What Is Multimodal RAG and How It Retrieves Across Images, Tables, and Text
Multimodal RAG isn't text RAG with images bolted on. Learn how unified embeddings, text summaries, and vision-first retrieval handle images, tables, and text.
Knowledge Retrieval Systems
Structured knowledge integration, document parsing, metadata filtering, and multimodal retrieval for production knowledge systems.
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What topics does this domain cover?
4 topicsEach topic below is a key concept in this domain. Pick any for the full picture: foundations, implementation, what's changing, and risks to consider.
Document Parsing and Extraction →
Document parsing and extraction is the preprocessing step that turns PDFs, scanned pages, tables, and images into clean, …
5 articles
Knowledge Graphs for RAG →
Knowledge Graphs for RAG use structured graph representations of entities and their relationships to retrieve …
7 articles
Metadata Filtering →
Metadata filtering is the practice of constraining vector search results using structured attributes such as dates, …
5 articles
Multimodal RAG →
Multimodal RAG extends retrieval-augmented generation beyond plain text so a system can search and reason over images, …
5 articles
Four perspectives on this domain
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Updated May 6, 2026
Concepts covered
What Is Metadata Filtering and How It Constrains Vector Search Beyond Semantic Similarity
Metadata filtering attaches typed key-value payloads to each vector and applies predicates during search, narrowing results beyond pure semantic similarity.
GraphRAG Prerequisites: Knowledge Graphs and Where Vector RAG Falls Short
GraphRAG inherits chunking, embeddings, and entity extraction from vector RAG. Learn what you need first and where the underlying pipeline breaks.
How OCR, Layout Analysis, and VLMs Turn PDFs Into Clean Text
Document parsing converts PDFs into structured text via layout analysis, OCR, and VLMs. Here is how each component works and where each one breaks.
Indexing Cost, Token Blowup, and the Hard Engineering Limits of GraphRAG at Scale
GraphRAG indexing costs scale with token recursion, not document size. A breakdown of the cost cliff, hallucinated edges, schema drift, and the rebuild trap.
Multimodal RAG Prerequisites: Vision-Language Models, Cross-Modal Alignment
Before multimodal RAG works, you need vision-language models, shared embeddings, and a theory of cross-modal retrieval. Here's the prerequisite stack.
OCR to Layout-Aware Models: Prerequisites and Hard Limits
Document parsing breaks in predictable ways. Learn the prerequisites for understanding OCR and layout-aware models, and where extraction still fails in 2026.
Pre-Filter vs Post-Filter vs Filtered-HNSW: Metadata Filtering at Scale
Why metadata filtering breaks vector search at scale — the HNSW prerequisites, payload indexing, and Boolean predicates needed to reason about recall.
What Is GraphRAG? Multi-Hop Reasoning with Knowledge Graphs
GraphRAG turns documents into a knowledge graph and uses community summaries to answer multi-hop questions vector retrieval cannot reach. Here is the mechanism.
