plexmix 0.1.3

AI-powered Plex playlist generator using mood-based queries

PlexMix

AI-powered Plex playlist generator using mood-based queries

PlexMix syncs your Plex music library to a local SQLite database, generates semantic embeddings for tracks, and uses AI to create personalized playlists based on mood descriptions.

Features

• ✨ Simple Setup - Only requires a Google API key to get started
• 🎵 Smart Sync - Syncs Plex music library with incremental updates
• 🤖 AI-Powered - Uses Google Gemini, OpenAI GPT, or Anthropic Claude
• 🏷️ AI Tagging - Automatically generates tags, environments, and instruments for tracks
• 🔍 Semantic Search - FAISS vector similarity search for intelligent track matching
• 🎨 Mood-Based - Generate playlists from natural language descriptions
• ⚡ Fast - Local database with optimized indexes and full-text search
• 🎯 Flexible - Filter by genre, year, rating, artist, environment, and instrument

Quick Start

# Install from PyPI
pip install plexmix

# Run setup wizard
plexmix config init

# Sync your Plex library (generates embeddings automatically)
plexmix sync full

# Generate AI tags for tracks (enhances search quality)
plexmix tags generate

# Create a playlist
plexmix create "upbeat morning energy"

# With filters
plexmix create "chill evening vibes" --genre jazz --year-min 2010 --limit 30

# Filter by environment and instrument
plexmix create "focus music" --environment study --instrument piano

# Use alternative AI provider
plexmix create "workout motivation" --provider openai

# If you encounter issues (e.g., "0 candidate tracks")
plexmix doctor

# Force regenerate all tags and embeddings
plexmix doctor --force

Installation

From PyPI (Recommended)

pip install plexmix

From Source

git clone https://github.com/izzoa/plexmix.git
cd plexmix
poetry install

Configuration

PlexMix uses Google Gemini by default for both AI playlist generation and embeddings, requiring only a single API key!

Required

• Plex Server: URL and authentication token
• Google API Key: For Gemini AI and embeddings (Get one here)

Optional Alternative Providers

• OpenAI API Key: For GPT models and text-embedding-3-small
• Anthropic API Key: For Claude models (AI only, no embeddings)
• Cohere API Key: For Command R7B and Embed v4 models
• Local Embeddings: sentence-transformers (free, offline, no API key needed)

Getting a Plex Token

1. Open Plex Web App
2. Play any media item
3. Click the three dots (...) → Get Info
4. View XML
5. Copy the X-Plex-Token from the URL

Usage

Configuration Commands

# Interactive setup wizard
plexmix config init

# Show current configuration
plexmix config show

Sync Commands

# Full sync with embedding generation
plexmix sync full

# Sync without embeddings
plexmix sync full --no-embeddings

Database Health Check

# Diagnose and fix database issues
plexmix doctor

# Force regenerate all tags and embeddings
plexmix doctor --force

What does doctor do?

• Detects orphaned embeddings (embeddings that reference deleted tracks)
• Shows database health status (track count, embeddings, orphans)
• Interactively removes orphaned data
• Regenerates missing embeddings
• Rebuilds vector index

What does --force do?

• Deletes ALL tags, environments, instruments, and embeddings
• Regenerates tags for all tracks using Gemini AI
• Regenerates embeddings with the new tags
• Rebuilds the complete vector index from scratch

When to use:

• After "No tracks found matching criteria" errors
• When playlist generation finds 0 candidates
• After database corruption or manual track deletion
• Periodic maintenance to keep database healthy
• Use --force when you want to start completely fresh with AI-generated data

Tag Generation

# Generate AI tags for all untagged tracks
plexmix tags generate

# Use alternative AI provider
plexmix tags generate --provider openai

# Skip embedding regeneration (faster, but tags won't be in search)
plexmix tags generate --no-regenerate-embeddings

Embedding Generation

# Generate embeddings for tracks without them
plexmix embeddings generate

# Regenerate all embeddings from scratch
plexmix embeddings generate --regenerate

What are tags? AI-generated metadata (per track) that enhances semantic search:

• Tags (3-5): Mood descriptors like energetic, melancholic, upbeat, chill, intense
• Environments (1-3): Best-fit contexts like work, study, focus, relax, party, workout, sleep, driving, social
• Instruments (1-3): Most prominent instruments like piano, guitar, saxophone, drums, bass, synth, vocals, strings

All metadata is automatically included in embeddings for more accurate mood-based playlist generation.

