Retrieval Augmented Generation (RAG) models have been developed to improve the capabilities of language models by incorporating external knowledge from large text corpora. Despite their success in various natural language processing tasks, RAG models still have limitations such as missing content, reasoning mismatch, and challenges in dealing with multimodal data. To address these shortcomings, a new approach called multimodal RAG (mmRAG) has been introduced. This solution aims to enhance large language models (LLMs) and visual language models (VLMs) using LangChain capabilities through Amazon Bedrock, a managed service offering foundation models from leading AI companies.
The mmRAG architecture involves extracting different data types individually, generating text summarizations, embedding text summaries and raw data, and storing data in a vector database and document store. This architecture allows for advanced reasoning and retrieval mechanisms to integrate text, table, and image data for cross-modal understanding and retrieval.
The integration of advanced technologies like Anthropic Claude 3, Amazon Titan, and LangChain in Amazon Bedrock enables the system to provide comprehensive and accurate outputs by handling multimodal data effectively. The system can generate image captions, store vectors, objects, raw image file names, and source documents in Amazon OpenSearch Serverless for efficient retrieval of relevant information.
Fusion and decomposition methods in RAG enhance search capabilities by using multi-faceted query generation and Reciprocal Rank Fusion for re-ranking search outcomes. This approach addresses the limitations of traditional search methods and improves the accuracy and effectiveness of information retrieval.
The mmRAG system enables multimodal content comprehension by integrating advanced RAG and multimodal retrieval engines. By combining textual, tabular, and visual information, the system can understand and generate answers to multimodal queries. The system operates seamlessly across vector databases, object stores, and image-to-image searches, improving the efficiency and accuracy of information retrieval.
While the mmRAG system offers advanced features for content comprehension, it also has limitations, such as increased inference latency due to multi-step processes like query decomposition and fusion. However, these limitations are mitigated by the system’s ability to provide detailed and accurate analysis for complex data.
In conclusion, the mmRAG system, powered by Amazon Bedrock and advanced technologies, offers a comprehensive solution for multimodal generative AI assistants. By leveraging cutting-edge tools and techniques, businesses can gain deeper insights, make informed decisions, and drive innovation with more accurate data. Ongoing research aims to further enhance the system’s reliability and reusability, paving the way for more advanced multimodal content understanding.
Article Source
https://aws.amazon.com/blogs/machine-learning/create-a-multimodal-assistant-with-advanced-rag-and-amazon-bedrock/