v0.1.2
  [187b0558] ConstructionBase v1.6.0
  [adafc99b] CpuId v0.3.1
  [9a962f9c] DataAPI v1.16.0
⌅ [864edb3b] DataStructures v0.18.22
  [b4f34e82] Distances v0.10.12
  [ffbed154] DocStringExtensions v0.9.5
  [5789e2e9] FileIO v1.17.0
  [076d061b] HashArrayMappedTries v0.2.0
  [615f187c] IfElse v0.1.1
  [3587e190] InverseFunctions v0.1.17
  [92d709cd] IrrationalConstants v0.2.4
  [c3a54625] JET v0.10.7
⌅ [033835bb] JLD2 v0.5.15
  [aa1ae85d] JuliaInterpreter v0.10.5
  [70703baa] JuliaSyntax v1.0.2
  [10f19ff3] LayoutPointers v0.1.17
  [2ab3a3ac] LogExpFunctions v0.3.29
  [6f1432cf] LoweredCodeUtils v3.4.3
  [1914dd2f] MacroTools v0.5.16
  [d125e4d3] ManualMemory v0.1.8
  [e1d29d7a] Missings v1.2.0
  [bac558e1] OrderedCollections v1.8.1
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.18
  [1d0040c9] PolyesterWeave v0.2.2
  [aea7be01] PrecompileTools v1.3.3
  [21216c6a] Preferences v1.5.0
  [92933f4c] ProgressMeter v1.11.0
  [ae029012] Requires v1.3.1
  [94e857df] SIMDTypes v0.1.0
  [7e506255] ScopedValues v1.5.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.0
  [a2af1166] SortingAlgorithms v1.2.2
  [aedffcd0] Static v1.2.0
  [0d7ed370] StaticArrayInterface v1.8.0
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.7.1
⌅ [2913bbd2] StatsBase v0.33.21
  [7792a7ef] StrideArraysCore v0.5.8
  [8290d209] ThreadingUtilities v0.5.5
  [3bb67fe8] TranscodingStreams v0.11.3
  [3a884ed6] UnPack v1.0.2
  [0dad84c5] ArgTools v1.1.2
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [ade2ca70] Dates v1.11.0
  [8ba89e20] Distributed v1.11.0
  [f43a241f] Downloads v1.7.0
  [7b1f6079] FileWatching v1.11.0
  [b77e0a4c] InteractiveUtils v1.11.0
  [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2 v1.11.0
  [8f399da3] Libdl v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [56ddb016] Logging v1.11.0
  [d6f4376e] Markdown v1.11.0
  [a63ad114] Mmap v1.11.0
  [ca575930] NetworkOptions v1.3.0
  [44cfe95a] Pkg v1.13.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization v1.11.0
  [6462fe0b] Sockets v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [f489334b] StyledStrings v1.11.0
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test v1.11.0
  [cf7118a7] UUIDs v1.11.0
  [4ec0a83e] Unicode v1.11.0
  [e66e0078] CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] LibCURL_jll v8.16.0+0
  [e37daf67] LibGit2_jll v1.9.1+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2025.9.9
  [4536629a] OpenBLAS_jll v0.3.29+0
  [458c3c95] OpenSSL_jll v3.5.2+0
  [efcefdf7] PCRE2_jll v10.46.0+0
  [bea87d4a] SuiteSparse_jll v7.10.1+0
  [83775a58] Zlib_jll v1.3.1+2
  [3161d3a3] Zstd_jll v1.5.7+1
  [8e850b90] libblastrampoline_jll v5.13.1+0
  [8e850ede] nghttp2_jll v1.67.1+0
  [3f19e933] p7zip_jll v17.6.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
Test Summary:              | Pass  Total   Time
test database abstractions |   56     56  12.3s
Precompiling packages...
 106290.6 ms  ✓ JET
  1 dependency successfully precompiled in 107 seconds. 38 already precompiled.
