Multimodal Retrieval-Augmented Generation (RAG)

What is Multimodal RAG?

Definition: RAG with multiple data types like images, text, tables

Why it’s important? Enterprise (unstructured) data is often spread across multiple modalities, e.g. images or PDFs containing a mix of text tables, charts, and diagrams.

• Goal: Improve retrieval accuracy and provide richer, context-aware responses.
• Applications: Virtual assistants, recommendation systems, content generation.

Multimodal Capabilities in RAG

Multimodal: Involves more than one type of data (e.g., text, images, audio).

Enhanced Context: Combining modalities provides a fuller understanding, helping models answer more complex queries.

Example Use Case: Searching images and text databases to answer a visual question.

Why Multimodal RAG is Challenging?

• Data Spread: Unstructured data across modalities (e.g., images, PDFs).
• Unique Modality Challenges: Each data type has specific retrieval requirements.
• Data Alignment: Combining text and image data meaningfully.
• Latency: Increased computational requirements.