Build Modular Industrial Inspection Agents with MCP-Native Tooling Using BeeAI and FastAPI

"""
Production implementation for building modular industrial inspection agents.
Provides secure, scalable operations using FastAPI and BeeAI.
"""

from typing import Dict, Any
import os
import logging
import httpx
from fastapi import FastAPI, HTTPException, Request
from pydantic import BaseModel, conlist
from contextlib import asynccontextmanager

# Logger setup
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    beeai_api_url: str = os.getenv('BEEAI_API_URL')

# Pydantic model for input validation
class InspectionData(BaseModel):
    id: int
    fields: Dict[str, Any]

# Input validation function
async def validate_input(data: InspectionData) -> bool:
    """Validate request data.
    
    Args:
        data: InspectionData to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if data.id <= 0:
        raise ValueError('ID must be a positive integer')
    return True

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

# Data transformation function
async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize input data for processing.
    
    Args:
        data: Data to normalize
    Returns:
        Normalized data
    """
    return {k: v.lower() for k, v in data.items()}

# Core processing function
async def process_batch(data: conlist(InspectionData), batch_size: int = 5):
    """Process a batch of inspection data.
    
    Args:
        data: List of InspectionData
        batch_size: Number of records to process per batch
    Returns:
        Processed results
    """
    results = []
    for i in range(0, len(data), batch_size):
        batch = data[i:i + batch_size]
        normalized_batch = [await normalize_data(item.fields) for item in batch]
        results.extend(normalized_batch)
    return results

# Integration function to fetch data from BeeAI
async def fetch_data(api_url: str, headers: Dict[str, str]) -> Dict[str, Any]:
    """Fetch data from BeeAI API.
    
    Args:
        api_url: API URL to fetch data from
        headers: Headers for the request
    Returns:
        JSON response from API
    Raises:
        HTTPException: If request fails
    """
    async with httpx.AsyncClient() as client:
        response = await client.get(api_url, headers=headers)
        response.raise_for_status()  # Raise exception for HTTP errors
        return response.json()

# Integration function to save to the database
async def save_to_db(data: Dict[str, Any]) -> bool:
    """Save data to the database.
    
    Args:
        data: Data to save
    Returns:
        True if successful
    Raises:
        Exception: If saving fails
    """
    # Simulate database saving logic here
    logger.info('Saving data to the database')
    return True

# Utility function for formatting output
def format_output(data: Any) -> str:
    """Format data for output.
    
    Args:
        data: Data to format
    Returns:
        Formatted string
    """
    return f'Result: {data}'

# Main orchestrator class
class InspectionAgent:
    def __init__(self, config: Config):
        self.config = config

    async def run_inspection(self, data: InspectionData) -> str:
        """Run the inspection process.
        
        Args:
            data: InspectionData to process
        Returns:
            Result of the inspection
        """
        await validate_input(data)  # Validate input
        sanitized_data = await sanitize_fields(data.fields)  # Sanitize data
        await save_to_db(sanitized_data)  # Save to DB
        return format_output(sanitized_data)  # Format output

# FastAPI application setup
app = FastAPI()

@app.post('/inspect', response_model=str)
async def inspect(data: InspectionData):
    """Endpoint for inspection.
    
    Args:
        data: InspectionData to inspect
    Returns:
        Inspection result
    Raises:
        HTTPException: If validation or processing fails
    """
    try:
        agent = InspectionAgent(Config())  # Create inspection agent
        result = await agent.run_inspection(data)  # Run inspection
        return result
    except ValueError as e:
        logger.error(f'Validation error: {e}')
        raise HTTPException(status_code=400, detail=str(e))  # Handle validation errors
    except Exception as e:
        logger.error(f'Processing error: {e}')
        raise HTTPException(status_code=500, detail='Internal Server Error')  # Handle other errors

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