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 20005  20005  4.2s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.6s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.3s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.788427 seconds (1000 allocations: 78.125 KiB)
  9.965370 seconds (1000 allocations: 78.125 KiB)
  3.486009 seconds (1000 allocations: 78.125 KiB)
  4.156062 seconds (1000 allocations: 78.125 KiB)
  4.008262 seconds (1000 allocations: 78.125 KiB)
  3.943521 seconds (1000 allocations: 78.125 KiB)
  3.547274 seconds (1000 allocations: 78.125 KiB)
  3.910934 seconds (1000 allocations: 78.125 KiB)
 15.787999 seconds (1000 allocations: 78.125 KiB)
 15.742972 seconds (1000 allocations: 78.125 KiB)
 28.801066 seconds (1000 allocations: 78.125 KiB)
 28.654204 seconds (1000 allocations: 78.125 KiB)
 20.926277 seconds (6.23 k allocations: 388.672 KiB)
 21.190895 seconds (1000 allocations: 78.125 KiB)
 18.216322 seconds (1.00 k allocations: 78.141 KiB)
 17.294035 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m42.2s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  1.883340 seconds (1000 allocations: 78.125 KiB)
  2.448619 seconds (1000 allocations: 78.125 KiB)
 28.909257 seconds (1000 allocations: 78.125 KiB)
 28.424477 seconds (1000 allocations: 78.125 KiB)
 28.867514 seconds (1000 allocations: 78.125 KiB)
 28.789230 seconds (1000 allocations: 78.125 KiB)
  3.871835 seconds (1000 allocations: 78.125 KiB)
  3.935684 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m11.0s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  8.543255 seconds (1000 allocations: 78.125 KiB)
  8.525240 seconds (1000 allocations: 78.125 KiB)
  8.515329 seconds (1000 allocations: 78.125 KiB)
  8.522783 seconds (1000 allocations: 78.125 KiB)
  8.466097 seconds (1000 allocations: 78.125 KiB)
  8.489609 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  54.2s
  0.045829 seconds (1.00 k allocations: 78.141 KiB)
  0.045431 seconds (1000 allocations: 78.125 KiB)
  0.041033 seconds (1000 allocations: 78.125 KiB)
  0.040949 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  3.4s
  0.054018 seconds (1000 allocations: 78.125 KiB)
  0.054207 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.061778 seconds (2.37 M allocations: 131.707 MiB, 1.74% gc time, 99.95% compilation time)
ParallelExhaustiveSearch allknn: 1.479932 seconds (604.62 k allocations: 31.776 MiB, 11.76% gc time, 99.87% compilation time)
Test Summary: | Pass  Total     Time
allknn        |    5      5  1m02.4s
quantile(length.(hsp_knns), 0:0.1:1) = [2.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0]
Test Summary: | Total  Time
HSP           |     0  3.2s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:38.148
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-21T19:19:38.184
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=2 ep=2 n=2 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-21T19:19:39.409
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 14, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:39.813
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000006, 0x0000000b, 0x0000000c, 0x00000012, 0x00000013, 0x00000018, 0x0000001a, 0x0000001d, 0x00000021, 0x00000058]
