Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1184 (441ebf9584*) started at 2025-09-24T07:00:15.415


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.41s


################################################################################
# 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 v4.4.0
  [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
  [431bcebd] + SciMLPublic v1.0.0
  [7e506255] + ScopedValues v1.5.0
  [0e966ebe] + SearchModels v0.4.1
  [053f045d] + SimilaritySearch v0.13.0
  [a2af1166] + SortingAlgorithms v1.2.2
  [aedffcd0] + Static v1.3.0
  [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 3.89s


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

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 110.34s


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

     Testing SimilaritySearch
      Status `/tmp/jl_CcWRXp/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_CcWRXp/Manifest.toml`
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.4.0
  [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
  [431bcebd] SciMLPublic v1.0.0
  [7e506255] ScopedValues v1.5.0
  [0e966ebe] SearchModels v0.4.1
  [053f045d] SimilaritySearch v0.13.0
  [a2af1166] SortingAlgorithms v1.2.2
  [aedffcd0] Static v1.3.0
  [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  12.1s
Precompiling packages...
 103188.2 ms  ✓ JET
  1 dependency successfully precompiled in 103 seconds. 38 already precompiled.
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 20005  20005  3.9s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.7s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.2s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 10.707619 seconds (1000 allocations: 78.125 KiB)
 10.834575 seconds (1000 allocations: 78.125 KiB)
  4.020766 seconds (1000 allocations: 78.125 KiB)
  4.009745 seconds (1000 allocations: 78.125 KiB)
  3.990881 seconds (1000 allocations: 78.125 KiB)
  3.321643 seconds (1000 allocations: 78.125 KiB)
  3.977442 seconds (1000 allocations: 78.125 KiB)
  3.889301 seconds (1000 allocations: 78.125 KiB)
 13.016836 seconds (1000 allocations: 78.125 KiB)
 14.423056 seconds (1000 allocations: 78.125 KiB)
 25.753025 seconds (1000 allocations: 78.125 KiB)
 27.393382 seconds (1000 allocations: 78.125 KiB)
 20.519805 seconds (6.23 k allocations: 388.672 KiB)
 18.928054 seconds (1000 allocations: 78.125 KiB)
 16.687100 seconds (1.00 k allocations: 78.141 KiB)
 16.837370 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m30.2s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  2.893269 seconds (1000 allocations: 78.125 KiB)
  3.149309 seconds (1000 allocations: 78.125 KiB)
 28.895387 seconds (1000 allocations: 78.125 KiB)
 28.566062 seconds (1000 allocations: 78.125 KiB)
 29.100091 seconds (1000 allocations: 78.125 KiB)
 28.458862 seconds (1000 allocations: 78.125 KiB)
  3.941387 seconds (1000 allocations: 78.125 KiB)
