RAG-Anything Tutorial: Build a Multimodal Retrieval Pipeline for Text, Tables, Equations, and Images in Colab

In this tutorial, we build a RAG-Anything workflow and use it to explore how multimodal retrieval works across text, tables, equations, and images. We start by preparing the Colab environment, installing the required packages, and securely entering our OpenAI API key at runtime to keep the notebook practical and safe to run. We then create a synthetic multimodal report, generate a chart and PDF, convert the content into RAG-Anything’s direct content_list format, and insert it into the retrieval system. As we move through the tutorial, we configure clean OpenAI-based chat, vision, and embedding functions, initialize RAG-Anything, and test different retrieval modes such as naive, local, global, and hybrid.

Installing RAG-Anything Dependencies

import os

import re

import sys