How I Built the AI That's Making Senior Developers Nervous

When Limitations Spark Innovation

Every developer knows the frustration of diving into a new codebase. Hours spent scrolling through files, tracing function calls, and piecing together how different components interact. This challenge becomes even more daunting with large repositories or those lacking comprehensive documentation.

For one developer, this common pain point sparked an ingenious solution. Facing the constraints of existing AI tools like ChatGPT, which couldn't ingest entire GitHub repositories due to context limitations, they set out to build something better: a specialized chatbot that could understand and answer questions about any GitHub repository.

This article was inspired by an innovative project shared on Reddit's r/MachineLearning community by user jsonathan, who built a chatbot that lets you talk to any GitHub repository. Their creative approach sparked this exploration of how similar functionality can be built using BotStacks.

The Spark of an Idea

The concept was straightforward yet powerful – create a tool that could analyze entire codebases, understand their structure and functionality, and allow developers to ask questions in natural language. Instead of manually feeding code snippets into ChatGPT or navigating through endless files, developers could simply ask: "How does the authentication system work?" or "What's the main functionality of this project?"

The solution required combining several technologies: static code analysis to understand the structure, vector search for finding relevant code segments, and integration with powerful language models to generate insightful responses.

Building the Repository Whisperer

The development process began with tackling the fundamental challenge – how to ingest and process entire repositories efficiently. The solution involved creating a system that could:

First, clone or download the complete repository structure, analyzing the files, directories, and their relationships. This provided the skeletal understanding of the codebase's organization.

Next, transform the code content into vector embeddings that captured semantic relationships between different parts of the codebase. This crucial step enabled the system to understand not just what code existed, but how different components related to each other conceptually.

When a user asked a question, the system would perform a semantic search against these embeddings to identify the most relevant code sections. Rather than simple keyword matching, this approach understood the meaning behind the query.

Finally, these relevant sections would be provided as context to a language model like GPT-3.5 or GPT-4, which would generate comprehensive responses that addressed the developer's questions.

The resulting tool allowed developers to have conversations with codebases, extracting insights and understanding complex systems without manually sifting through files.

Community Response and Validation

When shared with the developer community on platforms like Reddit, the tool generated significant excitement. One user called the creator "an absolute Mad Chad," while another commented that "Reading code is hard! Having something to help you gain insights into existing code would be incredible."

The reception validated the need for such a tool, particularly for developers working with unfamiliar or poorly documented codebases. Discussions highlighted several key use cases, from accelerating onboarding for new team members to supplementing inadequate documentation.

Some developers even envisioned these tools potentially replacing traditional documentation entirely, with one suggesting implementing "pre-processed prompts for new repositories" to automatically generate tutorial-style content based on the codebase itself.

Honest Conversations About Limitations

Not all feedback was without concerns. Some developers questioned the reliability of AI-generated explanations, asking, "How do you trust that it's not lying through its teeth?"

This sparked thoughtful discussions about verification and the proper role of AI assistants. As one developer insightfully noted: "If the choice is between spending 1 hour of searching, reviewing, and experimenting to do X, and spending 15 minutes iterating and verifying, it is an easy choice."

Another compared the tool to working with a junior developer – you verify their work rather than accepting it blindly. This verification-oriented perspective positions these tools as time-saving assistants rather than infallible authorities.

The tool also faced technical challenges. Some users reported it worked better with certain programming languages (particularly Python) than others. Complex, highly interdependent codebases sometimes proved challenging for the system to analyze effectively.

Building Your Own Repository Chatbot with BotStacks

Inspired by this innovative tool, developers can create similar functionality using platforms like BotStacks. Here's how to approach building your own repository-aware chatbot using BotStacks' conversational AI platform:

Setting Up Your BotStack

Begin by creating a BotStack within the platform. This will serve as the foundation for your repository chatbot. Navigate to the dashboard and click "Create new BotStack," providing a descriptive name for your assistant.

Leveraging the Brain Vault for Repository Knowledge

BotStacks' Brain Vault feature is perfect for ingesting repository data. You can add repository content through multiple methods:

For a comprehensive approach, use the site crawling feature to automatically scrape an entire repository website. Alternatively, add individual URLs pointing to specific repository files or documentation.

You can also directly upload code files or documentation from the repository to provide your assistant with the necessary context about the codebase.

Designing the Conversation Flow

Using BotStacks' Sequence Studio, design a conversation flow optimized for code understanding. This requires several key nodes:

Start with a Listen Node to capture the user's question about the repository. Connect this to an LLM Node that processes the query and searches for relevant information within your Brain Vault data.

For the LLM Node, craft a system prompt that instructs the model to act as a code expert. For example: "You are an expert code analyst. When answering questions about the repository, provide clear explanations about functionality, structure, and implementation details. When appropriate, include code examples to illustrate your points."

Configure the LLM Node to use the Brain Vault by enabling the "Use Brain Vault" option. This ensures that when users ask questions, the assistant searches through the repository data you've added.

Finally, connect the LLM Node to a Response Node that delivers the answer to the user, followed by another Listen Node to create a continuous conversation loop.

Enhancing with API Integration

For more advanced functionality, incorporate the API Node to connect with external services:

Integrate with GitHub's API to fetch real-time repository data, such as recent commits or issues.

Use code analysis APIs to perform deeper static analysis on repository code when needed.

Testing and Refining Your Assistant

Test your chatbot with various questions about the repository, ranging from high-level architecture queries to specific implementation details. Use the sandbox environment to interact with your assistant and refine its responses.

Pay attention to how well it handles different types of questions. If you notice patterns of inaccuracy, adjust your system prompt or add more specific examples to guide the model's responses.

Deploying Your Repository Chatbot

Once satisfied with the performance, publish your BotStack and deploy it using the web widget. This allows you to embed the chatbot directly on repository pages or documentation sites, making it easily accessible to developers working with the codebase.

The Evolution of Repository-Aware AI Assistants

The journey of creating repository-aware AI assistants continues to evolve. Some developers are already exploring more comprehensive approaches, combining repository understanding with code generation capabilities.

As one developer shared, they developed "a tool that uses multiple GPT calls to get context on the codebase and when you give it a task it uses the context to understand where to implement the code changes and suggests the code to implement."

Others are looking toward using open-source models to build similar tools, expressing hopes for implementations using models like "qwen or llama3." This trend could make repository-aware AI assistance more accessible and customizable for development teams of all sizes.

A New Era of Code Comprehension

The creation of repository-aware chatbots represents a significant advancement in developer tooling. Moving beyond simple code completion, these tools provide holistic understanding of entire codebases, dramatically reducing the time required to comprehend unfamiliar code.

While they don't replace human expertise or careful code review, these AI companions significantly accelerate the process of navigating complex repositories. They serve as collaborative assistants that reduce cognitive load and direct attention to relevant areas of the codebase.

For teams constantly onboarding new developers or working with diverse libraries and frameworks, these tools offer a promising way to reduce the friction of code comprehension and increase overall productivity.

As one developer aptly put it: "WOW. This is what I needed long time ago!"

Ready to build your own repository chatbot? Explore BotStacks' AI assistant platform and join thousands of developers creating specialized AI tools without complex coding.

References:

1. jsonathan. (2023). "[P] I built a chatbot that lets you talk to any Github repository." Reddit r/MachineLearning. link