Introduction to RAG and Multimodal RAG {#introduction}
What is RAG (Retrieval-Augmented Generation)?
RAG is a technique that enhances Large Language Models (LLMs) by providing them with relevant context from external knowledge sources before generating responses.
Think of it like this: Instead of an AI relying solely on its training data (which might be outdated), RAG allows it to "look up" information from a knowledge base, similar to how you'd Google something before answering a question.
Traditional RAG Workflow:
text
User Question → Search Knowledge Base → Retrieve Documents → LLM → Answer
What is Multimodal RAG?
Multimodal RAG extends traditional RAG to handle multiple types of data:
📝 Text (documents, articles)
📊 Tables (structured data with rows/columns)
🖼️ Images (charts, diagrams, photographs)
📈 Graphs and visualizations
Why Multimodal RAG?
Most documents contain a mixture of content - text, tables, and images. Traditional RAG ignores images, losing valuable information. Multimodal RAG captures ALL information types and uses them together for better answers.
- System Architecture {#architecture}
Complete System Flow Diagram
text
┌─────────────────────────────────────────────────────────────────────────────┐
│ MULTIMODAL RAG SYSTEM ARCHITECTURE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ PDF/ │ │ EXTRACT │ │ SUMMARIZE │ │
│ │ Document │───▶│ Elements │───▶│ Content │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌──────────────────────────────────────────────────────────────────────┐ │
│ │ STORAGE LAYER (Two-Store System) │ │