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python3-numpy-2.2.4-1.lbn36.x86_64: NumPy is a general-purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays. NumPy is built on the Numeric code base and adds features introduced by numarray as well as an extended C-API and the ability to create arrays of arbitrary type. There are also basic facilities for discrete fourier transform, basic linear algebra and random number generation. Also included in this package is a version of f2py that works properly with NumPy.

python3-notebook-shim-0.2.3-1.lbn36.noarch: Notebook Shim This project provides a way for JupyterLab and other frontends to switch to Jupyter Server for their Python Web application backend. Basic Usage Install from PyPI: pip install notebook_shim This will automatically enable the extension in Jupyter Server. Usage This project also includes an API for shimming traits that moved from NotebookApp in to ServerApp in Jupyter Server. This can be used by applications that subclassed NotebookApp to leverage the Python server backend of Jupyter Notebooks. Such extensions should now switch to ExtensionApp API in Jupyter Server and add NotebookConfigShimMixin in their inheritance list to properly handle moved traits. For example, an application class that previously looked like: from notebook.notebookapp import NotebookApp class MyApplication(NotebookApp): should switch to look something like: from jupyter_server.extension.application import ExtensionApp from notebook_shim.shim import NotebookConfigShimMixin class MyApplication(Note

python3-notebook-7.2.0-1.lbn36.noarch: The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Notebook has support for multiple programming languages, sharing, and interactive widgets.

python3-networkx-3.4.2-1.lbn36.noarch: NetworkX is a Python 3 package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

python3-needle-python-0.4.0-1.lbn36.noarch: Needle Python Library This Python library provides convenient acccess to Needle API. There are various methods and data types which, we believe will help you explore Needle API quickly. There may be some functionality available in REST API earlier than this Python library. In any case, we recommend to take look the the complete documentation. Thank you for flying with us. 🚀 Installation This library requires Python >3.8 and pip to use. You don't need the sources unless you want to modify it. Install with: pip install needle-python Usage ⚡️ To get started, generate an API key for your account in developer settings menu at Needle. Note that your key will be valid until you revoke it. Set the following env variable before you run your code: export NEEDLE_API_KEY=<your-api-key> NeedleClient reads the API key from the environment by default. If you like to override this behaviour you can pass it in as a parameter. Retrieve context from Needle from needle.v1 import NeedleClient from need

python3-needle-0.5.0-2.lbn36.noarch: Needle is a tool for testing your CSS and visuals with Selenium and nose. It checks that visuals (CSS/fonts/images/SVG/etc.) render correctly by taking screenshots of portions of a website and comparing them against known good screenshots. It also provides tools for testing calculated CSS values and the position of HTML elements.

python3-nbsphinx-0.8.7-2.fc36.noarch: nbsphinx is a Sphinx extension that provides a source parser for *.ipynb files. Custom Sphinx directives are used to show Jupyter Notebook code cells (and of course their results) in both HTML and LaTeX output. Unevaluated notebooks, i.e. notebooks without stored output cells, will be automatically executed during the Sphinx build process.

python3-nbformat-5.9.2-1.lbn36.noarch: This package contains the base implementation of the Jupyter Notebook format, and Python APIs for working with notebooks.

python3-nbconvert-7.16.4-1.lbn36.noarch: nbconvert Jupyter Notebook Conversion The nbconvert tool, jupyter nbconvert, converts notebooks to various other formats via Jinja templates. The nbconvert tool allows you to convert an .ipynb notebook file into various static formats including: HTML LaTeX PDF Reveal JS Markdown (md) ReStructured Text (rst) executable script Usage From the command line, use nbconvert to convert a Jupyter notebook (input) to a a different format (output). The basic command structure is: $ jupyter nbconvert --to <output format> <input notebook> where <output format> is the desired output format and <input notebook> is the filename of the Jupyter notebook. Example: Convert a notebook to HTML Convert Jupyter notebook file, mynotebook.ipynb, to HTML using: $ jupyter nbconvert --to html mynotebook.ipynb This command creates an HTML output file named mynotebook.html. Dev Install Check if pandoc is installed (pandoc --version); if needed, install: sudo apt-get install pandoc Or brew install pandoc I

python3-mistralclient-4.5.0-1.lbn36.noarch: Python client for Mistral REST API. Includes python library for Mistral API and Command Line Interface (CLI) library.

python3-mistralai-1.7.0-1.lbn36.noarch: Mistral Python Client Mistral AI API: Our Chat Completion and Embeddings APIs specification.

python3-litellm+proxy-1.69.0-1.lbn36.noarch: This is a metapackage bringing in proxy extras requires for python3-litellm. It makes sure the dependencies are installed.

python3-litellm-1.69.0-1.lbn36.noarch: 🚅 LiteLLM Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.] LiteLLM manages: Translate inputs to provider's completion, embedding, and image_generation endpoints Consistent output, text responses will always be available at ['choices'][0]['message']['content'] Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - Router Set Budgets & Rate limits per project, api key, model LiteLLM Proxy Server (LLM Gateway)

python3-langwatch-0.1.16-1.lbn36.noarch: LangWatch Python SDK Go to https:/docs.langwatch.ai to get started. Contributing After changing code, to test all integrations are working, run the examples integration test manually (you will need all env vars to be set up): poetry run pytest tests/test_examples.py -p no:warnings -s -x Or to test only a specific example, run: poetry run pytest tests/test_examples.py -p no:warnings -s -x -k <example_name>

python3-language-server-0.36.2-6.fc35.noarch: A Python implementation of the Language Server Protocol.

python3-langflow+postgresql-1.4.0-1.lbn36.noarch: This is a metapackage bringing in postgresql extras requires for python3-langflow. It makes sure the dependencies are installed.

python3-langflow+nv-ingest-1.4.0-1.lbn36.noarch: This is a metapackage bringing in nv-ingest extras requires for python3-langflow. It makes sure the dependencies are installed.

python3-langflow+local-1.4.0-1.lbn36.noarch: This is a metapackage bringing in local extras requires for python3-langflow. It makes sure the dependencies are installed.

python3-langflow+deploy-1.4.0-1.lbn36.noarch: This is a metapackage bringing in deploy extras requires for python3-langflow. It makes sure the dependencies are installed.

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