🤖 GitHub AI Assistant with RAG and LangChain 💀

This repository contains a GitHub AI assistant implemented in Python, leveraging Retrieval-Augmented Generation (RAG) and LangChain. This agent is designed to help users interact with GitHub repositories by retrieving and summarizing information such as issues, pull requests, and more.

Table of Contents

• Features
• Prerequisites
• Installation
• Usage
• Project Structure
• Example
• Contributing
• License
• Acknowledgments
• Contact

Features

• GitHub Repository Interaction: Select a GitHub repository and retrieve information such as open issues, pull requests, or file contents.
• Issue Summarization: Summarizes issues for better understanding and quicker decision-making.
• Retrieval-Augmented Generation (RAG): Combines information retrieval with generative AI for precise and context-aware responses.
• LangChain and Hugging Face Integration: Uses LangChain for efficient chaining of multiple language model queries and Hugging Face models for generative responses.
• Poetry Dependency Management: Simplifies dependency management and project setup.

Prerequisites

Before you begin, ensure you have the following installed on your system:

• Python 3.8 or higher
• Poetry (for dependency management)

Installation

1. Clone the repository:

   git clone https://github.com/sakhileln/GitHub-Agent.git
   cd GitHub-Agent/

2. Install dependencies using Poetry:

   poetry install

3. Activate the virtual environment:

   poetry shell

Usage

1. Ensure you have access to the GitHub repository you wish to analyze and have a GitHub personal access token (PAT) with appropriate permissions.

2. Configure your environment by adding the GitHub token:

   export GITHUB_TOKEN=your_personal_access_token

3. Run the AI assistant:

   python main.py

4. Select a repository and interact with the AI agent to retrieve and summarize data.

Example commands:

• "Summarize the open issues in this repository."
• "List the pull requests and their statuses."

Project Structure

• main.py: The entry point of the application.
• retriever.py: Handles interactions with the GitHub API and retrieves repository data.
• summarizer.py: Uses LangChain and Hugging Face models to generate summaries from retrieved data.
• pyproject.toml: Poetry configuration file for dependencies.
• README.md: Documentation for the project.

Example

To get a quick sense of how it works, try the following after running the script:

1. Select a GitHub repository.
2. Ask the assistant to summarize open issues.

Example Query:

Summarize the open issues in this repository.

Example Response:

There are 5 open issues:
1. Bug in the login flow causing 500 errors (critical).
2. Feature request: Add dark mode support.
...

Contributing

Contributions are welcome! If you'd like to contribute. See the CONTRIBUTING file for details.

1. Fork the repository.
2. Create a new branch for your feature/bug fix:

   git checkout -b feature/YourFeature

3. Make your changes and test thoroughly.
4. Submit a pull request explaining your changes.

License

This project is licensed under the GPL v3.0 License. See the LICENSE file for details.

Acknowledgments

• LangChain: For providing robust tools to handle language model operations.
• Hugging Face: For providing versatile and high-quality machine learning models.
• GitHub: For offering a robust platform for collaboration and version control.

Contact

• Sakhile III
• LinkedIn Profile
• GitHub Profile