semvec/pypi-packages huggingface dataset:
usdm3-excel 0.2.1
A python package for importing and exporting the CDISC TransCelerate USDM, version 3, using Excel

# USDM4_Excel

Library for import and export of USDM Version 3 via MS Excel. The package exports from USDM4 data.

# Build Package

Build steps for deployment to pypi.org

- Build with `python3 -m build --sdist --wheel`
- Upload to pypi.org using `twine upload dist/*`
moin 2.0.0b4
MoinMoin is an easy to use, full-featured and extensible wiki software package
Requires Python: >=3.10
Keywords: wiki, web

MoinMoin — a lightweight open-source wiki engine
================================================

<img src="https://github.com/moinwiki/moin/blob/master/src/moin/static/logos/moinmoin.svg?raw=true" alt="MoinMoin Logo" width="120" align="right">

MoinMoin is an easy-to-use, full-featured, and extensible wiki software package
written in Python. You can run it on all common operating systems (Windows,
macOS, Linux, etc.).

This wiki software can fulfill a wide range of roles, such as a personal notes
organizer deployed on a laptop or home web server, a company knowledge base
deployed on an intranet, or an Internet server open to individuals sharing the
same interests, goals, or projects.


Documentation and Support
=========================

> [!NOTE]
> Moin2 is not released yet.

You will find the latest documentation at https://moin-20.readthedocs.io/en/latest.

There is a wiki page collecting all Moin2-specific information, links to
support resources, and information about MoinMoin’s development status and plans:
https://moinmo.in/MoinMoin2.0

In general, ensure that the documentation you are reading
is written for the Moin version you are using. Much of the information
you find in the wiki refers to Moin 1.9 and does not apply to Moin2.

For support, please try the documentation, the home page, the IRC channel,
the mailing lists, and GitHub issues before contacting the MoinMoin
authors directly.

If you have trouble with any web server configuration, please try reading
the web server's documentation. The same applies to any other third-party
software commonly used with Moin but not written by the MoinMoin developers.


Contributing
============

You're welcome to help us make MoinMoin even better! Whether you have experience
with Python, HTML, or CSS, or are just starting out, there are many ways you
can contribute — please see [CONTRIBUTING](https://github.com/moinwiki/moin/blob/master/CONTRIBUTING.md).


Acknowledgments
================

We thank the many people who have contributed their valuable ideas, time, and
code — please see the [contributors](https://github.com/moinwiki/moin/graphs/contributors) page.
NEXTProtocol-s3file 0.1.2
A package to upload files to S3
Requires Python: >=3.9

# NEXTProtocol S3File

Petit client OVH S3 pensé pour des uploads publics simples.

## Installation

```bash
pip install NEXTProtocol-s3file
# mettre à jour si besoin
pip install --upgrade NEXTProtocol-s3file
```

## Démarrage rapide

```python
from s3file import OvhS3

s3 = OvhS3(
    endpoint="https://s3.gra.io.cloud.ovh.net",
    bucket="your-bucket-name",
    region="gra",
    key_id="YOUR_ACCESS_KEY",
    secret="YOUR_SECRET_KEY",
)

url = s3.upload_file_public("local/path/to/file.jpg")
# -> https://your-bucket.s3.gra.io.cloud.ovh.net/default_path/file.jpg

url = s3.upload_file_public(
    "photo.jpg",
    "images/2024/photo.jpg",
)
# -> https://your-bucket.s3.gra.io.cloud.ovh.net/images/2024/photo.jpg
```

## API

### `OvhS3(endpoint, bucket, gra, key_id, secret)`
- prépare un client boto3 configuré pour OVH
- garde l'endpoint public dans `virtual_endpoint`

### `upload_file_public(file_path, bucket_file_path=None)`
- crée la clé distante `default_path/<nom>` si aucun chemin n'est donné
- passe l'ACL `public-read`
- renvoie l'URL publique complète

## Bonnes pratiques
- vérifie tes identifiants OVH avant l'appel
- pilote les erreurs via un try/except si tu veux une gestion custom
- lance `pip install --upgrade NEXTProtocol-s3file` pour récupérer la dernière version

## Prérequis
- Python >= 3.9
- boto3 >= 1.26.0

## Licence
MIT
openmemory-python 1.0.0
Python implementation of OpenMemory - Long-term memory for AI systems
Requires Python: >=3.8

# OpenMemory (Python)

Python implementation of OpenMemory - Long-term memory for AI systems with cognitive architecture.

This is a reimplementation of [OpenMemory](https://github.com/CaviraOSS/OpenMemory) in Python, maintaining the same cognitive memory architecture while enabling integration with Python-based AI systems.

## Features

- **Multi-sector memory** - Episodic, semantic, procedural, emotional, and reflective memory types
- **Automatic decay** - Memories fade naturally unless reinforced
- **Graph associations** - Waypoint-based memory linking
- **Pattern recognition** - Regex-based sector classification
- **User isolation** - Per-user memory spaces
- **Pluggable embeddings** - OpenAI, Sentence-Transformers, or local models
- **MCP Server** - Built-in Model Context Protocol server

## Installation

```bash
pip install -r requirements.txt
```

## Quick Start

```python
from openmemory import MemorySystem

# Initialize memory system
memory = MemorySystem(db_path="memory.db")

# Add a memory
result = memory.add_memory(
    content="User prefers dark mode in their IDE",
    user_id="user123"
)

# Query memories
results = memory.query(
    query="What are the user's preferences?",
    user_id="user123",
    k=5
)

for mem in results:
    print(f"Score: {mem.score:.3f} | {mem.content}")
```

## Architecture

OpenMemory uses a cognitive architecture with five memory sectors:

- **Episodic** (decay: 0.015/day) - Time-based experiences
- **Semantic** (decay: 0.005/day) - Facts and knowledge
- **Procedural** (decay: 0.008/day) - How-to instructions
- **Emotional** (decay: 0.020/day) - Feelings and moods
- **Reflective** (decay: 0.001/day) - Insights and wisdom

Each memory is:
- Classified into sectors using pattern matching
- Embedded with sector-specific models
- Connected via waypoint graphs
- Tracked with salience scores (0-1)
- Subject to exponential decay

## MCP Server

Run the MCP server:

```bash
python -m openmemory.mcp.server
```

Or use with Claude Desktop (add to config):

```json
{
  "mcpServers": {
    "openmemory": {
      "command": "python",
      "args": ["-m", "openmemory.mcp.server"]
    }
  }
}
```

## Development

```bash
# Install dev dependencies
pip install -r requirements-dev.txt

# Run tests
pytest tests/

# Format code
black openmemory/
```

## License

MIT License - Copyright (c) 2025

## Credits

Based on [OpenMemory by CaviraOSS](https://github.com/CaviraOSS/OpenMemory)

Python reimplementation by @danielsimonjr