v2.10.3

2.10.3 (2024-04-02)

Make

• Create missing directories (39cf7c3)

v2.10.2

2.10.2 (2024-04-01)

Make

• Cleaner SQLite CI (e0bc522), closes #376 #373 #371

v2.10.1

2.10.1 (2024-04-01)

Fix

• Strip shared lib extension (9c10855), closes #373

Improve

• SQLite download process (47f94ef)

Make

• usearch.h path (d04f898)
• Override OSX arch target (aff6293)
• Versioning releases from main (27897e8)

Broader Compatibility in USearch v2.10.0

USearch v2.10.0 brings major compatibility improvements:

• Better CMake thanks to @Ngalstyan4 🤗
• Pre-build JavaScript binaries thanks to @sroussey 🤗
• Better docs thanks to @simonw 🤗

Notable new features include:

• Use AVX in Windows Python images (c694880)
• Standalone SQLite binaries (227083b)
• Introspecting SIMD capabilities in Rust builds (16fa90f)
• Pretty printing the index with __repr_pretty__ (7087138)
• Improved bit-casting for negative float-s (1d03a9d)

Moreover, new USearch ships 50 binaries to PyPi, covering more platforms than NumPy with 35, but still fewer than SimSIMD and StringZilla with 105. More improvements to come to the most portable fast approximate nearest neighbors search engine 🥳

v2.9.2

2.9.2 (2024-03-05)

Docs

• Comparison tolerance (01c5c89)
• New shields (8a964ec)

Fix

• index.vectors computed property (fe710cc), closes #349
• multi initialization in C tests (7f4dee4)
• Add BigInt validation in index creation,Node Support (5c61c95)
• Bugfix for importlib attrib error (1bc24dd)
• No Pearson correlation for 1d data (3b09259)
• ordering keys before comparison (6bf1df8)
• Reordering vectors before comparison (a4fb305)

Make

• Upgrade SimSIMD & StringZilla (e7aac02)

Hashes

• docs.tar.gz : 71c0d872c29a98d2fdbfbd27946682372138f54512e701d3f40810cb8566ab0d
• usearch-v2.9.2.tar.gz : 16736360f30c9e27d9544ac1fc7662939e02ea223c748e29e8e18ba282a2eadf
• usearch-v2.9.2.zip : 572988a414ddc8ebe6091648c6fe12edfe3fb01ddb2c4000a3538d56e547aa76
• usearch_linux_amd_2.9.2.deb : 4025bf124e3ee32029407a85b8eb7011e9a3ce1f19f5057cc674391264947ea2
• usearch_linux_arm_2.9.2.deb : a9ad80f35789b5f661ad8fd2d01663661d036c61ff04a9109a6e8ef9d20f3a99
• usearch_macOS_arm64_2.9.2.zip : ebfc16c1da6029110260dbb3bd5b3e1c20855cc5ffaa69ee00d30436f2bf377b
• usearch_macOS_x86_64_2.9.2.zip : 7860f96675d87ff3afc0587e5940a3732e54c933057ad1290802ae8110a945f0
• usearch_wasm_linux_arm64_2.9.2.tar.gz : a610edde242ee047156945b82dcf9ad02448fce5c815ae57f87296ffa81ddecd
• usearch_wasm_linux_x86_64_2.9.2.tar.gz : 4e389a217fad0c7f2802697330c8b19cbbee09201c9e85542404f694d17426e2
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• usearch_wasm_windows_x64_2.9.2.tar.gz : 80a8aac793c5bd537eef7b16c22186886ae1f6146a50fb4c06068eb3a8894d10
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• usearch_windows_x86_2.9.2.tar : bf851998175614f406ba31395c07f4d65a9f98ef1f09003e2fe00cb180c13ad0

v2.9.1

2.9.1 (2024-02-27)

Fix

• Explicit narrowing conversions (25436ae)
• Match enum values between C and C# (4a69086)
• Recovering quantization settings in load (fd53619), closes #353 #343
• wildcard ES module imports (#351) (d5091ac), closes #351

Improve

• Report missing arguments in debug builds (ce5bbaf)
• Test reopening index (24be07b)

Make

• Clean-up the C# dir (70d646c)
• invalid C++17 Clang arg (23684aa)

Hashes

• docs.tar.gz : 80b75daa7f627b9a96607fff2498d695f71dc4a12f3695bc9a3521ae8fc12f6a
• usearch-v2.9.1.tar.gz : 69f22afb6da763c11840ca28307523b1cd1154d6925b0f05235af9e49674eba0
• usearch-v2.9.1.zip : 771f434c5cd818c4e6b395ab5cc7ac755b167417a724a9bb70f95954f21cb603
• usearch_linux_amd_2.9.1.deb : e12ba570f5f8eba17221218e1d49f9fae5f5936cb7c21727df1f72f6712d3490
• usearch_linux_arm_2.9.1.deb : ce6fb426af55a96324f77d1ada9ab58cf741ea787358e681e17805601f359279
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• usearch_wasm_linux_arm64_2.9.1.tar.gz : 470440a59551fb9f3a980a5837f5bbe95b37f0527dbaa41cd5d0e662f9e51551
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v2.9.0

