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. 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. Text-RAG on PDFs throws away most of the document. A financial report's Q3 revenue growth is usually in a chart; a medical report's findings are in annotated images; a legal contract's signature block is a layout fact, not a text fact. The text-RAG pipeline: PDF → text via OCR / pdftotext. Text →…
ColPali and Vision-Native Document RAG: Traditional RAG parses PDFs into text, splits into chunks, embeds chunks, stores vectors. Every step loses signal: OCR…
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