Multimodal RAG Made Simple: RAG-Anything + Milvus Instead of 20 Separate Tools

This post details the integration of RAG-Anything with Milvus to create a unified multimodal RAG system. It explains how RAG-Anything's "1 + 3 + N" architecture (core engine, 3 modal processors, N parsers) handles diverse content types in parallel. Milvus is used as the vector database to store and search across various embedding types generated from text, images, tables, and equations, simplifying the infrastructure by replacing multiple specialized tools with a single, scalable vector search solution. A hands-on demo with Milvus Lite, Alibaba Cloud API, and specific models (qwen-vl-max, tongyi-embedding-vision-plus) is provided, along with project structure, dependencies, and configuration details.