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python3-ibm-cloud-security-advisor-findingsapi-sdk-2.0.7-1.lbn36.noarch: NOTE : This repo is planned to be sunset by May 31st 2020, new SDK is available at ibm_security_advisor_findings_api_sdkThis repository contains the released Python client SDK for IBM Cloud Security Advisor Findings API . Check out our [API documentation]( for more details. NoticeSupport for Python versions 2.x and versions < 3.4 is deprecated and will be officially dropped in the next major...

python3-ibm-cloud-security-advisor-2.0.0-1.lbn36.noarch: This repository contains the released python client SDK for IBM Cloud Security Advisor Findings and Notifications APIs

python3-ibm-cloud-sdk-core-3.23.0-1.lbn36.noarch: This project contains core functionality required by Python code generated by the IBM Cloud OpenAPI SDK Generator (openapi-sdkgen).

python3-ibm-cloud-networking-services-0.24.0-1.lbn36.noarch: Python client library to interact with various IBM Cloud Networking Service APIs.

python3-ibm-cloud-databases-0.1.0-1.lbn36.noarch: Python client library to interact with various IBM Cloud Cloud Databases APIs.

python3-ibm-cloud-cis-services-0.0.0-1.lbn36.noarch: IBM Cloud Internet Services Python SDK

python3-orjson-3.8.10-1.fc36.x86_64: orjosn is a fast, correct Python JSON library supporting dataclasses, datetimes, and numpy

python3-kerberos-1.3.0-25.lbn36.x86_64: This Python package is a high-level wrapper for Kerberos (GSSAPI) operations. The goal is to avoid having to build a module that wraps the entire Kerberos framework, and instead offer a limited set of functions that do what is needed for client/server Kerberos authentication based on <http://www.ietf.org/rfc/rfc4559.txt>.

python3-jsonpickle-3.0.1-1.lbn36.noarch: jsonpickle is a library for the two-way conversion of complex Python objects and JSON. jsonpickle builds upon the existing JSON encoders, such as simplejson, json, and ujson. Warning jsonpickle can execute arbitrary Python code. Please see the Security section for more details.

python3-jsonpath-python-1.0.6-1.lbn36.noarch: - [jsonpath-python](jsonpath-python) - [Features](features) - [JSONPath Syntax](jsonpath-syntax) - [Operators](operators) - [Examples](examples) - [Select Fields](select-fields) - [Recursive Descent](recursive-descent) - [Slice](slice) - [Filter Expression](filter-expression) - [Sorter Expression](sorter-expression) - [Field-Extractor Expression](field-extractor- expression) - [Appendix:...

python3-langtable-0.0.58-1.fc36.noarch: This package contains a Python module to query the data from langtable-data.

python3-langsmith-0.3.43-1.lbn36.noarch: LangSmith Client SDK This package contains the Python client for interacting with the LangSmith platform. LangSmith helps you and your team develop and evaluate language models and intelligent agents. It is compatible with any LLM Application and provides seamless integration with LangChain, a widely recognized open-source framework that simplifies the process for developers to create powerful language model applications.

python3-langserve-0.0.27-1.lbn36.noarch: 🦜️🏓 LangServe 🚩 We will be releasing a hosted version of LangServe for one-click deployments of LangChain applications. Sign up here to get on the waitlist. Overview LangServe helps developers deploy LangChain runnables and chains as a REST API. This library is integrated with FastAPI and uses pydantic for data validation. In addition, it provides a client that can be used to call into runnables deployed on a server. A javascript client is available in LangChainJS. Features Input and Output schemas automatically inferred from your LangChain object, and enforced on every API call, with rich error messages API docs page with JSONSchema and Swagger (insert example link) Efficient /invoke, /batch and /stream endpoints with support for many concurrent requests on a single server /stream_log endpoint for streaming all (or some) intermediate steps from your chain/agent Playground page at /playground with streaming output and intermediate steps Built-in (optional) tracing to LangSmith, ju

python3-langchain-0.3.25-1.lbn36.noarch: 🦜️🔗 LangChain Building applications with LLMs through composability 🤔 What is this? Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.

