🔍 Code Extractor

Initializes six predefined approval workflow types (STANDARD, PARALLEL, DEPARTMENT, QUALITY, REGULATORY, EXECUTIVE) in a Neo4j database with their configurations including min/max steps.

Purpose

This function sets up the foundational approval workflow types in a Neo4j graph database. It creates or updates ApprovalWorkflowType nodes with specific characteristics for different approval processes used in organizational workflows. Each workflow type has constraints on the number of approval steps and a descriptive purpose. The function uses MERGE operations to ensure idempotency - creating nodes if they don't exist or updating them if they do. This is typically called during application initialization or database setup to ensure all required workflow types are available for the approval system.

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": "PARALLEL",
                "description": "All approvers approve in parallel",
                "min_steps": 1,
                "max_steps": 1
            },
            {
                "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
            },
            {
                "name": "EXECUTIVE",
                "description": "Executive-level approval workflow",
                "min_steps": 1,
                "max_steps": 2
            }
        ]
        
        with driver.session() as session:
            for wf_type in workflow_types:
                try:
                    session.run(
                        """
                        MERGE (wt:ApprovalWorkflowType {name: $name})
                        ON CREATE SET 
                            wt.UID = apoc.create.uuid(),
                            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
                    )
                except Exception as e:
                    logger.warning(f"Error creating workflow type {wf_type['name']}: {e}")
        
        logger.info("Initialized approval workflow types")
        return True
        
    except Exception as e:
        logger.error(f"Error initializing approval workflow types: {e}")
        logger.error(traceback.format_exc())
        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 manages database sessions and transactions for executing Cypher queries.

Return Value

Type: bool

Returns a boolean value: True if all workflow types were successfully initialized or updated in the database, False if any error occurred during the initialization process. The function catches exceptions and logs errors, returning False on failure while still attempting to process all workflow types.

Dependencies

• neo4j
• logging
• traceback
• typing
• datetime
• uuid
• CDocs

Required Imports

from neo4j import Driver
import logging
import traceback

Conditional/Optional Imports

These imports are only needed under specific conditions:

from CDocs import guard_execution

from CDocs import db

import uuid

from datetime import datetime

Usage Example

from neo4j import GraphDatabase
import logging

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

# Create Neo4j driver
driver = GraphDatabase.driver(
    'bolt://localhost:7687',
    auth=('neo4j', 'password')
)

try:
    # Initialize 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

• Ensure the Neo4j APOC plugin is installed before calling this function, as it uses apoc.create.uuid()
• Call this function during application initialization or database setup, not during regular runtime operations
• The function is idempotent - safe to call multiple times as it uses MERGE operations
• Always close the Neo4j driver after use to prevent connection leaks
• Ensure proper logging configuration is in place before calling this function to capture initialization status
• The function continues processing remaining workflow types even if one fails, check logs for partial failures
• Consider running this in a transaction if you need all-or-nothing behavior across all workflow types
• Verify database permissions allow MERGE operations on ApprovalWorkflowType nodes before execution

Tags

Similar Components

AI-powered semantic similarity - components with related functionality:

• function initialize_approval_workflow_types 97.7% similar

  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.

  From: /tf/active/vicechatdev/CDocs/db/schema_manager.py

• function initialize_schema_v1 64.7% similar

  Initializes a Neo4j database schema by creating constraints, indexes, a root CDocs node, audit trail, and migrating approval data and workflow types.

  From: /tf/active/vicechatdev/CDocs single class/db/schema_manager.py

• function migrate_approval_cycles 61.7% similar

  Migrates legacy Approval nodes and their related structures to the new ApprovalCycle model in a Neo4j graph database, creating ApprovalCycle nodes and ApproverAssignment nodes while maintaining relationships with DocumentVersion nodes.

  From: /tf/active/vicechatdev/CDocs/db/schema_manager.py

• function migrate_approval_data_v1 61.4% similar

  Migrates legacy single-step approval records in Neo4j to a new multi-step approval model by creating ApprovalStep nodes and Approver nodes with proper relationships.

  From: /tf/active/vicechatdev/CDocs single class/db/schema_manager.py

• function migrate_approval_data 59.7% similar

  Migrates legacy single-step approval records in Neo4j to a new multi-step approval model by creating ApprovalStep nodes and Approver nodes with proper relationships.

  From: /tf/active/vicechatdev/CDocs/db/schema_manager.py