  3.970861 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m12.9s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  8.141191 seconds (1000 allocations: 78.125 KiB)
  8.055030 seconds (1000 allocations: 78.125 KiB)
  8.169053 seconds (1000 allocations: 78.125 KiB)
  8.271598 seconds (1000 allocations: 78.125 KiB)
  8.279811 seconds (1000 allocations: 78.125 KiB)
  8.248880 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  51.9s
  0.044516 seconds (1.00 k allocations: 78.141 KiB)
  0.043639 seconds (1000 allocations: 78.125 KiB)
  0.039558 seconds (1000 allocations: 78.125 KiB)
  0.039731 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.8s
  0.058689 seconds (1000 allocations: 78.125 KiB)
  0.058456 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.8s
ExhaustiveSearch allknn: 3.841911 seconds (2.38 M allocations: 132.185 MiB, 1.31% gc time, 99.96% compilation time)
ParallelExhaustiveSearch allknn: 1.207667 seconds (609.25 k allocations: 31.917 MiB, 99.84% compilation time)
Test Summary: | Pass  Total     Time
allknn        |    5      5  1m02.4s
quantile(length.(hsp_knns), 0:0.1:1) = [3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 6.0]
Test Summary: | Total  Time
HSP           |     0  3.3s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:10.963
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-24T07:13:11.002
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) 2025-09-24T07:13:12.272
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:12.667
D.map = UInt32[0x00000001, 0x00000002, 0x00000005, 0x00000009, 0x0000000e, 0x00000013, 0x0000001f, 0x00000031, 0x00000042, 0x00000051, 0x00000053]
D.nn = Int32[1, 2, 2, 1, 5, 1, 5, 1, 9, 9, 9, 2, 9, 14, 5, 2, 2, 2, 19, 1, 5, 5, 19, 19, 9, 9, 5, 1, 2, 5, 31, 1, 2, 5, 9, 31, 1, 14, 1, 2, 2, 2, 31, 9, 31, 2, 9, 5, 49, 31, 9, 1, 1, 31, 2, 31, 1, 19, 31, 5, 1, 2, 9, 9, 2, 66, 1, 5, 5, 1, 1, 2, 2, 5, 31, 9, 2, 5, 49, 66, 81, 31, 83, 2, 9, 2, 9, 1, 1, 19, 2, 2, 2, 31, 2, 2, 19, 66, 81, 9]
D.dist = Float32[0.0, 0.0, 0.049019158, 0.07481098, 0.0, 0.06481308, 0.03142506, 0.07459116, 0.0, 0.09427118, 0.051882863, 0.07480472, 0.024345696, 0.0, 0.07047206, 0.09667134, 0.0405007, 0.04507774, 0.0, 0.04404682, 0.0057420135, 0.07045096, 0.009726465, 0.04489863, 0.008406758, 0.02982378, 0.07663536, 0.0474419, 0.051606596, 0.029211283, 0.0, 0.06032628, 0.04306984, 0.014180899, 0.021543086, 0.035372555, 0.012563825, 0.02741766, 0.08357793, 0.015414596, 0.071970046, 0.047418714, 0.07005131, 0.012132704, 0.003390193, 0.07254368, 0.0072228312, 0.0017539263, 0.0, 0.04213029, 0.081103146, 0.023998976, 0.046042323, 0.06690633, 0.055292368, 0.021152735, 0.015429795, 0.045173883, 0.055361986, 0.006441176, 0.0091513395, 0.04360825, 0.055020213, 0.051405072, 0.07213622, 0.0, 0.04482317, 0.012615502, 0.023421347, 0.04362011, 0.020111322, 0.040724635, 0.05869329, 0.020759821, 0.015973687, 0.06402367, 0.083678186, 0.08159423, 0.015893519, 0.043628335, 0.0, 0.07747394, 0.0, 0.021783412, 0.04697776, 0.012539148, 0.018229902, 0.058078945, 0.043622613, 0.030615866, 0.05101192, 0.057396054, 0.051689506, 0.047671914, 0.0140196085, 0.03379947, 0.044513524, 0.044246018, 0.03968537, 0.061301768]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  19.2s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:13.910
