Building a Multimodal RAG Pipeline: Chatting with PDFs, Images, and Tables

This tutorial video guides you through building a multimodal Retrieval-Augmented Generation (RAG) pipeline using LangChain and the Unstructured library. You'll learn how to create an AI-powered system that can query complex documents, such as PDFs containing text, images, tables, and plots, by harnessing the multimodal capabilities of advanced Language Learning Models (LLMs) like GPT-4 with vision.

We begin by setting up the Unstructured library to parse and pre-process various document formats, from images to text. Then, we use LangChain to establish a document retrieval system that integrates textual and visual data into a multimodal LLM, enabling comprehensive understanding and accurate, relevant responses. This method is perfect for tasks requiring insights across multiple data formats, such as technical documents, scientific papers, and presentations.

Whether you're a beginner in multimodal pipelines or looking to improve your RAG workflows, this step-by-step guide will help you create an intelligent document querying system that goes beyond text, broadening the scope for real-world applications. Don't miss this opportunity to make document intelligence genuinely multimodal!

Topics
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1. How can you set up the Unstructured library to parse and pre-process diverse document types?
2. Want to learn how to create a document retrieval system that utilizes both textual and visual data?
3. Discover how to integrate multimodal data into a LangChain-powered Retrieval-Augmented Generation pipeline!
4. Uncover the benefits of using a multimodal LLM for more comprehensive understanding and accurate responses.
5. Create an AI-powered document querying system that goes beyond text, expanding the possibilities for real-world applications.

Links
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🚀 Zero-to-hero AI Engineer Bootcamp: https://www.aibootcamp.dev/
👉 Code on this video: https://colab.research.google.com/gist/alejandro-ao/47db0b8b9d00b10a96ab42dd59d90b86/langchain-multimodal.ipynb
📽️ Introduction to RAG: https://youtu.be/wUAUdEw5oxM

☎️ Consulting for your company: https://link.alejandro-ao.com/consulting-call
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Timestamps
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0:00 Introduction
2:36 Diagram Explanation
11:45 Notebook Setup
16:52 Partition the Document
35:38 Summarize Each Chunk
46:14 Create the Vector Store
58:48 RAG Pipeline


Connect with me
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https://www.linkedin.com/in/alejandro-ao/
https://twitter.com/_alejandroao