How to Build a Multi-Modal RAG Pipeline in Python
A new technical tutorial has been published on Machine Learning Mastery demonstrating how to build a multi-modal RAG pipeline in Python that processes both text and images. The guide covers extracting and reasoning over diverse document formats using modern AI tools. This tutorial provides practical value for developers looking to enhance traditional text-based search systems with visual data processing capabilities. It helps bridge the gap between unstructured multi-modal data and accurate AI-driven retrieval. The pipeline integrates document parsing, vector embedding generation, and multi-modal reasoning to retrieve relevant content. It specifically targets developers working with Python who need to handle complex documents containing mixed media like images and text.
## BACKGROUND
Retrieval-Augmented Generation (RAG) is a technique that improves AI model outputs by retrieving relevant facts from external data sources before generating a response. Multi-modal RAG extends this concept by enabling the system to understand and reason over non-text elements such as images, tables, and charts alongside standard text documents.