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python3-pydantic-ai-slim+mistral-0.0.19-1.lbn36.noarch
This is a metapackage bringing in mistral extras requires for
python3-pydantic-ai-slim.
It makes sure the dependencies are installed.
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python3-pydantic-ai-slim+openai-0.0.19-1.lbn36.noarch
This is a metapackage bringing in openai extras requires for
python3-pydantic-ai-slim.
It makes sure the dependencies are installed.
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python3-pydantic-ai-slim+vertexai-0.0.19-1.lbn36.noarch
This is a metapackage bringing in vertexai extras requires for
python3-pydantic-ai-slim.
It makes sure the dependencies are installed.
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python3-pydantic-graph-0.0.19-1.lbn36.noarch
pydantic-graph
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python3-rapidfuzz-3.12.2-1.lbn36.x86_64
RapidFuzz is a fast string matching library for Python and C++, which is using
the string similarity calculations from FuzzyWuzzy. However there are a couple
of aspects that set RapidFuzz apart from FuzzyWuzzy:
- It is MIT licensed so it can be used whichever License you might want
to choose for your project, while you're forced to adopt the GPL license when
using FuzzyWuzzy
- It provides many string_metrics like hamming or jaro_winkler, which
are not included in FuzzyWuzzy
- It is mostly written in C++ and on top of this comes with a lot of Algorithmic
improvements to make string matching even faster, while still providing the same
results. For detailed benchmarks check the documentation
- Fixes multiple bugs in the partial_ratio implementation
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python3-scikit-build-0.18.1-2.lbn36.noarch
Improved build system generator for CPython C/C++/Fortran/Cython extensions.
Better support is available for additional compilers, build systems, cross
compilation, and locating dependencies and determining their build requirements.
The scikit-build package is fundamentally just glue between the setup-tools
Python module and CMake.
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python3-scikit-build-core-0.11.0-1.lbn36.noarch
A next generation Python CMake adapter and Python API for plugins
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python3-scikit-image-0.25.2-1.lbn36.x86_64
The scikit-image SciKit (toolkit for SciPy) extends scipy.ndimage to provide a
versatile set of image processing routines.
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python3-scikit-learn-1.6.1-1.lbn36.x86_64
Scikit-learn integrates machine learning algorithms in the tightly-knit
scientific Python world, building upon numpy, scipy, and matplotlib.
As a machine-learning module, it provides versatile tools for data mining
and analysis in any field of science and engineering. It strives to be
simple and efficient, accessible to everybody, and reusable
in various contexts.
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python3-scour-0.38.2-12.lbn36.noarch
Scour is an SVG optimizer/cleaner written in Python that reduces the
size of scalable vector graphics by optimizing structure and removing
unnecessary data.
It can be used to create streamlined vector graphics suitable for web
deployment, publishing/sharing or further processing.
The goal of Scour is to output a file that renders identically at a
fraction of the size by removing a lot of redundant information created
by most SVG editors. Optimization options are typically lossless but can
be tweaked for more aggressive cleaning.
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