semvec/pypi-packages huggingface dataset:
gs-ml-toolkit 0.1.3
Package per trattare dati di trasporto pubblico e semplificare l'utilizzo di questi in progetti AI
Requires Python: >=3.7

# gs_ml_tooklit

The **gs_ml_tooklit** is a library developed for standardizing and streamlining internal projects for the GreenShare Experiments team. This package provides a suite of tools to associate directional information with public transport data, specifically for occupancy (crowding) and evasion data provided by public transport companies. It also provides a GTFS processor class that enables the user to merge different GTFS files into one to later use it with the direction processing function.

## Installation

You can install the package from PyPI using `pip`:

```bash
pip install gs_ml_tooklit
```

For testing and development, you can also install it from TestPyPI:

```bash
pip install --index-url https://test.pypi.org/simple/ gs_ml_tooklit
```
## Running Tests

The package includes tests to verify functionality and ensure code quality. To run the tests, follow these steps:

1. **Install Testing Dependencies:**

   Ensure you have `pytest` and `pytest-cov` installed:
   
   ```bash
   pip install pytest pytest-cov
   ```

2. **Run Tests:**

   From the root directory of the project, run:
   
   ```bash
   pytest --cov=gs_ml_tooklit --cov-report=term-missing
   ```

   This command runs all tests and shows a coverage report in the terminal.

## Documentation

The full documentation for this package is generated using Sphinx and is available online (e.g., via Read the Docs or GitHub Pages).

To build the documentation locally:

1. Navigate to the `docs/` folder:
   
   ```bash
   cd docs
   ```

2. Build the HTML documentation:
   
   ```bash
   make html
   ```

3. Open the generated documentation in your browser by navigating to the `build/html/index.html` file.

Alternatively, you can use `sphinx-autobuild` for automatic rebuilding and live preview:

```bash
sphinx-autobuild . build/html
```

Then open `http://127.0.0.1:8000` in your browser.

## Contributing

Contributions are welcome! If you wish to contribute, please follow these guidelines:

- Fork the repository.
- Create a new branch for your feature or bugfix.
- Write tests for your changes.
- Ensure all tests pass and the coverage is maintained.
- Submit a pull request with a clear description of your changes.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Project URLs

- **Source Code:** [https://gitlab.greenshare.it/rombo-ai/gs_ml_toolkit.git](https://gitlab.greenshare.it/rombo-ai/gs_ml_toolkit.git)
- **Documentation:** (Link to your documentation site, e.g., Read the Docs or GitHub Pages)
Figma_to_Q 0.1.0
A CLI-Tool to publish Figma designs to Q
Requires Python: >=3.6

### How to install

```bash
python setup.py sdist bdist_wheel
pip install .
```

### How to use

In you project folder, create a file called `q.yaml`.
The file should contain a link to the q-infographic you
want to update, and the links to the figma frames that should be used.

```yaml
- type: infographic
  q: https://qv2.st.nzz.ch/editor/infographic/560x7350xf5bxb3a50cb836998edabcd
  staging: https://qv2.st-staging.nzz.ch/editor/infographic/560x7350xf5bxb3a50cb836998edabcd
  mw: https://www.figma.com/design/26LECd2dAce34lxzgCtala/Mein-Projekt?node-id=1-1787&node-type=FRAME
  cw: https://www.figma.com/design/26LECd2dAce34lxzgCtala/Mein-Projekt?node-id=1-1982&node-type=FRAME
  kw: https://www.figma.com/design/26LECd2dAce34lxzgCtala/Mein-Projekt?node-id=1-1983&node-type=FRAME
  fw: https://www.figma.com/design/26LECd2dAce34lxzgCtala/Mein-Projekt?node-id=1-1984&node-type=FRAME
```

Then run the script with the following command in the folder with the `q.yaml` file:

```bash
fig2q .
```

Only `mw` and `cw` are required. The other links are optional.
Also, the `staging` link is optional. If it is not provided, the script will not update the staging version of the q-infographic.

