semvec/pypi-packages huggingface dataset:
prov 2.1.1
A library for W3C Provenance Data Model supporting PROV-JSON, PROV-XML and PROV-O (RDF)
Requires Python: >=3.9
Keywords: provenance, graph, model, PROV, PROV-DM, PROV-JSON, W3C, PROV-XML, PROV-N, PROV-O, RDF

============
Introduction
============


.. image:: https://badge.fury.io/py/prov.svg
  :target: http://badge.fury.io/py/prov
  :alt: Latest Release
.. image:: https://github.com/trungdong/prov/workflows/CI/badge.svg
  :target: https://github.com/trungdong/prov/actions?workflow=CI
  :alt: CI Status
.. image:: https://img.shields.io/coveralls/trungdong/prov.svg
  :target: https://coveralls.io/r/trungdong/prov?branch=master
  :alt: Coverage Status
.. image:: https://img.shields.io/pypi/wheel/prov.svg
  :target: https://pypi.python.org/pypi/prov/
  :alt: Wheel Status
.. image:: https://img.shields.io/pypi/pyversions/prov.svg
  :target: https://pypi.python.org/pypi/prov/
  :alt: Supported Python version
.. image:: https://img.shields.io/pypi/l/prov.svg
  :target: https://pypi.python.org/pypi/prov/
  :alt: License


A library for W3C Provenance Data Model supporting PROV-O (RDF), PROV-XML, PROV-JSON import/export

* Free software: MIT license
* Documentation: http://prov.readthedocs.io/.
* Python 3 only.

Features
--------

* An implementation of the `W3C PROV Data Model <http://www.w3.org/TR/prov-dm/>`_ in Python.
* In-memory classes for PROV assertions, which can then be output as `PROV-N <http://www.w3.org/TR/prov-n/>`_
* Serialization and deserialization support: `PROV-O <http://www.w3.org/TR/prov-o/>`_ (RDF), `PROV-XML <http://www.w3.org/TR/prov-xml/>`_ and `PROV-JSON <http://www.w3.org/Submission/prov-json/>`_.
* Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG).
* Convert a PROV document to a `Networkx MultiDiGraph <https://networkx.github.io/documentation/stable/reference/classes/multidigraph.html>`_ and back.


Uses
^^^^

See `a short tutorial  <http://trungdong.github.io/prov-python-short-tutorial.html>`_ for using this package.

This package is used extensively by `ProvStore <https://openprovenance.org/store/>`_,
a free online repository for provenance documents.
conversao-moedas 0.0.2
Conversor de moedas
Requires Python: >=3.8

# conversor-moedas

Description. 
The package conversor-moedas is used to:
	- Convert currency
	- Get currency rate

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install conversor-moedas

```bash
pip install conversor-moedas
```

## Author
Guilherme Kenzo

## License
[MIT](https://choosealicense.com/licenses/mit/)
balajiwork 1.1.6
Balaji's personal utility library
Requires Python: >=3.8

# balajiwork

Utility functions for personal and professional use.

## Installation

You can install this package from PyPI using pip:

```bash
pip install balajiwork
```

Or, to upgrade to the latest version:

```bash
pip install --upgrade balajiwork
```

## Tools included:

### 🧾 ifc2xlsx
A GUI tool to convert IFC files to XLSX.

#### How to Run

You can launch the tool directly from the command line:

```cmd
ifc2xlsx
```

Or, if you have a Python script entry point:

```cmd
python -m balajiwork.ifc2xlsx
```

This will open the GUI window for selecting input and output directories and other options.

#### GUI Screenshot

![IFC to CSV Converter GUI](https://github.com/balajibalagurusami/python/raw/ab3ad0ebe9f5b99ac72f354e20e028ac351a10d9/ifc2xlsx.png)

### 🖨️ slowprint
Prints a PDF file one page at a time with a delay using SumatraPDF to avoid printer jamming.

Usage:
```python
from balajiwork import slow_print
slow_print(
    pdf_path=r"C:\pdf\paper2.pdf",
    sumatra_path=r"C:\Users\Balaj\AppData\Local\SumatraPDF\SumatraPDF.exe",
    printer_name="HP Smart Tank 660-670 series [HP7860CD]",
    delay=20
)
```

**Tip:**  
To find your exact printer name for the `printer_name` argument, open Command Prompt and run:

```cmd
wmic printer get name
```

This will list all available printers. Copy the exact name as shown for use
ragmetrics-client 0.2.1
Monitor your LLM calls. Test your LLM app.
Requires Python: >=3.6
Keywords: llm, evaluation, monitoring, rag

<h1 align="center">RagMetrics</h1>
    <p align="center">
        Monitor your LLM calls. Test your LLM app.
    </p>
<h4 align="center">
    <a href="https://pypi.org/project/ragmetrics-client/" target="_blank">
        <img src="https://img.shields.io/pypi/v/ragmetrics-client.svg" alt="PyPI Version">
    </a>
</h4>

[RagMetrics](https://ragmetrics.ai/) offers:

- The best LLM judge on the market (>95% human agreement)
- A/B testing for your entire LLM pipeline
- Evaluations for retrievals, not just generation

With this package, you can log your LLM calls to RagMetrics and use them as labeled data

# Quickstart

```shell
pip install ragmetrics-client
```

```python
import ragmetrics
from openai import OpenAI

## login to ragmetrics account via portal key
ragmetrics.login(key="your_ragmetrics_key")
openai_client = OpenAI()

## Monitor OpenAI, LangChain or LiteLLM clients
ragmetrics.monitor(openai_client)

## Then use as usual. All calls will be logged
resp = openai_client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": f"What is the capital of Spain?"}]
)
```
## Get your key and read docs at [RagMetrics.ai](https://ragmetrics.ai/)