semvec/pypi-packages huggingface dataset:
fortress-alchemy 0.1.0
Sqlalchemy ORM for Fortress
Requires Python: <4.0,>=3.11

# Fortress Alchemy

## Installation

You can install the client using pip. Simply run the following command:

```bash
pip install fortress-alchemy
```

## Quick Start

Here is a quick example to get you started with the client:

```python
from fortress_alchemy import FortressAlchemy

# Initialize the client with your API key
client = FortressAlchemy(org_id='your_org_id', api_key='your_api_key')

# Create an engine
engine = fortress_alchemy.create_engine("client1", echo=True)

# Create a database
```

## Documentation

Below is a list of the available functionality in the client. The client allows you to create SQLAlchemy engines to use with your existing SQLAlchemy infrastructure. You can also easily route data requests based on tenant names. For more detailed information, please refer to the [Fortress API documentation](https://docs.fortress.build).

- `create_engine(tenant_id: str, **kwargs: Any)`: Creates a new SQLAlchemy engine for the given tenant. The tenant ID is used to route data requests to the correct database.

## Configuration

To use the client, generate an API key from the Fortress dashboard. Also, provide the organization ID, which is available under the API Keys page on the platform website.

## License

This client is licensed under the MIT License.

## Support

If you have any questions or need help, don't hesitate to get in touch with our support team at founders@fortress.build.
eng-text-cleaner 0.0.5
This package is for clean the text as text processing
Requires Python: >=3.8

# Text Cleaning of English Language Python Package

Text Cleaning is a common preprocessing technique for almost all NLP task. Mainly I have designed the package for Text Classification Task. Also You can use it for other NLP task also. You are welcome to contribute the package.

**Install the package**

```bash
pip install eng-text-cleaner
```

There has number of methods to clean the text such as removing emoji, punctuation, html_tags, urls, characters not words or digits or underscore, digits, stopwords, spell correction, lemmatize the words. One Method named clean text will apply all the methods to clean the text at a glance. Let's explore the simple package.
```python
from eng_text_cleaner import preprocessing 
```
Start by removing punctuation
```python
text = "Neither too small nor too large, and nice resolution at a good price."
# create textcleaner instance
textcleaner = preprocessing.TextCleaner()
# remove punctuation
textcleaner.remove_punctuation(text)
```
Output:
```bash
Neither too small nor too large and nice resolution at a good price
```
For Clean the text totally
```python
# fully clean the text
textcleaner.clean_text(text)
```
Output:
```bash
neither small large nice resolution good price
```

Author:
* **Md Abdullah Al Hasib**