What is the best way to handle Multimodal RAG with images and complex tables in PDFs?

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To solve the "blind spot" in your RAG pipeline, you need to move toward a Vision-Language Model (VLM) for the indexing phase. Instead of just OCR, we’ve started using models like ColPali or specialized vision encoders to create embeddings for the entire page layout. This allows the vector database to retrieve the actual image of a chart rather than a messy text-converted version of it. When the user asks a question, the LLM receives the relevant text chunks alongside the visual crops of the tables. This multimodal grounding has reduced our hallucination rate by nearly 40% in financial document processing tasks.

   Answered 2024-06-14 by Patricia Wilson

Have you tried using a "layout-aware" parser like Unstructured.io or LayoutLM to identify which text sections actually refer to specific image captions?

   Answered 2024-06-15 by Robert Martinez

• We tried LayoutLM, but it struggles when the PDF has a non-standard multi-column format. It often links the wrong caption to the image. Do you know if there are any newer models that use a more "spatial" understanding to determine the relationship between a graph and its descriptive text in complex layouts?

     Commented 2024-06-17 by William Taylor

You might want to consider converting your PDF pages into high-resolution images and using a model like GPT-4V to summarize each page into a "searchable" metadata tag.

   Answered 2024-06-19 by Jennifer Garcia

• That’s a great suggestion, Jennifer. Summarizing visual content into text tags is a solid fallback when full multimodal vector search isn't available for your specific database.

     Commented 2024-06-20 by Christopher Evans