Playlist Generation

# Basic playlist (prompts for track count)
plexmix create "happy upbeat summer vibes"

# Specify track count
plexmix create "rainy day melancholy" --limit 25

# Filter by genre
plexmix create "energetic workout" --genre rock --limit 40

# Filter by year range
plexmix create "90s nostalgia" --year-min 1990 --year-max 1999

# Filter by environment (work, study, focus, relax, party, workout, sleep, driving, social)
plexmix create "workout energy" --environment workout

# Filter by instrument (piano, guitar, saxophone, drums, etc.)
plexmix create "piano jazz" --instrument piano

# Use specific AI provider
plexmix create "chill study session" --provider claude

# Custom playlist name
plexmix create "morning coffee" --name "Perfect Morning Mix"

# Don't create in Plex (save locally only)
plexmix create "test playlist" --no-create-in-plex

Architecture

PlexMix uses a two-stage retrieval system with AI-enhanced tagging:

1. AI Tagging → Tracks receive:
   • 3-5 descriptive tags (mood, energy, tempo, emotion)
   • 1-3 environments (work, study, focus, relax, party, workout, sleep, driving, social)
   • 1-3 instruments (piano, guitar, saxophone, drums, bass, synth, vocals, strings, etc.)
2. SQL Filters → Filter tracks by genre, year, rating, artist, environment, instrument
3. FAISS Similarity Search → Retrieve top-K candidates using semantic embeddings (includes all metadata)
4. LLM Selection → AI provider selects final tracks matching the mood

Technology Stack

• Language: Python 3.10+
• CLI: Typer with Rich console output
• Database: SQLite with FTS5 full-text search
• Vector Search: FAISS (CPU) with cosine similarity
• AI Providers: Google Gemini (default), OpenAI GPT, Anthropic Claude
• Embeddings: Google Gemini (3072d), OpenAI (1536d), Local (384-768d)
• Plex Integration: PlexAPI

Project Structure

plexmix/
├── src/plexmix/
│   ├── ai/               # AI provider implementations
│   │   ├── base.py       # Abstract base class
│   │   ├── gemini_provider.py
│   │   ├── openai_provider.py
│   │   ├── claude_provider.py
│   │   └── tag_generator.py  # AI-based tag generation
│   ├── cli/              # Command-line interface
│   │   └── main.py       # Typer CLI app
│   ├── config/           # Configuration management
│   │   ├── settings.py   # Pydantic settings
│   │   └── credentials.py # Keyring integration
│   ├── database/         # Database layer
│   │   ├── models.py     # Pydantic models
│   │   ├── sqlite_manager.py # SQLite CRUD
│   │   └── vector_index.py   # FAISS index
│   ├── plex/             # Plex integration
│   │   ├── client.py     # PlexAPI wrapper
│   │   └── sync.py       # Sync engine
│   ├── playlist/         # Playlist generation
│   │   └── generator.py  # Core generation logic
│   └── utils/            # Utilities
│       ├── embeddings.py # Embedding providers
│       └── logging.py    # Logging setup
└── tests/                # Test suite

Database Schema

PlexMix stores all music metadata locally:

• artists: Artist information
• albums: Album details with artist relationships
• tracks: Track metadata with full-text search, AI-generated tags (3-5), environments (1-3), and instruments (1-3)
• embeddings: Vector embeddings for semantic search (includes all AI-generated metadata)
• playlists: Generated playlist metadata
• sync_history: Synchronization audit log

