Package evaluation of SimilaritySearch on Julia 1.13.0-DEV.1162 (f0ece4ad9a*) started at 2025-09-18T19:03:39.674


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 10.13s


################################################################################
# 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 v2.0.0
  [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 v1.67.0+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 3.84s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 104.26s


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

     Testing SimilaritySearch
      Status `/tmp/jl_LfR2dx/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_LfR2dx/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 v2.0.0
  [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 v1.67.0+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
Test Summary: | Pass  Total  Time
heap          |   16     16  0.1s
Test Summary: |  Pass  Total  Time
KnnHeap       | 20005  20005  3.8s
Test Summary: |  Pass  Total  Time
XKnn          | 25005  25005  2.5s
Test Summary: | Pass  Total  Time
XKnn pop ops  | 9603   9603  1.2s
[ Info: (MatrixDatabase{Matrix{Float32}}, SubDatabase{MatrixDatabase{Matrix{Float32}}, Vector{Int64}})
 11.015362 seconds (1000 allocations: 78.125 KiB)
 11.075790 seconds (1000 allocations: 78.125 KiB)
  3.947562 seconds (1000 allocations: 78.125 KiB)
  3.918035 seconds (1000 allocations: 78.125 KiB)
  3.867247 seconds (1000 allocations: 78.125 KiB)
  3.793664 seconds (1000 allocations: 78.125 KiB)
  3.806194 seconds (1000 allocations: 78.125 KiB)
  3.820757 seconds (1000 allocations: 78.125 KiB)
 15.244642 seconds (1000 allocations: 78.125 KiB)
 15.802233 seconds (1000 allocations: 78.125 KiB)
 26.334219 seconds (1000 allocations: 78.125 KiB)
 25.251758 seconds (1000 allocations: 78.125 KiB)
 21.371975 seconds (6.23 k allocations: 388.672 KiB)
 20.166069 seconds (1000 allocations: 78.125 KiB)
 18.763631 seconds (1.00 k allocations: 78.141 KiB)
 18.948573 seconds (1000 allocations: 78.125 KiB)
Test Summary:                          | Pass  Total     Time
indexing vectors with ExhaustiveSearch | 8000   8000  3m38.5s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  3.434469 seconds (1000 allocations: 78.125 KiB)
  3.591725 seconds (1000 allocations: 78.125 KiB)
 29.706546 seconds (1000 allocations: 78.125 KiB)
 30.096520 seconds (1000 allocations: 78.125 KiB)
 29.719733 seconds (1000 allocations: 78.125 KiB)
 29.407397 seconds (1000 allocations: 78.125 KiB)
  4.646577 seconds (1000 allocations: 78.125 KiB)
  4.502402 seconds (1000 allocations: 78.125 KiB)
Test Summary:                            | Pass  Total     Time
indexing sequences with ExhaustiveSearch | 4000   4000  2m18.8s
[ Info: (VectorDatabase{Vector{Vector{Int64}}}, SubDatabase{VectorDatabase{Vector{Vector{Int64}}}, Vector{Int64}})
  7.767818 seconds (1000 allocations: 78.125 KiB)
  7.843217 seconds (1000 allocations: 78.125 KiB)
  7.459212 seconds (1000 allocations: 78.125 KiB)
