Adding the multimodal RAG tutorial with Amazon Nova and LangChain #18
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This notebook demonstrates how to implement a multi-modal Retrieval-Augmented Generation (RAG) system using Amazon Bedrock with Amazon Nova and LangChain. Many documents contain a mixture of content types, including text and images. Traditional RAG applications often lose valuable information captured in images. With the emergence of Multimodal Large Language Models (MLLMs), we can now leverage both text and image data in our RAG systems.
Adding the multimodal RAG tutorial with Amazon Nova and LangChain
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why they are commented out here? it might make sense to have a requirements.txt in the directory to lock the versions since langchain is updating/deprecating quite frequently.
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Line #16. with tqdm(total=len(items), desc="Generating embeddings", bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]") as pbar:
nit: consider to use a formatter "black[jupyter]". Some lines are a little long to read.
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This notebook demonstrates how to implement a multi-modal Retrieval-Augmented Generation (RAG) system using Amazon Bedrock with Amazon Nova and LangChain. Many documents contain a mixture of content types, including text and images. Traditional RAG applications often lose valuable information captured in images. With the emergence of Multimodal Large Language Models (MLLMs), we can now leverage both text and image data in our RAG systems.