📄 Docify-RAG: Chat with Your PDFs using RAG + Gemini AI

Docify-RAG is a full-stack AI-powered chatbot that lets you upload any PDF file and ask natural language questions about its contents. It uses Retrieval-Augmented Generation (RAG) with Gemini AI to deliver accurate, contextual answers — like ChatGPT, but focused on your documents.

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🚀 Tech Stack

Layer	Tech
Frontend	React (Vite) + Tailwind CSS
Backend	Flask (Python)
Vector DB	FAISS
Embeddings	Sentence Transformers
LLM	Gemini Pro (Google AI)
PDF Parsing	PyPDF
Auth	.env API key loading

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📸 Screenshots

🏠 Home Page

📤 File Upload

💬 Chat Interface

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📁 Folder Structure

project-root/
├── Docify-RAG/      # React frontend (Vite)
├── Server/          # Flask backend
├── .gitignore
├── package.json     # Runs both client and server

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⚙️ Setup Instructions

1. Clone the Repo

git clone https://github.com/your-username/docify-rag.git
cd docify-rag

2. Install Client & Server Dependencies

# Install React client
cd Docify-RAG
npm install

# (Optional) Add Tailwind if not already:
# npx tailwindcss init -p

# Go back and create virtual environment for Flask
cd ../Server
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt

3. Set Up Environment Variables

Create a .env file in /Server:

GOOGLE_API_KEY=your_google_api_key_here

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▶️ Run the App

At the root of the project:

npm install concurrently --save-dev
npm run dev

This will:

• Start Flask backend on http://localhost:5000
• Start React frontend on http://localhost:5173

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🧠 How It Works

1. Upload a PDF.

2. The backend extracts, chunks, and embeds the text.

3. When you ask a question:

   • It retrieves top-k relevant chunks using FAISS
   • Then it builds a prompt using Gemini + conversation history

4. Gemini generates a natural, contextual answer.

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✨ Features

• Natural chat interface
• Context-aware RAG using FAISS
• Supports any text-based PDF
• Realtime answers powered by Gemini
• Works locally, no external database needed

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📦 Future Improvements

• Streamed responses (typing effect)
• File management and history
• Support for image-based PDFs (OCR)
• Multi-user authentication

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📄 License

MIT License.