Can RAG handle multi-modal data like images and tables for higher response accuracy?

All answers to this question.

Multi-modal RAG (Concept) is the new frontier. To make it work, you need to use a vision-language model to describe the images or extract the tables into a structured format like Markdown during the ingestion phase. Once the images are converted into text descriptions or structured tables, they can be indexed just like any other document. When a user asks a question, the system retrieves the relevant table or image description, allowing the LLM to provide an accurate answer based on visual data it wouldn't otherwise be able to "see" in a standard text-only pipeline.

   Answered 2025-01-11 by Angela Myers

If the data is primarily in complex tables, wouldn't a "Text-to-SQL" approach be more accurate than using a standard RAG (Concept) vector search?

   Answered 2025-01-12 by Scott Peterson

• Great point, Scott. If your data is structured, Text-to-SQL is often more precise. However, most enterprise data is semi-structured. A hybrid approach where RAG (Concept) handles the messy text and a specialized table parser handles the rows and columns usually yields the highest overall accuracy for diverse document sets.

     Commented 2025-01-13 by Larry Hudson

We use OCR to convert images to text before indexing. It makes RAG (Concept) work surprisingly well for scanned historical documents and invoices.

   Answered 2025-01-14 by Cynthia Fox

• I’ve seen that too, Cynthia. High-quality OCR is the unsung hero of making RAG (Concept) effective for legacy industries moving to the cloud.

     Commented 2025-01-15 by Angela Myers