rpa-suite 1.7.5

Comprehensive Python toolkit for RPA automation: email, logging, database tracking, browser automation, OCR, desktop automation, and more. Essential utilities for building robust automation workflows with Selenium, Botcity, and custom solutions.

RPA Suite

A comprehensive Python toolkit for Robotic Process Automation (RPA) development

Documentation • Installation • Quick Start • Features • Contributing

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Overview

RPA Suite is a powerful and versatile Python library designed to streamline and optimize the development of Robotic Process Automation (RPA) projects. Built with simplicity and efficiency in mind, it provides a comprehensive set of tools that make automation development faster, more reliable, and more maintainable.

Whether you're working with Selenium, Botcity, or building custom automation solutions, RPA Suite offers the essential utilities you need to accelerate your development process.

Key Features

• 🕐 Time Management - Schedule executions, wait for specific times, and manage time-based automation flows
• 📧 Email Automation - Send emails via SMTP with HTML support and attachments
• 📝 Logging System - Comprehensive logging with file and stream support using Loguru
• 📁 File Operations - Screenshot capture, file counting, and flag file management
• 🗂️ Directory Management - Create and manage temporary directories with ease
• 🔍 Text Processing - Pattern matching, regex operations, and text validation
• 🌐 Browser Automation - Selenium-based browser control with Chrome support (optional)
• ⚡ Parallel & Async Execution - Run processes in parallel or asynchronously
• 🤖 Desktop Automation - PyAutoGUI-based desktop automation (Artemis module)
• 📄 OCR with AI - Document conversion with OCR capabilities (Iris module - optional)
• 💾 Database Tracking - Complete execution tracking and management system with multi-database support (SQLite, PostgreSQL, MySQL)
• 🎨 Colored Console Output - Beautiful terminal output with color-coded messages
• ✅ Data Validation - Email validation and pattern checking utilities

Installation

Basic Installation

Install RPA Suite using pip:

pip install rpa-suite

Or using conda:

conda install -c conda-forge rpa-suite

Optional Dependencies

For advanced features, install additional dependencies:

# Browser automation (Selenium)
pip install selenium webdriver-manager

# OCR with AI (Iris module)
pip install docling

# Desktop automation (Artemis - included by default)
# pyautogui is already included

Quick Start

After installation, import and use RPA Suite immediately:

from rpa_suite import rpa

# Send an email
rpa.email.send_smtp(
    email_user="[e-mail]",
    email_password="your_password",
    email_to="[e-mail]",
    subject_title="Hello from RPA Suite",
    body_message="<p>This is a test email</p>"
)

# Schedule a function to run at a specific time
rpa.clock.exec_at_hour('14:30', my_function, arg1, arg2)

# Wait before executing a function
rpa.clock.wait_for_exec(30, my_function)

# Take a screenshot
rpa.file.screen_shot(filename="screenshot.png")

# Print colored messages
rpa.success_print("Operation completed successfully!")
rpa.error_print("An error occurred!")

Database Module Example

Track your automation executions with the Database module:

from rpa_suite import rpa

# Initialize database (SQLite by default)
db = rpa.database()

# Start tracking an execution
exec_id = db.start_execution(automation_name="My Automation Bot")

# Add items to the processing queue
db.add_items(execution_id=exec_id, items=[
    {"item_identifier": "invoice_001", "item_data": {"value": 150.00}},
    {"item_identifier": "invoice_002", "item_data": {"value": 320.50}},
])

# Process the queue
while item := db.get_next_item_from_queue(execution_id=exec_id):
    db.start_processing_item(item["id"])
    try:
        # ... your processing logic here ...
        db.finish_item(item["id"], status="success")
    except Exception as e:
        db.finish_item(item["id"], status="failed", error_message=str(e))

# Log and finish the execution
db.add_log_info("All items processed", execution_id=exec_id)
db.finish_execution(exec_id, status="completed")

# Check the results
stats = db.get_statistics(execution_id=exec_id)

Requirements

Core Dependencies

• Python 3.11+
• colorama
• colorlog
• email-validator
• loguru
• pillow
• pyautogui
• requests
• opencv-python

Optional Dependencies

• selenium (for browser automation)
• webdriver-manager (for browser automation)
• docling (for OCR/AI features)
• psycopg2-binary (for PostgreSQL support)
• mysql-connector-python (for MySQL support)