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-24T07:13:13.910
LOG n.size quantiles:[0.0, 0.0, 0.0, 0.0, 0.0]
[ Info: neardup> range: 17:32, current elements: 5, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:13.911
[ Info: neardup> range: 33:48, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:13.911
[ Info: neardup> range: 49:64, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:13.911
[ Info: neardup> range: 65:80, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:13.911
[ Info: neardup> range: 81:96, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:13.912
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:13.912
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:13.912
D.map = UInt32[0x00000001, 0x00000002, 0x00000005, 0x00000009, 0x0000000e, 0x00000013, 0x0000001f, 0x00000031, 0x00000042, 0x00000051, 0x00000053]
D.nn = Int32[1, 2, 2, 1, 5, 1, 5, 1, 9, 9, 9, 2, 9, 14, 5, 2, 2, 2, 19, 1, 5, 5, 19, 5, 9, 9, 5, 1, 2, 5, 31, 1, 2, 5, 9, 31, 1, 14, 1, 2, 2, 2, 31, 9, 31, 2, 9, 5, 49, 31, 9, 1, 1, 31, 2, 31, 1, 19, 31, 5, 1, 2, 9, 9, 2, 66, 1, 5, 5, 1, 1, 2, 2, 5, 31, 9, 2, 5, 49, 2, 81, 31, 83, 2, 9, 2, 9, 1, 1, 19, 2, 2, 2, 31, 2, 2, 19, 66, 81, 9]
D.dist = Float32[0.0, 0.0, 0.049019158, 0.07481098, 0.0, 0.06481308, 0.03142506, 0.07459116, 0.0, 0.09427118, 0.051882863, 0.07480472, 0.024345696, 0.0, 0.07047206, 0.09667134, 0.0405007, 0.04507774, 0.0, 0.04404682, 0.0057420135, 0.07045096, 0.009726465, 0.04708445, 0.008406758, 0.02982378, 0.07663536, 0.0474419, 0.051606596, 0.029211283, 0.0, 0.06032628, 0.04306984, 0.014180899, 0.021543086, 0.035372555, 0.012563825, 0.02741766, 0.08357793, 0.015414596, 0.071970046, 0.047418714, 0.07005131, 0.012132704, 0.003390193, 0.07254368, 0.0072228312, 0.0017539263, 0.0, 0.04213029, 0.081103146, 0.023998976, 0.046042323, 0.06690633, 0.055292368, 0.021152735, 0.015429795, 0.045173883, 0.055361986, 0.006441176, 0.0091513395, 0.04360825, 0.055020213, 0.051405072, 0.07213622, 0.0, 0.04482317, 0.012615502, 0.023421347, 0.04362011, 0.020111322, 0.040724635, 0.05869329, 0.020759821, 0.015973687, 0.06402367, 0.083678186, 0.08159423, 0.015893519, 0.087617695, 0.0, 0.07747394, 0.0, 0.021783412, 0.04697776, 0.012539148, 0.018229902, 0.058078945, 0.043622613, 0.030615866, 0.05101192, 0.057396054, 0.051689506, 0.047671914, 0.0140196085, 0.03379947, 0.044513524, 0.044246018, 0.03968537, 0.061301768]
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: 2025-09-24T07:13:14.008
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-24T07:13:14.008
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: 2025-09-24T07:13:14.009
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:14.009
