semvec/pypi-packages huggingface dataset:
Yucebio-Notifier 0.0.3
通用钉钉消息推送工具
License: MIT
Requires Python: >=3.8

# 通用消息推送模块

- 支持发送给企业员工钉钉消息 和 钉钉群消息
- ~~支持发送邮件~~

# 安装

```bash
# 初始化环境，如通过conda初始化python>=3.8的环境
conda create -n python3.8 python=3.8

# 安装依赖包
pip install click icecream alibabacloud-dingtalk
# 安装
pip install -i https://test.pypi.org/simple/ Yucebio-Notifier
```

# Usage

```python
from yucebio_notifier import Notifier

# dingding, dingding-rebot
notifier = Notifier(backend='dingding')

notifier.send_message(
    user,   # 接受人员
    title,  # 消息主题、标题 或 简短消息
    markdown,   # markdown格式的复杂格式消息。可以通过 yucebio_notifier.utils.markdown.Markdown 创建
)
```


# 参考内容

## 钉钉消息

- [Refer]](https://developers.dingtalk.com/document/app/server-api-overview

### 文本消息

```json
{
    "msgtype": "text",
    "content": "一句话消息"

}
```
### markdown消息
```json
{
    "msgtype": "markdown",
    "markdown": {
        "title": "",
        "text": "str(Markdown(...))"
    }
}
```

### 配置 `yucebio_notifier config-dingding`
- corpid        钉钉开发者平台分配给企业的ID
- corpsecret    钉钉开发者平台分配给企业的Seccrect
- agent_id      发送消息时使用的微应用的AgentID
xyl-seq 1.0
a component that automatically generates sequences according to configuration
License: MIT
Requires Python: >=3.4.0
Keywords: xyl_seq,sequence

# XylSeq

## Introduction

the XylSeq means "行云流序列" in chinese. This is a component that automatically generates sequences according to configuration,
supports various types of data type arrangement, and does not require any human intervention after setup.

# Usage

If you want to use XylSeq in a Flask application. you can try it like the most of Flask components.

```python
from flask import Flask
from xyl_seq import XylSeq

app = Flask(__name__)

# form of separate define
seq = XylSeq()
seq.init_app(app=app)

# also
seq = XylSeq(app=app)

```

or only delivery the Flask-SqlAlchemy object to the parameter.

```python
seq = XylSeq(db=db)
```

then you can define sequences like following code.

```python
from xyl_seq import XylSeq

seq = XylSeq(db=db)
seq.create_seq_rule("po_number",
                    {"type": "static", "static_string": "PO"},
                    {"type": "year"},
                    {"type": "month", "width": 2},
                    {"type": "day", "width": 2},
                    {"type": "natural_seq", "width": 4}
                    )
```

```python
seq.get_code("po_number")
```
OUT:
`PO202107010001`

When executed repeatedly, the sequence is obtained

```python
seq.get_code("po_number") # PO202107010001
seq.get_code("po_number") # PO202107010002
seq.get_code("po_number") # PO202107010003
```

explain the `{"type": "static", "static_string": "PO", "width": 4}` structure.

the optional type of the rule as follows:

* static
* year
* month
* day
* hour
* hour_seq
* day_seq
* week_seq
* month_seq
* year_seq
* global_seq
* user_code
* department_code
* company_code
* natural_seq

when the type is `static`, the `static_string` take effect. the result string will display the `static_string` directly.

Fill the zero to reach some length by setting `width` parameter. example:

```python
seq.create_seq_rule("hour_seq_test",
                    {"type": "hour_seq", "width": "1"}
                    )

seq.get_code("hour_seq_test")
```

OUT: `8`

when set `width` parameter:

```python
seq.create_seq_rule("hour_seq_test",
                    {"type": "hour_seq", "width": "1"}
                    )
seq.get_code("hour_seq_test")
```

OUT: `08`
KinGBERT 0.0.2
Keywords extractor with Graph and BERT methods
License: MIT
Keywords: Keywords extractions Graph BERT

# KinGBERT

    KinGBERT(Keywords in Graph with BERT) is a minimal keyword extraction library with graph methods to extract keywords. Use Sentence-BERT embedding for founding the most significant keywords.
## Installation

KinGBERT is available on PyPI.

```
pip install KinGBERT
```

To clone this repository, run

```
git clone https://github.com/sokolheavy/KinGBERT.git
```

## How to use

We use the `KinGBERTExtractor` class, which can be configured to generate keywords from text.
```python
text = """What is data science?
    Data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. 
    Data science encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.
    Data preparation can involve cleansing, aggregating, and manipulating it to be ready for specific types of processing. Analysis requires the development and use of algorithms, analysis and AI models. It’s driven by software that combs through data to find patterns within to transform these patterns into predictions that support business decision-making. The accuracy of these predictions must be validated through scientifically designed tests and experiments. And the results should be shared through the skillful use of data visualization tools that make it possible for anyone to see the patterns and understand trends."""
```
Just extract 5 keywords from the text.

    extractor = KinGBERTExtractor()
    keywords = extractor.generate(doc)

```python
>>> from KinGBERT import KinGBERTExtractor
>>> extractor = KinGBERTExtractor(top_k=5)
>>> keywords = extractor.generate(text)
>>> print(keywords)
['data science', 'insights', 'analysis', 'experiments', 'algorithms']
```