📄 Ask PDF - Intelligent PDF Chat Assistant

Ask PDF is a high-performance, AI-driven backend that allows users to chat with their PDF documents. Using Retrieval-Augmented Generation (RAG), the system extracts text from PDFs, generates semantic embeddings, and provides context-aware answers to user questions using state-of-the-art language models.

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✨ Features

• 📂 PDF Processing – Extract text and metadata from PDF files using pypdf.
• 🧠 Semantic Search – Powered by SentenceTransformers (all-MiniLM-L6-v2) for fast and accurate context retrieval.
• ⚡ Lazy Loading – Optimized for cloud deployment (Render / Heroku / Vercel) to reduce cold start time.
• 💬 Contextual AI – Integrated with NVIDIA DeepSeek for high-quality, document-grounded responses.
• 🕒 Chat History – Persistent conversation tracking integrated with Supabase.
• 🛡️ Rate Limiting – Built-in protection using slowapi to ensure API stability.

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🚀 Live Demo

Frontend https://ask-pdf-gray.vercel.app

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🛠️ Tech Stack

• Framework: FastAPI
• Embeddings: Sentence-Transformers (CPU optimized)
• LLM API: NVIDIA Integrated (DeepSeek-v3)
• Database: Supabase (PostgreSQL)
• PDF Engine: PyPDF
• Security: Slowapi (Rate Limiting) & CORS

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🔧 Installation & Setup

1. Prerequisites

• Python 3.10 or higher
• A Supabase account (for chat history storage)
• An NVIDIA API key

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2. Clone the Repository

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

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3. Setup Virtual Environment

python -m venv venv

# Windows
.\venv\Scripts\activate

# Linux / macOS
source venv/bin/activate

pip install -r requirements.txt

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4. Configuration

Create a .env file in the root directory:

SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_anon_key
NVIDIA_API_KEY=your_nvidia_api_key

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5. Run Locally

python -m uvicorn main:app --reload

The API will be available at:

http://localhost:8000

API documentation:

http://localhost:8000/docs

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☁️ Deployment Guides

Deploy Frontend to Vercel

1. Push your code to a GitHub repository.
2. Go to Vercel → Add New → Project.
3. Import your repository.
4. Set Root Directory to frontend.
5. Add Environment Variables:

NEXT_PUBLIC_API_URL=https://your-render-backend-url
NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key

6. Click Deploy.

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Deploy Backend to Render

1. Create a Web Service on Render.
2. Build Command

pip install -r requirements.txt

3. Start Command

uvicorn main:app --host 0.0.0.0 --port $PORT

4. Add Environment Variables:

SUPABASE_URL
SUPABASE_KEY
NVIDIA_API_KEY

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📄 License

Distributed under the MIT License. See LICENSE for more information.

❤️ Made for the AI Community

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