  7.796287 seconds (1000 allocations: 78.125 KiB)
  7.812792 seconds (1000 allocations: 78.125 KiB)
  7.854909 seconds (1000 allocations: 78.125 KiB)
Test Summary:                       | Pass  Total   Time
indexing sets with ExhaustiveSearch | 3000   3000  48.9s
  0.044814 seconds (1.00 k allocations: 78.141 KiB)
  0.044377 seconds (1000 allocations: 78.125 KiB)
  0.038263 seconds (1000 allocations: 78.125 KiB)
  0.039722 seconds (1000 allocations: 78.125 KiB)
Test Summary:                                    | Pass  Total  Time
Normalized Cosine and Normalized Angle distances | 2000   2000  2.9s
  0.048986 seconds (1000 allocations: 78.125 KiB)
  0.048905 seconds (1000 allocations: 78.125 KiB)
Test Summary:           | Pass  Total  Time
Binary hamming distance | 1000   1000  1.4s
ExhaustiveSearch allknn: 4.109532 seconds (2.11 M allocations: 118.188 MiB, 1.67% gc time, 99.95% compilation time)
ParallelExhaustiveSearch allknn: 1.289519 seconds (604.62 k allocations: 31.768 MiB, 99.85% compilation time)
Test Summary: | Pass  Total     Time
allknn        |    5      5  1m02.7s
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  3.4s
[ Info: neardup> starting: 1:100, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:52.357
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-18T19:14:52.392
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) 2025-09-18T19:14:53.603
LOG n.size quantiles:[1.0, 1.0, 1.0, 1.0, 1.0]
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:54.007
D.map = UInt32[0x00000001, 0x00000004, 0x00000005, 0x00000008, 0x00000009, 0x00000019, 0x00000027, 0x00000029, 0x0000002b, 0x00000032]
D.nn = Int32[1, 1, 1, 4, 5, 5, 5, 8, 9, 8, 5, 9, 5, 9, 9, 9, 8, 9, 5, 1, 5, 8, 8, 4, 25, 9, 5, 4, 5, 9, 1, 5, 5, 5, 8, 1, 1, 5, 39, 39, 41, 1, 43, 25, 9, 4, 9, 9, 4, 50, 9, 8, 5, 9, 1, 50, 9, 8, 50, 50, 5, 5, 4, 1, 8, 5, 9, 4, 8, 9, 5, 5, 41, 9, 1, 9, 5, 41, 43, 9, 9, 1, 9, 4, 41, 41, 4, 5, 8, 5, 5, 5, 5, 5, 8, 5, 5, 41, 1, 50]
D.dist = Float32[0.0, 0.04692453, 0.06101066, 0.0, 0.0, 0.003852427, 0.007971585, 0.0, 0.0, 0.007607758, 0.008245528, 0.054189026, 0.021893382, 0.02986741, 0.040685594, 0.09939271, 0.0150536895, 0.025017083, 0.07211739, 0.081546545, 0.011937916, 0.060802817, 0.039512753, 0.038677633, 0.0, 0.040123582, 0.061636925, 0.03549266, 0.00177598, 0.02954185, 0.03285128, 0.032324314, 0.019365847, 0.05372423, 0.0978632, 0.0378986, 0.087117314, 0.042200446, 0.0, 0.07214308, 0.0, 0.047381938, 0.0, 0.004747629, 0.061680615, 0.064123094, 0.042306304, 0.062449694, 0.04820019, 0.0, 0.09412044, 0.04483396, 0.022848785, 0.024795115, 0.04801762, 0.06725085, 0.09441042, 0.042368352, 0.03300464, 0.014008522, 0.04598427, 0.0115202665, 0.033764303, 0.0053476095, 0.037991583, 0.050020754, 0.07609838, 0.016465902, 0.068941176, 0.09855455, 0.020055294, 0.050707757, 0.047141194, 0.024720669, 0.053334296, 0.033376098, 0.050505877, 0.029135585, 0.033845663, 0.025154948, 0.06655604, 0.050573826, 0.031345546, 0.01504004, 0.06812692, 0.04089117, 0.010480225, 0.078225315, 0.05122453, 0.043203354, 0.017992318, 0.04489714, 0.026842356, 0.04328257, 0.034752965, 0.03741449, 0.042967677, 0.01563567, 0.004542947, 0.041684747]
