RAG Paper Summarizer

A Python application that implements Retrieval Augmented Generation (RAG) to download and summarize academic papers. Currently configured to process the ReAct paper from arXiv.

Features

• Automatic paper download from arXiv
• PDF processing and text chunking
• Vector store creation using Chroma
• RAG-based summarization using OpenAI's GPT-4 and LangChain

Prerequisites

• Python 3.x
• OpenAI API key

Installation

1. Clone the repository:

git clone <repository-url>
cd rag-inboundsquare

2. Install the required dependencies:

pip install -r requirements.txt

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

OPENAI_API_KEY=your_api_key_here

Usage

Run the script using:

python rag.py

The script will:

1. Download the ReAct paper if not already present
2. Process the PDF and split it into chunks
3. Create a vector store using Chroma
4. Generate a comprehensive summary using RAG

Dependencies

• langchain
• openai
• chromadb
• arxiv
• python-dotenv
• requests

Note

The current implementation is configured to summarize the ReAct paper (arXiv:2210.03629). You can modify the process_pdf function to work with other papers or PDF documents.

License

[Add your license here]