Master Multimodal AI: Build RAG with VLM for Documents, Tables & Images (Full Pipeline)

Develop an advanced Retrieval-Augmented Generation (RAG) system capable of understanding and synthesizing information from complex, multimodal documents containing text, tables, and images. Modern knowledge bases are rarely text-only, and this project tackles the real-world challenge of building AI that can reason over visual and textual data simultaneously, a critical capability for industries like research, finance, and enterprise knowledge management.

Through this task, you will master the entire lifecycle of a multimodal AI system, from sophisticated data ingestion pipelines involving Optical Character Recognition (OCR) and table extraction to implementing cutting-edge multimodal embeddings. You will design and build a cross-modal retrieval system that can find relevant images from text queries and vice-versa. The final system's quality will be judged on its ability to generate accurate, visually-grounded answers by integrating with a powerful Vision-Language Model (VLM), demonstrating a deep understanding of production-level AI engineering.
github url:https://github.com/Satyanagapraveen/Multimodal-RAG-System-for-Document-and-Image-Analysis

Видео Master Multimodal AI: Build RAG with VLM for Documents, Tables & Images (Full Pipeline) канала Praveen Namburi