https://github.com/parvvaresh/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.
https://github.com/parvvaresh/rag-application
faiss llm rag
Last synced: 8 months ago
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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.
- Host: GitHub
- URL: https://github.com/parvvaresh/rag-application
- Owner: parvvaresh
- Created: 2025-02-26T18:48:49.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-28T08:15:54.000Z (8 months ago)
- Last Synced: 2025-02-28T14:35:15.556Z (8 months ago)
- Topics: faiss, llm, rag
- Language: HTML
- Homepage:
- Size: 16.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#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**:
```bash
git clone https://github.com/parvvaresh/RAG-Application.git
cd RAG-Application
```
2. **Install dependencies**:
```bash
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**:
```bash
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](LICENSE) file for more details.
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For any questions or issues, please open an issue on the [GitHub repository](https://github.com/parvvaresh/RAG-Application/issues).