📄 AskMyPDF AI — PDF RAG Chatbot

A smart digital assistant that lets you "talk" to your PDF documents and get instant answers — without reading through every page.

🔗 Live Demo: pdf-rag-chatbot-five.vercel.app

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✨ Features

• 📤 Upload any PDF — Support for single or multiple PDF documents
• 💬 Conversational Q&A — Ask questions in natural language and get context-aware answers
• 🔍 RAG-Powered Retrieval — Uses Retrieval-Augmented Generation to find the most relevant chunks before answering
• ⚡ Fast Vector Search — FAISS-powered semantic search for low-latency results
• 🧠 LLM Integration — Leverages large language models via LangChain for intelligent responses
• 🌐 Modern UI — Clean, responsive Next.js frontend

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🛠️ Tech Stack

Layer	Technology
Frontend	Next.js (TypeScript)
Backend	FastAPI (Python)
LLM	Groq (fast LLM inference)
Embeddings	Cohere Embeddings
AI Framework	LangChain
Vector DB	FAISS (Facebook AI Similarity Search)
Deployment	Vercel (frontend) Render (Backend)

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📁 Project Structure

pdf-chatbot/
├── backend/
│   ├── app/
│   │   ├── services/
│   │   │   └── ai_service.py       # RAG pipeline logic (embedding, retrieval, LLM)
│   │   ├── main.py                 # FastAPI app entry point
│   │   ├── models.py               # Pydantic request/response models
│   │   └── routes.py               # API route definitions
│   ├── venv/                       # Python virtual environment
│   ├── .env                        # Environment variables (API keys)
│   ├── .gitignore
│   ├── requirements.txt            # Python dependencies
│   └── runtime.txt                 # Python runtime version
│
└── frontend/
    ├── src/
    │   └── app/
    │       ├── globals.css
    │       ├── layout.tsx
    │       └── page.tsx            # Main UI page
    └── .gitignore

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🚀 Getting Started

Prerequisites

• Node.js >= 18
• Python >= 3.9
• A Groq API key for LLM inference
• A Cohere API key for embeddings

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1. Clone the repository

git clone https://github.com/Resham011/pdf-rag-chatbot.git
cd pdf-rag-chatbot

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2. Backend Setup

cd backend
python -m venv venv
source venv/bin/activate        # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Create a .env file in the backend/ directory:

GROQ_API_KEY=your_groq_api_key_here
COHERE_API_KEY=your_cohere_api_key_here

Start the FastAPI server:

uvicorn app.main:app --reload --port 8000

The backend will be available at http://localhost:8000.

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3. Frontend Setup

cd frontend
npm install

Create a .env.local file in the frontend/ directory:

NEXT_PUBLIC_API_URL=http://localhost:8000

Start the development server:

npm run dev

The app will be available at http://localhost:3000.

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🧠 How It Works

1. Upload — User uploads a PDF via the frontend.
2. Parse & Chunk — The backend extracts text and splits it into overlapping chunks.
3. Embed — Each chunk is converted into a vector embedding using Cohere Embeddings.
4. Index — Embeddings are stored in a FAISS vector index.
5. Query — User asks a question; it is embedded (via Cohere) and the top-k most relevant chunks are retrieved.
6. Generate — The retrieved context + question are sent to a Groq-powered LLM (via LangChain) to produce an accurate, grounded answer.

PDF Upload → Text Extraction → Chunking → Embedding → FAISS Index
                                                              ↓
User Question → Embedding → Similarity Search → Top-K Chunks → LLM → Answer

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📡 API Endpoints

Method	Endpoint	Description
POST	/upload	Upload and process a PDF file
POST	/ask	Ask a question about the uploaded PDF
GET	/health	Health check

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🌍 Deployment

• Frontend is deployed on Vercel.
• Backend is deployed on Render.

Make sure to set the appropriate environment variables in your deployment platform.

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🤝 Contributing

Contributions, issues, and feature requests are welcome!

1. Fork the repository
2. Create a new branch: git checkout -b feature/your-feature
3. Commit your changes: git commit -m 'Add your feature'
4. Push to the branch: git push origin feature/your-feature
5. Open a Pull Request

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📜 License

This project is open source. See the LICENSE file for details.

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👤 Author

Resham011
GitHub: @Resham011