semvec/pypi-packages huggingface dataset:
whiskerRAG 0.0.1a4
A utility package for RAG operations
Requires Python: <4.0,>=3.8.1

# WhiskerRAG-toolkit
petercat、whisker 项目使用的 rag 工具包，提供 rag 相关类型定义和方法

## 使用方式
```
pip install whiskerRAG
```
提供两个模块，分别是 whisker_rag_type、 whisker_rag_util

```
from whiskerRAG.github.fileLoader import GithubFileLoader
```


## 开发指南
安装 poetry 进行依赖管理

```bash
pip install poetry
```

## 本地测试

```bash
# 运行测试
poetry run pytest

# 带覆盖率报告
poetry run pytest --cov

# 查看HTML格式的覆盖率报告
poetry run pytest --cov --cov-report=html
open htmlcov/index.html

```

# 构建并发布

```bash
poetry build

poetry publish
```
PyRexBot 1.0.4
PyBotRex simplifies Telegram bot creation with a clean and intuitive interface for commands, messaging, buttons, and more.
Requires Python: >=3.7
Keywords: Telegram Bot, Python Telegram Bot, Telegram API, Bot Development, Python Bot Library, Interactive Bots, PyBotRex, Bot Framework, Telegram Python Bot, Telegram Bot Toolkit

# PyBotRex

<p align="center">
  <img src="https://i.ibb.co/vVx1xfb/Picsart-25-01-20-22-05-41-616.png" alt="PyBotRex Logo" width="300"/>
</p>


**PyBotRex** simplifies Telegram bot development with an intuitive interface for commands, messaging, buttons, and more. Build interactive Telegram bots effortlessly with a clean and reusable design.

---

## 🚀 Features

- **Command Handling**: Easily add commands with custom callbacks.
- **Message Handling**: Process messages with text or media filters.
- **Inline Buttons**: Create interactive inline keyboard buttons.
- **Advanced Features**: Inline queries, error handling, and custom updates.
- **Async Support**: Fully asynchronous with `python-telegram-bot` 20.0+.

---

## 🛠 Installation

Install PyBotRex using pip:

```bash
pip install PyBotRex
```

---

## 📦 Quick Start

Here’s how you can build a basic bot:

```python
from PyBotRex import PyBotRex
from telegram import Update
from telegram.ext import ContextTypes

# Initialize the bot
bot = PyBotRex("YOUR_TELEGRAM_BOT_TOKEN")

# Define a command
async def start_command(update: Update, context: ContextTypes.DEFAULT_TYPE):
    await bot.reply(update, "Welcome to PyBotRex!")

# Add the command
bot.add_command("start", start_command)

# Run the bot
bot.start()
```

---

## 📚 Documentation

Full documentation is available [here](https://github.com/TraxDinosaur/PyRexBot/wiki/PyRexBot).

---


## 🐛 Issues

Encountered a bug? Report it [here](https://github.com/TraxDinosaur/PyRexBot/issues).

---

## 📜 License

This project is licensed under the **CC-BY-SA 4.0**. See the [LICENSE](https://github.com/TraxDinosaur/PyRexBot/blob/main/LICENSE) for more details.

---

## 🌟 Acknowledgments

Thanks to the community and contributors of `python-telegram-bot` for providing the foundation that made PyBotRex possible.

---

## 📬 Contact

- **Author**: TraxDinosaur  
- **Website**: [TraxDinosaur.github.io](https://traxdinosaur.github.io)  
- **GitHub**: [TraxDinosaur](https://github.com/TraxDinosaur)  

---

Happy Bot Building with PyBotRex! 🦖🤖
py-bEaNs-lib 0.1.2
A library to encode and decode messages with the power of BEANS
Requires Python: >=3.6
Keywords: beans, code, encrypt, pybeans, translator

# Introducing py-bEaNs_lib
**The new easiest and not convoluted whatsoever way to talk to your friends**

Ever wanted to talk to your friends using only the word "beans"?

> BeANS 𝓑𝓮𝓪𝓝𝓼 𝓫𝓮𝓐𝓝𝓼 𝓫𝓮𝓐𝓝𝓼 𝚋𝚎𝚊𝚗𝚂 𝓫𝓮𝓪𝓝𝓼 𝓫𝓮𝓪𝓝𝓢 𝓑𝓮𝓐𝓝𝓢 𝚋𝚎𝚊𝚗𝚂 𝓑𝓔𝓐𝓷𝓢 𝓫𝓮𝓪𝓝𝓢 𝓑𝓮𝓐𝓷𝓢 𝚋𝚎𝚊𝚗𝚂 𝓑𝓔𝓪𝓷𝓼 𝓫𝓮𝓪𝓷𝓼 𝓫𝓮𝓪𝓝𝓼 𝚋𝚎𝚊𝚗𝚜 
-- ***"Well now you can" in bean language***