D.nn = Int32[1, 2, 3, 4, 3, 6, 6, 1, 3, 2, 11, 12, 1, 6, 6, 4, 3, 18, 19, 4, 6, 3, 3, 24, 11, 26, 18, 1, 29, 2, 11, 2, 33, 3, 1, 6, 19, 3, 19, 18, 11, 3, 24, 1, 2, 12, 18, 3, 12, 18, 24, 29, 2, 4, 4, 26, 26, 26, 29, 12, 29, 3, 3, 1, 11, 3, 18, 26, 12, 3, 29, 3, 3, 1, 2, 18, 6, 19, 1, 29, 6, 3, 3, 2, 18, 18, 29, 88, 2, 11, 3, 12, 3, 1, 12, 3, 29, 29, 18, 12]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.07486063, 0.0, 0.021095157, 0.071404934, 0.01787883, 0.015727818, 0.0, 0.0, 0.063878834, 0.053358495, 0.051314473, 0.09749043, 0.04339814, 0.0, 0.0, 0.03446573, 0.0029528737, 0.032815576, 0.05237961, 0.0, 0.062039495, 0.0, 0.059091687, 0.015738666, 0.0, 0.032797992, 0.04448372, 0.04340595, 0.0, 0.016829133, 0.031274855, 0.06402546, 0.034912944, 0.03139633, 0.027543485, 0.03874427, 0.04284191, 0.006607473, 0.039311767, 0.05373925, 0.0046414733, 0.010903358, 0.04923922, 0.051623046, 0.04046458, 0.061041653, 0.030447364, 0.044074178, 0.048812866, 0.0012171268, 0.022826076, 0.04897505, 0.034510076, 0.09031981, 0.0147410035, 0.001914382, 0.016673446, 0.01637888, 0.013514817, 0.034775138, 0.07243848, 0.019042134, 0.009768784, 0.041461885, 0.036889374, 0.06390798, 0.06290698, 0.048644483, 0.020626783, 0.033124506, 0.007345617, 0.017462194, 0.015136719, 0.039975345, 0.012091994, 0.025836408, 0.08078539, 0.025504768, 0.014266551, 0.025991797, 0.07138419, 0.043174982, 0.036760986, 0.0, 0.08581692, 0.043311775, 0.004829943, 0.022837698, 0.03540659, 0.04103917, 0.010202527, 0.036944747, 0.015726209, 0.015217721, 0.01533705, 0.059031248]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  18.5s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-21T19:19:41.001
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.001
[ Info: neardup> range: 33:48, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.001
[ Info: neardup> range: 49:64, current elements: 13, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.001
[ Info: neardup> range: 65:80, current elements: 13, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.001
[ Info: neardup> range: 81:96, current elements: 13, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.001
[ Info: neardup> range: 97:100, current elements: 14, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.001
[ Info: neardup> finished current elements: 14, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.001
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000006, 0x0000000b, 0x0000000c, 0x00000012, 0x00000013, 0x00000018, 0x0000001a, 0x0000001d, 0x00000021, 0x00000058]
D.nn = Int32[1, 2, 3, 4, 3, 6, 6, 1, 3, 2, 11, 12, 1, 6, 6, 4, 3, 18, 19, 4, 6, 3, 3, 24, 11, 26, 3, 1, 29, 2, 11, 2, 33, 3, 1, 6, 19, 3, 19, 18, 11, 3, 24, 1, 2, 12, 18, 3, 12, 18, 24, 29, 2, 4, 4, 26, 26, 26, 29, 12, 29, 3, 3, 1, 11, 3, 18, 26, 12, 3, 29, 3, 3, 1, 2, 18, 6, 19, 1, 29, 6, 3, 3, 2, 18, 18, 29, 88, 2, 11, 3, 12, 3, 1, 12, 3, 29, 29, 18, 12]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.07486063, 0.0, 0.021095157, 0.071404934, 0.01787883, 0.015727818, 0.0, 0.0, 0.063878834, 0.053358495, 0.051314473, 0.09749043, 0.04339814, 0.0, 0.0, 0.03446573, 0.0029528737, 0.032815576, 0.05237961, 0.0, 0.062039495, 0.0, 0.08187604, 0.015738666, 0.0, 0.032797992, 0.04448372, 0.04340595, 0.0, 0.016829133, 0.031274855, 0.06402546, 0.034912944, 0.03139633, 0.027543485, 0.03874427, 0.04284191, 0.006607473, 0.039311767, 0.05373925, 0.0046414733, 0.010903358, 0.04923922, 0.051623046, 