[ Info: neardup> range: 49:64, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:14.009
[ Info: neardup> range: 65:80, current elements: 17, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:14.009
[ Info: neardup> range: 81:96, current elements: 18, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:14.009
[ Info: neardup> range: 97:100, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:14.009
[ Info: neardup> finished current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:14.009
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x0000003a, 0x0000004b, 0x00000051]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 3, 16, 15, 4, 5, 12, 7, 7, 9, 11, 5, 12, 2, 5, 8, 6, 2, 5, 9, 16, 1, 13, 12, 2, 3, 2, 16, 9, 6, 12, 9, 5, 8, 6, 10, 1, 8, 8, 3, 6, 1, 58, 6, 5, 1, 2, 10, 12, 2, 3, 1, 5, 5, 4, 1, 2, 2, 5, 75, 10, 16, 4, 8, 3, 81, 16, 10, 2, 13, 2, 9, 12, 6, 12, 16, 12, 2, 13, 2, 2, 12, 75, 10, 11]
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, 0.013222575, 0.012793481, 0.08769429, 0.010212779, 0.0057420135, 0.012259603, 0.049020827, 0.013387203, 0.008406758, 0.0060215592, 0.07663536, 0.01592207, 0.051606596, 0.029211283, 0.070571005, 0.0047982335, 0.04306984, 0.014180899, 0.021543086, 0.054819345, 0.012563825, 0.01086843, 0.0051023364, 0.015414596, 0.020991087, 0.047418714, 0.016355097, 0.012132704, 0.060274363, 0.010476232, 0.0072228312, 0.0017539263, 0.036913335, 0.031346858, 0.05999869, 0.023998976, 0.016297042, 0.020855665, 0.01412487, 0.05888772, 0.015429795, 0.0, 0.012075484, 0.006441176, 0.0091513395, 0.04360825, 0.049440384, 0.04483688, 0.07213622, 0.07558286, 0.04482317, 0.012615502, 0.023421347, 0.029860258, 0.020111322, 0.040724635, 0.05869329, 0.020759821, 0.0, 0.003806293, 0.017119765, 0.016380072, 0.019697666, 0.009118438, 0.0, 0.012573898, 0.0050497055, 0.021783412, 0.021086574, 0.012539148, 0.018229902, 0.011978388, 0.0093794465, 0.04391098, 0.009967983, 0.014665127, 0.051689506, 0.04232049, 0.0140196085, 0.03379947, 0.04492736, 0.059503675, 0.038620234, 0.03192413]
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: 2025-09-24T07:13:21.706
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=5 n=5 2025-09-24T07:13:21.706
[ Info: neardup> range: 17:32, current elements: 5, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:21.712
[ Info: neardup> range: 33:48, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:21.712
[ Info: neardup> range: 49:64, current elements: 7, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:21.712
[ Info: neardup> range: 65:80, current elements: 8, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:21.712
[ Info: neardup> range: 81:96, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:21.712
[ Info: neardup> range: 97:100, current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:21.712
[ Info: neardup> finished current elements: 11, n: 100, ϵ: 0.1, timestamp: 2025-09-24T07:13:21.712
D.map = UInt32[0x00000001, 0x00000002, 0x00000005, 0x00000009, 0x0000000e, 0x00000013, 0x0000001f, 0x00000031, 0x00000042, 0x00000051, 0x00000053]
