Multimodal RAG: Retrieving Images, Tables, and Text

Standard RAG systems treat everything as text, but real-world knowledge bases are rich with images, charts, diagrams, tables, and other visual content. A technical manual might explain a concept through a diagram that contains information not captured in the surrounding text. A financial report might convey key trends through charts that are more informative than the accompanying narrative. Multimodal RAG extends the retrieval-augmented generation paradigm to handle these diverse content types, enabling AI systems to reason across text and visual information simultaneously.

This is not just a theoretical improvement. In many domains, visual content carries critical information that text-only RAG systems simply miss. Engineering diagrams, medical images, architectural drawings, and data visualizations all encode knowledge that cannot be adequately represented as text.

The Multimodal Challenge

Traditional RAG embeds text chunks as vectors and retrieves them based on semantic similarity to a text query. Multimodal RAG must solve several additional problems: How do you create embeddings for images that live in the same vector space as text embeddings? How do you retrieve an image based on a text query? How do you present visual content to a language model for answer generation?

Multimodal RAG is not about adding image search to a text pipeline. It is about creating a unified retrieval system where text and visual content are first-class citizens that can be searched and reasoned about interchangeably.

Architectures for Multimodal RAG

Approach 1: Text Descriptions of Visual Content

The simplest approach converts visual content to text before indexing. Images are captioned using vision models, tables are converted to Markdown or natural language descriptions, and charts are summarized into textual statements. Once everything is text, the standard RAG pipeline works without modification.

This approach is easy to implement but lossy. Captions cannot capture every detail in a complex diagram, and table-to-text conversion may lose structural relationships. However, for many use cases, it provides a significant improvement over ignoring visual content entirely.

Approach 2: Multimodal Embeddings

Models like CLIP and its successors create embeddings in a shared space where both images and text can be represented. A text query and a relevant image will have similar embeddings, enabling cross-modal retrieval. You can search for "network topology diagram" and retrieve relevant images even if those images have no associated text description.

This approach preserves the original visual content but requires a multimodal embedding model and a vector database that can store and retrieve heterogeneous content types. The quality of retrieval depends heavily on the embedding model's ability to capture domain-specific visual concepts.

Approach 3: Hybrid Pipeline

The most robust approach combines both strategies. Visual content is both embedded directly using multimodal models and converted to text descriptions. During retrieval, both the original visual content and its text description are considered. The vision-language model used for generation can then process both the text context and the original images to produce comprehensive answers.

Handling Tables in RAG

Tables are a particularly important type of visual content because they appear in virtually every domain. Financial tables, specification sheets, comparison matrices, and data summaries all encode structured information that is poorly served by simple text extraction.

Effective table handling strategies include:

• Structure-preserving extraction: Using tools like Camelot, Tabula, or Document AI to extract tables with their row-column structure intact
• Multi-representation indexing: Storing both the raw table data and natural language summaries, indexing each separately
• Row-level decomposition: Converting each row into a standalone statement that includes column headers for context
• SQL generation: For highly structured tables, storing data in a relational database and using text-to-SQL for precise queries

Handling Charts and Diagrams

Charts and diagrams present unique challenges because they encode information visually rather than textually. A bar chart showing quarterly revenue trends conveys information through bar heights, colors, and spatial relationships that have no direct textual equivalent.

Chart understanding models like ChartQA and DePlot can extract data points and trends from chart images. Vision-language models like GPT-4V and Gemini can directly interpret charts when provided as image inputs during generation. For architectural and engineering diagrams, specialized models trained on technical drawings may be needed.

Diagram Description Generation

For complex diagrams, generating detailed text descriptions during indexing enables text-based retrieval. A good diagram description captures the key elements, their relationships, and the overall message or structure depicted. Vision-language models can generate these descriptions automatically, though human review improves quality for critical content.

Vision-Language Models for Generation

The generation step in multimodal RAG benefits enormously from vision-language models (VLMs) that can process both text and images in their context window. Models like GPT-4o, Claude with vision, and Gemini can directly examine retrieved images, charts, and diagrams while generating answers.

This means the RAG pipeline can retrieve an image and pass it directly to the generator along with text context. The model can then synthesize information from both modalities to produce a more comprehensive answer than would be possible from text alone.

Vision-language models have transformed multimodal RAG from a complex multi-step pipeline into a more streamlined architecture where the generator can directly consume visual content alongside text.

Practical Implementation Considerations

Building a multimodal RAG system requires addressing several practical challenges:

1. Storage requirements: Images and other visual content require significantly more storage than text. Plan for object storage alongside your vector database.
2. Embedding costs: Multimodal embedding models are typically more expensive to run than text-only models. Budget for higher compute costs during indexing.
3. Latency: Retrieving and transmitting images adds latency compared to text-only retrieval. Consider thumbnail retrieval for initial ranking and full-resolution images for final generation.
4. Evaluation: Evaluating multimodal RAG is harder than text-only RAG because answers may depend on visual information that is difficult to capture in ground-truth annotations.

Multimodal RAG is rapidly maturing as vision-language models improve and multimodal embedding spaces become more capable. For organizations whose knowledge is heavily visual, such as manufacturing, healthcare, architecture, and engineering, multimodal RAG represents a transformative capability that text-only systems simply cannot match.