Knowledge Labs
Logo
Introduction to Machine LearningDeep Learning IntroductionProbability for Machine LearningStatistics for Machine LearningNatural Language Processing (NLP)Data Science IntroductionAI for EveryoneRetrieval Augmented Generation (RAG)AI for ArtistsComprehensive Guide to Model Context ProtocolComprehensive Guide to Economy & Financial SystemsComprehensive Guide to Babylon.js Game Engine
Retrieval Augmented Generation (RAG)
/Optimization & Extension (Advanced Customization)
/Multimodal Expansion

Multimodal Expansion

.

Multimodal Expansion

Expanding RAG beyond just text allows systems to work with images, audio, and other types of content. This creates more versatile and powerful applications:

Connecting Different Content Types:

  • CLIP: A system that understands both images and text in the same way, letting you search for images using words or find text related to images
  • ImageBind: Takes this further by connecting six different types of content (like text, images, audio) in a unified way
  • LLaVA/GPT-4V: Advanced systems that can look at images and understand them in context with text

Unified Storage Approach:

  • Creates a single storage system where different types of content (text, images, etc.) can be searched together
  • Allows searching across different content types with consistent results (like finding images related to text queries)
  • Supports documents that mix text, images, and other media while keeping their relationships intact

Processing Mixed-Media Documents:

  • Extracts text from visual elements like charts, graphs, and diagrams
  • Creates text descriptions of visual content so it can be searched and understood by the system
  • Preserves important relationships between text and nearby images or graphics

Creating Rich Responses:

  • Generates answers that include both text and relevant visuals when appropriate
  • Selects helpful images or diagrams to better explain concepts
  • Creates charts or visualizations based on retrieved information to make it easier to understand

These multimodal capabilities make RAG systems 40-60% more effective in fields that rely heavily on visual information, such as medicine, design, scientific research, and media content.