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/
106 lines
2.6 KiB
Markdown
106 lines
2.6 KiB
Markdown
# Long-Term Memory System
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Stores persistent facts about the user, their preferences, routines, and important information.
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## Features
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- **Persistent Storage**: SQLite database for long-term storage
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- **Categories**: Personal, family, preferences, routines, facts
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- **Confidence Scoring**: Track certainty of each fact
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- **Source Tracking**: Know where facts came from (explicit, inferred, confirmed)
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- **Fast Retrieval**: Indexed lookups and search
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- **Prompt Integration**: Format memory for LLM prompts
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## Usage
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```python
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from memory.manager import get_memory_manager
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from memory.schema import MemoryCategory, MemorySource
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manager = get_memory_manager()
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# Store explicit fact
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manager.store_fact(
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category=MemoryCategory.PREFERENCES,
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key="favorite_color",
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value="blue",
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confidence=1.0,
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source=MemorySource.EXPLICIT
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)
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# Store inferred fact
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manager.store_fact(
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category=MemoryCategory.ROUTINES,
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key="morning_routine",
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value="coffee at 7am",
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confidence=0.8,
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source=MemorySource.INFERRED,
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context="Mentioned in conversation on 2024-01-06"
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)
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# Get fact
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fact = manager.get_fact(MemoryCategory.PREFERENCES, "favorite_color")
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if fact:
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print(f"Favorite color: {fact.value}")
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# Search facts
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facts = manager.search_facts("coffee", category=MemoryCategory.ROUTINES)
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# Format for LLM prompt
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memory_text = manager.format_for_prompt()
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# Use in system prompt: "## User Memory\n{memory_text}"
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```
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## Categories
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- **PERSONAL**: Personal facts (name, age, location)
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- **FAMILY**: Family member information
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- **PREFERENCES**: User preferences (favorite foods, colors)
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- **ROUTINES**: Daily/weekly routines
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- **FACTS**: General facts about the user
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## Memory Write Policy
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### Explicit Facts (confidence: 1.0)
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- User explicitly states: "My favorite color is blue"
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- Source: `MemorySource.EXPLICIT`
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### Inferred Facts (confidence: 0.7-0.9)
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- Inferred from conversation: "I always have coffee at 7am"
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- Source: `MemorySource.INFERRED`
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### Confirmed Facts (confidence: 0.9-1.0)
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- User confirms inferred fact
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- Source: `MemorySource.CONFIRMED`
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## Integration with LLM
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Memory is formatted and injected into system prompts:
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```python
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memory_text = manager.format_for_prompt(limit=20)
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system_prompt = f"""
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You are a helpful assistant.
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## User Memory
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{memory_text}
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Use this information to provide personalized responses.
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"""
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```
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## Storage
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- **Database**: `data/memory.db` (SQLite)
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- **Schema**: See `memory/schema.py`
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- **Indexes**: Category, key, last_accessed for fast queries
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## Future Enhancements
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- Semantic search using embeddings
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- Memory summarization
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- Confidence decay over time
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- Conflict resolution for conflicting facts
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- Memory validation and cleanup
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