Semantically Search Equipment Specifications with Neo4j Knowledge Graphs and Transformers

                      
                     
"""
Production implementation for semantically searching equipment specifications using Neo4j knowledge graphs and transformers.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List
import os
import logging
import time
from neo4j import GraphDatabase

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

# Configuration class for environment variables
class Config:
    database_url: str = os.getenv('NEO4J_DATABASE_URL')
    database_user: str = os.getenv('NEO4J_DATABASE_USER')
    database_password: str = os.getenv('NEO4J_DATABASE_PASSWORD')

# Initialize the Neo4j driver
class Neo4jConnection:
    def __init__(self):
        self.driver = GraphDatabase.driver(
            Config.database_url,
            auth=(Config.database_user, Config.database_password)
        )

    def close(self):
        self.driver.close()

# Helper functions
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate search input data.

    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """  
    if 'query' not in data:
        raise ValueError('Missing query in input data.')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.

    Args:
        data: Input data to sanitize
    Returns:
        Sanitized input data
    """
    return {key: value.strip() for key, value in data.items()}

async def fetch_data(query: str, session) -> List[Dict[str, Any]]:
    """Fetch data from Neo4j based on the query.

    Args:
        query: Cypher query to execute
        session: Neo4j session object
    Returns:
        List of records from the database
    """  
    result = []
    try:
        result = session.run(query)
        return [record for record in result]
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        return []

async def transform_records(records: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Transform fetched records into desired format.

    Args:
        records: List of records from the database
    Returns:
        Transformed records
    """
    return [dict(record) for record in records]

async def process_search(query: str) -> List[Dict[str, Any]]:
    """Main processing function for search.

    Args:
        query: Search query
    Returns:
        List of search results
    """  
    with Neo4jConnection() as connection:
        with connection.driver.session() as session:
            cypher_query = f'MATCH (e:Equipment) WHERE e.specifications CONTAINS "{query}" RETURN e'
            records = await fetch_data(cypher_query, session)
            return await transform_records(records)

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

    async def search_equipment(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Search for equipment specifications.

        Args:
            data: Input data containing search query
        Returns:
            List of search results
        """  
        try:
            await validate_input(data)
            sanitized_data = await sanitize_fields(data)
            results = await process_search(sanitized_data['query'])
            return results
        except ValueError as e:
            logger.warning(f'Input validation error: {e}')
            return []
        except Exception as e:
            logger.error(f'Unexpected error: {e}')
            return []

# Main block
if __name__ == '__main__':
    import asyncio
    test_data = {'query': 'crane'}
    equipment_search = EquipmentSearch()
    results = asyncio.run(equipment_search.search_equipment(test_data))
    logger.info(f'Search results: {results}')