semvec/pypi-packages huggingface dataset:
langeval-cli 0.4.0
Evaluation for AI apps and agent.
Requires Python: >=3.7

# LangEval

Evaluation for AI apps and agent

[![PyPI - Version](https://img.shields.io/pypi/v/langeval-cli.svg)](https://pypi.org/project/langeval-cli)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/langeval-cli.svg)](https://pypi.org/project/langeval-cli)

```txt
▄▄▌   ▄▄▄·  ▐ ▄  ▄▄ • ▄▄▄ . ▌ ▐· ▄▄▄· ▄▄▌
██•  ▐█ ▀█ •█▌▐█▐█ ▀ ▪▀▄.▀·▪█·█▌▐█ ▀█ ██•
██▪  ▄█▀▀█ ▐█▐▐▌▄█ ▀█▄▐▀▀▪▄▐█▐█•▄█▀▀█ ██▪
▐█▌▐▌▐█ ▪▐▌██▐█▌▐█▄▪▐█▐█▄▄▌ ███ ▐█ ▪▐▌▐█▌▐▌
.▀▀▀  ▀  ▀ ▀▀ █▪·▀▀▀▀  ▀▀▀ . ▀   ▀  ▀ .▀▀▀
```

-----

## Table of Contents

- [Installation](#installation)
- [Documents](#documents)
- [How to use](#how-to-use)
- [Development](#development)
- [License](#license)

## Installation

```console
pip install langeval-cli
```

## Documents

TODOs:

- Refactor RAG Eval
- Support custom output parser
- Support more provider.

## How to use

see `./examples` for more details.

## Development

```bash
# Create virtual environment
hatch env create
# Activate virtual environment
hatch shell
# Run test
hatch run test
# Run lint
hatch run lint:style

# Version dump
hatch version patch/minor/major
# Build
hatch build
# Upload to pypi
hatch publish
```

## License

`LangEval` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
semser 0.2.1
A simple Semantic Scholar search CLI.
Requires Python: >=3.8,<4.0

# Semantic Scholar Search

Searches Semantic Scholar for papers or authors. Downloads the papers as pdfs if they are on arxive.

# Installation

```
pip install semser
```

or clone and install

```
git clone https://github.com/fergusfettes/semser
cd semser
poetry install
```

# Usage

Search for some terms. You will get a list of papers to choose from.

```
$ semser search "retrieval augmented generation"
1. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Bob et al., 15 Nov 2019
...
Papers to retrieve: 1
Downloading
```

Search for an author. You can select a likely looking author and you will be shown a selection of their papers.

```
$ semser author "Schmidhuber"
1. Jürgen Schmidhuber, 1 Jan 2015
...
Authors to retrieve: 1
1. Paper 1, 1 Jan 2015
2. Paper 2, 1 Jan 2015
Papers to retrieve: 1
Downloading
```

The 'limit' applies to authors and to papers by the author:
```
$ semser author "Schmidhuber" --limit 2
```

Will only return two papers by them. But you can grab their id and use it instead:

```
$ semser author 145341374 --limit 50
```

You can also search for a paper by its Semantic Scholar ID, or by its DOI, or just use the url from semantic scholar.

```
$ semser paper "10.1162/neco.1997.9.8.1735"
Downloading
```

Since it is made with typer, you can also just type
    
```
$ semser --help
$ semser search --help
```

to get a list of commands and options.
ehr 0.0.dev1
Data Science Toolkit for Healthcare EHR Data
Requires Python: >=3.11

# EHR: Data Science Toolkit for Healthcare EHR Data

## Goal

OHDSI OMOP CDM analytic tools are mainly R based. This repo aims to approach it using python tools and MIMIC dataset as an example.


