Turn Your Codebase into a Conversation Partner in Minutes and For Free: Introducing Pyragify

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Ever wished you could talk to your codebase? Imagine this: instead of wading through a spaghetti mess of functions, classes, and comments, you could just ask questions like:

• “Where’s the bug hiding in this module?”
• “What does this legacy function even do?”
• “What’s the next feature to implement?”

Or even turn it into a podcast, and almost no code?

Sounds too good to be true? With pyragify and tools like NotebookLM (or any other RAG-focused app), this future is already here. Whether you’re a developer, manager, or team lead, Py-Ragify makes your codebase not just readable — but conversational.

Chatting With Your Codebase in Minutes — Image by Author

Why Developers and Managers Need This

We’ve all been there:

• A legacy codebase that no one remembers writing.
• A bug report pointing vaguely at a “problematic module.”
• A sprint meeting where no one knows what the next feature should be.
• Or worse, a potential vulnerability waiting to be exploited.

Py-Ragify is here to help. By organizing your code into semantic chunks and connecting it with tools like NotebookLM, you can now:

• Reverse-engineer legacy systems: Ask high-level questions and trace them directly to their code source.
• Find your next features: Spot gaps, opportunities, and hidden gems in your codebase.
• Identify vulnerabilities: Isolate fragile or insecure code for your next iteration.
• Improve trust in results: NotebookLM cites exact code sources, so you know you can rely on the answers.

What Makes Pyragify Stand Out?

Pyragify isn’t just a tool — it’s a bridge between you and your codebase. Here’s what makes it special:

1. NotebookLM Integration: Trust Built In

Unlike other tools, NotebookLM cites the exact code source for every insight or answer. This means:

• You always know where an answer comes from.
• You can trust the system for critical decisions like bug fixes, features, or vulnerability analysis.

This level of transparency is a game-changer for developers and managers alike.

2. Make Legacy Code Manageable

Legacy codebases are like black boxes — complex, fragile, and full of surprises. Py-Ragify simplifies the process:

• Breaks down massive codebases into functions, classes, and comments.
• Splits Markdown into readable sections for documentation clarity.
• Outputs plain text files ready for tools like NotebookLM to process.

Suddenly, the black box isn’t so intimidating anymore.

3. Ask Questions, Get Answers

With Py-Ragify, your codebase becomes a conversation partner. Managers can ask:

• “What are the top priorities for refactoring?”
• “What are the next features we can add?”

Developers can ask:

• “How does this function connect to other parts of the system?”
• “What’s causing this legacy bug?”

How Py-Ragify Works

Using Pyragify is simple and intuitive. Here’s how you can start making sense of your codebase:

1. Install Py-Ragify

Install it via pip:

pip install pyragify

2. Prepare a Configuration

Create a config.yaml to define your settings:

repo_path: /path/to/repository
output_dir: /path/to/output
max_words: 200000
skip_patterns:
  - "*.log"
  - "uv.lock"
skip_dirs:
  - "__pycache__"
  - ".git"
verbose: true

Here’s a concise bullet-point summary of the configuration options for the blog post:

Key Configuration Options in config.yaml

• repo_path: /path/to/repository

Specifies the path to the repository to process. Example: /home/user/projects/my-code-repo.

• output_dir: /path/to/output

Defines where processed output files are saved. Example: /home/user/outputs.

• max_words: 200000

Sets the maximum number of words per output file, ensuring compatibility with tools like NotebookLM. Larger repositories may need higher limits; smaller values create modular chunks.

• skip_patterns:

Ignores files matching specified patterns. Example patterns:

• *.log: Skips log files.
• uv.lock: Ignores lock files.

You can extend the list at your will

• skip_dirs:

Excludes specific directories from processing. Example: __pycache__ to skip Python's bytecode cache or .git to exclude Git metadata.

• verbose: true

Enables detailed logging for transparency during execution. Set to false for a quieter run.

3. Run the Tool

For the best experience, use uv to ensure reproducibility:

uv run python -m pyragify --config-file config.yaml

Alternatively, run it directly:

python -m pyragify.cli process-repo --repo-path /my/repo --output-dir /my/output

4. Check the Outputs

Your codebase will be transformed into semantic chunks saved as .txt files, organized into ./output/remaining/chunk_0.txt

Why It’s a Game-Changer for Teams

For Managers:

• Plan better sprints: Understand your codebase well enough to identify the next steps.
• Validate results: Get trusted answers backed by code citations.
• Discuss vulnerabilities: Work with your team to fix insecure areas.

For Developers:

• Simplify debugging: Pinpoint problems directly in the code source.
• Reverse-engineer effectively: Trace legacy code functionality with clear, chunked outputs.
• Integrate seamlessly: Feed your RAG workflows with high-quality, structured inputs.

Real-Life Use Cases

Case 1: Debugging Legacy Code

A team inherited a massive repository with no documentation. Using Py-Ragify, they chunked the code and asked NotebookLM:

• “What does this legacy function do?”
• “Are there any functions with missing documentation?”

Answers were sourced and cited directly from the codebase, allowing the team to trust and act on the results.

Case 2: Planning New Features

In a sprint meeting, the manager used NotebookLM with Py-Ragify to ask:

• “What unused methods could be extended for feature X?” The system not only found the method but also cited where it was implemented, streamlining the discussion.

Future-Proofing Your Codebase

We’re not stopping here. The roadmap for Py-Ragify includes:

• PDF Support: Output directly as PDFs for easy sharing with non-technical stakeholders.
• Cloud Integration: Seamlessly upload processed files to S3 or Google Drive.
• Multimodal Expansion: Add support for richer data sources like audio or video.

Try It Today

Turn your codebase into a trusted discussion, planning, and decision-making partner. Whether you’re a manager looking for clarity or a developer solving tough problems, Py-Ragify is here to help.

Install Py-Ragify today and let the conversation with your codebase begin:

pip install pyragify

Have questions or feedback? Let us know on GitHub. Together, let’s make every codebase a little smarter. Pyragify is an extension of this script.