Test Summary:        | Pass  Total   Time
neardup single block |    3      3  19.6s
[ Info: neardup> starting: 1:16, current elements: 0, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.201
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-18T19:14:55.202
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: 2025-09-18T19:14:55.202
[ Info: neardup> range: 33:48, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.202
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.202
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.202
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.202
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.202
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.202
D.map = UInt32[0x00000001, 0x00000004, 0x00000005, 0x00000008, 0x00000009, 0x00000019, 0x00000027, 0x00000029, 0x0000002b, 0x00000032]
D.nn = Int32[1, 1, 1, 4, 5, 5, 5, 8, 9, 8, 5, 9, 5, 9, 9, 9, 8, 9, 5, 1, 5, 8, 8, 4, 25, 9, 5, 4, 5, 9, 1, 5, 5, 5, 8, 1, 1, 5, 39, 39, 41, 1, 43, 25, 9, 4, 9, 9, 4, 50, 9, 8, 5, 9, 1, 50, 9, 8, 4, 4, 5, 5, 4, 1, 8, 5, 9, 4, 8, 9, 5, 5, 41, 9, 1, 9, 5, 41, 43, 9, 9, 1, 9, 4, 41, 41, 4, 5, 8, 5, 5, 5, 5, 5, 8, 5, 5, 41, 1, 50]
D.dist = Float32[0.0, 0.04692453, 0.06101066, 0.0, 0.0, 0.003852427, 0.007971585, 0.0, 0.0, 0.007607758, 0.008245528, 0.054189026, 0.021893382, 0.02986741, 0.040685594, 0.09939271, 0.0150536895, 0.025017083, 0.07211739, 0.081546545, 0.011937916, 0.060802817, 0.039512753, 0.038677633, 0.0, 0.040123582, 0.061636925, 0.03549266, 0.00177598, 0.02954185, 0.03285128, 0.032324314, 0.019365847, 0.05372423, 0.0978632, 0.0378986, 0.087117314, 0.042200446, 0.0, 0.07214308, 0.0, 0.047381938, 0.0, 0.004747629, 0.061680615, 0.064123094, 0.042306304, 0.062449694, 0.04820019, 0.0, 0.09412044, 0.04483396, 0.022848785, 0.024795115, 0.04801762, 0.06725085, 0.09441042, 0.042368352, 0.0702095, 0.07626486, 0.04598427, 0.0115202665, 0.033764303, 0.0053476095, 0.037991583, 0.050020754, 0.07609838, 0.016465902, 0.068941176, 0.09855455, 0.020055294, 0.050707757, 0.047141194, 0.024720669, 0.053334296, 0.033376098, 0.050505877, 0.029135585, 0.033845663, 0.025154948, 0.06655604, 0.050573826, 0.031345546, 0.01504004, 0.06812692, 0.04089117, 0.010480225, 0.078225315, 0.05122453, 0.043203354, 0.017992318, 0.04489714, 0.026842356, 0.04328257, 0.034752965, 0.03741449, 0.042967677, 0.01563567, 0.004542947, 0.041684747]
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: 2025-09-18T19:14:55.295
LOG add_vertex! sp=1 ep=1 n=1 BeamSearch(bsize=4, Δ=1.0, maxvisits=1000000) 2025-09-18T19:14:55.296
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: 2025-09-18T19:14:55.296
[ Info: neardup> range: 33:48, current elements: 16, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.297
[ Info: neardup> range: 49:64, current elements: 19, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.297
[ Info: neardup> range: 65:80, current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.297
