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/
109 lines
2.5 KiB
Markdown
109 lines
2.5 KiB
Markdown
# LLM Logging & Metrics
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This module provides structured logging and metrics collection for LLM services.
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## Features
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- **Structured Logging**: JSON-formatted logs with all request details
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- **Metrics Collection**: Track requests, latency, tokens, errors
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- **Agent-specific Metrics**: Separate metrics for work and family agents
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- **Hourly Statistics**: Track trends over time
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- **Error Tracking**: Log and track errors
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## Usage
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### Logging
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```python
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from monitoring.logger import get_llm_logger
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import time
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logger = get_llm_logger()
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start_time = time.time()
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# ... make LLM request ...
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end_time = time.time()
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logger.log_request(
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session_id="session-123",
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agent_type="family",
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user_id="user-1",
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request_id="req-456",
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prompt="What time is it?",
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messages=[...],
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tools_available=18,
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start_time=start_time,
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end_time=end_time,
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response={...},
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tools_called=["get_current_time"],
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model="phi3:mini-q4_0"
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)
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```
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### Metrics
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```python
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from monitoring.metrics import get_metrics_collector
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collector = get_metrics_collector()
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# Record a request
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collector.record_request(
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agent_type="family",
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success=True,
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latency_ms=450.5,
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tokens_in=50,
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tokens_out=25,
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tools_called=1
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)
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# Get metrics
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metrics = collector.get_metrics("family")
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print(f"Total requests: {metrics['total_requests']}")
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print(f"Average latency: {metrics['average_latency_ms']}ms")
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```
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## Log Format
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Logs are stored in JSON format with the following fields:
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- `timestamp`: ISO format timestamp
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- `session_id`: Conversation session ID
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- `agent_type`: "work" or "family"
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- `user_id`: User identifier
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- `request_id`: Unique request ID
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- `prompt`: User prompt (truncated to 500 chars)
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- `messages_count`: Number of messages in context
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- `tools_available`: Number of tools available
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- `tools_called`: List of tools called
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- `latency_ms`: Request latency in milliseconds
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- `tokens_in`: Input tokens
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- `tokens_out`: Output tokens
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- `response_length`: Length of response text
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- `error`: Error message if any
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- `model`: Model name used
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## Metrics
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Metrics are tracked per agent:
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- Total requests
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- Successful/failed requests
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- Average latency
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- Total tokens (in/out)
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- Tools called count
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- Last request time
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## Storage
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- **Logs**: `data/logs/llm_YYYYMMDD.log` (JSON format)
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- **Metrics**: `data/metrics/metrics_YYYYMMDD.json` (JSON format)
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## Future Enhancements
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- GPU usage monitoring (when available)
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- Real-time dashboard
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- Alerting for errors or high latency
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- Cost estimation based on tokens
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- Request rate limiting based on metrics
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