DocuMind : RAG-Based PDF Chatbot

A full-stack application that allows users to upload PDF documents and chat with them using RAG (Retrieval-Augmented Generation). The application uses Google's Gemini Pro model for generation and Qdrant for vector storage.

Features

• 1. PDF Upload: Upload and index PDF documents.
• 2. Interactive Chat: Chat with your documents using natural language.
• 3. RAG Pipeline: Uses LangChain and Google Generative AI embeddings.
• 4. Modern UI: Built with React and Vite, featuring dark mode and Markdown support.
• 5. Fast Backend: Powered by FastAPI.

Tech Stack

• Frontend: React, Vite, Axios, React Markdown
• Backend: FastAPI, Uvicorn, Python
• AI/ML: Google Gemini (via google-genai), LangChain
• Vector DB: Qdrant (Docker)

Prerequisites

• Python 3.10+
• Node.js & npm
• Docker Desktop (for Qdrant)
• Google API Key

Installation & Setup

1. Clone the Repository

git clone https://github.com/parthpatidar03/RAG-Based-Pdf-Chatbot.git
cd RAG-Based-Pdf-Chatbot

2. Start Qdrant Vector DB

Ensure Docker is running, then start the Qdrant container:

docker-compose up -d

3. Backend Setup

Navigate to the root directory and install dependencies:

pip install -r requirements.txt

Create a .env file in the root directory and add your Google API key:

GOOGLE_API_KEY=your_api_key_here

Start the backend server:

python -m uvicorn server:app --reload

The backend will run at http://localhost:8000.

4. Frontend Setup

Navigate to the frontend directory:

cd frontend
npm install

Start the development server:

npm run dev

The frontend will run at http://localhost:5173.

Usage

1. Open the frontend URL.
2. Upload a PDF file using the "Upload PDF" button.
3. Once processed, type your questions in the chat box and send!