D.nn = Int32[1, 2, 2, 1, 5, 1, 5, 1, 9, 9, 9, 2, 9, 14, 5, 2, 2, 2, 19, 1, 5, 5, 19, 5, 9, 9, 5, 1, 2, 5, 31, 1, 2, 5, 9, 31, 1, 14, 1, 2, 2, 2, 31, 9, 31, 2, 9, 5, 49, 31, 9, 1, 1, 31, 2, 31, 1, 19, 31, 5, 1, 2, 9, 9, 2, 66, 1, 5, 5, 1, 1, 2, 2, 5, 31, 9, 2, 5, 49, 2, 81, 31, 83, 2, 9, 2, 9, 1, 1, 19, 2, 2, 2, 31, 2, 2, 19, 66, 81, 9]
D.dist = Float32[0.0, 0.0, 0.049019158, 0.07481098, 0.0, 0.06481308, 0.03142506, 0.07459116, 0.0, 0.09427118, 0.051882863, 0.07480472, 0.024345696, 0.0, 0.07047206, 0.09667134, 0.0405007, 0.04507774, 0.0, 0.04404682, 0.0057420135, 0.07045096, 0.009726465, 0.04708445, 0.008406758, 0.02982378, 0.07663536, 0.0474419, 0.051606596, 0.029211283, 0.0, 0.06032628, 0.04306984, 0.014180899, 0.021543086, 0.035372555, 0.012563825, 0.02741766, 0.08357793, 0.015414596, 0.071970046, 0.047418714, 0.07005131, 0.012132704, 0.003390193, 0.07254368, 0.0072228312, 0.0017539263, 0.0, 0.04213029, 0.081103146, 0.023998976, 0.046042323, 0.06690633, 0.055292368, 0.021152735, 0.015429795, 0.045173883, 0.055361986, 0.006441176, 0.0091513395, 0.04360825, 0.055020213, 0.051405072, 0.07213622, 0.0, 0.04482317, 0.012615502, 0.023421347, 0.04362011, 0.020111322, 0.040724635, 0.05869329, 0.020759821, 0.015973687, 0.06402367, 0.083678186, 0.08159423, 0.015893519, 0.087617695, 0.0, 0.07747394, 0.0, 0.021783412, 0.04697776, 0.012539148, 0.018229902, 0.058078945, 0.043622613, 0.030615866, 0.05101192, 0.057396054, 0.051689506, 0.047671914, 0.0140196085, 0.03379947, 0.044513524, 0.044246018, 0.03968537, 0.061301768]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.7s
computing farthest point 1, dmax: Inf, imax: 29, n: 30
computing farthest point 2, dmax: 1.1424581, imax: 15, n: 30
computing farthest point 3, dmax: 1.0378888, imax: 10, n: 30
computing farthest point 4, dmax: 0.9249033, imax: 14, n: 30
computing farthest point 5, dmax: 0.80869925, imax: 28, n: 30
computing farthest point 6, dmax: 0.7086113, imax: 4, n: 30
computing farthest point 7, dmax: 0.67421824, imax: 3, n: 30
computing farthest point 8, dmax: 0.62495315, imax: 25, n: 30
computing farthest point 9, dmax: 0.5696905, imax: 27, n: 30
computing farthest point 10, dmax: 0.5292703, imax: 20, n: 30
Test Summary:            | Pass  Total  Time
farthest first traversal |    3      3  1.6s
Test Summary: | Pass  Total  Time
AdjacencyList |   15     15  1.6s
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-24T07:13:30.039
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=16, Δ=0.975, maxvisits=106) 2025-09-24T07:13:43.811
LOG n.size quantiles:[2.0, 2.0, 3.0, 3.0, 3.0]
(i, j, d) = (23, 696, -1.1920929f-7)
(i, j, d, :parallel) = (23, 696, -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 => 21.729733901000003, :exact => 0.947419162)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  23.2s
  5.654532 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.005124 seconds
SEARCH Exhaustive 2: 0.005194 seconds
