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python3-hpack-4.1.0-1.lbn36.noarch: HTTP/2 Header Encoding for Python This module contains a pure-Python HTTP/2 header encoding (HPACK) logic for use in Python programs that implement HTTP/2. It also contains a compatibility layer that automatically enables the use of nghttp2 if it's available.

python3-msrestazure-0.6.4-9.fc36.noarch: The runtime library "msrest" for AutoRest generated Python clients

python3-h2-4.2.0-1.lbn36.noarch: HTTP/2 Protocol Stack This repository contains a pure-Python implementation of a HTTP/2 protocol stack. It's written from the ground up to be embeddable in whatever program you choose to use, ensuring that you can speak HTTP/2 regardless of your programming paradigm.

python3-google-resumable-media-2.7.2-1.lbn36.noarch: google-resumable-media Utilities for Google Media Downloads and Resumable Uploads See the docs_ for examples and usage... _docs: asyncio Support While still in development and subject to change, this library has asyncio support at google._async_resumable_media. Supported Python Versions - Python > 3.7Unsupported Python Versions Python 2.7, Python 3.5, Python 3.6.The last version of this...

python3-weaviate-client-4.14.0-1.lbn36.noarch: A python native client for easy interaction with a Weaviate instance. The client is tested for python 3.8 and higher. Visit the official Weaviate website for more information about the Weaviate and how to use it in production. Articles Here are some articles on Weaviate: Semantic Search Queries Return More Informed Results Getting Started with Weaviate Python Library A sub-50ms neural search with DistilBERT and Weaviate Documentation Weaviate Python client overview. Weaviate documentation. Additional reference documentation Support Use our Forum for support or any other question. Use our Slack Channel for discussions or any other question. Use the weaviate tag on StackOverflow for questions. For bugs or problems, submit a GitHub issue. Contributing To contribute, read How to Contribute.

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

python3-openai-1.77.0-1.lbn36.noarch: The OpenAI Python library provides convenient access to the OpenAI API from applications written in the Python language. It includes a pre-defined set of classes for API resources that initialize themselves dynamically from API responses which makes it compatible with a wide range of versions of the OpenAI API.

python3-ntlm-auth-1.5.0-2.lbn36.noarch: This package allows Python clients running on any operating system to provide NTLM authentication to a supporting server.

python3-xmltodict-0.13.0-4.lbn36.noarch: xmltodict is a Python module that makes working with XML feel like you are working with JSON. It's very fast (Expat-based) and has a streaming mode with a small memory footprint, suitable for big XML dumps like Discogs or Wikipedia.

python3-portalocker-2.10.0-1.lbn36.noarch: Library to provide an easy API to file locking

python3-pinecone-plugin-inference-0.4.1-1.lbn36.noarch: Inference API plugin for python SDK Installation The plugin is distributed separately from the core python sdk. pip install pinecone-client pip install pinecone-plugin-inference Usage Interact with Pinecone's Inference APIs, e.g. create embeddings (currently in preview). Models currently supported: multilingual-e5-large Generate embeddings The following example highlights how to use an embedding model to generate embeddings for a list of documents and a user query, with the ultimate goal of retrieving similar documents from a Pinecone index. from pinecone import Pinecone pc = Pinecone(api_key="<<PINECONE_API_KEY>>") model = "multilingual-e5-large" text = [ "Turkey is a classic meat to eat at American Thanksgiving.", "Many people enjoy the beautiful mosques in Turkey.", ] text_embeddings = pc.inference.embed( model=model, inputs=text, parameters={"input_

python3-pinecone-client-4.1.2-1.lbn36.noarch: Pinecone Python Client The official Pinecone Python client. For more information, see the docs at https:/www.pinecone.io/docs/ Documentation If you are upgrading from a 2.2.x version of the client, check out the v3 Migration Guide. Reference Documentation Example code Many of the brief examples shown in this README are using very small vectors to keep the documentation concise, but most real world usage will involve much larger embedding vectors. To see some more realistic examples of how this client can be used, explore some of our many Jupyter notebooks in the examples repository. Prerequisites The Pinecone Python client is compatible with Python 3.8 and greater. Installation There are two flavors of the Pinecone python client. The default client installed from PyPI as pinecone-client has a minimal set of dependencies and interacts with Pinecone via HTTP requests. If you are aiming to maximimize performance, you can install additional gRPC dependencies to access an alternate clie

python3-immutabledict-4.2.1-2.lbn36.noarch: Implements the complete mapping interface and can be used as a drop-in replacement for dictionaries where immutability is desired. The immutabledict constructor mimics dict, and all of the expected interfaces (iter, len, repr, hash, getitem) are provided.

python3-hdfs+kerberos-2.7.3-1.lbn36.noarch: This is a metapackage bringing in kerberos extras requires for python3-hdfs. It makes sure the dependencies are installed.

python3-google-analytics-admin-0.22.7-1.lbn36.noarch: Python Client for Analytics Admin |preview| |pypi| |versions|Analytics Admin_: allows you to manage Google Analytics accounts and properties.- Client Library Documentation_ - Product Documentation_.. |previe .. |pyp .. |versions| image::

python3-qdrant-client-1.10.1-1.lbn36.noarch: Python Client library for the Qdrant vector search engine. Python Qdrant Client Client library and SDK for the Qdrant vector search engine. Python Client API Documentation is available here. Library contains type definitions for all Qdrant API and allows to make both Sync and Async requests. Client allows calls for all Qdrant API methods directly. It also provides some additional helper methods for frequently required operations, e.g. initial collection uploading. See QuickStart for more details! Installation pip install qdrant-client Features Type hints for all API methods Local mode - use same API without running server REST and gRPC support Minimal dependencies Extensive Test Coverage Local mode Python client allows you to run same code in local mode without running Qdrant server. Simply initialize client like this: from qdrant_client import QdrantClient client = QdrantClient(":memory:") client = QdrantClient(path="path/to/db") # Persists changes to disk Local mo

python3-pymysql-1.1.1-1.lbn36.noarch: PyMySQL This package contains a pure-Python MySQL client library, based on PEP249.

python3-pymssql-2.3.0-3.lbn36.x86_64: A simple database interface for Python that builds on top of FreeTDS to provide a Python DB-API (PEP-249) interface to Microsoft SQL Server.

python3-pykerberos-1.2.4-1.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 <

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