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, Anthropic Claude, Cohere Command, or fully local Gemma/Mistral models
• 🔌 Bring-Your-Own LLM - Point PlexMix at any OpenAI-compatible local endpoint (Ollama, LM Studio, llama.cpp)
• 🏷️ 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
• 🛡️ Resilient - Automatic database recovery if deleted or corrupted

Quick Start

Option 1: Command Line Interface (Recommended)

# Install from PyPI
pip install plexmix

# Run setup wizard
plexmix config init

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

# 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

# Regenerate all tags and embeddings from scratch (WARNING: destructive)
plexmix sync regenerate

Option 2: Web User Interface (Alpha)

Note: The Web UI is currently in Alpha status. The CLI is the recommended way to interact with PlexMix for production use.

# Install with UI extras
pip install "plexmix[ui]"

# Or if using poetry
poetry install -E ui

# Launch the web UI
plexmix ui

# Optional: Specify host and port
plexmix ui --host 0.0.0.0 --port 8000

Then open your browser to http://localhost:3000

Screenshots

Dashboard with configuration status and library statistics

AI-powered playlist generator with mood-based queries

Browse and manage your music library with advanced filtering

Configure Plex, AI providers, and embeddings

Web UI Features

The web interface provides a modern, intuitive way to interact with PlexMix:

• 📊 Dashboard - Overview of library stats, configuration status, and quick actions
• ⚙️ Settings - Configure Plex, AI providers, and embeddings with real-time validation
• 📚 Library Manager - Browse, search, and sync your music library with live progress tracking
• 🎵 Playlist Generator - Create mood-based playlists with advanced filters and instant preview
• 🏷️ AI Tagging - Batch generate tags for tracks with progress monitoring
• 📜 Playlist History - View, export, and manage all generated playlists

Key UI Features

• 🌓 Dark/Light Mode - Toggle between themes with automatic logo switching
• Real-time Progress - Live updates for sync, tagging, and generation operations
• Form Validation - Instant feedback on configuration settings
• Loading States - Skeleton screens and spinners for smooth UX
• Error Handling - User-friendly error messages with recovery options
• Responsive Design - Works on desktop and tablet devices

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)
• Local LLM: Run Gemma 3 1B/4B, Liquid LFM 1.2B, Yarn-Mistral 7B-128K, or hook into an Ollama/LM Studio endpoint

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

# Test Plex server connection
plexmix config test

# Show current configuration
plexmix config show

Troubleshooting Connection Issues:

If you get a "400 Bad Request" error when connecting to Plex:

1. Check your Plex token - Make sure there are no extra spaces when copying
2. Try HTTPS - Use https:// instead of http:// if your server requires secure connections
3. Verify the URL - Ensure the server address and port (default: 32400) are correct
4. Check server settings - In Plex Server settings, look for network/authentication requirements
5. Test the connection - Run plexmix config test to diagnose the issue

Common Plex Server URLs:

• Local: http://localhost:32400
• Remote: http://192.168.1.X:32400 (replace X with your server's IP)
• Secure: https://your-server:32400

Sync Commands

PlexMix offers three sync modes:

# Incremental sync (default) - Only syncs new/changed/deleted tracks
plexmix sync

# Same as above, but explicit
plexmix sync incremental

# Regenerate everything from scratch (WARNING: Deletes ALL tags and embeddings)
plexmix sync regenerate

# Legacy alias for incremental sync
plexmix sync full

# Sync without embeddings (faster, but you'll need to generate them later)
plexmix sync --no-embeddings

Sync Mode Comparison:

Mode	Tracks	Tags	Embeddings	Use Case
incremental (default)	✅ Syncs changes only	✅ Preserves existing	✅ Preserves existing	Regular updates, new tracks added
full (alias)	✅ Syncs changes only	✅ Preserves existing	✅ Preserves existing	Same as incremental (kept for compatibility)
regenerate	✅ Syncs everything	⚠️ DELETES ALL	⚠️ DELETES ALL	Starting fresh, fixing corrupt data

When to use each:

• plexmix sync → Default for daily use, adding new music
• plexmix sync regenerate → When you want to completely regenerate all AI data (tags, embeddings)

Database Health Check

# Diagnose and fix database issues
plexmix doctor

# Force regenerate all tags and embeddings (DEPRECATED: use 'plexmix sync regenerate' instead)
plexmix doctor --force

What does plexmix 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

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

Note: For complete regeneration of all tags and embeddings, use plexmix sync regenerate instead of doctor --force

Database Management

# Show database information and statistics
plexmix db info

# Reset database and embeddings (with automatic backup)
plexmix db reset

# Reset without backup (not recommended)
plexmix db reset --no-backup

# Skip confirmation prompt
plexmix db reset --force

What gets deleted:

• SQLite database (~/.plexmix/plexmix.db)
• FAISS embeddings index (~/.plexmix/embeddings.index)
• All synced music metadata
• User-applied tags (moods, environments, instruments)
• Playlist history
• AI-generated embeddings

What gets preserved:

• Your music files on Plex server (unchanged)
• Plex server metadata (unchanged)
• PlexMix configuration (.env, config.yaml)
• API keys

When to use:

• Complete fresh start
• Switching embedding providers
• Database corruption that doctor can't fix
• Testing or development

After reset:

1. Run plexmix sync to re-sync your library
2. (Optional) Run plexmix tags generate to re-tag tracks

By default, a timestamped backup is created in ~/.plexmix/backups/ before deletion.