You can also use the script to update a scroll_graphic. In this case, the `q.yaml` file should look like this:

```yaml
- type: scroll_graphic
  q: https://qv2.st.nzz.ch/editor/scroll_graphic/599cb06749192d5221a2f8e4dcabcdef
  staging: https://qv2.st-staging.nzz.ch/editor/infographic/560x7350xf5bxb3a50cb836998edabcd
  steps:
      - mw: https://www.figma.com/design/26LECd2dAce34lxzgCtala/Untitled?node-id=1-2&node-type=frame&t=dVqDEKkaO47UMmde-11
        cw: https://www.figma.com/design/26LECd2dAce34lxzgCtala/Untitled?node-id=1-17&node-type=frame&t=dVqDEKkaO47UMmde-11
        text: Text of the first textbox
      - mw: https://www.figma.com/design/26LECd2dAce34lxzgCtala/Untitled?node-id=1-18&node-type=frame&t=dVqDEKkaO47UMmde-11
        cw: https://www.figma.com/design/26LECd2dAce34lxzgCtala/Untitled?node-id=1-19&node-type=frame&t=dVqDEKkaO47UMmde-11
        text: Text of the second textbox
```

## Use it without Figma

If you are not using Figma or don't want to link Frames directly, you can also add image paths.

```yaml
- type: infographic
  q: https://qv2.st.nzz.ch/editor/infographic/560x7350xf5bxb3a50cb836998edabcd
  mw: pngs/mw.png
  cw: pngs/cw.png
```
RabbitMQPubSub 1.4.0
RabbitMQ helpers for pubsub.

# RabbitMQ Pub-Sub

This project provides a simple implementation of the publish-subscribe pattern using RabbitMQ, making it easy to set up communication between different parts of your application.

## Prerequisites

Ensure you have the following installed before proceeding:

- RabbitMQ server
- Python 3

## Installation

1. Install the required Python dependencies:

   ```bash
   pip install .
   ```

## Usage

While this project was initially standalone, it is now integrated into a larger project. For practical usage, we recommend utilizing the `mrkutil` library, which includes implemented methods for this library. Check out the [mrkutil GitHub repository](https://github.com/ivke-99/mrkutil/) for more details and examples. However, feel free to adapt this library for your own needs.

## Authors

- [@nmrkic](https://github.com/nmrkic)

## Deployment to PyPI

To deploy this package to PyPI, use the following commands:

```bash
flit build
flit publish
```

## Contributing

Contributions are always welcome! If you have any suggestions or improvements, feel free to submit a pull request or open an issue.
microsoft-store-scraper 0.0.2
Microsoft-Store-Scraper provides APIs to easily crawl the Microsoft Store for Python without any external dependencies!
Requires Python: >=3.11

# Microsoft-Store-Scraper
Microsoft-Store-Scraper provides APIs to easily crawl the Microsoft Store for Python without any external dependencies!


## Getting started

```
from microsoft_store_scraper import app

results = app(app_id="9nht9rb2f4hd")
print(results)
```


## Thanks to our Resources
[google-play-scracper](https://github.com/JoMingyu/google-play-scraper)
taolue-multi-retrival 1.0.9
韬略问答Agent多跳策略的多轮检索模块
Requires Python: >=3.10

# 多跳策略的多轮检索模块

## 使用方法
```
from taolue_multi_retrival.multi_retrieve import efficient_rag

efficient_rag(search_rerank_func, query, top_k, libs, kb_ids, kb_file_ids)

search_rerank_func函数：
def search_rerank_func(query, top_k, libs, kb_ids, kb_file_ids)
```

## 参数说明
参数	类型	默认值	说明
search_rerank_func	函数		传入执行检索+精排操作的函数
query	str		原始query
libs	List[str]	["es_chunk","es_summary","es_embedding","es_summary_embedding"]	一般情况下检索所有库，第二轮检索开始只检索es切片库、es摘要库，不检索向量库。
kb_ids	List[int]	/	哪个字段有值用哪个，Tuple结构：(知识库id, 文件id列表)
kb_file_ids	List[Tuple[int, List[int]]] 	/
top_k	int	10	每轮检索的数据量