[ Info: neardup> range: 81:96, current elements: 20, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.297
[ Info: neardup> range: 97:100, current elements: 21, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.297
[ Info: neardup> finished current elements: 21, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:14:55.297
D.map = UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000006, 0x00000007, 0x00000008, 0x00000009, 0x0000000a, 0x0000000b, 0x0000000c, 0x0000000d, 0x0000000e, 0x0000000f, 0x00000010, 0x00000027, 0x00000028, 0x0000002b, 0x00000032, 0x00000055]
D.nn = Int32[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 10, 15, 10, 3, 5, 3, 10, 4, 7, 12, 13, 4, 5, 12, 2, 6, 6, 11, 10, 1, 3, 12, 39, 40, 12, 3, 43, 13, 12, 6, 9, 16, 4, 50, 13, 3, 6, 9, 2, 40, 13, 10, 4, 4, 12, 6, 4, 1, 8, 13, 12, 4, 10, 14, 6, 10, 12, 9, 2, 9, 6, 2, 43, 9, 16, 3, 12, 4, 85, 2, 4, 13, 2, 5, 6, 11, 7, 10, 3, 5, 7, 12, 1, 50]
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, 0.0027287602, 0.018804848, 0.04281372, 0.06502277, 0.011937916, 0.00330621, 0.018277168, 0.038677633, 0.08658862, 0.003915429, 0.013593078, 0.03549266, 0.00177598, 0.014345467, 0.01192832, 0.023900032, 0.0064560175, 0.030590475, 0.09415412, 0.0378986, 0.009761035, 0.0049524307, 0.0, 0.0, 0.04609835, 0.009148657, 0.0, 0.08585352, 0.022782862, 0.046665013, 0.042306304, 0.01921171, 0.04820019, 0.0, 0.08406764, 0.01265353, 0.020631194, 0.024795115, 0.019782603, 0.035830736, 0.035857737, 0.027872264, 0.0702095, 0.07626486, 0.0078050494, 0.003113389, 0.033764303, 0.0053476095, 0.037991583, 0.03259653, 0.054220557, 0.016465902, 0.038449585, 0.045515716, 0.01609999, 0.020402014, 0.035440743, 0.024720669, 0.0232957, 0.033376098, 0.029319942, 0.0115453005, 0.033845663, 0.025154948, 0.018584669, 0.025279284, 0.03006655, 0.01504004, 0.0, 0.0064370036, 0.010480225, 0.026970863, 0.023237526, 0.043203354, 0.016626954, 0.027079284, 0.020887434, 0.034128904, 0.028071165, 0.03741449, 0.014351189, 0.03824383, 0.004542947, 0.041684747]
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: 2025-09-18T19:15:02.932
LOG append_items! ExhaustiveSearch{SimilaritySearch.DistanceWithIdentifiers{CosineDistance, MatrixDatabase{Matrix{Float32}}}, VectorDatabase{Vector{UInt32}}} sp=0 ep=5 n=5 2025-09-18T19:15:02.932
[ Info: neardup> range: 17:32, current elements: 5, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:15:02.938
[ Info: neardup> range: 33:48, current elements: 6, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:15:02.938
[ Info: neardup> range: 49:64, current elements: 9, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:15:02.939
[ Info: neardup> range: 65:80, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:15:02.939
[ Info: neardup> range: 81:96, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:15:02.939
[ Info: neardup> range: 97:100, current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:15:02.939
[ Info: neardup> finished current elements: 10, n: 100, ϵ: 0.1, timestamp: 2025-09-18T19:15:02.939
