Agentic-Rag-Student-Career-Guidance 🤖🎓

Welcome to Agentic-Rag-Student-Career-Guidance: an AI-powered platform designed to help students navigate their study journey and career path. Combining state-of-the-art Retrieval-Augmented Generation (RAG), document processing, and a modern frontend, this project delivers personalized guidance and answers to student questions.

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🚀 Introduction

Agentic-Rag-Student-Career-Guidance leverages advanced AI (Azure OpenAI) and robust retrieval mechanisms (FAISS) to deliver intelligent, context-aware responses to student queries. Upload documents (PDF/TXT/MD), chat with the bot, and get tailored guidance for your academic and career choices—all through a sleek, responsive interface.

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✨ Features

• Document Processing: Load, clean, and chunk PDFs, TXT, and Markdown files for knowledge ingestion.
• Retrieval-Augmented Generation (RAG): Embeddings and FAISS-based retrieval for relevant context.
• Azure OpenAI Integration: High-quality answer generation using LLMs.
• Modern Frontend: Responsive UI built with Next.js, Tailwind CSS, and Gradio; optimized for all devices.
• Multi-Document Support: Auto-load documents from a directory for seamless knowledge updates.
• Customizable Themes: Light/dark mode toggle and stylish visuals.
• Student-Focused Chatbot: Instant answers to educational and career questions.

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🛠️ Installation

Backend Setup

1. Clone the repository

   git clone https://github.com/your-org/Agentic-Rag-Student-Career-Guidance.git
   cd Agentic-Rag-Student-Career-Guidance

2. Create a virtual environment and install dependencies

   python3 -m venv venv
   source venv/bin/activate
   pip install -r backend/requirements.txt

3. Set up environment variables

   • Create a .env file in backend/ with your Azure OpenAI credentials.

Frontend Setup

1. Navigate to frontend directory

   cd frontend

2. Install Node.js dependencies

   npm install

3. Start the frontend

   npm run dev

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📚 Usage

1. Add your study documents

   • Place PDF, TXT, or MD files into the data/ directory.

2. Start the backend server

   • Run your Gradio app:

     python backend/app.py

   • Or run the Next.js frontend:

     npm run dev

3. Interact with the chatbot

   • Visit the local URL shown in your terminal.
   • Upload or select documents, ask questions, and get instant, context-rich answers.

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🤝 Contributing

We welcome contributions! To get started:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Commit your changes and push (git push origin feature/your-feature).
4. Open a Pull Request and describe your changes.

Please check CONTRIBUTING.md for guidelines.

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📄 License

This project is licensed under the MIT License.
Feel free to use, modify, and share—just keep the credits!

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🗂️ Project Structure

Agentic-Rag-Student-Career-Guidance/
│
├── backend/
│   ├── llm.py          # Azure OpenAI answer generation
│   ├── processing.py   # Document loading & chunking
│   ├── rag.py          # Embeddings & FAISS retrieval
│   └── app.py          # Gradio app server
│
├── frontend/
│   ├── app/            # Next.js app, global styles, layouts
│   ├── components/     # Chat input, chat interface, UI components
│   ├── components.json # UI config
│   └── README.md       # Frontend-specific docs
│
└── data/               # Uploaded study documents

💡 Get In Touch

Questions, suggestions, or feedback?
Open an issue or reach out via GitHub Discussions!

Empowering students with intelligent guidance.
Start your learning journey today! 🚀

License

This project is licensed under the MIT License.

🔗 GitHub Repo: https://github.com/Tharanika-R-Git/Agentic-Rag-Student-Career-Guidance