ml6team/fondant-usecase-RAG
Data pipelines and notebooks for RAG tuning using Fondant
This project helps machine learning engineers and data scientists refine their Retrieval Augmented Generation (RAG) systems. It takes in your raw data and RAG configuration parameters, then processes it through data pipelines to produce an optimized RAG system capable of generating more accurate and relevant responses. The primary users are those building and improving AI-powered question-answering or content generation applications.
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Use this if you are a machine learning engineer or data scientist looking to systematically improve the performance and output quality of your RAG applications.
Not ideal if you are looking for a simple, out-of-the-box RAG solution without needing to customize or fine-tune its underlying data processing.
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Jupyter Notebook
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Last pushed
Mar 17, 2024
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