Features in Detail

Time Management (Clock Module)

Control execution timing and scheduling:

• exec_at_hour() - Execute functions at specific times
• wait_for_exec() - Wait before executing functions
• exec_and_wait() - Execute and wait pattern

Email (Email Module)

Send emails with full SMTP support:

• HTML email support
• File attachments
• Custom SMTP configuration
• Email validation

Logging (Log Module)

Comprehensive logging system based on Loguru:

• File and console logging
• Multiple log levels (debug, info, warning, error, critical)
• Configurable log formats
• Automatic log rotation

File Operations (File Module)

File and screenshot management:

• Screenshot capture with custom naming
• Flag file creation/deletion for process tracking
• File counting with extension filtering

Database Tracking (Database Module)

Complete execution lifecycle management:

• Multi-database support (SQLite, PostgreSQL, MySQL)
• Execution tracking with status management
• Item queue processing
• Automatic interruption detection
• Reprocessing capabilities
• Comprehensive statistics and reporting
• Structured logging integration

Module Structure

Core Modules

• clock - Time management and scheduling
• date - Date and time formatting utilities
• email - SMTP email sending
• file - File operations and screenshots
• directory - Directory management
• log - Logging system
• printer - Colored console output
• regex - Pattern matching and regex operations
• validate - Data validation utilities

Advanced Modules

• database - Execution tracking and database management
• browser - Selenium-based browser automation (optional)
• parallel - Parallel process execution
• async - Asynchronous execution
• artemis - Desktop automation with PyAutoGUI
• iris - OCR and document conversion (optional)

Database Module Methods

Execution Management:

• start_execution() - Start tracking a new execution
• finish_execution() - Complete an execution
• get_execution() - Retrieve execution details
• get_executions() - List executions with filtering
• detect_and_mark_interrupted_executions() - Auto-detect and mark interrupted executions

Item Processing:

• add_item() - Add item to processing queue
• add_items() - Batch add items
• get_next_item_from_queue() - Get next item to process
• start_processing_item() - Mark item as processing
• update_checkpoint() - Update processing checkpoint
• finish_item() - Complete item processing
• get_item() - Get item details
• get_items() - List items with filtering
• detect_and_mark_interrupted_items() - Auto-detect and mark interrupted items

Reprocessing:

• is_reprocessable() - Check if an item is eligible for reprocessing
• can_reprocess_execution() - Check if execution can be reprocessed
• reprocess_interrupted_execution() - Restart interrupted execution
• can_reprocess_item() - Check if item can be reprocessed
• reprocess_interrupted_item() - Restart interrupted item
• reprocess_items_from_execution() - Batch reprocess eligible items from an execution

Maintenance (Safe):

• clear_pending_items() - Remove pending items
• clear_interrupted_items() - Remove interrupted items
• clear_interrupted_executions() - Remove interrupted executions

Maintenance (Protected - requires confirmation code):

• clear_successful_items() - Remove successful items
• clear_failed_items() - Remove failed items
• clear_successful_executions() - Remove successful executions
• clear_failed_executions() - Remove failed executions

Maintenance (Full - requires confirmation):

• clear_executions_table() - Clear all executions
• clear_items_table() - Clear all items
• clear_logs_table() - Clear all logs
• clear_database() - Clear entire database

Logging:

• add_log() - Add log entry with custom level
• add_log_debug() - Add DEBUG level log
• add_log_info() - Add INFO level log
• add_log_warn() / add_log_warning() - Add WARNING level log
• add_log_error() - Add ERROR level log
• add_log_critical() - Add CRITICAL level log
• add_log_success() - Add SUCCESS level log
• get_logs() - Retrieve execution logs
• clear_logs() - Clear execution logs

Statistics:

• get_statistics() - Get comprehensive statistics

Documentation

For detailed documentation, usage examples, and API reference, visit:

• GitHub Wiki - Complete documentation and guides
• PyPI Project Page - Package information and releases

Contributing

Contributions are welcome! If you'd like to contribute to RPA Suite:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Camilo Costa de Carvalho

• GitHub: @CamiloCCarvalho
• LinkedIn: camilocostac
• Email: [e-mail]

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