Build Advanced Multimodal RAG End-to-End | Visual RAG with LangChain + Pinecone

In a world where AI reads words but fails to see the full picture, traditional R app understands text [music] yet leaves behind tables, charts, diagrams, and visual intelligence. But what if AI could understand every part of a document? Introducing Visual RAB, a multimodal retrieval system built for real world intelligence. Now ask deeper questions,

unlock hidden insights, retrieve meaning from text, tables, charts, and visual evidence instantly. This is the future of retrieval. This is production ready visual ra. >> Hi there everyone. My name is BPI and you are welcome to my YouTube channel. So guys uh in this video we'll be developing another amazing and super important advanced rag technique which

is multimodel rag. And uh probably this is the most requested uh video guys I'm creating uh because many people uh have requested me for this particular video uh like uh they want to see how we can create the multimodel rag. We also call it as a visual rag. Okay. Some of you also call

it as a visual rag. So visual rag multimodel multimodel rag. Okay. Both are same. Uh I think someone will name it as visual rag. Someone will name it as multimodel rag. Multimodel rag is the um let's say actual name you have to call it right. So why we call it as a multimodel rag

we'll try to understand and how it solves one amazing problem amazing use case each and everything we'll try to see in a theoretical understanding as well as the practical development. So everything I'm going to show you from scratch we'll try to develop the entire rag application from scratch okay completely end to end. So make sure you

watch this video till the end and if you have any question please try to ask me in the comment section and if you found this video interesting guys please try to hit the like and please try to subscribe to my channel. So guys uh you know that I started an advanced rag series on my YouTube channel. So this is