0.04046458, 0.061041653, 0.030447364, 0.044074178, 0.048812866, 0.0012171268, 0.022826076, 0.04897505, 0.034510076, 0.09031981, 0.0147410035, 0.001914382, 0.016673446, 0.01637888, 0.013514817, 0.034775138, 0.07243848, 0.019042134, 0.009768784, 0.041461885, 0.036889374, 0.06390798, 0.06290698, 0.048644483, 0.020626783, 0.033124506, 0.007345617, 0.017462194, 0.015136719, 0.039975345, 0.012091994, 0.025836408, 0.08078539, 0.025504768, 0.014266551, 0.025991797, 0.07138419, 0.043174982, 0.036760986, 0.0, 0.08581692, 0.043311775, 0.004829943, 0.022837698, 0.03540659, 0.04103917, 0.010202527, 0.036944747, 0.015726209, 0.015217721, 0.01533705, 0.059031248]
Test Summary:       | Pass  Total  Time
neardup small block |    3      3  0.0s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.096
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-21T19:19:41.096
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.096
[ Info: neardup> range: 33:48, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.096
[ Info: neardup> range: 49:64, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.096
[ Info: neardup> range: 65:80, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.097
[ Info: neardup> range: 81:96, current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.097
[ Info: neardup> range: 97:100, current elements: 21, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.097
[ Info: neardup> finished current elements: 21, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:41.097
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000013, 0x00000018, 0x0000001d, 0x00000043, 0x00000058]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 3, 7, 19, 9, 6, 3, 3, 24, 11, 13, 3, 1, 29, 2, 8, 2, 16, 3, 1, 7, 19, 3, 19, 12, 11, 9, 24, 1, 10, 12, 7, 8, 5, 3, 24, 29, 2, 4, 4, 15, 1, 8, 29, 12, 29, 9, 9, 1, 13, 3, 67, 13, 5, 3, 29, 3, 3, 13, 2, 19, 7, 19, 1, 29, 14, 3, 9, 2, 67, 67, 29, 88, 10, 8, 3, 12, 8, 13, 12, 5, 29, 29, 67, 12]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04339814, 0.078828275, 0.0, 0.021820068, 0.0029528737, 0.032815576, 0.05237961, 0.0, 0.062039495, 0.09166354, 0.08187604, 0.015738666, 0.0, 0.032797992, 0.024262965, 0.04340595, 0.028744817, 0.016829133, 0.031274855, 0.05840683, 0.034912944, 0.03139633, 0.027543485, 0.07160163, 0.04284191, 0.0037443042, 0.039311767, 0.05373925, 0.0045645833, 0.010903358, 0.05544603, 0.027806759, 0.0068540573, 0.08522576, 0.030447364, 0.044074178, 0.048812866, 0.0012171268, 0.022826076, 0.032805324, 0.055143356, 0.026762128, 0.0147410035, 0.001914382, 0.016673446, 0.0100902915, 0.0060925484, 0.034775138, 0.005568087, 0.019042134, 0.0, 0.027357638, 0.02410227, 0.06390798, 0.06290698, 0.048644483, 0.020626783, 0.010396183, 0.007345617, 0.08575553, 0.0032547712, 0.039975345, 0.012091994, 0.025836408, 0.00688231, 0.025504768, 0.0066351295, 0.025991797, 0.06717718, 0.04766363, 0.036760986, 0.0, 0.033427358, 0.028605998, 0.004829943, 0.022837698, 0.035004497, 0.024455309, 0.010202527, 0.017201126, 0.015726209, 0.015217721, 0.03508526, 0.059031248]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  0.1s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:48.528
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=7 n=7 2025-09-21T19:19:48.529
[ Info: neardup> range: 17:32, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:48.538
