semvec/pypi-packages huggingface dataset:
rishabh-ask-ai 1.0.0.post2
This is a python package which can return outputs given by ChatGPT using groq
Requires Python: >=3.10

# Ask AI 
## Features
* Uses ChatGPT to answer users questions
* Powered by groq module
* You can use ask function to use this library
## Usage
```python
# Import the ask function from the package
from rishabh-ask-ai import ask

# Use the ask function to send a query to AI
response = ask("Hello, what is your name?")
print(response)
```
Topsis-Vihaan-102303658 1.1.1
A Python package to implement TOPSIS for MCDM.
Requires Python: >=3.6

# Topsis-Vihaan-102303658

Is a Python package for implementing the **Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)**.

## Installation

```bash
pip install Topsis-Vihaan-102303658

Usage:

Run the package via command line as follows:

<<Terminal>>
topsis <InputDataFile> <Weights> <Impacts> <OutputResultFileName>

Example : 
<<Terminal>>
topsis data.csv "1,1,1,1,1" "+,-,+,+,+" result.csv
qililab 0.31.0
Fundamental package for fast characterization and calibration of quantum chips.
Requires Python: >=3.10
Keywords: quantum control

# Qililab

[![codecov](https://codecov.io/gh/qilimanjaro-tech/qililab/branch/main/graph/badge.svg?token=gSfTPmCeJw)](https://codecov.io/gh/qilimanjaro-tech/qililab)
[![Documentation Status](https://readthedocs.org/projects/qililab/badge/?version=latest)](https://qaas.readthedocs.io/projects/qililab/en/latest/?badge=latest)

Qililab is a generic and scalable quantum control library used for fast characterization and calibration of quantum chips. Qililab also offers the ability to execute high-level quantum algorithms with your quantum hardware.

You can find Qililab's documentation [here](https://qaas.readthedocs.io/projects/qililab/en/latest/index.html)
## Contributions

Thank you for your interest in our project. While we appreciate your enthusiasm and interest in contributing, we would like to clarify our policy regarding external contributions.

### Our Contribution Policy

This project is primarily intended for reference purposes, and we do not actively accept or manage external contributions, including pull requests and issue reports. Our development team maintains this codebase for internal use and does not have the capacity to review or merge contributions from the community.

### Why We Have This Policy

Our decision to limit external contributions is based on our specific project goals, resource constraints, and internal policies. While we understand the value of collaboration and open-source contributions, we have chosen to maintain this project as a reference rather than a collaborative, community-driven effort.

### Seeking Help and Support

If you have questions about using this project or encounter issues, please feel free to open an issue in the repository for discussion. However, please be aware that our ability to provide support or address issues may be limited.

Thank you for your understanding and for considering our project. We hope that you find it useful for your needs, and we wish you the best in your open-source endeavors.

Sincerely,

Qilimanjaro Quantum Tech
ChameleonIQ 2.0.0
A tool for NEMA NU 2-2018 Image Quality analysis.
Requires Python: >=3.11

<p align="center">
  <img
    alt="Logo Banner"
    src="https://raw.githubusercontent.com/EdAlita/nema_analysis_tool/main/data/banner.png"
  >
</p>

<p align="center">
  <a href="https://github.com/EdAlita/nema_analysis_tool/actions/workflows/tests.yml">
    <img alt="Tests" src="https://github.com/EdAlita/nema_analysis_tool/actions/workflows/tests.yml/badge.svg?branch=main">
  </a>
  <a href="https://github.com/psf/black">
    <img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg">
  </a>
  <a href="https://github.com/EdAlita/nema_analysis_tool/blob/main/LICENSE">
    <img alt="License: Apache-2.0" src="https://img.shields.io/badge/License-Apache_2.0-blue.svg">
  </a>
  <a>
    <img alt="Python" src="https://img.shields.io/badge/language-Python-blue?logo=python">
  </a>
  <a>
    <img alt="Git Download" src="https://img.shields.io/github/downloads/EdAlita/nema_analysis_tool/total">
  </a>
  <a>
    <img alt="Git Release" src="https://img.shields.io/github/v/release/EdAlita/ChameleonIQ">
  </a>
</p>


# ChameleonIQ: Nema-aware Image Quality Tool for Python

This project is a Python-based tool for the automated analysis of PET image quality based on the NEMA NU 2-2018 standard, specifically focusing on Section 7.4.1.

## Features

*   Calculates Percent Contrast (Q_H,j), Percent Background Variability (N_j), and Accuracy of Corrections (ΔC_lung,i).
*   Utilizes 3D Regions of Interest (ROIs) based on the NEMA Body Phantom.
*   Loads nii image data with user-defined dimensions and voxel spacing.
*   Automatic postions of ROIs on given centers

## How to get Started?
Read these:
- [**Installation instructions**](https://github.com/EdAlita/ChameleonIQ/wiki/Installation)
- [**Usage instructions**](https://github.com/EdAlita/ChameleonIQ/wiki/Usage)
- [**How it works?**](https://github.com/EdAlita/ChameleonIQ/wiki/How-it-works)

Additional information:
- [**What will change?**](https://github.com/EdAlita/ChameleonIQ/wiki/Changelog)

## License
This project is licensed under the Apache Lincese 2.0 - see the [LICENSE.md](Lhttps://github.com/EdAlita/ChameleonIQ/blob/main/LICENSE.txt) file for details.

[//]: # (- [Ignore label]&#40;documentation/ignore_label.md&#41;)
llama-index-vector-stores-volcengine-mysql 0.2.0
llama-index vector_stores Volcengine MySQL integration
Requires Python: <4.0,>=3.9

# LlamaIndex Vector_Stores Integration: Volcengine MySQL

This integration provides a `VolcengineMySQLVectorStore` that leverages the native vector index capabilities of Volcano engine Cloud Database for MySQL. It allows you to use RDS for MySQL as a fully-functional vector store in LlamaIndex for high-performance similarity search.

To learn more about the native vector feature in Volcengine RDS for MySQL, you can refer to its official documentation. This integration is specifically designed for Volcengine's enhanced MySQL and is not intended for standard community MySQL instances.

## Installation

