🚀 Ragfolio: AI-Powered RAG Portfolio

Ragfolio is a modern, high-performance personal portfolio featuring an integrated AI Chatbot. It uses RAG (Retrieval-Augmented Generation) to answer questions about your professional experience using your resume as the primary knowledge source.

---

🛠️ Tech Stack

• Frontend: React, Vite, Tailwind CSS, Framer Motion.
• Backend: FastAPI (Python), Uvicorn.
• AI/RAG: ChromaDB (Vector Store), FastEmbed (Embeddings), Google Gemini Flash 1.5 (LLM).
• Package Management: uv (Python), npm (Node.js).

---

📋 Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.12+
• uv (Extremely fast Python package manager)
• Node.js & npm
• A Google Gemini API Key (Get it from Google AI Studio)

---

💻 Local Development

1. Setup Environment Variables

Create a .env file in the root of the project:

GEMINI_API_KEY=your_api_key_here

Also, ensure frontend/.env points to the local backend:

VITE_API_BASE_URL=http://localhost:8000/api

2. Ingest Resume Data

This converts everything in rag/input-data/ into a searchable vector database (recommended: split your info across multiple files, not just a single resume).

uv run rag/create-embeddings.py

3. Start Backend (Terminal 1)

cd backend
uv run python main.py

Backend runs at http://localhost:8000.

4. Start Frontend (Terminal 2)

cd frontend
npm install
npm run dev

Frontend runs at http://localhost:5000 (proxied to port 8000 for /api calls).

---

🌐 Production Deployment (Render)

This project is optimized for a Unified Deployment on Render (serving both Frontend and Backend from a single Python service).

Step-by-Step Guide:

1. Push to GitHub: Ensure all changes are committed and pushed to your repo.
2. Create Web Service: In the Render Dashboard, click New + -> Web Service.
3. Connect Repository: Select your ragfolio repository.
4. Configuration:
   • Runtime: Python
   • Build Command: ./render-build.sh
   • Start Command: python -m uvicorn backend.main:app --host 0.0.0.0 --port $PORT
5. Advanced / Environment Variables:
   • Add GEMINI_API_KEY: (Your actual key)
   • Add PYTHON_VERSION: 3.12.0
6. Deploy: Click Create Web Service.

How the Unified Build Works:

The ./render-build.sh script automatically:

1. Builds the React production files into frontend/dist.
2. Installs Python dependencies from requirements.txt.
3. Runs the RAG ingestion script to prepare the database on the server.
4. The FastAPI backend serves the dist folder as static files while maintaining the /api endpoints for the chatbot.

---

🏗️ Architecture Note

• API Prefixing: All backend routes are prefixed with /api to avoid collisions with frontend routes.
• SPA Support: The backend includes a catch-all route that serves index.html for any non-API path, allowing React Router to work perfectly in production.

---

�️ Related Projects

git-lrc: Free, Unlimited AI Code Reviews That Run on Commit. Stop bugs before they land.