RAG Application

This repository contains a Retrieval-Augmented Generation (RAG) application, which combines the power of retrieval-based and generative models to provide accurate and contextually relevant responses. The application is designed to enhance question-answering systems by leveraging external knowledge sources and advanced natural language processing techniques.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Contributing
• License

Introduction

Retrieval-Augmented Generation (RAG) is a hybrid approach that integrates the strengths of both retrieval-based and generative models. The retrieval component fetches relevant documents or passages from a knowledge base, while the generative component synthesizes the information to produce a coherent and contextually appropriate response.

Features

• Retrieval Component: Efficiently retrieves relevant documents or passages from a knowledge base.
• Generative Component: Generates coherent and contextually appropriate responses based on retrieved information.
• Customizable Knowledge Base: Easily integrate your own knowledge base or dataset.
• Scalable: Designed to handle large-scale datasets and high query volumes.
• User-Friendly Interface: Simple and intuitive API for easy integration into existing systems.

Installation

To get started with the RAG application, follow these steps:

1. Clone the repository:

   git clone https://github.com/parvvaresh/RAG-Application.git
   cd RAG-Application

2. Install dependencies:

   pip install -r requirements.txt

3. Set up the knowledge base:

   • Place your documents or passages in the knowledge_base/ directory.
   • Update the configuration file to point to your knowledge base.

4. Run the application:

   python app.py

Example Usage

Contributing

We welcome contributions from the community! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Commit your changes and push to your fork.
4. Submit a pull request with a detailed description of your changes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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For any questions or issues, please open an issue on the GitHub repository.