RAG (Rapid Answer Generator)

🚀 Project Overview

RAG (Rapid Answer Generator) is an intelligent web application designed to transform your PDF documents into a personal, queryable knowledge base. Upload your PDFs, ask questions, and receive precise, context-aware answers instantly. Whether for research, study, or decision-making, RAG empowers you to extract maximum value from your documents efficiently.

✨ Features

• Advanced PDF Processing: Upload up to two PDF documents (max 5MB each). The system automatically detects and performs OCR on scanned PDFs using ocrmypdf to ensure text is always extractable.
• Retrieval-Augmented Generation (RAG): Documents are chunked, vectorized using Google's embeddings, and stored in a ChromaDB vector store for efficient similarity searches.
• Context-Aware Chat: Ask questions and receive answers based on the content of your uploaded documents. The conversation history is used to maintain context.
• Gemini Fallback: If an answer isn't found within the provided documents, the app seamlessly queries the powerful Google Gemini model for a general knowledge response.
• Voice-to-Text Input: Use the built-in voice search to ask questions hands-free, powered by the browser's Speech Recognition API.
• Secure User Authentication: Robust authentication system with Appwrite, supporting both email/password and Google OAuth for quick and secure access.
• Conversation Management: View, revisit, and delete past conversations. Your chat history is saved and linked to your user account.
• Usage Limits: Implements a daily prompt limit per user to manage resource consumption, which resets every 24 hours.
• Fully Responsive UI: A sleek and modern interface built with React, Tailwind CSS, and shadcn/ui that works beautifully on desktop and mobile devices.

🛠️ Tech Stack

The application is built with a modern, full-stack architecture, leveraging powerful tools for both the frontend and backend.

Frontend

• Framework: React.js
• Build Tool: Vite
• Styling: Tailwind CSS
• UI Components: shadcn/ui
• State Management: Redux Toolkit
• Routing: React Router DOM
• Icons: Lucide React

Backend

• Server: Flask (Python)
• BaaS (Backend as a Service): Appwrite
• AI/RAG Framework: LangChain (Python)
• AI Model: Google Gemini API
• Vector Database: ChromaDB
• PDF Processing: PyPDFLoader & ocrmypdf

🏗️ Architecture

Below is a high-level overview of the RAG application's architecture:

📺 Demo Video

Watch a quick demonstration of RAG in action, from setting up to querying your documents:

🚀 Getting Started

Follow these instructions to set up and run the RAG application on your local machine.

Prerequisites

• Node.js (v18 or higher)
• Python (v3.9 or higher)
• npm or Yarn
• ocrmypdf (install via pip install ocrmypdf or your system's package manager)

1. Clone the Repository

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

2. Backend Setup (Python Flask)

Navigate to the server directory:

cd server

Create a Python virtual environment and activate it:

# On Windows
python -m venv venv
.\venv\Scripts\activate

# On macOS/Linux
python3 -m venv venv
source venv/bin/activate

Install the required Python packages:

pip install -r requirements.txt

Create a .env file in the server directory by copying .env.sample and fill in your credentials:

# server/.env
FRONTEND_URL="http://localhost:5173"
VITE_APPWRITE_ENDPOINT="https://cloud.appwrite.io/v1"
VITE_APPWRITE_PROJECT_ID="YOUR_APPWRITE_PROJECT_ID"
VITE_APPWRITE_API_KEY="YOUR_APPWRITE_API_KEY"
VITE_APPWRITE_DATABASE_ID="YOUR_APPWRITE_DATABASE_ID"
VITE_APPWRITE_CONVERSATIONS_COLL_ID="YOUR_CONVERSATIONS_COLLECTION_ID"
VITE_APPWRITE_MESSAGES_COLL_ID="YOUR_MESSAGES_COLLECTION_ID"
VITE_APPWRITE_USER_LIMITS_COLL_ID="YOUR_USER_LIMITS_COLLECTION_ID"
GOOGLE_API_KEY="YOUR_GOOGLE_GEMINI_API_KEY"

Run the Flask server:

flask run

The backend server will typically run on http://127.0.0.1:5000.

3. Frontend Setup (React Vite)

Open a new terminal and navigate to the client directory:

cd client

Install the Node.js dependencies:

npm install

Create a .env file in the client directory by copying .env.sample and fill in your credentials:

# client/.env
VITE_APPWRITE_PROJECT_ID="YOUR_APPWRITE_PROJECT_ID"
VITE_APPWRITE_ENDPOINT="https://cloud.appwrite.io/v1"
VITE_APPWRITE_DATABASE_ID="YOUR_APPWRITE_DATABASE_ID"
VITE_APPWRITE_USERS_COLL_ID="YOUR_USERS_COLLECTION_ID"
VITE_APPWRITE_USER_LIMITS_COLL_ID="YOUR_USER_LIMITS_COLLECTION_ID"
VITE_GOOGLE_OAUTH_CLIENT_ID="YOUR_GOOGLE_OAUTH_CLIENT_ID"
VITE_EMAIL_ADDRESS="your-contact-email@example.com"
VITE_BACKEND_URL="http://127.0.0.1:5000"
VITE_BASE_URL="http://localhost:5173"

Start the React development server:

npm run dev

The frontend application will open in your browser at http://localhost:5173.

🤝 Contributing

We welcome contributions to the RAG project! If you have suggestions, bug reports, or want to contribute code, please feel free to:

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