breakpilot-lehrer/voice-service/services/enhanced_task_orchestrator.py

"""
Enhanced Task Orchestrator - Multi-Agent Integration

Extends the existing TaskOrchestrator with Multi-Agent support:
- Session management with checkpoints
- Message bus integration for inter-agent communication
- Quality judge integration via BQAS
- Heartbeat-based liveness
"""

import structlog
import asyncio
from typing import Optional, Dict, Any
from datetime import datetime

from services.task_orchestrator import TaskOrchestrator, Intent
from models.task import Task, TaskState

# Import agent-core components
import sys
sys.path.insert(0, '/Users/benjaminadmin/Projekte/breakpilot-pwa/agent-core')

from sessions.session_manager import SessionManager, AgentSession, SessionState
from sessions.heartbeat import HeartbeatMonitor, HeartbeatClient
from brain.memory_store import MemoryStore
from brain.context_manager import ContextManager, MessageRole
from orchestrator.message_bus import MessageBus, AgentMessage, MessagePriority
from orchestrator.task_router import TaskRouter, RoutingStrategy

logger = structlog.get_logger(__name__)


class EnhancedTaskOrchestrator(TaskOrchestrator):
    """
    Enhanced TaskOrchestrator with Multi-Agent support.

    Extends the existing TaskOrchestrator to integrate with:
    - Session management for persistence and recovery
    - Message bus for inter-agent communication
    - Quality judge for response validation
    - Memory store for long-term learning
    """

    def __init__(
        self,
        redis_client=None,
        db_pool=None,
        namespace: str = "breakpilot"
    ):
        """
        Initialize the enhanced orchestrator.

        Args:
            redis_client: Async Redis/Valkey client
            db_pool: Async PostgreSQL connection pool
            namespace: Namespace for isolation
        """
        super().__init__()

        # Initialize agent-core components
        self.session_manager = SessionManager(
            redis_client=redis_client,
            db_pool=db_pool,
            namespace=namespace
        )

        self.memory_store = MemoryStore(
            redis_client=redis_client,
            db_pool=db_pool,
            namespace=namespace
        )

        self.context_manager = ContextManager(
            redis_client=redis_client,
            db_pool=db_pool,
            namespace=namespace
        )

        self.message_bus = MessageBus(
            redis_client=redis_client,
            db_pool=db_pool,
            namespace=namespace
        )

        self.heartbeat = HeartbeatMonitor(
            timeout_seconds=30,
            check_interval_seconds=5,
            max_missed_beats=3
        )

        self.task_router = TaskRouter()

        # Track active sessions by voice session ID
        self._voice_sessions: Dict[str, AgentSession] = {}
        self._heartbeat_clients: Dict[str, HeartbeatClient] = {}

        logger.info("Enhanced TaskOrchestrator initialized with agent-core")

    async def start(self) -> None:
        """Starts the enhanced orchestrator"""
        await self.message_bus.start()
        await self.heartbeat.start_monitoring()

        # Subscribe to messages directed at this orchestrator
        await self.message_bus.subscribe(
            "voice-orchestrator",
            self._handle_agent_message
        )

        logger.info("Enhanced TaskOrchestrator started")

    async def stop(self) -> None:
        """Stops the enhanced orchestrator"""
        # Stop all heartbeat clients
        for client in self._heartbeat_clients.values():
            await client.stop()
        self._heartbeat_clients.clear()

        await self.heartbeat.stop_monitoring()
        await self.message_bus.stop()

        logger.info("Enhanced TaskOrchestrator stopped")

    async def create_session(
        self,
        voice_session_id: str,
        user_id: str = "",
        metadata: Optional[Dict[str, Any]] = None
    ) -> AgentSession:
        """
        Creates a new agent session for a voice session.

        Args:
            voice_session_id: The voice session ID
            user_id: Optional user ID
            metadata: Additional metadata

        Returns:
            The created AgentSession
        """
        # Create session via session manager
        session = await self.session_manager.create_session(
            agent_type="voice-orchestrator",
            user_id=user_id,
            context={"voice_session_id": voice_session_id},
            metadata=metadata
        )

        # Create conversation context
        self.context_manager.create_context(
            session_id=session.session_id,
            system_prompt=self._get_system_prompt(),
            max_messages=50
        )

        # Start heartbeat for this session
        heartbeat_client = HeartbeatClient(
            session_id=session.session_id,
            monitor=self.heartbeat,
            interval_seconds=10
        )
        await heartbeat_client.start()

        # Register heartbeat for monitoring
        self.heartbeat.register(session.session_id, "voice-orchestrator")

        # Store references
        self._voice_sessions[voice_session_id] = session
        self._heartbeat_clients[session.session_id] = heartbeat_client

        logger.info(
            "Created agent session",
            session_id=session.session_id[:8],
            voice_session_id=voice_session_id
        )

        return session

    async def get_session(
        self,
        voice_session_id: str
    ) -> Optional[AgentSession]:
        """Gets the agent session for a voice session"""
        return self._voice_sessions.get(voice_session_id)

    async def end_session(self, voice_session_id: str) -> None:
        """
        Ends an agent session.

