The Problem with Text-Based RAG
Traditional RAG systems have a fundamental limitation: they focus exclusively on text data. While this works for simple document queries, it misses critical insights that exist in other formats throughout enterprise data.
Consider a financial report that includes a combination of text, tables, and charts. A text-only RAG system would process the written narrative but cannot interpret the visual information contained in the tables and charts-often where the most important data lives.
Why This Matters
Enterprise documents are inherently multimodal:
Financial Reports
Revenue trends are shown in line charts. Cost breakdowns appear in pie charts. Quarterly comparisons live in tables. The narrative text often just summarizes what the visuals show in detail.
Technical Documentation
Architecture diagrams explain system relationships. Flowcharts illustrate processes. Screenshots demonstrate interfaces. These visuals carry meaning that text alone cannot capture.
Research Papers
Experimental results are presented in graphs. Data distributions are shown in histograms. Methodology is explained through diagrams. Missing these means missing the evidence.
Elastiq Discover’s Multimodal Capabilities
We built Elastiq Discover to process five distinct data types:
1. Charts
Analyzing bar charts, line graphs, and pie charts in PDFs for accurate insights. The system understands axes, legends, trends, and relationships-not just the pixels.
2. Tables
Extracting and interpreting data from embedded tables. This includes understanding headers, row/column relationships, and performing calculations across cells.
3. No-text Images
Understanding visual instructions, diagrams, and images with minimal text. The system infers meaning from visual structure and relationships.
4. Videos
Extracting information from explainer and demo videos. Key frames are analyzed, transcripts are generated, and visual content is indexed for search.
5. Audio
Processing call recordings and audio files to locate specific information. Speaker identification, topic segmentation, and semantic search across spoken content.
How Multimodal RAG Works
The architecture combines multiple specialized models:
Embedding Generation
Each modality requires its own embedding approach:
- Text: Dense embeddings from language models
- Images: Visual embeddings from vision transformers
- Tables: Structured embeddings preserving cell relationships
- Audio/Video: Temporal embeddings capturing sequential content
Unified Search
All embeddings map to a shared vector space, enabling cross-modal search:
- Text queries can find relevant charts
- Image uploads can find similar diagrams
- Questions about audio can surface relevant video clips
Contextual Synthesis
When generating answers, the system combines evidence from all relevant modalities, presenting a unified response with citations to specific visual elements, timestamps, or table cells.
Real-World Example
Query: “What was the quarter-over-quarter revenue growth in Q3?”
Text-only RAG: Might find a sentence mentioning “strong Q3 performance” but miss the actual numbers.
Multimodal RAG:
- Locates the revenue chart in the quarterly report
- Extracts the Q2 and Q3 values from the bar chart
- Calculates the percentage growth
- Returns: “Q3 revenue was $12.4M, up 18% from Q2’s $10.5M” with a citation to the specific chart.
The Key Benefit
By combining textual and multimodal data, the solution provides more comprehensive and accurate understanding, enabling more informative and relevant responses to a wider range of queries.
This isn’t about replacing text-based RAG-it’s about extending it to handle the full complexity of enterprise data as it actually exists.
Conclusion
As data volume and variety continue growing, multimodal RAG represents a fundamental advancement in deriving insights from diverse, complex datasets. Organizations that limit themselves to text-only approaches are leaving significant value on the table-often literally, in charts and tables they can’t process.