[ Info: neardup> range: 33:48, current elements: 12, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:48.538
[ Info: neardup> range: 49:64, current elements: 13, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:48.539
[ Info: neardup> range: 65:80, current elements: 13, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:48.539
[ Info: neardup> range: 81:96, current elements: 13, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:48.539
[ Info: neardup> range: 97:100, current elements: 14, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:48.539
[ Info: neardup> finished current elements: 14, n: 100, ϵ: 0.1, timestamp: 2025-09-21T19:19:48.539
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000006, 0x0000000b, 0x0000000c, 0x00000012, 0x00000013, 0x00000018, 0x0000001a, 0x0000001d, 0x00000021, 0x00000058]
D.nn = Int32[1, 2, 3, 4, 3, 6, 6, 1, 3, 2, 11, 12, 1, 6, 6, 4, 3, 18, 19, 4, 6, 3, 3, 24, 11, 26, 3, 1, 29, 2, 11, 2, 33, 3, 1, 6, 19, 3, 19, 18, 11, 3, 24, 1, 2, 12, 18, 3, 12, 18, 24, 29, 2, 4, 4, 26, 26, 26, 29, 12, 29, 3, 3, 1, 11, 3, 18, 26, 12, 3, 29, 3, 3, 1, 2, 18, 6, 19, 1, 29, 6, 3, 3, 2, 18, 18, 29, 88, 2, 11, 3, 12, 3, 1, 12, 3, 29, 29, 18, 12]
D.dist = Float32[0.0, 0.0, 0.0, 0.0, 0.07486063, 0.0, 0.021095157, 0.071404934, 0.01787883, 0.015727818, 0.0, 0.0, 0.063878834, 0.053358495, 0.051314473, 0.09749043, 0.04339814, 0.0, 0.0, 0.03446573, 0.0029528737, 0.032815576, 0.05237961, 0.0, 0.062039495, 0.0, 0.08187604, 0.015738666, 0.0, 0.032797992, 0.04448372, 0.04340595, 0.0, 0.016829133, 0.031274855, 0.06402546, 0.034912944, 0.03139633, 0.027543485, 0.03874427, 0.04284191, 0.006607473, 0.039311767, 0.05373925, 0.0046414733, 0.010903358, 0.04923922, 0.051623046, 0.04046458, 0.061041653, 0.030447364, 0.044074178, 0.048812866, 0.0012171268, 0.022826076, 0.04897505, 0.034510076, 0.09031981, 0.0147410035, 0.001914382, 0.016673446, 0.01637888, 0.013514817, 0.034775138, 0.07243848, 0.019042134, 0.009768784, 0.041461885, 0.036889374, 0.06390798, 0.06290698, 0.048644483, 0.020626783, 0.033124506, 0.007345617, 0.017462194, 0.015136719, 0.039975345, 0.012091994, 0.025836408, 0.08078539, 0.025504768, 0.014266551, 0.025991797, 0.07138419, 0.043174982, 0.036760986, 0.0, 0.08581692, 0.043311775, 0.004829943, 0.022837698, 0.03540659, 0.04103917, 0.010202527, 0.036944747, 0.015726209, 0.015217721, 0.01533705, 0.059031248]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.4s
computing farthest point 1, dmax: Inf, imax: 11, n: 30
computing farthest point 2, dmax: 1.2125546, imax: 22, n: 30
computing farthest point 3, dmax: 1.0838422, imax: 29, n: 30
computing farthest point 4, dmax: 0.8957876, imax: 19, n: 30
computing farthest point 5, dmax: 0.8699598, imax: 26, n: 30
computing farthest point 6, dmax: 0.74996, imax: 16, n: 30
computing farthest point 7, dmax: 0.74898404, imax: 30, n: 30
computing farthest point 8, dmax: 0.72563195, imax: 28, n: 30
computing farthest point 9, dmax: 0.59692687, imax: 14, n: 30
computing farthest point 10, dmax: 0.5473573, imax: 18, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.7s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.7s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-21T19:19:57.782
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=8, Δ=0.95, maxvisits=108) 2025-09-21T19:20:10.878
LOG n.size quantiles:[2.0, 2.0, 3.0, 3.0, 3.0]
