https://github.com/rvats20/rag-llm-app
This repo contains useful OpenAI-Python-Scripts for automating small tasks. This app integrates retrieval mechanisms to fetch relevant documents and uses LLMs to generate coherent and informative outputs based on the retrieved information.
https://github.com/rvats20/rag-llm-app
ai chainlink chatgpt gpt llms ollama openai python rag scripts
Last synced: 6 months ago
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This repo contains useful OpenAI-Python-Scripts for automating small tasks. This app integrates retrieval mechanisms to fetch relevant documents and uses LLMs to generate coherent and informative outputs based on the retrieved information.
- Host: GitHub
- URL: https://github.com/rvats20/rag-llm-app
- Owner: rvats20
- Created: 2024-10-28T19:45:27.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-01-01T10:57:21.000Z (9 months ago)
- Last Synced: 2025-02-06T16:59:10.587Z (8 months ago)
- Topics: ai, chainlink, chatgpt, gpt, llms, ollama, openai, python, rag, scripts
- Language: Jupyter Notebook
- Homepage: https://rvats20.github.io/portfolio/
- Size: 80.1 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: newsletterreader.py
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README
# RAG-LLM App
## Overview
The RAG-LLM App is a sophisticated Python application that leverages Retrieval-Augmented Generation (RAG) techniques with Large Language Models (LLMs) to provide enhanced and contextually relevant responses. This app integrates retrieval mechanisms to fetch relevant documents and uses LLMs to generate coherent and informative outputs based on the retrieved information.
## Features
- **Newsletter Reading**: Efficiently retrieves relevant information from a large corpus of emails.
- **Language Generation**: Utilizes state-of-the-art LLMs to generate detailed and contextually appropriate responses.
- **Hybrid Approach**: Combines retrieval and generation for improved accuracy and relevance.
- **Customizable Pipelines**: Easily customizable retrieval and generation pipelines to suit various use cases.
- **Scalable Architecture**: Designed to handle large-scale data and high query volumes.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/rag-llm-app.git
```
2. Navigate to the project directory:
```bash
cd rag-llm-app
```
3. Create a virtual environment and activate it:
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
4. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Prepare your data corpus and place it in the `data/` directory.
2. Run the application:
```bash
python app.py
```
3. Use the provided API endpoints or command-line interface to interact with the app.
## Configuration
- **config.yaml**: Customize the retrieval and generation settings in the `config.yaml` file.
- **models/**: Place your pre-trained models in the `models/` directory or specify the paths in the configuration file.
## Requirements
- Python 3.8+
- transformers
- faiss
- pandas
- numpy
- flask (for API deployment)
## Contributing
We welcome contributions! Please fork the repository and submit a pull request with your changes. Ensure your code adheres to the project's coding standards and includes appropriate tests.
## License
This project is license free so feel free to use it as needed.
## Contact
For any questions or suggestions, please contact rahul.rkumar23@gmail.com