semvec/pypi-packages huggingface dataset:
chatproxy 0.0.1
Language Model Proxy
Requires Python: >=3.10
Keywords: chatbot,ai,ml,llm

# ChatProxy

The goal of ChatProxy is providing python library that enables developers to effortlessly interface with various LLMs such as OpenAI's GPT-4, Google's BERT, and others. 

This package encapsulates the complex interactions with these LLMs behind intuitive and user-friendly proxy classes, allowing developers to focus on leveraging the power of these advanced models, without being bogged down by intricate API details and interfacing code.

## Features (WIP)

1. **Multiple LLM Support:** The package supports multiple LLMs, including but not limited to GPT-4, BERT, and others. It is also designed with flexibility in mind to accommodate future models.

2. **Uniform API:** It provides a uniform API for different LLMs, allowing developers to switch between models without the need to extensively change the codebase.

3. **Error Handling:** It includes built-in error handling and retry logic, ensuring that your application remains resilient against minor network hiccups and transient errors.

4. **Optimized Performance:** With caching and other optimizations, this package makes sure you get the best possible performance out of your chosen LLM.

5. **Asynchronous Support:** For developers who require high performance and non-blocking code execution, this package offers asynchronous methods.

## Installation

You can install the `chatproxy` package via pip:

```bash
pip install chatproxy
```

## Quickstart

Here's an example of how to create a ChatGPT proxy and use it to generate text:

```python
import chatproxy

session = chatproxy.Session()
while (prompt := input("you : ")) != "exit":
    message = session.send(prompt)
    print(f"\n{message['role']} : {message['content']}\n")
```

## Documentation

For detailed information on using this package, please refer to our [documentation](link_to_documentation).

## Contributing

We welcome contributions! Please see our [contributing guidelines](link_to_contributing_guidelines) for details.

## License

This project is licensed under the terms of the MIT license. See [LICENSE](link_to_license) for more details.
chinesename 0.1.1
get a chinesename by random

# 中文取名
[![Build Status](https://travis-ci.org/mouday/chinesename.svg?branch=master)](https://travis-ci.org/mouday/chinesename)
[![PyPI](https://img.shields.io/pypi/v/chinesename.svg)](https://pypi.org/project/chinesename/)
![PyPI - Downloads](https://img.shields.io/pypi/dm/chinesename.svg)

## 说明
项目包含《百家姓》姓氏504个，常用于名字的汉字2812个
名字汉字中包含大部分《楚辞》、《诗经》词汇

pypi地址： https://pypi.python.org/pypi/chinesename

## 安装模块

```
pip install chinesename
```

## 代码示例：

```python
from chinesename import ChineseName  # 导包

cn = ChineseName()  # 实例化

cn.getName()  # 获取一个姓名
'广袁'

cn.getNames(10)  # 获取多个姓名
['笪递', '慎彭徨', '席具', '锺谦', '任西', '公羊狱', '华桑', '利节', '燕角', '任彪']

cn.getNameGenerator(10)  # 获取生成器
<generator object ChineseName.getNameGenerator at 0x1045cfa40>

cn.getLastName()  # 获取姓氏
'舒'

cn.getFirstName()  # 获取名字
'崔'

cn.getName(lastname="彭")  # 设置姓氏
'彭圭'

cn.getName(char_count=2)  # 设置名字长度
'倪吏渚'

cn.getName(sex="boy")  # 设置性别 （boy | girl）
'骆留'

```

## 更新记录

2018年1月22日 增加姓名生成器函数，getNameGenerator()


2018年3月19日 修复导入lastnames 和 firstname 文件失败的问题，将两文件内容直接写到chinesename中，并删除两文件


2018年6月20日 修复资源文件导入问题，增加性别参数，区分男孩姓名和女孩姓名
感谢 JoffreyN 提出的建议 [古语云：男楚辞，女诗经。名可以从诗经、楚辞里面取](https://github.com/mouday/chinesename/issues/2)
在原有基础上进行了扩展
遗留问题：取词不准确，需要进一步优化


2018年7月23日 修复文件open时引发的编码问题，增加参数：encoding="utf-8"

