Multimodal RAG

Overview

Multimodal RAG extends traditional text-based RAG to handle multiple modalities—text, images, video, audio—in a unified system. It enables queries like "find images similar to this description" or "what does this video show?"

Key Components

Multimodal Embeddings

• Gemini Embedding 2 (text, image, video, audio)
• CLIP (text and images)
• ImageBind (six modalities)
• Jina Embeddings v4 (text and images)

Vector Database

Stores embeddings from all modalities in unified space

Multimodal LLMs

• GPT-4 Vision
• Gemini Pro Vision
• Claude 3 (vision-enabled)

How It Works

1. Index: Embed documents, images, videos into same vector space
2. Query: User provides text, image, or other modality
3. Retrieve: Find similar items across all modalities
4. Generate: Multimodal LLM generates response using retrieved context

Use Cases

• Visual question answering
• Medical image diagnosis with clinical notes
• E-commerce ("find products like this image")
• Video content search and summarization
• Education (diagrams + text explanations)

Challenges

• Alignment between modalities
• Higher computational costs
• Complex preprocessing
• Modality-specific optimization

Pricing

Depends on embedding and LLM providers. Typically higher than text-only RAG.