Orchestrate Equipment Monitoring Agents with llama-agents and FastAPI

                      
                     
"""
Production implementation for orchestrating equipment monitoring agents.
Provides secure and scalable operations for monitoring equipment using llama-agents.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import httpx
import asyncio
from fastapi import FastAPI, HTTPException, BackgroundTasks
from pydantic import BaseModel, Field

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    database_url: str = os.getenv('DATABASE_URL')
    llama_agent_url: str = os.getenv('LLAMA_AGENT_URL')

class Equipment(BaseModel):
    id: str = Field(..., description="Unique identifier for the equipment.")
    status: str = Field(..., description="Current status of the equipment.")
    last_checked: str = Field(..., description="Timestamp of the last check.")

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'id' not in data:
        raise ValueError('Missing id')
    if 'status' not in data:
        raise ValueError('Missing status')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input data to sanitize
    Returns:
        Dict[str, Any]: Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}

async def fetch_data(equipment_id: str) -> Optional[Equipment]:
    """Fetch equipment data from the database.
    
    Args:
        equipment_id: ID of the equipment
    Returns:
        Optional[Equipment]: Equipment data if found
    """
    # Simulating database fetch operation
    logger.info("Fetching data for equipment ID: %s", equipment_id)
    return Equipment(id=equipment_id, status="operational", last_checked="2023-10-01T12:00:00Z")

async def save_to_db(equipment: Equipment) -> None:
    """Save equipment status to the database.
    
    Args:
        equipment: Equipment data to save
    """
    logger.info("Saving equipment status for ID: %s", equipment.id)
    # Simulating a database save operation

async def call_api(equipment: Equipment) -> None:
    """Call external API for equipment.
    
    Args:
        equipment: Equipment data to send
    """
    logger.info("Calling external API for equipment ID: %s", equipment.id)
    async with httpx.AsyncClient() as client:
        response = await client.post(Config.llama_agent_url, json=equipment.dict())
        if response.status_code != 200:
            logger.error("Failed to call external API: %s", response.text)
            raise HTTPException(status_code=response.status_code, detail="API call failed")

async def process_batch(equipments: List[Dict[str, Any]]) -> None:
    """Process a batch of equipment statuses.
    
    Args:
        equipments: List of equipment data
    """
    for equipment_data in equipments:
        try:
            await validate_input(equipment_data)  # Validate input data
            sanitized_data = await sanitize_fields(equipment_data)
            equipment = Equipment(**sanitized_data)  # Create Equipment object
            await save_to_db(equipment)  # Save to DB
            await call_api(equipment)  # Call external API
        except Exception as e:
            logger.error("Error processing equipment %s: %s", equipment_data.get('id'), e)

app = FastAPI()

@app.post("/monitor/equipment/")
async def monitor_equipment(equipments: List[Dict[str, Any]], background_tasks: BackgroundTasks) -> Dict[str, str]:
    """Endpoint to monitor equipment statuses.
    
    Args:
        equipments: List of equipment data to process
    Returns:
        Dict[str, str]: Confirmation message
    """
    background_tasks.add_task(process_batch, equipments)  # Process in background
    return {"message": "Monitoring started."}

if __name__ == '__main__':
    # Example usage
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)