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python3-pyautogen+anthropic-0.9.1-1.lbn36.noarch: This is a metapackage bringing in anthropic extras requires for python3-pyautogen. It makes sure the dependencies are installed.

python3-pinecone-plugin-interface-0.0.7-1.lbn36.noarch: This small package is used to distribute a few classes and utilities used to build and consume plugins for the Pinecone Python SDK. At present this package is experimental and intended for internal use by Pinecone engineers iterating on new features. It should not be used by end-users or partners.

python3-pinecone-6.0.2-1.lbn36.noarch: Pinecone Python SDK The official Pinecone Python SDK. Documentation Conceptual docs and guides Python Reference Documentation Upgrading the SDK [!NOTE] The official SDK package was renamed from pinecone-client to pinecone beginning in version 5.1.0. Please remove pinecone-client from your project dependencies and add pinecone instead to get the latest updates. For notes on changes between major versions, see Upgrading Prerequisites The Pinecone Python SDK is compatible with Python 3.9 and greater. It has been tested with CPython versions from 3.9 to 3.13. Before you can use the Pinecone SDK, you must sign up for an account and find your API key in the Pinecone console dashboard at https:/app.pinecone.io. Installation The Pinecone Python SDK is distributed on PyPI using the package name pinecone. By default the pinecone has a minimal set of dependencies, but you can install some extras to unlock additional functionality. Available extras: pinecone[asyncio] will add a dependency

python3-optuna-4.3.0-1.lbn36.noarch: Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters.

python3-openapi-spec-validator-0.7.1-1.lbn36.noarch: ********************** OpenAPI Spec validator ********************** :target:

python3-openapi-schema-validator-0.6.3-1.lbn36.noarch: About Openapi-schema-validator is a Python library that validates schema against: OpenAPI Schema Specification v3.0 which is an extended subset of the JSON Schema Specification Wright Draft 00. OpenAPI Schema Specification v3.1 which is an extended superset of the JSON Schema Specification Draft 2020-12. Installation Recommended way (via pip): $ pip install openapi-schema-validator Alternatively you can download the code and install from the repository: $ pip install -e git+https:/github.com/p1c2u/openapi-schema-validator.git#egg=openapi_schema_validator Usage By default, the latest OpenAPI schema syntax is expected. To validate an OpenAPI v3.1 schema: from openapi_schema_validator import validate schema = { "type": "object", "required": [ "name" ], "properties": { "name": { "type": "string" }, "age": { "type": ["integer", "null"], "format": "int32", "minimum": 0,

python3-openapi-core-0.18.2-1.lbn36.noarch: About Openapi-core is a Python library that adds client-side and server-side support for the OpenAPI v3.0 and OpenAPI v3.1 specification. Key features Validation and unmarshalling of request and response data (including webhooks) Integration with popular libraries (Requests, Werkzeug) and frameworks (Django, Falcon, Flask, Starlette) Customization with media type deserializers and format unmarshallers Security data providers (API keys, Cookie, Basic and Bearer HTTP authentications) Documentation Check documentation to see more details about the features. All documentation is in the “docs” directory and online at openapi-core.readthedocs.io Installation Recommended way (via pip): pip install openapi-core Alternatively you can download the code and install from the repository: pip install -e git+https:/github.com/python-openapi/openapi-core.git#egg=openapi_core First steps Firstly create your OpenAPI object. from openapi_core import OpenAPI openapi = OpenAPI.from_file_path('op

python3-openapi-client-0.21.2-3.lbn36.noarch: openapi-python-client Generate modern Python clients from OpenAPI 3.0 and 3.1 documents. This generator does not support OpenAPI 2.x FKA Swagger. If you need to use an older document, try upgrading it to version 3 first with one of many available converters. This project is still in development and does not support all OpenAPI features Why This? This tool focuses on creating the best developer experience for Python developers by: Using all the latest and greatest Python features like type annotations and dataclasses. Having documentation and usage instructions specific to this one generator. Being written in Python with Jinja2 templates, making it easier to improve and extend for Python developers. It's also much easier to install and use if you already have Python. Installation I recommend you install with pipx so you don't conflict with any other packages you might have: pipx install openapi-python-client --include-deps. Note the --include-deps option makes ruff available in your

python3-openai-wandb-1.6.1-1.lbn36.noarch: Install support for Weights and Biases

python3-openai-embeddings-1.6.1-1.lbn36.noarch: Install dependencies for embedding utilities.

python3-openai-datalib-1.6.1-1.lbn36.noarch: Data libraries like numpy and pandas are not installed by default due to their size. They’re needed for some functionality of this library, but generally not for talking to the API.

python3-openai+voice_helpers-1.77.0-1.lbn36.noarch: This is a metapackage bringing in voice_helpers extras requires for python3-openai. It makes sure the dependencies are installed.

python3-openai+realtime-1.77.0-1.lbn36.noarch: This is a metapackage bringing in realtime extras requires for python3-openai. It makes sure the dependencies are installed.

python3-ollama-0.4.7-1.lbn36.noarch: Ollama Python Library The Ollama Python library provides the easiest way to integrate Python 3.8+ projects with Ollama. Prerequisites Ollama should be installed and running Pull a model to use with the library: ollama pull <model> e.g. ollama pull llama3.2 See Ollama.com for more information on the models available. Usage from ollama import chat from ollama import ChatResponse response: ChatResponse = chat(model='llama3.2', messages=[ { 'role': 'user', 'content': 'Why is the sky blue?', }, ]) print(response['message']['content']) print(response.message.content)

python3-numpy-f2py-2.2.4-1.lbn36.x86_64: This package includes a version of f2py that works properly with NumPy.

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

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