semvec/pypi-packages huggingface dataset:
qran 1.0.1
Quran text API
Requires Python: >=3.9
Keywords: quran, nlp, grapheme, archigrapheme, kabikaj

# qran

Quran structured text API.

Python package to extract Quranic text in graphemic and archigraphemic representations, different Quranic encodings and Latin transliteration. The package works as a an API for the stuctured Quran.

This package can be very helpful for semiautomating the transcription of old Quranic manuscripts or fast lookups of script and typographic analysis.

## Installation

```bash
pip install qran
```

## Usage

As a python library

```python
from qran import get_text, Index

>>> text = get_text(
...     ini_index=Index(sura=1, verse=1, word=4, block=2),
...     end_index=Index(sura=1, verse=2, word=2, block=-1),
...     args={"blocks": True},
... )
... 
>>> for grapheme_ar, grapheme, lt, archigrapheme_ar, arhigrapheme_lt in text:
...     print(grapheme_ar, grapheme, lt, archigrapheme_ar, arhigrapheme_lt)
...     
لرَّ LRᵚᵃ لر LR 1:1:4:2
حِيمِ GᵢB₂Mᵢ حٮم GBM 1:1:4:3
ا A ا A 1:2:1:1
لْحَمْدُ LᵒGᵃMᵒDᵘ لحمد LGMD 1:2:1:2
لِلَّهِ LᵢLᵚᵃHᵢ لله LLH 1:2:2:1
```

Alternatively, you can use a tuple of integers instead of an Index object:

```python
>>> text = get_text(
...     ini_index=(1, 1, 4, 2),
...     end_index=(1, 2, 2, -1),
...     args={"blocks": True},
... )
```

As a unix-like command:

```bash
$ qran 1:1:4:2-1:2:2 --blocks
لرَّ	LRᵚᵃ	لر	LR	1:1:4:2
حِيمِ	GᵢB₂Mᵢ	حٮم	GBM	1:1:4:3
ا	A	ا	A	1:2:1:1
لْحَمْدُ	LᵒGᵃMᵒDᵘ	لحمد	LGMD	1:2:1:2
لِلَّهِ	LᵢLᵚᵃHᵢ	لله	LLH	1:2:2:1
```

## License

This project is licensed under the [MIT License](./LICENSE) for its source code.

It includes Quran text from the [Tanzil Project](http://tanzil.net/), 
which is licensed under the [Creative Commons Attribution 3.0 License](https://creativecommons.org/licenses/by/3.0/).

© 2007–2021 Tanzil Project. The text is unmodified and used under Tanzil's terms of use: http://tanzil.net/docs/license

## Author

Alicia González Martínez
vald-client-python 1.7.17
a client library for Vald (https://github.com/vdaas/vald).

# vald-client-python

[![PyPI version](https://badge.fury.io/py/vald-client-python.svg)](https://badge.fury.io/py/vald-client-python)
[![Vald version](https://img.shields.io/github/release/vdaas/vald.svg?style=flat-square)](https://github.com/vdaas/vald/releases/latest)
![Build version](https://img.shields.io/badge/python-3.10-green)

A Python gRPC client library for [Vald](https://github.com/vdaas/vald).

## Install

Using pip,

```sh
pip install vald-client-python
```

## Usage

To use v1 APIs,

```python
import grpc
from vald.v1.vald import insert_pb2_grpc
from vald.v1.vald import search_pb2_grpc
from vald.v1.vald import update_pb2_grpc
from vald.v1.vald import remove_pb2_grpc
from vald.v1.vald import flush_pb2_grpc
from vald.v1.payload import payload_pb2

## create a channel by passing "{host}:{port}"
channel = grpc.insecure_channel("gateway.vald.vdaas.org:80")

## create stubs for calling RPCs
istub = insert_pb2_grpc.InsertStub(channel)
sstub = search_pb2_grpc.SearchStub(channel)
ustub = update_pb2_grpc.UpdateStub(channel)
rstub = remove_pb2_grpc.RemoveStub(channel)
fstub = flush_pb2_grpc.FlushStub(channel)

## call RPCs: Insert
vec = payload_pb2.Object.Vector(id='vector_id_1', vector=[0.1, 0.2, 0.3])
icfg = payload_pb2.Insert.Config(skip_strict_exist_check=True)
istub.Insert(payload_pb2.Insert.Request(vector=vec, config=icfg))


## call RPCs: Search
### num: number of results
### timeout: 3 seconds
scfg = payload_pb2.Search.Config(num=10, radius=-1.0, epsilon=0.01, timeout=3000000000)
res = sstub.Search(payload_pb2.Search.Request(vector=[0.1, 0.2, 0.3], config=scfg))


## call RPCs: Update
uvec = payload_pb2.Object.Vector(id='vector_id_1', vector=[0.1, 0.2, 0.3])
ucfg = payload_pb2.Update.Config(skip_strict_exist_check=True)
ustub.Update(payload_pb2.Update.Request(vector=uvec, config=ucfg))


## call RPCs: Remove
rcfg = payload_pb2.Remove.Config(skip_strict_exist_check=True)
rid = payload_pb2.Object.ID(id='vector_id_1')
rstub.Remove(payload_pb2.Remove.Request(id=rid, config=rcfg))

## call RPCs: Flush
fstub.Flush(payload_pb2.Flush.Request())

## close channel
channel.close()
```
taxi-zebra 5.0.0
Zebra backend for Taxi
Requires Python: >=3.10