(i, j, d) = (2, 131, -1.1920929f-7)
(i, j, d, :parallel) = (2, 131, -1.1920929f-7, :parallel)
[ Info: NOTE: the exact method will be faster on small datasets due to the preprocessing step of the approximation method
[ Info: ("closestpair computation time", :approx => 21.458221547, :exact => 1.043779402)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  23.1s
  5.755082 seconds (1.00 k allocations: 140.742 KiB)
SEARCH Exhaustive 1: 0.005710 seconds
SEARCH Exhaustive 2: 0.005639 seconds
SEARCH Exhaustive 3: 0.005746 seconds
typeof(seq) = ExhaustiveSearch{SqL2Distance, MatrixDatabase{Matrix{Float32}}}
typeof(ectx) = GenericContext{KnnSorted}
typeof(q) = SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}
typeof(res) = KnnSorted{Vector{IdWeight}}
[ Info: ===================== minrecall ==============================
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-21T19:20:40.470
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
LOG add_vertex! sp=295 ep=299 n=294 BeamSearch(bsize=4, Δ=1.21275, maxvisits=182) 2025-09-21T19:20:46.458
LOG n.size quantiles:[1.0, 2.0, 3.0, 3.0, 3.0]
[ Info: RECALL BAJO!! recall: 0.2375, #objects: 11224, #queries: 32
[ Info: [0.3645728826522827, 0.37720146775245667, 0.8330537676811218, 0.24754022061824799, 0.17208164930343628, 0.3419845998287201, 0.6844384670257568, 0.22826805710792542, 0.3441331386566162, 0.45004668831825256, 0.3497995138168335, 0.3056269884109497, 0.6020161509513855, 0.29216402769088745, 0.15497049689292908, 0.23863480985164642, 0.20301412045955658, 0.4373682141304016, 0.33919134736061096, 0.2298060804605484, 0.2536603510379791, 0.45199108123779297, 1.1091656684875488, 0.2179090827703476, 0.2597670555114746, 0.2336317002773285, 0.14472056925296783, 0.2982045114040375, 0.17747646570205688, 0.4605778157711029, 0.1446564793586731, 0.3484553396701813]
(g, r) = (Set(Int32[255, 5170, 8647, 1302, 2525, 3700, 843, 7237, 4945, 11169]), Set(Int32[638, 7816, 9767, 9476, 9465, 7546, 8720, 2880, 9937, 10038]))
(g, r) = (Set(Int32[10251, 4684, 10964, 6778, 4075, 2967, 1327, 8059, 5536, 413]), Set(Int32[5611, 6319, 668, 8854, 4098, 3100, 7507, 3030, 1026, 2145]))
(g, r) = (Set(Int32[1758, 2277, 5983, 5868, 4293, 6146, 1512, 969, 11114, 1930]), Set(Int32[5886, 638, 8815, 6541, 7481, 6717, 9331, 6058, 6994, 7325]))
(g, r) = (Set(Int32[2954, 9690, 2433, 6665, 11188, 2183, 5519, 9623, 7971, 7620]), Set(Int32[9690, 1103, 6162, 3861, 5519, 5405, 1094, 1241, 11192, 7326]))
(g, r) = (Set(Int32[2565, 2089, 965, 7060, 7252, 4131, 9543, 10849, 6058, 176]), Set(Int32[2089, 6892, 5346, 7060, 7252, 8001, 4131, 6485, 7235, 176]))
(g, r) = (Set(Int32[8668, 1283, 8234, 10161, 7370, 6457, 9838, 925, 4014, 2427]), Set(Int32[9498, 10581, 8234, 6166, 6988, 7481, 9186, 8947, 10161, 8668]))
(g, r) = (Set(Int32[7978, 4966, 4623, 3556, 6309, 2039, 10457, 675, 8508, 7367]), Set(Int32[3956, 7185, 3070, 6549, 5847, 6309, 4188, 3539, 5672, 5638]))
(g, r) = (Set(Int32[10884, 6295, 6813, 4095, 4021, 10100, 10352, 5386, 4255, 5445]), Set(Int32[10884, 2927, 6813, 4095, 4021, 9986, 10100, 8969, 10352, 5386]))
(g, r) = (Set(Int32[6481, 8122, 5453, 563, 7365, 8445, 9317, 9785, 6409, 741]), Set(Int32[5122, 2541, 7605, 3956, 793, 5453, 7365, 5893, 5982, 7187]))
(g, r) = (Set(Int32[5336, 9196, 5397, 9420, 8886, 10760, 4191, 742, 6114, 9039]), Set(Int32[547, 2926, 8928, 197, 82, 1292, 382, 2498, 2755, 275]))
