semvec/pypi-packages huggingface dataset:
nextgen-mft 0.2.2
Secure, AI-powered Managed File Transfer (SFTP/HTTPS + Crypto + Anomaly Detection)
License: MIT
Requires Python: >=3.7

# nextgen-mft

`nextgen-mft` is a modular Python library designed for building secure, intelligent, and automated file transfer systems. It enables seamless SFTP, HTTPS, and AI-driven anomaly detection, along with powerful cryptographic utilities like RSA, ECDHE, ChaCha20, and X.509 certificate handling.

---

##  **Key Features**

**SFTP & HTTPS Transfers**
- Upload/download files securely over SFTP and HTTPS  
- SSH key authentication and TLS client cert support  

**AI-Powered Anomaly Detection**  
- Detect abnormal transfer behaviors using `IsolationForest`  
- Simple API for training and scoring  

**Strong Cryptography**
- Generate RSA, ECDHE, and DH keys  
- AES (CBC) and ChaCha20 encryption support  
- Load/save certificates and keys in PEM  

**Command-Line Interface (CLI)**  
- Built-in CLI using [Typer](https://typer.tiangolo.com)  
- Easily upload files, generate keys, and run anomaly detection  

**Production-Ready** 
- Modular architecture  
- Ready for pipelines, automation, and cloud workflows  

##  **Use Cases**

- Enterprise Managed File Transfer (MFT) systems  
- Secure partner integrations via HTTPS/SFTP  
- Compliance-driven cryptographic workflows  
- Real-time anomaly detection in file movements  
- CLI-based DevOps automation

## **Installation**

   **Recommended process**

There are several different ways to install nextgen-mft. However it is recommended to both install and use the package inside python virtual environment.

At the command line using "pip"

```
$ pip install nextgen-mft
```
Or, if you have virtualenvwrapper installed.

```
$ mkvirtualenv nextgen-mft
$ python3 -m pip install nextgen-mft
```

## **Installation with Package Manager**

Alternatively it is possible to install nextgen-mft using package manager.

Such as yum or dnf

``` $ yum|dnf install nextgen-mft ```

## **Quick CLI Usage**

## **Upload a file via SFTP**
```
$ python3 -m nextgen_mft.cli.main sftp-upload \
  --host sftp.example.com \
  --username user \
  --key-file ~/.ssh/id_rsa \
  --local-file ./file.txt \
  --remote-path /inbox/file.txt
```

```
## **Detect anomalies from file transfer logs**
$ python3 -m nextgen_mft.cli.main detect-anomalies \
  --train-file train_data.json \
  --test-file new_data.json
```

## **Project Structure**

```
nextgen_mft/
|--transfer/     # SFTP & HTTPS clients
|--security/     # Crypto utils (AES, RSA, ChaCha20, X.509)
|--ai/           # ML anomaly detection
|--cli/          # Typer-based CLI
```

##  **Dependencies**

