Bridging the Gap: A Chatbot That Explains Code to Non-Technical Stakeholders
As technology continues to evolve, the divide between developers and non-technical stakeholders is becoming more apparent. A common challenge faced by business leaders, managers, or clients with limited technical backgrounds is understanding the technical complexities of the products they’re investing in. Code, a fundamental building block of software development, often becomes a mystery for these stakeholders. How do they understand what’s going on beneath the surface? How can they ensure that the technical implementation aligns with their business goals without having to become a developer themselves?
This is the problem my latest project addresses — creating a chatbot that can converse with the code, interpret it, and explain it in simple terms to non-technical stakeholders. The result is a tool that empowers clients, managers, and even business executives to ask questions about the codebase and get clear, understandable explanations, all through a conversational interface.
The Problem: Lack of Understanding Between Technical and Non-Technical Teams
In the real world, businesses often rely on developers to build products and features that are central to their operations. However, most of the time, non-technical stakeholders are left out of the loop when it comes to understanding how the code that powers these products works. This lack of understanding can lead to:
- Communication Gaps: Non-technical stakeholders often struggle to communicate their needs effectively to the technical teams. Without a common language, misunderstandings can happen, leading to features being built incorrectly or not as expected.
- Poor Decision-Making: Without a clear understanding of the underlying code, stakeholders may make decisions based on incomplete or inaccurate information. This can affect budgeting, timelines, and the overall direction of the project.
- Increased Dependency on Developers: Non-technical stakeholders must constantly rely on developers to explain or provide updates on the code, which takes time away from the developers’ core tasks.
In a fast-paced, ever-changing tech environment, these problems can hinder project efficiency, collaboration, and ultimately, success.
The Solution: A Chatbot That Talks to the Code
This project leverages the power of LangChain, a framework for building conversational AI, to bridge the gap between technical and non-technical teams. The chatbot, which I developed, can read the code from a GitHub repository, understand its structure, and then explain it in simple terms through a conversational interface.
Here’s how it works:
- Code Extraction: The chatbot clones a GitHub repository and loads the code files into memory.
- Code Understanding: Using a variety of techniques like document loaders and text splitters, the code is parsed and analyzed to extract meaningful chunks of information.
- Embedding and Vectorization: The code is converted into vector representations using OpenAI embeddings, allowing the chatbot to search and retrieve relevant code snippets efficiently.
- Conversational Interface: When a user asks a question about the code, the chatbot searches for the relevant portions of the code and generates a human-readable answer.
The end result is a chatbot that can answer specific questions about the code. For example, a non-technical stakeholder could ask, “What are the deep learning models used in this project?” or “Can you explain how hyperparameter tuning is handled in this project?” and receive an explanation that doesn’t require any programming knowledge.
Real-World Applications of Code Chatbots
The potential applications for a tool like this are vast. Here are just a few examples where it could be useful:
1. Business Stakeholders Understanding Product Development
In any software development project, business leaders need to ensure that the product being developed aligns with their vision. This can be challenging when they don’t have a deep understanding of the underlying code. With the code chatbot, these stakeholders can interact with the code directly and get clear explanations about what’s being developed, how it works, and whether it meets their requirements.
For instance, if a project manager is overseeing the development of a machine learning product, they might want to know how the model was trained, which algorithms were used, and how the performance was optimized. The chatbot can provide those answers in simple, business-friendly language.
2. Client Communication in Software Development
Software development often involves iterative work with clients who need updates on progress. Rather than constantly relying on developers to provide explanations, the chatbot allows clients to directly query the code and understand exactly how features are being built. This fosters a deeper understanding and can help clients make more informed decisions during the development process.
Imagine a scenario where a client is unsure about a new feature being added to their app. They can simply ask the chatbot, “What does this new feature do in the code?” and receive a detailed explanation, removing ambiguity from the communication.
3. Onboarding New Team Members
Onboarding new team members — especially non-technical ones — can be a time-consuming process. Understanding an existing codebase is often one of the hardest challenges for new hires. This chatbot can serve as an assistant to help new employees (who may not be developers) get up to speed with the project by answering questions about how different components of the code work.
For example, a new product manager joining the team could use the chatbot to learn about the code’s structure and understand the business logic behind the application without having to rely on senior developers for every little detail.
