A guide on RAG Pipeline with kerasHub models

[laxmareddyp]

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Summary of Changes

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I've implemented a comprehensive guide on building a Retrieval-Augmented Generation (RAG) pipeline using KerasHub models for brain MRI analysis. This guide demonstrates how to retrieve similar medical images and generate detailed diagnostic reports, showcasing the benefits of RAG over traditional Vision-Language Models for domain-specific tasks.

Highlights

• RAG Pipeline for Brain MRI Analysis: I've added a new guide that implements a complete RAG pipeline for analyzing brain MRI images, focusing on image retrieval, context-aware report generation, and comparison with traditional VLM approaches.
• Integration of KerasHub Models: The guide showcases the use of various KerasHub models, including MobileNetV3 for image feature extraction and Gemma3 (1B and 4B variants) for text generation and benchmarking.
• Medical Data Handling and Visualization: I've included utilities for preparing and visualizing real brain MRI data from the OASIS dataset, demonstrating practical application in a medical context.
• Performance Comparison and RAG Advantages: The guide provides a clear comparison between the RAG pipeline and a direct VLM approach, emphasizing RAG's superior accuracy, efficiency, scalability, and interpretability for domain-specific tasks.
• Code and Documentation Formats: The new content is provided as a Jupyter notebook (.ipynb), a runnable Python script (.py), and a Markdown guide (.md), ensuring accessibility and ease of use across different platforms.

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