Robert Helewka
ecf37658ce
Complete project scaffolding and core implementation of an AI-powered telephony system that calls companies, navigates IVR menus, waits on hold, and transfers to the user when a human answers. Key components: - FastAPI server with REST API, WebSocket, and MCP (SSE) interfaces - SIP/VoIP call management via PJSUA2 with RTP audio streaming - LLM-powered IVR navigation using OpenAI/Anthropic with tool calling - Hold detection service combining audio analysis and silence detection - Real-time STT (Whisper/Deepgram) and TTS (OpenAI/Piper) pipelines - Call recording with per-channel and mixed audio capture - Event bus (asyncio pub/sub) for real-time client updates - Web dashboard with live call monitoring - SQLite persistence via SQLAlchemy with call history and analytics - Notification support (email, SMS, webhook, desktop) - Docker Compose deployment with Opal VoIP and Opal Media containers - Comprehensive test suite with unit, integration, and E2E tests - Simplified .gitignore and full project documentation in README
325 lines
12 KiB
Python
325 lines
12 KiB
Python
"""
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Call Analytics Service — Tracks call metrics and generates insights.
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Monitors call patterns, hold times, success rates, and IVR navigation
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efficiency. Provides data for the dashboard and API.
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"""
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import logging
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from collections import defaultdict
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from datetime import datetime, timedelta
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from typing import Any, Optional
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from models.call import ActiveCall, AudioClassification, CallMode, CallStatus
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logger = logging.getLogger(__name__)
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class CallAnalytics:
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"""
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In-memory call analytics engine.
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Tracks:
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- Call success/failure rates
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- Hold time statistics (avg, min, max, p95)
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- IVR navigation efficiency
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- Human detection accuracy
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- Per-number/company patterns
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- Time-of-day patterns
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In production, this would be backed by TimescaleDB or similar.
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For now, we keep rolling windows in memory.
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"""
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def __init__(self, max_history: int = 10000):
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self._max_history = max_history
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self._call_records: list[CallRecord] = []
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self._company_stats: dict[str, CompanyStats] = defaultdict(CompanyStats)
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# ================================================================
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# Record Calls
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# ================================================================
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def record_call(self, call: ActiveCall) -> None:
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"""
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Record a completed call for analytics.
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Called when a call ends (from CallManager).
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"""
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record = CallRecord(
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call_id=call.id,
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remote_number=call.remote_number,
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mode=call.mode,
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status=call.status,
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intent=call.intent,
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started_at=call.created_at,
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duration_seconds=call.duration,
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hold_time_seconds=call.hold_time,
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classification_history=[
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r.audio_type.value for r in call.classification_history
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],
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transcript_chunks=list(call.transcript_chunks),
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services=list(call.services),
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)
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self._call_records.append(record)
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# Trim history
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if len(self._call_records) > self._max_history:
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self._call_records = self._call_records[-self._max_history :]
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# Update company stats
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company_key = self._normalize_number(call.remote_number)
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self._company_stats[company_key].update(record)
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logger.debug(
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f"📊 Recorded call {call.id}: "
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f"{call.status.value}, {call.duration}s, hold={call.hold_time}s"
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)
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# ================================================================
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# Aggregate Stats
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# ================================================================
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def get_summary(self, hours: int = 24) -> dict[str, Any]:
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"""Get summary statistics for the last N hours."""
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cutoff = datetime.now() - timedelta(hours=hours)
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recent = [r for r in self._call_records if r.started_at >= cutoff]
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if not recent:
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return {
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"period_hours": hours,
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"total_calls": 0,
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"success_rate": 0.0,
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"avg_hold_time": 0.0,
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"avg_duration": 0.0,
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}
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total = len(recent)
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successful = sum(1 for r in recent if r.status in (
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CallStatus.COMPLETED, CallStatus.BRIDGED, CallStatus.HUMAN_DETECTED
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))
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failed = sum(1 for r in recent if r.status == CallStatus.FAILED)
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hold_times = [r.hold_time_seconds for r in recent if r.hold_time_seconds > 0]
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durations = [r.duration_seconds for r in recent if r.duration_seconds > 0]
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hold_slayer_calls = [r for r in recent if r.mode == CallMode.HOLD_SLAYER]
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hold_slayer_success = sum(
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1 for r in hold_slayer_calls
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if r.status in (CallStatus.BRIDGED, CallStatus.HUMAN_DETECTED)
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)
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return {
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"period_hours": hours,
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"total_calls": total,
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"successful": successful,
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"failed": failed,
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"success_rate": round(successful / total, 3) if total else 0.0,
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"avg_duration": round(sum(durations) / len(durations), 1) if durations else 0.0,
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"max_duration": max(durations) if durations else 0,
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"hold_time": {
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"avg": round(sum(hold_times) / len(hold_times), 1) if hold_times else 0.0,
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"min": min(hold_times) if hold_times else 0,
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"max": max(hold_times) if hold_times else 0,
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"p95": self._percentile(hold_times, 95) if hold_times else 0,
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"total": sum(hold_times),
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},
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"hold_slayer": {
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"total": len(hold_slayer_calls),
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"success": hold_slayer_success,
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"success_rate": round(
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hold_slayer_success / len(hold_slayer_calls), 3
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) if hold_slayer_calls else 0.0,
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},
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"by_mode": self._group_by_mode(recent),
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"by_hour": self._group_by_hour(recent),
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}
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def get_company_stats(self, number: str) -> dict[str, Any]:
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"""Get stats for a specific company/number."""
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key = self._normalize_number(number)
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stats = self._company_stats.get(key)
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if not stats:
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return {"number": number, "total_calls": 0}
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return stats.to_dict(number)
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def get_top_numbers(self, limit: int = 10) -> list[dict[str, Any]]:
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"""Get the most-called numbers with their stats."""
