Retrieval-Augmented Generation (RAG) Model for Question Answering

This repository contains code for implementing a Retrieval-Augmented Generation (RAG) model to answer questions based on contextual information from Stephen Hawking's "A Brief History of Time" and "The Universe in a Nutshell".

Overview

The project utilizes the following components:

• Dataset Preparation: Text excerpts from the aforementioned books are used to create a dataset containing text chunks along with their embeddings. FAISS is integrated for efficient vector search within the dataset.

• Model Setup: Two powerful models are utilized:

  • Embedding Model: all-mpnet-base-v2 is used for encoding text into embeddings.
  • Generative Model: Gemma model is employed for language modeling and response generation within the RAG framework.

• Query and Retrieval: Users can input queries related to the content of the books. FAISS facilitates fast and efficient vector search to retrieve relevant text chunks from the dataset.

• Prompt Creation: Prompts for the RAG model are created by combining user queries with retrieved text chunks as context. This prompts the model to generate accurate and informative responses.

Usage

1. Clone the Repository:

git clone https://github.com/SahilJain8/RAG-PIPELINE
cd RAG-PIPELINE

2. Install requirements

pip install -r requirements.txt

2. Run the project

python main.py

3.Command Line Arguments

--model_name: Specifies the name of the model to be used. Defaults to 'google/gemma-2b-it'.
--encoding_model_name: Specifies the name of the encoding model. Defaults to 'all-mpnet-base-v2'.
--use_quantization: Specifies whether to use quantization or not. Defaults to True.

Credits

This project is inspired by the works of Stephen Hawking and builds upon research in natural language processing. The RAG model implementation leverages open-source libraries and frameworks.