Research Paper Q&A System

This repository provides a system for extracting information from research papers and answering questions based on their content. It leverages vector embeddings and a Qdrant vector database for efficient information retrieval, combined with a summarization agent to generate concise answers. This system was developed to assist in the process of writing a systematic literature review thesis.

---

Features

• PDF Processing: Automatically ingests PDF documents from a specified directory.
• Text Embedding: Converts document chunks into numerical vector representations using sentence-transformers/all-MiniLM-L6-v2.
• Vector Database (Qdrant): Stores and retrieves document embeddings for fast similarity searches.
• Question Answering: Takes a list of predefined questions and retrieves relevant document snippets.
• Summary Generation: Utilizes a SummaryAgent to synthesize retrieved information into coherent answers.
• Output: Appends generated answers and original questions to an output.txt file.
• Database Management: Clears the Qdrant database upon completion.

---

Getting Started

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

Prerequisites

• Python 3.12+
• pip (Python package installer)
• Qdrant (follow this guide)
• Google Gemini API key

Installation

1. Clone the repository:

   git clone https://github.com/Peppe-elefante/AI-SLR.git

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

   The requirements.txt file includes:

   qdrant_client
   fastembed
   google-generativeai
   docling
   dotenv
   

4. Set up environment variables: Create a .env file in the root directory of your project.

   GOOGLE_API_KEY = Your google api_key
   QDRANT_URL = your Qdrant URL
   

Usage

1. Place your PDF studies: Inside the folder named Studi place all the studies needed for your SLR

2. Define your questions: Ensure your utils/questions.py file contains a list of strings, where each string is a question you want the system to answer. For example:

   # utils/questions.py
   questions = [
       "What is the main finding of the first study?",
       "How does the second study approach data analysis?",
       "What are the limitations mentioned in the third paper?"
   ]

3. Run the main script:

   python main.py

After execution, the output.txt file in the root directory will contain the generated answers for each question.