semvec/pypi-packages huggingface dataset:
yt-dlp-transcript 0.1.21
Handy yt-dlp wrapper to download Youtube videos transcripts
Requires Python: >=3.11

# yt-dlp-transcript

A handy wrapper for `yt-dlp` to download transcripts for YouTube videos.

## Usage

    uvx yt-dlp-transcript https://www.youtube.com/watch?v=dQw4w9WgXcQ

or:

    pip install yt-dlp-transcript
    yt-dlp-transcript https://www.youtube.com/watch?v=dQw4w9WgXcQ

## Options:

    > yt-dlp-transcript -h
    usage: yt-dlp-transcript [-h] [-l LANGUAGE] [-v] url

    positional arguments:
      url                   Youtube URL

    options:
      -h, --help            show this help message and exit
      -l LANGUAGE, --language LANGUAGE
                            subtitles language (default: en)
      -v, --verbose         verbose mode (default: False)

## Usage in Python code

    from yt_dlp_transcript import yt_dlp_transcript
    print(yt_dlp_transcript("https://www.youtube.com/watch?v=dQw4w9WgXcQ"))
levers 0.2.0
A Python package for visualizing lever mechanisms and animated geometry
Requires Python: >=3.12
Keywords: animation, geometry, lever, mechanics, mechanism, simulation

# Levers
**Levers** is a Python package for visualizing lever mechanisms and animated geometry.  
It lets you describe a moving mechanism or geometric construction in just a few lines of code and visualize it using pluggable renderers.

<p align="center">
  <img src="media/demo2-1c.gif" width="400" alt="Chebyshev lambda linkage">
</p>

## Features

- Concise and natural syntax for describing mechanical linkages and geometry
- Optional rendering backends — choose what fits your use case
- No dependencies required for the core functionality

## Renderers and Installation

Although the project core has no external dependencies, vizualisation is performed via renderer plugins, each with its own requirements. You can choose optional dependencies at installation depending on the renderer you plan to use.

Currently, the package includes two renderer plugins:

- **PyGameRenderer** — install with:  
  ```bash
  pip install levers[pygame]
  ```
- **PyQtGraphRenderer** — install with:
  ```bash
  pip install levers[pyqt]
  ```
To install both:
```bash
pip install levers[all]
```

## Examples

Check the `examples/` folder to quickly learn how to use Levers!
poma-chunker 0.1.3
Compiled chunking + retrieval engine for .poma documents
Requires Python: >=3.10

# poma-chunker

[![PyPI version](https://img.shields.io/pypi/v/poma-chunker.svg)](https://pypi.org/project/poma-chunker/)  
[![License: Proprietary](https://img.shields.io/badge/License-Proprietary-blue.svg)](LICENSE.txt)

> **Part of the POMA toolkit.** For the complete documentation, see the [organization README](https://github.com/poma-science/.github).

A high-performance, compiled chunking & retrieval engine for `.poma` archives outputting chunksets.

> **A chunkset** is the entire path from the document root to a leaf sentence. This keeps full context.

---

## 🚀 Installation

**From PyPI**

```bash
pip install poma-chunker
```


---

## 🏁 Basic Usage

```python
from poma_chunker import process, get_relevant_chunks, generate_cheatsheet

# 1. Chunk your .poma archive
result = process("doc.poma", { ...chunker config... })
chunks, chunksets = result["chunks"], result["chunksets"]

# 2. Pick some IDs (e.g. via vector search)
ids = chunksets[0]["chunks"]

# 3. Enrich & format
relevant = get_relevant_chunks(ids, chunks)
print(generate_cheatsheet(relevant))
```

## 📄 API

```python
process(archive_path: str, config: dict) -> dict
get_relevant_chunks(chunk_ids: list, chunks: list) -> list
generate_cheatsheet(relevant_chunks: list) -> str
```

* `process()` - Process a .poma archive to generate structure-aware chunks and chunksets
* `get_relevant_chunks()` - Enrich chunk IDs with their full context
* `generate_cheatsheet()` - Create a compact, deduplicated, context-rich summary

---

## 🔗 See Also

All configuration details & end-to-end examples live in the **org-level README**:
🔗 [https://github.com/poma-science/.github](https://github.com/poma-science/.github)

---

## 📜 License

Proprietary — see [LICENSE.txt](LICENSE.txt)

Free for non-commercial and evaluation use. Commercial use is currently free but [registration is encouraged](https://poma.science/register) and subject to future licensing terms. Patents pending.

---

## 🧑‍🔬 Acknowledgments

This package uses the following open source libraries at runtime:

- [litellm](https://github.com/BerriAI/litellm) (MIT)
- [pandas](https://github.com/pandas-dev/pandas) (BSD-3)
- [tiktoken](https://github.com/openai/tiktoken) (MIT)
- [beautifulsoup4](https://www.crummy.com/software/BeautifulSoup/) (MIT)
- [chardet](https://github.com/chardet/chardet) (LGPL)
- [transformers](https://github.com/huggingface/transformers) (Apache 2.0)