agent-task-executor/core/task_executor.py

import time
import uuid
from typing import List, Dict, Any
from dataclasses import dataclass
from enum import Enum
import json
import logging
import asyncio
from .llm_executor import LLMExecutor

class TaskStatus(Enum):
    PENDING = "pending"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    FAILED = "failed"

@dataclass
class StepState:
    step_id: str
    status: TaskStatus
    required_info: List[str]
    available_info: List[str]
    missing_info: List[str]
    resources_used: Dict[str, Any]

class TaskExecutor:
    MAX_RETRIES = 3
    TIMEOUT = 300  # seconds
    CHECKPOINT_INTERVAL = 5  # steps

    def __init__(self, llm_model: str = None):
        """Initialize TaskExecutor."""
        self.task_id = str(uuid.uuid4())
        self.start_time = time.time()
        self.checkpoints = []
        self.execution_path = []
        self.current_step = None
        self.retry_count = 0
        self.llm_executor = LLMExecutor(model=llm_model)
        self.task_input = None
        
        # Configure logging
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        self.logger = logging.getLogger(f"TaskExecutor-{self.task_id}")

    def get_status_update(self) -> dict:
        """Generate a status update for the current execution state."""
        return {
            "task_id": self.task_id,
            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
            "current_step": self.current_step,
            "checkpoints": self.checkpoints,
            "resources": {
                "used": {
                    "time": time.time() - self.start_time,
                    "memory": "N/A"  # To be implemented
                },
                "available": {
                    "time": self.TIMEOUT - (time.time() - self.start_time),
                    "memory": "N/A"  # To be implemented
                }
            },
            "execution_path": self.execution_path,
            "status": TaskStatus.COMPLETED.value if self.current_step and self.current_step.get("status") == TaskStatus.COMPLETED.value else TaskStatus.IN_PROGRESS.value
        }

    async def execute_step(self, step_id: str, step_data: Dict[str, Any]) -> bool:
        """Execute a single step of the task using LLM."""
        try:
            self.current_step = {
                "id": step_id,
                "name": step_data.get("name", "Unknown"),
                "status": TaskStatus.IN_PROGRESS.value,
                "progress": 0
            }
            
            # Check if execution should continue
            if time.time() - self.start_time > self.TIMEOUT:
                raise TimeoutError("Task execution timeout")
            
            # Execute step using LLM
            step_result = await self.llm_executor.execute_step(
                step_instruction=step_data.get("instruction", ""),
                step_input=step_data.get("input", {}),
                context=step_data.get("context", {})
            )
            
            if not step_result["success"]:
                raise Exception(f"Step failed: {step_result['error']}")
            
            # Update step status
            self.execution_path.append({
                "step_id": step_id,
                "result": step_result["output"]
            })
            
            self.current_step["status"] = TaskStatus.COMPLETED.value
            self.current_step["progress"] = 100
            
            # Create checkpoint if needed
            if len(self.execution_path) % self.CHECKPOINT_INTERVAL == 0:
                self.create_checkpoint()
                
            return True
            
        except Exception as e:
            self.current_step["status"] = TaskStatus.FAILED.value
            return self.handle_error(e)

    def validate_input(self, input_data: Dict[str, Any]) -> bool:
        """Validate the input data for the task."""
        return True

    def create_checkpoint(self):
        """Create a checkpoint of the current execution state."""
        checkpoint = {
            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
            "task_id": self.task_id,
            "current_step": self.current_step,
            "execution_path": self.execution_path.copy(),
            "resources": {
                "used": {
                    "time": time.time() - self.start_time,
                    "memory": "N/A"  # To be implemented
                }
            }
        }
        self.checkpoints.append(checkpoint)
        self.logger.info(f"Created checkpoint: {json.dumps(checkpoint, indent=2)}")

    def rollback_to_checkpoint(self, checkpoint_index: int):
        """Rollback the execution to a specific checkpoint."""
        if 0 <= checkpoint_index < len(self.checkpoints):
            checkpoint = self.checkpoints[checkpoint_index]
            # Implement state restoration logic
            self.logger.info(f"Rolling back to checkpoint: {checkpoint['timestamp']}")
            return True
        return False

    def get_next_actions(self) -> List[str]:
        """Determine the next possible actions based on current state."""
        actions = []
        if self.current_step and self.current_step["status"] == TaskStatus.FAILED.value:
            actions.extend(["retry", "rollback", "abort"])
        elif self.current_step and self.current_step["status"] == TaskStatus.COMPLETED.value:
            actions.extend(["continue", "checkpoint"])
        return actions

    def handle_error(self, error: Exception):
        """Handle execution errors."""
        self.logger.error(f"Error occurred: {str(error)}")
        self.retry_count += 1
        
        if self.retry_count >= self.MAX_RETRIES:
            self.logger.error("Max retries reached. Terminating execution.")
            return False
            
        # Implement error recovery logic
        return True

    async def execute(self, task_input: Dict[str, Any]) -> Dict[str, Any]:
        """Execute the complete task."""
        try:
            # Store task input
            self.task_input = task_input
            
            if not self.validate_input(task_input):
                raise ValueError("Invalid task input")

            self.logger.info(f"Starting task execution: {self.task_id}")
            
            # Execute each step in sequence
            if hasattr(self, 'task_steps'):
                for step in self.task_steps:
                    if not await self.execute_step(step["id"], step):
                        raise Exception(f"Step {step['id']} failed")
                        
                    if len(self.execution_path) % self.CHECKPOINT_INTERVAL == 0:
                        self.create_checkpoint()
            else:
                raise ValueError("No task steps defined")
            
            return self.get_status_update()
            
        except Exception as e:
            self.logger.error(f"Task failed: {str(e)}")
            return self.get_status_update()