Multimodal RAG
Definition
Multimodal RAG combines vision and language capabilities in the retrieval pipeline. Traditional RAG systems retrieve and process only text; multimodal RAG indexes and retrieves images, charts, tables, PDFs with figures, and other visual content. Two primary approaches exist: (1) extract text descriptions or captions from images during indexing so they are retrievable via text embeddings, or (2) use multimodal embedding models that produce a shared embedding space for both text and images (e.g., CLIP-based models), allowing image-to-text and text-to-image retrieval. At generation time, a vision-language model (VLM) receives both retrieved text chunks and relevant images as context.
Why It Matters
Many real-world knowledge bases contain critical information in non-textual formats: architecture diagrams, error message screenshots, UI walkthrough images, data tables, and schematic charts. A text-only RAG system simply cannot answer questions about these visuals. For 99helpers customers who upload product documentation with screenshots or technical diagrams to their knowledge base, multimodal RAG enables the chatbot to answer questions like 'How do I find the API key in the dashboard?' by retrieving and describing the relevant screenshot rather than only returning text that references it.
How It Works
Multimodal RAG implementation involves: (1) ingestion—detect and extract images from documents (PDFs, web pages), generate captions or OCR text, and index both text and visual embeddings; (2) retrieval—use text or multimodal embeddings to find relevant chunks and images; (3) generation—pass retrieved text and images to a vision-language model like GPT-4V or Claude. For tables, specialized parsers extract tabular data as structured text or images. The ColPali model family produces direct image embeddings from PDF pages without OCR, enabling retrieval over entire page layouts including text positioning and visual context.
Multimodal RAG — Mixed-Type Retrieval Pipeline
Text Query
"How does the checkout flow work?"
Image Query
Screenshot of checkout error (optional)
Multi-Modal Encoder
Projects all modalities into joint embedding space
Joint Embedding Search — Mixed Modality Index
Text Chunks
paragraphs, FAQs
Image Descriptions
alt text, captions
Table Extracts
structured data
Diagram Captions
annotated figures
Mixed-Type Retrieved Results
Multimodal LLM
Synthesizes answer from text + image + table context
Real-World Example
A 99helpers customer uploads a SaaS product manual containing setup screenshots. A user asks: 'Where is the SSO configuration button?' Text-only RAG finds a paragraph mentioning 'navigate to the SSO section in Settings' but no screenshot. Multimodal RAG indexes the accompanying screenshot with a generated caption 'Settings panel with SSO toggle highlighted in red.' The chatbot retrieves both the text and screenshot, providing the user with a visual reference alongside the textual instructions.
Common Mistakes
- ✕Assuming OCR-extracted text is sufficient—tables and diagrams lose structural meaning when flattened to text, often requiring image-level retrieval.
- ✕Using text-only embedding models with image captions—multimodal embedding models align image and text representations more accurately.
- ✕Ignoring the significantly higher storage and processing costs of image embeddings compared to text-only RAG.
Related Terms
Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language model responses by first retrieving relevant documents from an external knowledge base and then using that retrieved content as context when generating an answer.
Document Ingestion
Document ingestion is the process of importing, parsing, and indexing external documents — PDFs, Word files, web pages, CSVs, and more — into a knowledge base or AI retrieval system. It transforms raw files into searchable, retrievable content that an AI can use to answer questions.
Embedding Model
An embedding model is a machine learning model that converts text (or other data) into dense numerical vectors that capture semantic meaning, enabling similarity search and serving as the foundation of RAG retrieval systems.
Document Parsing
Document parsing is the extraction of structured or clean text content from various file formats — PDF, DOCX, HTML, CSV, PPTX, and more — as part of a knowledge base ingestion pipeline. A robust parser handles format-specific complexities and produces clean, well-structured text ready for chunking and indexing.
GraphRAG
GraphRAG combines retrieval-augmented generation with knowledge graph structures, enabling multi-hop reasoning across connected entities and relationships rather than retrieving isolated text chunks.
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