Multimodal RAG: Enhancing RAG outputs with image results

Integrating detailed image descriptions generated by multimodal large language models into Retrieval-Augmented Generation (RAG) workflows can enhance contextual depth and quality in information synthesis by recreating images through stored base64 encodings in the metadata of retrieved chunks. This approach, demonstrated using Jay Alammar's "The Illustrated Transformer," showcases how visual data can enrich question-and-answer interactions by providing context-aware responses. The example queries illustrate the self-attention mechanism and transformer decoder processes within machine learning, highlighting the transformation of input words into vectors for context understanding and the sequence-to-sequence task of language translation, respectively. The initiative encourages users to explore these capabilities with their own files using the Unstructured Platform, offering a 14-day free trial to facilitate engagement with this dynamic intersection of visual and textual AI.