semvec/pypi-packages huggingface dataset:
hupi-pay-sdk 0.1
A brief description of your package
Requires Python: >=3.6

## Using the Demo

以下是一个使用 `hupipay_sdk` 的示例代码，展示了如何使用虎皮椒支付SDK进行支付操作：

```python
import json
import time

from hupipay_sdk import hupi_pay

def test_hupi_pay_sdk(fee):
    """
    使用虎皮椒支付SDK进行支付测试。
    
    :param fee: 支付金额（单位：元）
    """
    # 初始化Hupi实例并发起支付请求
    r = hupi_pay.Hupi(
        appid='201906124557',
        appsecret='35e717b331196138a1340e6c4da0b508',
        notify_url='https://demo.cc/paynotify',
        return_url='https://demo.cc/payreturn',
        callback='https://demo.cc',
        headers='https://demo.cc',
        payment_api='https://go.vrmrgame.com/payment/do.html'
    ).Pay(total_fee=fee)

    # 将支付结果转换为格式化的JSON字符串，中文字符不转义
    r = json.dumps(r, indent=4, ensure_ascii=False)
    print(r)

if __name__ == '__main__':
    test_hupi_pay_sdk(fee=0.01)
ontmont 0.0.8
A package to analyze split reads of ONT (Oxford Nanopore Technologies) long read data
Requires Python: >=3.7

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░█──░█ ░█░█░█ ─░█── █─▀─█ ░█──░█ ░█░█░█ ─░█──<br>
░█▄▄▄█ ░█──▀█ ─░█── ▀───▀ ░█▄▄▄█ ░█──▀█ ─░█──<br>
<br>
Package for analyzing split reads from ONT (Oxford Nanopore Technologies) alignment data. 

## Install
This simple script will get things going:
```bash
pip install ontmont
```

## Test
Please check out whether the unit tests work before you begin using the module, by:
```bash
cd /path/to/ontmont
pytest
```
Ideally you should get some output like:
```bash
============================ test session starts =============================
platform linux -- Python 3.11.8, pytest-8.1.1, pluggy-1.4.0
rootdir: /data1/shahs3/users/chois7/projects/ontmont
configfile: pyproject.toml
plugins: cov-5.0.0, anyio-4.3.0
collected 49 items

tests/test_collect.py ........                                         [ 16%]
tests/test_datatypes.py ...........                                    [ 38%]
tests/test_irs.py .......................                              [ 85%]
tests/test_process.py ....                                             [ 93%]
tests/test_utils.py ...                                                [100%]

============================= 49 passed in 2.49s =============================
```

## Usage
Documentation about the modules are in the [Read the Docs](https://ontmont.readthedocs.io/en/latest/) pages.

## Development
Do build and upload to pypi:
    1. `python -m build`
    2. `python -m twine upload --repository pypi dist/*`
bestrag 0.3.3
bestrag: Library for storing and searching document embeddings in a Qdrant vector database using hybrid embedding techniques.
Requires Python: >=3.6


<img src="https://github.com/user-attachments/assets/e23d11d5-2d7b-44e2-aa11-59ddcb66bebc" align=left height=180px>

![Supported python versions](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)
[![PEP8](https://img.shields.io/badge/code%20style-pep8-black.svg)](https://www.python.org/dev/peps/pep-0008/)
[![License](https://img.shields.io/badge/License-MIT%202.0-blue.svg)](LICENSE)
[![Run Pytest](https://github.com/samadpls/BestRAG/actions/workflows/pytest.yml/badge.svg?branch=main)](https://github.com/samadpls/BestRAG/actions/workflows/pytest.yml)
![GitHub stars](https://img.shields.io/github/stars/samadpls/BestRAG?color=red&label=stars&logoColor=black&style=social)
![PyPI - Downloads](https://img.shields.io/pypi/dm/bestrag?style=social)



Introducing **BestRAG**! This Python library leverages a hybrid Retrieval-Augmented Generation (RAG) approach to efficiently store and retrieve embeddings. By combining dense, sparse, and late interaction embeddings, **BestRAG** offers a robust solution for managing large datasets.


## ✨ Features

🚀 **Hybrid RAG**: Utilizes dense, sparse, and late interaction embeddings for enhanced performance.  
🔌 **Easy Integration**: Simple API for storing and searching embeddings.  
📄 **PDF Support**: Directly store embeddings from PDF documents.  

## 🚀 Installation

To install **BestRAG**, simply run:

