semvec/pypi-packages huggingface dataset:
easycheck-doiteasy 0.1.1
A simple Python package example

**Nombre de la librería**
doit-monitoring-api

Descripción
La librería doit-monitoring-api es una API que se encarga de monitorear otros sistemas de tipo ETL. Esta librería ha sido desarrollada por DoIteasy.

Instalación
Para instalar esta librería, puedes clonar el repositorio desde GitHub:

Uso
La API está compuesta por varios endpoints que permiten la monitorización de sistemas ETL. A continuación se presentan los endpoints disponibles:

/health: endpoint para verificar el estado de la API.
/jobs: endpoint para listar los trabajos monitoreados por la API.
/jobs/{job_id}: endpoint para obtener detalles sobre un trabajo específico.
/jobs/{job_id}/runs: endpoint para listar las ejecuciones de un trabajo específico.
/jobs/{job_id}/runs/{run_id}: endpoint para obtener detalles sobre una ejecución específica.
Contributing
Si deseas contribuir a esta librería, por favor, sigue estos pasos:

Haz un fork del repositorio.
Crea una rama para tu contribución (git checkout -b feature/your-feature).
Haz tus cambios y haz commit de los mismos (git commit -am 'Add some feature').
Empuja tus cambios a la rama (git push origin feature/your-feature).
Abre un pull request.
Licencia
Esta librería se distribuye bajo la licencia MIT. Ver el archivo LICENSE para más detalles.
MIT License
Copyright (c) 2023 Doiteasy
Permission is here by granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
PassionShell 0.1.0
The PassionShell for the Passion programming language
Requires Python: >=3.10

# About
PassionShell is a Bash-like sub-language made in Python for the Passion programming langauge.

# How to Use
In the shell, type `pip install PassionShell`, and in your Python code, `from PassionShell import PassionShell`.

# Functions and Classes
## Classes
PassionShell comes with one class, `PassionShell`. To use it, create an instance of the class, and add this code:
```
from PassionShell import PassionShell
ps = PassionShell()
ps.loop()
```
First, it gets the class and imports it into the local namespace. Next, it creates an instance of the class, `ps`, and finally, calls the `loop` method to continously take input, and execute it.

## Functions
PassionShell currently does not come with any top-level functions.
haystack-threshold-node 0.0.1
This component filters documents based on a threshold percentage, ensuring only the documents above the threshold get passed down the pipeline
Requires Python: >=3.10

# haystack_threshold_node
This component filters documents based on a threshold percentage, ensuring only the documents above the threshold get passed down the pipeline.
This allows you to query your document store for a larger top_k, but then filter the results down to those which are above a set confidence score.

## Installation

`pip install haystack-threshold-node`

## Usage

Include it in your pipeline - example as follows:

```python
import logging
import re

from datasets import load_dataset
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import PromptNode, PromptTemplate, AnswerParser, BM25Retriever
from haystack.pipelines import Pipeline
from haystack_lemmatize_node import LemmatizeDocuments


logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
logging.getLogger("haystack").setLevel(logging.INFO)

document_store = InMemoryDocumentStore(use_bm25=True)

dataset = load_dataset("bilgeyucel/seven-wonders", split="train")
document_store.write_documents(dataset)

retriever = BM25Retriever(document_store=document_store, top_k=10)

lfqa_prompt = PromptTemplate(
    name="lfqa",
    prompt_text="Given the context please answer the question using your own words. Generate a comprehensive, summarized answer. If the information is not included in the provided context, reply with 'Provided documents didn't contain the necessary information to provide the answer'\n\nContext: {documents}\n\nQuestion: {query} \n\nAnswer:",
    output_parser=AnswerParser(),
)

prompt_node = PromptNode(
    model_name_or_path="text-davinci-003",
    default_prompt_template=lfqa_prompt,
    max_length=500,
    api_key="sk-OPENAIKEY",
)

# The value you pass for threshold is the lowest % score you will accept. Whole numbers only.
# In this example, the threshold is set to 80%.
threshold = DocumentThreshold(threshold=80) 

pipe = Pipeline()
pipe.add_node(component=retriever, name="Retriever", inputs=["Query"])
pipe.add_node(component=threshold, name="Threshold", inputs=["Retriever"])
pipe.add_node(component=prompt_node, name="prompt_node", inputs=["Threshold"])

query = "What does the Rhodes Statue look like?"
  
output = pipe.run(query)

print(output['answers'][0].answer)
```