🚀 FirstPR - AI Mentor for Open Source Contributors

An AI-powered multi-agent mentorship system that helps beginner developers make their first open-source contributions.

FirstPR dynamically ingests any GitHub repository on demand, builds a structure-aware index, and uses a team of local AI agents to explain codebases, answer questions, estimate issue difficulty, and provide actionable contribution paths.

✨ Key Features

• 🤖 100% Local AI Execution - Runs entirely on your machine using Ollama (privacy-first, zero API costs)
• 🧠 Multi-Agent RAG System - Issue Analyzer, Retrieval Agent, Reasoning Agent, and Q&A Agent working together
• 💾 Persistent ChromaDB Storage - Vector embeddings saved locally, no re-ingestion needed
• 🔍 Hybrid Search - Combines semantic embeddings with BM25 keyword search for accurate code retrieval
• 📊 Issue Difficulty Analysis - Automatically categorizes issues by type, difficulty, and required skills
• 🎯 Step-by-Step Contribution Guides - Get beginner-friendly explanations with optional code patches
• 💬 Interactive Codebase Q&A - Ask questions about any part of the repository
• ⚡ Optimized for Apple Silicon - Highly optimized for M-series chips (also supports AMD/NVIDIA GPUs)
• 🌐 Modern React Frontend - Beautiful, responsive UI built with React, Vite, and Tailwind CSS

🏗️ Architecture

FirstPR consists of two main components:

Backend (FastAPI + Ollama + ChromaDB)

• FastAPI server with rate limiting and CORS support
• Ollama running qwen2.5-coder:1.5b for 32k context AI reasoning
• ChromaDB for persistent vector storage and metadata
• HuggingFace BGE-base for local text embeddings
• Hybrid RAG Pipeline combining semantic and keyword search

Frontend (React + Vite + Tailwind)

• React 19 with modern hooks and components
• Vite for lightning-fast development and builds
• Tailwind CSS 4 for responsive, beautiful UI
• Framer Motion for smooth animations
• Lucide React for crisp, modern icons

📋 Prerequisites

Before getting started, ensure you have:

1. Python 3.9+ - Download Python
2. Node.js 18+ - Download Node.js
3. Git - Download Git
4. Ollama - Download Ollama

🚀 Quick Start

1. Clone the Repository

git clone https://github.com/AkibDa/FirstPR.git
cd FirstPR

2. Backend Setup

# Navigate to backend directory
cd backend

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Pull the AI model (required for first-time setup)
ollama pull qwen2.5-coder:1.5b

# Start Ollama server (in a separate terminal)
ollama serve

# Start the FastAPI backend
uvicorn main:app --reload

Backend will be available at http://127.0.0.1:8000

3. Frontend Setup

# Navigate to frontend directory (from project root)
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

Frontend will be available at http://localhost:5173

📖 Usage

API Endpoints

Visit the interactive API documentation at http://127.0.0.1:8000/docs

1. Load a Repository

POST /api/load-repo

{
  "repo_url": "https://github.com/TheAlgorithms/Python"
}

2. Analyze an Issue

POST /api/analyze-issue?generate_patch=true

{
  "repo_url": "https://github.com/TheAlgorithms/Python",
  "issue_url": "https://github.com/TheAlgorithms/Python/issues/42"
}

Or provide manual issue details:

{
  "repo_url": "https://github.com/TheAlgorithms/Python",
  "issue_title": "Fix binary search edge case",
  "issue_text": "The binary search fails when array is empty..."
}

3. Ask Questions About the Codebase

POST /api/ask

{
  "repo_url": "https://github.com/TheAlgorithms/Python",
  "question": "How does the binary search algorithm handle edge cases?"
}

📂 Project Structure

FirstPR/
├── backend/                 # FastAPI backend
│   ├── main.py             # Application entry point
│   ├── api.py              # API route definitions
│   ├── services.py         # RAG pipeline and AI agents
│   ├── schemas.py          # Pydantic data models
│   ├── utils.py            # Helper functions
│   ├── config.py           # Configuration management
│   ├── limiter.py          # Rate limiting
│   ├── requirements.txt    # Python dependencies
│   └── chroma_db/          # Persistent vector storage (auto-generated)
│
├── frontend/               # React frontend
│   ├── src/                # Source code
│   ├── public/             # Static assets
│   ├── index.html          # HTML entry point
│   ├── package.json        # Node dependencies
│   ├── vite.config.js      # Vite configuration
│   └── eslint.config.js    # ESLint configuration
│
└── README.md               # This file

🛠️ Tech Stack

Backend

• FastAPI - Modern Python web framework
• Ollama - Local LLM inference engine
• ChromaDB - Vector database for embeddings
• HuggingFace Transformers - BGE-base embeddings
• SlowAPI - Rate limiting middleware
• Pydantic - Data validation

Frontend

• React 19 - UI library
• Vite 8 - Build tool and dev server
• Tailwind CSS 4 - Utility-first CSS framework
• Framer Motion - Animation library
• Lucide React - Icon library

AI Models

• qwen2.5-coder:1.5b - 32k context window, optimized for code
• BAAI/bge-base-en-v1.5 - Embeddings for semantic search

🌟 Features in Detail

Multi-Agent System

1. Issue Analyzer Agent

   • Categorizes issue type (bug, feature, documentation, etc.)
   • Estimates difficulty level (beginner, intermediate, advanced)
   • Identifies required skills and technologies

2. Retrieval Agent

   • Hybrid search using semantic embeddings + BM25 keyword matching
   • Traces imports and file dependencies
   • Smart filtering (ignores SVGs, CSVs, lockfiles)

3. Reasoning Agent

   • Reads raw source code with full context
   • Generates step-by-step contribution guides
   • Optionally creates unified diff patches

4. Q&A Agent

   • Allows free-form codebase exploration
   • Contextual answers based on repository structure
   • Helps users learn and understand the project

Performance Optimizations

• Asynchronous processing for large repositories
• Batch embedding to prevent VRAM overflow
• Persistent storage eliminates re-ingestion
• Smart chunking for optimal context windows
• Throttled requests for stable performance

🔒 Privacy & Security

• ✅ 100% local execution - No data sent to external APIs
• ✅ No telemetry - Your code stays on your machine
• ✅ Rate limiting - Protection against abuse
• ✅ CORS configured - Secure cross-origin requests

🤝 Contributing

Contributions are welcome! This project itself is designed to help beginners contribute to open source.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is open source and available under the MIT License.

👥 Authors

• Sk Akib Ahammed - GitHub
• Jeet Sarkar - GitHub

🙏 Acknowledgments

• Ollama - For making local AI accessible
• ChromaDB - For efficient vector storage
• HuggingFace - For open-source embeddings
• FastAPI - For the excellent Python web framework
• React & Vite - For modern frontend development

📞 Support

Having issues?

• Check the Backend README for detailed backend setup
• Check the Frontend README for frontend configuration
• Open an issue on GitHub

🚀 Deployment

The application can be deployed using:

• Backend: Any cloud platform supporting Python (Railway, Render, Fly.io)
• Frontend: Vercel, Netlify, or any static hosting service

Current deployment: https://firstpr-mentor.vercel.app

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Built with 💚 to help developers make their first open-source contributions