AI Provider Comparison

Provider	Model	Context Window	Default Temp	Speed	Quality	Cost	Best For
Google Gemini ⭐	gemini-2.5-flash	1M tokens	0.7	⚡⚡⚡ Fast	⭐⭐⭐⭐ Excellent	💰 Low	General use, RAG, large contexts
OpenAI	gpt-5-mini	400K tokens	0.7	⚡⚡ Moderate	⭐⭐⭐⭐⭐ Outstanding	💰💰 Medium	High-quality responses, reasoning
OpenAI	gpt-5-nano	400K tokens	0.7	⚡⚡⚡ Fast	⭐⭐⭐⭐ Excellent	💰 Low	Speed-optimized, efficient
Cohere	command-r7b-12-2024	128K tokens	0.3	⚡⚡⚡ Fast	⭐⭐⭐⭐ Excellent	💰 Low	RAG, tool use, agents
Cohere	command-r-plus-08-2024	128K tokens	0.3	⚡⚡ Moderate	⭐⭐⭐⭐⭐ Outstanding	💰💰 Medium	Multilingual, complex tasks
Cohere	command-r-08-2024	128K tokens	0.3	⚡⚡⚡ Fast	⭐⭐⭐⭐ Excellent	💰 Low	Balanced performance
Anthropic	claude-sonnet-4-5	200K tokens	0.7	⚡⚡ Moderate	⭐⭐⭐⭐⭐ Outstanding	💰💰💰 High	Advanced reasoning, analysis
Anthropic	claude-3-5-haiku-20241022	200K tokens	0.7	⚡⚡⚡ Fast	⭐⭐⭐⭐ Excellent	💰 Low	Fast responses, efficiency

Legend:

• ⭐ Default/recommended option
• Speed: ⚡ Slow, ⚡⚡ Moderate, ⚡⚡⚡ Fast
• Quality: ⭐ Basic → ⭐⭐⭐⭐⭐ Outstanding
• Cost: 💰 Low, 💰💰 Medium, 💰💰💰 High

Embedding Provider Comparison

Provider	Model	Dimensions	Quality	Speed	Cost	API Key	Best For
Google Gemini ⭐	gemini-embedding-001	3072	⭐⭐⭐⭐⭐ Outstanding	⚡⚡ Moderate	💰 Low	Required	High-dimensional, accurate semantic search
OpenAI	text-embedding-3-small	1536	⭐⭐⭐⭐ Excellent	⚡⚡⚡ Fast	💰💰 Medium	Required	Balanced performance, OpenAI ecosystem
Cohere	embed-v4	256/512/1024/1536	⭐⭐⭐⭐ Excellent	⚡⚡⚡ Fast	💰 Low	Required	Flexible dimensions (Matryoshka), multimodal
Local	all-MiniLM-L6-v2	384	⭐⭐⭐ Good	⚡⚡⚡ Fast	💰 Free	None	Offline use, privacy, no API costs

Key Features:

• Gemini: Highest dimensions (3072d) for maximum semantic precision
• OpenAI: Industry standard, excellent ecosystem integration
• Cohere: Configurable dimensions (256/512/1024/1536), supports images with v4
• Local: Completely free, offline, private, no internet required

Dimension Trade-offs:

• Higher dimensions = Better semantic understanding but larger storage
• Lower dimensions = Faster search but slightly less accurate
• Cohere's Matryoshka embeddings allow dynamic dimension selection

Development

Setup Development Environment

# Clone repository
git clone https://github.com/izzoa/plexmix.git
cd plexmix

# Install with development dependencies
poetry install

# Run tests
poetry run pytest

# Format code
poetry run black src/

# Lint
poetry run ruff src/

# Type check
poetry run mypy src/

Running Tests

poetry run pytest
poetry run pytest --cov=plexmix --cov-report=html

Troubleshooting

"No music libraries found"

• Ensure your Plex server has a music library
• Verify your Plex token is correct
• Check server URL is accessible

"Failed to generate embeddings"

• Verify API keys are configured correctly
• Check internet connection
• Try local embeddings: --embedding-provider local

"No tracks found matching criteria"

• First, try: plexmix doctor to check for database issues
• Ensure library is synced: plexmix sync full
• Check filters aren't too restrictive
• Verify embeddings were generated

"0 candidate tracks" or "No orphaned embeddings"

• This usually means embeddings reference old track IDs
• Solution: Run plexmix doctor to detect and fix orphaned embeddings
• The doctor will clean up orphaned data and regenerate embeddings

Performance Tips

• Use local embeddings for faster offline operation
• Run sync during off-peak hours for large libraries
• Adjust candidate pool size based on library size
• Use filters to narrow search space

FAQ

How does PlexMix work?