(g, r) = (Set(Int32[2818, 10832, 4690, 3014, 3851, 4253, 6174, 8726, 6398, 4218]), Set(Int32[2980, 3115, 2548, 1020, 2888, 1387, 1683, 2566, 1681, 1524]))
(g, r) = (Set(Int32[774, 10176, 2025, 8060, 3384, 5510, 6775, 2389, 679, 2941]), Set(Int32[2025, 2955, 11051, 2709, 1836, 7907, 2828, 2941, 2362, 1649]))
(g, r) = (Set(Int32[2297, 8327, 5313, 10950, 3427, 1429, 3770, 10948, 10023, 2699]), Set(Int32[11051, 7868, 2555, 9546, 3073, 7702, 7022, 2712, 2362, 3278]))
(g, r) = (Set(Int32[1674, 5556, 2243, 5007, 8851, 10710, 6254, 3372, 2690, 5017]), Set(Int32[4184, 8151, 1546, 8851, 10710, 3372, 9462, 7870, 267, 9314]))
(g, r) = (Set(Int32[1873, 11054, 756, 5268, 4837, 3644, 10264, 2440, 3559, 7576]), Set(Int32[1873, 7140, 1156, 8559, 4837, 3644, 10264, 2440, 3559, 7576]))
(g, r) = (Set(Int32[4907, 2902, 2958, 1621, 7132, 1114, 1929, 1682, 8329, 9231]), Set(Int32[4907, 2902, 4359, 10856, 4654, 158, 11147, 414, 4150, 3413]))
(g, r) = (Set(Int32[5800, 1945, 72, 7489, 5025, 5358, 9596, 8712, 1815, 3653]), Set(Int32[1945, 72, 3342, 2220, 5025, 1356, 1187, 7255, 1815, 2621]))
(g, r) = (Set(Int32[775, 6209, 2569, 10554, 8876, 2185, 10545, 3623, 5766, 8973]), Set(Int32[10189, 7546, 10358, 8720, 10006, 8295, 7561, 9343, 9560, 7712]))
(g, r) = (Set(Int32[1153, 2030, 781, 2084, 4369, 6743, 4350, 6694, 6330, 9676]), Set(Int32[7722, 7247, 4841, 4963, 5626, 3892, 1225, 6989, 1215, 89]))
(g, r) = (Set(Int32[9168, 6867, 5150, 932, 3032, 1606, 1657, 7945, 1029, 7089]), Set(Int32[1486, 932, 386, 1606, 1657, 1029, 1957, 8336, 2126, 4801]))
(g, r) = (Set(Int32[7902, 7573, 3829, 3850, 3723, 9644, 6282, 6984, 4406, 3086]), Set(Int32[2023, 3829, 3723, 2821, 241, 6282, 2662, 6258, 3086, 2743]))
(g, r) = (Set(Int32[9305, 9741, 5754, 8684, 3566, 9730, 4974, 9924, 2417, 3549]), Set(Int32[3197, 7383, 5566, 3568, 6299, 5763, 2339, 7995, 2448, 8991]))
(g, r) = (Set(Int32[9604, 3217, 4618, 8026, 1980, 7528, 319, 8793, 10, 4755]), Set(Int32[1696, 11189, 6807, 4877, 5553, 10794, 4055, 4280, 3640, 3180]))
(g, r) = (Set(Int32[2053, 2274, 10292, 1707, 2696, 9344, 5297, 4111, 10853, 8242]), Set(Int32[2053, 3169, 721, 6285, 8670, 682, 236, 3234, 2962, 32]))
(g, r) = (Set(Int32[11156, 2798, 8090, 135, 2615, 10142, 5596, 8187, 152, 6114]), Set(Int32[3618, 10593, 192, 838, 3950, 1003, 5681, 152, 742, 5264]))
(g, r) = (Set(Int32[8145, 1508, 10561, 10326, 8378, 6967, 434, 9701, 5923, 4790]), Set(Int32[10561, 8975, 9689, 10326, 8378, 9701, 8479, 9131, 7618, 8861]))
(g, r) = (Set(Int32[4357, 5473, 10885, 3826, 5917, 1019, 4098, 6639, 10009, 6281]), Set(Int32[4357, 5473, 10885, 3826, 5917, 1019, 7784, 4098, 6639, 6281]))
(g, r) = (Set(Int32[939, 7132, 1573, 5291, 5954, 9840, 5456, 10577, 7923, 122]), Set(Int32[4907, 4359, 2902, 10856, 7988, 3125, 375, 3319, 414, 3413]))
(g, r) = (Set(Int32[4183, 830, 4903, 1880, 2040, 10935, 762, 7992, 2576, 355]), Set(Int32[4183, 6623, 4903, 4179, 1880, 6411, 2040, 10935, 762, 6824]))
(g, r) = (Set(Int32[287, 7250, 6560, 9637, 6758, 179, 4699, 10129, 9765, 7632]), Set(Int32[54, 2110, 6560, 8320, 10096, 2931, 1686, 9515, 7595, 7632]))
(g, r) = (Set(Int32[7901, 3070, 1234, 488, 120, 5891, 3539, 4458, 3381, 2891]), Set(Int32[2869, 3070, 3420, 5891, 488, 120, 6549, 3539, 5701, 3381]))