2.9.0 (2024-02-22)

Add

• SQLite binding (222de55)
• String distances to SQLite (ae4d0f0)

Docs

• Header refreshed (7465c29)
• Py and SQLite extensions (550624b)
• README.md link to Joins (#327) (1279c54), closes #327

Fix

• bug reports were immediately marked invalid (c5fc825)
• Error handling, mem safety bugs #335 (#339) (4747ef4), closes #335 #339
• Passing SQLite tests (6334983)
• Reported number of levels (9b1a06a)
• Skip non-Linux SQLite tests (b02d262)
• SQLite cosine function + tests (55464fb)
• undefined var error in remove api (8d86a9e)

Improve

• Multi property lookup (e8bf02c)
• Support multi-column vectors (66f1716)

Make

• npi ci (#330) (5680920), closes #330
• Add 3.12 wheels (d66f697)
• Change include paths (21db294)
• invalid C++17 Clang arg (2a6d779)
• Link libpthread for older Linux GCC builds (#324) (6f1e5dd), closes #324
• Parallel CI for Python wheels (a9ad89e)
• Upgrade SimSIMD & StringZilla (5481bdf)

Revert

• Postpone Apache Arrow integration (5d040ca)

Hashes

• docs.tar.gz : 068f9712d50aa9734dd636ffce9a58453544754bc476f7df2aa07b3634816d46
• usearch-v2.9.0.tar.gz : f2ceef55ba874b1ab14b115f8a8487848ebdd1384425497b1a76a6e4c1ab6718
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v2.8.16

2.8.16 (2024-01-24)

Docs

• Downloads numbers (13cc624)

Fix

• SimSIMD dispatch (c8dc3b7), closes #320

Make

• Fix Node version for SemVer (a68fca6)
• Upgrade dependencies (6ab2150)
• Upgrade Node environment (f5b6750)

v2.8.15

2.8.15 (2024-01-09)

Fix

• exporting quantization enums in Go (#336) (93c74c3), closes #336

Hashes

• docs.tar.gz : 1083f4feafdcb4e2f58df423302cf0bf66cf55e3d03b14d902e462760a055a9f
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Faster Double-Precision Math

As was discussed in the SciPy integration thread, Python libraries use double-precision floating-point numbers by default.
So, in this release, I've extended the spatial distance functions in the underlying SimSIMD - Cos, L2sq, IP with support for double arguments with specialized implementations on AVX-512-capable x86 CPUs and SVE-capable Arm CPUs.

Benchmarking SimSIMD vs. SciPy on Intel Sapphire Rapids CPU

• Vector dimensions: 1536
• Vectors count: 1000
• Hardware capabilities: serial, x86_avx2, x86_avx512, x86_avx2fp16, x86_avx512fp16, x86_avx512vpopcntdq, x86_avx512vnni
• NumPy BLAS dependency: openblas64
• NumPy LAPACK dependency: dep140640983012528

Between 2 Vectors, Batch Size: 1

Datatype	Method	Ops/s	SimSIMD Ops/s	SimSIMD Improvement
f64	scipy.cosine	63,612	572,605	9.00 x
f64	scipy.sqeuclidean	238,547	915,596	3.84 x
f64	numpy.inner	449,499	986,522	2.19 x

Between 2 Vectors, Batch Size: 1,000

Datatype	Method	Ops/s	SimSIMD Ops/s	SimSIMD Improvement
f64	scipy.cosine	68,962	1,457,172	21.13 x
f64	scipy.sqeuclidean	247,727	1,535,547	6.20 x
f64	numpy.inner	463,509	1,512,004	3.26 x

Benchmarking SimSIMD vs. SciPy on AWS Graviton 3

• Vector dimensions: 1536
• Vectors count: 1000
• Hardware capabilities: serial, arm_neon, arm_sve
• NumPy BLAS dependency: openblas64
• NumPy LAPACK dependency: openblas64

Between 2 Vectors, Batch Size: 1

Datatype	Method	Ops/s	SimSIMD Ops/s	SimSIMD Improvement
f64	scipy.cosine	40,729	725,382	17.81 x
f64	scipy.sqeuclidean	160,812	728,114	4.53 x
f64	numpy.inner	473,443	767,374	1.62 x
f64	scipy.jensenshannon	15,684	38,528	2.46 x
f64	scipy.kl_div	49,983	61,811	1.24 x

Between 2 Vectors, Batch Size: 1,000

Datatype	Method	Ops/s	SimSIMD Ops/s	SimSIMD Improvement
f64	scipy.cosine	41,130	1,460,850	35.52 x
f64	scipy.sqeuclidean	162,147	1,486,255	9.17 x
f64	numpy.inner	473,856	1,580,136	3.33 x

Hashes

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