Module 18: Vector Quantization and Multimodal RAG

Module Overview

This module covers two scaling techniques for production RAG: compressing the vector index so large corpora stay fast and cheap, and retrieving directly over visual documents using the ColPali late-interaction paradigm.

Learning Objectives

• Compare scalar, binary, and product quantization for vector search at scale.
• Explain the ColPali late-interaction paradigm for no-OCR document retrieval.
• Plan a multimodal indexing pipeline with layout-aware chunking and VL embeddings.
• Add a VL reranker to refine multimodal retrieval results.

Topics Covered

Vector Quantization for Scaling

• Scalar quantization
• Binary quantization
• Product quantization

Multimodal RAG

• The ColPali paradigm (and ColQwen-style late-interaction document retrieval)
• Single-stage and dual-stage data parsing
• Layout detection
• The OCR paradigm
• Structure / layout-aware chunking
• Vision-language (VL) embeddings
• VL rerankers
• Multimodal LLMs in the retrieval loop

Key Concepts & Terminology

Product/scalar/binary quantization, late-interaction patch embeddings, MaxSim scoring, VL reranker, layout-aware chunking.

Tools & Frameworks Referenced

Qdrant (multi-vector / MaxSim), ColPali / ColQwen, layout detection libraries, VL rerankers.

Prerequisites

Modules 14, 16, 17 (embeddings and RAG); Module 11–12 (vision/VLMs) for multimodal RAG.

Further Reading

• Late Interaction Retrieval Models: ColBERT, ColPali, ColQwen (Weaviate)
• Multimodal RAG with Document Retrieval (ColPali) and VLMs (Hugging Face)