From Naïve RAG to MultiModal RAG: Revolutionizing AI Assistance with Enhanced Reasoning and Retrieval

🚀 #Multimodal #Assistant with #advanced #RAG 🚀

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RAG models have shown potential in improving the capabilities of #LLMs. Despite attempts to rectify some issues like those found in #Naïve RAG, such as missing content and challenges with multimodal data, there are still difficulties in effectively interpreting and reasoning over content. This can lead to inaccuracies, logical errors, and inconsistencies in the models’ outputs.

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#Approach:
MultiModal RAG (#mmRAG) intends to address the limitations observed for genAI assistant use cases. Researchers claim that, the Experiments include potential solutions to enhance the capabilities LLMs, VLMs, Advanced #LangChain capabilities for effective handling of the multimodal data. Few of the solutions for eg: #BedRock, a fully managed service that offers a choice of higher performing Foundation models.

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#SolutionArchitecture:
mmRAG is based on the concept of extracting different datatypes separately Text Summarizations with #VLM from different data types, embed text summaries along with raw data to a vector db and store document store. The Query will prompt the #LLM to retrieve relevant…