2018年11月23日 修复pyhton2下安装报错'IOError: [Errno 2] No such file or directory: "README.md"'的问题
gaiacalc 1.1.0
Gaia white dwarfs parameter interpolator
Requires Python: >=3.7

Gaia calculator for White Dwarfs

Gaia is a space observatory of the European Space Agency (ESA). The mission aims to construct the largest and most precise 3D space catalog.
EDR3 - Early Data Release 3 - was released on 3 December 2020.
DR3 - 3rd data release - was released on 13 June 2022.
Those catalogs include 1811709771 objects. Data is freely downloadable from ESA servers.
The DR3 database contains more than 200 parameters for each space object.

The Universitat Politècnica de Catalunya · BarcelonaTech (UPC) makes available a tool to compute the mass and temperature of White Dwarfs stars. This tool is based on data abacus, spatial index, and interpolation.

Any input file with CSV format is accepted, provided that is contains at least 3 columns with values for BP-RP, Gabs and probability.
The tool will add 2 more columns with mass and Teff of the star.

The tool is available as :
- online access here : http://gaia.doligez.fr
- MacOS application
- Windows exe file
- Python package
- linux debian package
- GitHub source code

See PACKAGING.txt to be able to generate any or all of these deliveries from the source code.

Julie Doligez
csstuning 0.0.3
Configurable Software System Tuning Benchmark
Requires Python: >=3.8

# CSSTuning - Configurable Software Configuration Optimization Benchmark

CSSTuning is a comprehensive benchmark tool that caters to various real-world software configuration optimization problems. It offers a simple and rapid setup process for creating a software configuration optimization environment on Linux systems, accurately replicating real software configuration challenges.

## Introduction

Optimizing software configurations is a crucial aspect of performance tuning for a wide array of applications and systems. CSSTuning aims to address this need by providing an extensive collection of benchmark scenarios, each representing authentic software configuration optimization problems. Whether you are a developer, researcher, or enthusiast, CSSTuning provides an invaluable resource for assessing and optimizing the performance of diverse software configurations.

## Benchmark List

Compiler

Data base manage system (DBMS) //to do

Web Server //to do

Big data system //to do

## Features

- Diverse Benchmark Scenarios: CSSTuning presents a rich set of benchmark scenarios that accurately emulate real-world software configuration optimization problems for multiple software systems.
- Multi-objective Optimization: For each software system, CSSTuning incorporates various officially recognized performance evaluation metrics, allowing the construction of multi-objective optimization problems.
- Containerized Benchmarks: All benchmarks in CSSTuning are containerized, eliminating dependency issues and ensuring easy reproducibility across different environments.
- Industrial Data-driven Tuning: CSSTuning goes beyond traditional benchmarks by providing the ability to optimize software systems using real-world industrial data, making the optimization process more relevant and practical.

## installation

**Prerequisites:**

- POSIX System (i.e. Linux, MacOS)
- Docker installed
- Python 3.8+

```
git clone https://github.com/neeetman/csstuning.git
cd path/to/csstuning
#modify PAPI permissions for compiler benchmark
sudo sh -c "echo -1 > /proc/sys/kernel/perf_event_paranoid"
##installation
python setup.py install --user
```

## Usage

### 1.Compiler Benchmarks

# Use command line #

# GCC compiler #

```
# View the available benchmarks for GCC #
csstuning compiler:gcc list benchs
# View the available flags for GCC #
csstuning compiler:gcc list flags
# Help command #
csstuning --help
# Run GCC with specific benchmark and flags #
csstuning compiler:gcc run benchs=cbench-automotive-bitcount flags="ftree-loop-vectorize,ftree-partial-pre"
```

# LLVM compiler #

```
# View the available benchmarks for LLVM #
csstuning compiler:llvm cc list benchs
# View the available flags for LLVM #
csstuning compiler:llvm list flags
# Help command #
csstuning --help
# Run LLVM with specific benchmark and flags #
csstuning compiler:llvm run benchs=cbench-automotive-bitcount flags="vetor-combine"
```

# Use in python #

### 2.DBMS Benchmarks