Multimodal RAG: Retrieval with Images, Tables, and Text — The 2026 Guide
Text-only RAG was just the beginning. In 2026, real-world documents are packed with charts, tables, diagrams, screenshots, and infographics. Standard RAG pipelines that only process text miss critical information. This guide shows you how to build multimodal RAG systems that understand the full richness of your documents.
Why Text-Only RAG Falls Short
Consider a typical enterprise PDF: it might contain financial tables, process diagrams, product screenshots, and technical drawings. A text-only RAG pipeline will:
- Miss entirely: Charts, graphs, and images that contain key insights.
- Lose structure: Tables converted to plain text lose row/column relationships.
- Ignore visual hierarchy: Page layouts convey importance that text extraction destroys.
Architecture Patterns for Multimodal RAG
1. Embed-Then-Retrieve: Embed all modalities (text, images, tables) into a shared vector space. At query time, retrieve chunks from any modality. Models like CLIP and ColPali enable this.
2. Retrieve-Then-Rerank: First retrieve candidates using cheap text embeddings, then rerank using expensive multimodal models. Best for large document collections.
3. Unified Multimodal Indexing: Parse documents into a unified representation where text, images, and tables are all indexed together. Most accurate but most complex to build.
Embedding Models for Multimodal
| Model | Modalities | Strength |
|---|---|---|
| CLIP | Image + Text | General purpose alignment |
| ColPali | PDF pages | Page-level visual retrieval |
| ColNemb | Multi-vector | Fine-grained chunk matching |
| Jina CLIP v2 | Multilingual | 8-language support |
Building a Multimodal RAG Pipeline
from colpali_engine.models import ColPali
from qdrant_client import QdrantClient
from google.generativeai import GenerativeModel
# 1. Parse documents (unstructured.io ormarker-pdf)
# 2. Create multi-vector embeddings
model = ColPali.from_pretrained("vidore/colpali-v1.2")
# 3. Index in vector DB (Qdrant with multi-vector support)
client = QdrantClient("localhost", port=6333)
collection = client.create_collection(
"multimodal_docs",
vectors_config={"page_vectors": {"size": 128, "distance": "Cosine"}}
)
# 4. Query: embed question, retrieve relevant pages, generate with Gemini
def query(question: str):
q_embed = model.encode_question(question)
results = client.search("multimodal_docs", query_vector=q_embed, limit=5)
# Pass retrieved pages (images) to Gemini for answer generation
model = GenerativeModel("gemini-2.0-flash")
response = model.generate_content([question] + [r.payload["page_image"] for r in results])
return response.text
Table Handling for RAG
Tables require special treatment in RAG systems:
- Extraction: Use models like TableFormer or AWS Textractor to extract structured data.
- SQL augmentation: Convert extracted tables to SQLite, use text-to-SQL for querying.
- Dual indexing: Index both the original table image and the extracted CSV/markdown.
Benchmarks
| Benchmark | Tests | Top Model |
|---|---|---|
| DocVQA | Document visual QA | GPT-4o + ColPali: 82% |
| ChartQA | Chart understanding | Gemini 2.0: 78% |
| InfoVQA | Infographic QA | GPT-4o: 71% |
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