Multimodal RAG
Retrieving and synthesizing knowledge from documents containing mixed text, tables, diagrams, and images.
Multimodal RAG extends Retrieval-Augmented Generation to process complex documents containing tables, charts, diagrams, and embedded images. Architectural patterns include: 1) Text Summarization RAG (extracting images/tables, generating text summaries using Vision LLMs, and indexing summaries in standard vector DBs), 2) Native Multimodal Embedding RAG (using CLIP / ColPali to embed image patches directly), and 3) Multimodal VLM Generation (passing retrieved raw images alongside text to Vision LLMs like GPT-4o).
Three Multimodal RAG Architectural Patterns
MULTIMODAL RAG PATTERNS
┌──────────────────────────┬──────────────────────────┬──────────────────────────┐
│ 1. SUMMARY-BASED RAG │ 2. NATIVE IMAGE EMBEDDING│ 3. COLPALI VISUAL INDEX │
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
│ VLM generates text summary│ CLIP / Contrastive model │ Embeds PDF page image │
│ of images/tables; index │ embeds image & text in │ patches directly; zero │
│ text summary in Vector DB│ shared vector space. │ OCR pipeline required! │
└──────────────────────────┴──────────────────────────┴──────────────────────────┘
Pattern 1: Summary-Based Text RAG (Standard Enterprise)
Best for leveraging existing text-only Vector DB pipelines:
PDF Document ──► Layout Parser (Unstructured.io) ──► Extract Images & Tables
│
▼
[ Vision LLM (GPT-4o) ]
"Summarize table/chart in detail"
│
▼
User Query ──► Vector Search ──► Match Summary ──► Fetch Raw Image + Text ──► Pass to VLM
Pattern 3: ColPali (End-to-End Visual Retrieval)
ColPali (Fuyu-8B architecture) eliminates OCR completely:
PDF Page (Image) ──► [ Vision Transformer ] ──► Multi-Vector Patch Embeddings
│
▼
User Query Text ──► [ Text Encoder ] ──► [ Late Interaction ColBERT ] ──► Top Pages
- Pros: Zero layout destruction! Captures complex multi-column tables, fonts, diagrams, and logos perfectly.
- Cons: Higher index storage footprint (multiple patch vectors per page).
Multimodal Generation Pipeline
Once relevant visual chunks are retrieved:
# Pass retrieved image bytes directly to Multimodal LLM
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Based on this chart, what was Q3 revenue?"},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{retrieved_base64_image}"}}
]
}
]
)
Say this out loud
"Multimodal RAG retrieves and synthesizes knowledge from tables, diagrams, and images. Standard pipelines use Vision LLMs to generate text summaries of visual elements for indexing in text Vector DBs. Cutting-edge frameworks like ColPali embed PDF page images directly as visual patches, bypassing lossy OCR and preserving table layouts."
Follow-ups to expect
- What is Unstructured.io / Marker? Advanced document parsing libraries that automatically segment PDFs into structured Markdown blocks, extracting embedded images, tables, and headers cleanly.
- How do you evaluate Multimodal RAG? Use MM-Needle-In-A-Haystack benchmarks to evaluate visual retrieval precision, testing whether VLM models can locate specific small chart numbers inside 50-page PDF document contexts.
Check yourself
Question 1 of 3
How does ColPali (Fuyu-based Multimodal Retrieval) revolutionize PDF document retrieval compared to traditional OCR text extraction?