Multimodal RAG: Chat with PDFs (Images & Tables) [2025]

About this Video

• Video Title: Multimodal RAG: Chat with PDFs (Images & Tables) [2025]
• Channel: Alejandro AO - Software & Ai
• Speakers: Alejandro
• Duration: 01:11:04

Introduction

This video demonstrates how to build a multimodal Retrieval Augmented Generation (RAG) pipeline to interact with PDFs. The pipeline considers images, tables, and text within the PDF to generate responses to user queries. The tutorial focuses on the process and provides a code walkthrough in a subsequent lesson.

Key Takeaways

1. Multimodal RAG: The video explains how to create a RAG system that handles not only text but also images and tables from a PDF.
2. Unstructured Library: The unstructured library is used for efficiently extracting structured data (images, tables, text) from unstructured documents like PDFs.
3. Chunking Strategy: A chunking strategy (e.g., by_title) is implemented to group related elements within the PDF, improving the context for the language model.
4. Multimodal Language Model: A multimodal language model (like GPT-4 0 mini) is necessary to process both text and image data for accurate responses.
5. Vector Store and Document Store: The video details how to use a vector store for summaries and a separate document store for the original elements, linked by a unique ID.