```shell
pip install llama-index-vector-stores-volcengine-mysql
```

## Usage

```python
from llama_index.vector_stores.volcengine_mysql import (
    VolcengineMySQLVectorStore,
)

# Initialize the vector store
vector_store = VolcengineMySQLVectorStore.from_params(
    host="your-rds-instance-host",
    port=3306,
    user="your-username",
    password="your-password",
    database="your-database",
    table_name="llama_index_vector_store",
    embed_dim=1536,  # Example: OpenAI embedding dimension
)
```

### Requirements

To use this integration, you need a Volcengine RDS for MySQL instance with the following features enabled:

- The `loose_vector_index_enabled` parameter set to `ON`.
- Support for `VECTOR(N)` data type for columns.
- Support for `VECTOR INDEX` (HNSW) for indexing.
- Access to vector functions like `TO_VECTOR(...)`, `L2_DISTANCE(...)`, and `COSINE_DISTANCE(...)`.
bvloader 1.0.1
Light loader library that fetches and runs BV.py remotely
Requires Python: >=3.7

# bvloader

مكتبة بايثون خفيفة  
عند استدعائها تقوم بتحميل وتشغيل ملف BV.py من GitHub تلقائيًا.

## التثبيت
```bash
pip install bvloader
python-cloudkittyclient 6.0.0
API client of cloudkitty, Rating as a Service project.
Requires Python: >=3.10

================
CloudKittyClient
================

.. image:: https://governance.openstack.org/badges/python-cloudkittyclient.svg
    :target: https://governance.openstack.org/reference/tags/index.html

This is a client for CloudKitty_. It provides a Python api (the
``cloudkittyclient`` module), a command-line script (``cloudkitty``), and an
`OpenStack Client`_ extension (``openstack rating``).

The client is available on PyPi_.

.. _OpenStack Client: https://docs.openstack.org/python-openstackclient/latest/
.. _CloudKitty: https://github.com/openstack/cloudkitty
.. _PyPi:  https://pypi.org/project/python-cloudkittyclient/
rdcompiler 0.2.1
A library to compile Rhythm Doctor custom levels.
Requires Python: >=3.8

# RD-Compiler

## Introduction
This is a Python library that "compiles" Rhythm Doctor custom levels.

It actually rearranges level structure, making it difficult for human to read in both the game editor and text editors.

## Installation & Running
```shell
pip install rdcompiler
python -m rdcompiler -h
```

## Building
This library uses [Hatch](https://github.com/pypa/hatch) to build.

Install Hatch and run the following command in the root directory of this repository:
```shell
hatch build
```
python-heatclient 5.0.0
OpenStack Orchestration API Client Library
Requires Python: >=3.10

=================
python-heatclient
=================

.. image:: https://governance.openstack.org/tc/badges/python-heatclient.svg

.. Change things from this point on

.. image:: https://img.shields.io/pypi/v/python-heatclient.svg
    :target: https://pypi.org/project/python-heatclient/
    :alt: Latest Version


OpenStack Orchestration API Client Library

This is a client library for Heat built on the Heat orchestration API. It
provides a Python API (the ``heatclient`` module) and a command-line tool
(``heat``).

* Free software: Apache license
* `PyPi`_ - package installation
* `Online Documentation`_
* `Launchpad project`_ - release management
* `Blueprints`_ - feature specifications
* `Bugs`_ - issue tracking
* `Source`_
* `Specs`_
* `Template`_
* `How to Contribute`_

.. _PyPi: https://pypi.org/project/python-heatclient
.. _Online Documentation: https://docs.openstack.org/python-heatclient/latest
.. _Launchpad project: https://launchpad.net/python-heatclient
.. _Blueprints: https://blueprints.launchpad.net/python-heatclient
.. _Bugs: https://storyboard.openstack.org/#!/project/openstack/python-heatclient
.. _Source: https://opendev.org/openstack/python-heatclient
.. _How to Contribute: https://docs.openstack.org/infra/manual/developers.html
.. _Specs: https://specs.openstack.org/openstack/heat-specs/
.. _Template: https://opendev.org/openstack/heat-templates/