Most RAG systems ignore everything that isn’t text. They skip diagrams, discard images, and flatten tables into incomprehensible strings. In my experience building document intelligence systems, this limitation throws away 30-50% of the information in typical enterprise documents. Multimodal RAG changes this.

Through implementing multimodal RAG systems for clients with image-heavy documentation (technical manuals, product catalogs, scientific papers), I’ve developed patterns that actually work in production. This guide covers how to build RAG systems that understand visual content alongside text.

Why Multimodal RAG Matters

Real-world documents contain more than text:

Technical documentation includes architecture diagrams, flowcharts, and screenshots. The diagram often explains what paragraphs of text struggle to convey.

Product information relies on images. Customers ask “what does this look like?” or “where is this button?” Text-only RAG fails these queries.

Scientific papers contain figures, charts, and tables that carry key findings. The abstract doesn’t contain everything.

Business documents mix text with charts, org diagrams, and embedded images. PDFs especially blend modalities.

A text-only RAG system can’t answer questions about this content. It retrieves text that references the diagram but not the diagram itself. Users get partial, sometimes misleading answers.

For foundational RAG concepts, see my RAG implementation guide. This guide extends those patterns to multimodal content.

Multimodal Architecture Options

Several approaches exist for handling multimodal content. Each has trade-offs:

Approach 1: Text Extraction Only

The simplest approach extracts text descriptions of visual elements:

OCR extracts text from images and adds it to the document text.

Table parsing converts tables to structured text or markdown format.

Diagram annotation relies on captions and surrounding text to describe visual content.

Pros: Uses existing text-only infrastructure. Simple to implement.

Cons: Loses visual information. Can’t answer “what does the architecture look like?” Can’t interpret charts.

This works when visual content is supplementary, not primary. It fails when images carry essential information.

Approach 2: Vision-Language Model Processing

Modern VLMs (GPT-4V, Claude with vision, Gemini) can interpret images:

Image summarization sends images to a VLM to generate text descriptions for indexing.

Visual Q&A retrieves images and uses VLMs to answer questions about them.

Combined processing interprets document pages as images, capturing both text and visual layout.

Pros: Captures visual semantics. Can answer questions about visual content.

Cons: Processing cost. VLM latency. Quality depends on description quality.

This is the most powerful approach for rich visual understanding.

Approach 3: Multimodal Embeddings

Use embedding models that handle both text and images:

CLIP-style models embed text and images into the same vector space.

Cross-modal retrieval finds relevant images from text queries and relevant text from image queries.

Combined indexes store text and image embeddings together.

Pros: Native multimodal retrieval. Fast at query time.

Cons: Embedding quality varies. May not capture fine-grained visual detail.

This works well for image search and retrieval, less well for complex visual understanding.

Approach 4: Hybrid Pipeline

Combine approaches for comprehensive coverage:

1. Extract text using OCR and parsing
2. Generate image descriptions using VLMs
3. Index both text content and image descriptions
4. Retrieve relevant content across modalities
5. Generate responses using retrieved text and images as context

This is what I recommend for production systems with diverse content types. It balances capability with cost.

Implementation: Document Processing Pipeline

Building multimodal RAG starts with robust document processing:

PDF Processing with Visual Awareness

PDFs are the most common multimodal format. Process them comprehensively:

Page-level rendering converts pages to images for visual processing.

Text extraction pulls text content while preserving layout information.

Image extraction identifies and extracts embedded images.

Table detection locates and extracts tables for structured processing.

Region classification distinguishes text regions, images, headers, and other elements.

Tools like PyMuPDF, pdfplumber, and layout analysis models (LayoutLM, DiT) enable this extraction.

Image Description Generation

Convert images to searchable text using VLMs:

Contextual prompting includes surrounding document text for better descriptions. “This diagram appears in a section about authentication. Describe what it shows.”

Structured output generates consistent description formats: subject, key elements, relationships, text visible in image.

Quality validation filters low-quality or irrelevant descriptions before indexing.

Batch processing generates descriptions efficiently rather than one at a time.

Store both the description and the original image reference. You may want to retrieve the actual image for response generation.

Table Processing

Tables carry structured information that doesn’t fit traditional chunking:

Table detection identifies table boundaries in documents.

Structure extraction parses rows, columns, headers, and cell relationships.

Multiple representations create both structured (JSON) and prose versions of tables.

Metadata preservation tracks which document and section each table comes from.

Tables often answer specific queries well. “What is the price of X?” retrieves the pricing table directly. My hybrid database solutions guide covers handling structured data in RAG.

Chart and Graph Interpretation

Charts visualize data that may not exist elsewhere in the document:

Chart type detection identifies bar charts, line graphs, pie charts, etc.

Data extraction attempts to recover underlying data points when possible.

Visual description generates text explaining what the chart shows and key insights.

Trend and comparison summary captures the chart’s message in searchable form.

Charts are particularly important for financial documents, reports, and dashboards.

Chunking Strategies for Multimodal Content

Multimodal documents need adapted chunking strategies:

Content-Type Aware Chunking

Different content types need different handling:

Text sections chunk using standard semantic chunking principles.