SEARCH Exhaustive 3: 0.005397 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) 2025-09-24T07:14:12.847
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=1.1025, maxvisits=168) 2025-09-24T07:14:19.130
LOG n.size quantiles:[1.0, 2.0, 3.0, 3.0, 3.0]
[ Info: RECALL BAJO!! recall: 0.265625, #objects: 4989, #queries: 32
[ Info: [0.28810906410217285, 0.4682541787624359, 0.3340613842010498, 0.5467998385429382, 0.5581090450286865, 0.5591040253639221, 0.18912504613399506, 0.689606249332428, 0.44633662700653076, 0.8585449457168579, 0.2955743968486786, 0.5822293758392334, 1.0660127401351929, 0.2886512875556946, 0.3898615539073944, 0.22229865193367004, 0.33960720896720886, 0.2399187982082367, 0.32243531942367554, 0.36239245533943176, 0.648032009601593, 0.3113001585006714, 0.19899225234985352, 0.2949386239051819, 0.6885350346565247, 0.17803232371807098, 0.15577642619609833, 0.5297441482543945, 0.40528690814971924, 0.13483133912086487, 0.1459840089082718, 0.47372832894325256]
(g, r) = (Set(Int32[502, 2761, 3069, 2487, 2103, 2028, 2467, 704, 1172, 2621]), Set(Int32[2761, 2607, 2822, 3069, 2487, 2103, 4168, 3640, 3300, 2621]))
(g, r) = (Set(Int32[24, 1674, 1269, 2390, 701, 3979, 2448, 2451, 518, 2270]), Set(Int32[1674, 4251, 2930, 3171, 2827, 1509, 1911, 3329, 2909, 245]))
(g, r) = (Set(Int32[1194, 2699, 726, 996, 3725, 2127, 1984, 2120, 2199, 821]), Set(Int32[996, 2785, 2127, 2026, 1984, 2120, 2176, 1213, 2199, 1194]))
(g, r) = (Set(Int32[1182, 2940, 1397, 3835, 4000, 2967, 3049, 3724, 2823, 4105]), Set(Int32[2598, 3223, 2400, 3327, 1387, 2911, 2391, 1049, 2940, 1602]))
(g, r) = (Set(Int32[1289, 3141, 3859, 38, 1492, 135, 4805, 183, 2169, 1478]), Set(Int32[3168, 1944, 2625, 3043, 2978, 2762, 1492, 926, 316, 1353]))
(g, r) = (Set(Int32[4970, 2547, 4516, 4332, 88, 3644, 8, 2862, 1196, 3685]), Set(Int32[2547, 2575, 2983, 3014, 2271, 2296, 2242, 3137, 2862, 3246]))
(g, r) = (Set(Int32[881, 3328, 942, 963, 646, 2455, 4827, 1615, 940, 2143]), Set(Int32[4822, 422, 963, 1615, 940, 646, 1541, 4827, 1311, 3630]))
(g, r) = (Set(Int32[3360, 4823, 3750, 1804, 4717, 247, 3273, 2342, 1117, 4313]), Set(Int32[1565, 1698, 3786, 1601, 1987, 1990, 3072, 2554, 3105, 3705]))
(g, r) = (Set(Int32[4771, 1969, 2877, 2368, 1719, 1903, 1980, 3584, 732, 1388]), Set(Int32[2324, 1668, 3581, 3522, 4772, 4342, 714, 4419, 3536, 2415]))
(g, r) = (Set(Int32[4575, 3036, 2232, 4347, 3893, 1816, 925, 2090, 3540, 1285]), Set(Int32[2625, 1561, 3065, 1710, 3786, 1765, 1549, 2144, 3902, 492]))
(g, r) = (Set(Int32[1755, 4488, 3614, 1597, 2227, 2787, 1527, 2252, 3437, 455]), Set(Int32[3171, 2787, 1064, 3055, 3736, 2228, 3945, 2252, 43, 3437]))
(g, r) = (Set(Int32[4607, 3690, 2817, 3342, 1178, 2375, 859, 4862, 4758, 1469]), Set(Int32[4607, 2139, 3994, 2578, 3933, 4910, 1494, 2064, 4758, 2145]))
(g, r) = (Set(Int32[3066, 1940, 3248, 1666, 2215, 2443, 4968, 1825, 3549, 2054]), Set(Int32[4607, 3171, 2812, 3065, 2434, 2556, 1494, 3945, 3705, 2758]))