```bash
pip install bestrag
```

## 📦 Usage

Here’s how you can use **BestRAG** in your projects:

```python
from bestrag import BestRAG

rag = BestRAG(
    url="https://YOUR_QDRANT_URL", 
    api_key="YOUR_API_KEY", 
    collection_name="YOUR_COLLECTION_NAME"
)

# Store embeddings from a PDF
rag.store_pdf_embeddings("your_pdf_file.pdf", "pdf_name")

# Search using a query
results = rag.search(query="your search query", limit=10)
print(results)

# Delete particular pdf embeddings
rag.delete_pdf_embeddings("home/notes.pdf")
```

> **Note**: Qdrant offers a free tier with 4GB of storage. To generate your API key and endpoint, visit [Qdrant](https://qdrant.tech/).

## 🤝 Contributing

Feel free to contribute to **BestRAG**! Whether it’s reporting bugs, suggesting features, or submitting pull requests, your contributions are welcome. 

## 📝 License

This project is licensed under the [MIT License](LICENSE).

---

Created by [samadpls](https://github.com/samadpls) 🎉
advent-tools 1.0.0b1
Cool set of tools for working with Advent of Code challenges
Requires Python: >=3.13
Keywords: advent, advent of code, advent tools, aoc

# [advent-tools](https://github.com/Achxy/advent-tools)
Python interface to conveniently download and interact with advent data with a high-level-wrapper.

### Installation
A [Virtual Environment](https://docs.python.org/3/library/venv.html) is strongly recommended to install the library

```bash
# Linux/macOS
python3 -m pip install -U advent-tools

# Windows
py -3 -m pip install -U advent-tools
```

---
### Quickstart
First we need to get the `session` cookie (🍪), in order to do that
1) Open your broswer's Devtools ([Chrome](https://developer.chrome.com/docs/devtools/open) / [Firefox](https://firefox-source-docs.mozilla.org/devtools-user/) guide)
2) Head on over to `Application` tab, then to `Cookies`
3) Find and copy `session` cookie value
!["Steps to get session cookie"](./assets/steps_to_get_session_cookie.jpg)
4) set `AOC_SESSION=<session-cookie-value>` environment variable (make sure terminal restart doesn't reset the value)\
**OR**\
Enter the following command in your terminal which will create an `.env` file storing your session cookie.
```bash
# Same for all Windows, Linux and macOS
echo AOC_SESSION=<session-cookie-value> > .env
# Make sure to replace <session-cookie-value> with your cookie value
```
Make sure version control system ignores your token container in case you are using one, cause your token is sensitive and can be used for log-ins.

---
### Usage
`year` and `day` can be given as kwargs when subclassing `advent.Advent` or can be provided using it's `__getitem__` behaviour like `Advent[year:day]`
```py
from advent import Advent


class Solution(Advent, year=2020, day=3):
    def __init__(self, data: str) -> None:
        self.data = ...

    def part_1(self):
        ...

    def part_2(self):
        ...
```
Example 1:
```py
from advent import Advent


class Solution(Advent, year=2022, day=2):
    PLAY = {"A": ["Z", "X", "Y"], "B": ["X", "Y", "Z"], "C": ["Y", "Z", "X"]}

    def __init__(self, data: str) -> None:
        self.data = [line.split() for line in data.splitlines()]

    def part_1(self):
        return sum(self.PLAY[oppo].index(me) * 3 + ord(me) - 87 for oppo, me in self.data)

    def part_2(self):
        return sum((fate := ord(res) - 88) * 3 + ord(self.PLAY[oppo][fate]) - 87 for oppo, res in self.data)
```
Example 2:
```python
from heapq import nlargest
from advent import Advent

class Solution(Advent[2022:1]):
    def __init__(self, data: str) -> None:
        self.max, *self.top = nlargest(3, [sum(map(int, chunk.split())) for chunk in data.split("\n\n")])

    def part_1(self):
        return self.max

    def part_2(self):
        return self.max + sum(self.top)
```