Renders the Extended OpenAI Conversation prompt/function definitions and
checks the model's first tool-call choice against test phrases, bypassing
Home Assistant.
Add automated validation workflows for HACS and Hassfest to ensure repository meets quality standards for HACS default store inclusion.
Workflows run on:
- Every push and pull request
- Daily at midnight UTC
- Manual trigger via Actions tab
Validates:
- HACS repository structure and metadata
- Home Assistant integration standards
Major documentation updates:
- Streamline README by removing redundant sections
- Link to example files instead of duplicating configuration
- Fix typos and update model recommendations
- Add GPU acceleration documentation link
- Remove outdated "Prompt Optimization" section
Test suite improvements:
- Add comprehensive queue management tests (5 new tests)
- Add shuffle control tests (3 new tests)
- Fix combined query references to match current system prompt
- Update total test count to 53
- Renumber all test sections for consistency
- TODO refresh
- Playlist searches now automatically check personal playlists before public ones
- Remove 'playlist' and 'playlists' from search queries for better matching
- Eliminate user_playlist type - just use playlist for all playlist searches
- Update all documentation and examples to reflect new behavior
- Add podcast support as future feature to TODO.md
Features:
- Exact matching for all search types (artists, albums, tracks, playlists)
- User playlist search (type=user_playlist) with exact and partial matching
- Performance caching for user playlist data
- Comprehensive documentation and test cases
- HACS integration support (hacs.json)
All search types now use limit=10 and prefer exact name matches to avoid
Spotify's personalized recommendations, ensuring you get what you ask for.
- Clarify that integration provides search, but LLM determines accuracy/speed
- Recommend qwen3:4b as good starting point for local voice pipelines
- Link to Performance section for optimization guidance
- Add shuffle_on and shuffle_off actions to control_playback function
- Update prompt to automatically enable shuffle when playing artists
- Add artist radio mode feature to README
- Update all example configurations with shuffle support
- Simplify control_playback YAML syntax (if/then vs choose/conditions)
- Add shuffle examples to voice command demonstrations
- Add 'Why Natural Language Matters' section comparing intent patterns vs LLM
- Show both standard patterns (that work everywhere) and conversational examples (LLM advantage)
- Add explicit default speaker configuration in system prompt
- Include note about when default speaker is used
- Demonstrate conversational playback control examples
- Replace symlink instructions with Docker bind mount setup (primary method)
- Add clear guidance on when to restart vs reload integration
- Document that Python changes require full restart, YAML only needs reload
- Add comprehensive troubleshooting section for common issues
- Include performance notes about 15-50x caching improvement
- Add code quality standards and pre-commit checks
- Clarify symlink limitations in Docker environments
Makes all prerequisites clear upfront:
- Spotify Premium account
- HA Spotify integration (OAuth)
- Extended OpenAI Conversation or similar LLM agent
- At least one Spotify Connect device
Helps users quickly determine if this integration is right for them.
- Explain that Music Assistant and this integration work together
- MA is excellent for UI control, this is for custom voice assistants
- Music started via voice appears in MA interface
- They complement each other, don't conflict
Voice-controlled Spotify playback integration for Home Assistant
Features:
- Natural language music control via Extended OpenAI Conversation
- Exact match artist search (finds Coldplay when you ask for Coldplay)
- Support for artist, album, track, and playlist searches
- Smart caching for 15-50x performance improvement
- Works with any Spotify Connect device
- Zero additional authentication (reuses HA Spotify OAuth)
- Enterprise-grade code quality with defensive programming
Technical improvements:
- Client caching with automatic validation
- Input validation and error handling
- Defensive attribute checks
- Lazy logging for performance
- Clear error messages for users
bhetherman
Chad Auld