## References
* OHDSI 
    * software tools: https://www.ohdsi.org/software-tools/
    * CDM v6.0: https://ohdsi.github.io/CommonDataModel/cdm60.html
* Others
  * https://realpython.com/pypi-publish-python-package/
tiny-dungeon 0.9.0
Character Generator for the Tiny Dungeon RPG (1st Edition)
Requires Python: >=3.8
Keywords: Dungeons,Dragons,Tiny Dungeon,RPG

# tiny-dungeon-chargen
Python package to generate player characters for the
[Tiny Dungeon](https://www.drivethrurpg.com/product/144545/Tiny-Dungeon-Print-and-Play-Bundle)
tabletop roleplaying game (1st edition) by Brandon McFaddon.

## Installation
This package may be installed from pypi via pip:
```
python -m pip install tiny-dungeon
```

## Usage
Instantiate a character
```python
>>> from tiny_dungeon.char import TinyDungeonPC
>>>
>>> pc = TinyDungeonPC()
>>>
>>> # view as dictionary
>>> pc.as_dict()
{'race': 'Fey', 'hp': 6, 'race_trait': 'Bow Mastery', 'race_trait_desc': 'You have Mastered bows and have Advantage when using them. This is in addition to the Mastered weapon chosen at Adventurer Creation.', 'trait1': 'Acrobat', 'trait1_desc': 'You gain Advantage when Testing to do acrobatic tricks such as tumbling, long-distance jumps, climbing, and maintaining balance.', 'trait2': 'Marksman', 'trait2_desc': 'When you Focus, your next attack with a ranged weapon is successful on a Test of 3, 4, 5, or 6.', 'trait3': 'Nimble Fingers', 'trait3_desc': 'You gain Advantage when Testing to pick locks, pick pockets, or steal.', 'weapon_proficiency': 'Ranged', 'weapon': 'Throwing Knives', 'gold_pieces': 10, 'equipment': ['bedroll', 'flint and steel', 'belt pouch', 'rucksack', 'lantern', 'empty waterskin', 'oil (3 pints)', "50' rope", 'rations (7 days)', 'torch', 'cloak'], 'family_trade': ''}
>>>
>>> # print character to STDOUT
>>> pc.print_char()


NAME: _________________________________

RACE: Fey

HIT POINTS: 6

WEAPON PROFICIENCY: Ranged

WEAPON: Throwing Knives

MASTERED WEAPON: ______________________

TRAITS
------------
Bow Mastery: You have Mastered bows and have Advantage when using them. This is in addition to the Mastered weapon chosen at Adventurer Creation.
Acrobat: You gain Advantage when Testing to do acrobatic tricks such as tumbling, long-distance jumps, climbing, and maintaining balance.
Marksman: When you Focus, your next attack with a ranged weapon is successful on a Test of 3, 4, 5, or 6.
Nimble Fingers: You gain Advantage when Testing to pick locks, pick pockets, or steal.

EQUIPMENT
------------
bedroll
flint and steel
belt pouch
rucksack
lantern
empty waterskin
oil (3 pints)
50' rope
rations (7 days)
torch
cloak

GOLD PIECES: 10

```
catsay 1.0.4
A python package for no one.

# CatSay

## Make your cat say Anything you like

## Overview

The ASCII Art Cat Speaking package provides a simple Python function to display a cat "speaking" a message using ASCII art. The cat's message is presented in a thought bubble beside the cat.

## Installation

```bash
pip install catsay
```
## Usage

1. Import the `catsay` function from the `cat` package.
2. Call the function, passing the desired message as an argument.

Example:

```python
from cat import catsay

message = "Hello, I am a cat!"
catsay(message)
```
```
     ------------------       
    (Hello, I am a cat!)
     ------------------   
                   \/     
                 /\_/\
                ( o.o )
                 > ^ <  ,"",
                 ( " ) :
                  (|)""
```
This will print a cat with a thought bubble beside it, containing the specified message.

### Customization
Feel free to customize the ASCII art or modify the function according to your preferences. Or make a pull request, You can change the cat's appearance, the thought bubble, or any other aspect of the output.

