Breakthrough in Multimodal RAG: New Framework Handles Text, Images, and Tables

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:

1. Architecture diagram (image)
2. Authentication section (text)
3. API endpoints table (structured data)

Multimodal Fusion

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

DEVELOPERpython
# 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):

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:

DEVELOPERpython
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.