D.map = UInt32[0x00000001, 0x00000004, 0x00000005, 0x00000008, 0x00000009, 0x00000019, 0x00000027, 0x00000029, 0x0000002b, 0x00000032]
D.nn = Int32[1, 1, 1, 4, 5, 5, 5, 8, 9, 8, 5, 9, 5, 9, 9, 9, 8, 9, 5, 1, 5, 8, 8, 4, 25, 9, 5, 4, 5, 9, 1, 5, 5, 5, 8, 1, 1, 5, 39, 39, 41, 1, 43, 25, 9, 4, 9, 9, 4, 50, 9, 8, 5, 9, 1, 50, 9, 8, 4, 4, 5, 5, 4, 1, 8, 5, 9, 4, 8, 9, 5, 5, 41, 9, 1, 9, 5, 41, 43, 9, 9, 1, 9, 4, 41, 41, 4, 5, 8, 5, 5, 5, 5, 5, 8, 5, 5, 41, 1, 50]
D.dist = Float32[0.0, 0.04692453, 0.06101066, 0.0, 0.0, 0.003852427, 0.007971585, 0.0, 0.0, 0.007607758, 0.008245528, 0.054189026, 0.021893382, 0.02986741, 0.040685594, 0.09939271, 0.0150536895, 0.025017083, 0.07211739, 0.081546545, 0.011937916, 0.060802817, 0.039512753, 0.038677633, 0.0, 0.040123582, 0.061636925, 0.03549266, 0.00177598, 0.02954185, 0.03285128, 0.032324314, 0.019365847, 0.05372423, 0.0978632, 0.0378986, 0.087117314, 0.042200446, 0.0, 0.07214308, 0.0, 0.047381938, 0.0, 0.004747629, 0.061680615, 0.064123094, 0.042306304, 0.062449694, 0.04820019, 0.0, 0.09412044, 0.04483396, 0.022848785, 0.024795115, 0.04801762, 0.06725085, 0.09441042, 0.042368352, 0.0702095, 0.07626486, 0.04598427, 0.0115202665, 0.033764303, 0.0053476095, 0.037991583, 0.050020754, 0.07609838, 0.016465902, 0.068941176, 0.09855455, 0.020055294, 0.050707757, 0.047141194, 0.024720669, 0.053334296, 0.033376098, 0.050505877, 0.029135585, 0.033845663, 0.025154948, 0.06655604, 0.050573826, 0.031345546, 0.01504004, 0.06812692, 0.04089117, 0.010480225, 0.078225315, 0.05122453, 0.043203354, 0.017992318, 0.04489714, 0.026842356, 0.04328257, 0.034752965, 0.03741449, 0.042967677, 0.01563567, 0.004542947, 0.041684747]
Test Summary:                               | Pass  Total  Time
neardup small block with filterblocks=false |    3      3  7.6s
computing farthest point 1, dmax: Inf, imax: 27, n: 30
computing farthest point 2, dmax: 1.2812308, imax: 3, n: 30
computing farthest point 3, dmax: 0.9023207, imax: 15, n: 30
computing farthest point 4, dmax: 0.90037274, imax: 4, n: 30
computing farthest point 5, dmax: 0.8779225, imax: 13, n: 30
computing farthest point 6, dmax: 0.75074637, imax: 23, n: 30
computing farthest point 7, dmax: 0.5881884, imax: 1, n: 30
computing farthest point 8, dmax: 0.5746349, imax: 19, n: 30
computing farthest point 9, dmax: 0.56322247, imax: 28, n: 30
computing farthest point 10, dmax: 0.55957514, imax: 30, 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) 2025-09-18T19:15:11.827
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=0.9047619, maxvisits=100) 2025-09-18T19:15:24.808
LOG n.size quantiles:[2.0, 2.0, 3.0, 3.0, 3.0]
(i, j, d) = (7, 225, -1.1920929f-7)
(i, j, d, :parallel) = (7, 225, -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 => 20.986244966999998, :exact => 0.924100087)
Test Summary: | Pass  Total   Time
closestpair   |    4      4  22.4s
  5.905273 seconds (1.00 k allocations: 140.711 KiB)
SEARCH Exhaustive 1: 0.005513 seconds
SEARCH Exhaustive 2: 0.005369 seconds
