ColPali and Vision-Native Document RAG

Traditional RAG parses PDFs into text, splits into chunks, embeds chunks, stores vectors. Every step loses signal: OCR drops chart data, chunking breaks table rows, text embeddings ignore figures. ColPali (Faysse et al., July 2024) asked the simpler question: why extract text at all? Embed the page image directly via PaliGemma, use ColBERT-style late interaction for retrieval, and keep all the layout, figures, fonts, and formatting signal the document carries. Published benchmarks: 20-40% better end-to-end accuracy than text-RAG on visually-rich documents. ColQwen2, ColSmol, and VisRAG extended the pattern. This lesson reads the vision-native RAG thesis and builds a tiny ColPali-like indexer.

Build 3 hr Python (stdlib, multi-vector indexer + MaxSim scorer)

Prerequisites: Phase 11 (LLM Engineering — RAG basics), Phase 12 · 05 (LLaVA)

#Learning Objectives

Explain the difference between bi-encoder retrieval (one vector per document) and late-interaction retrieval (many vectors per document).
Describe ColBERT's MaxSim operation and how ColPali generalizes it from text tokens to image patches.
Build a tiny ColPali-like indexer: page → patch embeddings → MaxSim over query-term embeddings → top-k pages.
Compare ColPali + Qwen2.5-VL generator vs text-RAG + GPT-4 on an invoices / financial reports use case.

#Learning Objectives

Keep going. The rest of this lesson is one step away.