Build Your Own AI-Driven Code Analysis Chatbot for Developers with the GenAI Stack

The topic of GenAI is everywhere now, but even with so much interest, many developers are still tryi

The topic of GenAI is everywhere now, but even with so much interest, many developers are still trying to understand what the real-world use cases are. Last year, Docker hosted an , and genuinely interesting projects were submitted.  In this , we will dive into a winning submission, , in the hope that it sparks project ideas for you.  For developers, understanding and navigating codebases can be a constant challenge. Even popular AI assistant tools like ChatGPT can fail to understand the context of your projects through code access and struggle with complex logic or unique project requirements. Although large language models (LLMs) can be valuable companions during development, they may not always grasp the specific nuances of your codebase. This is where the need for a deeper understanding and additional resources comes in. Imagine you’re working on a project that queries datasets for both cats and dogs. You already have functional code in that retrieves dog data using pagination (a technique for fetching data in parts). Now, you want to update to achieve the same functionality for cat data. Wouldn’t it be amazing if you could ask your AI assistant to reference the existing code in and guide you through the modification process?  This is where , an AI-powered chatbot comes in.  The following demo, which was submitted to the AI/ML Hackathon, provides an overview of Code Explorer (Figure 1). Code Explorer helps you find answers about your code by searching relevant information based on the programming language and folder location. Unlike chatbots, Code Explorer goes beyond generic coding knowledge. It leverages a powerful AI technique called retrieval-augmented generation (RAG) to understand your code’s specific context. This allows it to provide more relevant and accurate answers based on your actual project. Code Explorer supports a variety of programming languages, such as *.swift, *.py, *.java, *.cs, etc. This tool can be useful for learning or debugging your code projects, such as Xcode projects, Android projects, AI applications, web dev, and more. Benefits of the CodeExplorer include: Use cases include: Code Explorer leverages the power of a RAG-based AI framework, providing context about your code to an existing LLM model. Figure 2 shows the magic behind the scenes. The user selects a codebase folder through the Streamlit app. The function in the file is called. This function performs the following actions: This step is triggered only after the codebase has been processed (Step 1). Two LLM chains are created: Langchain is used to orchestrate the chatbot pipeline/flow. The Streamlit app provides a chat interface for users to ask questions about their code. The user interacts with the Streamlit app’s chat interface, and user inputs are stored and used to query the LLM or the QA/Agent models. Based on the following factors, the app chooses how to answer the user: To get started with Code Explorer, check the following: Then, complete the four steps explained below. Open a terminal window and run the following command to clone the sample application. You should now have the following files in your directory: Before running the GenAI stack services, open the and modify the following variables according to your needs. This file stores environment variables that influence your application’s behavior. Run the following command to build and bring up Docker Compose services: This gets the following output: The logs indicate that the application has successfully started all its components, including the LLM, Neo4j database, and the main application container. You should now be able to interact with the application through the user interface. You can view the services via the Docker Desktop dashboard (Figure 3). The Code Explorer stack consists of the following services: You may notice that service exits with , which indicates successful execution. This service is designed just to download the LLM model ( ). Once the download is complete, there’s no further need for this service to remain running. Exiting with signifies that the service finished its task successfully (downloading the model). You can now view your Streamlit app in your browser by accessing (Figure 5). In the sidebar, enter the path to your code folder and select (Figure 6). Then, you can start asking questions about your code in the main chat. You will find a toggle switch in the sidebar. By default is enabled. Under this mode, the QA RAG chain chain is used ( ) . This mode leverages the processed codebase for in-depth answers.  When you toggle the switch to another mode ( ), the Agent chain gets selected. This is similar to how one AI discusses with another AI to create the final answer. In testing, the agent appears to summarize rather than a technical response as opposed to the QA agent only. Here’s a result when (Figure 7): Figure 8 shows a result when : Code Explorer, powered by the , offers a compelling solution for developers seeking AI assistance with coding. This chatbot leverages RAG to delve into your codebase, providing insightful answers to your specific questions. Docker containers ensure smooth operation, while Langchain orchestrates the workflow. Neo4j stores code representations for efficient analysis.  Explore and the GenAI Stack to unlock the potential of AI in your development journey!