HusseinLezzaik / payments-accelerated

Can we replace COBOL mainframe legacy code with new accelerated computing?

Integration Process Documentation

Overview

This document outlines the process of integrating a COBOL batch payment processing program with the Groq Inference engine. The integration enables the COBOL program to leverage Groq's accelerated computing for enhanced transaction processing.

Components

• COBOL Program (batch_pay.cbl): Processes batch payment transactions and outputs transaction details.
• Python Script (groq_integration.py): Executes the COBOL program, parses its output, and interacts with the Groq API for transaction summarization.
• Groq Inference Engine: Provides AI-powered insights and summarization for transaction data.

Setup

1. Install gnucobol3 compiler for COBOL program compilation.
2. Set Groq API key as an environment variable (GROQ_API_KEY).
3. Install Groq Python library (version 0.5.0) for API interaction.

COBOL Program

• File: batch_pay.cbl
• Function: Reads TRANSACTION.DAT file, processes transactions, and writes to OUTPUT.DAT.
• Output Format: 6-digit account number, amount with 2 decimal places, transaction code, and description.

Python Integration Script

• File: groq_integration.py
• Functions:
  • execute_cobol_program(): Compiles and runs the COBOL program.
  • parse_cobol_output(): Parses the OUTPUT.DAT file to extract transaction details.
  • process_transactions_with_groq(): Sends transaction details to Groq API and retrieves summarized content.
  • update_transaction_logic(): Updates transactions with new balance based on Groq's response.
• Transaction Codes: 'EP' for deposit, 'DL' for withdrawal.

Groq API Interaction

• The script formats transaction data and sends it to the Groq API with a prompt specifying the desired operation.
• Groq processes the data and returns a summarized output, which is then logged by the script.

Potential Improvements

• Automate the retrieval and setting of the Groq API key.
• Enhance error handling and logging within the Python script for better debugging.
• Optimize the COBOL program to handle larger datasets and more complex transactions.
• Implement additional features in the Groq API interaction to cover a wider range of transaction types.
• Adding a user interface for easier monitoring and interaction with the integration process.
• Explore the possibility of real-time processing by streaming transaction data to the Groq API.
• Improve regex patterns to handle even more varied response formats from Groq.
• Explore parallel processing to further speed up transaction processing.