semvec/pypi-packages huggingface dataset:
simstring-fast 0.6.0
A fork of the Python implementation of the SimString by (Katsuma Narisawa), a simple and efficient algorithm for approximate string matching. Uses mypyc to improve speed
Requires Python: >=3.9

# simstring
[![PyPI - Status](https://img.shields.io/pypi/status/simstring-fast.svg)](https://pypi.org/project/simstring-fast/)
[![PyPI version](https://badge.fury.io/py/simstring-fast.svg)](https://badge.fury.io/py/simstring-fast)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/simstring-fast)
[![MIT License](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](LICENSE)

![icon](simstring/docs/strings_icon.png)

A Python implementation of the [SimString](http://www.chokkan.org/software/simstring/index.html.en), a simple and efficient algorithm for approximate string matching.

Docs are [here](https://banking-circle-advanced-analytics.github.io/simstring-fast/)

## Features
With this library, you can extract strings/texts which has certain similarity from large amount of strings/texts. It will help you when you develop applications related to language processing.

This library supports variety of similarity functions such as Cossine similarity, Jaccard similarity, and supports Word N-gram and Character N-gram as features. You can also implement your own feature extractor easily.

SimString has the following features:

* Fast algorithm for approximate string retrieval.
* 100% exact retrieval. Although some algorithms allow misses (false positives) for faster query response, SimString is guaranteed to achieve 100% correct retrieval with fast query response.
* Unicode support.
* Extensibility. You can implement your own feature extractor easily.
* no japanese support
[Please see this paper for more details](http://www.aclweb.org/anthology/C10-1096).


## Install
```
pip install simstring-fast
```

## Usage
```python
from simstring.feature_extractor.character_ngram import CharacterNgramFeatureExtractor
from simstring.measure.cosine import CosineMeasure
from simstring.database.dict import DictDatabase
from simstring.searcher import Searcher

db = DictDatabase(CharacterNgramFeatureExtractor(2))
db.add('foo')
db.add('bar')
db.add('fooo')

searcher = Searcher(db, CosineMeasure())
results = searcher.search('foo', 0.8)
print(results)
# => ['foo', 'fooo']
```

If you want to use other feature, measure, and database, simply replace these classes. You can replace these classes easily by your own classes if you want.

```python
from simstring.feature_extractor.word_ngram import WordNgramFeatureExtractor
from simstring.measure.jaccard import JaccardMeasure
from simstring.database.dict import DictDatabase
from simstring.searcher import Searcher

db = DictDatabase(WordNgramFeatureExtractor(2))
db.add('You are so cool.')

searcher = Searcher(db, JaccardMeasure())
results = searcher.search('You are cool.', 0.8)
print(results)
```

## Supported String Similarity Measures
- Cosine
- Dice
- Jaccard
- Overlap
- Left Overlap


## Supported database backends
- dictionary
- diskcache (sqlite)
- redis (in development #37)
cliz 0.1.4
A useful command-line agent help you work with your favorite tools
Requires Python: >=3.11

# cliz

🤖 Hello human, my name is cliz (cli + z), A useful command-line agent help you work with your favorite tools!

## Table of Contents

- [Features](#features)
- [Examples](#examples)
- [Install](#install)
- [Roadmap](#roadmap)

## Features

- 🚀 Lightweight & Fast: cliz is just `10M+` and run extremely fast.
- 🌟 CLI Agent: cliz is a `command-line agent`, help you work with your favorite cli tools!
- 📚 Chat History: cliz can remember chat history, and continue the conversation.
- 🛡️ Secure: all tool-call requests need to be approved by user, We also has `auto` mode.

Enjoy your cliz!

## Examples

Help me download video `https://www.bilibili.com/video/BV1GJ411x7h7` to local folder `videos/`.

![example](docs/asserts/example1.gif)

Fetch deepseek-v3 github homepage, and save to deepseek-v3.md.

