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python3-langchain+google-vertexai-0.3.25-1.lbn36.noarch: This is a metapackage bringing in google-vertexai extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+fireworks-0.3.25-1.lbn36.noarch: This is a metapackage bringing in fireworks extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+community-0.3.25-1.lbn36.noarch: This is a metapackage bringing in community extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+cohere-0.3.25-1.lbn36.noarch: This is a metapackage bringing in cohere extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+azure-ai-0.3.25-1.lbn36.noarch: This is a metapackage bringing in azure-ai extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+anthropic-0.3.25-1.lbn36.noarch: This is a metapackage bringing in anthropic extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-joblib-1.4.2-5.lbn36.noarch: Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: * transparent disk-caching of the output values and lazy re-evaluation (memorize pattern) * easy simple parallel computing * logging and tracing of the execution

python3-inkex-1.4.1-1.lbn36.noarch: This package supports Inkscape extensions. It provides - a simplification layer for SVG manipulation through lxml - base classes for common types of Inkscape extensions - simplified testing of those extensions - a user interface library based on GTK3 At its core, Inkscape extensions take in a file, and output a file. - For effect extensions, those two files are SVG files. - For input extensions, the input file may be any arbitrary file and the output is an SVG. - For output extensions, the input is an SVG file while the output is an arbitrary file. - Some extensions (e.g. the extensions manager) don't manipulate files. This folder also contains the stock Inkscape extensions, i.e. the scripts that implement some commands that you can use from within Inkscape. Most of these commands are in the Extensions menu, or in the Open / Save dialogs.

python3-ibm-watsonx-ai-1.3.1-1.lbn36.noarch: Welcome to ibm-watsonx-ai ibm-watsonx-ai is a library that allows to work with watsonx.ai service on IBM Cloud and IBM Cloud for Data. Train, test and deploy your models as APIs for application development, share with colleagues using this python library. Package documentation ==========================================

python3-ibm-cos-sdk-s3transfer-2.14.0-1.lbn36.noarch: s3transfer - An IBM COS Transfer Manager for Pythons3transfer is a Python library for managing IBM COS transfers.

python3-ibm-cos-sdk-core-2.14.0-1.lbn36.noarch: ibm-cos-sdk-python-coreA low-level interface to IBM Cloud Object Storage based on the ibm_botocore package. This core package is the foundation for the [ibm- cos-sdk-python]( package. DocumentationDocumentation for ibm-cos-sdk-python- core can be found [here]( Getting HelpFeel free to use GitHub issues for tracking bugs and feature requests, but for help please use one of the following...

python3-ibm-cos-sdk-2.14.0-1.lbn36.noarch: IBM Cloud Object Storage - Python SDKThis package allows Python developers to write software that interacts with [IBM Cloud Object Storage]( It is a fork of the [boto3 library]( and can stand as a drop-in replacement if the application needs to connect to object storage using an S3-like API and does not make use of other AWS services. NoticeIBM has added a [Language Support Policy](language-...

python3-huggingface-hub-0.30.2-1.lbn36.noarch: The huggingface_hub library allows you to interact with the Hugging Face Hub, a machine learning platform for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the hundreds of machine learning apps hosted on the Hub. You can also create and share your own models and datasets with the community. The huggingface_hub library provides a simple way to do all these things with Python.

python3-pandas-gbq-0.19.1-1.lbn36.noarch: pandas-gbq |preview| |pypi| |versions| **pandas-gbq** is a package providing an interface to the Google BigQuery API from pandas.- Library Documentation_ - Product Documentation_.. |previe .. |pyp .. |version .. _Library Documentation:

python3-pandas-gbq-0.19.1-1.lbn36.noarch: pandas-gbq |preview| |pypi| |versions| **pandas-gbq** is a package providing an interface to the Google BigQuery API from pandas.- Library Documentation_ - Product Documentation_.. |previe .. |pyp .. |version .. _Library Documentation:

python3-pandas-datareader-0.10.0-2.fc36.noarch: Data readers extracted from the pandas codebase, should be compatible with recent pandas versions.

python3-maturin-1.4.0-1.lbn36.x86_64: There are four main commands: maturin new creates a new cargo project with maturin configured. maturin publish builds the crate into python packages and publishes them to pypi. maturin build builds the wheels and stores them in a folder (target/wheels by default), but doesn't upload them. It's possible to upload those with twine or maturin upload. maturin develop builds the crate and installs it as a python module directly in the current virtualenv. Note that while maturin develop is faster, it doesn't support all the feature that running pip install after maturin build supports. pyo3 and rust-cpython bindings are automatically detected, for cffi or binaries you need to pass -b cffi or -b bin. maturin doesn't need extra configuration files and doesn't clash with an existing setuptools-rust or milksnake configuration. You can even integrate it with testing tools such as tox. There are examples for the different bindings in the test-crates folder. The name of the package will be the name of the cargo project, i.e. the name field in the [package] section of Cargo.toml. The name of the module, which you are using when importing, will be the name value in the [lib] section (which defaults to the name of the package). For binaries, it's simply the name of the binary generated by cargo.

python3-maturin-1.4.0-1.lbn36.x86_64: There are four main commands: maturin new creates a new cargo project with maturin configured. maturin publish builds the crate into python packages and publishes them to pypi. maturin build builds the wheels and stores them in a folder (target/wheels by default), but doesn't upload them. It's possible to upload those with twine or maturin upload. maturin develop builds the crate and installs it as a python module directly in the current virtualenv. Note that while maturin develop is faster, it doesn't support all the feature that running pip install after maturin build supports. pyo3 and rust-cpython bindings are automatically detected, for cffi or binaries you need to pass -b cffi or -b bin. maturin doesn't need extra configuration files and doesn't clash with an existing setuptools-rust or milksnake configuration. You can even integrate it with testing tools such as tox. There are examples for the different bindings in the test-crates folder. The name of the package will be the name of the cargo project, i.e. the name field in the [package] section of Cargo.toml. The name of the module, which you are using when importing, will be the name value in the [lib] section (which defaults to the name of the package). For binaries, it's simply the name of the binary generated by cargo.

python3-marshmallow-sqlalchemy-1.0.0-1.lbn36.noarch: ********************** marshmallow-sqlalchemy **********************|pypi- package| |build-status| |docs| |marshmallow3| |black|Homepage: < integration with the marshmallow < (de)serialization library.Declare your models .. code- block:: python import sqlalchemy as sa from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import scoped_session, sessionmaker, relationship,...

python3-jupyterlab_pygments-0.1.2-5.fc36.noarch: This package contains a syntax coloring theme for pygments making use of the JupyterLab CSS variables.

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