SEARCH Exhaustive 3: 0.005459 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) 2025-09-18T19:15:54.377
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=1.1, maxvisits=166) 2025-09-18T19:16:00.731
LOG n.size quantiles:[2.0, 2.0, 2.0, 2.0, 2.0]
[ Info: RECALL BAJO!! recall: 0.16874999999999998, #objects: 11224, #queries: 32
[ Info: [0.28599992394447327, 0.2830814719200134, 0.35334840416908264, 0.24872468411922455, 0.767204225063324, 0.6613260507583618, 0.2676694393157959, 0.2909555435180664, 0.2560093402862549, 0.22115854918956757, 0.42409276962280273, 0.4453642666339874, 0.23491087555885315, 0.7128263115882874, 0.2831970155239105, 0.3498385548591614, 0.35820677876472473, 0.19165687263011932, 0.1569005846977234, 0.5475488901138306, 0.4251658618450165, 0.29828473925590515, 0.24237462878227234, 0.21017630398273468, 0.506109356880188, 0.6419256925582886, 0.24141010642051697, 0.2737509608268738, 0.6305056810379028, 0.5650492310523987, 0.3853415846824646, 0.25755518674850464]
(g, r) = (Set(Int32[10318, 6152, 2684, 6449, 205, 8101, 4539, 5354, 6342, 2848]), Set(Int32[2682, 5967, 1144, 7570, 882, 5326, 3888, 4345, 6342, 2848]))
(g, r) = (Set(Int32[10870, 7150, 8010, 7047, 9945, 2098, 4239, 3195, 8867, 4323]), Set(Int32[2456, 4769, 7150, 9259, 2615, 5437, 3483, 10747, 3047, 5389]))
(g, r) = (Set(Int32[991, 7519, 10089, 4271, 9148, 6368, 9673, 212, 7771, 1547]), Set(Int32[1898, 2849, 991, 4271, 6368, 1547, 3644, 3641, 3426, 6228]))
(g, r) = (Set(Int32[1752, 722, 3070, 10711, 4160, 5578, 7077, 4689, 11143, 11223]), Set(Int32[1782, 722, 6, 140, 7184, 6606, 7154, 351, 10142, 5578]))
(g, r) = (Set(Int32[3089, 6514, 111, 1858, 3620, 10113, 480, 8917, 8702, 6410]), Set(Int32[6894, 6721, 7626, 1039, 6011, 3159, 8021, 2067, 4644, 5851]))
(g, r) = (Set(Int32[10912, 7381, 10199, 3203, 6493, 4604, 6037, 1434, 528, 5774]), Set(Int32[10476, 9634, 2657, 9984, 8223, 8473, 8637, 10159, 10431, 7935]))
(g, r) = (Set(Int32[9799, 1863, 7929, 7434, 10384, 2364, 8942, 7887, 1007, 484]), Set(Int32[5865, 1863, 7434, 5675, 6769, 8942, 6412, 1983, 5513, 6997]))
(g, r) = (Set(Int32[444, 4334, 11058, 10074, 9070, 1457, 6675, 1301, 1315, 7122]), Set(Int32[10074, 11058, 10451, 8443, 8640, 1517, 3979, 9560, 10924, 866]))
(g, r) = (Set(Int32[7980, 911, 4361, 9356, 6595, 2331, 4032, 2773, 9977, 6985]), Set(Int32[9010, 1729, 7655, 8046, 8673, 5704, 9047, 1446, 10791, 1983]))
(g, r) = (Set(Int32[8569, 9618, 2673, 7483, 10900, 6928, 10339, 6093, 275, 56]), Set(Int32[4834, 1613, 2679, 2673, 2864, 657, 6604, 6093, 3547, 56]))
(g, r) = (Set(Int32[503, 8092, 10801, 5460, 2065, 10250, 4080, 3166, 32, 9278]), Set(Int32[7268, 4599, 7023, 3862, 5460, 3922, 4724, 4080, 7401, 4082]))
(g, r) = (Set(Int32[6295, 5070, 9117, 4693, 5327, 6628, 2505, 4111, 815, 1794]), Set(Int32[1912, 1804, 1890, 707, 383, 2505, 297, 1910, 256, 1794]))
(g, r) = (Set(Int32[9, 3141, 5745, 6999, 10244, 790, 728, 7416, 3414, 1126]), Set(Int32[3141, 8564, 5116, 6999, 3995, 790, 728, 7416, 9, 1126]))
(g, r) = (Set(Int32[5095, 4742, 8963, 8795, 4617, 240, 3539, 9756, 5277, 5840]), Set(Int32[7464, 4723, 5441, 5417, 6665, 4841, 7176, 5728, 7088, 5851]))
