Multimodal RAG: Text, Images, Tables & Audio Pipeline

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Explore multimodal Retrieval-Augmented Generation (RAG) with this comprehensive video.

Learn how to build an end-to-end RAG pipeline that handles text, images, graphs, tables, and audio data using Weaviate as a vector database.

This video covers everything from data collection to system testing, with a focus on ESG and Finance applications. Perfect for AI engineers, data scientists, and machine learning enthusiasts looking to expand their skills in building versatile and powerful RAG systems.

ℹ️ CHAPTERS OF THE VIDEO

0:00 - Introduction
0:53 - Overview of Multimodal RAG
5:50 - Text, Images, Tables, and Audio Data Collection & Preprocessing
41:34 - Set Up Weaviate
49:40 - Data Ingestion into Weaviate
54:21 - Implementing the Retriever Component
58:47 - Building the Augmented Generation Component
01:03:41 - Testing and Optimizing the RAG System
01:09:42 - Clean Workspace
01:09:54 - Conclusion and Next Steps

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#artificialintelligence #gpt4 #openai #largelanguagemodels

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This video deserves way more views. It was BRILLIANT!

eventsjamaicamobileapp

So you can input multi-modal sources.
On the retrieval side (let’s say a table and an image of vacuum cleaner ). The LLM could be informed by the information in the table.

Could I retrieve the image of the complete table and/or vacuum cleaner? (The objects )

robertboroughs

Great video. What if it were multiple PDF documents in a single folder. What code would have to be changed?

eventsjamaicamobileapp

Hi. thank for a good video. i try to replicate you code and have got error: "Error during transcription: [WinError 2] The system cannot find the file specified". even the mp3-file is created and exists in the directory. Where can i check possible solutions for my problem?

annapetmikel

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