[split-required] [guardrail-change] Enforce 500 LOC budget across all services
Install LOC guardrails (check-loc.sh, architecture.md, pre-commit hook) and split all 44 files exceeding 500 LOC into domain-focused modules: - consent-service (Go): models, handlers, services, database splits - backend-core (Python): security_api, rbac_api, pdf_service, auth splits - admin-core (TypeScript): 5 page.tsx + sidebar extractions - pitch-deck (TypeScript): 6 slides, 3 UI components, engine.ts splits - voice-service (Python): enhanced_task_orchestrator split Result: 0 violations, 36 exempted (pipeline, tests, pure-data files). Go build verified clean. No behavior changes — pure structural splits. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Benjamin Admin
285
voice-service/services/enhanced_task_helpers.py
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285
voice-service/services/enhanced_task_helpers.py
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"""
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Enhanced Task Orchestrator - Helper Mixin
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Provides routing, quality checks, memory storage, message handling,
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and session recovery logic for EnhancedTaskOrchestrator.
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Separated from enhanced_task_orchestrator.py for the 500 LOC budget.
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"""
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import structlog
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import asyncio
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from typing import Optional, Dict, Any
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from datetime import datetime
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from models.task import Task, TaskState
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from sessions.session_manager import AgentSession, SessionState
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from sessions.heartbeat import HeartbeatClient
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from orchestrator.message_bus import AgentMessage, MessagePriority
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from orchestrator.task_router import RoutingStrategy
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logger = structlog.get_logger(__name__)
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class EnhancedTaskHelpersMixin:
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"""
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Mixin providing internal helper methods for EnhancedTaskOrchestrator.
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This class should not be instantiated directly. It is mixed into
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EnhancedTaskOrchestrator to provide:
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- Agent routing (_route_to_agent, _needs_specialized_agent)
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- Quality checks (_run_quality_check, _needs_quality_check)
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- Memory storage (_store_task_result)
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- Message handling (_handle_agent_message)
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- Session recovery (recover_session, _recover_pending_tasks)
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- System prompt (_get_system_prompt)
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All attributes (message_bus, task_router, memory_store, heartbeat,
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session_manager, _voice_sessions, _heartbeat_clients, _tasks) are
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expected to be set by the main class __init__.
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"""
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def _needs_specialized_agent(self, task: Task) -> bool:
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"""Check if task needs routing to a specialized agent"""
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from models.task import TaskType
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# Tasks that benefit from specialized agents
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specialized_types = [
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TaskType.PARENT_LETTER, # Could use grader for tone
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TaskType.FEEDBACK_SUGGEST, # Quality judge for appropriateness
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]
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return task.type in specialized_types
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def _needs_quality_check(self, task: Task) -> bool:
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"""Check if task result needs quality validation"""
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from models.task import TaskType
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# Tasks that generate content should be checked
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content_types = [
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TaskType.PARENT_LETTER,
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TaskType.CLASS_MESSAGE,
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TaskType.FEEDBACK_SUGGEST,
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TaskType.WORKSHEET_GENERATE,
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]
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return task.type in content_types
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async def _route_to_agent(
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self,
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task: Task,
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session: Optional[AgentSession]
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) -> None:
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"""Routes a task to a specialized agent"""
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# Determine target agent
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intent = f"task_{task.type.value}"
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routing_result = await self.task_router.route(
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intent=intent,
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context={"task": task.parameters},
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strategy=RoutingStrategy.LEAST_LOADED
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)
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if not routing_result.success:
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# Fall back to local processing
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logger.warning(
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"No agent available for task, using local processing",
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task_id=task.id[:8],
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reason=routing_result.reason
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)
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await super().process_task(task)
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return
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# Send to agent via message bus
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try:
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response = await self.message_bus.request(
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AgentMessage(
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sender="voice-orchestrator",
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receiver=routing_result.agent_id,
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message_type=f"process_{task.type.value}",
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payload={
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"task_id": task.id,
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"task_type": task.type.value,
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"parameters": task.parameters,
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"session_id": session.session_id if session else None
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},
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priority=MessagePriority.NORMAL
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),
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timeout=30.0
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)
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task.result_ref = response.get("result", "")
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task.transition_to(TaskState.READY, "agent_processed")
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except asyncio.TimeoutError:
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logger.error(
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"Agent timeout, falling back to local",
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task_id=task.id[:8],
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agent=routing_result.agent_id
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)
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await super().process_task(task)
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async def _run_quality_check(
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self,
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task: Task,
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session: Optional[AgentSession]
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) -> None:
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"""Runs quality check on task result via quality judge"""
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try:
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response = await self.message_bus.request(
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AgentMessage(
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sender="voice-orchestrator",
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receiver="quality-judge",
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message_type="evaluate_response",