        Args:
            voice_session_id: The voice session ID
        """
        session = self._voice_sessions.get(voice_session_id)
        if not session:
            return

        # Stop heartbeat
        if session.session_id in self._heartbeat_clients:
            await self._heartbeat_clients[session.session_id].stop()
            del self._heartbeat_clients[session.session_id]

        # Unregister from heartbeat monitor
        self.heartbeat.unregister(session.session_id)

        # Mark session as completed
        session.complete()
        await self.session_manager.update_session(session)

        # Clean up
        del self._voice_sessions[voice_session_id]

        logger.info(
            "Ended agent session",
            session_id=session.session_id[:8],
            duration_seconds=session.get_duration().total_seconds()
        )

    async def queue_task(self, task: Task) -> None:
        """
        Queue a task with session checkpointing.

        Extends parent to add checkpoint for recovery.
        """
        # Get session for this task
        session = self._voice_sessions.get(task.session_id)

        if session:
            # Checkpoint before queueing
            session.checkpoint("task_queued", {
                "task_id": task.id,
                "task_type": task.type.value,
                "parameters": task.parameters
            })
            await self.session_manager.update_session(session)

        # Call parent implementation
        await super().queue_task(task)

    async def process_task(self, task: Task) -> None:
        """
        Process a task with enhanced routing and quality checks.

        Extends parent to:
        - Route complex tasks to specialized agents
        - Run quality checks via BQAS
        - Store results in memory for learning
        """
        session = self._voice_sessions.get(task.session_id)

        if session:
            session.checkpoint("task_processing", {
                "task_id": task.id
            })

        # Check if this task should be routed to a specialized agent
        if self._needs_specialized_agent(task):
            await self._route_to_agent(task, session)
        else:
            # Use parent implementation for simple tasks
            await super().process_task(task)

        # Run quality check on result
        if task.result_ref and self._needs_quality_check(task):
            await self._run_quality_check(task, session)

        # Store in memory for learning
        if task.state == TaskState.READY and task.result_ref:
            await self._store_task_result(task)

        if session:
            session.checkpoint("task_completed", {
                "task_id": task.id,
                "state": task.state.value
            })
            await self.session_manager.update_session(session)

    def _needs_specialized_agent(self, task: Task) -> bool:
        """Check if task needs routing to a specialized agent"""
        from models.task import TaskType

        # Tasks that benefit from specialized agents
        specialized_types = [
            TaskType.PARENT_LETTER,      # Could use grader for tone
            TaskType.FEEDBACK_SUGGEST,   # Quality judge for appropriateness
        ]

        return task.type in specialized_types

    def _needs_quality_check(self, task: Task) -> bool:
        """Check if task result needs quality validation"""
        from models.task import TaskType

        # Tasks that generate content should be checked
        content_types = [
            TaskType.PARENT_LETTER,
            TaskType.CLASS_MESSAGE,
            TaskType.FEEDBACK_SUGGEST,
            TaskType.WORKSHEET_GENERATE,
        ]

        return task.type in content_types

    async def _route_to_agent(
        self,
        task: Task,
        session: Optional[AgentSession]
    ) -> None:
        """Routes a task to a specialized agent"""
        # Determine target agent
        intent = f"task_{task.type.value}"
        routing_result = await self.task_router.route(
            intent=intent,
            context={"task": task.parameters},
            strategy=RoutingStrategy.LEAST_LOADED
        )

        if not routing_result.success:
            # Fall back to local processing
            logger.warning(
                "No agent available for task, using local processing",
                task_id=task.id[:8],
                reason=routing_result.reason
            )
            await super().process_task(task)
            return

