Retrieval-Augmented Generation (RAG) ==================================== .. raw:: html

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- **Supervisor**: Masrour Tawfik |linkedin_Masrour| .. |linkedin_Masrour| image:: Documentation/images/LinkedIn_Logo.png :width: 16 :height: 16 :target: https://www.linkedin.com/in/tawfik-masrour-43163b85/ - **Realized by**: MAALOUF Imad |linkedin_imad-maalouf| .. |linkedin_imad-maalouf| image:: Documentation/images/LinkedIn_Logo.png :width: 16 :height: 16 :target: https://www.linkedin.com/in/imad-maalouf-811b25264/ .. raw:: html

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link to github repository: `link `__ .. figure:: /Documentation/images/introo.jpg :width: 100% :alt: Alternative text for the image :name: logo .. raw:: html

In this article, I will explain the concept of Retrieval-Augmented Generation (RAG), providing numerous examples and discussing the advantages of using RAG models. RAG effectively combines the capabilities of retrieval systems and generative models to create more informed and contextually relevant outputs. Let’s dive into RAG in detail. To illustrate the application of RAG, I will demonstrate how it can be implemented in a Streamlit application. Here, we can build an interactive interface that showcases the differences between various language models

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link to github repository: `link `__ .. raw:: html

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- **Supervisor**: Masrour Tawfik |linkedin_Masrour| .. |linkedin_Masrour| image:: Documentation/images/LinkedIn_Logo.png :width: 16 :height: 16 :target: https://www.linkedin.com/in/tawfik-masrour-43163b85/ - **Realized by**: MAALOUF Imad |linkedin_imad-maalouf| .. |linkedin_imad-maalouf| image:: Documentation/images/LinkedIn_Logo.png :width: 16 :height: 16 :target: https://www.linkedin.com/in/imad-maalouf-811b25264/ .. raw:: html

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