ilia
bdbf09a9ac
✅ TICKET-006: Wake-word Detection Service - Implemented wake-word detection using openWakeWord - HTTP/WebSocket server on port 8002 - Real-time detection with configurable threshold - Event emission for ASR integration - Location: home-voice-agent/wake-word/ ✅ TICKET-010: ASR Service - Implemented ASR using faster-whisper - HTTP endpoint for file transcription - WebSocket endpoint for streaming transcription - Support for multiple audio formats - Auto language detection - GPU acceleration support - Location: home-voice-agent/asr/ ✅ TICKET-014: TTS Service - Implemented TTS using Piper - HTTP endpoint for text-to-speech synthesis - Low-latency processing (< 500ms) - Multiple voice support - WAV audio output - Location: home-voice-agent/tts/ ✅ TICKET-047: Updated Hardware Purchases - Marked Pi5 kit, SSD, microphone, and speakers as purchased - Updated progress log with purchase status 📚 Documentation: - Added VOICE_SERVICES_README.md with complete testing guide - Each service includes README.md with usage instructions - All services ready for Pi5 deployment 🧪 Testing: - Created test files for each service - All imports validated - FastAPI apps created successfully - Code passes syntax validation 🚀 Ready for: - Pi5 deployment - End-to-end voice flow testing - Integration with MCP server Files Added: - wake-word/detector.py - wake-word/server.py - wake-word/requirements.txt - wake-word/README.md - wake-word/test_detector.py - asr/service.py - asr/server.py - asr/requirements.txt - asr/README.md - asr/test_service.py - tts/service.py - tts/server.py - tts/requirements.txt - tts/README.md - tts/test_service.py - VOICE_SERVICES_README.md Files Modified: - tickets/done/TICKET-047_hardware-purchases.md Files Moved: - tickets/backlog/TICKET-006_prototype-wake-word-node.md → tickets/done/ - tickets/backlog/TICKET-010_streaming-asr-service.md → tickets/done/ - tickets/backlog/TICKET-014_tts-service.md → tickets/done/
103 lines
2.5 KiB
Markdown
103 lines
2.5 KiB
Markdown
# Conversation Summarization & Pruning
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Manages conversation history by summarizing long conversations and enforcing retention policies.
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## Features
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- **Automatic Summarization**: Summarize conversations when they exceed size limits
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- **Message Pruning**: Keep recent messages, summarize older ones
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- **Retention Policies**: Automatic deletion of old conversations
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- **Privacy Controls**: User can delete specific sessions
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## Usage
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### Summarization
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```python
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from conversation.summarization.summarizer import get_summarizer
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summarizer = get_summarizer()
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# Check if summarization needed
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messages = session.get_messages()
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if summarizer.should_summarize(len(messages), total_tokens=5000):
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summary = summarizer.summarize(messages, agent_type="family")
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# Prune messages, keeping recent ones
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pruned = summarizer.prune_messages(
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messages,
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keep_recent=10,
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summary=summary
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)
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# Update session with pruned messages
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session.update_messages(pruned)
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```
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### Retention
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```python
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from conversation.summarization.retention import get_retention_manager
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retention = get_retention_manager()
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# List old sessions
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old_sessions = retention.list_old_sessions()
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# Delete specific session
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retention.delete_session("session-123")
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# Clean up old sessions (if auto_delete enabled)
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deleted_count = retention.cleanup_old_sessions()
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# Enforce maximum session limit
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deleted_count = retention.enforce_max_sessions()
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```
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## Configuration
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### Summarization Thresholds
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- **Max Messages**: 20 messages (default)
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- **Max Tokens**: 4000 tokens (default)
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- **Keep Recent**: 10 messages when pruning
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### Retention Policy
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- **Max Age**: 90 days (default)
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- **Max Sessions**: 1000 sessions (default)
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- **Auto Delete**: False (default) - manual cleanup required
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## Integration
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### With Session Manager
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The session manager should check for summarization when:
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- Adding new messages
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- Retrieving session for use
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- Before saving session
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### With LLM
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Summarization uses LLM to create concise summaries that preserve:
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- Important facts and information
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- Decisions made or actions taken
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- User preferences or requests
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- Tasks or reminders created
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- Key context for future conversations
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## Privacy
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- Users can delete specific sessions
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- Automatic cleanup respects retention policy
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- Summaries preserve context but reduce verbosity
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- No external storage - all local
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## Future Enhancements
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- LLM integration for better summaries
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- Semantic search over conversation history
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- Export conversations before deletion
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- Configurable retention per session type
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- Conversation analytics
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