Phase 4: Multimodal Deployment · ~180 minutes · Python

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.

What you will learn

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.

Introduction

Type: Build
Languages: Python (stdlib, multi-vector indexer + MaxSim scorer)
Prerequisites: the ML & AI Engineering course, LLaVA and Visual Instruction Tuning
Time: ~180 minutes

Objective

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.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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