### License
This package is provided under the MIT License.

Feel free to use and modify it as needed!
django-mentor-ds-myenglish 1.4.0
mentor_pro porting for django
Requires Python: >=3.8

# django-mentor_ds_myenglish

django-mentor_ds_myenglish 는 mentor-pro v4.8.0을 장고에 맞게 포팅한 장고앱이다.


> 프로젝트에 설치하기
> 1. mentor 앱과 demian_parts 앱을을 프로젝트 settings.py 의 INSTALLED_APPS 에 추가한다.
> ```python
> import os
> INSTALLED_APPS = [
>     ...
>     'demian_parts',
>     'mentor',
> ]
> ```
> 2. 프로젝트의 urls.py에 mentor url을 추가한다.
> ```python
> from django.urls import path, include
> 
> urlpatterns = [
>     ...
>     path('', include('mentor.urls')),
> ]
> ```
> 3. 케이스를 입력하기 위해서 프로젝트에 mentor 데이터베이스를 생성한다.
> ```commandline
>     python manage.py makemigrations mentor
>     python manage.py migrate
>     python manage.py createsuperuser
> ```
> 4. http://127.0.0.1:8000/admin 으로 접속하여 블로그를 입력한다.

> 프로젝트 구조 생성하기
> 1. 개별 업체의 static 폴더 경로를 INSTALLED_APPS 에 추가한다.
> ```python
> import os
> STATICFILES_DIRS = [
>   os.path.join(BASE_DIR, '_static/'),
> ]
> ```
> 2. _data 폴더를 생성하고 contents.py내에 데이터를 형식에 맞게 입력한다.
> 3. _static 폴더를 생성하고 각종 이미지 등을 형식에 맞게 저장한다.

> 참고 : SCSS 설치하기 - 프로젝트에 SCSS를 설치해야 앱이 작동한다.    
> https://www.accordbox.com/blog/how-use-scss-sass-your-django-project-python-way/   
> 1. django_compressor, django-libsass를 설치한다. (앱을 설치하면 자동으로 설치된다.)
> ```commandline
> pip install django_compressor django-libsass
> ```
> 2. 프로젝트 settings.py 의 INSTALLED_APPS 에 다음을 추가한다.
> ```python
> import os
> INSTALLED_APPS = [
>     ...
>     'compressor',
> ]
> 
> COMPRESS_PRECOMPILERS = (
>     ('text/x-scss', 'django_libsass.SassCompiler'),
> )
> 
> STATICFILES_FINDERS = [
>     'django.contrib.staticfiles.finders.FileSystemFinder',
>     'django.contrib.staticfiles.finders.AppDirectoriesFinder',
>     'compressor.finders.CompressorFinder',
> ]
> 
> # compressor 앱을 실행하기 위해서는 STATIC_ROOT가 설정되어 있어야 한다.
> STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')
> ```
desota 0.0.5
DeSOTA Tools Package
Requires Python: >=3.7

![Logo_DeSota](https://github.com/DeSOTAai/pip/assets/140865429/6fc2c7e2-bea0-4823-8ce2-4dfed8d974a6)
# DeSOTA Preferred Installer Program
```
pip install desota
```

# Packages
## detools
```
from desota import detools
```
### Tool Methods

* [detools.get_model_req](https://github.com/DeSOTAai/pip/blob/7bcc88958e23d32eaa45b99284fdafc15b6b5c54/src/desota/detools.py#L96)

    Converts the model request, a YAML file created BY [DeRunner](https://github.com/DeSOTAai/DeRunner), into a python dictionary.

