Create a multimodal assistant with advanced RAG and Amazon Bedrock

This article discusses creating a multimodal assistant using advanced Retrieval Augmented Generation (RAG) and Amazon Bedrock.

Specifically, the article covers:

• The solution architecture for a multimodal RAG (mmRAG) system, which combines text, table, and image data into a unified vector representation for cross-modal understanding and retrieval.
• Configuring Amazon Bedrock with LangChain to work with Anthropic's Claude 3 models and Amazon Titan embeddings.
• Parsing and embedding multimodal data (text, tables, images) from various sources.
• Storing embedded vectors and data in an Amazon OpenSearch Serverless vector store.
• Advanced RAG techniques like query decomposition, reciprocal re-ranking, and answer fusion to improve reasoning.
• Multimodal retrieval from vector databases and object stores.
• Potential use cases and limitations of the mmRAG approach.