🌟 Gen Wagon - Personalized Learning Marketplace

Gen Wagon is a modern GenAI-powered learning platform that delivers personalized, modular course bundles based on users’ skills, goals, and preferences. It analyzes resumes and chats, retrieves targeted content via embeddings and FAISS, and orchestrates intelligent agents using LangGraph.

Built with:

• 🔧 FastAPI + Pydantic AI for structured backend logic
• 🧠 LangGraph + GPT-4o for LLM-powered decision making
• 🔍 FAISS for fast semantic course matching
• ⚛️ React + Chakra UI for a seamless frontend experience

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🚀 Quickstart (Local Development)

🧰 Prerequisites

• Python ≥ 3.10
• Node.js ≥ 18
• npm ≥ 9

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1. Backend Setup

cd backend
python -m venv .venv
source .venv/bin/activate  # or .venv/Scripts/activate on Windows
pip install -r requirements.txt
cp .env.example .env

✏️ Edit .env and set your API keys:

OPENAI_API_KEY=sk-...

Start the server:

uvicorn main:app --reload

📚 API Docs: http://localhost:8000/docs
📡 Default Port: 8000

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2. Frontend Setup

cd frontend
npm install
npm run dev

🎨 Dev Server: http://localhost:5173

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🧠 Key Features

• Resume Agent: Extracts skills, experience, preferences from free-text resumes
• Conversation Agent: Understands user goals, budget, preferences from chat
• Course Retriever: Embedding + FAISS-powered semantic search
• Module Selector Agent: Picks relevant subtopics using GPT-4o with rationale
• Pricing Agent: Optimizes bundles within budget
• Quiz Agent: Generates assessments from selected modules
• API: /recommend-bundle — Returns a complete personalized learning plan

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📦 API Reference

POST /recommend-bundle

Request:

{
  "resume_text": "Experienced data scientist with Python and SQL...",
  "chat_transcript": "I want to transition into ML engineering on a budget of €500..."
}

Response:

{
  "summary": "...",
  "skills": ["Python", "SQL"],
  "target_role": "ML Engineer",
  "goal_skills": ["MLOps", "Deep Learning"],
  "skills_gap": ["MLOps", "Model Deployment"],
  "recommended_modules": [
    {
      "course_title": "Advanced ML Engineering",
      "module_title": "MLOps Fundamentals",
      "selected_subtopics": ["CI/CD", "Model Monitoring"],
      "why_selected": "Covers core deployment strategies for ML workflows."
    }
  ],
  "final_bundle": [...]
}

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🧩 Project Structure

│
├── backend/
│   ├── api/              # FastAPI routes
│   ├── llm_agents/       # Modular LLM agents
│   ├── embeddings/       # Course catalog + FAISS logic
│   ├── graph/            # LangGraph DAG orchestration
│   ├── main.py           # Entry point
│   └── .env              # Your secrets (not committed)
│
├── frontend/
│   ├── src/
│   │   ├── components/   # Reusable UI elements
│   │   ├── pages/        # React pages
│   │   └── theme.js      # Chakra UI customization
└──   └── package.json

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🔒 Security Best Practices

• ✅ Secrets managed via .env (auto-loaded with python-dotenv)
• ✅ Inputs validated with Pydantic
• ✅ No hardcoded credentials
• ✅ Responses are sanitized & structured

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🛠 Troubleshooting

Issue	Fix
Backend not starting	Check Python version, .env file, and pip install -r requirements.txt
Frontend error	Run npm install after deleting node_modules
CORS issues	Ensure both frontend and backend are running on correct ports

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📍 Roadmap

• Modular agent design (Pydantic AI)
• LangGraph DAG orchestration
• Resume & Chat understanding
• FAISS + Embeddings
• Frontend polish & dashboard UX
• Session persistence
• Payment integration (Stripe, PayPal)

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👨‍💻 Contributing

We welcome pull requests and ideas!

1. Fork the repo
2. Create a feature branch (git checkout -b feat/your-feature)
3. Submit a PR with context
4. Tag us if it's urgent ✉️

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✨ Authors & Maintainers

• Harshith Naganna, Revan Kumar Dhanasekaran, Paul Gaikwad, Philipp Peter, Edilbert Christhuraj — Lead Developer
• Community Contributors — You!

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📜 License

MIT License

© 2025 Gen Wagon. All rights reserved.