This project provides a streamlined way to ingest GitHub repositories, process their contents, and use Retrieval-Augmented Generation (RAG) with a Language Model to answer questions about the code. This project runs locally and requires Ollama with the LLM of your choice installed.

• GitHub Repository Ingestion: Extracts code summaries, file structures, and content from a given GitHub repository.
• File Storage: Saves ingested data to a local file for persistence.
• Retrieval-Augmented Generation (RAG): Uses a vector-based search system to query indexed documents.
• LLM-Powered Queries: Utilizes an Ollama-powered model to answer questions about the ingested codebase.
• Web Interface: A Flask-based front-end with Bootstrap styling for a clean UI.

Ensure you have Python installed (recommended: Python 3.8+). Then install the required dependencies:

pip install llama-index torch gitingest

Additionally, install Ollama and the LLM of your choice:

ollama pull <your-llm-model>

1. Clone the repository:

   git clone https://github.com/SHASWATSINGH3101/Git_RAG
   cd Git_RAG

2. Run the Flask application:

   python app.py

3. Open your browser and go to:

   http://127.0.0.1:5000/
   

1. Enter a GitHub repository URL to ingest code.
2. Specify directories and embedding model parameters.
3. Input a query to retrieve insights about the code.
4. View results in the web interface.

project_root/
│── data/
│   ├── data.txt
│── index/
│── app.py
│── requirements.txt
│── README.md

You can modify key parameters in app.py:

• Embedding Model: Change embed_model_name to another supported model.
• LLM Model: Update llm_model_name for different LLM variants.
• Index & Data Storage: Adjust index_dir and docs_dir paths.

Which function is used for severity detection in the code?

• Implement user authentication
• Support for multiple repositories
• Additional visualization tools

This project is licensed under the MIT License.

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