Text-only RAG silently discards charts, tables, and diagrams that often contain the actual answers in enterprise documents. This guide covers three multimodal RAG architectures for 2026: caption-and-index (simplest), unified vision embeddings using Cohere Embed 4 or voyage-multimodal-3.5 (single-vector, API-based), and page-as-image with late interaction using ColPali/ColQwen2.5 (highest recall, highest storage cost). A reference architecture on OpenSearch uses parallel BM25, text k-NN, and image k-NN fields fused with Reciprocal Rank Fusion. Key trade-offs include storage (tens of GB for single-vector vs. single-digit TB for ColPali), per-query cost, and recall on visually complex queries. The guide also covers PDF parsers, VLM generation patterns, chart hallucination mitigations, and common production anti-patterns like caption drift and modality leakage.
Table of contents
Why Text-Only RAG Fails on Real Enterprise DocumentsWhat Multimodal Means in a RAG PipelineEmbedding Models That MatterColPali and the Page-as-Image ApproachPDF Parsing When You Still Need Structured TextReference Architecture on OpenSearchGeneration with Vision-Capable LLMsCost Per Query Across the Three ArchitecturesUse Cases Where Multimodal RAG Pays OffProduction Patterns and Anti-PatternsKey TakeawaysSort: