atlas/home-voice-agent/monitoring

LLM Logging & Metrics

This module provides structured logging and metrics collection for LLM services.

Features

• Structured Logging: JSON-formatted logs with all request details
• Metrics Collection: Track requests, latency, tokens, errors
• Agent-specific Metrics: Separate metrics for work and family agents
• Hourly Statistics: Track trends over time
• Error Tracking: Log and track errors

Usage

Logging

from monitoring.logger import get_llm_logger
import time

logger = get_llm_logger()

start_time = time.time()
# ... make LLM request ...
end_time = time.time()

logger.log_request(
    session_id="session-123",
    agent_type="family",
    user_id="user-1",
    request_id="req-456",
    prompt="What time is it?",
    messages=[...],
    tools_available=18,
    start_time=start_time,
    end_time=end_time,
    response={...},
    tools_called=["get_current_time"],
    model="phi3:mini-q4_0"
)

Metrics

from monitoring.metrics import get_metrics_collector

collector = get_metrics_collector()

# Record a request
collector.record_request(
    agent_type="family",
    success=True,
    latency_ms=450.5,
    tokens_in=50,
    tokens_out=25,
    tools_called=1
)

# Get metrics
metrics = collector.get_metrics("family")
print(f"Total requests: {metrics['total_requests']}")
print(f"Average latency: {metrics['average_latency_ms']}ms")

Log Format

Logs are stored in JSON format with the following fields:

• timestamp: ISO format timestamp
• session_id: Conversation session ID
• agent_type: "work" or "family"
• user_id: User identifier
• request_id: Unique request ID
• prompt: User prompt (truncated to 500 chars)
• messages_count: Number of messages in context
• tools_available: Number of tools available
• tools_called: List of tools called
• latency_ms: Request latency in milliseconds
• tokens_in: Input tokens
• tokens_out: Output tokens
• response_length: Length of response text
• error: Error message if any
• model: Model name used

Metrics

Metrics are tracked per agent:

• Total requests
• Successful/failed requests
• Average latency
• Total tokens (in/out)
• Tools called count
• Last request time

Storage

• Logs: data/logs/llm_YYYYMMDD.log (JSON format)
• Metrics: data/metrics/metrics_YYYYMMDD.json (JSON format)

Future Enhancements

• GPU usage monitoring (when available)
• Real-time dashboard
• Alerting for errors or high latency
• Cost estimation based on tokens
• Request rate limiting based on metrics