Multimodal-RAG-Survey and UniversalRAG
These are ecosystem siblings—the survey provides a comprehensive taxonomy and analysis of multimodal RAG approaches that UniversalRAG exemplifies as a practical implementation handling diverse modalities and granularities.
About Multimodal-RAG-Survey
llm-lab-org/Multimodal-RAG-Survey
A Survey on Multimodal Retrieval-Augmented Generation
This is a curated collection of research papers and resources related to Multimodal Retrieval-Augmented Generation (RAG). It provides a structured overview of the field, categorizing advancements, datasets, and applications for AI researchers. The resource takes in new research papers, updates, and analysis, and provides a continuously updated survey for those working in AI and natural language processing.
About UniversalRAG
wgcyeo/UniversalRAG
UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities
This framework helps AI developers build advanced Retrieval-Augmented Generation (RAG) systems capable of searching across various data types like text, images, and videos. It takes diverse data corpora and user queries, then intelligently directs each query to the most relevant data source, providing a more accurate and nuanced context for generative AI models. AI researchers and machine learning engineers who are building next-generation AI applications will find this valuable.
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