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sorted_stats = sorted(
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self._company_stats.items(),
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key=lambda x: x[1].total_calls,
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reverse=True,
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)[:limit]
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return [stats.to_dict(number) for number, stats in sorted_stats]
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# ================================================================
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# Hold Time Trends
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# ================================================================
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def get_hold_time_trend(
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self,
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number: Optional[str] = None,
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days: int = 7,
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) -> list[dict]:
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"""
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Get hold time trend data for graphing.
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Returns daily average hold times for the last N days.
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"""
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cutoff = datetime.now() - timedelta(days=days)
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records = [r for r in self._call_records if r.started_at >= cutoff]
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if number:
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key = self._normalize_number(number)
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records = [r for r in records if self._normalize_number(r.remote_number) == key]
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# Group by day
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by_day: dict[str, list[int]] = defaultdict(list)
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for r in records:
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day = r.started_at.strftime("%Y-%m-%d")
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if r.hold_time_seconds > 0:
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by_day[day].append(r.hold_time_seconds)
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trend = []
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for i in range(days):
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date = (datetime.now() - timedelta(days=days - 1 - i)).strftime("%Y-%m-%d")
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times = by_day.get(date, [])
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trend.append({
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"date": date,
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"avg_hold_time": round(sum(times) / len(times), 1) if times else 0,
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"call_count": len(times),
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"max_hold_time": max(times) if times else 0,
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})
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return trend
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# ================================================================
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# Helpers
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# ================================================================
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@staticmethod
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def _normalize_number(number: str) -> str:
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"""Normalize phone number for grouping."""
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# Strip formatting, keep last 10 digits
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digits = "".join(c for c in number if c.isdigit())
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return digits[-10:] if len(digits) >= 10 else digits
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@staticmethod
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def _percentile(values: list, pct: int) -> float:
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"""Calculate percentile value."""
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if not values:
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return 0.0
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sorted_vals = sorted(values)
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idx = int(len(sorted_vals) * pct / 100)
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idx = min(idx, len(sorted_vals) - 1)
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return float(sorted_vals[idx])
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@staticmethod
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def _group_by_mode(records: list["CallRecord"]) -> dict[str, int]:
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"""Group call counts by mode."""
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by_mode: dict[str, int] = defaultdict(int)
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for r in records:
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by_mode[r.mode.value] += 1
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return dict(by_mode)
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@staticmethod
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def _group_by_hour(records: list["CallRecord"]) -> dict[int, int]:
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"""Group call counts by hour of day."""
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by_hour: dict[int, int] = defaultdict(int)
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for r in records:
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by_hour[r.started_at.hour] += 1
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return dict(sorted(by_hour.items()))
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@property
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def total_calls_recorded(self) -> int:
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return len(self._call_records)
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# ================================================================
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# Data Models
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# ================================================================
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class CallRecord:
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"""A completed call record for analytics."""
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def __init__(
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self,
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call_id: str,
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remote_number: str,
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mode: CallMode,
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status: CallStatus,
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intent: Optional[str] = None,
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started_at: Optional[datetime] = None,
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duration_seconds: int = 0,
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hold_time_seconds: int = 0,
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classification_history: Optional[list[str]] = None,
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transcript_chunks: Optional[list[str]] = None,
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services: Optional[list[str]] = None,
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):
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self.call_id = call_id
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self.remote_number = remote_number
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self.mode = mode
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self.status = status
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self.intent = intent
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self.started_at = started_at or datetime.now()
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self.duration_seconds = duration_seconds
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self.hold_time_seconds = hold_time_seconds
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self.classification_history = classification_history or []
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self.transcript_chunks = transcript_chunks or []
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self.services = services or []
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class CompanyStats:
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"""Aggregated stats for a specific company/phone number."""
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def __init__(self):
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self.total_calls = 0
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self.successful_calls = 0
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self.failed_calls = 0
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self.total_hold_time = 0
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self.hold_times: list[int] = []
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self.total_duration = 0
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self.last_called: Optional[datetime] = None
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self.intents: dict[str, int] = defaultdict(int)
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def update(self, record: CallRecord) -> None:
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"""Update stats with a new call record."""
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self.total_calls += 1
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self.total_duration += record.duration_seconds
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self.last_called = record.started_at
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if record.status in (CallStatus.COMPLETED, CallStatus.BRIDGED, CallStatus.HUMAN_DETECTED):
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self.successful_calls += 1
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elif record.status == CallStatus.FAILED:
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self.failed_calls += 1
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if record.hold_time_seconds > 0:
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self.total_hold_time += record.hold_time_seconds
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self.hold_times.append(record.hold_time_seconds)
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if record.intent:
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self.intents[record.intent] += 1
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def to_dict(self, number: str) -> dict[str, Any]:
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return {
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"number": number,
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"total_calls": self.total_calls,
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"successful_calls": self.successful_calls,
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"failed_calls": self.failed_calls,
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"success_rate": round(
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self.successful_calls / self.total_calls, 3
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) if self.total_calls else 0.0,
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"avg_hold_time": round(
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self.total_hold_time / len(self.hold_times), 1
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) if self.hold_times else 0.0,
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"max_hold_time": max(self.hold_times) if self.hold_times else 0,
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"avg_duration": round(
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self.total_duration / self.total_calls, 1
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) if self.total_calls else 0.0,
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"last_called": self.last_called.isoformat() if self.last_called else None,
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"top_intents": dict(
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sorted(self.intents.items(), key=lambda x: x[1], reverse=True)[:5]
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),
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}
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