NotebookLM Clone

This project is a clone of Google's NotebookLM, designed to help users interact with their documents using AI. It consists of a Python Flask backend and a Next.js frontend.

Live Demo

You can access the live, deployed version of the application here:

• Frontend: https://nblm.akshaylilani.com
• Backend API: https://notebooklm-clone-backend-h5xf.onrender.com

Features

• Document Upload: Upload PDF documents for processing.
• AI Chat: Ask questions and get answers based on the content of your uploaded PDFs.
• Citations: Get citations from the source documents for the AI-generated answers.

Technologies Used

Backend

• Framework: Flask
• Vector Database: Chroma DB for storing and retrieving document embeddings.
• Embeddings: OpenAI's text-embedding-ada-002 model to create vector representations of the text.
• Language Model: OpenAI's o3 for generating responses.
• PDF Parsing: LlamaParse for extracting text from PDF documents.
• Text Splitting: Langchain's RecursiveCharacterTextSplitter to divide the extracted text into smaller chunks.
• Token Counting: Tiktoken to count the number of tokens in the text.

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.12+ (for the backend)
• Node.js 20+ and npm (for the frontend)

Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Create and activate a virtual environment (recommended):

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Environment Variables:

   Create a .env file in the backend/ directory based on .env.sample:

   • OPENAI_API_KEY: Your API key from OpenAI.
   • LLAMA_CLOUD_API_KEY: For document parsing

5. Run the Backend Server:

   gunicorn --worker-class gevent --workers 1 --bind 0.0.0.0:5000 app:app
   # Or for development with Flask's built-in server:
   # flask run --port 5000

   The backend server should now be running at http://localhost:5000 (or your specified port).

Frontend Setup

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Environment Variables:

   Create a .env file in the frontend/ directory based on .env.sample:

   NEXT_PUBLIC_API_URL="http://0.0.0.0:5000"

   • NEXT_PUBLIC_API_URL: The URL of your backend server. Make sure this matches the port you set for the backend.

4. Run the Frontend Development Server:

   npm run dev

   The frontend application should now be accessible at http://localhost:3000 (or another port if 3000 is in use).

Usage

1. Ensure both the backend and frontend servers are running.
2. Open your web browser and navigate to http://localhost:3000.
3. Upload a PDF document using the provided interface.
4. Once the document is processed, you can start asking questions related to its content.

Docker Setup

You can also run the application using Docker.

1. Build Docker Images:

   From the project root directory, run:

   docker build -t notebooklm-backend ./backend
   docker build -t notebooklm-frontend ./frontend

2. Run Docker Containers:

   Ensure you have your .env files configured in both backend/ and frontend/ directories as described above.

   docker run -d -p 5000:5000 --name notebooklm-backend notebooklm-backend
   docker run -d -p 3000:3000 --name notebooklm-frontend notebooklm-frontend

   The backend will be accessible at http://localhost:5000 and the frontend at http://localhost:3000.