Build-Retrieval-Augmented-Generation-pipelines

Project Overview

The project aims to showcase the process of constructing and evaluating Retrieval Augmented Generation pipelines. The provided notebook (llm_evaluation_ragas.ipynb) demonstrates the evaluation of Retrieval Augmented Generation (RAG) pipelines using the RAGAS framework, utilizing OpenAI's Wiki page as a dataset.

Files

• Python Notebook: llm_evaluation_ragas.ipynb
  • Jupyter notebook providing a step-by-step guide on building and evaluating Retrieval Augmented Generation pipelines using the RAGAS framework.

Usage

1. Clone the repository:

   git clone https://github.com/Praveen76/Build-Retrieval-Augmented-Generation-pipelines.git
   cd Build-Retrieval-Augmented-Generation-pipelines

2. Open and run the Jupyter notebook:

   jupyter notebook llm_evaluation_ragas.ipynb

3. Follow the instructions in the notebook to understand and implement Retrieval Augmented Generation pipelines using RAGAS.

Contributing

If you have a Data Science mini-project that you'd like to share, please follow the guidelines in CONTRIBUTING.md.

Code of Conduct

Please adhere to our Code of Conduct in all your interactions with the project.

License

This project is licensed under the MIT License.

Contact

For questions or inquiries, feel free to contact me on Linkedin.

Happy building and evaluating!!

About Me:

I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.