        # Send to agent via message bus
        try:
            response = await self.message_bus.request(
                AgentMessage(
                    sender="voice-orchestrator",
                    receiver=routing_result.agent_id,
                    message_type=f"process_{task.type.value}",
                    payload={
                        "task_id": task.id,
                        "task_type": task.type.value,
                        "parameters": task.parameters,
                        "session_id": session.session_id if session else None
                    },
                    priority=MessagePriority.NORMAL
                ),
                timeout=30.0
            )

            task.result_ref = response.get("result", "")
            task.transition_to(TaskState.READY, "agent_processed")

        except asyncio.TimeoutError:
            logger.error(
                "Agent timeout, falling back to local",
                task_id=task.id[:8],
                agent=routing_result.agent_id
            )
            await super().process_task(task)

    async def _run_quality_check(
        self,
        task: Task,
        session: Optional[AgentSession]
    ) -> None:
        """Runs quality check on task result via quality judge"""
        try:
            response = await self.message_bus.request(
                AgentMessage(
                    sender="voice-orchestrator",
                    receiver="quality-judge",
                    message_type="evaluate_response",
                    payload={
                        "task_id": task.id,
                        "task_type": task.type.value,
                        "response": task.result_ref,
                        "context": task.parameters
                    },
                    priority=MessagePriority.NORMAL
                ),
                timeout=10.0
            )

            quality_score = response.get("composite_score", 0)

            if quality_score < 60:
                # Mark for review
                task.error_message = f"Quality check failed: {quality_score}"
                logger.warning(
                    "Task failed quality check",
                    task_id=task.id[:8],
                    score=quality_score
                )

        except asyncio.TimeoutError:
            # Quality check timeout is non-fatal
            logger.warning(
                "Quality check timeout",
                task_id=task.id[:8]
            )

    async def _store_task_result(self, task: Task) -> None:
        """Stores task result in memory for learning"""
        await self.memory_store.remember(
            key=f"task:{task.type.value}:{task.id}",
            value={
                "result": task.result_ref,
                "parameters": task.parameters,
                "completed_at": datetime.utcnow().isoformat()
            },
            agent_id="voice-orchestrator",
            ttl_days=30
        )

    async def _handle_agent_message(
        self,
        message: AgentMessage
    ) -> Optional[Dict[str, Any]]:
        """Handles incoming messages from other agents"""
        logger.debug(
            "Received agent message",
            sender=message.sender,
            type=message.message_type
        )

        if message.message_type == "task_status_update":
            # Handle task status updates
            task_id = message.payload.get("task_id")
            if task_id in self._tasks:
                task = self._tasks[task_id]
                new_state = message.payload.get("state")
                if new_state:
                    task.transition_to(TaskState(new_state), "agent_update")

        return None

    def _get_system_prompt(self) -> str:
        """Returns the system prompt for the voice assistant"""
        return """Du bist ein hilfreicher Assistent für Lehrer in der Breakpilot-App.

Deine Aufgaben:
- Hilf beim Erstellen von Arbeitsblättern
- Unterstütze bei der Korrektur
- Erstelle Elternbriefe und Klassennachrichten
- Dokumentiere Beobachtungen und Erinnerungen

Halte dich kurz und präzise. Nutze einfache, klare Sprache.
Bei Unklarheiten frage nach."""

    # Recovery methods

    async def recover_session(
        self,
        voice_session_id: str,
        session_id: str
    ) -> Optional[AgentSession]:
        """
        Recovers a session from checkpoint.

        Args:
            voice_session_id: The voice session ID
            session_id: The agent session ID to recover

        Returns:
            The recovered session or None
        """
        session = await self.session_manager.get_session(session_id)

        if not session:
            logger.warning(
                "Session not found for recovery",
                session_id=session_id
            )
            return None

        if session.state != SessionState.ACTIVE:
            logger.warning(
                "Session not active for recovery",
                session_id=session_id,
                state=session.state.value
            )
            return None

        # Resume session
        session.resume()

        # Restore heartbeat
        heartbeat_client = HeartbeatClient(
            session_id=session.session_id,
            monitor=self.heartbeat,
            interval_seconds=10
        )
        await heartbeat_client.start()
        self.heartbeat.register(session.session_id, "voice-orchestrator")

        # Store references
        self._voice_sessions[voice_session_id] = session
        self._heartbeat_clients[session.session_id] = heartbeat_client

        # Recover pending tasks from checkpoints
        await self._recover_pending_tasks(session)

        logger.info(
            "Recovered session",
            session_id=session.session_id[:8],
            checkpoints=len(session.checkpoints)
        )

        return session

    async def _recover_pending_tasks(self, session: AgentSession) -> None:
        """Recovers pending tasks from session checkpoints"""
        for checkpoint in reversed(session.checkpoints):
            if checkpoint.name == "task_queued":
                task_id = checkpoint.data.get("task_id")
                if task_id and task_id in self._tasks:
                    task = self._tasks[task_id]
                    if task.state == TaskState.QUEUED:
                        # Re-process queued task
                        await self.process_task(task)
                        logger.info(
                            "Recovered pending task",
                            task_id=task_id[:8]
                        )