Key Takeaways
Your Application is Talking, Are You Listening?
As I sit here on the tarmac in Charlotte, NC just waiting for the maintenance team to show up to fix an issue on our airplane, I'm thankful that the pilot listened to the warnings from the plane and turned back to have the issue checked out. It makes me think about companies receiving signals and messages from their applications and source code. Whether it is as simple as "hey, you should really update my framework and package versions a little more often", or maybe it's a little more serious: "hey, I am just LOADED with security vulnerabilities"; when our applications talk, we should listen. But, I know what you're asking: how can I listen to my applications / source code? Well, you're in luck! Today's blog is about how you can turn your source code into a knowledge file (or set of files) and train a custom gpt on your source code and listen to what it is telling you. So, without further ado, let's see how we can listen to what our code is telling us.
In the ever-evolving landscape of software development, the ability to quickly adapt and learn from your environment is key. But what if I told you that your application could literally "talk" to you, offering insights and solutions directly from its source code? Enter the intriguing world of GPT (Generative Pre-trained Transformer) models and the GPT-GitHub-crawler, a dynamic duo that transforms your application's code into a conversational partner. Yes, you read that right. Your codebase isn't just a collection of files and functions—it's a wealth of knowledge waiting to be tapped into. Let's dive into how you can leverage GPT to listen closely to what your application has to say.
Step 1: Crawling Your Code with GPT-GitHub-Crawler
The journey begins with the GPT-GitHub-crawler, a tool designed to traverse your GitHub repositories and prepare your codebase for GPT-based interactions. Here’s a simple guide to get you started:
- Setup GPT-GitHub-crawler: First, ensure you have access to the crawler. This involves downloading the tool or cloning it from its repository. You can find the tool here: https://github.com/phloai/gpt-github-crawler. The directions for setting up the tool are pretty simple, and contained within a readme file on the GitHub site, so I won't really cover those in this blog post.
- Authenticate Your GitHub Account: You’ll need to provide the crawler with access to your GitHub account. This usually involves generating a personal access token in GitHub and configuring the crawler to use this token.
- Configure the Crawl: Specify the repositories you want to crawl. You can customize the crawl to focus on specific branches, directories, or file types, depending on your needs. You can also generate knowledge files from source code that you have locally on your machine.
- Run the Crawler: With everything set up, execute the crawler. It will meticulously go through your specified repositories, extracting and organizing the code into a digestible format for GPT models.
Step 2: Engaging in Dialogue with Your Application
Once your application’s source code has been processed, it’s time to start the conversation. Using a GPT model fine-tuned with your codebase, you can ask questions or seek advice on various aspects of your application. Here are some fascinating use cases:
Debugging Assistance
Encountered a tricky bug? Ask your model about potential causes and solutions based on similar issues it has seen in your code. It’s like having a seasoned developer review your code, except it's instant and available 24/7.
Code Reviews
Imagine conducting code reviews with insights drawn directly from your application’s history and patterns. The GPT model can highlight inconsistencies, suggest improvements, and even reference parts of your application that adhere to best practices.
Feature Suggestions
Discuss new features or enhancements with your GPT model. It can suggest implementation strategies by drawing parallels with existing code, potentially uncovering more efficient or innovative approaches.
Learning and Documentation
New team members can converse with the GPT model to better understand the application’s structure, functions, and workflows. It’s an interactive documentation source that answers queries with context and examples.
Making It Work for You
Integrating GPT-GitHub-crawler and GPT models into your workflow can be a game-changer, but it's important to approach this innovation responsibly:
- Data Privacy and Security: Always ensure your activities comply with your data governance policies. When working with sensitive code, consider where you're training and hosting your GPT model. Opt for secure, private environments like a private team in OpenAI or your own Azure OpenAI instance. Safeguarding your information is crucial.
- Continuous Updates: Regularly update the knowledge files generated by the crawler to reflect the latest state of your application. This ensures your GPT model's insights remain relevant and valuable.
- Customization and Training: While generic GPT models are powerful, fine-tuning them with your specific codebase and use cases can provide even more tailored insights.
Ready to Start the Conversation? We're Here to Help!
Embarking on this journey may seem daunting, but you're not alone. Our consultancy specializes in helping businesses like yours leverage the latest in AI and machine learning technologies to revolutionize how you interact with your codebase. From setting up GPT-GitHub-crawler to fine-tuning your GPT models, our team of experts is here to guide you every step of the way. Let's unlock the full potential of your application together.
Are you ready to listen to what your application has to say? Contact us today, and let's transform your codebase into a dynamic, conversational partner. Remember, in the world of software development, those who listen closely are the ones who lead the way. Safeguard your information, and let's start this exciting conversation together.
Also, in case you were wondering how things went from the tarmac in Charlotte...the plane was fixed, and we're on our way to our destination. Happy coding! I'll follow up with an example of training a GPT on the CSLA framework. The CSLA is a favorite open-source framework that we use quite frequently and like very much.
