Multi-Vector Retriever for RAG on tables, text, and images

Seamless question-answering across diverse data types, including images, text, and tables, has been a major objective in the field of Retrieval-Augmented Generation (RAG), with recent advancements focusing on multi-vector retrieval techniques. Three new cookbooks have been released to demonstrate the application of these techniques on documents with mixed content types, highlighting the potential of multimodal models like GPT4-V and LLaVA for enhancing RAG capabilities on images. RAG allows Large Language Models (LLMs) to enhance their factual recall by integrating reasoning capabilities with external data sources, beneficial for enterprise data handling. Techniques to improve RAG involve various strategies such as metadata filtering and multi-stage retrieval processes. The multi-vector retriever, introduced earlier, enables the decoupling of documents from references, allowing for a more efficient answer synthesis process without losing context, and can be applied to both semi-structured data and multiple modalities. The document also discusses how to partition documents by types using tools like Unstructured, which supports RAG by extracting tables, images, and text from various file formats. This approach allows for the generation of summaries optimized for retrieval, passing the full document context to LLMs when necessary. Moreover, the cookbooks propose methods for using multimodal embeddings and LLMs to process image data within RAG, offering privacy-conscious solutions that can be executed locally with open-source components.

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