📄 PDF QA RAG

A powerful, production-ready PDF Question-Answering (QA) system using Retrieval-Augmented Generation (RAG). This project lets you query PDFs with natural language and get precise, context-aware answers, leveraging state-of-the-art NLP models.

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🚀 Features

• Ask Anything: Query one or multiple PDFs with natural language.
• Retrieval-Augmented Generation (RAG): Combines retrieval of relevant passages & generative AI for accurate answers.
• Scalable Backend: Designed for production & large-scale document sets.
• Easy to Extend: Modular code—swap models, add new features, or integrate with your stack.
• Docker Support: One command to run anywhere.

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🛠️ Tech Stack

• Backend: Python (FastAPI or Flask)
• NLP: HuggingFace Transformers, Sentence Transformers
• Vector Store: FAISS / ChromaDB / Pinecone
• PDF Parsing: PyPDF2 / pdfplumber
• Containerization: Docker

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📦 Installation

1. Clone the Repo

git clone https://github.com/VarunKumar-05/pdf_QA_RAG.git
cd pdf_QA_RAG

2. Install Dependencies

pip install -r requirements.txt

3. (Optional) Build & Run with Docker

docker build -t pdf_qa_rag .
docker run -p 8000:8000 pdf_qa_rag

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💡 Usage

1. Upload PDFs

Place your PDF files in the designated folder (e.g., data/).

2. Start the API

python main.py

3. Query PDFs

Send a POST request to the /ask endpoint:

curl -X POST "http://localhost:8000/ask" \
     -H "Content-Type: application/json" \
     -d '{"question": "What is the main topic of the document?", "pdf": "sample.pdf"}'

Or use the included web UI (if available).

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🧩 Example

Q: "Summarize the key findings on page 5."
A: “Page 5 discusses...”

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⚙️ Configuration

• Model selection: Change the model in config.py (e.g., sentence-transformers/all-MiniLM-L6-v2)
• Vector Store: Switch between Qdrant in vectorstore.py.
• Chunk size, max docs, etc.: Tunable in config.py.

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📝 Project Structure

pdf_QA_RAG/
├── data/                # PDF files to be processed
├── main.py              # Entry point/API server
├── modules/             # Core modules (retriever, reader, etc.)
├── requirements.txt     # Python dependencies
├── Dockerfile           # Containerization config
└── README.md            # This file

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🛣️ Roadmap

• Add web frontend for interactive QA
• Support for more file formats (Word, HTML)
• Multi-lingual support
• Authentication & user management

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🤝 Contributing

Pull requests, issues, and feature requests are welcome!
See CONTRIBUTING.md for details.

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🛡️ License

MIT License

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📬 Contact

Varun Kumar
GitHub Profile

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Star ⭐ the repo if you find it useful!