Introduction

A collaborative team from Stanford and Google DeepMind has published research on MM-RAG (Multimodal Retrieval-Augmented Generation), a framework that seamlessly handles retrieval across text, images, tables, and charts within a single system.

The Multimodal Challenge

Traditional RAG systems focus on text, but real-world documents contain: • Images and diagrams • Tables and spreadsheets • Charts and graphs • Mixed layouts

Existing approaches either ignore non-text content or process each modality separately, leading to fragmented understanding.

MM-RAG Architecture

Unified Embedding Space

MM-RAG uses CLIP-based encoders to project all modalities into a shared embedding space:

`` Text → Text Encoder → Images → Vision Encoder → [Shared 1024-dim space] → Vector DB Tables → Table Encoder → `

Cross-Modal Retrieval

The system can retrieve: • Text for text queries (standard RAG) • Images for visual questions • Tables for data queries • Mixed results for complex queries

Example query: "Show me the architecture diagram and explain the authentication flow"

Retrieves: Architecture diagram (image) Authentication section (text) API endpoints table (structured data)

Multimodal Fusion

Retrieved multimodal content is processed by GPT-4V or Gemini Pro Vision:

`python Pseudocode query = "Compare Q3 revenue across regions"

Retrieve mixed modalities results = mm_rag.retrieve(query, k=5) Returns: [chart_image, revenue_table, text_analysis]

Generate answer using multimodal LLM answer = gpt4v.generate( text_prompt=query, images=[r for r in results if r.type == 'image'], tables=[r for r in results if r.type == 'table'], context=[r for r in results if r.type == 'text'] ) `

Benchmark Results

Tested on newly created MixedQA benchmark (10K questions across modalities):

| Query Type | Baseline | MM-RAG | Improvement | |------------|----------|--------|-------------| | Text-only | 78.2% | 79.1% | +1.2% | | Image-only | 45.3% | 74.8% | +65.1% | | Table-only | 52.1% | 81.3% | +56.0% | | Mixed | 31.2% | 68.7% | +120.2% | | Overall | 51.7% | 75.9% | +46.8% |

Key Innovations

Layout-Aware Chunking

MM-RAG preserves document layout during chunking: • Keeps images with their captions • Maintains table structure • Preserves figure references

Modality Routing

Automatically determines which modalities to retrieve based on query:

`python query_intent = analyze_query(query)

if query_intent.needs_visual: retrieve_images = True

if query_intent.needs_data: retrieve_tables = True

Always retrieve text as context retrieve_text = True ``

Cross-Modal Reranking

After retrieval, a cross-modal reranker scores relevance: • Text-to-image relevance • Table-to-query relevance • Overall coherence of mixed results

Applications

MM-RAG excels in:

Scientific Research • Retrieve figures from papers • Answer questions about experimental results • Compare data across studies

Business Intelligence • Query dashboards and reports • Extract insights from charts • Analyze tabular data

Technical Documentation • Find relevant diagrams • Understand architecture from visuals • Connect text explanations with illustrations

Education • Visual learning materials • Interactive textbook Q&A • Diagram-based explanations

Implementation Considerations

Computational Costs

Processing images and tables is expensive: • Image encoding: 10x slower than text • Table parsing: 5x slower than text • Multimodal LLMs: 2-3x more expensive

Storage Requirements

Embedding all modalities increases storage: • Text: 768-1536 dimensions • Images: 512-1024 dimensions + original image • Tables: Structured representation + embeddings

Estimated: 3-5x storage increase vs. text-only RAG

Quality Dependencies

MM-RAG quality depends on: • OCR accuracy for scanned documents • Table extraction quality • Image resolution and clarity • Multimodal LLM capabilities

Open Source Release

The team has released: • MM-RAG framework (Apache 2.0 license) • MixedQA benchmark dataset • Pretrained cross-modal encoders • Evaluation scripts

Available at: github.com/stanford-futuredata/mm-rag

Industry Adoption

Early adopters include: • Technical documentation platforms • Legal document analysis tools • Scientific literature search engines • Business intelligence providers

Limitations

Current limitations include: • Video not yet supported • Audio processing limited • Real-time performance challenges • High resource requirements

Future Work

Planned improvements: • Video frame retrieval • Audio transcription integration • Reduced computational overhead • Better handling of complex layouts

Conclusion

MM-RAG represents a significant step toward truly multimodal AI assistants that can understand and reason across all content types in documents, not just text. As multimodal LLMs improve, systems like MM-RAG will become increasingly practical for real-world applications.