Your RAG reads text. That is it. Someone uploads a PDF with a table, a chart, a diagram and your RAG skips all of it. The answer was right there in the image and your agent acted like it did not exist. Then someone asks a question that needs information scattered across three different documents. A name in one, a date in another, a policy in a third. Your RAG cannot connect them because it treats every chunk like it lives alone with no relationship to anything else.

In this session you learn three types of RAG and build each one. Text RAG done right with semantic chunking, reranking, and metadata filtering so you retrieve the right chunk not just a similar one. Multimodal RAG using Gemini so your agent actually sees the charts, images, tables, and diagrams it was ignoring before. Graph RAG where entities and relationships across documents are stored in a knowledge graph so your agent connects facts across hundreds of files instead of searching one chunk at a time. You build all three on real messy data, see where each one wins, where each one breaks, and learn when to use one vs combine all three.