# Multimodal RAG

> Retrieval-Augmented Generation extended to handle multiple modalities including text, images, video, and audio. Uses multimodal embeddings like Gemini Embedding 2 or CLIP to enable cross-modal search and generation.

- **URL:** https://analyticsvidhya.com/courses/mastering-multimodal-rag
- **Category:** Concepts & Definitions
- **Tags:** Multimodal, Rag, Embeddings
- **Featured:** yes
- **Updated:** 2026-03-15 12:39
- **Canonical page:** https://vectordb.ever.works/items/multimodal-rag

## Details

## 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.

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