(g, r) = (Set(Int32[2972, 2705, 2945, 3711, 3791, 2898, 1551, 2564, 1817, 3833]), Set(Int32[2705, 2945, 2898, 3786, 1737, 218, 3813, 2384, 2564, 2972]))
(g, r) = (Set(Int32[1396, 1120, 199, 4045, 2458, 4054, 3228, 1056, 3519, 1784]), Set(Int32[338, 53, 1120, 1322, 3519, 1056, 2960, 1474, 764, 3934]))
(g, r) = (Set(Int32[2846, 3143, 2623, 2478, 3973, 3452, 431, 4280, 1735, 4508]), Set(Int32[2846, 2623, 2490, 2478, 3973, 3452, 431, 900, 1735, 4508]))
(g, r) = (Set(Int32[2658, 2193, 1099, 3749, 94, 3731, 2970, 131, 78, 4665]), Set(Int32[2680, 2463, 3112, 4682, 2436, 2820, 3731, 2970, 3245, 4094]))
(g, r) = (Set(Int32[909, 1673, 4123, 4232, 185, 2667, 2587, 561, 4194, 2122]), Set(Int32[2048, 4324, 4240, 3748, 2587, 2719, 39, 4258, 334, 4641]))
(g, r) = (Set(Int32[4254, 4495, 1979, 2999, 1056, 129, 2644, 820, 2267, 1784]), Set(Int32[1218, 2105, 4254, 3481, 2593, 3102, 1170, 2267, 4449, 1920]))
(g, r) = (Set(Int32[1317, 2002, 4435, 3740, 1710, 3067, 1694, 3807, 2009, 1477]), Set(Int32[3268, 2052, 3065, 3136, 2844, 3067, 4030, 2237, 2485, 2532]))
(g, r) = (Set(Int32[3411, 1755, 3000, 1438, 1222, 1113, 2712, 4147, 4651, 278]), Set(Int32[1075, 3132, 3065, 3171, 1435, 1777, 2853, 3902, 2532, 1295]))
(g, r) = (Set(Int32[2925, 2219, 3956, 4577, 1508, 4567, 2287, 4048, 4172, 664]), Set(Int32[3199, 211, 648, 2287, 1440, 2965, 3457, 1599, 1400, 2666]))
(g, r) = (Set(Int32[671, 2184, 4444, 4636, 4602, 2309, 2312, 3370, 2349, 3219]), Set(Int32[2184, 4444, 1982, 2309, 2312, 1465, 539, 330, 353, 671]))
(g, r) = (Set(Int32[2847, 4276, 4225, 196, 4012, 4674, 3131, 1875, 4219, 2590]), Set(Int32[4933, 3286, 62, 192, 2851, 4897, 904, 757, 3832, 4391]))
(g, r) = (Set(Int32[1510, 1514, 1678, 1707, 3864, 2229, 1708, 387, 4904, 3967]), Set(Int32[2835, 2902, 3171, 3055, 2729, 2556, 3945, 2434, 267, 2792]))
(g, r) = (Set(Int32[526, 3441, 1588, 1507, 3658, 4342, 3839, 674, 132, 1081]), Set(Int32[526, 1588, 3658, 4342, 3364, 1412, 3839, 407, 132, 1081]))
(g, r) = (Set(Int32[2761, 1369, 691, 1640, 4519, 4050, 1242, 4618, 1582, 2621]), Set(Int32[1369, 1640, 4519, 4050, 1242, 2535, 4618, 4876, 1582, 659]))
(g, r) = (Set(Int32[2575, 2897, 4176, 681, 678, 3895, 3623, 1005, 3696, 1927]), Set(Int32[2575, 2983, 2296, 3014, 3137, 3140, 2242, 612, 3170, 2862]))
(g, r) = (Set(Int32[1998, 608, 1405, 4296, 3723, 1281, 4253, 3981, 3652, 1499]), Set(Int32[2274, 3663, 4365, 4704, 3723, 3463, 4119, 2934, 3981, 4393]))
(g, r) = (Set(Int32[2025, 3192, 940, 4032, 1579, 3734, 4049, 1853, 4831, 1691]), Set(Int32[2025, 3192, 4228, 2864, 4032, 1579, 3734, 4049, 139, 89]))
(g, r) = (Set(Int32[2025, 744, 2027, 91, 2673, 4241, 2751, 4049, 1853, 2113]), Set(Int32[2025, 744, 91, 2673, 4241, 2751, 1579, 4049, 2113, 4831]))
(g, r) = (Set(Int32[1180, 1943, 970, 2839, 2502, 3009, 920, 1525, 4514, 4224]), Set(Int32[478, 4587, 3464, 4259, 2375, 4862, 4121, 2207, 4541, 3955]))
collect(Int32, IdView(p)) = Int32[3069, 2487, 2621, 2761, 2103, 2822, 3640, 3300, 4168, 2607]
collect(Int32, IdView(p)) = Int32[1674, 2827, 2930, 4251, 2909, 1911, 3171, 3329, 245, 1509]
