Multimodal RAG
Plain Explanation
Many documents hide crucial facts in figures, tables, and screenshots that plain text search misses. Traditional RAG only retrieves text, so answers can ignore a chart that actually contains the key number or a diagram that shows the system layout. Multimodal RAG solves this by letting the system retrieve and read both the words and the visuals that support an answer. Think of it like asking a librarian not only for the right pages, but also for the right figures taped to those pages. The system finds relevant chunks of text and the associated images, then a vision-capable model reads them together to produce an explanation that cites both. Concretely, images get extracted and captioned, and both the captions and images are turned into vectors for search. Some systems store text and image vectors in separate collections and query both, then combine results; others embed text and images into a shared space with models like CLIP. Orchestrators can send text plus base64-encoded images to a vision-capable model so it can synthesize an answer grounded in the retrieved evidence.
Examples & Analogies
- Troubleshooting from equipment manuals: A technician asks, “Which valve is V3 in this layout?” The system retrieves the page with the piping diagram and the nearby paragraph, then the vision-capable model points to the correct label and summarizes the related safety note.
- Policy review with charts in slide decks: An analyst queries a compliance change and gets back the slide text plus the bar chart image that illustrates the new thresholds. The answer references the chart and explains the cutoff shown in the figure.
- Research Q&A over PDFs with figures: A student asks for the difference between two model variants; the system retrieves the ablation table image and the methods section. The model reads the table and highlights the row that shows the accuracy delta.
At a Glance
| Text-only RAG | Multimodal RAG (shared space) | Multimodal RAG (separate stores) | |
|---|---|---|---|
| Inputs | Text chunks | Text + images | Text + images |
| Embeddings | Text only | One vector space (e.g., CLIP) for both | Text vectors + image vectors stored separately |
| Retriever changes | Standard text vector search | Swap to multimodal encoder; reuse infra | Query two indexes; merge/re-rank |
| Generation model | Text-only LLM | Vision-language model | Vision-language model |
| Strengths | Simple, cheap | Simple pipeline swap | Flexible: best text + best image encoders |
| Limits | Misses visuals | Needs strong unified encoder | More orchestration and re-ranking |
You pick shared-space or separate-stores based on whether you prefer simplicity or the flexibility to mix the best encoders and re-rank across modalities.
Where and Why It Matters
- Figure-aware document QA: Adds support to extract figures, generate captions, store caption embeddings, and present images with text to a vision-capable model, so answers can reference charts and photos.
- LlamaIndex MultiModalVectorIndex-style pipelines: Index and retrieve text and images together, often with separate collections (for example, text embeddings and CLIP image embeddings) combined at query time.
- Caption generation and storage: Pipelines increasingly produce image captions and embed them, so queries like "flowchart of onboarding" can match both prose and the relevant figure.
- Re-ranking as a quality gate: A vision-language re-ranker can suppress irrelevant images before the generator sees them, reducing hallucinated visual evidence.
- Operational cost control: Image download, resizing, and base64 expansion add latency, so production systems usually cap image count and prefer captions when pixels are not needed.
Common Misconceptions
- Myth: Any LLM can read images once you retrieve them → Reality: You need a vision-capable model to interpret images alongside text.
- Myth: Multimodal RAG requires one universal embedding for all data → Reality: You can embed text and images into a shared space or keep separate stores and re-rank results.
- Myth: Images are optional; text retrieval already covers the facts → Reality: Many PDFs and slides carry key info in figures and tables; skipping them loses critical evidence.
How It Sounds in Conversation
- "Let’s turn on MULTIMODAL=true so ingestion keeps figures and generates captions we can embed."
- "We’ll try CLIP for image embeddings and keep text in our existing index, then merge and re-rank at query time."
- "The orchestrator will send text + base64 images to the vision-capable model so the answer can cite the chart."
- "Relevance dropped after we raised top_k for images—let’s add a listwise re-rank step before generation."
- "For cost control, caption once during ingestion and reuse the captionVector instead of re-captioning at query time."
Related Reading
References
- mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation
Research framing of retrieval, re-ranking, and generation stages for multimodal RAG.
- Multimodality - GPT-RAG
Azure GPT-RAG docs: ingestion, figure extraction, caption embeddings, and multimodal orchestration.
- What is multimodal RAG?
High-level intro: modality encoders, shared spaces, and grounding across modalities.
- An Easy Introduction to Multimodal Retrieval-Augmented Generation
Overview of three common multimodal RAG approaches and trade-offs.
- Multi-Modal RAG: Indexing And Retrieval Guide
LlamaIndex abstractions for multimodal LLMs, embeddings, and a MultiModalVectorIndex.