python3-jupyterlab-vpython-3.1.6-1.lbn36.noarch: jupyterlab_vpython A VPython extension for JupyterLab Requirements JupyterLab >= 4.0.0 Install To install the extension, execute: pip install jupyterlab_vpython Uninstall To remove the extension, execute: pip uninstall jupyterlab_vpython Contributing Development install Note: You will need NodeJS to build the extension package. The jlpm command is JupyterLab's pinned version of yarn that is installed with JupyterLab. You may use yarn or npm in lieu of jlpm below. pip install -e "." jupyter labextension develop . --overwrite jlpm build You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.

python3-jupyterlab-server-2.27.2-1.lbn36.noarch: jupyterlab server Motivation JupyterLab Server sits between JupyterLab and Jupyter Server, and provides a set of REST API handlers and utilities that are used by JupyterLab. It is a separate project in order to accommodate creating JupyterLab-like applications from a more limited scope. Install pip install jupyterlab_server To include optional openapi dependencies, use: pip install jupyterlab_server[openapi] To include optional pytest_plugin dependencies, use: pip install jupyterlab_server[test] Usage See the full documentation for API docs and REST endpoint descriptions. Extending the Application Subclass the LabServerApp and provide additional traits and handlers as appropriate for your application. Contribution Please see CONTRIBUTING.md for details.

python3-jupyterlab-irods-3.0.5-1.lbn36.noarch

python3-jupyterlab-heroku-0.2.1-1.lbn36.noarch: jupyterlab-heroku[![npm]( [![pypi]( extension to manage and deploy applications to Heroku.The extension enables 1-click deployments of [voila]( dashboards.![screencast]( Requirements Create an Heroku account_If you already have an Heroku account, you can skip to the next section_.

python3-jupyterlab-dash-0.1.0a3-1.lbn36.noarch: jupyterlab-dash A JupyterLab extension for rendering Plotly Dash apps as a separate window in JupyterLab :tada: Note:: This extension does not currently support Windows or Python 2 Prerequisites JupyterLab 1.0.0 Dash Installation The jupyterlab-dash library requires both a Python package and a JupyterLab extension. First, install the Python package using either pip... $ pip install "jupyterlab>=1.0" jupyterlab-dash==0.1.0a3 or conda (but not both!). $ conda install -c plotly -c defaults -c conda-forge "jupyterlab>=1.0" jupyterlab-dash=0.1.0a3 Then, install the JupyterLab extension. v6.17.1 of node works, and node 8.x.x or 10+ should also work. $ jupyter labextension install jupyterlab-dash@0.1.0-alpha.3 Development Installation If you'd like to install jupyterlab-dash for development $ git clone https:/github.com/plotly/jupyterlab-dash $ cd jupyterlab-dash $ pip install -e . $ npm install # or yarn

python3-jupyterlab-celltagsclasses-0.3.2-1.lbn36.noarch: jupyterlab_celltagsclasses An extension to expose cell tags as CSS classes Requirements JupyterLab >= 4.0.0 Install To install the extension, execute: pip install jupyterlab_celltagsclasses Uninstall To remove the extension, execute: pip uninstall jupyterlab_celltagsclasses What it does CSS classes each cell has its widget (the DOM element) classes kept in sync in terms of the cell's tags; for example, adding tag foobar will result in the current cell having class cell-tag-foobar added specifically the DOM elements that are decorated have the .jpCell class set by jlab, like so, where we have set tag celltagsclasses-test1 metadata management helper functions it also exports utilities to manage a cell's metadata, specifically for getting, setting or unsetting a key/value pair adding, removing items in a list inside the metadata (e.g. tags) cleaning the metadata for empty/useless items to that effect, see the md_get and similar functions in metadata.ts (that can be imported right f

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