semvec/pypi-packages huggingface dataset:
pytest-gee 0.8.1
The Python plugin for your GEE based packages.
License: MIT
Requires Python: >=3.9
Keywords: Python, skeleton


pytest-gee
==========

.. |license| image:: https://img.shields.io/badge/License-MIT-yellow.svg?logo=opensourceinitiative&logoColor=white
    :target: LICENSE
    :alt: License: MIT

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   :target: https://conventionalcommits.org
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   :target: https://github.com/astral-sh/ruff
   :alt: ruff badge

.. |prettier| image:: https://img.shields.io/badge/code_style-prettier-ff69b4.svg?logo=prettier&logoColor=white
   :target: https://github.com/prettier/prettier
   :alt: prettier badge

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    :target: https://pre-commit.com/
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    :target: https://pypi.org/project/pytest-gee/
    :alt: PyPI version

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    :target: https://anaconda.org/conda-forge/pytest-gee
    :alt: conda-forge version badge

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    :target: https://github.com/gee-community/pytest-gee/actions/workflows/unit.yaml
    :alt: build

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    :target: https://codecov.io/gh/gee-community/pytest-gee
    :alt: Test Coverage

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    :target: https://pytest-gee.readthedocs.io/en/latest/
    :alt: Documentation Status

|license| |commit| |ruff| |prettier| |pre-commmit| |pypi| |conda| |build| |coverage| |docs|

Overview
--------

.. image:: https://raw.githubusercontent.com/gee-community/pytest-gee/main/docs/_static/logo.svg
    :width: 20%
    :align: right

``pytest-gee`` provides some fixtures that make it easy to generate independent tests that require Earth Engine asset filesystem.
It also able to help maintaining tests that generate Earth Engine server side data.

This plugin uses a data directory (courtesy of ``pytest-datadir``) to store expected data files,
which are stored and used as baseline for future test runs.
You can also define your own data directory directly as described in the ``pytest_regression`` documentation.

Credits
-------

This package was created with `Copier <https://copier.readthedocs.io/en/latest/>`__ and the `@12rambau/pypackage <https://github.com/12rambau/pypackage>`__ 0.1.16 project template.
django-slack-processor 0.1.9
Django app to manage a slack bot with support for custom message processors
License: MIT
Requires Python: >=3.9

## SlackBot

Django app to manage a slack bot with support for custom message processors


Available settings:

* `SLACKBOT_BOT_TOKEN` - ...
* `SLACKBOT_APP_TOKEN` - ...
* `SLACKBOT_USER_MODEL` - ...

## Testing

...
ragchunker 0.1.4
A lightweight RAG chunking and preprocessing library
License: MIT
Requires Python: >=3.8

# ragchunker

A modular Python package for Retrieval-Augmented Generation (RAG) preprocessing. It supports:

- **File Ingestion**: Load documents from various formats (TXT, PDF, DOCX, PPTX, images with OCR, audio with Whisper, CSV/Excel/Parquet, HTML, JSON, ZIP/TAR).
- **Chunking**: Split documents into chunks using fixed-length, semantic, or recursive strategies.
- **Provenance**: Track metadata, checksums, and token counts for chunks.
- **Embeddings**: Generate embeddings using Sentence-Transformers or OpenAI.
- **Storage**: Save chunks and embeddings to JSONL, Parquet, SQLite, NumPy, or FAISS; optional integration with Pinecone or ChromaDB.

## Installation

Install the core package:

```bash
pip install ragchunker



For Testing:

from ragchunker.rag_pipeline import run_rag_pipeline
from ragchunker.storage import store_from_result

VECTOR_DB = "chroma"  # "faiss" or "chroma"


## For Open Source Embeddings
result = run_rag_pipeline(
    data_dir="data",                # Folder with your PDFs, docs, or txt files
    output_dir="output",            # Where JSONL, Chunks and embeddings will be saved
    chunk_strategy="semantic",      # or "fixed", "recursive"
    chunk_size=800,
    overlap=100,
    embed_model="all-MiniLM-L6-v2", # or any other
    embed_provider="sentence-transformers",
    openai_api_key=None                    # or "sk-your-openai-key" if using OpenAI
)

print("\n✅ Pipeline executed successfully!")


# For Open AI Embeddings
result = run_rag_pipeline(
    data_dir="data",                # Folder with your PDFs, docs, or txt files
    output_dir="output",            # Where JSONL, Chunks and embeddings will be saved
    chunk_strategy="semantic",      # or "fixed", "recursive"
    chunk_size=800,
    overlap=100,
    embed_model="text-embedding-3-small", # or any other
    embed_provider="openai",
    openai_api_key=""                   # or "sk-your-openai-key" if using OpenAI
)

print("\n✅ Pipeline executed successfully!")


print(f"\nStoring embeddings + chunks to: {VECTOR_DB}")
store_info = store_from_result(result, VECTOR_DB)
print("Store info:", store_info)
print("Done.")
xml-sitemap-writer 0.7.0
Python3 package for writing large XML sitemaps with no external dependencies
License: MIT
Requires Python: >=3.9

# py-xml-sitemap-writer
[![PyPI](https://img.shields.io/pypi/v/xml-sitemap-writer.svg)](https://pypi.python.org/pypi/xml-sitemap-writer)
[![Downloads](https://pepy.tech/badge/xml-sitemap-writer)](https://pepy.tech/project/xml-sitemap-writer)
[![Coverage Status](https://coveralls.io/repos/github/pigs-will-fly/py-xml-sitemap-writer/badge.svg?branch=master)](https://coveralls.io/github/pigs-will-fly/py-xml-sitemap-writer?branch=master)

Python3 package for writing large [XML sitemaps](https://www.sitemaps.org/index.html) with no external dependencies.

```
pip install xml-sitemap-writer
```

## Usage

This package is meant to **generate sitemaps with hundred of thousands of URLs** in **memory-efficient way** by
making use of **iterators to populate sitemap** with URLs.

```python
from typing import Iterator
from xml_sitemap_writer import XMLSitemap

def get_products_for_sitemap() -> Iterator[str]:
    """
    Replace the logic below with a query from your database.
    """
    for idx in range(1, 1000001):
        yield f"/product/{idx}.html"  # URLs should be relative to what you provide as "root_url" below

with XMLSitemap(path='/your/web/root', root_url='https://your.site.io') as sitemap:
    sitemap.add_section('products')
    sitemap.add_urls(get_products_for_sitemap())
```

`sitemap.xml` and `sitemap-00N.xml.gz` files will be generated once this code runs:

```xml
<?xml version="1.0" encoding="UTF-8"?>
<sitemapindex xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
	<!-- Powered by https://github.com/pigs-will-fly/py-xml-sitemap-writer -->
	<!-- 100000 urls -->
	<sitemap><loc>https://your.site.io/sitemap-products-001.xml.gz</loc></sitemap>
	<sitemap><loc>https://your.site.io/sitemap-products-002.xml.gz</loc></sitemap>
    ...
</sitemapindex>
```

And gzipped sub-sitemaps with up to 15.000 URLs each:

```xml
<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
	<url><loc>https://your.site.io/product/1.html</loc></url>
	<url><loc>https://your.site.io/product/2.html</loc></url>
	<url><loc>https://your.site.io/product/3.html</loc></url>
    ...
</urlset>
<!-- 15000 urls in the sitemap -->
```

For easier discovery of your sitemap add its URL to `/robots.txt` file:

```
Sitemap: https://your.site.io/sitemap.xml
```