semvec/pypi-packages huggingface dataset:
nebari-plugin-airflow 0.0.2
MetroStar Onyx - Airflow plugin for Nebari platform
Requires Python: >=3.10

# Nebari Plugin - Airflow

[![PyPI - Version](https://img.shields.io/pypi/v/nebari-plugin-airflow.svg)](https://pypi.org/project/nebari-plugin-airflow)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/nebari-plugin-airflow.svg)](https://pypi.org/project/nebari-plugin-airflow)
scihub_dmunozg 0.2.0
Unofficial API for retrieving documents from SciHub
Requires Python: >=3.11,<4.0

Sci-Hub lib
[![Python](https://img.shields.io/badge/Python-3%2B-blue.svg)](https://www.python.org)
=======

Forked from [Scihub Downloader](https://github.com/ezxpro/scihub-downloader) which is a fork of [scihub.py](https://github.com/zaytoun/scihub.py).

Usage
-----

First, import the module and create an instance of the `SciHub` object. At this point, you can optionally specify which Sci-Hub mirror will be employed.

```python
from scihub import SciHub

sh = SciHub(base_url="https://sci-hub.ru")
```

The `fetch` method will obtain a URL for direct downloading of an specified reference:

```python
sh.fetch("https://doi.org/10.1111/gwmr.12285")
```

<pre>> 'http://dacemirror.sci-hub.ru/journal-article/0b1ec328b87368b809e0913c8591b9bc/miller2018.pdf?download=true'</pre>

The `download` method will automatically download the PDF from the reference into a specified location:

```python
sh.download(
    reference="https://doi.org/10.1111/gwmr.12285",
    output_dir="./",
    pdf_filename = "paper.pdf" # This is optional
  )
```

CLI tool
--------

The package features a tool to download PDF files from the command line.

```bash
scihub "https://doi.org/10.1111/gwmr.12285"
```

It can be called with the arguments `--output`/`-o` to specify where to save the PDF file, and with `--sci-hub-url` to specify the Sci-Hub mirror.

```bash
scihub "https://doi.org/10.1111/gwmr.12285" -o here.pdf --sci-hub-url "https://sci-hub.ru"
```

License
-------

MIT
nicellm 0.0.2
NiceLLM is a wrangler of API connections of Large Language Models (LLMs) in Python.

# NiceLLM: A Python LLM toolkit

## About

NiceLLM is a Python package that homologates mainstream library functions of Large Language Models (LLMs), such as OpenAI, MistralAI, and Google Generative AI, into a unique and simple interface for the user.

## Main Features

- Simplified handling of API connections
- Simplified interface to use images in the prompt

## Installation

The source code can be found at https://github.com/idealab-udec/nicellm.

```
pip install nicellm
```

## Initial config

To initialize the functions of `nicellm`, you will need to set some environment variables, which will contain the API keys for OpenAI and Google Generative AI. We suggest to create a file called `.env` in your root folder with the following information:

```
APIKEY_GOOGLE="YOUR-API-KEY-FROM-GENERATIVE-AI"
APIKEY_OPENAI="YOUR-API-KEY-FROM-OPEN-AI"
APIKEY_MISTRAL="YOUR-API-KEY-FROM-MISTRAL"
```

It is not mandatory to set up all env variables. However, you must set up at least the ones corresponding to the models you want to use.

To obtain your API keys, please visit:

- OpenAI: https://platform.openai.com/account/api-keys
- Google Generative AI: https://makersuite.google.com/app/apikey
- Mistral AI: https://console.mistral.ai/user/api-keys/

## Usage

Interact with `gpt-3.5-turbo`:

```
import nicellm as llm

llm.get_response(
    model_id="gpt-3.5-turbo",
    prompt="Who is the best LLM in the world?"
)
```

The same question can be easily replicated using `gemini-pro`:

```
llm.get_response(
    model_id="gemini-pro",
    prompt="Who is the best LLM in the world?"
)
```

By using `mistral-tiny`:

```
llm.get_response(
    model_id="mistral-tiny",
    prompt="Who is the best LLM in the world?"
)
```

## Contributions

This project is maintained by the Interdisciplinary Education & Artificial Intelligence Lab, IDEALAB, from the University of ConcepciÃ³n, Chile.

All contributions and ideas are welcome and well received. Please send us your PRs, and we will revise them as soon as possible.

## License

This project is under an MIT-License.