---

* [detools.get_model_args](https://github.com/DeSOTAai/pip/blob/7bcc88958e23d32eaa45b99284fdafc15b6b5c54/src/desota/detools.py#L121)

    Get Pro-Arguments from DeSOTA Model request

---
---

### Model Methods

* [detools.get_request_audio](https://github.com/DeSOTAai/pip/blob/7bcc88958e23d32eaa45b99284fdafc15b6b5c54/src/desota/detools.py#L220)

    Get (download) Audio Arguments from DeSOTA Model request

---

* [detools.get_request_video](https://github.com/DeSOTAai/pip/blob/7bcc88958e23d32eaa45b99284fdafc15b6b5c54/src/desota/detools.py#L249)

    Get (download) Video Arguments from DeSOTA Model request

---

* [detools.get_request_image](https://github.com/DeSOTAai/pip/blob/7bcc88958e23d32eaa45b99284fdafc15b6b5c54/src/desota/detools.py#L191)

    Get (download) Image Arguments from DeSOTA Model request

---

* [detools.get_request_file](https://github.com/DeSOTAai/pip/blob/7bcc88958e23d32eaa45b99284fdafc15b6b5c54/src/desota/detools.py#L171)

    Get (download) File Arguments from DeSOTA Model request

---

* [detools.get_request_qa](https://github.com/DeSOTAai/pip/blob/7bcc88958e23d32eaa45b99284fdafc15b6b5c54/src/desota/detools.py#L279)

    Get Question-Answer Arguments from DeSOTA Model request

---

* [detools.get_request_url](https://github.com/DeSOTAai/pip/blob/7bcc88958e23d32eaa45b99284fdafc15b6b5c54/src/desota/detools.py#L332)

    Get URL Arguments from DeSOTA Model request

---

* [detools.get_request_html](https://github.com/DeSOTAai/pip/blob/7bcc88958e23d32eaa45b99284fdafc15b6b5c54/src/desota/detools.py#L430)

    Get HTML Files from DeSOTA Model request

---
---
simple-blast-actapia 0.0.5
A simple library for executing BLAST searches with ncbi-blast+
Requires Python: >=3.11

# simple_blast

This is a library that provides a very basic wrapper around ncbi-blast+.
Currently, the library supports searches with `blastn` only, but I may expand
the library to include wrappers for other BLAST executables if I need them.

## Requirements

This library depends on Pandas for parsing BLAST output. The library has been
tested with Pandas 1.5.3, but it likely works with other versions.

Of course, this library assumes that ncbi-blast+ is installed. The library has
been tested with ncbi-blast 2.12.0+, and it likely works with newer versions of
the software as well.

## Basic usage

You can define a `blastn` search to be carried out using the `BlastnSearch`
class. `BlastnSearch`objects are constructed with two required
arguments&mdash;the subject sequence and the query sequence files, in that
order. For example, to set up a `balstn` search for sequences in `seqs1.fasta`
against those in `seqs2.fasta`, you could construct a `BlastnSearch` object like
this:

```python
from simple_blast import BlastnSearch

search = BlastnSearch("seqs2.fasta", "seqs1.fasta")
```

The BLAST search is not carried out until you ask for the results by accessing
the `hits` property of the search. This property returns a Pandas dataframe
containing the HSPs identified in the BLAST search.

```python
results = search.hits
```

The columns in the output may be configured by passing either the `out_columns`
or `additional_columns` arguments when constructing the `BlastnSearch`. The
former argument overrides the set of output columns; the latter argument is
added to the list of default output columns.

You can also specify an e-value cutoff through the `evalue` argument.

## DB caches

When the same sequence file is used as a subject in multiple searches, it can be
efficient to build a BLAST database up front. The `BlastDBCache` class can be
used to handle this mostly automatically. To make a `BlastDBCache`, you need
to specify the location of the on the file system.

```python
from simple_blast import BlastDBCache

cache = BlastDBCache("cache_dir")
```

To add a file to the cache, use the `makedb` method.

```python
cache.makedb("seqs2.fasta")
```

When constructing a `BlastnSearch` object, give it the `BlastDBCache` as the
`db_cache` parameter to make the `BlastnSearch` object use the cache for
searches.

```python
search = BlastnSearch("seqs2.fasta", "seqs1.fasta", db_cache=cache)
```

Now `search` will use the database we created for `seqs2.fasta`.