Zebra backend for Taxi
======================

This is the Zebra backend for `Taxi <https://github.com/liip/taxi>`_. It
exposes the ``zebra`` protocol to push entries and fetch projects and
activities from Zebra.

Installation
------------

    taxi plugin install zebra

Usage
-----

In your ``.taxirc`` file, use the ``zebra`` protocol for your backend. For example,
if your Zebra instance is hosted at https://zebra.example.com/, after generating a
token in your Zebra profile, you'll use the following configuration::

    [backends]
    my_zebra_backend = zebra://token@zebra.example.com

Contributing
------------

To setup a development environment, create a virtual environment and run the
following command in it::

    pip install -e .

To use a specific version of Taxi, eg. if you need to also make changes to Taxi,
install it in the virtual environment in editable mode::

    pip install -e /path/to/taxi

To run the tests::

    pip install .[testing]
    pytest


Uploading a new release
-----------------------

To upload a new release just run the ``release.sh`` file which will commit and push a tag. A Github action will then upload the new release.
glyphsh 1.3.1
Social media reinvented. In a terminal.
Requires Python: <3.13,>=3.10

# ![image](https://github.com/user-attachments/assets/be679c9d-479f-4441-8c16-02b7f88b66ef)
𝕒 𝕥𝕖𝕣𝕞𝕚𝕟𝕒𝕝-𝕓𝕒𝕤𝕖𝕕 𝕤𝕠𝕔𝕚𝕒𝕝 𝕞𝕖𝕕𝕚𝕒 𝕤𝕖𝕣𝕧𝕚𝕔𝕖.
todolist-mcp-server 0.1.1
TodoList MCP Server - A Model Context Protocol server for managing todo lists
Requires Python: >=3.12

# TodoList MCP Server

A Model Context Protocol (MCP) server for managing todo lists with intelligent task tracking and auto-clear functionality.

## Features

- **In-memory todo storage** with session-based persistence
- **Intelligent auto-clear** - automatically clears completed tasks
- **Task validation** with duplicate ID prevention
- **Priority levels** (high, medium, low) and status tracking
- **MCP-compliant** tools for seamless integration

## Installation

### Requirements
- Python 3.12+
- Poetry (recommended) or pip

### Setup
```bash
# Clone the repository
git clone https://github.com/hicaosen/todolist.git
cd todolist

# Install dependencies
poetry install
# or with pip
pip install -e .
```

## Usage

### Running the Server
```bash
# Using the installed script
todolist-mcp-server

# Or directly with Python
python -m src.server
```

### MCP Tools

The server provides two main tools:

#### `todo_read`
Returns the current todo list. Use frequently to track progress.

```python
# No parameters required
todos = todo_read()
```

#### `todo_write`
Creates and manages todo items. Use for complex multi-step tasks.

```python
todos = [
    {
        "id": "task-1",
        "content": "Implement user authentication",
        "priority": "high",
        "status": "pending"
    },
    {
        "id": "task-2", 
        "content": "Write unit tests",
        "priority": "medium",
        "status": "in_progress"
    }
]
todo_write(todos)
```

### Todo Item Structure

Each todo item must contain:
- `id`: Unique string identifier
- `content`: Task description (non-empty string)
- `priority`: One of "high", "medium", "low"
- `status`: One of "pending", "in_progress", "completed"

### Auto-Clear Behavior

When all todos reach "completed" status, the list automatically clears to maintain a clean workspace.

## Development

### Code Quality Tools
```bash
# Lint code
ruff check

# Format code
ruff format

# Type checking
pyright
```

### Project Structure
```
todolist/
├── src/
│   ├── __init__.py
│   └── server.py          # Main MCP server implementation
├── pyproject.toml         # Project configuration
├── poetry.lock           # Dependency lock file
└── README.md
```

## Configuration

The server uses the following configuration:
- **Line length**: 88 characters
- **Python version**: 3.12+
- **Code style**: Ruff with comprehensive rule set
- **Type checking**: Pyright with strict mode

## License

This project is licensed under the MIT License.

## Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests and linting
5. Submit a pull request

## Support

For issues and questions, please use the GitHub issue tracker.