Using our comprehensive **bean dictionary**™, which includes:
- A to Z in uppercase *and* lowercase 
<sup><sub>That's crazy!</sub></sup>
- The numbers 0 to 9
<sup><sub>How remarkable!</sub></sup>
- And some punctuation but only the ones I could be bothered to put in
<sup><sub>How definitely not lazy at all!</sub></sup>

## Installation
Simply type

    pip install py-bEaNs_lib

into your console, and you're ready to bean
## Usage
Firstly, import the module:

    import py_bEaNs_lib
    from py_bEans_lib import pybeans
  Then to encode use:.
  

    pybeans.bean("This code is a sacrifice to the bean lords")
   and to decode:
   

    pybeans.debean("Im too lazy to generate some actually encoded text for this tutorial")

## Reviews
480g of my 500g can of baked beans recommend this product!
Don't believe me? Look at some of our amazing reviews:

> "*Bean*"
> -- **Mr Bean**

> "*Beans, beans, beans. Everywhere I look, there's more beans. HELP ME I CAN'T GET OUT  MY LIFE IS A LIE OH GOD PLEASE HAVE MER--*"
> -- **Guy who used to work at the heinz factory**
lpdensity 2.4.3
Implements local polynomial point estimation with robust bias-corrected uniform confidence intervals.
Requires Python: >=3.7

# LPDENSITY

The `lpdensity` package provides Stata and R implementations of bandwidth selection, point estimation and inference procedures for local polynomial distribution and density methods.

This work was supported by the National Science Foundation through grant SES-1459931, SES-1459967, SES-1947805 and SES-1947662.

## Authors

Matias D. Cattaneo (<cattaneo@princeton.edu>)

Xinwei Ma (<x1ma@ucsd.edu>)

Michael Jansson (<mjansson@econ.berkeley.edu>)

Rajita Chandak (maintainer) (<rchandak@princeton.edu>)

## Website

https://nppackages.github.io/lpdensity/

## Manual
https://github.com/nppackages/lpdensity/tree/master/Python/lpdensity/docs/build/latex/lpdensity.pdf

## Installation
To install/update use pip
```
pip install lpdensity
```

# Usage
```
from lpdensity import lpdensity, lpbwdensity
```
## Dependencies
- numpy
- pandas
- math
- scipy
- sympy
- plotnine

## References

For overviews and introductions, see [lpdensity website](https://nppackages.github.io/lpdensity/)

### Software and Implementation

- Cattaneo, Jansson and Ma (2022): [lpdensity: Local Polynomial Density Estimation and Inference](https://nppackages.github.io/references/Cattaneo-Jansson-Ma_2022_JSS.pdf).<br>
_Journal of Statistical Software_ 101(2): 1-25.

### Technical and Methodological

- Cattaneo, M. D., M. Jansson, and X. Ma (2020). [Simple Local Polynomial Density Estimators](https://nppackages.github.io/references/Cattaneo-Jansson-Ma_2020_JASA.pdf).<br>
_Journal of the American Statistical Association_, 115(531): 1449-1455.<br>
[Supplemental appendix](https://nppackages.github.io/references/Cattaneo-Jansson-Ma_2020_JASA--Supplement.pdf).

- Cattaneo, M. D., M. Jansson, and X. Ma (2023). [Local Regression Distribution Estimators](https://nppackages.github.io/references/Cattaneo-Jansson-Ma_2023_JoE.pdf). <br>
_Journal of Econometrics_, forthcoming.<br>
[Supplemental Appendix](https://nppackages.github.io/references/Cattaneo-Jansson-Ma_2022_JoE--Supplement.pdf).

- Calonico, S., M. D. Cattaneo, and M. H. Farrell (2018): [On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference](https://nppackages.github.io/references/Calonico-Cattaneo-Farrell_2018_JASA.pdf).<br>
_Journal of the American Statistical Association_ 113(522): 767-779.

- Calonico, S., M. D. Cattaneo, and M. H. Farrell (2022): [Coverage Error Optimal Confidence Intervals for Local Polynomial Regression](https://cattaneo.princeton.edu/papers/Calonico-Cattaneo-Farrell_2022_Bernoulli.pdf).<br>
_Bernoulli_ 28(4): 2998-3022.


- Cattaneo, M. D., M. Jansson, and X. Ma (2022). [lpdensity: Local Polynomial Density Estimation and Inference](https://nppackages.github.io/references/Cattaneo-Jansson-Ma_2022_JSS.pdf). <br>
_Journal of Statistical Software_, 101(2), 1–25.


<br><br>