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payload={
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"task_id": task.id,
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"task_type": task.type.value,
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"response": task.result_ref,
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"context": task.parameters
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},
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priority=MessagePriority.NORMAL
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),
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timeout=10.0
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)
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quality_score = response.get("composite_score", 0)
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if quality_score < 60:
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# Mark for review
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task.error_message = f"Quality check failed: {quality_score}"
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logger.warning(
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"Task failed quality check",
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task_id=task.id[:8],
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score=quality_score
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)
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except asyncio.TimeoutError:
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# Quality check timeout is non-fatal
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logger.warning(
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"Quality check timeout",
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task_id=task.id[:8]
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)
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async def _store_task_result(self, task: Task) -> None:
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"""Stores task result in memory for learning"""
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await self.memory_store.remember(
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key=f"task:{task.type.value}:{task.id}",
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value={
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"result": task.result_ref,
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"parameters": task.parameters,
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"completed_at": datetime.utcnow().isoformat()
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},
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agent_id="voice-orchestrator",
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ttl_days=30
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)
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async def _handle_agent_message(
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self,
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message: AgentMessage
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) -> Optional[Dict[str, Any]]:
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"""Handles incoming messages from other agents"""
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logger.debug(
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"Received agent message",
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sender=message.sender,
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type=message.message_type
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)
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if message.message_type == "task_status_update":
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# Handle task status updates
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task_id = message.payload.get("task_id")
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if task_id in self._tasks:
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task = self._tasks[task_id]
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new_state = message.payload.get("state")
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if new_state:
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task.transition_to(TaskState(new_state), "agent_update")
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return None
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def _get_system_prompt(self) -> str:
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"""Returns the system prompt for the voice assistant"""
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return """Du bist ein hilfreicher Assistent für Lehrer in der Breakpilot-App.
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Deine Aufgaben:
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- Hilf beim Erstellen von Arbeitsblättern
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- Unterstütze bei der Korrektur
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- Erstelle Elternbriefe und Klassennachrichten
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- Dokumentiere Beobachtungen und Erinnerungen
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Halte dich kurz und präzise. Nutze einfache, klare Sprache.
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Bei Unklarheiten frage nach."""
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# Recovery methods
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async def recover_session(
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self,
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voice_session_id: str,
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session_id: str
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) -> Optional[AgentSession]:
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"""
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Recovers a session from checkpoint.
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Args:
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voice_session_id: The voice session ID
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session_id: The agent session ID to recover
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Returns:
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The recovered session or None
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"""
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session = await self.session_manager.get_session(session_id)
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if not session:
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logger.warning(
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"Session not found for recovery",
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session_id=session_id
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)
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return None
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if session.state != SessionState.ACTIVE:
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logger.warning(
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"Session not active for recovery",
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session_id=session_id,
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state=session.state.value
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)
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return None
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# Resume session
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session.resume()
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# Restore heartbeat
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heartbeat_client = HeartbeatClient(
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session_id=session.session_id,
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monitor=self.heartbeat,
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interval_seconds=10
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)
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await heartbeat_client.start()
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self.heartbeat.register(session.session_id, "voice-orchestrator")
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# Store references
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self._voice_sessions[voice_session_id] = session
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self._heartbeat_clients[session.session_id] = heartbeat_client
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# Recover pending tasks from checkpoints
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await self._recover_pending_tasks(session)
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logger.info(
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"Recovered session",
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session_id=session.session_id[:8],
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checkpoints=len(session.checkpoints)
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)
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return session
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async def _recover_pending_tasks(self, session: AgentSession) -> None:
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"""Recovers pending tasks from session checkpoints"""
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for checkpoint in reversed(session.checkpoints):
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if checkpoint.name == "task_queued":
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task_id = checkpoint.data.get("task_id")
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if task_id and task_id in self._tasks:
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task = self._tasks[task_id]
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if task.state == TaskState.QUEUED:
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# Re-process queued task
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await self.process_task(task)
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logger.info(
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"Recovered pending task",
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task_id=task_id[:8]
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)
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