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Initializes default approval workflow types in a Neo4j database by creating or updating four predefined workflow type nodes (STANDARD, DEPARTMENT, QUALITY, REGULATORY) with their configurations.

Purpose

This function sets up the foundational approval workflow types in a Neo4j graph database. It's designed to be run during application initialization or database setup to ensure that standard workflow types exist. The function uses MERGE operations to be idempotent - it creates nodes if they don't exist or updates them if they do. Each workflow type has constraints on minimum and maximum approval steps, making it suitable for different organizational approval processes.

Source Code

def initialize_approval_workflow_types(driver: Driver) -> bool:
    """
    Initialize default approval workflow types in the database.
    
    Args:
        driver: Neo4j driver instance
        
    Returns:
        Boolean indicating success
    """
    try:
        workflow_types = [
            {
                "name": "STANDARD",
                "description": "Standard sequential approval workflow",
                "min_steps": 1,
                "max_steps": 5
            },
            {
                "name": "DEPARTMENT",
                "description": "Department-level approval workflow",
                "min_steps": 1,
                "max_steps": 3
            },
            {
                "name": "QUALITY",
                "description": "Quality assurance approval workflow",
                "min_steps": 1,
                "max_steps": 3
            },
            {
                "name": "REGULATORY",
                "description": "Regulatory affairs approval workflow",
                "min_steps": 1,
                "max_steps": 4
            }
        ]
        
        with driver.session() as session:
            for wf_type in workflow_types:
                session.run(
                    """
                    MERGE (wt:ApprovalWorkflowType {name: $name})
                    ON CREATE SET 
                        wt.UID = randomUUID(),
                        wt.description = $description,
                        wt.min_steps = $min_steps,
                        wt.max_steps = $max_steps,
                        wt.createdDate = datetime()
                    ON MATCH SET 
                        wt.description = $description,
                        wt.min_steps = $min_steps,
                        wt.max_steps = $max_steps
                    """,
                    wf_type
                )
        
        logger.info("Initialized approval workflow types")
        return True
        
    except Exception as e:
        logger.error(f"Error initializing approval workflow types: {e}")
        return False

Parameters

Parameter Details

driver: A Neo4j Driver instance that provides the connection to the Neo4j database. This should be an active, authenticated driver object created using neo4j.GraphDatabase.driver(). The driver is used to create database sessions for executing Cypher queries.

Return Value

Type: bool

Returns a boolean value: True if all workflow types were successfully initialized/updated in the database, False if any exception occurred during the process. The function logs success or error messages using the logger.

Dependencies

• neo4j
• logging

Required Imports

from neo4j import Driver
import logging

Usage Example

from neo4j import GraphDatabase
import logging

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

# Create Neo4j driver
uri = "bolt://localhost:7687"
username = "neo4j"
password = "your_password"
driver = GraphDatabase.driver(uri, auth=(username, password))

try:
    # Initialize approval workflow types
    success = initialize_approval_workflow_types(driver)
    
    if success:
        print("Workflow types initialized successfully")
    else:
        print("Failed to initialize workflow types")
finally:
    driver.close()

Best Practices

• This function should be called during application startup or database initialization phase
• Ensure the Neo4j driver is properly configured and connected before calling this function
• The function is idempotent - safe to call multiple times without creating duplicates
• Always close the driver connection after use to prevent resource leaks
• Monitor the logs for successful initialization or error messages
• The function uses MERGE operations which are atomic and safe for concurrent execution
• Consider running this in a transaction if combining with other initialization operations
• The workflow types are hardcoded - modify the workflow_types list if different types are needed

Tags

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