(g, r) = (Set(Int32[2761, 8231, 1530, 10938, 6416, 2197, 3209, 4235, 3348, 4632]), Set(Int32[1514, 2086, 1530, 2197, 1960, 3209, 1662, 1580, 1604, 1138]))
(g, r) = (Set(Int32[9524, 5883, 6615, 10133, 2061, 6991, 8768, 7812, 5108, 4060]), Set(Int32[8954, 7268, 6483, 427, 6690, 3242, 6738, 7744, 2690, 411]))
(g, r) = (Set(Int32[553, 2710, 6529, 3498, 3514, 7918, 6164, 6161, 9529, 7869]), Set(Int32[1724, 1584, 29, 2115, 1084, 2697, 8782, 654, 2657, 1815]))
(g, r) = (Set(Int32[5473, 6863, 10565, 4524, 843, 9968, 8941, 3403, 4014, 5931]), Set(Int32[10605, 8597, 8843, 10565, 10358, 9968, 8941, 10434, 9597, 7372]))
(g, r) = (Set(Int32[7407, 10651, 3646, 327, 5701, 4424, 1080, 8128, 6646, 8973]), Set(Int32[7407, 10651, 3646, 327, 5701, 4424, 5926, 8128, 10665, 8755]))
(g, r) = (Set(Int32[6079, 7081, 7900, 10982, 10166, 5624, 4319, 3108, 2891, 4529]), Set(Int32[4127, 3394, 2028, 4252, 4319, 4702, 4260, 4561, 3698, 1938]))
(g, r) = (Set(Int32[524, 6516, 3352, 3976, 1527, 6935, 2531, 5110, 7689, 9299]), Set(Int32[10614, 5266, 6298, 8714, 5358, 6586, 6031, 6804, 6420, 7237]))
(g, r) = (Set(Int32[8901, 7924, 10565, 8526, 8048, 6855, 5741, 4258, 9857, 6126]), Set(Int32[8301, 6128, 1749, 1056, 4780, 4479, 5741, 6126, 1138, 6229]))
(g, r) = (Set(Int32[2410, 1944, 2271, 6374, 10969, 2375, 8514, 8081, 1683, 9608]), Set(Int32[2410, 1480, 1590, 3576, 2208, 4006, 1652, 1683, 2270, 2145]))
(g, r) = (Set(Int32[4929, 2306, 7933, 2310, 2128, 8716, 9535, 7225, 877, 7768]), Set(Int32[338, 2335, 2128, 2310, 10403, 9364, 8916, 845, 877, 863]))
(g, r) = (Set(Int32[9505, 5178, 1720, 7170, 1065, 7106, 8089, 8732, 1497, 1625]), Set(Int32[7656, 10608, 10101, 10869, 7773, 364, 5075, 3177, 11069, 7009]))
(g, r) = (Set(Int32[7687, 7760, 4446, 9365, 2556, 6773, 8639, 4038, 5827, 2252]), Set(Int32[2840, 7599, 8363, 1144, 7206, 10644, 8021, 8473, 8855, 3006]))
(g, r) = (Set(Int32[7877, 9581, 2266, 7747, 10959, 3920, 2206, 9176, 5795, 3697]), Set(Int32[6379, 7329, 9581, 7024, 5566, 10471, 1855, 10959, 7594, 2248]))
(g, r) = (Set(Int32[10636, 1232, 2456, 2511, 5657, 136, 2805, 2063, 2187, 3992]), Set(Int32[10767, 5373, 2851, 10467, 10432, 2805, 9049, 1478, 9744, 3992]))
(g, r) = (Set(Int32[5581, 4543, 10541, 8237, 6882, 6605, 2772, 7338, 7644, 664]), Set(Int32[7658, 632, 104, 2555, 2548, 216, 9350, 9494, 266, 8872]))
(g, r) = (Set(Int32[3718, 1072, 802, 10720, 8853, 6362, 7535, 3865, 5648, 5159]), Set(Int32[685, 2163, 6766, 5139, 1167, 5648, 780, 7587, 6122, 1726]))
(g, r) = (Set(Int32[4742, 714, 10471, 1907, 10900, 5466, 5142, 5840, 232, 6123]), Set(Int32[7464, 6705, 4723, 5441, 5417, 5903, 7176, 6053, 8585, 7088]))
(g, r) = (Set(Int32[9932, 6347, 4908, 8808, 6747, 1575, 8017, 1682, 4845, 9437]), Set(Int32[224, 53, 30, 172, 9884, 826, 271, 1339, 5594, 10298]))
collect(Int32, IdView(p)) = Int32[6342, 2848, 3888, 882, 5326, 2682, 4345, 7570, 5967, 1144]
collect(Int32, IdView(p)) = Int32[7150, 10747, 2456, 9259, 3483, 4769, 5389, 2615, 3047, 5437]