PlexMix syncs your Plex music library to a local SQLite database, generates AI-powered tags (mood, instruments, environments) for each track, creates semantic embeddings, and uses vector similarity search combined with LLM intelligence to generate playlists from natural language mood descriptions.

Do I need an API key?

Yes, but only one! Google Gemini is the default provider for both AI and embeddings. You can get a free API key at Google AI Studio. Alternative providers (OpenAI, Anthropic, local embeddings) are optional.

How much does it cost to run?

Google Gemini (default):

• Embedding generation: ~$0.10-0.30 for 10,000 tracks (one-time)
• Tag generation: ~$0.20-0.50 for 10,000 tracks (one-time)
• Playlist creation: ~$0.01 per playlist (ongoing)

Alternatives:

• Local embeddings are completely free (no API key needed)
• OpenAI and Anthropic have similar costs

How long does initial sync take?

• Metadata sync: 5-15 minutes for 10,000 tracks
• Tag generation: 30-60 minutes for 10,000 tracks
• Embedding generation: 15-30 minutes for 10,000 tracks

Total: ~1-2 hours for a large library. You can interrupt and resume at any time.

Can I use this without internet?

Partially. After initial sync and tag/embedding generation, you can:

• ✅ Browse your database offline
• ✅ Use local embeddings (no API needed)
• ❌ Generate new playlists (requires AI API)
• ❌ Generate tags for new tracks (requires AI API)

What's the difference between tags, environments, and instruments?

• Tags (3-5): Mood and vibe descriptors like "energetic", "melancholic", "upbeat", "chill"
• Environments (1-3): Best contexts for listening like "work", "study", "workout", "party"
• Instruments (1-3): Most prominent instruments like "piano", "guitar", "saxophone", "drums"

All three are automatically generated by AI and improve playlist quality.

Why am I getting "0 candidate tracks"?

This usually means:

1. No embeddings generated: Run plexmix embeddings generate
2. Database out of sync: Run plexmix doctor to fix
3. Filters too restrictive: Remove some filters and try again
4. Empty library: Ensure plexmix sync full completed successfully

Can I use multiple Plex libraries?

Not yet. Currently PlexMix supports one music library at a time. Multi-library support is on the roadmap.

Does this modify my Plex server?

Only when creating playlists. PlexMix:

• ✅ Reads metadata from Plex (read-only)
• ✅ Creates playlists in Plex (if enabled with --create-in-plex)
• ❌ Does NOT modify tracks, albums, or artists
• ❌ Does NOT delete anything from Plex

What happens if I delete tracks from Plex?

Run plexmix sync full to update your local database. The sync will:

• Remove deleted tracks from the database
• Clean up orphaned embeddings
• Update the vector index

Or use plexmix doctor to clean up orphaned data.

Can I backup my database?

Yes! Your database is stored at ~/.plexmix/plexmix.db. Simply copy this file and the ~/.plexmix/embeddings.index file to backup all your data, tags, and embeddings.

How do I update PlexMix?

pip install --upgrade plexmix

After updating, run plexmix sync full --no-embeddings to apply any database migrations.

Can I contribute?

Absolutely! See CONTRIBUTING.md for guidelines. We welcome bug reports, feature requests, and pull requests.

Roadmap

• Docker support
• Web UI dashboard
• Multi-library support
• Playlist templates
• Smart shuffle and ordering
• Export/import playlists (M3U, JSON)
• Audio feature analysis integration

Contributing

Contributions welcome! Please read CONTRIBUTING.md for guidelines.

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

License

MIT License - see LICENSE for details

Acknowledgments

• Built with Typer and Rich
• Plex integration via python-plexapi
• Vector search powered by FAISS
• AI providers: Google, OpenAI, Anthropic

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Made with ❤️ for music lovers