![example](docs/asserts/example2.gif)

## Install

Step 1: install from pip:

```bash
pip install cliz
```

Step 2: make config file:

```bash
mkdir -p ~/.cliz
vim ~/.cliz/cliz.yaml
```

This is a template yaml, write your LLMs provider, and add any tools you like. This is my [config](cliz.yaml):

```yaml
auto: false # disable auto mode
chat_history: true # enable chat history
respond_language: "English" # you can switch language to "中文"
llm: # set your favorite LLMs
  base_url: "xxx"
  api_key: "xxx"
  model: "xxx"
tools: # add more tools you like!
  - name: "zstd"
    description: "Zstandard - Fast real-time compression algorithm"
  - name: "rsync"
    description: "Rsync is a fast and extraordinarily versatile file copying tool for both remote and local file"
  - name: "uv"
    description: "An extremely fast Python package and project manager, written in Rust"
  - name: "yt-dlp"
    description: "A feature-rich command-line audio/video downloader"
```

Step 3: Run cliz!

```bash
cliz "build a operation system for me. :D"
```

Also run `auto` mode with `--auto` or set `auto: true` in config file:

```bash
cliz --auto "build a operation system for me. :D"
```

## Roadmap

- [ ] Add agent memory
- [ ] Release to homebrew
browzy 0.1.1
A simple terminal-based web browser with Google search integration
Requires Python: >=3.6
Keywords: browser terminal web-browser search google cli

# Browzy

A simple terminal-based web browser that allows you to search and read web content directly from your terminal.

## Installation

```bash
pip install browzy
```

## Usage

Just type:
```bash
browzy
```

Available commands:
- s(keyword) - search for content
- o(number) - open webpage in browser
- ow(number) - read webpage in terminal
- exit - exit program

## Features

- Google search integration
- Terminal-based web content reader
- Browser integration
- Colorful interface

## Requirements

- Python 3.6+
- Internet connection

## License

MIT License
openstack-doc-tools 4.0.1
Tools for OpenStack Documentation
Requires Python: >=3.8

========================
Team and repository tags
========================

.. image:: https://governance.openstack.org/tc/badges/openstack-doc-tools.svg
    :target: https://governance.openstack.org/tc/reference/tags/index.html

.. Change things from this point on

OpenStack Doc Tools
~~~~~~~~~~~~~~~~~~~

This repository contains tools used by the OpenStack Documentation
project.

For more details, see the `OpenStack Documentation Contributor Guide
<https://docs.openstack.org/contributor-guide/>`_.

* License: Apache License, Version 2.0
* Source: https://opendev.org/openstack/openstack-doc-tools
* Bugs: https://bugs.launchpad.net/openstack-doc-tools

Prerequisites
-------------

You need to have Python 2.7 installed for using the tools.

This package needs a few external dependencies including lxml. If you
do not have lxml installed, you can either install python-lxml or have
it installed automatically and build from sources. To build lxml from
sources, you need a C compiler and the xml and xslt development
packages installed.

To install python-lxml, execute the following based on your
distribution.

On Fedora, RHEL 7, and CentOS 7::

    $ yum install python-lxml

On openSUSE::

    $ zypper in python-lxml

On Ubuntu::

    $ apt-get install python-lxml

For building from source,  install the dependencies of lxml.

On Fedora, RHEL 7, and CentOS 7::

    $ yum install python-devel libxml2-devel libxslt-devel

On openSUSE::

    $ zypper in libxslt-devel

On Ubuntu::

    $ apt-get install libxml2-dev libxslt-dev
django-infra 0.1.11
A infra for Django engineers.
Requires Python: >=3.11

# Django Infra

A collection of utilities to streamline Django development with enhanced testing, optimized database operations, and flexible queryset exporting.

## Quality
[![codecov](https://codecov.io/gh/Occy88/django-infra/graph/badge.svg?token=ER1K5KC1WV)](https://codecov.io/gh/Occy88/django-infra)


## Pytest Enhancements

- Python-based database management avoids rebuilding migrations on every test run.
- Directory-level model setup decouples test models from production models for abstract model testing.

## django_infra.db

- **bulk_update_queryset**  
  Optimized function for batch updating fields based on annotations.

- **UpdatableModel**  
  Enables direct model instance updates without needing to call `.save()`.

## django_infra.exporter

- Export app for converting querysets into various formats.

*Refer to additional documentation for installation and detailed usage instructions.*
browzy-quhu 0.1.0
A simple terminal-based web browser with Google search integration
Requires Python: >=3.6
Keywords: browser terminal web-browser search google cli

# Browzy

A simple terminal-based web browser that allows you to search and read web content directly from your terminal.

## Installation

```bash
pip install browzy
```

## Usage

Just type:
```bash
browzy
```

Available commands:
- s(keyword) - search for content
- o(number) - open webpage in browser
- ow(number) - read webpage in terminal
- exit - exit program

## Features

- Google search integration
- Terminal-based web content reader
- Browser integration
- Colorful interface

## Requirements

- Python 3.6+
- Internet connection

## License

MIT License