collect(Int32, IdView(p)) = Int32[4271, 6368, 1547, 991, 3644, 6228, 3641, 3426, 1898, 2849]
collect(Int32, IdView(p)) = Int32[722, 5578, 6606, 10142, 6, 140, 7184, 351, 7154, 1782]
collect(Int32, IdView(p)) = Int32[6894, 5851, 3159, 6721, 6011, 8021, 7626, 1039, 2067, 4644]
collect(Int32, IdView(p)) = Int32[9984, 10431, 10159, 10476, 9634, 7935, 8473, 2657, 8223, 8637]
collect(Int32, IdView(p)) = Int32[8942, 7434, 1863, 6997, 5675, 6412, 6769, 5865, 1983, 5513]
collect(Int32, IdView(p)) = Int32[11058, 10074, 10924, 9560, 1517, 3979, 10451, 8443, 866, 8640]
collect(Int32, IdView(p)) = Int32[7655, 8673, 1446, 1729, 9010, 5704, 8046, 10791, 1983, 9047]
collect(Int32, IdView(p)) = Int32[56, 6093, 2673, 3547, 6604, 2864, 4834, 657, 2679, 1613]
collect(Int32, IdView(p)) = Int32[4080, 5460, 4599, 7023, 3922, 4082, 4724, 3862, 7268, 7401]
collect(Int32, IdView(p)) = Int32[2505, 1794, 707, 1912, 1804, 297, 1910, 256, 1890, 383]
collect(Int32, IdView(p)) = Int32[790, 1126, 7416, 9, 728, 3141, 6999, 8564, 3995, 5116]
collect(Int32, IdView(p)) = Int32[5417, 4723, 5441, 7088, 7464, 6665, 4841, 7176, 5728, 5851]
collect(Int32, IdView(p)) = Int32[1530, 3209, 2197, 1514, 1662, 2086, 1960, 1604, 1138, 1580]
collect(Int32, IdView(p)) = Int32[6738, 8954, 7744, 2690, 7268, 427, 411, 3242, 6483, 6690]
collect(Int32, IdView(p)) = Int32[2697, 1584, 8782, 1724, 2115, 1084, 654, 1815, 2657, 29]
collect(Int32, IdView(p)) = Int32[9968, 10565, 8941, 7372, 10434, 10358, 9597, 10605, 8843, 8597]
collect(Int32, IdView(p)) = Int32[10651, 327, 7407, 8128, 3646, 5701, 4424, 8755, 5926, 10665]
collect(Int32, IdView(p)) = Int32[4319, 4702, 2028, 4252, 4260, 4561, 3698, 1938, 3394, 4127]
collect(Int32, IdView(p)) = Int32[6586, 5358, 6804, 6298, 6031, 6420, 7237, 10614, 8714, 5266]
collect(Int32, IdView(p)) = Int32[5741, 6126, 4780, 6128, 4479, 1138, 6229, 8301, 1749, 1056]
collect(Int32, IdView(p)) = Int32[1683, 2410, 2270, 2208, 3576, 2145, 4006, 1480, 1652, 1590]
collect(Int32, IdView(p)) = Int32[2310, 877, 2128, 845, 863, 8916, 10403, 338, 2335, 9364]
collect(Int32, IdView(p)) = Int32[11069, 7773, 10869, 10101, 7656, 10608, 364, 5075, 3177, 7009]
collect(Int32, IdView(p)) = Int32[8021, 3006, 7599, 7206, 8473, 2840, 8855, 8363, 1144, 10644]
collect(Int32, IdView(p)) = Int32[10959, 9581, 1855, 10471, 6379, 7329, 2248, 7024, 7594, 5566]
collect(Int32, IdView(p)) = Int32[2805, 3992, 5373, 1478, 10467, 2851, 9049, 10432, 9744, 10767]
collect(Int32, IdView(p)) = Int32[632, 9350, 104, 2555, 9494, 266, 7658, 216, 2548, 8872]
collect(Int32, IdView(p)) = Int32[5648, 1167, 1726, 6766, 780, 5139, 2163, 7587, 685, 6122]
collect(Int32, IdView(p)) = Int32[7088, 8585, 5903, 7176, 5417, 4723, 6705, 5441, 7464, 6053]
collect(Int32, IdView(p)) = Int32[1339, 5594, 224, 10298, 9884, 826, 53, 271, 30, 172]
quantile(neighbors_length.(Ref(index.adj), 1:length(index)), 0:0.1:1.0) = [1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0, 6.0, 8.0, 37.0]
     Testing SimilaritySearch tests passed 

Testing completed after 618.54s

PkgEval succeeded after 752.58s