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ColPali (Multimodal RAG)

ColPali-style multimodal RAG treats PDF pages as images and retrieves relevant pages using vision embeddings — often without an OCR-first pipeline.

This matters when the answer depends on:

  • tables / multi-column layout
  • charts / diagrams
  • forms and scanned documents

Learning goals​

  • Build a page-image index for PDFs
  • Retrieve top-k page images from a text query
  • Combine visual retrieval with text (hybrid) when needed

The pipeline​

  1. Render PDF → page images
  2. Create embeddings for each page image
  3. Store embeddings + page metadata
  4. Query with text → retrieve pages
  5. Answer with citations (page numbers)
text
query(text)
  → embed(text)
  → ANN search over page-image vectors
  → return page images
  → VLM answers grounded in top-k pages

Hybrid strategy (often best)​

  • Use OCR for searchable text
  • Use multimodal retrieval for layout-sensitive pages
  • Merge results (RRF fusion) and rerank

What to log​

For debuggability:

  • retrieved page numbers
  • similarity scores
  • the exact rendered page images used

Mini-lab (optional)​

Build a “visual PDF QA” demo:

  • upload a PDF
  • query it
  • show the top-3 retrieved pages as images
  • answer with page citations