semvec/pypi-packages huggingface dataset:
ytrag 2.0.0
YouTube transcripts to RAG-ready volumes
License: MIT
Requires Python: >=3.10
Keywords: llm, rag, subtitles, transcripts, youtube

# ytrag

YouTube transcripts → RAG-ready volumes.

Download YouTube subtitles and consolidate them into LLM-ready volumes.

## Quick Start

```bash
# Install from PyPI
pipx install ytrag

# Download and process a YouTube channel
ytrag all "https://youtube.com/@ChannelName"
```

That's it! The transcripts will be cleaned and organized into volumes ready for use with LLMs.

## Installation

### With pipx (recommended)

```bash
pipx install ytrag
```

### With pip

```bash
pip install ytrag
```

### From Source

```bash
git clone https://github.com/chakkyy/ytrag.git
cd ytrag
pip install .
```

## Usage

```bash
# Process a YouTube channel
ytrag all "https://youtube.com/@ChannelName"

# Process a single video
ytrag all "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

# Process a playlist
ytrag all "https://www.youtube.com/playlist?list=PLxxxxxxx"
```

### Options

```bash
# Specify languages (default: auto-detect from video)
ytrag all "https://..." --lang es,en,pt

# Custom output directory
ytrag all "https://..." --output ./my-transcripts

# Adjust transcripts per volume (default: 100)
ytrag all "https://..." --per-volume 50

# Check status of current directory
ytrag status

# Show version
ytrag --version
```

## Output Structure

After running `ytrag all`, your directory will look like:

```
./
└── ChannelName/
    ├── ChannelName_Vol01.txt      # LLM-ready volume
    ├── ChannelName_Vol02.txt
    ├── manifest.json              # Metadata
    └── .ytrag_archive             # Resume tracking
```

Each volume contains cleaned, consolidated transcripts ready for use with LLMs and RAG systems.

## Features

- **Simple**: One command does everything
- **Auto language detection**: Defaults to video's original language
- **Resume support**: Re-run to continue where you left off
- **Smart deduplication**: Skips regional variants (en-US if en exists)
- **Adaptive rate limiting**: Automatically handles YouTube rate limits
- **Clean output**: No intermediate files, just the volumes you need

## Requirements

- Python 3.10+

## Troubleshooting

### "No subtitles found"

Some videos don't have subtitles enabled. Try:

- Using `--lang` with different language codes
- Auto-generated subtitles are downloaded by default

### Rate limiting errors

YouTube may rate-limit downloads. ytrag handles this automatically with exponential backoff, but for large channels you may need to wait or run again later.

## License

MIT
weconnect-cupra-lordyavin 0.50.15
Python API for the Cupra Born online services
License: MIT
Requires Python: >=3.7
Keywords: weconnect_cupra, weconnect, we connect, carnet, car net, volkswagen, vw, telemetry, cupra



# WeConnect-Cupra-python
[![GitHub sourcecode](https://img.shields.io/badge/Source-GitHub-green)](https://github.com/lordyavin/WeConnect-Cupra-python)
[![GitHub release (latest by date)](https://img.shields.io/github/v/release/lordyavin/WeConnect-Cupra-python)](https://github.com/lordyavin/WeConnect-Cupra-python/releases/latest)
[![GitHub](https://img.shields.io/github/license/lordyavin/WeConnect-Cupra-python)](https://github.com/lordyavin/WeConnect-Cupra-python/blob/master/LICENSE)
[![GitHub issues](https://img.shields.io/github/issues/lordyavin/WeConnect-Cupra-python)](https://github.com/lordyavin/WeConnect-Cupra-python/issues)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/weconnect-cupra-daern?label=PyPI%20Downloads)](https://pypi.org/project/weconnect-cupra-daern/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/weconnect-cupra-daern)](https://pypi.org/project/weconnect-cupra-daern/)

Python API for the MyCupra Services. If you are not a developer and ended up here you probably want to check out a project using this library (see below).

## Projects in which the library is used
- [Cupra WeConnect Home Assistant Integration](https://github.com/lordyavin/cupra_we_connect)

## Install
```
pip3 install weconnect-cupra-lordyavin[Images]
```

## Package
```
python3 -m pip install --upgrade build
python3 -m build --sdist
python3 -m build --wheel
```

## Distribute
```
python3 -m pip install --upgrade twine
python3 -m twine upload --repository pypi dist/*
```

## Getting started
- To get started have a look in the [examples folder](https://github.com/lordyavin/WeConnect-Cupra-python/tree/main/examples)

## Tested with
- Cupra Born Model year 2022/23

## Login & Consent
WeConnect-python is based on the new WeConnect ID API that was introduced with the new series of ID cars. If you use another car or hybrid you probably need to agree to the terms and conditions of the WeConnect ID interface. Easiest to do so is by installing the WeConnect ID app on your smartphone and login there. If necessary you will be asked to agree to the terms and conditions.

## Reporting Issues
Please feel free to open an issue at [GitHub Issue page](https://github.com/lordyavin/WeConnect-Cupra-python/issues) to report problems you found.

See also https://github.com/daernsinstantfortress/WeConnect-Cupra-python/issues.

### Known Issues
- The API is in alpha state and may change unexpectedly at any time! Please conscider this and pin to a specific version if you depend on it.
- Examples and API documentation is missing

## Credits
Largely based on
* WeConnect-python for Cupra https://github.com/daernsinstantfortress/WeConnect-Cupra-python by daern
* WeConnect-python for VW from https://github.com/tillsteinbach/WeConnect-python, with the initial Cupra port developed by Alan Gibson
Inspired by [TA2k/ioBroker.vw-connect](https://github.com/TA2k/ioBroker.vw-connect/) that gave me a point to start working with the API