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This repository is an incubator of multiple OpenAI GPT-based utilities. Fresh developments of an LLM technology make it possible to implement ideas that used to be hard or impossible for software implementation until now. As of now, the repository consists of:

Scanner of eBay listings

The scanner looks at fresh auctions and BUY IT NOW listings and filters out items using various heuristics (static, customizable and AI-based), craving to find the best deals according to the custom catalog. The scanner is designed to be run as a cron job. The idea is to "cherry-pick" the most prominent items with the best price/quality ratio. eBay is a great marketplace to apply LLM-based tools for scanning, as it is a very competitive and fuzzy environment that is not easy to navigate for a human, yet the prices are often well below the manufacturer's suggested retail price (MSRP) or recommended retail price (RRP). There are various challenges for auto-scans (like how many inline skate wheels are being sold in a set? Or what is bundled within a lot?), so there is an idea in a backlog to use machine vision to recognize some important aspects of a lot.

This project has been participating in Backdrop Build hackathon (look for GptMonkey demo video and usage instructions)

Batch vulnerability scanner

The script splits source code files into chunks (trying to split between methods), uploads these files recursively to GPT and asks it to scan the code for vulnerabilities. Findings are then grouped and pretty-printed. Note: depending on prompt and model used, findings may be too paranoid and consist almost entirely from false positives. You may want to adjust the temperature parameter and prompts/vulnerabilities/analyze prompts to your case

Usage

export OPENAI_API_KEY=<your API key>
GptMonkey/vuln_scanner.py --project_path /tmp/WordPress/wp-admin

Or, if you want to tune prompts, you may consider uploading just a sample of few files for analysis to speed up the process and save costs:

export OPENAI_API_KEY=<your API key>
GptMonkey/vuln_scanner.py --project_path /tmp/WordPress/wp-admin --samples 5

Example of output:

----------------------------------------
File Path: edit-form-comment.php
Finding: Possible XSS vulnerability
Code:
    <div class="misc-pub-section misc-pub-comment-status" id="comment-status">
    <?php _e( 'Status:' ); ?> <span id="comment-status-display">
    <?php
    switch ( $comment->comment_approved ) {
    	case '1':
    		_e( 'Approved' );
    		break;
    	case '0':
    		_e( 'Pending' );
    		break;
    	case 'spam':
    		_e( 'Spam' );
    		break;
    }
    ?>
    </span>
----------------------------------------
File Path: edit-form-comment.php
Finding: Potential Cross-Site Scripting (XSS) vulnerability in the 'comment_status' field
Code:
    <label><input type="radio"<?php checked( $comment->comment_approved, '1' ); ?> name="comment_status" value="1" /><?php _ex( 'Approved', 'comment status' ); ?></label><br />

Code Generation Tool leveraging OpenAI GPT APIs

Welcome to the language-agnostic Code Generation Tool, powered by OpenAI GPT (Generative Pre-trained Transformer) APIs. This tool harnesses the capabilities of GPT LLM to iteratively generate code based on user prompts. Think of it as of giving a GPT model the "vim" access to your project, allowing it to assist in code creation based on freeform text user requests.

Disclaimer

⚠️ Disclaimer: This README contains information on features and functionality that have not been implemented yet. Almost everything described in this README is rather a placeholder, "vision" or "concept" than an existing production-ready thing. Please wait until the first release, or feel free to contribute and suggest ideas. Use this document only as a reference for planned features and future developments. ⚠️

Key Features

This code generator employs state-of-the-art techniques to maximize the use of the context window effectively by loading the most relevant parts of existing code and requirements It leverages multiple Language Model (LLM) prompts to ensure that the generated code aligns with the context of your existing codebase and adheres to freeform requirements.

Technologies and Approaches

This tool is built on a foundation of advanced methodologies, including:

  • Chain of Thought: This technique ensures that the code generation process is a coherent sequence of logical steps, increasing the quality and relevance of resulting code

  • ReAct (React and Act): s at the heart of the code generation process. It begins with a prompt, adapts to the project's context, and continuously learns from the existing codebase to produce the relevant code. It allows the model to maintain a "train of thought" across multiple interactions.

  • PAL: Program-aided Language Models in scope of current project, PAL approach looks like giving the model the programmatic access to project files and documentation

For more in-depth information on these approaches, you can refer to the following articles:

Getting Started

To start using the Code Generation Tool, follow these steps:

  1. TBD

Support and Feedback

If you encounter any issues or have suggestions for improvement, please feel free to open an issue on the GitHub repository. Your feedback is valuable, and the aim is to make this tool as effective as possible for your code generation needs.

Happy coding!