📚 RAG Document Assistant

A full-stack AI application that enables users to upload PDF documents and engage in natural language conversations with them. Built using Retrieval-Augmented Generation (RAG) to provide accurate, context-aware answers based on document content.

🚀 Features

• 📄 Smart PDF Ingestion: Uploads, cleans, and chunks PDF text automatically.
• 🧠 Context-Aware AI: Uses Google Gemini Pro to answer questions based only on the uploaded document.
• ⚡ Vector Search: Powered by Pinecone for high-speed semantic similarity search.
• 🛡️ Robust Backend: Built with FastAPI, featuring rate-limit handling and secure environment management.
• ✨ Modern UI: Clean, responsive interface built with Next.js 14 and Tailwind CSS.

🛠️ Tech Stack

• Frontend: Next.js, React, Tailwind CSS, Axios
• Backend: Python, FastAPI, Uvicorn
• AI & Logic: LangChain, Google Gemini API (LLM & Embeddings)
• Database: Pinecone (Vector Database)

⚙️ Installation & Setup

1. Clone the Repository

git clone [https://github.com/AKG451/rag-document-assistant.git](https://github.com/AKG451/rag-document-assistant.git)
cd rag-document-assistant

2. Backend Setup

The backend handles document processing, vector storage, and AI interactions.

cd backend

Create and activate a virtual environment:

# Windows
python -m venv venv
venv\Scripts\activate

# Mac/Linux
python3 -m venv venv
source venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Set up your environment variables (see Configuration below).

Start the FastAPI server:

uvicorn main:app --reload

Backend runs at:
👉 http://localhost:8000

API Docs:
👉 http://localhost:8000/docs

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3. Frontend Setup

Open a new terminal and navigate to the frontend directory:

cd ../frontend

Install dependencies:

npm install

Start the development server:

npm run dev

Frontend runs at:
👉 http://localhost:3000

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🔑 Configuration

Create a .env file inside the backend directory.

# AI Provider
GOOGLE_API_KEY=your_gemini_api_key_here

# Vector Database
PINECONE_API_KEY=your_pinecone_api_key_here
PINECONE_INDEX_NAME=rag-app

Get your API keys:

• Google Gemini API
• Pinecone API

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📖 Usage

Upload a Document

• Click Upload PDF
• Select a PDF file
• Wait for processing (Clean → Chunk → Embed → Index)

Ask Questions

• Type a question in the chat bar
• Example:
  • "Summarize section 3"
  • "What are the main conclusions?"

The AI retrieves relevant chunks and generates an answer using Gemini Pro.

Clear Context

• Use the Reset button to clear the document and chat history.

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📂 Project Structure

rag-document-assistant/
├── backend/
│   ├── app/
│   │   ├── api/          # API Endpoints
│   │   ├── core/         # Config & Security
│   │   ├── services/     # RAG Logic
│   │   └── utils/        # PDF Parsing & Chunking
│   ├── main.py
│   └── requirements.txt
├── frontend/
│   ├── components/      # React Components
│   ├── app/             # Next.js Pages
│   └── public/
└── README.md

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🔮 Future Roadmap

• Add support for .docx and .txt files
• Implement chat history persistence (SQL Database)
• Add source citations for answers
• Deploy frontend to Vercel and backend to Render

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🤝 Contributing

Contributions are welcome!
Feel free to open an issue or submit a pull request.

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📄 License

This project is licensed under the MIT License.