semvec/pypi-packages huggingface dataset:
kryten-playlist 0.3.2
Kryten playlist management service - handles video queue, playlist operations, and catalog enrichment with LLM
License: MIT
Requires Python: <4.0,>=3.10
Keywords: cytube, moderation, chat, nats, microservices, catalog, llm, enrichment

"# Kryten Playlist

Kryten playlist management service - handles video queue and playlist operations for CyTube.

## Features

- Real-time playlist management
- Video queue operations
- Event-driven architecture using NATS
- Playlist synchronization
- **Catalog Management**: Ingest and enrich media catalogs with LLM-powered metadata

## Installation

### Prerequisites

- Python 3.10 or higher
- Poetry
- NATS server running
- kryten-py library

### Setup

1. Install dependencies:
```bash
poetry install
```

2. Copy the example configuration:
```bash
cp config.example.json config.json
```

3. Edit `config.json` with your settings:
```json
{
  "nats_url": "nats://localhost:4222",
  "nats_subject_prefix": "kryten",
  "service_name": "playlist"
}
```

## Usage

### Running the Service

Using Poetry:
```bash
poetry run kryten-playlist --config config.json
```

Using the startup script (PowerShell):
```powershell
.\start-playlist.ps1
```

Using the startup script (Bash):
```bash
./start-playlist.sh
```

### Catalog Management

Kryten Playlist includes tools to manage and enrich your media catalog.

#### Ingest
Ingest catalog from a MediaCMS instance:

```bash
# Using config file (recommended)
poetry run kryten-ingest --config config.json

# Or with manual arguments
poetry run kryten-ingest --base-url https://mediacms.example.com --db data/catalog.db
```

#### Enrich
Enrich metadata using LLM (OpenAI, Anthropic, OpenRouter, etc.):

```bash
# Enrich all unenriched items (recommended)
poetry run kryten-enrich --config config.json

# Manual enrichment with specific model
export LLM_API_KEY="your-key-here"
poetry run kryten-enrich --model gpt-4o --limit 100 --verify
```

**Options:**
- `--verify`: Enable 2-pass verification (double-checks facts with LLM)
- `--tv-only` / `--movies-only`: Filter by type
- `--dry-run`: Preview changes without saving
- `--force-all`: Re-process items even if already enriched

### Command Line Options

- `--config PATH`: Path to configuration file (default: `/etc/kryten/playlist/config.json`)
- `--log-level LEVEL`: Set logging level (DEBUG, INFO, WARNING, ERROR)

## Event Handling

The service currently listens for:

- **queue**: Video queue events
- **delete**: Video deletion events
- **moveVideo**: Video position changes
- **setTemp**: Temporary video status changes

## Development

### Running Tests

```bash
poetry run pytest
```

### Linting

```bash
poetry run ruff check .
```

### Formatting

```bash
poetry run black .
```

## License

MIT License - see LICENSE file for details
"
hyptorch 1.3.1
Suite of hyperbolic neural networks in PyTorch
License: MIT
Requires Python: >=3.13
Keywords: pytorch, deep-learning, hyperbolic, neural-networks, machine-learning

# HypTorch

[![Documentation Status](https://readthedocs.org/projects/hyptorch/badge/?version=latest)](https://hyptorch.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/hyptorch.svg)](https://badge.fury.io/py/hyptorch)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.13+](https://img.shields.io/badge/python-3.13+-blue.svg)](https://www.python.org/downloads/)

A PyTorch library for hyperbolic deep learning. HypTorch provides hyperbolic neural network layers, optimizers, and utilities for building deep learning models in hyperbolic space, with a focus on the Poincaré ball model.