Database Command Reference:

Command	Purpose	When to Use
plexmix db info	Show database stats	Check database health, view track/embedding counts
plexmix db reset	Delete and reset database	Fresh start, switching providers, unfixable corruption
plexmix sync	Incremental sync	Regular updates, new tracks
plexmix sync regenerate	Regenerate all data	Regenerate tags/embeddings, fix data quality
plexmix doctor	Fix orphaned data	After errors, periodic maintenance

Tag Generation

# Generate AI tags for all untagged tracks
plexmix tags generate

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

# Use the offline/local provider
plexmix tags generate --provider local

# 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

# Force the offline/local provider
plexmix create "ambient focus" --provider local

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

# Adjust candidate pool multiplier (default: 25x playlist length)
plexmix create "diverse mix" --limit 20 --pool-multiplier 50

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

Architecture

PlexMix uses a multi-stage pipeline for intelligent playlist generation:

1. AI Tagging (One-time setup) → 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. Playlist Generation Pipeline:

   • SQL Filters → Apply optional filters (genre, year, rating, artist, environment, instrument)
   • Candidate Pool → Search using FAISS vector similarity (default: 25x playlist length)
   • Diversity Selection → Apply algorithmic diversity rules:
     • Max 3 tracks per artist
     • Max 2 tracks per album
     • No duplicate track/artist combinations
   • Final Playlist → Return curated, diverse track list

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, Cohere, Local Gemma/Yarn presets or any OpenAI-compatible endpoint
• 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
│   │   ├── cohere_provider.py
│   │   ├── local_provider.py   # Managed Hugging Face + custom endpoint support
│   │   └── 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
│   ├── ui/               # Web UI (Reflex)
│   │   ├── app.py        # Main Reflex app
│   │   ├── pages/        # UI pages
│   │   ├── states/       # State management
│   │   ├── components/   # Reusable components
│   │   └── utils/        # UI utilities
│   └── utils/            # Utilities
│       ├── embeddings.py # Embedding providers
│       └── logging.py    # Logging setup
└── tests/                # Test suite
    └── ui/               # UI tests

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
OpenAI	gpt-5-mini	400K tokens	0.7	⚡⚡ Moderate	⭐⭐⭐⭐⭐ Outstanding	💰💰 Medium	High-quality responses, reasoning
Anthropic	claude-sonnet-4-5	200K tokens	0.7	⚡⚡ Moderate	⭐⭐⭐⭐⭐ Outstanding	💰💰💰 High	Advanced reasoning, analysis
Cohere	command-r-plus-08-2024	128K tokens	0.3	⚡⚡ Moderate	⭐⭐⭐⭐⭐ Outstanding	💰💰 Medium	Multilingual, complex tasks
Google Gemini ⭐	gemini-2.5-flash	1M tokens	0.7	⚡⚡⚡ Fast	⭐⭐⭐⭐ Excellent	💰 Low	General use, RAG, large contexts
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-08-2024	128K tokens	0.3	⚡⚡⚡ Fast	⭐⭐⭐⭐ Excellent	💰 Low	Balanced performance
Anthropic	claude-haiku-4-5	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
Local	mixedbread-ai/mxbai-embed-large-v1	1024	⭐⭐⭐⭐ Excellent	⚡⚡ Moderate	💰 Free	None	Highest-quality offline retrieval when you can store larger vectors
Local	google/embeddinggemma-300m	768 (Matryoshka)	⭐⭐⭐⭐ Excellent	⚡⚡ Fast	💰 Free	None	Flexible local embeddings with truncation to 128/256/512d
Cohere	embed-v4	256/512/1024/1536	⭐⭐⭐⭐ Excellent	⚡⚡⚡ Fast	💰 Low	Required	Flexible dimensions (Matryoshka), multimodal
OpenAI	text-embedding-3-small	1536	⭐⭐⭐⭐ Excellent	⚡⚡⚡ Fast	💰💰 Medium	Required	Balanced performance, OpenAI ecosystem
Local	nomic-ai/nomic-embed-text-v1.5	768 (Matryoshka)	⭐⭐⭐ Excellent	⚡⚡ Fast	💰 Free	None	Open-source local embeddings with Matryoshka support
Local	sentence-transformers/all-MiniLM-L6-v2	384	⭐⭐⭐ Good	⚡⚡⚡ Fast	💰 Free	None	Offline use on modest hardware

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, with multiple Hugging Face options (MiniLM, MXBAI, EmbeddingGemma, Nomic) to balance speed vs. recall

* EmbeddingGemma and Nomic embeddings support Matryoshka truncation if you need smaller vectors (128/256/512d) without retraining.

