Hey! I wanted to share a tool I've been working on. It's still very early and a work in progress, but I've found it incredibly helpful when working with Claude and OpenAI's models.
What it does:
I created a Python script that dumps your entire Git repository into a single file. This makes it much easier to use with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.
Key Features:
- Respects .gitignore patterns
- Generates a tree-like directory structure
- Includes file contents for all non-excluded files
- Customizable file type filtering
Why I find it useful for LLM/RAG:
- Full Context: It gives LLMs a complete picture of my project structure and implementation details.
- RAG-Ready: The dumped content serves as a great knowledge base for retrieval-augmented generation.
- Better Code Suggestions: LLMs seem to understand my project better and provide more accurate suggestions.
- Debugging Aid: When I ask for help with bugs, I can provide the full context easily.
How to use it:
Example: python dump.py /path/to/your/repo output.txt .gitignore py js tsx
Again, it's still a work in progress, but I've found it really helpful in my workflow with AI coding assistants (Claude/Openai). I'd love to hear your thoughts, suggestions, or if anyone else finds this useful!
https://github.com/artkulak/repo2file
P.S. If anyone wants to contribute or has ideas for improvement, I'm all ears!
What it does: I created a Python script that dumps your entire Git repository into a single file. This makes it much easier to use with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.
Key Features: - Respects .gitignore patterns - Generates a tree-like directory structure - Includes file contents for all non-excluded files - Customizable file type filtering
Why I find it useful for LLM/RAG: - Full Context: It gives LLMs a complete picture of my project structure and implementation details. - RAG-Ready: The dumped content serves as a great knowledge base for retrieval-augmented generation. - Better Code Suggestions: LLMs seem to understand my project better and provide more accurate suggestions. - Debugging Aid: When I ask for help with bugs, I can provide the full context easily.
How to use it: Example: python dump.py /path/to/your/repo output.txt .gitignore py js tsx
Again, it's still a work in progress, but I've found it really helpful in my workflow with AI coding assistants (Claude/Openai). I'd love to hear your thoughts, suggestions, or if anyone else finds this useful!
https://github.com/artkulak/repo2file
P.S. If anyone wants to contribute or has ideas for improvement, I'm all ears!