(g, r) = (Set(Int32[4926, 6105, 2718, 3068, 8271, 8716, 9893, 6862, 7651, 6772]), Set(Int32[561, 2248, 6539, 7824, 2909, 7181, 9155, 7651, 6421, 5434]))
collect(Int32, IdView(p)) = Int32[9937, 9465, 10038, 9476, 9767, 7816, 2880, 7546, 8720, 638]
collect(Int32, IdView(p)) = Int32[3030, 7507, 5611, 3100, 2145, 668, 6319, 4098, 1026, 8854]
collect(Int32, IdView(p)) = Int32[6717, 9331, 6058, 7481, 8815, 6994, 7325, 5886, 6541, 638]
collect(Int32, IdView(p)) = Int32[5519, 9690, 1241, 1094, 11192, 7326, 1103, 6162, 3861, 5405]
collect(Int32, IdView(p)) = Int32[176, 7060, 4131, 7252, 2089, 8001, 5346, 6892, 6485, 7235]
collect(Int32, IdView(p)) = Int32[8234, 8668, 10161, 8947, 7481, 9498, 6988, 10581, 6166, 9186]
collect(Int32, IdView(p)) = Int32[6309, 7185, 5672, 3956, 3070, 5638, 4188, 3539, 5847, 6549]
collect(Int32, IdView(p)) = Int32[10352, 10884, 4095, 5386, 4021, 10100, 6813, 8969, 9986, 2927]
collect(Int32, IdView(p)) = Int32[5453, 7365, 5122, 5893, 793, 5982, 7187, 7605, 3956, 2541]
collect(Int32, IdView(p)) = Int32[2755, 275, 547, 2926, 8928, 382, 2498, 1292, 197, 82]
collect(Int32, IdView(p)) = Int32[2566, 2980, 2548, 3115, 1020, 1681, 1387, 2888, 1524, 1683]
collect(Int32, IdView(p)) = Int32[2025, 2941, 2362, 2828, 1649, 7907, 2955, 1836, 11051, 2709]
collect(Int32, IdView(p)) = Int32[7702, 3073, 2712, 2555, 7022, 9546, 2362, 3278, 11051, 7868]
collect(Int32, IdView(p)) = Int32[10710, 8851, 3372, 7870, 9462, 267, 9314, 4184, 8151, 1546]
collect(Int32, IdView(p)) = Int32[3559, 7576, 10264, 1873, 2440, 3644, 4837, 7140, 8559, 1156]
collect(Int32, IdView(p)) = Int32[2902, 4907, 10856, 4359, 414, 158, 4150, 11147, 3413, 4654]
collect(Int32, IdView(p)) = Int32[1815, 72, 1945, 5025, 2220, 1356, 2621, 3342, 1187, 7255]
collect(Int32, IdView(p)) = Int32[8720, 7546, 8295, 7561, 10006, 9343, 10358, 9560, 10189, 7712]
collect(Int32, IdView(p)) = Int32[4841, 4963, 1225, 6989, 89, 1215, 7247, 7722, 5626, 3892]
collect(Int32, IdView(p)) = Int32[1606, 1657, 1029, 932, 1957, 8336, 386, 2126, 1486, 4801]
collect(Int32, IdView(p)) = Int32[3086, 3829, 6282, 3723, 2743, 2821, 6258, 241, 2023, 2662]
collect(Int32, IdView(p)) = Int32[3568, 7995, 7383, 5763, 6299, 2339, 2448, 8991, 3197, 5566]
collect(Int32, IdView(p)) = Int32[3640, 11189, 10794, 4280, 6807, 4055, 1696, 3180, 4877, 5553]
collect(Int32, IdView(p)) = Int32[2053, 8670, 682, 236, 3169, 3234, 2962, 721, 32, 6285]
collect(Int32, IdView(p)) = Int32[152, 1003, 3950, 192, 742, 5264, 5681, 3618, 10593, 838]
collect(Int32, IdView(p)) = Int32[10561, 10326, 8378, 9701, 8479, 9131, 8975, 9689, 8861, 7618]
collect(Int32, IdView(p)) = Int32[4357, 5473, 6281, 10885, 4098, 6639, 5917, 3826, 1019, 7784]
collect(Int32, IdView(p)) = Int32[4359, 4907, 375, 7988, 2902, 10856, 414, 3413, 3319, 3125]
collect(Int32, IdView(p)) = Int32[2040, 4183, 10935, 1880, 762, 4903, 6824, 6411, 4179, 6623]
collect(Int32, IdView(p)) = Int32[6560, 7632, 2931, 7595, 54, 2110, 8320, 1686, 9515, 10096]
collect(Int32, IdView(p)) = Int32[3539, 120, 3070, 488, 3381, 5891, 6549, 3420, 5701, 2869]
collect(Int32, IdView(p)) = Int32[7651, 6421, 5434, 7181, 9155, 6539, 561, 2909, 2248, 7824]
quantile(neighbors_length.(Ref(index.adj), 1:length(index)), 0:0.1:1.0) = [1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 4.0, 5.0, 6.0, 8.0, 41.0]
     Testing SimilaritySearch tests passed 

Testing completed after 725.2s

PkgEval succeeded after 860.81s