4. Technical Support for Non-Technical Users
This chatbot can also be used in customer support scenarios where users or clients encounter technical issues. Instead of contacting the technical team for every minor issue, users can ask the chatbot about the functionality or behavior of certain code features, reducing the burden on the tech team and speeding up troubleshooting.
Why This Project Matters
This project is about democratizing access to technical knowledge. By enabling non-technical stakeholders to engage with the code directly through a chatbot, we reduce dependency on developers for every minor query. This leads to:
- Improved Collaboration: Developers and non-technical teams can communicate more effectively, with both sides having a better understanding of the project.
- Faster Decision-Making: With easy access to code-related explanations, stakeholders can make decisions faster and with more confidence.
- Increased Efficiency: Developers can spend more time coding and less time explaining the same concepts repeatedly.
Ultimately, this project is about making code more accessible. It’s about breaking down the barriers between technical and non-technical people, creating a more efficient, collaborative, and understanding work environment.
A Step-by-Step Breakdown of How the Chatbot Works
Now that we understand the high-level problem and solution, let’s dive into the technical implementation of the chatbot itself. Below, I’ll walk you through the key steps involved in setting up this chatbot, using the powerful LangChain framework and various tools for processing code, embeddings, and generating responses.
Step 1: Cloning the GitHub Repository
The first step is to clone the GitHub repository containing the code that the chatbot will interact with. Here, we’re using Python’s Git library to clone the repository directly from GitHub:
from git import Repo
repo_path = "Path-to-Repo"
repo = Repo.clone_from("https://github.com/Ravjot03/Fashion-Recommendation-System", to_path=repo_path)Step 2: Loading and Parsing the Code
Once the repository is cloned, we load the code files using the GenericLoader from LangChain’s community loaders. This allows us to filter and parse only the relevant files (in this case, Python files):
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import LanguageParser
from langchain_text_splitters import Languageloader = GenericLoader.from_filesystem(
repo_path,
glob="**/*",
suffixes=[".ipynb"],
exclude=["**/non-utf8-encoding.ipynb"],
parser=LanguageParser(language=Language.PYTHON, parser_threshold=500),
)
documents = loader.load()Step 3: Splitting the Code into Chunks
After loading the code, it is split into smaller chunks to make processing easier. LangChain’s RecursiveCharacterTextSplitter handles this efficiently:
from langchain_text_splitters import RecursiveCharacterTextSplitter
python_splitter = RecursiveCharacterTextSplitter.from_language(
language=Language.PYTHON, chunk_size=1200, chunk_overlap=200
)
texts = python_splitter.split_documents(documents)Step 4: Creating the Embedding Database
Next, the code is embedded using OpenAI’s embeddings model, which converts the text into vector representations for efficient searching:
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddingsdb = Chroma.from_documents(
texts,
OpenAIEmbeddings(openai_api_key="MYAPIKEY", disallowed_special=())
)
retriever = db.as_retriever(
search_type="mmr",
search_kwargs={"k": 8},
)Step 5: Setting Up the Question-Answering Chain
With the embeddings in place, we can use a question-answering chain to generate answers based on the retrieved code:
from langchain.chains.question_answering import load_qa_chain
from langchain_openai import OpenAIchain = load_qa_chain(OpenAI(), chain_type="stuff")Step 6: Running Inference and Answering Queries
Finally, we can perform inference by asking questions about the code, and the chatbot will return relevant answers:
def inference(query):
docs = db.similarity_search(query)
result = chain.run(input_documents=docs, question=query)
return resultquery1 = "What are the deep learning models used in the repo, can you explain them?"
result = inference(query1)
print("Query:", query1)
print("Answer:", result)
query2 = "Explain the code used for hyperparameter tuning in the above repo"
result = inference(query2)
print("Query:", query2)
print("Answer:", result)
Step 7: Final Results
Once the inference is complete, the chatbot provides an explanation of the code, allowing non-technical stakeholders to understand complex technical concepts such as deep learning models or hyperparameter tuning — all in plain, business-friendly language.
The Future of Code Chatbots
As AI continues to advance, we can imagine even more sophisticated versions of this chatbot that can understand more complex programming languages, offer deeper insights, and integrate with more systems. Future iterations might even be able to provide suggestions for improving the code or automatically generate documentation for the codebase.
The possibilities are endless. For now, this project represents a meaningful step toward making code more transparent, understandable, and accessible for everyone, no matter their technical background.