collect(Int32, IdView(p)) = Int32[996, 2199, 1194, 1984, 2127, 2120, 2785, 2176, 2026, 1213]
collect(Int32, IdView(p)) = Int32[2940, 2911, 3327, 1602, 2391, 2400, 1387, 3223, 2598, 1049]
collect(Int32, IdView(p)) = Int32[1492, 3043, 2978, 316, 3168, 1944, 2625, 1353, 926, 2762]
collect(Int32, IdView(p)) = Int32[2862, 2547, 2242, 2575, 3014, 2983, 3246, 2271, 2296, 3137]
collect(Int32, IdView(p)) = Int32[646, 963, 4827, 1615, 940, 1541, 1311, 4822, 422, 3630]
collect(Int32, IdView(p)) = Int32[1601, 1987, 2554, 1990, 3072, 3786, 3105, 3705, 1565, 1698]
collect(Int32, IdView(p)) = Int32[3522, 2415, 2324, 1668, 4772, 4342, 714, 3581, 4419, 3536]
collect(Int32, IdView(p)) = Int32[1561, 3786, 2144, 1765, 3902, 492, 3065, 1710, 1549, 2625]
collect(Int32, IdView(p)) = Int32[3437, 2252, 2787, 3055, 1064, 3945, 3171, 3736, 2228, 43]
collect(Int32, IdView(p)) = Int32[4607, 4758, 3994, 2139, 1494, 2578, 3933, 4910, 2064, 2145]
collect(Int32, IdView(p)) = Int32[3171, 2556, 2434, 2812, 3065, 4607, 1494, 2758, 3945, 3705]
collect(Int32, IdView(p)) = Int32[2945, 2564, 2898, 2705, 2972, 218, 1737, 2384, 3813, 3786]
collect(Int32, IdView(p)) = Int32[1120, 1056, 3519, 1322, 338, 2960, 1474, 53, 764, 3934]
collect(Int32, IdView(p)) = Int32[4508, 3973, 2846, 2623, 3452, 1735, 431, 2478, 2490, 900]
collect(Int32, IdView(p)) = Int32[3731, 2970, 4682, 2436, 4094, 2820, 3112, 2680, 3245, 2463]
collect(Int32, IdView(p)) = Int32[2587, 4258, 4240, 334, 3748, 2048, 2719, 4641, 4324, 39]
collect(Int32, IdView(p)) = Int32[4254, 2267, 2593, 4449, 1920, 3102, 2105, 1170, 1218, 3481]
collect(Int32, IdView(p)) = Int32[3067, 2052, 3065, 2485, 2532, 2237, 3268, 4030, 3136, 2844]
collect(Int32, IdView(p)) = Int32[1075, 1295, 3065, 1777, 3132, 1435, 3171, 2853, 3902, 2532]
collect(Int32, IdView(p)) = Int32[2287, 1400, 3199, 1599, 1440, 648, 2666, 2965, 211, 3457]
collect(Int32, IdView(p)) = Int32[2184, 2309, 2312, 4444, 671, 539, 330, 1465, 353, 1982]
collect(Int32, IdView(p)) = Int32[3286, 4391, 62, 192, 757, 3832, 2851, 4897, 904, 4933]
collect(Int32, IdView(p)) = Int32[2902, 2792, 2556, 3055, 3171, 2835, 3945, 267, 2729, 2434]
collect(Int32, IdView(p)) = Int32[3658, 4342, 3839, 1081, 132, 526, 1588, 3364, 1412, 407]
collect(Int32, IdView(p)) = Int32[1582, 1369, 1640, 4519, 4618, 4050, 1242, 2535, 659, 4876]
collect(Int32, IdView(p)) = Int32[2575, 2862, 2983, 3014, 2242, 612, 2296, 3137, 3140, 3170]
collect(Int32, IdView(p)) = Int32[3723, 3981, 4365, 3463, 4119, 4393, 2274, 4704, 3663, 2934]
collect(Int32, IdView(p)) = Int32[2025, 4049, 3734, 4032, 1579, 3192, 4228, 89, 2864, 139]
collect(Int32, IdView(p)) = Int32[744, 2751, 2025, 91, 4049, 2113, 2673, 4241, 4831, 1579]
collect(Int32, IdView(p)) = Int32[3464, 3955, 4587, 4259, 4541, 2375, 4862, 4121, 478, 2207]
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, 40.0]
     Testing SimilaritySearch tests passed 

Testing completed after 709.33s

PkgEval succeeded after 851.99s