How the “Local” Provider Works

When you choose local on the Settings page, PlexMix instantiates the selected Hugging Face sentence-transformers model directly in-process—no HTTP endpoints, API keys, or port configuration are needed. The model weights download once into your Hugging Face cache (e.g., ~/.cache/huggingface) and subsequent embedding calls run entirely on your machine, which keeps everything offline and private.

Set PLEXMIX_LOCAL_EMBEDDING_DEVICE (default cpu) if you want to force a specific device (e.g., cpu to avoid macOS MPS instability, or cuda when running on a GPU server). The UI and CLI will reuse that cached model/device combination whenever local embeddings are needed.

Local LLM Presets & Custom Endpoints

You can now generate playlists with fully local LLMs—no outbound network traffic required. The AI tab in the UI (or plexmix config init) lets you choose between:

• Managed downloads (same workflow as local embeddings) with curated Hugging Face repos:
  • google/gemma-3-1b — fast, CPU-friendly drafts (8K context / ~768 new tokens)
  • liquid/lfm2-1.2b — lightweight music-focused reasoning (32K context)
  • google/gemma-3-4b — higher-quality 4B param model (32K context)
  • NousResearch/Yarn-Mistral-7b-128k — 7B param 128K context for huge playlists (GPU recommended)
• Custom endpoints that speak the OpenAI Chat Completions API (Ollama, LM Studio, llama.cpp server, OpenRouter running on your LAN, etc.)

When you select "Local (Offline)" as the AI provider you can toggle between Managed (Downloaded) and Custom Endpoint modes:

1. Managed (Downloaded)

   • Click Download / Warm Up Model to prefetch weights into your Hugging Face cache
   • Models are loaded in a background worker and reused across tagging/playlist runs
   • Set PLEXMIX_LOCAL_LLM_DEVICE to cpu, cuda, or mps to force device placement (defaults to auto)

2. Custom Endpoint

   • Point PlexMix at any OpenAI-compatible URL (e.g., http://localhost:11434/v1/chat/completions for Ollama)
   • Optionally provide a bearer token; PlexMix will include it as Authorization: Bearer <token>
   • Responses must return a JSON payload with choices[0].message.content

From the CLI you can force the local provider as well:

# Use the configured local model for tagging
plexmix tags generate --provider local

# Run the playlist doctor flow with your offline LLM
plexmix doctor --force

If you ever want to nuke cached weights, delete the relevant directories under ~/.cache/huggingface.

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

Optimal Setup

Online (Best Latency & Reasoning)

• AI Provider: gemini-2.5-flash (default). For more advanced reasoning, upgrade to gpt-5-mini or claude-sonnet-4-5 if you have the budget.
• Embeddings: gemini-embedding-001 for maximum semantic precision, or text-embedding-3-small if you want faster generation with a slightly smaller vector size.
• Network Tips: Keep API keys in ~/.plexmix/credentials and run plexmix config init to verify connectivity. Use plexmix ui --reload during development to check the status cards.

Hybrid (Cloud AI + Local Embeddings)

• AI Provider: Keep using gemini-2.5-flash (or gpt-5-mini) for playlist prompts so you get the latest reasoning updates.
• Embeddings: Run mixedbread-ai/mxbai-embed-large-v1 locally so FAISS never leaves your machine while still benefiting from high-quality vectors.
• Workflow Tips: Regenerate embeddings locally after every sync, but keep the AI provider online. This gives you the best of both worlds—fast semantic search without exposing track metadata, plus cloud-scale LLM quality.

Fully Local (Offline-Friendly)

• AI Provider: OpenRouter (planned) or a self-hosted LLM (future). Until then, use Gemini with cached responses if you need to stay mostly offline.
• Embeddings: mixedbread-ai/mxbai-embed-large-v1 (1024d) for the best similarity recall while keeping everything on disk.
• Device: Set PLEXMIX_LOCAL_EMBEDDING_DEVICE=cpu (or cuda if you have a local GPU) so sentence-transformers always uses the right hardware.
• Storage Tips: Keep FAISS index on SSD (~/.plexmix/embeddings.index) and prune unused tracks to reduce RAM usage when generating playlists.

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
• 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 multiplier based on library size (default: 25x playlist length)
  • Smaller libraries: Use lower multiplier (10-15x) for faster generation
  • Larger libraries: Use higher multiplier (30-50x) for better diversity
• 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 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 to update your local database. The incremental sync will:

• Detect deleted tracks from Plex
• Remove them 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 --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
• 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, Cohere

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Made with ❤️ for music lovers