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python3-astrapy-2.0.1-1.lbn36.noarch: AstraPy A pythonic client for DataStax Astra DB. This README targets AstraPy version 1.0.0+, which introduces a whole new API. Click here for the pre-existing API (fully compatible with newer versions). Quickstart Install with pip install astrapy. Get the API Endpoint and the Token to your Astra DB instance at astra.datastax.com. Try the following code after replacing the connection parameters: import astrapy ASTRA_DB_APPLICATION_TOKEN = "AstraCS:..." ASTRA_DB_API_ENDPOINT = "https:/01234567-....apps.astra.datastax.com" my_client = astrapy.DataAPIClient() my_database = my_client.get_database( ASTRA_DB_API_ENDPOINT, token=ASTRA_DB_APPLICATION_TOKEN, ) my_collection = my_database.create_collection( "dreams", dimension=3, metric=astrapy.constants.VectorMetric.COSINE, ) my_collection.insert_one({"summary": "I was flying", "$vector": [-0.4, 0.7, 0]}) my_collection.insert_many( [ { "_id": astrapy.ids.UUID("018e65c9-e33d-749b-9386-e848739582f0"

python3-astra-assistants+tools-2.2.12-1.lbn36.noarch: This is a metapackage bringing in tools extras requires for python3-astra-assistants. It makes sure the dependencies are installed.

python3-astra-assistants-2.2.12-1.lbn36.noarch: astra-assistants Astra Assistants is a drop in replacement for OpenAI's assistant API that supports the full Assistants v2 API (including streaming and vector_stores). This python library wraps the OpenAI SDK with additional capabilities and provides syntactic sugar for passing credentials for third party LLMs. How to use Install astra_assistants using your python package manager of choice: poetry add astra_assistants import and patch your client: from openai import OpenAI from astra_assistants import patch client = patch(OpenAI()) Server The astra-assistants server code is now open source (Apache2)! Check it out here https:/github.com/datastax/astra-assistants-api Authentication Provide api keys for third party LLMs via environment variables. We support LLM completions through litellm and support litellm environmental variables and models. Rename the .env.bkp to .env and fill in the appropriate values for the LLMs you want to use.

python3-assemblyai-0.35.1-1.lbn36.noarch: AssemblyAI's Python SDK Build with AI models that can transcribe and understand audio With a single API call, get access to AI models built on the latest AI breakthroughs to transcribe and understand audio and speech data securely at large scale.

python3-appium-python-client-4.0.0-1.lbn36.noarch: Appium Python Client[![PyPI version]( [![Downloads]( Status]( style: black]( extension library for adding [WebDriver Protocol]( and Appium commands to the Selenium Python language binding for use with the mobile testing framework [Appium]().

python3-apify-shared-1.1.2-1.lbn36.noarch: apify-shared

python3-apify-client-1.8.1-1.lbn36.noarch: apify-client

python3-best-ec2-1.1.4-1.lbn36.noarch: Best EC2 Best EC2, the smart solution designed to optimize your Amazon EC2 instance type selection process. The app simplifies the challenge of choosing the optimal EC2 instance type that matches your specific requirements, balancing performance, cost, and computing needs. Prerequisites python >=3.8.0 AWS Credentials Install pip install best-ec2 The IAM policy grants minimal permissions necessary for the application { "Version": "2012-10-17", "Statement": [ { "Sid": "General", "Effect": "Allow", "Action": [ "ec2:DescribeInstanceTypes" ], "Resource": "*" }, { "Sid": "OnDemand", "Effect": "Allow", "Action": [ "pricing:GetProducts" ], "Resource": "*" }, { "Sid": "Spot", "Effect": "Allow", "Action": [ "ec2:DescribeAvailabilityZones",

python3-websockets-14.1-2.lbn36.x86_64: websockets is a library for developing WebSocket servers and clients in Python. It implements RFC 6455 with a focus on correctness and simplicity. It passes the Autobahn Testsuite. Built on top of Python’s asynchronous I/O support introduced in PEP 3156, it provides an API based on coroutines, making it easy to write highly concurrent applications.

python3-google-genai-1.14.0-1.lbn36.noarch: Google Gen AI SDK Documentation: https:/googleapis.github.io/python-genai/ Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications. It supports the Gemini Developer API and Vertex AI APIs. Installation pip install google-genai Imports from google import genai from google.genai import types Create a client Please run one of the following code blocks to create a client for different services (Gemini Developer API or Vertex AI). client = genai.Client(api_key='GEMINI_API_KEY') client = genai.Client( vertexai=True, project='your-project-id', location='us-central1' ) (Optional) Using environment variables: You can create a client by configuring the necessary environment variables. Configuration setup instructions depends on whether you're using the Gemini Developer API or the Gemini API in Vertex AI. Gemini Developer A

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-pandas-2.2.3-1.lbn36.x86_64: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

python3-pandas-2.2.3-1.lbn36.x86_64: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

python3-pandas-2.2.3-1.lbn36.x86_64: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

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-matplotlib-tk-3.10.0-3.lbn36.x86_64: Tk backend for python3-matplotlib

python3-matplotlib-tk-3.10.0-3.lbn36.x86_64: Tk backend for python3-matplotlib

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