```
- paramiko  
- requests  
- cryptography  
- scikit-learn  
- pandas  
- numpy  
- typer
```

## **License**

MIT License © Raghava Chellu

## **Project Links**

- PyPI: [https://pypi.org/project/nextgen-mft](https://pypi.org/project/nextgen-mft)
- Docs: *coming soon*
- Support: *via GitHub issues or email*
vaticinator 0.1.0
Yet another Python fortune implementation
License: MIT
Requires Python: <4,>=3.11
Keywords: fortune, games

vaticinator(6)
==============
v0.1.0
======

Vaticinator is yet another Python implementation of the
ancient and (in)famous `fortune` program from the 
`bsdgames` package.

My motivation for writing it was more to have a 
portable library I could use to fetch fortunes for
use in other projects.  It's possible that one or
more of the existing ones have this (I looked
albeit briefly), but it was an itch I didn't mind
scratching.

It is still alpha maturity level, though the majority 
of `fortune` behavior is implemented at the moment.

Example integration
=====================

This is a the code for a Django template tag that
displays a random fortune inside a template (with
all the options from the command line available).
This is basically why I created this project::

	from django.template import Library
	from vaticinator.vaticinator import Vaticinator

	register = Library()
	vaticinator = Vaticinator()

	@register.simple_tag
	def random_fortune(*args, **kwargs):
		vaticinator.set_default_options()
		vaticinator.process_options(*args, **kwargs)
		return vaticinator.fortune

And the template::

	<span>{% random_fortune 'short' 'all' %}</span>
quickchat-sdk 0.1.1
A simple SDK for interfacing with the Quick Chat chatting platform.
License: MIT
Requires Python: >=3.6


# Quick Chat Python SDK

The **Quick Chat Python SDK** allows developers to integrate **Quick Chat** messaging functionality into their Python applications. This package provides a simple, easy-to-use interface for sending messages programmatically, whether for notifications, alerts, or other use cases.

🌐 Visit the Quick Chat website: [Quick Chat](https://qc1159.github.io/Quick-Chat/)

---

## Installation

To install the Quick Chat Python SDK, simply use **pip**:

```bash
pip install quickchat_sdk
```

## Features

Simple API: Easily send messages with just a few lines of code.

Cross-platform: Use in any Python environment.

Lightweight: No unnecessary dependencies.

## Documentation

Detailed documentation and examples can be found on the GitHub repository.

[Quick Chat Python SDK GitHub](https://github.com/qc1159/quickchat-python-sdk)

## Contributing

We welcome contributions! To help improve the Quick Chat SDK, please feel free to:

* Open issues or suggest features

* Submit pull requests for bug fixes or enhancements

## License

This project is licensed under the MIT License.

## Disclaimer

"Quick Chat" may be a trademark of its respective owner. This project is not affiliated with or endorsed by any entity owning the "Quick Chat" trademark. Use at your own discretion.

## Contact

For support or inquiries, please contact us at quickchatplatform@hotmail.com or open an issue on the GitHub repository.
prepembd 0.3.0
prepembd
License: MIT
Requires Python: >=3.12

# prepembd

[![PyPi](https://img.shields.io/pypi/v/inka2)](https://pypi.org/project/prepembd)
[![Tests CI](https://img.shields.io/github/actions/workflow/status/sysid/prepembd/test.yml?branch=main)](https://github.com/sysid/inka2/actions/workflows/prepembd.yml)
[![Codecov](https://codecov.io/gh/sysid/prepembd/branch/main/graph/badge.svg?token=8IL9MN4FK5)](https://codecov.io/gh/sysid/prepembd)


## Installation

Install **prepembd**:

```shell
python3 -m pip install prepembd --upgrade
```

### Requirements

- [Python](https://www.python.org/) >= 3.10

## Why

I've been using markdown now for a long time to take notes in every possible scenario. I even manage my Anki cards with markdown ([inka2](https://github.com/sysid/inka2)) so finding relevant information again is paramount.
With the advent of semantic search via Embeddings search became so much more powerfull. However, to create the
embeddings out of markdown the files have to be prepared in order to reduce noice and create the correct chunk sizes.

This Python script automates the process and creates a json representation of all the markdown files which then can be fed into an embedding model. It is basically just a thin wrapper aroung LangChain combined with some bespoke filter to eliminated noise.


## Usage
```bash
prepembd tokenize --prefix '$VIMWIKI_PATH/' <directory> | tee -a output.ndjson

# cat output.ndjson:
{
  "id": "$VIMWIKI_PATH/help/qk/quarkus.md:0",
  "content": "..."
}
{
  "id": "$VIMWIKI_PATH/help/qk/quarkus.md:1",
  "content": "..."
}
{
  "id": "$VIMWIKI_PATH/help/qk/quarkus.md:2",
  "content": "..."
}
```

This script integrates particularly well with [bkmr](https://github.com/sysid/bkmr).