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.
- 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.
- Clone the repository:
git clone https://github.com/yourusername/rag-llm-app.git
- Navigate to the project directory:
cd rag-llm-app - Create a virtual environment and activate it:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
- Install the required dependencies:
pip install -r requirements.txt
- Prepare your data corpus and place it in the
data/directory. - Run the application:
python app.py
- Use the provided API endpoints or command-line interface to interact with the app.
- config.yaml: Customize the retrieval and generation settings in the
config.yamlfile. - models/: Place your pre-trained models in the
models/directory or specify the paths in the configuration file.
- Python 3.8+
- transformers
- faiss
- pandas
- numpy
- flask (for API deployment)
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.
This project is license free so feel free to use it as needed.
For any questions or suggestions, please contact [email protected]