RAG Powered Question-Answering System

This project is a Retrieval-Augmented Generation (RAG) based Question-Answering system. It uses a language model to answer questions based on the content of a provided PDF or text file. The system is built with a FastAPI backend and a Next.js frontend.

Features

• File Upload: Upload PDF or text files to be used as the knowledge base.
• Question-Answering: Ask questions about the content of the uploaded file and get detailed answers.
• Dynamic UI: The user interface is built with Next.js + React and provides a ChatGPT-style experience.
• Markdown, LaTeX & Code Rendering: Responses render Markdown, KaTeX equations, tables, and syntax-highlighted code blocks.
• Responsive Layout: Optimised for both desktop and mobile viewing.

Installation

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

• Python 3.9+
• Pip (or Conda)
• Node.js 18+

Setup Using uv

1. Clone the repository:

   git clone https://github.com/Ghost-141/PDF-QA-System.git
   cd PDF-QA-System

2. Create a virtual environment (recommended):

   uv sync
   .\.venv\Scripts\activate   # Windows
   source .venv/bin/activate  # macOS / Linux

3. Install the dependencies:

   pip install -r requirements.txt

Setup Using Conda

1. Clone the repository:

   git clone https://github.com/Ghost-141/PDF-QA-System.git
   cd PDF-QA-System

2. Create a Conda environment:

   conda create --name pdf_qa python=3.9.23
   conda activate pdf_qa

3. Install the dependencies:

   pip install -r requirements.txt

4. Install the frontend dependencies:

   cd frontend
   npm install

GPU Support (NVIDIA)

For GPU acceleration, you need to install PyTorch with CUDA support. Make sure you have the correct NVIDIA drivers and CUDA Toolkit version installed.

1. Check your NVIDIA driver and CUDA version: You can check your NVIDIA driver version by running nvidia-smi in your terminal. This will also show the highest version of CUDA that is supported.

2. Install PyTorch with CUDA: Visit the PyTorch website to find the correct command for your specific CUDA version. For example, to install PyTorch with CUDA 12.6, you would run:

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

   Note: Using a version of PyTorch with CUDA support is essential for running the model on an NVIDIA GPU. If you do not have a compatible GPU, the embedding model will run on the CPU, which will be significantly slower while processing the pdf.

Configuration

Create a .env file in the root of the project and add your keys:

GROQ_API_KEY=your-groq-api-key
GROQ_MODEL_NAME=openai/gpt-oss-120b   # optional override

Uploads are stored in data/raw and the vector database persists in data/vector_db (created automatically).

Running the Application

Start the backend and frontend in separate terminals.

Backend (FastAPI)

uvicorn backend.main:app --reload

The API is available at http://localhost:8000.

Frontend (Next.js)

cd frontend
npm install      # first time only
npm run dev

The UI is available at http://localhost:3000.

Tip: Set NEXT_PUBLIC_API_URL inside frontend/.env.local if your API is not running on the default host/port.

Project Structure (relevant parts)

• backend/main.py: FastAPI app factory and CORS setup.
• backend/api/: API routers for files and QA (/upload-file, /process-file, /ask).
• backend/services/: File processing, vector store management, QA pipeline, uploads.
• backend/data/: Runtime data; data/raw for uploads, data/vector_db for Chroma persistence.
• frontend/: Next.js client.

Usage

1. Upload a file: Use the upload card in the UI to select a PDF or text file.
2. Process the file: Click "Upload & Process" to push the file to the backend and populate the vector store.
3. Ask a question: Type your question in the chat composer. Responses appear in a ChatGPT-style transcript with full formatting.

API Documentation

The FastAPI backend provides the following endpoints:

• POST /upload-file: Accepts a multipart file upload and stores it in data/raw.
  • Form Field: file (UploadFile, required)
  • Success Response: {"filename": "<stored-filename>"}
• POST /process-file: Processes a previously uploaded file.
  • Query Parameter: filename (string, required)
  • Success Response: {"message": "File '<filename>' processed...", "num_docs": <number>}
  • Error Response: {"detail": "File '<filename>' not found..."}
• POST /ask: Asks a question to the model.
  • Request Body: {"query": "<your-question>"}
  • Success Response: {"answer": "<model-answer>"}
  • Error Response: {"detail": "QA pipeline not initialized..."}

Libraries & Frameworks Used

• FastAPI
• Next.js
• React
• LangChain
• ChromaDB
• PyPDF / PyMuPDF

Upcoming Features

• Support for Bangla Languge
• Process Images and Complex Pdf
• Support for different documents
• Local llm support