Images become their own chunks with generated descriptions as searchable content.

Tables stay as complete units. Don’t split tables across chunks.

Figure-caption pairs stay together. The caption provides context for the figure.

Section coherence keeps related text, images, and tables together when they discuss the same topic.

Relationship Preservation

Maintain links between related content:

Image-to-text references connect images to paragraphs that reference them (“see Figure 3”).

Table-to-text references link tables to their explanatory text.

Cross-references track when one section refers to another.

These relationships enable retrieval that surfaces complete context, not isolated fragments.

Metadata for Multimodal Content

Rich metadata enables better retrieval:

Content type distinguishes text, image, table, chart chunks.

Visual properties for images include size, position, detected objects.

Structural context tracks where in the document hierarchy each chunk lives.

Quality scores from image descriptions or extraction confidence.

Use metadata filtering to retrieve specific content types when queries warrant it.

Retrieval for Multimodal Systems

Query processing adapts for multimodal content:

Query Understanding

Determine what modalities the query needs:

Visual intent detection identifies queries about visual content: “what does X look like,” “show me,” “diagram of.”

Structured data intent identifies tabular queries: “price of,” “list of,” “comparison between.”

Text intent identifies standard text retrieval needs.

Route queries to appropriate content types based on intent.

Cross-Modal Retrieval

Find relevant content across modalities:

Text-to-image retrieval finds relevant images from text queries using multimodal embeddings or image descriptions.

Image-to-text retrieval finds explanatory text for retrieved images.

Unified ranking combines results from different modalities into coherent result sets.

Relevance Scoring

Score multimodal results appropriately:

Modality-specific scoring accounts for different similarity distributions across content types.

Query-type weighting emphasizes visual content for visual queries, text for text queries.

Diversity ensures result sets cover multiple relevant modalities.

Generation with Multimodal Context

Response generation uses retrieved multimodal content:

Vision-Enabled Generation

When retrieved context includes images:

Include images in context for VLM-capable models. They can reference visual content directly.

Describe images for text-only models using generated descriptions when VLMs aren’t available.

Attribute visual sources so users know which image supports which claim.

Response Formatting

Multimodal responses need formatting consideration:

Image references tell users which images to examine: “See Figure 3 for the architecture diagram.”

Table rendering presents tabular results in readable format.

Mixed media responses combine text explanation with visual references.

Handling Visual Limitations

When visual content can’t be fully conveyed:

Acknowledge limitations rather than inventing descriptions.

Provide references to original documents for visual examination.

Summarize key points from visual content that can be textualized.

Production Considerations

Multimodal RAG adds complexity. Address these production concerns:

Processing Costs

VLM calls for image description are expensive:

Batch during ingestion rather than real-time processing.

Cache descriptions since images don’t change.

Selective processing describes important images, skips decorative ones.

Quality thresholds determine when VLM description is worth the cost.

My RAG cost optimization guide covers cost management strategies that apply here.

Storage Requirements

Multimodal content requires more storage:

Image storage for original images (if needed for response generation).

Description storage for generated text.

Multiple representations for tables (structured and prose).

Plan storage architecture for larger corpus sizes than text-only RAG.

Latency Management

Multimodal retrieval and generation take longer:

Parallel retrieval across modalities prevents serial delays.

Progressive loading shows text results while images load.

VLM response streaming displays generation as it happens.

Caching at multiple levels reduces repeated processing.

Quality Assurance

Evaluate multimodal-specific quality:

Image description accuracy ensures generated descriptions correctly represent images.

Visual query coverage measures whether visual queries find relevant images.

Cross-modal consistency ensures retrieved images match retrieved text.

End-to-end evaluation on queries that require visual understanding.

Use Case Examples

Multimodal RAG enables applications that text-only can’t support:

Technical Documentation Assistant

Users ask about complex products with diagrams:

“What does the network architecture look like?” “Where is the reset button on the device?” “How do the components connect together?”

Multimodal RAG retrieves relevant diagrams and generates responses that reference them.

Product Information System

E-commerce and catalogs rely on images:

“Show me blue dresses under $100” “What does the medium size look like on someone?” “Is this compatible with my existing setup?”

Visual retrieval and comparison becomes possible.

Scientific Literature Search

Research papers contain crucial figures:

“Find papers with results showing X trend” “What methodology does this diagram illustrate?” “Compare the architectures in these papers”

Multimodal RAG surfaces relevant figures and their explanations.

For more on building comprehensive document systems, see my multimodal AI development guide and document retrieval guide.

Getting Started with Multimodal RAG

Start with an incremental approach:

1. Audit your content to understand what visual elements exist and their importance
2. Add table extraction as a first step (high value, moderate complexity)
3. Generate image descriptions for key images using VLMs
4. Index multimodal content alongside text
5. Implement query routing to direct visual queries appropriately
6. Enable VLM generation for queries needing visual context

Each step adds capability. You don’t need everything at once to start delivering value from multimodal content.

Ready to build multimodal RAG systems? Join the AI Engineering community where engineers share implementation patterns for complex document processing and help each other build production-grade systems.