Welcome to the repository for demonstrating end-to-end implementations of Retrieval-Augmented Generation (RAG) using Google's PaLM model. This repository will serve as a learning resource for anyone interested in understanding and applying RAG-based models in real-world scenarios.
Retrieval-Augmented Generation (RAG) is a powerful technique that combines retrieval mechanisms with generative models to enhance the generation of relevant and accurate responses. By leveraging Google's PaLM model, this repository aims to provide comprehensive examples and tutorials on how to implement and utilize RAG models effectively.
This repository includes:
- Introduction to RAG Models: A basic overview of RAG models, their architecture, and use cases.
- Google PaLM Integration: Instructions and examples on how to integrate Google PaLM with RAG models.
- Code Samples: Various Python scripts and Jupyter notebooks demonstrating RAG model implementations.
- Step-by-Step Guides: Detailed walkthroughs of each example, including data preprocessing, model setup, retrieval techniques, and evaluation.
- Best Practices: Tips and tricks to optimize the performance of RAG models.
Before you begin, ensure you have the following installed:
- Python 3.7 or higher
- Required Python packages (listed in
requirements.txt) - Access to Google PaLM API
-
Clone this repository to your local machine:
git clone https://github.com/SankarPatnaik/lang-chain/lang-chain.git
-
Navigate to the project directory:
cd lang-chain -
Install the required Python packages:
pip install -r requirements.txt
Explore the different folders and scripts in this repository to understand how RAG models work with Google PaLM. You can run the Jupyter notebooks to see the code in action and modify them to fit your specific use case.
Contributions are welcome! If you have ideas for new examples, optimizations, or improvements, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
If you have any questions or feedback, please reach out to me through Sankar Patnaik.
Happy coding!